DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to arguments and amendments entered on July 2, 2026 for the patent application 18/381,021 filed on October 17, 2023. Claims 1 and 11 are amended. Claims 4 and 14 are cancelled. Claims 1-3, 5-13 and 15-22 are pending. The first office action of December 1, 2023; the second office action of February 8, 2024; the third office action of May 15, 2024; the fourth office action of December 6, 2024; the fifth office action of March 28, 2025; the sixth office action of July 22, 2025; the seventh office action of September 8, 2025; the eighth office action of November 20, 2025; and the ninth office action of January 20, 2026 are fully incorporated by reference into this Final Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-13 and 15-22 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.
Step 1 – “Statutory Category Identification”
Claim 1 is directed to “an apparatus” (i.e. a machine); and claim 11 is directed to “a method” (i.e. a process), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to an abstract idea of “modifying educational content,” in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“receiving user data including information about a user;
generating a digital avatar based on the user data by utilizing a digital avatar model, wherein the digital avatar model is trained using a plurality of user data items and digital avatar training data comprising information from a plurality of pre-existing digital avatars from a digital avatar database;
communicating a first set of educational content comprising a visual element data structure associated with a visual element, wherein the visual element data structure is configured to execute at least one rule for displaying the visual element, to a user;
receiving a reaction datum comprising image data based on an interaction between the user and the digital avatar,
wherein receiving the reaction datum comprises:
receiving image data;
processing the received image data, wherein the machine vision system is configured to extract at least one reaction feature from the received image data; and
determining a user sentiment, wherein the user sentiment indicates a response of the user as a function of the first set of educational content, and wherein determining the user sentiment further comprises applying a trained first machine learning model to the at least one reaction feature;
determining a content modification model as based on the reaction datum, wherein the content modification model comprises a toxicity reduction model, wherein the toxicity reduction model is configured to remove portions of the first set of educational content, and wherein the removed portions of the first set of educational content comprise profanity and gory photographs,
wherein the toxicity reduction model comprises a toxicity reduction machine learning model trained with training data
comprising a plurality of educational content data correlated with a plurality of educational content with undesirable element data, and wherein is further configured to:
receive a content request;
collect a second set of educational content based on the content request;
determine a filtered second set of educational content based on the second set of educational content and the content modification model, wherein the filtered second set of education content is altered as a function of the toxicity reduction model;
determine an updated visual element data structure as a function of the second set of educational content and the filtered second set of education content;
communicate the filtered second set of educational content,
wherein records a user reaction datum to the filtered second set of education content and
wherein sends a second communication in accordance with the updated visual element data structure wherein the at least one rule is configured to direct display an altered version of the filtered second set of educational content to the user based on the user reaction datum;
notify the user that changes have been made to the filtered second set of educational content and allow the user to view an original version of the filtered second set of educational content; and
calibrate and train the data structure, using a second machine learning model, such that data associated with the data structure is configured to be modified.”
Per claim 11:
“receiving user data including information about a user;
generating a digital avatar based on the user data by utilizing a digital avatar model, wherein the digital avatar model is trained using a plurality of user data items and digital avatar training data comprising information from a plurality of pre-existing digital avatars from a digital avatar database;
communicating a first set of educational content to a user comprising a visual element data structure associated with a visual element, wherein the visual element data structure is configured to execute at least one rule for displaying the visual element, to a user;
receiving a reaction datum comprising image data based on an interaction between the user and the digital avatar,
wherein receiving the reaction datum comprises:
receiving image data;
processing the received image data, configured to extract at least one reaction feature from the received image data; and
determining a user sentiment, wherein the user sentiment indicates a response of the user as a function of the first set of educational content, and wherein determining the user sentiment further comprises applying a trained first machine learning model to the at least one reaction feature;
determining a content modification model as based on the reaction datum, wherein the content modification model comprises a toxicity reduction model, wherein the toxicity reduction model is configured to remove portions of the first set of educational content, and wherein the removed portions of the first set of educational content comprise profanity and gory photographs,
wherein the toxicity reduction model comprises a toxicity reduction machine learning model trained with training data comprising a plurality of educational content data correlated with a plurality of educational content with undesirable element
receiving a content request;
collecting a second set of educational content based on the content request;
determining a filtered second set of educational content based on the second set of educational content and the content modification model, wherein the filtered second set of education content is altered as a function of the toxicity reduction model;
determining an updated visual element data structure as a function of the second set of educational content and the filtered second set of education content;
communicating the filtered second set of educational content,
wherein which records a user reaction datum to the filtered second set of education content and
wherein sends a second communication in accordance with the updated visual element data structure wherein the at least one rule is configured to direct the user to display an altered version of the filtered second set of educational content to the user based on the user reaction datum;
notify the user that changes have been made to the filtered second set of educational content and allow the user to view an original version of the filtered second set of educational content; and
calibrating and training the data structure using a second machine learning model, such that data associated with the data structure is configured to be modified.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim “a practical application” because to the extent that, e.g., “a machine vision system further comprising an image sensor,” “at least a processor,” “a user device” and “an optical sensor,” are claimed, as this is merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed “modifying educational content,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a machine vision system further comprising an image sensor,” “at least a processor,” “a user device” and “an optical sensor,” are claimed, these are a generic, well-known, and conventional data gather computing elements. As evidence that these are generic, well-known, and a conventional data gathering computing elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the claimed “a machine vision system,” isn’t adequately described in the written description of the specification as originally filed.
