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 .
This office action is in response to filing on 3/6/2026 in response to claims amendments in application 19/050,562 filed 2/11/2025.
This is a continuation of 17/969,303 filed on 10/19/2022.
Also a continuation to PCT/US2023/031343 of 8/29/2023.
The Pre-Grant publication # 20250182643 is published on 6/5/2025.
Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/6/2026 has been entered.
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 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 11 and 17 are directed to a system, method and readable media providing augmented tutoring (Step 1, Yes).
Claims 1, 11 and 17 are directed to obtaining image data, wherein the image data is descriptive of one or more images, one or more pages; determining a prompt based on the image data, wherein the prompt is descriptive of a request for a response; determining with a first machine-learned model, the image data is associated with a particular type of problem based on one or more identified features in response to determining the image data is associated with the particular type of problem, determining, with a second machine-learned model separate from the first machine-learned model a multi-part response to the prompt, wherein the multi-part response comprises a plurality of individual responses associated with the prompt; as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This type of mental process can be practically performed in the human mind, for instance by a human teacher.
The providing of second augmented image for display,; generating one or more augmented images based on the correction action and the one or more images, wherein the one or more augmented images comprise one or more user interface elements rendered into the one or more images, wherein the one or more user interface elements comprise text superimposed over at least a portion of the one or more images, or features within one or more image data is associated with the particular type of problem, wherein the text of the one or more user interface elements comprise informational data descriptive of the corrective action; generating a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response and providing the one or more augmented images for display based on the corrective action as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation as certain method of organizing human activities as managing relationship and information between people such as tutor for corrective action and display. The mere nominal recitation of at least one processor performing these steps does not take the claim limitation outside of the mental processes grouping. Thus, the claim recites an abstract idea (Step 2A, Prong 1: yes).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", " generating a first augmented image that comprises an overlay superimposed over at least a portion of the one or more pages of the one or more images, wherein the overlay is descriptive of a first action of the multi-part response”, ´”features within one or more image data is associated with the particular type of problem” merely use generic computer functions and computer parts. The determining, by the computing system and with a first machine-learned model, the image data is associated with a particular type of problem based on one or more identified features within the one or more images; determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model associated with the particular type based on the image data being associated with the particular type of problem; in response to determining the image data is associated with the particular type of problem, determining, with the second machine-learned model separate from the first machine- learned model by computing devices is not improving the functionality of the machines involved. Since a computer is an electronic device that accepts input, processes data Perform calculations, (execute instructions, and manage data flow), stores information, manage flow of data and outputs results . It works to perform these and some other related functions that are common and routine functions. When a following instructions from software programs uses these common hardware components to perform tasks like a machine-learned data processing a problem-specific particular type machine-learned model based on the image data, it still performing common routine function of input, analysis and output. Though It may be associated with the particular type of problem but on a generic machine with generic Computer Parts and their Functions such as accepting input, processing data to perform calculations, (execute instructions, and manage data flow), storing information, managing flow of data and outputting results. Hence not a additionally making a significantly more practical application. Selecting of augmented image and of instruction from input pages from storage would not be an indicative of integration of a practical application (Step 2A: Prong 2 No).
With respect to the dependent claims, one or more processors, one or more non-transitory computer-readable media, one or more image sensors of a mobile computing device (Claims15,16); a computing system generating a plurality of augmented-reality user interface elements based on the multi-part response, determining a prompt based on the image data, semantic understanding model (claims 2-10) one or more methods on computing devices, threshold amount of time (claim 12-14). It is particularly noted that the use of a computing device " augmented-reality generation block", a transformer model to generate the multi-part response to perform an abstract method and steps that only amount to extra solution activity are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Regarding these limitations: Applicant's specification only describes these features in a highly generic manner by stating that " one or more processors 132 can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory 134 can include one or more non-transitory computer-readable storage mediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magnetic disks, etc., and combinations thereof " in the Applicant’s specification, page. 16, para. [0074, 0075]. There is no indication in the Specification that Applicants have achieved an advancement or improvement in computer technology, camera sensor technology and/or OCR technology. Dependent claims 2 – 10, 12 – 16 and 18 – 20 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
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.
