DETAILED ACTION
This office action is based on the claim set filed on 08/17/2026.
Claims 1, 15, and 30, have been amended.
Claims 1-30 are currently pending and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/17/2026 has been entered.
Claim Objections
Claims 1 and 15 are objected to because of the following informalities:
Claim 1 and 15 recites "analyzing said patient exercise data historical record for determining patient progress of said treatment over said period of time/ determination of whether the patient is properly progressing from said therapy". The phrase "properly progression" does not have any measurable form and is indefinite in the claims. Appropriate correction is respectfully requested.
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.
Claim 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-30 are drawn to a method, which is within the four statutory categories (i.e., a machine and a process). Claims 1-30 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process evaluating patient speech disorder and suggest a treatment. Providing a patient on a device speech exercise and obtain the patient data according to interaction with the device to be analyzed for assessment and suggest a treatment, are steps that is/are an abstract idea that could have been performed by a human actor interacting with a system/interface to implement the abstract idea performing the steps providing, performing sending, receiving, which both the instant claims and the abstract idea are defined as Certain Methods of Organizing Human Activity (managing personal behavior or interactions between people)
Independent Claim 1 recites the steps of:
“preparing a baseline profile of the facial structure of the patient based on initial facial data of the facial structure of the patient
executing software on a patient smart device for performing the steps of:
providing one or more speech exercises to the patient to support a speech exercise session,
the patient performing said one or more speech exercises during said speech exercise session using the patient smart device at an arbitrary location of the patient,
said patient smart device monitoring the performance of the patient performing said one or more speech exercises,
said monitoring including capturing responses of the patient while performing the exercises to generate patient exercise data,
repeating said monitoring of the performance of the patient over a plurality of said exercise sessions over a period of time and
the smart device sending the patient exercise data for each of said exercise sessions over a communication network to a remote server;
said remote server receiving said patient exercise data;
said remote server saving said patient exercise data for accumulating a historical record of said patient exercise data over a plurality of different exercise sessions, wherein said historical record includes a history of the facial structures of the patient over the period of time
said remote server executing software for performing the steps of:
analyzing said patient exercise data to determine a current facial structure of the patient based at least in part on a recent exercise session,
comparing the current facial structure of the patient to the baseline facial structure of the patient to determine current changes in the facial structure of the patient;
tracking the progress of the patient by comparing the current changes in the facial structure of the patient to the historical record of the facial structures of the patient for determining patient progress of said treatment over said period of time,
generating assessment data of the patient progress based on said analyzing, wherein said assessment data includes a determination of whether the patient is properly progressing from said therapy,
the server sending the assessment data over a communication network to a medical provider smart device of a medical provider located remotely from the patient;
said medical provider smart device providing said assessment data for access by the medical provider to review whether the patient is properly progressing from the speech therapy treatment.”
Independent Claim 15 recites similar steps as in Claim 1 including:
capturing data from images of the face of the patient including data of facial structures of the patient for generating a patient baseline for the patient
analyzing said patient exercise data for determining patient progress, by comparing said patient exercise data taken overtime with said patient baseline
generating an assessment report of the patient progress based on said analyzing, wherein said assessment report includes an automatically generated assessment of the progress of the patient including a determination of whether the patient is properly progressing from said therapy
Independent Claim 30 recites similar steps as in Claim 1 including:
analyzing said patient exercise data for determining patient progress by utilizing said patient exercise data taken over time
generating an assessment report of the patient progress based on said analyzing, wherein said assessment report includes:
suggested changes to said therapy determined using a machine based learning algorithm executing on said remote server, and face mask data for generating a patient face mask of the face of the patient changing over time;
wherein said assessment report is provided to said medical provider to evaluate the therapy treatment being provided to the patient, and wherein displaying a video are displayed showing a face mask of the patient over time that is generated based on the historical record of patient exercise data
These limitations, as drafted, given the broadest reasonable interpretation, cover performance of the limitations by a human user/actor interaction with computing device(s) that constitute Certain Methods of Organizing Human Activity along with Mental process. For example, the limitations encompass a user the ability to provide exercise data to a patient and collect the patient interaction to include facial and speech data overtime to be analyzed and generate and assessment report of the patient progress and suggest changes to the patient therapy to be evaluated by a provider, which are steps that that could have been performed by a human actor using generic computing components/units to implement the abstract idea. These limitations encompass activity of a single person or multiple people and a computer, interacting with other users and with computing system(s) to perform the steps of the claimed invention for evaluating patient speech disorder, which constitutes Certain Methods of Organizing Human Activity. Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “smart device, server, machine based learning algorithm” are computing components that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions “e.g., display[ing], store[ing]”, and are recited at a high level of generality, for example, “the machine based learning”, is recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific process how these elements are implemented to perform a task is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent) with 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, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), adding insignificant extra-solution activity to the judicial exception, i.e. “the smart device sending the patient exercise data over a communication network to a remote server; remote server receiving said patient exercise data; the server sending the assessment data over a communication network”, and such as mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d)&(g). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f) adding insignificant extra-solution activity to the judicial exception and mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d)&(g), and Symantec., TLI Communication LLC. 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 mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-14 and 16-29 include all of the limitations of claim(s) 1 and 15, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2-9, 17, 20-25, and 29 the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human interaction constituting Certain methods of organizing human activity but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible.
