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 .
Response to Amendment
Claims 1-20 were previously pending in this application. The amendment filed 12 September 2025 has been entered and the following has occurred: Claims 1, 3-5, 8, 10-11, 16-17, & 19-20 have been amended. Claim 14 has been cancelled. Claim 21 has been added.
Claims 1-13 & 15-21 remain pending in the application
Drawings
The drawings filed on 12 September 2025 accepted.
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 1-13 & 15-21 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.
The claims recite subject matter within a statutory category as a process (claims 1-13, 15-16 & 19-21) and a machine (claims 17-18) which recite steps of:
receiving, by the computing device, training behavioral data associated with one or more subjects, wherein at least a portion of the subjects are individuals characterized as having a neurodevelopmental disorder (NDD), and wherein the training behavioral data associated with the each of the plurality of subjects comprises data collected during delivery of a behavior therapy intervention and indicative of behavior of the subject, mood of the subject, emotions of the subject, treatment goals for the subject, instruction given to the subject, or combinations thereof; and
evaluating, by the computing device, the treatment data associated with the subject via a therapy notes (TN) model, wherein the TN model is configured to evaluate the treatment data associated with the subject to generate one or more therapy note features.; and/or
processing the training behavioral data associated with the plurality of subjects to yield a therapy notes (TN) model, wherein the TN model is a machine-learning model selected from the group consisting of a deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof wherein the TN model is configured to evaluate the behavioral treatment data associated with a subject to generate one or more therapy note features; and/or
an application in communication with the computing device, wherein the application is configured to receive one or more therapy note features from the computing device.
These steps of receiving treatment data, evaluating the treatment data to generate one or more therapy note features, and/or processing training data associated with the plurality of subjects to yield a therapy notes model, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the receiving treatment data associated with a patient/subject language, receiving treatment data in the context of this claim encompasses a mental process of the user or doctor reading an electronic health record or other medical file to receive treatment data for a patient. Similarly, the limitation of evaluating the treatment data, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as a doctor reviewing the information to make a potential diagnosis and/or future treatments for the subject/patient. For example, but for the processing the training data to yield a therapy notes model language, processing the training data to yield a model in the context of this claim encompasses a mental process of the user being trained to identify certain aspects of the treatment data in order to classify or create a model for analyzing future treatment data, and while recited for generic computer components, such as electronic processing and/or a computerized model, these aspects under BRI could reasonably be performed in the human mind or at least performed by an entity using a generic computer as a tool to achieve the steps recited. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components or mere implementation of a generic computer, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-13, 15-16, 18, & 20-21, reciting particular aspects of how recording, modifying and/or manipulating treatment data to create therapy notes may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of a computing device, a therapy notes (TN) model, a processor, a non-transitory computer-readable medium, a mobile device, amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [00108] for a computing device; [00085] & [00105] for a therapy notes (TN) model and various models recited such as deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof; [00063] for a processor; [00058] for a non-transitory computer-readable medium; [00109] for a mobile device, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving the treatment data associated with a subject, receiving one or more therapy note features from the computing device amounts to mere data gathering, recitation of evaluating the treatment data associated with the subject via a therapy notes (TN) model, wherein the TN model is configured to evaluate the treatment data associated with the subject to generate one or more therapy note features amounts to selecting a particular data source or type of data to be manipulated, recitation of evaluating the treatment data associated with the subject via a therapy notes (TN) model, i.e. application of a computational model, amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as specifying the treatment data to be behavior data, mood data, emotions data, goals data, instruction data, or combinations thereof, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13, 15-16, 18, & 20-21, additional limitations which amount to invoking computers as a tool to