The factual inquiries 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 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Noble et al. (US 2019/0272775 A1) in view of US 20230244938 A1 to Wei et al.(Wei).
Claim 1. Noble teaches a computing system (Fig.1 ), the system comprising: one or more processors (Para 0197); and one or more non-transitory computer-readable media that collectively store instructions (Para 0209) that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
obtaining image data, wherein the image data is descriptive of one or more images, wherein the one or more images are descriptive of one or more pages (Para 0041,0042 response image data as that relates to automated response-step extraction);
determining a prompt based on the image data, wherein the prompt is descriptive of a request for a response (Para 0114, 0115 descriptive prompt or question);
determining with a first machine-learned model, the image data is associated with a particular type of problem based on one or more identified features in response to determining the image data or features within one or more image data is associated with the particular type of problem, accessing the multi-part response (Fig.61 element 1306, 1310, 1312 practice and progress with a first machine-learned model for image data to access an order of operation in a problem; response could easily be a multi-part prompt because of interactive interface.
Noble may not explicitly be determining features within one or more image data is associated with the particular type of problem. Wei, however teaches the determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model based on the associated image data (Para 0057-0060 a machine-learned model i.e. a possible second machine-learned model for a muti-part prompt includes a neural network trained to understand and interpret images or other data inputs more generally to extract meaning therefrom, including to respond to instructions provided in such inputs to be interpreted from images; Para 0122 implementing multiple parallel instances of a machine-learned model such as for a mulit-part system in figure element 1020 of Figure 10A) .
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model associated with the particular type based on the image data being associated with the particular type of problem, as taught by Wei, into the image retrieval system of Noble, in order to provide an improved secondary instance of machine learning model associated with additional specific type of problem efficiently contribute towards an overall goal.
Noble in combination with Wei teaches in response to determining, with a second machine-learned model associated with particular type of problem separate from the first machine-learned model determining a multi-part response to the prompt, wherein the multi-part response comprises a plurality of individual responses associated with the prompt (Fig.61 through Fig.73 progress multi-part response to prompts indicating a best photo multi-part response when for example a paper containing solution steps in fig. 66 and/or a solution to the question is placed on a contrasting background as a multi-part action depicted in fig.63 for example for first machine learned model; Fig.61 element 1312 is a progress response button; Para 0482 Fig.65 element 1328, Fig.66 element 1330 simultaneous multi-part image data and display with respect to a second machine-learned model).
wherein the second machine-learned model was trained to generate a proof illustrating how to solve the particular type of problem ( Fig.65 1328, Fig,66 element 1338 accuracy of the conversion as a proof solving particular problem evaluation via use of slider 1338, whereas accuracy of conversion can be evaluated via the toggling between panes 1328, 1330, via the overlaying of content from the panes 1328, 1330, via the simultaneous display of the panes 1328, 1330, or the like for how a problem is solved)
generating a first augmented image that comprises an overlay superimposed over at least a portion of the one or more pages of the one or more images, wherein the overlay is descriptive of a first action of the multi-part response (Fig.67 Para 0482 overlay of contents);
providing the first augmented image for display (Fig.64, Fig.65; Image augmentation involves applying various transformations to training images generating slightly modified versions of the original data)
storing the prompt and the multi-part response (Para 0524 storing tasks to include storing of prompt)
obtaining additional image data, wherein the additional image data is descriptive of one or more additional images, wherein the one or more additional images are descriptive of the one or more pages with user-generated text (Fig.68 one or more additional images) ;
processing the additional image data with an optical character recognition model to generate additional text data, wherein the additional text data is descriptive of the user-generated text on the one or more pages (Fig.68 Para 0479 possible user generation from Optical Character Recognition model);
determining the user-generated text is descriptive of a first part of the multi- part response being performed (Fig.65 elements 1328, 1332 first part of multi-part response), based on comparing the user-generated text of the additional image data against the multi-part response without having to continually redetermine semantics of an environment for determining multi-part response deviation ( Fig. 63 element 1316 1318; question 1316, the correctness of the user provided answer with a correctness indicator 1341, The correctness indicator 1341 can display whether the answer provided by the user is correct or incorrect. The results compared in response screenshot can further display the answer provided by the user, and specifically, the answer captured in photo data or from image data generated by the user and broken into step determining multi-part response deviation without redetermining semantics of a background environment)
generating a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response (Para 0360 alert notification; Fig.68 notifications like “collect and combine like terms”, “add” notification or a second action of a multi-part response); and
providing the second augmented image for display (Fig.70 a second augment image of solution screen shot display).