As for claims 10-14, 16, 18-19, and 26-28, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human interaction constituting Certain methods of organizing human activity but for, the recitation of the generic computer components which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible. The claims recite additional elements “machine based learning, server, smart device, communication network”. In particular, the claims recite the additional elements that implement the identified abstract idea. These computing components are recited at a high level of generality, for example, the machine based learning is recited in the claims in a high level of generality and is in described in the specification in an arbitrary form without disclosing a specific process how these elements are implemented to perform a task as such is a mere in instruction(s) that may be performed by human that it amounts no more than adding the words "apply it" (or an equivalent). Similarly, as mentioned above Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. 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 integration of the abstract idea into a practical application, the additional element of “e.g. machine based learning, server, smart device” to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
Claim Rejections - 35 USC § 103
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(s) 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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 5-13, 15-20, 23-24, 26-28, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Samuels et al. (US 2023/0401969 A1- “Samuels”) in view of Prout (US 11,178,956 Bl) in view of Aicher (US 2013/0204625 Al)
Regarding Claim 1 (Currently Amended), Samuels teaches a method of evaluating a speech therapy treatment for supporting the speech therapy of a patient Samuels discloses a computer-implemented automated speech and language system to assist in correcting speech and language disorders (Samuels: [0009], [0031]), comprising the steps of:
preparing a baseline profile of the facial structure of the patient based on initial facial data of the facial structure of the patient Samuels discloses determining a baseline for a user based on initial evaluation of the user facial structure (symmetry, anomalous movement), jaw assessment (mobility and symmetry), bite and teeth assessment, lip assessment, sound assessment, and tongue assessment (Samules: [0026])
executing software on a patient smart device Samuels discloses a user device operates remotely for executing the exercise software (Samuels: [0009], [0025], [0031-0032], [0035], [0038]), for performing the steps of:
providing one or more speech exercises to the patient to support a speech exercise session Samuels discloses the system determined a user profile and individualized plan of care (IPOC) and provide the user a set of exercises to achieve a treatment goal and setting a baseline to identify problems to be corrected such as unexpected sounds and/or disorders and facial structure (Samuels: [0009], [0025-0026], [0031-0032], [0038]),
the patient performing said one or more speech exercises during said speech exercise session using the patient smart device at an arbitrary location of the patient, Samuels discloses the user performing on the remotely operating deice, the exercise(s) and detecting the user’s video stream and sound using mobile device, personal computer, tablet, game console, etc. (Samuels: [0032], [0035], [0039], [0044-0045], [0047-0048], [0051])
said patient smart device monitoring the performance of the patient performing said one or more speech exercises, said monitoring including capturing responses of the patient while performing the exercises to generate patient exercise data, Samuels discloses assisting and monitoring the user performing the exercises such as tracking voice and facial features and response of the user to instructions (Samuels: [0026], [0035], [0040], [0043-0044], [0046], [0052])
repeating said monitoring of the performance of the patient over a plurality of said exercise sessions over a period of time Samuels discloses a repeating the exercises and monitoring of the performance of the user exercises for each exercise performed each time period (Samuels: [0028], [0052], [0056])
the smart device sending the patient exercise data for each of said exercise session over a communication network to a remote server;
said remote server receiving said patient exercise data