perform the abstract idea, claims 3-4, 7-13, & 16, which recites limitations relating to recording treatment data into a TN application by a computing device or by a user, sending a notification to the user, receiving treatment data via varying sensors, conveying a dictation, recording the note features into the TN application to yield therapy notes, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, claims 5-6, 9-13, & 20, which recite limitations relating to modifying treatment data recorded in the TN application, submitting the therapy note for approval, converting the dictation into one or more therapy note features, revising the therapy note, submitting the therapy notes and/or signaling the TN application to submit the therapy notes for approval, converting video data, converting the audio data, processing the training data by natural language processing computer vision, etc., additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated, claims 2-13, 15-16, 18, & 20-21, which generally recite limitations relating to specifying the type of application, e.g. TN application, the type of notes, therapy notes, sensor types for capturing data, specifying the type of TN model, further specifying the NDD comprising specific disorders, further specifying the type of computing device, e.g. mobile device and/or specifying the TN model being a natural language processing model, computer vision model, and/or specifying the treatment field such as ABA, speech language, physical, occupational therapy or combinations thereof, additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as receiving behavioral treatment/training data associated with a subject during delivery of a behavior therapy intervention and indicative of various aspects of the subject, receive one or more therapy note features from a computing device, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); evaluating the treatment data associated with the subject via a therapy notes (TN) model, wherein the TN model is configured to evaluate the treatment data associated with the subject to generate one or more therapy note features, such as by applying a TN model, the TN model being one or more models that are well-understood, routine, and/or conventional in prior art systems as suggested by Applicant’s Specification [00085] & [00105], e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more treatment data and/or subject data records, maintain one or more therapy notes, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing treatment data associated with a subject, storing computerized instructions, storing a therapy notes model, storing model parameters associated with one or more types of machine-learning models, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); receiving the treatment data associated with a subject, which could include, under BRI, feature extraction, optical character recognition, and/or natural language processing efforts of scanning a physical document or record, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13, 15-16, 18, & 20-21, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 3-4, 7-13, 16, & 21, which recites limitations relating to recording treatment data into a TN application by a computing device or by a user, sending a notification to the user, receiving treatment data via varying sensors, conveying a dictation, recording the note features into the TN application to yield therapy notes, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 5-6, 9-13, & 20, which recite limitations relating to modifying treatment data recorded in the TN application, submitting the therapy note for approval, converting the dictation into one or more therapy note features, revising the therapy note, submitting the therapy notes and/or signaling the TN application to submit the therapy notes for approval, converting video data, converting the audio data, processing the training data by natural language processing computer vision, etc., e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 2-16, 18, & 20, which recite limitations relating to maintaining one or more therapy notes, maintaining one or more TN model parameters, maintaining one or more patient/subject records, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2-13, 15-16, 18, & 20, which recite limitations relating to storing computerized instructions for performing the computerized steps recited, storing therapy notes, storing one or more TN applications, storing one or more TN models, storing one or more sensor data, storing dictation, etc., e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2, 9, & 20, which recite limitations relating to one or more therapy note features being extracted, such as via a structured text field, a free text section, a therapy session narrative, etc., dictation conversion/conveyance, converting audio features into therapy note features, converting video data into one or more therapy note feature, use of natural language processing algorithms or speech processing algorithms, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v); claim 2, which recites interface features similar to button functionality, e.g., a web browser’s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-13 & 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Khaleghi et al. (U.S. Patent Publication No. 2020/0084595), hereinafter “Khaleghi”.
