Detailed Notice
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-20 are pending.
Claims 1 and 17 are amended.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-19 are directed to a method (i.e., process) and claim 20 is directed to a system (i.e., machine). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 17, and 20 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A computer-implemented method comprising: receiving, from a user device, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity and/or at least one cognitive assessment activity performed by a patient; processing the audio data to obtain speech information in the form of transcribed text, wherein the speech information comprises semantic information and/or speech features; processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient, and wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data; extracting at least one gait or balance measurement from the pose information; extracting at least one cognitive measurement or cognitive normative score from the speech information; and generating, via a machine learning model, a prediction of a patient outcome using the at least one gait or balance measurement and/or the at least one cognitive measurement or cognitive normative score as input, wherein the machine learning model is trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments and cognitive assessments”.
The limitations of receiving, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity and/or at least one cognitive assessment activity performed by a patient; processing the audio data to obtain speech information in the form of transcribed text, wherein the speech information comprises semantic information and/or speech features; processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient, and wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data; extracting at least one gait or balance measurement from the pose information; extracting at least one cognitive measurement or cognitive normative score from the speech information; and generating, a prediction of a patient outcome using the at least one gait or balance measurement and/or the at least one cognitive measurement or cognitive normative score as input, wherein the machine learning model is trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments and cognitive assessments, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, processing, extracting, and generating, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 17 recites: “A computer-implemented method comprising: receiving, from a user device, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity; processing the audio data to obtain speech information in the form of transcribed text, wherein processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp to each of the one or more speech cues; assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment; processing the video data of the at least one segment to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient; extracting at least one gait or balance measurement from the pose information; and generating, via a machine learning model, a prediction of a patient outcome using the at least one gait or balance measurement as input, wherein the machine learning model has been trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments”.
The limitations of receiving, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity; processing the audio data to obtain speech information in the form of transcribed text, wherein processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp to each of the one or more speech cues; assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment; processing the video data of the at least one segment to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient; extracting at least one gait or balance measurement from the pose information; and generating, a prediction of a patient outcome using the at least one gait or balance measurement as input, wherein the model has been trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, processing, identifying, assigning, extracting, and generating, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Additionally, claim 20 recites: “A system comprising: an electronic medical records (EMR) subsystem comprising one or more databases to receive and store video and audio data of patient assessments and patient assessment results; and a data analysis and prediction subsystem comprising one or more processors executing processor-readable instructions causing the one or more processors to perform the method of claim 1”.
The limitations of claim 1, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of receiving, processing, identifying, assigning, extracting, and generating, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Dependent claims 2-16 and 18-19 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 17 and 20. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “user device”, “machine learning model”, “system”, “data analysis subsystem”, “processors”, and “electronic medical records (EMR) subsystem”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0046], [0055], and [0078], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “from a user device”, “via a machine learning model”, “wherein the machine learning model has been trained”, and “an electronic medical records (EMR) subsystem comprising one or more databases to receive and store video and audio data of patient assessments and patient assessment results; and a data analysis and prediction subsystem comprising one or more processors executing processor-readable instructions causing the one or more processors to perform”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving, from a user device, video data and audio data of a patient assessment”, “obtain speech information in the form of transcribed text”, “obtain pose information”, and “receiving, from a user device, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-16 and 18-19 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 17, and 20, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-16 and 18-19 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 17, and 20, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving, from a user device, video data and audio data of a patient assessment” and “receiving, from a user device, video data and audio data of a patient assessment, wherein the patient assessment comprises at least one gait or balance assessment activity”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Segal et al. (US 20240115159 A1), hereinafter Segal, in view of Pascual-Leone et al. (US 20230255564 A1), hereinafter Pascual-Leone, and Rao et al. (US 20190110754 A1), hereinafter Rao.