said remote server saving said patient exercise data for accumulating a historical record of said patient exercise data over a plurality of different exercise sessions, wherein said historical record includes a history of the facial structures of the patient over the period of time Samules discloses collecting facial impression for different exercises over period of time (Samules [0042] temporal appearance of the user's face features and temporal sequence of 2D, 3D or 3.5D appearance of the facial features are extracted to optimize a face key point data (temporal-spatial) model and [0043]; for different exercises the generalized facial temporal-spatial model is used, but different sub meshes of facial mesh can be selected for tracking the user's facial impressions),
said remote server executing software for performing the steps of:
analyzing said patient exercise data to determine a current facial structure of the patient based at least in part on a recent exercise session, Samuels discloses extracting the user’s features when the user performs the exercise involving facial expression (the face key point data model) to determine an actual data model of the user performing the exercise in real-time and analyze the facial features when exercise preformed (Samuels: [0028], [0042], [0045], [0047-0048], [0051])
comparing the current facial structure of the patient to the baseline facial structure of the patient to determine current changes in the facial structure of the patient; Samules discloses comparing facial features to reference model to evaluate actual changes in real-time (Samules: [0029], [0051])
tracking the progress of the patient by comparing the current changes in the facial structure of the patient to the historical record of the facial structure of the patient for determining patient progress of said treatment over side period of time, Samules discloses tracking and evaluating face key points in sequence of image frames over time to determine actual data of the user performing the exercise in real-time (Samules [0043, [0044] tracking the positions of the face's key points or any other representation of the user's face body in the sequence of image frames, the user's movements when performing the exercise can be evaluated, [0047-0048])
generating assessment data of the patient progress based on said analyzing, wherein said assessment data includes a determination of whether the patient is properly progressing from said therapy Samuels discloses the system provides real-time feedback during the exercise and grading upon completion of each exercise and assess the user performing the exercise such as generate a rating of the performance to assists the user improving and/or increasing muscle strength, agility, and stability for improving speech output quality, for example, assessing, rating and provide a progress report such as if the user properly pronounces a sound such that if the user is performing the exercise correctly showing a proregression in the therapy (Samuels: [0025], [0028], [0048], [0052], [0081])
Examiner note: the limitation contains an indefinite term “properly progression” which is a broad and unmeasurable feature and accordingly Examiner interpreters it as any progression/improvement in therapy.
the server sending the assessment data over a communication network to a medical provider smart device of a medical provider located remotely from the patient
said medical provider smart device providing said assessment data for access by the medical provider to review whether the patient is properly progressing from the speech therapy treatment Samuels discloses the system based on the real-time feedback during the exercise and grading upon completion of each exercise, generates progress reports to be accessed by the treating trained speech-language pathologists (SLPs) reviewing the report indicating whether or not the user is following the proper form of the exercise or properly makes the required sound (Samuels: [0025], [0052-0053], [0080-0081]).
Samuels discloses using a computer vision system that identifies facial key features and forming a baseline profile of the facial information and collecting facial data over time to be analyzed, tracking changes, and comparing reference model [0037-0051]. In addition, Samuels discloses the system comprising devices that communicate via servers over a network such that between the user and treating professionals, such as SLPs, special care centers, schools, hospitals, insurance companies ([0035], [0065], [0086]). However, Samuels does not expressly teach comparing the collected facial data to baseline facial data and the communication process features as underlined.