Claim 1 –
Regarding Claim 1, Khaleghi discloses a method implemented via a computing device (See Khaleghi Abstract for a computing device; See Khaleghi Par [0119] for a computerized method implemented via computing device), the method comprising:
receiving, by the computing device, behavioral treatment data associated with a subject, the behavioral treatment data associated with the subject comprising data collected during delivery of a behavior therapy intervention and indicative of behavior of the subject, mood of the subject, emotions of the subject, treatment goals for the subject, instruction given to the subject, or combinations thereof (See Khaleghi Par [0022] which discloses receiving or extraction of patient-related data including sound and/or video recordings, scans, test results, contact information, calendaring information, biographical data, patient-related team data, etc.; See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment, i.e. data collected during delivery of a behavior therapy intervention; See Khaleghi Par [0092] which discloses receiving new or updated biographical, medical, clinical, therapeutic, and/or diary data, i.e. treatment data; See Khaleghi Par [0094] which discloses the check-in user interface possibly enabling a user to provide updates on how a patient or the user is doing, i.e. emotions of the subject; See Khaleghi Par [0084] which discloses a medication record including fields that may be populated including the goal of the medication, i.e. treatment goal; See Khaleghi Par [0053] which discloses recording follow-up information indicating whether the patient is taking the medication, following other instructions provided by the doctor/specialist (i.e. instructions given to the subject), the patient's physical condition and health, etc.; See Khaleghi Par [0088]-[0089] which discloses said virtual diary being updated during each new appointment, intervention, etc., the interventions including those listed in Khaleghi Par [0053] & [0110] which discloses example forms of prescribed therapies for a patient, including physical therapy, speech therapy, counseling sessions, etc.) and
evaluating, by the computing device, the behavioral treatment data associated with the subject via a therapy notes (TN) model, wherein the TN model is configured to evaluate the behavioral treatment data associated with the subject to generate one or more therapy note features (See Khaleghi Par [0022] which discloses receiving or extraction of patient-related data including sound and/or video recordings, scans, test results, contact information, calendaring information, biographical data, patient-related team data, etc.; See Khaleghi Par [0053] & [0055] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0089] which discloses an example health timeline generated by the system using collected data, including receipt of new or updated diagnosis and/or treatment data for a subject; See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences),
the TN model is a machine-learning model selected from the group consisting of a deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof (See Khaleghi Par [0054] & [0111] which discloses the use of a voice-to-text module, using language modeling and statistical analysis, and can possibly make use of neural networks (trainable brain-like computer models that reliably recognize patterns) and/or pattern matching, pattern and feature analysis, language modeling and statistical analysis, etc.).
As disclosed above, Khaleghi discloses language modeling and statistical analysis, and can possibly make use of neural networks (trainable brain-like computer models that reliably recognize patterns) and/or pattern matching, pattern and feature analysis, language modeling and statistical analysis, etc. However, Khaleghi does not explicitly disclose the full list of models recited in the limitation “the TN model is a machine-learning model selected from the group consisting of a deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof” and/or being applied during “behavior treatment” session per se. However, because Khaleghi Par [0022]; [0053]; & [0092] identify the need for automated transcription of medical sessions, such as via a learning algorithm, and while not explicitly during “ behavioral treatment” sessions per se, Khaleghi further suggests at Par [0054] & [0111] that the use of one of the species found amongst the machine learning models found in the list above can accomplish said task of automated transcription of medical sessions, it is understood that the list of other machine learning models are mere alternatives to the one Khaleghi discloses and would also have the same reasonable expectation of success, i.e. transcription of therapy notes during a therapy session. As a result, it would have been obvious to try one of the identified, predictable solutions, i.e. machine learning algorithms, with a reasonable expectation of success by one of ordinary skill in the art before the effective filing date of the claimed invention. Furthermore, while it is understood that Khaleghi renders the claim above obvious and therefore not being relied upon, Examiner also points to Co et al. and Roh et al. for additional teachings regarding viable, known alternative machine learning algorithms for accomplishing the same or similar results as Khaleghi to further demonstrate said obviousness to try.
Claim 2 –
Regarding Claim 2, Khaleghi discloses the method of claim 1 in its entirety. Khaleghi further discloses a method, wherein:
the one or more therapy note features comprise a dropdown menu, a checkbox, a structured text field, a free text section, a therapy session narrative, a content thereof, or combinations thereof (See Khaleghi Par [0123] which discloses a user selecting a control, menu selection, or the like, and other user input modalities, including include voice commands, text entry, gestures, text fields, menu selection, drop down menu, check box, selectable icons, etc.; See Khaleghi Par [0040] which discloses the use of free form text fields being used as user input; Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), constituting therapy session narrative, the electronic notebook generating and/or provide user interfaces textually providing information about the treatment).
Claim 3 –
Regarding Claim 3, Khaleghi discloses the method of claim 1 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data is recorded into a TN application in communication with the computing device (See Khaleghi Par [0014] which discloses some or all of the information communicated between a user terminal app and a remote system are transmitted securely, i.e. an application is in communication with a remote computing system/device; See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment).
