DETAILED ACTION
Response to Amendment
The Applicant’s Amendment filed 07/21/2026 has been entered. Claims 1-20 are pending in the Application.
Response to Arguments
Applicant's arguments filed 07/21/2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Regarding claims 1 and 11, the Applicant submitted that the amended claims integrate any judicial exception into a practical application and separately recite significantly more than the exception because they describe a specific technique for maintaining a patient’s stress level by jointly and adaptively controlling task difficulty and empathetic content via a shared adaptive algorithm. The Examiner respectfully disagrees. The Examiner submitted that the additional features such as the adaptive algorithm and generative AI model are recited at a high-level of generality (e.g. generating novel content/empathetic feedback using a generative AI model, adjusting difficulty using an adaptive algorithm. No further information/improvement regarding the adaptive algorithm and/or the generative AI model is provided) such that it amounts to no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. See MPEP 2106.05 Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception
Based on the reasoning above, the rejection should be maintained. Please see below for the detailed rejection.
Applicant's arguments filed 07/21/2026 with respect to the prior art rejection have been fully considered but are moot upon a new ground of rejection under 35 U.S.C. 103 as being unpatentable over Bohbot US 20140315169, and in view of Crabtree et al US 20250352907.
Regarding claims 1 and 11, the Applicant argues that the cited arts fail to teach the newly amended limitations “adjust a difficulty level of the secondary task based on the determined stress level, using an adaptive algorithm configured to maintain the determined stress level within a target range; determine a level of empathy based on the determined stress level and the adjusted difficulty level, using an adaptive algorithm configured to maintain the determined stress level withing the target range”. However, the newly cited Crabtree disclose adjusting a difficulty level of a task based on the determined stress level, using an adaptive algorithm configured to maintain the determined stress level within a target range (see para 0480, A cornerstone of this is the “relaxation index,” a sophisticated metric that dynamically adjusts game difficulty and pacing based on real-time assessment of player stress levels); determine a level of empathy based on the determined stress level and the adjusted difficulty level, using an adaptive algorithm configured to maintain the determined stress level withing the target range (see para 0480, This index utilizes a combination of biometric data (if available through wearable or other devices), behavioral analysis, and contextual awareness to create a personalized comfort zone for each player. For instance, if the system detects elevated stress levels, perhaps through increased heart rate or erratic mouse movements, it might subtly simplify puzzle mechanics, slow the day-night cycle, or introduce calming environmental elements like soft rainfall or gentle animal companions). Therefore, it would have been obvious to modify the dual-task therapy system of Bohbot and incorporate the generative AI model and adaptive algorithm to adjust difficulty, generate content and empathetic feedback. The motivation for doing so is to utilize artificial intelligence to improve the system and provide better user interaction experiences as taught by Crabtree (see para 0481).
Based on the reasoning above, the rejection has been modified to address the newly amended limitations. Please see below for the detailed rejection.
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 1-20 are directed to Mental Processes. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract ideas.
Claims 1 and 11 recite in part a system and method for dual-task neurological therapy including generating novel task for a patient using a generative AI model, receiving feedback from the patient to determine stress level, adjusting a difficulty level of the task based on determined stress level and generating empathetic feedback using the generative AI model. The limitation is directed to concepts performed in the human mind, via the use of generic computer components, such as Mental Processes (including an observation, evaluation, judgement, opinion). Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements such as patient interface comprising a visual output device, an aural output device, processor, memory, programming instructions, adaptive algorithm and generative AI model which are well-known part of a generic computer. The generic computer components are recited at a high-level of generality (e.g. generating novel content/empathetic feedback using a generative AI model, adjusting difficulty using an adaptive algorithm) such that it amounts to no more than mere instruction to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Next the claims as a whole are analyzed to determine whether any element, or combination of elements, is sufficient to ensure the claim amounts to significantly more than an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of patient interface, processor, memory, programming instructions, adaptive algorithm and generative AI model are merely additional elements performing the abstract idea on a generic device i.e., abstract idea and apply it. There is no improvement to computer technology or computer functionality MPEP 2106.05(a) nor a particular machine MPEP 2106.05(b) nor a particular transformation MPEP 2106.05(c). Given the above reasons, the additional elements of patient interface, processor, memory, programming instructions and generative AI model are not Inventive Concepts. Thus, the claims are not patent eligible.
The dependent claims 2-10 and 12-20 have been given the full two-part analysis (Step 2A- 2 -prong tests and step 2B) including analyzing the additional limitations both individually and in combination. The Dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional limitations of the dependent claim(s) when considered individually and as ordered combination do not amount to significantly more than the abstract idea.