Regarding claim 1, Segal teaches a computer-implemented method comprising: receiving, from a user device, video data and audio data of a patient assessment (Segal, [0020], [0041], [0028]), wherein the patient assessment comprises at least one gait or balance assessment activity and/or at least one cognitive assessment activity performed by a patient (Segal, [0021], [0023], and [0041]); processing the audio data to obtain speech information in the form of transcribed text, wherein the speech information comprises semantic information and/or speech features (Segal, [0028], [0076], [0166], [0171], and [0177]); processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient (Segal, [0071]-[0095]: “According to some embodiments, each of engines 102, 104 and 106 may be configured to extract features 110, such as, computer vision extracted features 110 a (e.g., a gait velocity, a step length, cadence, gait stance, gait swing, gait symmetry, lower extremity range of motion, mean and max joint angle, coupling metrics, stride to stride variability, gait variability over time etc.)”, [0118]: “the computer vision/sensor data engine 102 of the present disclosure may be configured for analyzing in-depth gait, posture, kinesthesis, and/or proprioception including spatio-temporal, kinematic and kinetic parameters, such as joint and skeletal spatial position, limbs angles, stride length and width, trunk, and head/neck spatial position, among others”, and [0124]: “a motion capture and/or posture capture and analysis module of the present disclosure may be based on, e.g., mechanical capture systems which directly track body joint angles”); extracting at least one gait or balance measurement from the pose information (Segal, [0041]-[0042], [0052], [0056]-[0058], and [0197]-[0198]).
Segal does not teach extracting at least one cognitive measurement or cognitive normative score from the speech information; and generating, via a machine learning model, a prediction of a patient outcome using the at least one gait or balance measurement and/or the at least one cognitive measurement or cognitive normative score as input, wherein the machine learning model is trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments and cognitive assessments.
However, Pascual-Leone teaches extracting at least one cognitive measurement or cognitive normative score from the speech information (Pascual-Leone, [0032]-[0035] and [0046]-[0048]); and generating, via a machine learning model (Pascual-Leone, [0033], [0036], and [0040]), a prediction of a patient outcome using the at least one gait or balance measurement and/or the at least one cognitive measurement or cognitive normative score as input (Pascual-Leone, [0032]-[0035], [0046]-[0048], [0062], [0070], and [0073]), wherein the machine learning model is trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments and cognitive assessments (Pascual-Leone, [0046]-[0048], [0062], [0070], and [0073]-[0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Segal and Pascual-Leone does not teach wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data.
However, Rao teaches wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data (Rao, [0117]: “The raw video and audio data usually needs to go through several stages of preparation before it can be used to train models. These stages include data preprocessing (e.g., trimming video/audio, cropping video, adjusting audio gain, subsampling or super sampling time series, temporal smoothing, etc.), normalization (e.g., aligning audio clips to standard template, transforming face image to canonical view, detecting object of interest and cropping around it, etc.), and feature extraction (e.g., deriving Mel Frequency Cepstral Coefficients (MFCC) from acoustic data, computing optical flow features for video data, extracting and representing actions such as blinks or finger taps, etc.)” and [0127]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal and Pascual-Leone to incorporate the teachings of Rao and account for a system which can, either on its own or in conjunction with a physician, accurately diagnose a specific neurological disorder in a patient without the need for the patient or physician to have any prior training in diagnosing such conditions (Rao, Abstract and [0002]-[0027]).
Regarding claim 2 Segal further teaches determining a gait or balance normative score for the at least one gait or balance assessment activity based on the at least one gait or balance measurement (Segal, FIG. 5, [0021], [0071]-[0074], and [0142]-[0147]); and wherein the input for the machine learning model further comprises the gait or balance normative score (Segal, FIG. 4, [0140], [0198], and [0200]).
Regarding claim 3 Segal does not teach the cognitive normative score is extracted from the semantic information of the speech information.
However, Pascual-Leone teaches the cognitive normative score is extracted from the semantic information of the speech information (Pascual-Leone, [0033] and [0088] .
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 4 Segal does not teach determining at least one additional cognitive normative score based on the at least one cognitive measurement; and wherein the input for the machine learning model further comprises the at least one additional cognitive normative score.