Claim 4 –
Regarding Claim 4, Khaleghi discloses the method of claim 3 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data is directly recorded into the TN application by a user (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0092] which discloses receiving new or updated biographical, medical, clinical, therapeutic, and/or diary data, i.e. treatment data; See Khaleghi Par [0094] which discloses the check-in user interface possibly enabling a user to provide updates on how a patient or the user is doing).
Claim 5 –
Regarding Claim 5, Khaleghi discloses the method of claim 4 in its entirety. Khaleghi further discloses a method, wherein:
the user, responsive to the one or more therapy note features generated by the TN model, modifies the behavioral treatment data recorded into the TN application to yield a therapy note (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0092] which discloses receiving new or updated biographical, medical, clinical, therapeutic, and/or diary data, i.e. treatment data; See Khaleghi Par [0094] which discloses the check-in user interface possibly enabling a user to provide updates on how a patient or the user is doing; See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences).
Claim 6 –
Regarding Claim 6, Khaleghi discloses the method of claim 5 in its entirety. Khaleghi further discloses a method, wherein:
the TN application submits the therapy note for approval (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert, i.e. notification, can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised), wherein
the therapy note is approved or requires revisions (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert, i.e. notification, can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised).
Claim 7 –
Regarding Claim 7, Khaleghi discloses the method of claim 6 in its entirety. Khaleghi further discloses a method, wherein:
when the therapy note requires revisions, the TN application sends a notification to the user (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert, i.e. notification, can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised).
Claim 8 –
Regarding Claim 8, Khaleghi discloses the method of claim 3 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data is received via an audio sensor, a video device, or both (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment).
Claim 9 –
Regarding Claim 9, Khaleghi discloses the method of claim 8 in its entirety. Khaleghi further discloses a method, wherein:
the audio sensor conveys a dictation from a user to the TN application (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, and in some instances, the complete speech-to-text file for a given recording may be inserted/accessible for each past and future appointment, an indication as to whether there are one or more appointment recordings and any associated other files for the treatment (e.g. drug tests, blood test, psychiatric reports, orthopedic reports, prescriptions, imaging reports, etc.), wherein
the dictation comprises at least one word of portion thereof (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences), and wherein
the TN model converts the dictation into the one or more therapy note features (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences).
Claim 10 –
Regarding Claim 10, Khaleghi discloses the method of claim 8 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data acquired via the audio sensor comprises audio data (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences), wherein
the audio data comprises vocal sounds produced by the subject, vocal sounds produced by a caregiver, ambient sounds, or combinations thereof (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences), wherein
the vocal sounds comprise onomatopoeic sounds, words, sentences, sentence portions, phrases, phrase portions, conversations, humming, singing, whispering, yelling, sound pattern data, pattern of vocal sounds produced by the subject, pattern of vocal sounds produced by a caregiver, sound volume fluctuations, or combinations thereof (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences), and wherein
the TN model converts the audio data into the one or more therapy note features (See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences).
Claim 11 –
Regarding Claim 11, Khaleghi discloses the method of claim 8 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data acquired via the video device comprises video data and optionally audio data (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment), wherein
the video data comprises visually observable elements of the subject, the subject's spatial position, visually observable elements of a caregiver, the caregiver's spatial position, a body feature thereof, a movement thereof, visually observable elements of the subject's environment, or combinations thereof (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0100] which discloses real-time or recorded videos or images (optionally with an associated voice track) of the patient and/or significant other, caregiver, family member, etc., to be transmitted, such that the visual content may provide significant or critical information in making mental and/or physical health assessments, such as detecting if someone has suffered a stroke or heart attack), and wherein
the TN model converts the video data and optionally the audio data, respectively, into the one or more therapy note features (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; Ss Khaleghi Par [0018] & [0053] which discloses an electronic notebook automatically or upon activation by a user, recording, processing, and reproducing video recordings and associated audio track,, and/or other treatment information in an electronic notebook for future access by varying entities with access, without requiring that the user, patient, and/or other medical professional(s) manually write or type in notes during the appointment).