Prout teaches
comparing the current facial structure of the patient to the baseline facial structure of the patient to determine current changes in the facial structure of the patient; Prout discloses analyzing and comparing historical (or saved) facial images of a user, including the most recent or current image with a baseline facial model to detect face properties (Prout: [Fig. 5], [col. 8, line 44-46]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of Samules providing facial feature points baseline and tracking facial exercise and comparing to reference model with teaching of Prout providing comparing facial images of a user to a baseline facial model to detect changes to the user facial properties since the combination of the two references is merely simple substitution of one known element for a comparing to baseline (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself-that is, in the substitution of the baseline facial model of the secondary reference(s) for the reference model of the primary reference. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Aicher teaches
the smart device sending the patient exercise data over a communication network to a remote server; Aicher discloses an exercise and learning concept comprises a network system for transmitting via communications server digital information between at least one digital input device and at least one portable exercise and learning module device where the exercise and learning module is transmitted and downloaded to indented patient(s) device(s) and after the end of the exercise and learning phase or after closure of the exercise and learning module, the monitoring protocol is stored and/or sent automatically to the communications server (Aicher: [0007], [0025-0027], [0032], [0036], [0040])
said remote server receiving said patient exercise data Aicher discloses the monitoring protocol is stored and/or sent automatically to the communications server and the protocols are retrieved by the server (Aicher: [0007], [0032], [0036], [0040])
said remote server saving said patient exercise data for accumulating a historical record of said patient exercise data over a plurality of different exercise sessions, Aicher discloses execution and success of the plurality of exercise is/are recorded in a monitoring protocol on the communications server such that after the end of the exercise and learning phase, the monitoring protocol is sent automatically to the communications server (Aicher: [0007], [0026], [0032], [0036], [0040])
the server sending the assessment data over a communication network to a medical provider smart device of a medical provider located remotely from the patient Aicher discloses a therapist can download and evaluate the monitoring protocols from the communications server on his input device (Aicher: [0033], [0036], [0043])
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Samuels providing and monitoring speech and language disorders to incorporate exercise and learning communication platform between a user and provider, as taught by Aicher, which helps solving the increasingly issues for low availability of therapists, doctors and care workers and the cost savings in health care (Aicher: [0010]).
Regarding Claim 2 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said assessment data includes data for generating a patient face mask of the face of the patient Samuels discloses a "mask" in different form is used to provide the user with visual aids for proper positioning and uses facial features to determine a mask input of the face key features (Samuels: [0040-0042]).
Regarding Claim 6 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 5, wherein said assessment data includes a plurality of said images of the face of the patient at different times Samuels discloses acquiring a sequence of images or images frames, such as a video sequence, of the user's face movement at different times (Samuels: [0034], [0043-0044]).
Regarding Claim 7 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 6, wherein said plurality of said images is formed into a video timeline for viewing by the medical provider to show patient progress from the treatment over time Samuels discloses acquiring a sequence of images, such as a video sequence, of the user's face movement at different times (Samuels: [0034], [0043]).
Regarding Claim 8 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said assessment data includes an automatically generated assessment of the progress of the treatment for the medical provider Samuels discloses the system provides real-time feedback during the exercise and grading upon completion of each exercise (Samuels: [0025], [0052-0053]).
Regarding Claim 9 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said assessment data includes an automatically generated assessment report of the progress of the patient Samuels discloses the system based on the real-time feedback during the exercise and grading upon completion of each exercise, generates a progress reports (Samuels: [0025], [0052-0053]).
Regarding Claim 10 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 9, wherein automatically generated assessment report includes suggested changes in said therapy determined using a machine based learning algorithm executing on said remote server Samuels discloses the computer program include a computer vision (CV) module connected to an algorithmic modules and artificial neural networks (ANN) where the CV module monitors the user performance and provides a recommendation and/or modifications to the plan (Samuels: [0037], [0041], [0051-0053]).
Regarding Claim 11 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said speech exercises include oral motor exercises that are recorded by the patient smart device for generating said patient exercise data Samuels discloses using a camera to capture images of a user for evaluation of facial structure, jaw, lip, sound, and tongue assessment while performing an exercise that assists the user to improve and increase muscle strength, agility and stability that in tum helps the user in improving her/his speech output quality [oral motor exercises] (Samuels: [0009], [0026], [0031], [0033]).
Regarding Claim 12 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said speech exercises include using said patient smart device for displaying images to said patient and requesting a patient response to said images for generating said patient exercise data Samuels discloses displaying sections of the user on the user device display and provide instruction images and response to the request(s) (Samuels: [Fig. 5], [0051], [0072]).
Regarding Claim 13 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said speech exercises include using said patient smart device for detecting a speech impediment including one or more of stuttering, an inability to recite words; and/or forgetting words Samuels discloses detecting speech disorders, for example, difficulty of forming specific words or sounds correctly [stuttering], or difficulty with making or pronouncing full words or sentences (Samuels: [0022]).