Claim 2. Noble teaches the system of claim 1, wherein the first augmented image is generated with an augmented-reality generation block that generates one or more augmented-reality user interface elements based on the multi-part response; and wherein the second augmented image is generated with the augmented-reality generation block that generates a plurality of augmented-reality user interface elements based on the multi-part response (Para 0482, simultaneous display of the panes for augmented-reality generation block).
Claim 3. Noble teaches the system of claim 1, wherein the overlay comprises a point-of-interest indicator and instructions for completing the first action, and wherein the point-of-interest indicator further indicates a prompt type associated with the prompt (Para 0491 user can manipulate a slider 1338 to transition between the raw pane point-of-interest indicator) .
Claim 4. Noble teaches the system of claim 1, wherein determining the multi-part response to the prompt comprises: processing the prompt and the image data with a search engine to determine one or more search results; and processing the prompt, the image data, and the one or more search results with a transformer model to generate the multi-part response (Para 0188 search streams available).
Claim 5. Noble teaches the system of claim 1, wherein determining the prompt based on the image data comprises: determining the image data is associated with a particular type of problem; and generating the prompt based on the particular type of problem (Fig.66 particular arithmetical type of problem).
Claim 6. Noble teaches the system of claim 5, wherein determining the multi-part response to the prompt comprises: in response to determining the image data is associated with the particular type of problem, processing the image data with a machine-learned model to generate a proof for a detected problem in the one or more images, wherein the proof is descriptive of a multi-part response to the detected problem (Para 0189 verified source proof).
Claim 7. Noble teaches the system of claim 1, wherein the overlay comprises text descriptive of the first action of the multi-part response (Para 0482 overlaying of contents from panes).
Clam 8. Noble teaches the system of claim 1, wherein determining a prompt based on the image data comprises: generating the prompt based on processing the image data with a machine-learned semantic understanding model (Para 0244 machine-learned semantic analysis processor).
Claim 9. Noble teaches the system of claim 8, wherein the semantic understanding model comprises a language model trained for multi-part reasoning (Para 0256 natural language processing, semantic analysis model training purposes).
Claim 10. Noble teaches the system of claim 9, wherein the language model was trained on a plurality of mathematical proofs (Fig. 67 element 1341 response processor can employ, for example, natural language processing, semantic analysis, or the like in determining the correctness or incorrectness of the received responses).
Claim 11. Noble teaches a computer-implemented method, the method (Para 0009) comprising: obtaining, by a computing system comprising one or more processors (Para 0197), image data, wherein the image data is descriptive of one or more images, wherein the one or more images are descriptive of one or more pages (Para 0041,0042 response image data as that relates to automated response-step extraction); determining, by the computing system, a prompt based on the image data, wherein the prompt is descriptive of a request for a response;
wherein determining the prompt comprises: detecting one or more objects within an environment determining a focal point based on the one or more objects within the environment and generating the prompt based on the focal point ( Fig.61 element 1308,1310, 1312 launch button generating overall progress based on determined focal points for one or more learning objects e.g. not started (35), completed (0) in a progress report window; prompts could be displayed accordingly )
determining, by the computing system, a multi-part response to the prompt, wherein the multi-part response comprises a plurality of individual responses associated with the prompt; generating, by the computing system, a first augmented image that comprises an overlay superimposed over at least a portion of the one or more pages of the one or more images, wherein the overlay is descriptive of a first action of the multi-part response; providing, by the computing system, the first augmented image for display; storing the prompt and the multi-part response ( Para 0524 storing tasks to include storing of prompt ).