Claim 12 –
Regarding Claim 12, Khaleghi discloses the method of claim 8 in its entirety. Khaleghi further discloses a method, wherein:
the one or more therapy note features are recorded into the TN application to yield the therapy notes (See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0018] & [0053] which discloses an electronic notebook automatically or upon activation by a user, recording, processing, and reproducing video recordings and associated audio track, and/or other treatment information in an electronic notebook for future access by varying entities with access, without requiring that the user, patient, and/or other medical professional(s) manually write or type in notes during the appointment); wherein
a user assesses, in the TN application, the therapy notes (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised), wherein
the user revises the therapy notes and/or signals the TN application to submit the therapy notes for approval (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised), and wherein
the therapy notes are approved or require revisions (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised).
Claim 13 –
Regarding Claim 13, Khaleghi discloses the method of claim 12 in its entirety. Khaleghi further discloses a method, wherein:
when the therapy notes require revisions, the TN application sends a notification to the user (See Khaleghi Par [0096] which discloses a percentage likelihood of identifying phrases used in information provided during a check-in, and in some instances of slurred speech or unintelligible speech, an alert, i.e. notification, can be provided to a treating professional that the check-in information, i.e. in the note, needs to be urgently reviewed, i.e. revised).
Claim 15 –
Regarding Claim 15, Khaleghi discloses the method of claim 1 in its entirety. Khaleghi further discloses a method, wherein:
the subject is characterized as having as a neurodevelopmental disorder (NDD), and wherein the NDD comprises disorders on the autism spectrum or autism spectrum disorder (ASD); attention deficit hyperactivity disorder (ADHD), other specified ADHD, unspecified ADHD; motor disorders, developmental coordination disorder, stereotypic movement disorder, tic disorders, Tourette's disorder or syndrome, persistent or chronic motor or vocal tic disorder, provisional tic disorder, other specified tic disorder, unspecified tic disorder; cerebral palsy; Rett syndrome; intellectual disabilities, intellectual developmental disorder, global developmental delay, unspecified intellectual disability, unspecified intellectual developmental disorder, communication disorders, language disorder, speech sound disorder or phonological disorder, childhood-onset fluency disorder or stuttering; social or pragmatic communication disorder, unspecified communication disorder; specific learning disorder; other NDDs, other specified NDD, unspecified NDD; or combinations thereof (See Khaleghi Par [0045] which discloses a patient category that can be populated, including the patient having Autism Spectrum Disorder; See Khaleghi Par [0046]-[0047] which discloses a patient category for characterizing the patient, including developmental disorders, learning disorders, emotional disorder, psychiatric disorder, chemical dependency, comorbid substance abuse problem, mental illness, etc.; See Khaleghi Par [0077] which discloses diagnoses and conditions for characterizing a patient, including drug addiction, aging, dementia, special needs, seizure disorder, Parkinson’s, cancer, hyperactivity, i.e. ADHD, and/or by treatment side effect associated with various medications, e.g. sleeplessness, nausea, anxiety, etc.).
Claim 16 –
Regarding Claim 16, Khaleghi discloses the method of claim 1 in its entirety. Khaleghi further discloses a method, wherein:
the behavioral treatment data is derived from treatment provided to the subject (See Khaleghi Par [0022] which discloses receiving or extraction of patient-related data including sound and/or video recordings, scans, test results, contact information, calendaring information, biographical data, patient-related team data, etc.; See Khaleghi Par [0053] & [0055] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0089] which discloses an example health timeline generated by the system using collected data, including receipt of new or updated diagnosis and/or treatment data for a subject), and wherein
the treatment comprises applied behavioral analysis (ABA) therapy, speech therapy, language therapy, physical therapy, occupational therapy, or combinations thereof (See Khaleghi Par [0053] & [0110] which discloses example forms of prescribed therapies for a patient, including physical therapy, speech therapy, counseling sessions, etc.).