Regarding Claim 15 (Currently Amended), Samuels teaches a method of evaluating a speech therapy treatment for supporting the speech therapy of a patient, comprising the steps of:
capturing data from images of the face of the patient including data of facial structure of the patient for generating a patient baseline for the patient Samuels discloses using a camera to capture images of a user for determining a baseline for each user via evaluation of facial structure (symmetry, anomalous movement), jaw assessment (mobility and symmetry), bite and teeth assessment, lip assessment, sound assessment, and tongue assessment (Samuels: [0009], [0026], [0033)];
analyzing said patient exercise data for determining patient progress, by comparing said patient history of facial structure taken overtime with said facial structure of the patient baseline Samuels discloses extracting the user’s features when the user performs the exercise involving facial expression (the face key point data model) comparing facial features to reference model to evaluate actual changes in real-time to determine an actual data model of the user performing the exercise in real-time and analyze the facial features when exercise preformed (Samuels: [0026-0028], [0042], [0044-0048], [0051-0052], [0075], [0076])
generating an assessment report of the patient progress based on said analyzing, wherein said assessment report includes an automatically generated assessment of the progress of the patient Samuels discloses the system provides real-time feedback during the exercise and grading upon completion of each exercise generating a progress report(s) and assess the user performing the exercise such as generate a rating of the performance (Samuels: [0025], [0052-0053]).
the other claim limitations recite substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above.
Regarding Claim 16 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 15, wherein said update to the therapy treatment includes the steps of:
using said medical provider smart device to send updates to said speech exercises to said remote server over a communication network; and said remote server sending one or more updates for speech exercises to said patient smart device over a communication network Aicher discloses sending a new exercise and learning modules to reach again to the patient following same path of communication (Aicher: [0033], [0036]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 15, and incorporated herein.
Regarding Claim 17 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 16, wherein said method is performed using the updates of the speech exercises to generate patient exercise data Samuels discloses the IPOC assigns a series of exercises that can be modified by the system according to the user's progress (Samuels: [0028], [0052]).
Regarding Claim 18 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 16, wherein automatically generated assessment includes suggested changes to said therapy determined using a machine based learning algorithm executing on said remote server, and wherein said updates for said speech exercises are determined utilizing said suggested changes Samuels discloses the computer program include a computer vision (CV) module connected to an algorithmic modules and artificial neural networks (ANN) where the CV module monitors the user performance and provides a recommendation and/or modifications to the individualized plan of care (IPOC) (Samuels: [0028], [0037-0038], [0041], [0051-0053]).
Regarding Claim 19-20, 23-24, and 26-28, the claims recite substantially similar limitations to claims 5-7 and 10-13 as such, are rejected for similar reasons as given above.
Regarding Claim 30 (Currently Amended), Samuels teaches a method of evaluating a speech therapy treatment for supporting the speech therapy of a patient, comprising the steps of:
analyzing said patient exercise data for determining patient progress by utilizing said patient exercise data taken over time Samuels discloses creating a baseline for the user and sets the treatment based on comparison between the baseline and expected performance and extracting and analyzing the user’s features during the performance of the exercise (Samuels: [0026-0027], [0042], [0051-0052], [0075], [0076])
generating an assessment report of the patient progress based on said analyzing, Samuels discloses the system provides real-time feedback during the exercise and grading upon completion of each exercise generating a progress report(s) and assess the user performing the exercise such as generate a rating of the performance (Samuels: [0025], [0052-0053])
wherein said assessment report includes:
suggested changes to said therapy determined using a machine based learning algorithm executing on said remote server, and face mask data for generating a patient face mask of the face of the patient changing over time; Samuels discloses the computer program include a computer vision (CV) module connected to an algorithmic modules and artificial neural networks (ANN) where the CV module monitors the user performance and data generated to include face mask data parameters indicating the movements of the user's face features and muscles to determine a multi-dimensional face data model, including face mesh, temporal, semantic (meaningful features, face parts specific) and key points data, and provides a recommendation and/or modifications to the plan (Samuels: [0028], [0034], [0037-0038], [0041-0042], [0051-0053]).
wherein said assessment report is provided to said medical provider to evaluate the therapy treatment being provided to the patient, and wherein displaying a video showing a face mask of the patient over time that is generated based on the historical record of the patient exercise data Samuels discloses the system based on the real-time feedback during the exercise and grading upon completion of each exercise, generates progress reports to be accessed by the treating trained speech-language pathologists (SLPs) reviewing the report indicating whether or not the user is following the proper form of the exercise including set of image frames acquired in sequence of images such as video sequence comprising a set of facial key points, their temporal and semantic parameters indicating the movements of the user's face features and muscles to determine a multi-dimensional face data model including face mesh, temporal appearance of the face mesh, semantic (meaningful features, face parts specific) and key points data (Samuels: [Fig. 3], [0025], [0034], [0041-0044], [0052-0053], [0080-0081]).
the other claim limitations recite substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above.