Therefore, claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1 and 11, the claims recite the limitations “adjusting a difficulty level… using an adaptive algorithm” and “determining a level of empathy… using an adaptive algorithm”. It is unclear whether two different algorithms are used, or the same algorithm is used for the adjusting and determining steps. Clarification is suggested.
The other claims are rejected because they are dependent of the rejected claims above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bohbot US 20140315169, and in view of Crabtree et al US 20250352907.
Regarding claim 1, Bohbot teaches a system for dual-task neurological therapy (see figure 1 and 45), comprising:
a patient interface comprising a visual output device, an aural output device, or both, and being configured to present visual content, or aural content, or both to a patient (display 150, see para 0077, the VR engine 140 may be configured to generate a virtual 3D environment on a VR graphics display user interface 150 to interact with a user);
a computing device comprising a processor and a memory; a first plurality of programming instructions stored in the memory which, when operating on the processor (control module 120, see para 0074, control module 120 may be hosted on a generic computing device with an operating system for running various software modules and the memory training modules as described herein), causes the computing device to:
receive patient information relevant to generation of a secondary task for performance by patient (see para 0074, The selection of which memory training module is retrieved for execution may be determined by the control module based on a particular user's profile);
generate a novel secondary task for performance by patient (see para 0085, control module 120 may generate new training modules for use in a participant's memory training program);
generate novel content for the secondary task, the novel content comprising visual content, aural content, or both; present the novel content to the patient during performance of the secondary task using the patient interface (see para 0107, The virtual tasks that form the training program and the transfer tests described below were constructed using a 3D gaming editor);
receive feedback input data relative to the patient's performance of the secondary task; determine a stress level of the patient from the feedback input data (see para 0082, control module 120 may also receive feedback from sensors indicating the level of brain activity in particular regions of the brain);
adjust a difficulty level of the secondary task based on the determined stress level (see para 0082, based on this feedback, control module 120 may modify the training program to either increase or decrease the level of difficulty of the selected memory training modules);
and present empathetic feedback to the patient during performance of the secondary task using the patient interface (see para 0087, the virtual coach can provide the user with feedback on how the user did, and may provide the user with congratulations for doing well, or providing encouragement).
But, Bohbot fails to teach generating the novel content for the secondary task using a generative AI model, adjusting the difficulty level using an adaptive algorithm configured to maintain the determined stress level within the target range, determining a level of empathy based on the determined stress level and the adjusted difficulty level using an adaptive algorithm configured to maintain the determined stress level within the target range and generating empathetic feedback for the patient based on the determined level of empathy using the generative AI model.
However, Crabtree teaches generating the novel content for the secondary task using a generative AI model (see para 0065, The platform supports content mashups, custom scenario generation, and adaptive AI behaviors, enabling the creation of unique and engaging digital environments across various media formats), adjusting the difficulty level using an adaptive algorithm configured to maintain the determined stress level within the target range (see para 0480, A cornerstone of this is the “relaxation index,” a sophisticated metric that dynamically adjusts game difficulty and pacing based on real-time assessment of player stress levels), determining a level of empathy based on the determined stress level and the adjusted difficulty level using an adaptive algorithm configured to maintain the determined stress level within the target range (see para 0480, This index utilizes a combination of biometric data (if available through wearable or other devices), behavioral analysis, and contextual awareness to create a personalized comfort zone for each player. For instance, if the system detects elevated stress levels, perhaps through increased heart rate or erratic mouse movements, it might subtly simplify puzzle mechanics, slow the day-night cycle, or introduce calming environmental elements like soft rainfall or gentle animal companions) and generating empathetic feedback for the patient based on the determined level of empathy using the generative AI model (see para 0481, an AI companion system represents another cozy-specific feature, offering players a uniquely empathetic and supportive virtual presence. This companion, powered by advanced natural language processing and emotional intelligence algorithms, goes beyond simple task assistance or dialogue options. It adapts its personality, conversation topics, and even its visual appearance to align with the player's emotional state and preferences).
Therefore, it would have been obvious to modify the dual-task therapy system of Bohbot and incorporate the generative AI model and adaptive algorithm to adjust difficulty, generate content and empathetic feedback.
The motivation for doing so is to utilize artificial intelligence to improve the system and provide better user interaction experiences as taught by Crabtree (see para 0481).
Regarding claim 2, Bohbot further teaches the determined stress level is used to adjust the difficulty of a primary task engaged in by user as part of a dual-task therapy as well as the secondary task (see para 0125, Two of these tasks may be described as dual-tasks, one as a form of task-switching).
