DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1-28-2026 has been entered.
Status of Claims
This action is a non-final rejection
Claims 1-13, 15, 18-20 are pending
Claims 14, 16, 17 were cancelled
Claims 1, 10, 15 were amended
Claims 1-13, 15, 18-20 are rejected under 35 USC § 101
Claims 1-13, 15, 18-20 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 12-20-2021
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7-6-2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13, 15, 18-20 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-13, 15, 18-20 the claims recite an abstract idea of “determining a health risk”.
Independent Claim 1 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites a method of determining health risk.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
receiving, ..., first signaling from a first source configured to monitor behavior of a patient;
receiving, ..., second signaling from a second source configured to monitor environmental data associated with the patient;
writing ... data that is based at least in part on a combination of the first signaling and the second signaling including data comprising at least in part the real time patient health data associated with the patient and real time environmental data associated with the patient;
establishing an individual developmental baseline based on the data including an estimated threshold for the patient at which a developmental delay becomes more likely;
determining and outputting how much and what data is needed to determine a health risk for the patient using a plurality of ... models, by continuously incorporating new data including the real time patient health data and the real time environmental data into the plurality of models
wherein a first one of the plurality of ... models uses the real time patient health data and at least a portion of the real time environmental data to perform multi-class classification;
a second one of the plurality of ... models comprises a ...model to perform multi-class classification on patient image data; and
a third one of the plurality of ... models comprises a speech recognition ... model to perform multi-class classification on patient audio data;
determining, ... a health risk for the patient based on the output of the plurality of ... models, the first signaling, and the second signaling, wherein determining the health risk comprises;
compiling the data, wherein the data comprises different types of data and different weights to increase an accuracy of the health risk determination, and determining the health risk within a health risk range based on the compiled data;
tracking regression of the patient based on the individual developmental baseline, output of the plurality of .. models, the first signaling, and the second signaling;
identifying, ... output data representative of a health risk response plan for the patient based at least in part on input data representative of the health risk the tracked regression, the individual developmental baseline and associated threshold and additional patient data stored ...; and
transmitting the output data representative of the health risk response plan via third signaling.
generating a dashboard including health risk trend charts associated with different metrics associated with the health risk using the output data.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “determining a health risk”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “determining a health risk”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
computing device;
first processing resource;
a personal medical device to monitor real time patient health data;
second processing resource;
memory resource;
trained machine learning models;
improve a performance of the computing device through iterations;
convolutional neural network machine learning model;
plurality of trained machine learning models.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 1 recites:
computing device;
first processing resource;
a personal medical device to monitor real time patient health data;
second processing resource;
memory resource;
trained machine learning models;
improve a performance of the computing device through iterations;
convolutional neural network machine learning model;
plurality of trained machine learning models.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Independent Claim 10 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 10 recites a non-transitory machine-readable medium comprising a processing resource in communication with a memory resource having instructions executable to determine health risk.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
receive ..., a plurality of input data from a plurality of sources, the plurality of sources comprising at least two of:
... a patient..., monitor behavior of the patient, manually received input;
wherein the plurality of input data comprises at least in part real time patient health data and real time environmental data;
write .. the received plurality of input data;
establish an individual developmental baseline based on the data including an estimated threshold for the patient at which a developmental delay becomes more likely;
determine and output how much and what data is needed to determine a developmental delay by continuously incorporating new data including the real time patient health data and the real time environmental data into the plurality of ... models wherein:
a first one of the plurality of .. models uses patient health data and environmental data to perform multi-class classification;
a second one of the plurality of .. models comprises a convolutional neural network machine learning model to perform multi-class classification on patient image data; and
a third one of the plurality of .. models comprises a speech recognition .. model to perform multi-class classification on patient audio data;
identify, based on the output of the using a plurality of .. models, at the processing resource or a different processing resource, output data representative of a developmental delay risk of the patient and a developmental delay plan including a proposed action to identify the developmental delay, address the developmental delay, or both, wherein identifying the developmental delay risk comprises:
compiling the data, wherein the data comprises different types of data and different weights to increase an accuracy of the development delay, and determining the developmental delay risk within a health risk range based on the compiled data;
track regression of the patient based on the individual developmental baseline, output of the plurality of .. models, the first signaling, and the second signaling;
transmit the output data representative of the developmental delay risk and the developmental delay plan to the patient, a caregiver, a health care provider, or any combination thereof;
generate a dashboard including health risk trend charts associated with different metrics associated with the developmental delay risk.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “determining a health risk”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “determining a health risk”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 10 recites:
processing resource;
memory resource;
a mobile device;
a personal medical device to monitor behavior of the patient;
a medical device;
environmental sensors;
trained machine learning models;
improve a performance of a computing device through iterations;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 10 recites:
processing resource;
memory resource;
a mobile device;
a personal medical device to monitor behavior of the patient;
a medical device;
environmental sensors;
trained machine learning models;
improve a performance of a computing device through iterations.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible
Independent Claim 15 is rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claim 15 recites a non-transitory machine-readable medium comprising a first processing resource in communication with a memory resource having instructions executable to determine health risk.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES):
receive, patient image data, patient audio data, including .. real time patient health data;
receive, ..., second signaling ... configured to monitor environmental data associated with the patient;
receive, patient health data and patient environmental data via second signaling configured to receive input from the patient, a health care provider, or any combination thereof;
write data that is based at least in part on a combination of the first signaling, the second signaling, and the third signaling, wherein in the data includes the patient health data, the patient environmental data and the patient image data, and the patient audio data;
establish an individual developmental baseline based on the data including an estimated threshold for the patient at which a developmental delay becomes more likely;
determine, and output, ..., how much and what data is needed to determine a health risk for the patient,...models, to improve a performance of the computing device through iterations by continuously incorporating new data including the real time patient health data into the plurality of trained machine learning models wherein;
a first one of the plurality of ... models uses the patient health data and at least a portion of the environmental data to perform multi-class classification;
a second one of the plurality of .. models comprises a .. model to perform multi-class classification on the patient image data; and
a third one of the plurality of .. models comprises a speech recognition .. model to perform multi-class classification on the patient audio data;
determine a developmental delay risk of the patient based on the output of the plurality of .. models, input data representative of the written patient health data and patient environmental data and the written patient image data, patient audio data, wherein determining the developmental delay risk comprises:;
compiling the data, wherein the data comprises different types of data and different weights to increase an accuracy of the developmental delay determination, and determining the developmental delay risk within a health risk range based on the compiled data;
track regression of the patient based on the individual developmental baseline, output of the plurality of trained machine learning models, the first signaling, and the second signaling;
identify, .., output data representative of a developmental delay plan for the patient using the determination and output of the plurality of .. models, input data representative of the written patient health data and patient environmental data, the written patient image data, patient audio data, the tracked regression, the individual developmental baseline and associated threshold and input data representative of the developmental delay risk; and
transmit the output data representative of the developmental delay treatment plan to the patient, a health care provider, a caregiver, or any combination thereof;
generate a dashboard including health risk trend charts associated with different metrics associated with the developmental delay risk.
belong to the grouping of mental processes under concepts performed in the human mind as it recites “determining a health risk”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “determining a health risk”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 15 recites:
non-transitory machine-readable medium;
first processing resource;
memory resource;
a personal medical device to monitor real time patient health data;
a sensor
second processing resource;
plurality of trained machine learning models;
convolutional neural network machine learning model.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 15 recites:
non-transitory machine-readable medium;
first processing resource;
memory resource;
a personal medical device to monitor real time patient health data;
a sensor
second processing resource;
plurality of trained machine learning models;
convolutional neural network machine learning model.
Amount to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs [0003-0004], [0043-0049]. (refer to MPEP 2106.05(f)) Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “determining a health risk”. These claim limitations include:
Claim 2: wherein identifying the output data representative of the health risk response plan comprises utilizing the plurality of .. models to identify the output data representative of the health risk response plan based on data associated with the first signaling, the second signaling, the health risk, and previously received signaling and associated data associated with previous health risk response plans.