Claims 3-5, 14, 21-22, 25, and 29, are rejected under 35 U.S.C. 103 as being unpatentable over Samuels et al. (US 2023/0401969 A1- “Samuels”) in view of Prout (US 11,178,956 Bl) in view of Aicher (US 2013/0204625 Al) in view of O'Donovan et al. (US 2021/0202090 Al -“O'Donovan”)
Regarding Claim 3 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, wherein said assessment data includes data identifying patient facial droop(s) shown by the exercises Samuels discloses identifying facial features but does not expressly disclose the underlined.
O'Donovan discloses video stream includes one or more video frames showing at least eyes and lips of the patient, and asymmetry detector includes a facial landmark detector to automatically identify a set of facial key points where the asymmetry detector identifies patient facial droop (O'Donovan: [Fig. 12], [0016-0017], [0066]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have the combination of Samuels, Prout, and Aicher providing and monitoring speech and language disorders and extracting facial features to incorporate identifying facial droop and degree, as taught by O'Donovan, which helps determining a medical concern or condition likelihood based on the calculated angle of the droop (O'Donovan: [0017]).
Regarding Claim 4 (Original), the combination of Samuels, Prout, Aicher, and O'Donovan teaches the method of claim 3, wherein said assessment data includes one or more images of the face of the patient showing an extent of patient facial droop(s)shown by the exercises O'Donovan discloses the and the asymmetry detector includes a facial landmark detector to automatically identify a set of facial key point(s) and the asymmetry detector may calculate the degree of facial droop by calculating a first line between each eye point; calculating a second line between each lip point; and calculating an angle between the first line and the second line, and present score the angle (O'Donovan: [0016-0017], [0066-0068]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 3, and incorporated herein.
Regarding Claim 5 (Original), the combination of Samuels, Prout, Aicher, and O'Donovan teaches the method of claim 3, wherein said assessment data includes one or more images of the face of the patient providing color indications identifying an extent of patient facial droop(s) shown by the exercises O'Donovan discloses using a neural network base don heat map regression where color coding may be providing a distinction of the calculated lines and degree (O'Donovan: [0164]).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 3, and incorporated herein.
Regarding Claim 14 (Original), the combination of Samuels, Prout, and Aicher teaches the method of claim 1, further comprising the step of the central server generating a SOAP report for the medical provider Samuels discloses generation of a report by the system (Samuels: [0052]).
However, the combination of Samuels and Aicher does not expressly disclose the underlined.
O'Donovan discloses generation of SOAP report (O'Donovan: [0050]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Samuels generation of a report to incorporate SOAP report, as taught by O'Donovan, which helps reporting a medical concern or condition likelihood based on the calculated (O'Donovan: [0017], [0132]).
Regarding Claim 21-22, 25, and 29, the claims recite substantially similar limitations to claims 2-4 and 14 as such, are rejected for similar reasons as given above.
Response to Amendment
Applicant's arguments filed 08/17/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 9-10.
On page 9 of the remarks, Applicant argues “The Examiner is incorrect in assessing that the claims are directed to an "abstract idea" with nothing more. First, the claims clearly provide a practical application ... This is a practical application because it allows a patient to conduct exercises remotely from the medical provider offices... This saves the patient time,... This process
improves the efficiency of treating patients and reduces the costs and time of travel...”, Examiner respectfully disagree. As described in the prior OA mailed 04/16/2026, the claims, given their broadest reasonable interpretation, recite an abstract idea which have been analyzed under Step 2A, Prong One reciting a process for providing and monitoring a user speech exercise and collect to analyze and assess the exercise data which define a judicial exception steps while using computing devices to perform the abstract steps, which are steps can be performed by a user interacting with a device(s) for performing the abstract idea which are concepts that falls within the Certain Methods of Organizing Human Activity grouping and sub grouping sub-grouping "managing personal behavior or interactions between people".