Noble may not explicitly be determining features within one or more image data is associated with the particular type of problem. Wei, however teaches the determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model based on the associated image data (Para 0057-0060 a machine-learned model i.e. a possible second machine-learned model for a muti-part prompt includes a neural network trained to understand and interpret images or other data inputs more generally to extract meaning therefrom, including to respond to instructions provided in such inputs to be interpreted from images; Para 0122 implementing multiple parallel instances of a machine-learned model such as for a mulit-part system in figure element 1020 of Figure 10A) .
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model associated with the particular type based on the image data being associated with the particular type of problem, as taught by Wei, into the image retrieval system of Noble, in order to provide an improved secondary instance of machine learning model associated with additional specific type of problem efficiently contribute towards an overall goal.
obtaining, by the computing system, additional image data, wherein the additional image data is descriptive of one or more additional images, wherein the one or more additional images are descriptive of the one or more pages with user-generated text Fig.61 through Fig.73 progress multi-part response to prompts indicating a best photo multi-part response when for example a paper containing solution steps and/or a solution to the question is placed on a contrasting background as a multi-part action depicted in fig.63; Fig.61 element 1312 is a progress response button; Para 0482 Fig.65 element 1328, Fig.66 element 1330 simultaneous multi-part image data and display);
processing, by the computing system, the additional image data with an optical character recognition model to generate additional text data, wherein the additional text data is descriptive of the user-generated text on the one or more pages; determining, by the computing system, the user-generated text is descriptive of a first part of the multi-part response being performed; accessing the multi-part response ( Fig 64 image data can be evaluated by a user device, to identify response steps additional text data is descriptive of the user-generated text and to convert these response steps to a machine readable format and/or file; This could include, for example, Optical Character Recognition OCR)
determining, by the computing system, the user-generated text is descriptive of a first part of the multi-part response being performed based on comparing the user-generated text of the additional image data against the multi-part response without having to continually redetermine semantics of an environment for determining multi-part response deviation ( Fig.65 elements 1328,1332 ,1334,1338 In some embodiments, the identifying of these steps and/or the conversion of these response steps can be performed according to one or several of the algorithms disclosed therein; Fig.66,67 identifying of these steps and/or the conversion of these response steps to user-generated text of the additional image data against the multi-part response without having to continually redetermine semantics for accuracy of conversion as in para 0483 Fig.67 element 1341 by slider movement or toggling between panes for deviation)
generating, by the computing system, a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response; and providing, by the computing system, the second augmented image for display {Fig.68 processing the additional image data with an optical character recognition model to generate additional text data, Fig.68 Para 0479 possible user generation from Optical Character Recognition model; determining the user-generated text is descriptive of a first part of the multi- part response being performed as in Fig.65 elements 1328, 1332 first part of multi-part response;
generating a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response as in Para 0360 alert notification; Fig.68 notifications like “collect and combine like terms”, “add” notification or a second action of a multi-part response; and providing the second augmented image for display as in Fig.70 a second augment image of solution screen shot display).
Claim 15. Noble teaches the method of claim 11, further comprising: obtaining, by a computing system comprising one or more processors, an audio input with one or more audio sensors of a user computing device; and wherein the prompt is generated based on the image data and the audio input (Para 0206, 0207 audio input devices).
Claim 16. Noble teaches the method of claim 15, wherein the image data is generated with one or more image sensors of the user computing device (Para 0208,0138 specialized image sensors for audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.
Claim 17. Noble teaches One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising: obtaining image data, wherein the image data is descriptive of one or more images, wherein the one or more images are descriptive of one or more pages; determining a prompt based on the image data, wherein the prompt is descriptive of a request for a response; determining a multi-part response to the prompt, wherein the multi-part response comprises a plurality of individual responses associated with the prompt; generating a first augmented image that comprises an overlay superimposed over at least a portion of the one or more pages of the one or more images, wherein the overlay is descriptive of a first action of the multi-part response; providing the first augmented image for display; obtaining additional image data, wherein the additional image data is descriptive of one or more additional images, wherein the one or more additional images are descriptive of the one or more pages with user-generated text; processing the additional image data with an optical character recognition model to generate additional text data (Fig. 74, element 1412 “OCR Image Data”; Para 0479, 0500 Optical Character Recognition (OCR) ; Analysis can include the OCRing of the image data to identify words, letter, symbols, characters, and/or numbers in the image data) ;
Noble may not explicitly be determining features within one or more image data is associated with the particular type of problem. Wei, however teaches the determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model based on the associated image data (Para 0057-0060 a machine-learned model i.e. a possible second machine-learned model for a muti-part prompt includes a neural network trained to understand and interpret images or other data inputs more generally to extract meaning therefrom, including to respond to instructions provided in such inputs to be interpreted from images; Para 0122 implementing multiple parallel instances of a machine-learned model such as for a mulit-part system in figure element 1020 of Figure 10A) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate determining, by the computing system, to obtain a second machine-learned model comprising a problem-specific machine-learned model associated with the particular type based on the image data being associated with the particular type of problem, as taught by Wei, into the image retrieval system of Noble, in order to provide an improved secondary instance of machine learning model associated with additional specific type of problem efficiently contribute towards an overall goal.