Also, “an image sensor,” as described in para. [0037] discloses the following: “In some cases, at least a camera may include an image sensor.” This is reasonably interpreted as an element of a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
Likewise, the claimed “at least a processor,” as described in para. [0008] discloses the following: “[0008] Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 modifying educational content is illustrated. Apparatus 100 may include a computing device. Apparatus 100 may include a processor. Processor may include, without limitation, any processor described in this disclosure. Processor may be included in a computing device. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone.” This is also reasonably interpreted as an element of a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
Finally, the claimed “a user device” and “an optical sensor,” as described in para. [0020] discloses the following: “[0020] Still referring to FIG. 1, user device 120 may include a device operated by a user, such as a smartphone, tablet, laptop computer, desktop computer, smartwatch, vehicle media player, or digital assistant device. In some embodiments, a user may request a set of educational content from apparatus 100 using user device 120. In some embodiments, apparatus 100 may communicate a set of educational content to user device 120. In some embodiments, apparatus 100 communicating a set of educational content to user device 120 may include configuring user device 120 to communicate the set of educational content to user operating user device 120. Such communication to user may be, in non-limiting examples, in a visual format such as an image or video, or in an audio format. In some embodiments, user device 120 may include a sensor, such as an optical sensor or an audio sensor, and may record a user reaction to a set of educational content.” Again, these are also reasonably interpreted as a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
As such, the Applicant’s claimed “a machine vision system further comprising an image sensor,” “at least a processor,” “a user device” and “an optical sensor,” are reasonably understood as generic, well-known, and conventional data gather computing elements. Therefore, these elements provide no details of anything beyond ubiquitous standard equipment and are reasonably understood as not providing anything significantly more. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-3, 5-10, 12-13 and 15-22 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-3, 5-10, 12-13 and 15-22 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1 or 11. Therefore, claims 1-3, 5-13 and 15-22 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
Claim Rejections - 35 USC § 112
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.
Claims 1-3, 5-13 and 15-22 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 11 recite the limitation “an optical sensor.” The limitation is originally introduced in claims 1 and 11, respectfully. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “[[an]] the optical sensor”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1 and 11. Therefore, claims 1 and 11 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 2-3, 5-10, 12-13 and 15-22 are also rejected 35 U.S.C. § 112(b), based on their respective dependencies to claim 1 or 11.
Claims 6, 7, 16 and 17 recite the limitation “machine vision system.” The limitation is originally introduced in claims 1 and 11, respectfully. As such, the subsequent limitation is either (1) not following antecedent basis (i.e. “the machine vision system”); or (2) is intended to be a new limitation which ambiguously conflicts with the previous limitation of claim 1 and 11. Therefore, claims 6, 7, 16 and 17 are rejected under 35 U.S.C. § 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claims 7 and 17 are further rejected 35 U.S.C. § 112(b), based on their respective dependencies to claim 6 or 16.
Response to Arguments
The Applicant’s arguments filed on July 20, 2026 related to claims 1-3, 5-13 and 15-22 are fully considered, but are not persuasive.
Rejection of claims under 35 U.S.C. § 101
Step 2A, Prong one
Mental Processes
The Applicant respectfully argues “The present claim recites machine-vision-based feature extraction and application of trained machine-learning models to determine user sentiment and dynamically modify educational content, which cannot practically be performed in the human mind.