However, Pascual-Leone teaches determining at least one additional cognitive normative score based on the at least one cognitive measurement (Pascual-Leone, [0046]-[0048], [0057], [0085], [0092]); and wherein the input for the machine learning model further comprises the at least one additional cognitive normative score (Pascual-Leone, [0046]-[0048], [0057], [0085], [0092]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 5 Segal further teaches the input for the machine learning model further comprises at least one of the pose information (Segal, [0117]-[0119], [0122]-[0125], and [0213]) and the speech information (Segal, [0028], [0076], [0166], [0171], and [0177]).
Regarding claim 6 Segal further teaches the patient outcomes in the training data comprise patient-reported outcomes (Segal, [0024], [0041], [0065], and [0186]).
Regarding claim 7 Segal further teaches the at least one gait or balance measurement comprises at least one of: step and stride measurements (Segal, [0021], [0071], and [0118]); gait cycle phases for each leg (Segal, [0210]); estimation of trunk sway (Segal, [0118]); asymmetry detection during movement (Segal, [0025], and Claim 27); joint angles (Segal, [0071]); and joint velocities (Segal, [0071]).
Regarding claim 8 Segal further teaches at least one of processing the video data and extracting the at least one gait or balance measurement are performed using at least one machine learning model trained using sensor data collected from physical sensors during a plurality of gait or balance assessments. (Segal, [0020], [0059], [0066], and [0071]).
Regarding claim 9 Segal does not teach at least one of processing the audio data and extracting the at least one cognitive measurement or cognitive normative score are performed using at least one machine learning model trained using transcribed and labeled text collected from a plurality of cognitive assessments conducted by trained experts.
However, Pascual-Leone teaches at least one of processing the audio data and extracting the at least one cognitive measurement or cognitive normative score are performed using at least one machine learning model trained using transcribed and labeled text collected from a plurality of cognitive assessments conducted by trained experts (Pascual-Leone, [0033], [0057], [0088], and [0127]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 10 Segal further teaches the at least one machine learning model is a large-language model (Segal, [0068]-[0070], [0126], and [0139])
Regarding claim 11 Segal does not teach processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp to each of the one or more speech cues.
However, Pascual-Leone teaches processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp to each of the one or more speech cues (Pascual-Leone, [0048]-[0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 12 Segal further teaches the video data further comprises cropping or segmenting the video data based on the respective timestamp of at least one speech cue of the one or more speech cues (Segal, [0052]-[0053] and [0195]).
Regarding claim 13 Segal does not teach the at least one gait or balance measurement comprises a timed measurement and one speech cue of the one or more speech cues indicates the start of the at least one gait or balance assessment activity, and wherein processing the video data further comprises assigning an activity start timepoint to the video data based on the respective timestamp of the one speech cue.
However, Pascual-Leone teaches the at least one gait or balance measurement comprises a timed measurement and one speech cue of the one or more speech cues indicates the start of the at least one gait or balance assessment activity (Pascual-Leone, [0032], [0048]-[0049], and [0130]), and wherein processing the video data further comprises assigning an activity start timepoint to the video data based on the respective timestamp of the one speech cue (Pascual-Leone, [0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 14 Segal does not teach the timed measurement comprises a measured time lag between the activity start timepoint and a movement start timepoint, wherein the movement start timepoint is assigned based on the pose information.
However, Pascual-Leone teaches the timed measurement comprises a measured time lag between the activity start timepoint and a movement start timepoint, wherein the movement start timepoint is assigned based on the pose information (Pascual-Leone, [0048]-[0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 15 Segal does not teach the at least one gait or balance assessment activity and the at least one cognitive assessment activity are performed simultaneously as a dual-task assessment.
However, Pascual-Leone teaches the at least one gait or balance assessment activity and the at least one cognitive assessment activity are performed simultaneously as a dual-task assessment (Pascual-Leone, [0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 16 Segal does not teach determining a respective cost factor for each of the at least one gait or balance assessment activity and the at least one cognitive assessment activity in comparison to another gait or balance assessment activity and cognitive assessment activity, respectively, performed independently.