wherein the additional text data is descriptive of the user- generated text on the one or more pages; determining the user-generated text is descriptive of a first part of the multi-part response being performed; generating a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response; and providing the second augmented image for display {Fig.68 processing the additional image data with an optical character recognition model to generate additional text data, Fig.68 Para 0479 possible user generation Character Recognition model; determining the user-generated text is descriptive of a first part of the multi- part response being performed as in Fig.65 elements 1328, 1332 first part of multi-part response; generating a second augmented image comprising a notification, wherein the notification is descriptive of a second action of the multi-part response as in Para 0360 alert notification; Fig.68 notifications like “collect and combine like terms”, “add” notification or a second action of a multi-part response; and providing the second augmented image for display as in Fig.70 a second augment image of solution screen shot display}..
Claim 18. Noble teaches the one or more non-transitory computer-readable media of claim 17, wherein the one or more pages comprise one or more diagrams (Fig.39 element 1064 domain graph; Fig. 47 Para 0467 content input frames with diagrams ).
Claim 19. Noble teaches the one or more non-transitory computer-readable media of claim 18, wherein determining the multi-part response to the prompt comprises: processing the image data and the prompt to generate a multi-part response based on the one or more diagrams (Fig.47 diagrams in solutions).
Claim 20. Noble teaches the one or more non-transitory computer-readable media of claim 17, wherein the operations further comprise: determining a focal point of the image based on a determined gaze of the user (Para 0499 capture and/or generation of image data with one or several cameras focus on intended focal point) ; and wherein at least one of the prompt or the multi-part response are determined based on the focal point ( Para 0513 image data can be captured showing the user response to the recommended content item focused; User may hand-write multi-part response to solution to a problem of the provided content item. When the solution can show one or several steps to solving the problem; Corresponding image data of this solution to the problem).
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Noble et al. (US 2019/0272775 A1) in view of US 20230244938 A1 to Wei et al.(Wei) and further in view of Scanlon et al. (US 12027061 B2)
Claim 12. Noble in combination with Wei teaches the method of claim 11, but not for further comprising of determining a threshold amount of time occurring without an action occurring based on retrieving and processing images obtained with a user computing device. Scanlon, however, teaches the determining a threshold amount of time occurring without an action occurring based on retrieving and processing images obtained with a user computing device (Col.8 lines 35-40 predetermined threshold for particular training level). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate determining a threshold amount of time occurring without an action occurring based on retrieving and processing images obtained with a user computing device, as taught by Scanlon, into the image retrieval system of Noble, in order to provide a better guidance through dynamically adjusted virtual reality experiences efficiently contribute towards an overall goal.
Claim 13. Noble teaches the method of claim 12, wherein at least one of the first augmented image or the second augmented image are generated in response to determining the threshold amount of time occurring without the action occurring based on retrieving and processing images obtained with the user computing device (Scanlon: col. 9 lines 31-42 conversation sessions at a first and second time of augmented image Virtual Reality environment).
Claim 14. Noble teaches the method of claim 11, wherein the first augmented image and the second augmented image are provided for display within an augmented-reality experience (Scanlon col.10 lines 53 second augmented image after the first one is provided for display within an augmented-reality experience of user).
Response to Arguments/Remarks
Applicant's arguments/amendments filed on March 6, 2026 have been considered.
Upon further consideration, a new ground of rejection statement made as necessitated by amendments changing the scope of the claims.