Amended claim 1 recites a sequence of computer-implemented operations that cannot practically be performed in the human mind. These include machine vision processing of reaction image data, extracting reaction features, determining sentiment using a trained machine learning model, refining data using a toxicity reduction model, filtering educational content, updating visual element data structure, and training and calibrating a data structure using a machine learning model. Such operations require processor-executed instructions operating on structured digital datasets and application of trained machine-learning models to structured image and content data, contributing to dynamic modification of educational content based on model outputs. These operations cannot be practically performed in the human mind or with pen and paper because they require automated processing of large structured datasets and execution of trained machine-learning models and calibration of data structures using processor-executed instructions. In particular, extraction of reaction features from image data using a machine vision system and determination of user sentiment using trained machine-learning models require automated computational processing that cannot practically be performed in the human mind.
Courts have recognized that claims involving several-step manipulation of data that cannot practically be performed mentally do not recite mental processes. See Synopsys, Inc. V. Mentor Graphics Corp., 839 F.3d 1138, 1148 (Fed. Cir. 2016) (noting that claims involving multi-step data manipulation that cannot conceivably be performed mentally are not directed to mental processes). Here, amended claim 1 recites a defined machine-learning and machine-vision workflow requiring extraction of reaction features from image data using a machine vision system, determination of user sentiment using a trained machine-learning model, application of a toxicity reduction machine-learning model to modify educational content, and generation and calibration of a visual element data structure. These limitations define a specific processor-implemented computational workflow operating on structured image and educational content data that cannot practically be performed in the human mind and therefore fall outside the mental processes grouping.”
The Examiner respectfully disagrees. First, actual mental performance of the abstract idea is not required, Further, the MPEP § 2106.04(a)(2)(III)(C) states that “claims can recite a mental process even if they are claimed as being performed on a computer” and that “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.” In the present case, the independent claim limitation performs steps that are performed on a generic “user device” using “a machine vision system” (i.e. which is reasonably considered a camera) to further provide a solution in a computer environment, and merely uses a computer as a tool to perform the concept. As such, the argument is not persuasive.
The Applicant respectfully argues “The present claim is also consistent with USPTO Subject Matter Eligibility Example 47, which explains that claims reciting specific machine-learning training and application steps that cannot practically be performed mentally are not directed to mental processes. Like the eligible claims in Example 47, amended claim 1 recites specific machine-vision and machine-learning operations, including extraction of reaction features from image data using a machine vision system, determination of user sentiment using a trained machine-learning model, application of a toxicity reduction machine-learning model to modify educational content, and generation and calibration of a visual element data structure within a defined computational workflow.”
The Examiner respectfully disagrees. As previously stated in past office actions, although “the examples” consist of hypothetical cases that may parallel Supreme Court decisions and Federal Circuit decisions, the examples are not considered precedential and are not fully considered as binding precedent on the USPTO. That being said, the Applicant’s argument with regard to “Example 47” does not super cede the Examiner’s subject-matter eligibility analysis using precedential Supreme Court decisions and Federal Circuit decisions. As such, the argument is not persuasive.
The Applicant respectfully argues “Moreover, the present claim is distinguishable from Electric Power Group v. Alstom, 830 F.3d 1350 (Fed. Cir. 2016). In Electric Power Group, the claims were directed to collecting, analyzing, and displaying information without reciting any specific technological means for performing those functions. In contrast, amended claim 1 recites specific technological operations including processing reaction image data using a machine vision system to extract reaction features, determining user sentiment using a trained machine-learning model, applying a toxicity reduction machine-learning model to modify educational content, generating and updating a visual element data structure based on the modified content, and calibrating the data structure using processor-executed machine-learning operations. These limitations define a particular processor-implemented technological workflow for automated detection of user reactions and dynamic modification of educational content, rather than merely reciting the result of collecting and analyzing information.
For at least these reasons, amended claim 1 recites specific computer-implemented machine-learning operations that cannot practically be performed in the human mind and therefore does not fall within the mental processes grouping of abstract ideas under MPEP §2106.04(a)(2)(III). Applicant respectfully submits that amended claim 1 does not recite a mental process and is not directed to an abstract idea under Step 2A, Prong One.”
The Examiner respectfully disagrees. The Applicants claims clearly read on “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Specifically, the multiple steps of “receiving … data,” are analogous to “collecting information.” The steps of “generating…based on a model,” and “determining…” are analogous to “analyzing it.” Finally, the steps of “communicating…,” “notifying the user” and “calibrating…” are outputs that are analogous to “displaying certain results of the collection analysis.” As such, the argument is not persuasive.