Furthermore, as described in the rejection above, the claim does not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., smart device, server, machine based learning algorithm”, to perform the steps abstract idea such as collecting/obtaining, analyzing, assessing, and displaying through leveraging computing technology in a well understood manner however improving upon an abstract idea does not make the abstract idea any less abstract. In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to save patient time and determine exercise effectiveness to reduce cost and time of travel to and from provider office, which is solving a health facility and patient administrative problem, using computers, for assessing the patient speech language. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept).
On page 10 of the remarks, Applicant argues “the system interactions with the patient using his own smart device to facilitate the exercises and monitor their progress, ... all amount to "significantly more" than the judicial exception, as the interaction of three remotely located computers to act in concert to support patient/medical provider interactions amounts to more than the operation of generic computing components, but instead results in an integrated interaction that provides benefits to both parties”, Examiner respectfully disagree. As mentioned above, the steps recited in independent claims, when viewed as a whole, recite a judicial exception. As mentioned above, the claims under BRI recites an abstract idea for procedure parameter while citing addition element described at high level and as tool(s) to perform the abstract idea as such the thrust of Applicant's invention is to improve the abstract idea for evaluating a user speech language through leveraging computing technology, e.g., smart device, server, machine based learning algorithm, in an understood manner. The fact that the judicial exception, identified in the rejection above, relies upon providing, collecting user speech data, analyzing, assessing the data, does not impart an improvement to any existing computer, or any other technology or technical field. At best, this gathering of data to be used by substance injector device may improve the abstract idea of improving managing substance injection. However, improving upon an abstract idea does not make the abstract idea any less abstract.
Therefore, the Applicant argument is found to be unpersuasive and Examiner remains the 101 rejections of claims which have been updated to address Applicant's argument.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 10-11.
On page 10 of the remarks, Applicant argues “Claim 1, as amended, recites the feature of "preparing a baseline profile of the facial... Nowhere does the prior art teach preparing such a baseline profile”, Examiner respectfully disagree. Although the Applicant argument(s) is/are directed to a newly added features that have not been considered in the prior examination, Examiner asserts that the feature, under BRI, is disclosed in “Samuels”, (see at least [0026]).
On page 10 of the remarks, Applicant argues “Nowhere does the prior art teach accumulating such a historical record of patient exercise data over a plurality of different exercise sessions.”, Examiner respectfully disagree. Although the Applicant argument(s) is/are directed to a newly added features that have not been considered in the prior examination, Examiner asserts that the feature, under BRI, is disclosed in “Samuels” as per the above rejection describing collected facial key features over time indicating historical record for different exercises such that the collected data from the initial assessment in “Samuels” describes a historical data.
On page 11 of the remarks, Applicant argues “The prior art also fails to teach any such comparison using any historical record of facial structures”, Examiner respectfully disagree. As mentioned above that the Applicant argument(s) is/are similarly directed to a newly added features that have not been considered in the prior examination, Examiner asserts that the feature, under BRI, is disclosed in “Samuels” as per the above rejection such that tracking facial key features image frames over time.
On page 11 of the remarks, Applicant argues “Claim 30 recites generating an assessment report that includes "suggested changes to said therapy determined using a machine-based learning algorithm... and hence is also patentable over the references for reasons similar to those discussed above ... claim 30, as amended, recites the feature of displaying a video "showing a face mask... a feature also not found in the prior art references, and hence claim 30 is also patentable over the references for this reason as well”, Examiner respectfully disagree. As described in the prior OA mailed 04/16/2026 and in the above rejection, “Samuels” discloses the use of artificial neural networks (ANN )and machine learning (ML_ in determining and modifying the individualized plan of care (IPOC) that contains a personalized set of exercises to achieve the treatment goal as such the feature described by the Applicant as a generic feature for using ML, (see Applicant [0150]), in addition Samuels discloses image frames acquired in sequence of images such as video sequence comprising a set of facial key points, their temporal and semantic parameters indicating the movements of the user's face features and muscles to determine a multi-dimensional face data model including face mesh, temporal appearance of the face mesh.
Therefore, Examiner finds the Applicant argument unpersuasive.
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
US 7762264 “Ramig” discloses speech and movement disorders helping to calibrate the amplitude of individual’s speech and movement.
The references are relevant since it discloses analyzing a user audio and face image to determine speech disorder.
Conclusion
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