Some of examiner’s response may cite a different portions of an applied reference but do not go further and merely elaborates upon, what is taught in the previously cited portion of a reference.
35USC101
Applicant on pages 9-13 of arguments filed 3/6/2025 indicated First, the Office Action analyzes the claim language only at a "high level of generality" without fully considering the interactions between all elements of the claim. For example, in the rejection of claim 1, the Office Action only briefly addresses the implementation of "generating a first augmented image that comprises an overlay superimposed over at least a portion of the one or more pages of the one or more images, wherein the overlay is descriptive of a first action of the multi-part response," alleging that the claim limitation merely uses "generic computer functions and computer parts." (Office Action, p. 3).
Examiner respectfully traverses and contends that the use of term generic computer functions and computer parts encompasses mostly the abstract idea of mental processes being abstract as a method of organizing human activity and/or mathematical calculations. Since a computer is an electronic device that accepts input, processes data Perform calculations, (execute instructions, and manage data flow), stores information, manage flow of data and outputs results. It works by following instructions from software programs and using hardware components to perform tasks. Applicant further argument on Office Action not giving sufficient weight to the evidence on record demonstrating the claimed invention providing a technical solution to a technical problem. In particular, understanding how to interact with a real-world environment can be non-intuitive and mere text searches may fail to obtain accurate information. Moreover, the identification and performance of multi-part tasks may further these potential issues.
As commented applicant further argues that
1) determining errors in an environment and figuring out how to correct the determined errors can be difficult. In particular, errors in text may be difficult to detect. Additionally, if errors go undetected, the error can lead to a propagation of further errors, which lead to further confusion. The lack of real-time error detection can lead to a user spending time on a problem without understanding when and where they went wrong.
2) Additionally, some errors and/or problems may include a difficult and intricate response in order to resolve the problem or error. Such difficult and intricate responses may be prone to user confusion; therefore, further errors may be generated when attempting to resolve the errors. Applicant's contends that the claimed invention provides solution(s) to these and other challenges existing in the field. As described in Applicant's Specification. The systems and methods of the present disclosure
provide a number of technical effects and benefits.
i) As one example, the system and methods can provide an augmented-reality tutor experience. In particular, the systems and methods disclosed herein can leverage optical character recognition, natural language processing, and augmented-reality rendering to provide an interactive experience for identifying errors and providing multi- part responses.
ii) Another second one is technical benefit of the systems and methods of the present disclosure is the ability to leverage one or more machine-learned models to understand an environment and provide a step by step process for completing a task. For example, the systems and methods can determine the semantics of an environment, can determine a prompt associated with the environment, can determine a multi-part response associated with the prompt, and can continually collect data to ensure a user
completes actions associated with the multi-part response.
iii) Another third example of technical effect and benefit relates to improved computational efficiency and improvements in the functioning of a computing system. For example, the systems and methods disclosed herein can leverage the storage of the determined prompt and multi-part response to continually compare the additionally obtained data against the multi-part response without having to continually redetermine the semantics of the environment for error detection.
Examiner respectfully traverses and finds that all the above argument are abstract in terms of being directed to the mental process of collecting data (e.g., image data, additional image data, machine learned models input, additionally obtained data against the multi-part response), analyzing that data (e.g., determining a prompt and a multi-part response to the prompt, generating images and additional image data, semantics of environment, leverage optical character recognition, natural language processing, and augmented-reality rendering to provide an interactive experience for identifying errors and providing multi- part responses, determination of prompt and multi-part response to continually compare the additionally obtained data against the multi-part response), and providing outputs (e.g., providing the first and second augmented images for display) based on that analysis. These are held by CAFC, e.g., Electric Power Group, University of Florida Research Foundation, and/or Yousician (non-precedential and characterized as being abstract for a method of organizing human activity in terms of a method of teaching/training human beings { MPEP2106.04(A)(2)}. It could also be characterized as being directed to training/employing a machine learning model in a particular technological environment (“AI chatbots”) and thereby abstract under the CAFC’s decision in Recentive Analytics. To the extent that claim, e.g., a computing system comprising one or more processors, employing OCR, employing machine learning models, these are all well-known, routine, and conventional devices and/or software techniques as evidence by the limited disclosure in Applicant’s specification (cite spec sections) in regard how to make and/or use these devices and thereby do not constitute “significantly more” than the claimed abstract idea(s). None of these devices are improved qua devices, in other words, in terms of none of them will, e.g., run faster, use less power, and/or be manufactured more cheaply as a result of embodying Applicant’s invention does not result in “improved computational efficiency with improvements in the functioning of a computing system”. To the extent that the claim employing “augmented reality” for visual output would not overcome 35USC101 rejections since not being “significantly more” because it is merely employing a certain visual display.