Certain Methods of Organizing Human Activity
The Applicant respectfully argues “The MPEP states that not all methods of organizing human activity are considered abstract ideas. According to MPEP §2106.04(a)(2)(II), the phrase "methods of organizing human activity" is used to describe concepts relating to:
fundamental economic principles or practices (including hedging, insurance, mitigating risk);
commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and
managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
Moreover, this grouping is limited to activity that falls within the enumerated sub-groupings of fundamental economic principles or practices, commercial or legal interactions, managing personal behavior, and relationships or interactions between people, and is not to be expanded beyond these enumerated sub-groupings except in rare circumstances as explained in MPEP §2106.04(a)(3). Claim 1, as amended, includes limitations directed to machine-vision-based extraction of reaction features from image data, determination of user sentiment using a trained machine-learning model, application of a toxicity reduction machine-learning model to modify educational content, and generation and calibration of a visual element data structure, which cannot be fairly characterized as reciting "managing personal behavior or interactions between people" as alleged in the Office Action. Office Action, p. 3.
As amended, claim 1 includes limitations directed to processing reaction image data using a machine vision system, extracting reaction features, determining user sentiment using a trained machine-learning model, applying a toxicity reduction machine-learning model to modify educational content, and generating and updating a visual element data structure based on the modified content. These limitations recite a specific processor-implemented technological workflow for automated detection of user reactions and dynamic modification of educational content, rather than any form of commercial interaction, economic practice, or management of personal behavior between people.”
The Examiner respectfully disagrees. It is worth noting in MPEP §2106 under “II. Certain Methods Of organizing Human Activity,” certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. As applied in this case, a person interacting with a computer for “modifying educational content,” reasonably constitutes identifying the Applicant’s claims as an abstract idea in the form of “certain methods of organizing human activity.” As such, the argument is not persuasive.
The Applicant respectfully argues “Under the USPTO's 2025 guidance addressing subject matter eligibility of artificial intelligence-related inventions, limitations directed to training a machine learning model do not recite an abstract idea. As such, at least the limitations describing limitations directed to training and application of machine-learning models do not recite an abstract idea.”
The Examiner respectfully disagrees. The Applicant is clearly misguided with regard to the revised guidance. Specifically, the mere mentioning of “machine learning” is not an automatic pathway to patentability. The Applicant is urged to revisit the guidelines. As such, the argument is not persuasive.
The Applicant respectfully argues “Similar to the claims in McRO, Inc. v. Bandai Namco Games America Inc., which were found eligible because they used specific rules and algorithms to automate a technical process and improve computer functionality, the present claim requires particularized operations including machine-vision-based extraction of reaction features from image data, determination of user sentiment using trained machine-learning models, application of a toxicity reduction machine-learning model to modify educational content, and dynamic generation and calibration of a visual element data structure. These limitations reflect a concrete technical implementation that improves the functioning and reliability of the computer-implemented system. Moreover, unlike the ineligible claims in Synopsys, Inc. v. Mentor Graphics Corp., which merely recited an abstract goal without specifying how to achieve it, the present claim recites defined machine-vision and machine-learning processing steps that constrain how reaction data is analyzed and how educational content is dynamically modified, thereby demonstrating that the claim is directed to a specific technological solution rather than an abstract objective.
Accordingly, because claim 1 as amended recites specific machine-vision and machine-learning operations for automated analysis of user reactions and modification of educational content, at least the above-mentioned limitations do not recite methods of organizing human activity.”
The Examiner respectfully disagrees. The Applicant’s claims are not “directed to a patentable, technological improvement...designed to achieve an improved technological result in conventional industry practice,” as applied to McRo. Here, Applicant’s claims can be practiced by a human without a highly specific skill set as identified in McRo. When compared to McRo, Applicant's claims are unlike the specialized claimed solution of “accurate and realistic lip synchronization and facial expressions in animated characters that previously could only be produced by human animators.” The Applicant’s claims do not go beyond requiring the collection, analysis, and display of available information in a particular field, stating those functions in general terms, without limiting them to technical means for performing the functions that are arguably an advance over conventional computer and network technology. The claims, defining a desirable information-based result and not limited to inventive means of achieving the result, fail under § 101. As such, the arguments are not persuasive.
