DETAILED ACTION
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/26/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
The disclosure of the prior Applications does NOT provide adequate support for one or more claims of this application such as using a generative AI model to generate novel content/empathetic feedback. Thus, the effective filing date of the claimed invention is the instant filing date of 08/26/2025.
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 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) 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 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 § 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 Gokce et al US Patent No. 12,518,461.
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, determining a level of empathy based on the determined stress level and generating empathetic feedback for the patient based on the determined level of empathy using the generative AI model.
However, Gokce teaches generating the novel content for the secondary task using a generative AI model (see the abstract, generating, by an artificial intelligence (AI) persona, at least one of a visual output and an audio output), determining a level of empathy based on the determined stress level (see col 11 ln 34-39, the CEAAG module 210 extracts facial expressions (e.g., muscle movement for happiness, sadness, frustration), stress level (e.g., sweat on the face, face twitches), facial micro-expressions (e.g., subtle emotional cues), and eye gaze patterns (e.g., indicative of user attention and focus)) and generating empathetic feedback for the patient based on the determined level of empathy using the generative AI model (see col 11 ln 52-54, The CEAAG module 210 generates the audio output 106 with empathy having a comforting tone).
Therefore, it would have been obvious to modify the dual-task therapy system of Bohbot and incorporate the generative AI model to generate content and empathetic feedback.
The motivation for doing so is to utilize artificial intelligent to improve the system and provide better user interaction experiences as taught by Gokce (see col 13 ln 14-16).
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, Gokce 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 col 7 ln 39-50, the AI persona 102 may be customized and trained by employing other data formats to ensure an adaptive and contextually rich experience with the AI persona 102. In addition to the face input 103 and the audio input 104, the user may submit a direct text input, such as, for example, from an input keyboard or a stylus (e.g., text input from hand movement). Other data formats include at least one website, documents (e.g., PDF, TXT, PPT, XML, HTML, and the like), and images (e.g., PNG, JPG, TIFF, GIF, SVG, BMP, and the like). Each of the data formats are received by the AI persona 102 for data ingestion and adaptation to the user for the AI persona 102).
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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pascual-Leone et al US 20230255564 discloses systems and methods for machine-learning-assisted cognitive evaluation and treatment
Barnett et al US 20200365275 discloses system and method for assessing physiological state via performance of cognitive tasks and providing feedback using an AI model
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/PHONG H DANG/Primary Examiner, Art Unit 2184
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