Claim 17 –
Regarding Claim 17, Khaleghi discloses a system comprising:
the computing device comprising a processor and a non-transitory computer readable medium (See Khaleghi Par [0120] which discloses non-transitory computer-readable storage medium coupled to the processor device such that the device can read information to perform the steps described throughout Khaleghi), wherein
the non-transitory computer-readable medium includes instructions configured to cause the processor to implement a therapy notes (TN) model, wherein the TN model is a machine-learning model selected from the group consisting of a deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof (See Khaleghi Par [0054] & [0111] which discloses the use of a voice-to-text module, using language modeling and statistical analysis, and can possibly make use of neural networks (trainable brain-like computer models that reliably recognize patterns) and/or pattern matching, pattern and feature analysis, language modeling and statistical analysis, etc.),
wherein the ML model, when implemented via the processor (See Khaleghi Par [0120] which discloses non-transitory computer-readable storage medium coupled to the processor device such that the device can read information to perform the steps described throughout Khaleghi; See Khaleghi Par [0054] & [0111] which discloses the use of a voice-to-text module, using language modeling and statistical analysis, and can possibly make use of neural networks (trainable brain-like computer models that reliably recognize patterns) and/or pattern matching, pattern and feature analysis, language modeling and statistical analysis, etc., and specifically mentions training the trainable brain-like computer models to reliably recognize patterns associated with varying users/patients), causes the computing device to:
receive behavioral treatment data associated with a subject having a neurodevelopmental disorder (NDD), the behavioral treatment data associated with the subject comprising data collected during delivery of a behavior therapy intervention and indicative of behavior of the subject, mood of the subject, emotions of the subject, goals for the subject, instruction given to the subject, or combinations thereof (See Khaleghi Par [0022] which discloses receiving or extraction of patient-related data including sound and/or video recordings, scans, test results, contact information, calendaring information, biographical data, patient-related team data, etc.; See Khaleghi Par [0053] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0092] which discloses receiving new or updated biographical, medical, clinical, therapeutic, and/or diary data, i.e. treatment data; See Khaleghi Par [0094] which discloses the check-in user interface possibly enabling a user to provide updates on how a patient or the user is doing); and
evaluate the data associated with the subject via the TN model, wherein the TN model is configured to evaluate the treatment data associated with the subject to generate one or more therapy note features; and an application in communication with the computing device, wherein the application is configured to receive the one or more therapy note features from the computing device (See Khaleghi Par [0022] which discloses receiving or extraction of patient-related data including sound and/or video recordings, scans, test results, contact information, calendaring information, biographical data, patient-related team data, etc.; See Khaleghi Par [0053] & [0055] which discloses recording control which enables video and/or audio of a given appointment, i.e. treatment (e.g. medication, exercise physical therapy, etc.), the electronic notebook generating and/or provide user interfaces textually providing information about the treatment; See Khaleghi Par [0089] which discloses an example health timeline generated by the system using collected data, including receipt of new or updated diagnosis and/or treatment data for a subject; See Khaleghi Par [0055] which discloses a voice recording or text file generated by the voice-to-text module, i.e. application, allowing for convenient and organized viewing of content vocalized in the electronic notebook, i.e. as a note or file, such that the system can recognize various words, phrases, sentences, or series of sentences).
As disclosed above, Khaleghi discloses language modeling and statistical analysis, and can possibly make use of neural networks (trainable brain-like computer models that reliably recognize patterns) and/or pattern matching, pattern and feature analysis, language modeling and statistical analysis, etc. However, Khaleghi does not explicitly disclose the full list of models recited in the limitation “the TN model is a machine-learning model selected from the group consisting of a deep learning model, a generative adversarial network model, a computational neural network model, a recurrent neural network model, a perceptron model, a classical tree-based machine-learning model, a decision tree type model, a regression type model, a classification model, a reinforcement learning model, and combinations thereof” and/or being applied during “behavior treatment” session per se. However, because Khaleghi Par [0022]; [0053]; & [0092] identify the need for automated transcription of medical sessions, such as via a learning algorithm, and while not explicitly during “ behavioral treatment” sessions per se, Khaleghi further suggests at Par [0054] & [0111] that the use of one of the species found amongst the machine learning models found in the list above can accomplish said task of automated transcription of medical sessions, it is understood that the list of other machine learning models are mere alternatives to the one Khaleghi discloses and would