35USC102/103
Applicant on Pages 14-16 of argument file on 3/5/2026 asserts that the prior art Noble et al. does not teach or disclose "determining to obtain a second machine-learned model comprising a problem-specific machine-learned model associated with the particular type based on the image data being associated with the particular type of problem," as recited as amended claim 1. The cited reference Scanlon also fail to cure the deficiencies of Noble et al..
Examiner however contends and finds that the additional prior art Wei et al. explicitly prompts multi-part response to machine learned models with diversified objectives. An instructive query can present substantially any type of problem and question that can include image processing queries, etc. ( paragraph 0060). Hence a second machine- learned model comprising a problem-specific machine-learned model associated with the particular type could be obtained based on the image data being associated with the particular type of problem," as recited by amended claim. Hence examiner finds that independent claims 1, 11, 17 is patentably not distinct from the cited references and is allowable. Independent claims 11 and 17 incorporate similar limitations to those amended into claim 1. In this regard, Applicant submits that independent claims 11 and 17 are patentably distinct from the cited reference combinations.
Applicant previously on pages 19-22 of the earlier remarks 10/6/2025 asserted the prior art Noble fails to teach obtaining additional image data after providing the one or more first augmented images for display. Instead, Noble merely teaches an image capture pane before a conversion pane. (Noble, paras. [0478] - [0480]). Therefore, Noble fails to teach or disclose "storing the prompt and the multi-part response; obtaining additional image data, wherein the additional image data is descriptive of one or more additional images, wherein the one or more additional images are descriptive of the one or more pages with user-generated text; processing the additional image data with an optical character recognition model to generate additional text data, wherein the additional text data is descriptive of the user-generated text on the one or more pages; accessing the multi-part response; determining the user-generated text is descriptive of a first part of the multi-part response being performed based on comparing the user-generated text of the additional image data against the multi-part response without having to continually redetermine semantics of an environment for determining multi-part response deviation; generating a second augmented image comprising a notification," as recited by amended claim 1.
Examiner had respectfully traversed applicant assertions by indication that prior art Noble et al. further found in Figs.61-73 depicting continuous interface interaction generating content delivery, obtaining possible additional image data that could provide one or more first augmented images for display (Noble et al. para 0477-0484 evaluation screen 1326 can further include features configured to allow the user to modify all or portions of the content of the conversion, which content can be, for example, shown in the conversion pane 1328; Retake and confirmation buttons are also provided to manipulate control of image data). The rejection statement have similarly been modified above. Para 0480 additional image data, Fig.65 element 1336 retake button. The evaluation screen 1326 can further include a retake button 1336 that when manipulated enables the regenerating of the photo data.
About the deficiencies of Noble et al. in combination with Scanlon et al., applicant had indicated previously that the combination fails to teach or disclose "storing the prompt and the multi-part response; . .. accessing the multi-part response; determining the user-generated text is descriptive of a first part of the multi-part response being performed based on comparing the user-generated text of the additional images. But the secondary art Scanlon in col.8 lines 35-40 is found to determines a predetermined threshold for particular training level that could involve storing prompt and the multi-part response for comparing the user-generated text accuracy achievement levels.
Hence 35USC102/103 rejections previously maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.:
US 11386624 B2 Su; Kai-Hung et al.
Artificial intelligence and augmented reality system and method
US 20210383711 A1 Panuganty; Ramesh et al.
Intelligent and Contextual System for Test Management
US 20200364448 A1 GOBLE; Matthew et al.
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/S.Z/Examiner, Art Unit 3715
June 12, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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