Regarding claim 3, Bohbot further teaches a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the patient information comprising a history of secondary tasks previously assigned to patient; and ensure that the secondary task is novel by comparing it to the patient information (see para 0074, the selection of which memory training module is retrieved for execution may be determined by the control module based on a particular user's profile).
Regarding claim 4, Bohbot further teaches one or more biometric sensors, each configured to capture biometric data about the patient during performance of the secondary task; and a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to: receive the biometric data from the one or more biometric sensors; and incorporate the biometric data into the determination of the stress level of the patient (see para 0082, control module 120 may also receive feedback from sensors indicating the level of brain activity in particular regions of the brain).
Regarding claim 5, Bohbot further teaches the one or more biometric sensors are drawn from the list of heart rate sensors, galvanic skin response sensors, microphones, and facial images captured by a camera and processed to determine one or more facial expressions (see para 0081, a VR helmet may be provided which may include sensors for conducting measurements of brain activity, and which may also include sensors for identifying which regions of the brain are the most active. Such sensors may be used to measure pre-training brain activity, post-training brain activity, or brain activity during the course of conducting a VR memory training session).
Regarding claim 6, Bohbot further teaches the novel secondary task is chosen to provide some therapeutic benefit to patient, either mental or physical (see the abstract, a computer-generated 3D virtual environment for improving memory (e.g. spatial, temporal, spatial-temporal, working and short-term memory)); and the novel secondary task is generated using one or more of the following parameters: task type, task difficulty, narrative context or theme of task, and visual or aural stimuli (see para 0085, the new training modules may be based on a VR environment containing a standard set of tasks, objects, number of paths, etc. which need to be modified to either increase or decrease the level of difficulty).
Regarding claim 7, Bohbot further teaches the novel content generated by generative AI model comprises one or more of sounds, speech, text, images, and video (see para 0080, providing a 3D VR environment, which in addition to a visual user interface may also include motion feedback and audio interaction, the participant may be more fully engaged with each memory training program. Further, the HPC is a multimodal association area that receives auditory, olfactory, somatosensory as well as visual information).
Regarding claim 8, Bohbot further teaches the novel content generated by generative AI model comprises one or more of: pathways for tasks involving mazes or other restricted exploration; worlds, environments, and locations for tasks involving open-world exploration; thematic variants for a given type of task; thematic backgrounds, objects, and textures suitable for a chosen theme; storylines for adventures or other games; and text and audio for reading tasks or as in-game prompts for virtual reality tasks (see para 0135, participants are asked to navigate in a different environment from the one used in the experimental task. They are asked to retrieve objects in a 12-arm radial maze; however, they are told that it is not possible to predict the location of the objects because they are assigned randomly by the program).
Regarding claim 9, Bohbot further teaches the novel content generated by the generative AI model content generated by generative AI model is varied by the type of secondary task or its modality (see para 0082, control module 120 may modify the training program to either increase or decrease the level of difficulty of the selected memory training modules. The level of difficulty may be increased, for example by increasing the number of tasks, placing a larger number of objects in a VR environment for recall, or making the VR environment more complex with the addition of doors, hallways, and paths and reduction of landmarks. Similarly, the level of difficulty can be decreased by reducing the number of tasks, using fewer objects, or making the VR environment less complex with more landmarks, and a reduced number of doors or paths for selection).
Regarding claim 10, Crabtree further teaches the generative AI model is trained using one or more of the following types of training data: therapeutic dialogues, cognitive behavioral therapy session transcripts, peer support group conversations, tutoring session recordings with emotional support elements, customer service empathy training materials, conflict resolution training transcripts, and human conversations labeled for empathy levels (see para 0661, a specialized BERT-based model trained specifically on domain-relevant corpora (e.g., sports commentary, social interactions at events) to ensure contextually appropriate responses and ambient dialogue generation, also see para 0567, It uses sophisticated models trained on real player feedback and behavioral data, also see para 0517, deep learning models trained on vast datasets of user interactions, visual aesthetics, and narrative contexts, also see para 0512, Using natural language processing models trained on cozy and heartwarming narratives, also see para 0413, state-of-the-art machine learning models trained on vast corpora of emotionally annotated text across multiple languages and cultures).
Regarding claim 11-20, please refer to the rejection of claims 1-9 since the claimed subject matter is substantially similar. The claims are directed to the method for dual-task therapy with AI-generated content that is performed by the system as described above.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H DANG whose telephone number is (571)272-0470. The examiner can normally be reached Monday-Friday 9:30AM - 6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henry Tsai can be reached at (571)272-4176. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHONG H DANG/Primary Examiner, Art Unit 2184
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