Step 2A, Prong two
The Applicant respectfully argues “As amended, Claim 1 recites a specific technological implementation that integrates any alleged judicial exception into a practical application. The Examiner characterizes the claimed machine vision system, processor, user device, and optical sensor as merely performing data gathering or presentation. However, the amended claim does not merely collect or display information. Rather, it requires a processor-implemented workflow in which reaction image data captured by an optical sensor is processed by a machine vision system to extract reaction features, a trained machine-learning model determines user sentiment from those extracted features, and a toxicity reduction machine-learning model dynamically modifies educational content based on the determined sentiment. The claim further requires generation and calibration of a visual element data structure used to control subsequent presentation of modified educational content. These limitations define a specific technological process for automated detection of user reactions and dynamic modification of digital educational content, not mere data gathering or presentation.
The claimed machine vision processing and application of trained machine-learning models are integral to the operation of the claimed system and cannot be characterized as insignificant extra-solution activity. The machine vision system is not merely collecting data but is specifically configured to process image data to extract reaction features that are then used as inputs to trained machine-learning models. Those models are applied to determine user sentiment and dynamically modify educational content using a toxicity reduction model. The generation and calibration of the visual element data structure further enable automated control of how modified educational content is then displayed. These operations improve the accuracy, responsiveness, and adaptability of computer-implemented educational content delivery systems by enabling real-time content modification based on detected user reactions. Accordingly, the additional elements are not merely linking a judicial exception to a technological environment under MPEP §2106.05(h), nor are they mere instructions to apply an abstract idea using a computer under MPEP §2106.05(f). Instead, they define a particular technological implementation that uses machine vision and trained machine-learning models to transform reaction image data into dynamically modified educational content through a structured processor-executed workflow.”
The Examiner respectfully disagrees. The Applicant’s argument is misguided as to the proper analysis of a “Practical Application” as required under Step 2A, Prong 2. Specifically, the Applicant’s argument appears to describe claimed utility, which is not the test. Instead, the Applicant’s claims are not considered a “Practical Application,” because the claims do not provide any of the following:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
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Furthermore, there are also several factors that reasonably explain that the Applicant’s claims are not indicative of integration into a practical application, which include:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
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Here, the Applicant’s claims are not providing any technological advancement as described in the first five bulleted factors and, as described above in the rejection, the Applicant’s claims are merely claimed to use a computer as a tool to perform an abstract idea and to generally link the use of a judicial exception to a particular technological environment or field of use. As such, the argument is not persuasive.
The Applicant respectfully argues “The August 2025 Subject Matter Eligibility Memorandum clarifies that claims reciting specific AI implementations that cannot practically be performed in the human mind and that improve computer functionality constitute practical applications rather than mere use of a computer as a tool. The present claim recites such an implementation, including machine-vision-based extraction of reaction features and application of trained machine-learning models to dynamically modify educational content.
Moreover, Ex Parte Desjardins (Dec 2025) confirms that claims directed to specific improvements in the operation of machine-learning systems and data processing pipelines constitute practical applications. As in Desjardins, the present claim improves the operation of a computer-implemented system by requiring machine-vision-based feature extraction, application of trained machine-learning models to determine user sentiment, and dynamic modification and calibration of educational content presentation based on those determinations. These limitations improve the functioning of the computer-implemented system itself rather than merely applying an abstract idea in a particular field of use. Consistent with Desjardins, the claim as amended improves the operation of a specifically arranged machine-learning system itself, as opposed to merely applying a generic model to a specific technological environment or field of use.
The claimed machine vision and machine-learning operations are integral to achieving the claimed modification of educational content and cannot be removed without rendering the claim inoperable, confirming that they are not insignificant extra-solution activity.
Accordingly, and for at least the above-described reasons, Applicant respectfully submits that claim 1 as amended is not directed to an abstract idea. Therefore, pursuant to Step 2A of the 35 U.S.C. § 101 analysis prescribed in Alice, claim 1 as amended recites patentable subject matter. Applicant respectfully requests that this rejection be withdrawn.”
The Examiner respectfully disagrees. In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks.
In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).
The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.
In the present case, the Applicant’s statement of “specific AI implementations,” is set forth in a conclusory manner. In fact, the written description of the specification as originally filed fails to provide any specific details beyond simply using AI and machine learning models. As such, the Applicant’s written description is a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, which fails to improve technology or a technical field. Therefore, the argument is not persuasive.