However, Pascual-Leone teaches determining a respective cost factor for each of the at least one gait or balance assessment activity and the at least one cognitive assessment activity in comparison to another gait or balance assessment activity and cognitive assessment activity, respectively, performed independently (Pascual-Leone, [0039] and [0130]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 20 Segal teaches a system comprising: an electronic medical records (EMR) subsystem comprising one or more databases to receive and store video and audio data of patient assessments and patient assessment results (Segal, [0029], [0066], and [0075]-[0076]); and a data analysis and prediction subsystem comprising one or more processors executing processor-readable instructions causing the one or more processors (Segal, [0158]-[00160] and [0174]).
A combination of Segal, Pascual-Leone, and Rao teaches claim 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal and Pascual-Leone to incorporate the teachings of Rao and account for a more complete, 3D pictures of a person's gait, it, too, places limits on its use in clinical applications, in part due to time-consuming processing of the marker-based data. As a result, the potential of gait as a diagnostic or early-stage screening tool for neurodegenerative conditions in a clinical context has not been realized to date (Rao, Abstract and [0003]).
Claim(s) 17-19 is rejected under 35 U.S.C. 103 as being unpatentable over Segal et al. (US 20240115159 A1), hereinafter Segal, in view of Pascual-Leone et al. (US 20230255564 A1), hereinafter Pascual-Leone
Regarding claim 17 Segal teaches receiving, from a user device, video data and audio data of a patient assessment (Segal, [0020], [0041], [0028]), wherein the patient assessment comprises at least one gait or balance assessment activity (Segal, [0021], [0023], and [0041]); processing the audio data to obtain speech information in the form of transcribed text (Segal, [0028], [0076], [0166], [0171], and [0177]); processing the video data of the at least one segment to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient (Segal, [0117]-[0119], [0122]-[0125], and [0213]).
Segal does not teach wherein processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp of the one or more speech cues; assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment; extracting at least one gait or balance measurement from the pose information; and generating, via a machine learning model, a prediction of a patient outcome using the at least one gait or balance measurement as input, wherein the machine learning model has been trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments.
However, Pascual-Leone teaches wherein processing the audio data further comprises identifying one or more speech cues in the speech information and assigning a respective timestamp of the one or more speech cues (Pascual-Leone, [0048]-[0049]); assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment (Pascual-Leone, [0048]-[0049]); extracting at least one gait or balance measurement from the pose information (Pascual-Leone, [0032]-[0035] and [0046]-[0048]); and generating, via a machine learning model, a prediction of a patient outcome using the at least one gait or balance measurement as input, wherein the machine learning model has been trained using training data comprising patient outcomes in association with a plurality of previous gait or balance assessments (Pascual-Leone, [0032]-[0035], [0046]-[0048], [0062], [0070], and [0073]-[0076]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 18 Segal does not teach extracting at least one cognitive measurement or cognitive normative score from the speech information, and wherein the input for the machine learning model further comprises the at least one cognitive measurement or cognitive normative score.
However, Pascual-Leone teaches extracting at least one cognitive measurement or cognitive normative score from the speech information, and wherein the input for the machine learning model further comprises the at least one cognitive measurement or cognitive normative score (Pascual-Leone, [0032]-[0035] and [0046]-[0048]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Regarding claim 19 Segal does not teach the at least one gait or balance assessment activity is performed simultaneously with at least one cognitive assessment activity; the method further comprises determining a cost factor for the at least one gait or balance assessment activity in comparison to another gait or balance assessment activity performed independently; and the input for the machine learning model further comprises the cost factor.