Step 2B
The Applicant respectfully argues “Although the Step 2B analysis by the Office is rendered moot in light of the amendments to claim 1 and the arguments provided above regarding Step 2A Prong 2, the Office nonetheless alleges that the additional elements of the claim as described "are reasonably understood as generic, well-known, and conventional data gather computing elements." Office Action, p. 8. In Berkheimer v. HP, Inc., a claim was found to recite an inventive concept when its additional elements are not well-understood, routine, or conventional in the field. 881 F.3d 1360 (Fed. Cir. 2018). Additionally, Alice states that in Step 2B, "Examiners should consider whether the claim "purports to improve the functioning of the computer itself' or 'any other technology or technical field.' (MPEP § 2106.05(a) (citing Alice)). The Examiner asserts that the additional limitations "do not provide anything significantly more." Office Action, p. 8. Applicant respectfully rebuts this assertion.
Under Berkheimer, whether a claim element is "well-understood, routine, and conventional" is a question of fact, and the Office must identify evidentiary support showing that the specific claimed combination is routine in the art. 881 F.3d 1360, 1369 (Fed. Cir. 2018). No such evidence is provided here. The amended claim recites a multi-stage machine-vision and machine-learning pipeline that is neither routine nor conventional. The claim requires processing reaction image data using a machine vision system to extract reaction features, determining user sentiment using a trained machine-learning model, applying a toxicity reduction machine-learning model to dynamically modify educational content based on the determined sentiment, generating and updating a visual element data structure configured to control presentation of the modified content, and calibrating the data structure using processor-executed machine-learning operations. The inventive concept lies, at least in part, in the ordered combination of machine-vision-based extraction of reaction features and application of multiple trained machine-learning models to automatically modify and control presentation of educational content based on detected user reactions. These transformations improve the computer's ability to automatically detect user reactions from image data and dynamically adapt digital educational content in real time using trained machine-learning models. This ordered combination of machine-vision processing and machine-learning-based content modification is neither well-understood, routine, nor conventional, and the Office provides no evidence to the contrary as required by Berkheimer.”
The Examiner respectfully disagrees. In a step 2B analysis, an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of four options:
Option 1 – Statement(s) by Applicant
Option 2 – Court Decisions in MPEP § 2106.05(d)(II)
Option 3 – Publication(s)
Option 4 – Official Notice
Here, the Examiner has chosen to apply Option 1 – Statement(s) by Applicant as support of the rejection at step 2B of the analysis.
Statement(s) by the Applicant is/are defined as the following:
(1) An explanation based on an express statement in the specification (e.g., citation to a relevant portion of the specification) that demonstrates the well-understood, routine, conventional nature of the additional element(s). A specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional element(s) as conventional (or an equivalent term); as a commercially available product; or, in a way that shows the element is widely prevalent or in common use; or
(2) A statement made by an applicant during prosecution, that demonstrates the well-understood, routine, conventional nature of the additional element(s).
Here, the Examiner has chosen to incorporate the first definition (i.e.(1)) within the subject-matter eligibility analysis as provided above in the rejection. Specifically, the Examiner has identified that the claims do not include additional elements that either alone or in combination are sufficient to amount to “significantly more” than the judicial exception because to the extent that, e.g. “a machine vision system further comprising an image sensor,” “at least a processor,” “a user device” and “an optical sensor,” are claimed these are generic, well-known, and conventional data gather computing elements.
The Examiner, provides evidence of this by citing an express statement in the specification (e.g., citation to a relevant portion of the specification) that demonstrates the well-understood, routine, conventional nature of the additional element(s). This requirement is achieved by citing the Applicant’s own written description of the specification as originally filled.
Specifically, the claimed “a machine vision system,” isn’t adequately described in the written description of the specification as originally filed.
Also, “an image sensor,” as described in para. [0037] discloses the following: “In some cases, at least a camera may include an image sensor.” This is reasonably interpreted as an element of a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
Likewise, the claimed “at least a processor,” as described in para. [0008] discloses the following: “[0008] Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 modifying educational content is illustrated. Apparatus 100 may include a computing device. Apparatus 100 may include a processor. Processor may include, without limitation, any processor described in this disclosure. Processor may be included in a computing device. Computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone.” This is also reasonably interpreted as an element of a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
Finally, the claimed “a user device” and “an optical sensor,” as described in para. [0020] discloses the following: “[0020] Still referring to FIG. 1, user device 120 may include a device operated by a user, such as a smartphone, tablet, laptop computer, desktop computer, smartwatch, vehicle media player, or digital assistant device. In some embodiments, a user may request a set of educational content from apparatus 100 using user device 120. In some embodiments, apparatus 100 may communicate a set of educational content to user device 120. In some embodiments, apparatus 100 communicating a set of educational content to user device 120 may include configuring user device 120 to communicate the set of educational content to user operating user device 120. Such communication to user may be, in non-limiting examples, in a visual format such as an image or video, or in an audio format. In some embodiments, user device 120 may include a sensor, such as an optical sensor or an audio sensor, and may record a user reaction to a set of educational content.” Again, these are also reasonably interpreted as a generic computer and/or computer system and provide no details of anything beyond ubiquitous standard equipment.