However, Pascual-Leone teaches the at least one gait or balance assessment activity is performed simultaneously with at least one cognitive assessment activity (Pascual-Leone, [0032]-[0035] and [0046]-[0048]); the method further comprises determining a cost factor for the at least one gait or balance assessment activity in comparison to another gait or balance assessment activity performed independently (Pascual-Leone, [0039] and [0130]); and the input for the machine learning model further comprises the cost factor (Pascual-Leone, [0039] and [0130]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Segal to incorporate the teachings of Pascual-Leone and account for cost effective, reliable, objective, noninvasive, accurate, systems to identify and track meaningful deviations in brain health and to detect cognitive impairment at its earliest stages. In addition, there is a growing need to optimize care and treatment recommendation, as well as dosage and personalization of existing and in-development therapies (Pascual-Leone, Abstract and [0002]-[0003]).
Response to Arguments
Applicant's arguments filed 11/27/2025 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 Rejection, Applicant argues the claims do not recite an abstract idea, but a technical methodology for data management and signal processing. Examiner respectfully disagrees. The limitations of receiving… video data and audio data, processing the audio data to obtain speech information, processing video data to obtain pose information, extracting at least one gait or balance measurement, extracting at least one cognitive measurement, generating… a prediction of patient outcome, and assigning one or more timepoints to the video data, are all steps that a person can perform via pen and paper, with other people, or via computer tools (see MPEP 2106.04(a)(2) states “the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the “certain methods of organizing human activity” grouping”).
Applicant also argues any alleged abstract idea is integrated into a practical application by reciting a technological solution of processing large, complex video files. Examiner respectfully disagrees. MPEP 2106.04(a)(2) states “Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”)”. Also, MPEP 2106.05(f) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)” .
Applicant also argues the application is directed to an improvement to the efficiency and operation of the computing device itself. Applicant also argues a human cannot mentally synchronize millisecond-level timestamps from an audio waveform to crop a video file in real-time to reduce processor instruction cycles. Examiner respectfully disagrees. The claims do not distinctly claim “synchronize millisecond-level timestamps from an audio waveform”, but are recited at a high level (e.g., assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment). MPEP 2106.05(a) states “Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology.”
Applicant also argues the claims amount to significantly more because the prior art does not teach the amendment of “processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient, and wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data”. Examiner respectfully disagrees. MPEP 2106.05 states “Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101”. Therefore, even if there is no art rejection, the claims would still be evaluated for eligibility under 101. Therefore, the 35 U.S.C. 101 Rejection is maintained.
Regarding the 35 U.S.C. 103 Rejection, Applicant argues the prior art does not teach the amendments of “processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient, and wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data” and “assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment”. Examiner respectfully disagrees. Applicant’s arguments with respect to the amendment of “processing the video data to obtain pose information, wherein the pose information comprises a plurality of joint points representing the patient, and wherein processing the video data further comprises cropping or segmenting the video data based at least in part on the speech information from the audio data”, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Pascual-Leone teaches at [0047]: “recording positional data of user interactions such as inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen) provided by a mobile device stylus while the patient performs a task or assessment on a mobile application, such as drawing a clock; eye tracking data while providing visual stimulus and requiring the patient to perform tasks that elicit their ability to perceive and respond to that stimulus; audio recording while providing audiovisual stimulus and requiring the patient to vocalize responses to that stimulus to elicit their ability to perceive and respond to such stimulus; video data recording of patients performing some task such as walking; accelerometer data recording of patients performing some task such as walking; functional neuroimaging or sensing (e.g., electroencephalography (EEG) recordings or functional magnetic resonance imaging (fMRI) of the patient performing any one of the assessments or tasks listed herein; neuroimaging data from metabolic or chemical sources (e.g., positron emission tomography), structural or vascular imaging; data captured by third-party devices either contemporaneously with user performance of an assessment, or historically captured during day to day activity”, [0048]: “health data may be associated with timestamps of when each data point is captured and therefore has an element of temporality”, and [0116]: “automatic speech recognition (ASR) software may be used to determine the accuracy of the response(s). In various embodiments, the voice of the test taker is analyzed to derive speech metrics such as pause rate, pitch, and/or speed” (e.g., “assigning one or more timepoints to the video data based on the respective timestamps of the one or more speech cues to define at least one segment”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
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