As such, the Applicant’s claimed “a machine vision system further comprising an image sensor,” “at least a processor,” “a user device” and “an optical sensor,” are reasonably understood as generic, well-known, and conventional data gather computing elements. Therefore, these elements provide no details of anything beyond ubiquitous standard equipment and are clearly and reasonably understood to be commercially available products that are widely prevalent and in common use.
Therefore, it is the express statement in the specification that demonstrates the well-understood, routine, conventional nature of the additional element(s) that satisfies the Examiner’s burden of providing evidence under the required Berkheimer analysis at step 2B and ultimately overcoming the Applicant’s inaccurate argument. As such, the argument is not persuasive.
The Applicant respectfully argues “The August 2025 Subject Matter Eligibility Memorandum explains that when a claim recites a specific technological implementation of machine-learning operations, such as processing reaction image data using a machine vision system, extracting reaction features from the image data, applying trained machine-learning models to determine user sentiment and dynamically modify educational content, and generating and calibrating a visual element data structure within a defined computational pipeline, such recited features constitute additional elements evidencing an improvement in computer functionality rather than an abstract idea. The amended claim recites exactly this type of specific technological implementation involving machine-vision-based feature extraction and processor-executed machine-learning models used to dynamically modify and control presentation of educational content.
Moreover, consistent with Alice and MPEP § 2106.05(a), the analysis at Step 2B must consider whether the claim purports to improve the functioning of the computer itself or another technology. By requiring the processor to process reaction image data using a machine vision system, extract reaction features from the image data, determine user sentiment using trained machine-learning models, dynamically modify educational content using a toxicity reduction machine-learning model, and generate and calibrate a visual element data structure configured to control presentation of the modified content, the amended claim improves the computer's ability to automatically detect user reactions and perform real-time modification and delivery of digital educational content. This constitutes an improvement in computer functionality and in automated digital content modification and machine-learning-based content delivery technology.
For at least these reasons, the amended claim contains significantly more than any alleged judicial exception and is patent-eligible under Step 2B. Applicant respectfully requests that the §101 rejection be withdrawn. As such, Applicant submits that claim 1 as amended is allowable under 35 U.S.C. §101, at least for the reasons stated above.
Claim 11 recites, substantially, the same limitations as claim 1. Therefore, Applicant submits that the rejection to claim 11 has been overcome for the same reasons as to claim 1. Applicant respectfully requests reconsideration and withdrawal of the rejection.
Claims 2-3, 5-10, 12-13, and 15-22 depend, directly or indirectly, on claims 1 or 11 and thus recite all of the same elements as claim 1 and claim 11. Applicant, therefore, submits that claims 2-3, 5-10, 12-13, and 15-22 overcome these rejections for at least the same reasons as discussed above with reference to claims 1 and 11.
The Examiner respectfully disagrees. First, the Applicant’s argument with regard to “The August 2025 Subject Matter Eligibility Memorandum,” has been asked and answered; and continues to be unpersuasive. Second, the Applicant’s argument is conclusory and has provided no evidence supporting the statement “the amended claim improves the computer's ability to automatically detect user reactions and perform real-time modification and delivery of digital educational content.” Finally, independent claims 1 and 11 continue to be deemed as ineligible subject-matter as they do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. Likewise, the dependent claims do not change the result of the previously detailed subject-matter eligibility analysis of the independent claims. As, such, the argument is not persuasive. Therefore, the rejections under 35 U.S.C. § 101 are not withdrawn.
Conclusion
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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT P BULLINGTON whose telephone number is (313)446-4841. The examiner can normally be reached on Mon.-Fri. 8:00-4:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Vasat, can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Robert P Bullington, Esq./
Primary Examiner, Art Unit 3715
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