Claim 3: wherein determining the health risk comprises utilizing the plurality of .. models to perform multi-class classification on tabular data, image data, and language data.
Claim 4: wherein determining the health risk comprises determining a likelihood that the patient is at risk of a particular health condition or currently has the particular health condition.
Claim 5: wherein identifying the output data representative of the health risks response plan comprises:
identifying an alert to transmit to .. the patient; and
identifying a proposed action and associated instructions to address the health risk of the patient
Claim 6: further comprising updating the health risk in response to receiving .. additional first signaling, second signaling, or any combination thereof and based at least in part on feedback received .. associated with outcomes of the output data representative of the health risk response plan.
Claim 7: further comprising:
receiving .. accessible by the patient .., manual input from the patient comprising personal patient data, patient health data, environmental data, health care provider data, or a combination thereof; and
writing .. data that is based at least in part on a combination of the first signaling, the second signaling, and the manual input.
Claim 8: wherein determining the health risk comprises determining a developmental delay risk.
Claim 9: wherein the first signaling, the second signaling, or both, comprise image sequences.
Claim 11: further comprising .. to identify the output data representative of the developmental delay plan based at least in part on generic developmental patient information and generic developmental delay treatment information ..
Claim 12: further comprising .. to identify the output data representative of the developmental delay plan based at least in part on patient medical history information ..
Claim 13: wherein the plurality of input data comprises patient health data, environmental data, or any combination thereof
Claim 18: receive the patient image data, patient audio data, or both via first signaling configured to monitor patient health data comprise receive the patient image data, patient audio data, or both via signaling from the patient;
Claim 19: wherein the patient health data, the patient environmental data, the patient image data, the patient audio data, or any combination thereof is received in real time;
Claim 20: wherein the patient environmental data comprises lighting, screen time, diet, humidity, temperature, sound, caregiver actions, community traits, socioeconomic status, caregiver traits, social interactions, or any combination thereof;
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 2: trained machine learning models;
Claim 3: trained machine learning models;
Claim 5: a computing device of the patient
Claim 6: first processing resource
Claim 7:
receiving at the first processing resource via an application of a computing device;
writing from the first processing resource to the memory resource coupled to the first processing resource;
Claim 11:
instructions executable to identify the output data;
information stored in a portion of the memory resource or other storage accessible by the processing resource;
Claim 12:
instructions executable to identify the output data;
information stored in a portion of the memory resource or other storage accessible by the processing resource;
Claim 18:
instructions executable to receive;
the first source;
instructions executable to receive the patient image data;
health sensor;
health monitor;
wearable device;
camera;
audio collection device;
mobile device.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, even in combination the additional elements of the claim do not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 2: trained machine learning models;
Claim 3: trained machine learning models;
Claim 5: a computing device of the patient
Claim 6: first processing resource
Claim 7:
receiving at the first processing resource via an application of a computing device;
writing from the first processing resource to the memory resource coupled to the first processing resource;
Claim 11:
instructions executable to identify the output data;
information stored in a portion of the memory resource or other storage accessible by the processing resource;
Claim 12:
instructions executable to identify the output data;
information stored in a portion of the memory resource or other storage accessible by the processing resource;
Claim 18:
instructions executable to receive;
the first source;
instructions executable to receive the patient image data;
health sensor;
health monitor;
wearable device;
camera;
audio collection device;
mobile device.
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.
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
non-obviousness..
Claims 1-13, 15, 18-20 are rejected under 35 U.S.C. 103 as being un-patentable by Vaughan et.al (US 20190088366 A1) hereinafter “Vaughan” in view of Cemgil et.al (US 20240394541 A1) hereinafter “Cemgil”
Regarding claims 1, 10, 15 Vaughan teaches:
A non-transitory machine-readable medium comprising a first processing resource in communication with a memory resource having instructions executable to: (See at least [0269] via: “… machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 1201, such as, for example, on the memory 1210 or electronic storage unit 1215. …. During use, the code can be executed by the processor 1205. In some cases, the code can be retrieved from the storage unit 1215 and stored on the memory 1210 for ready access by the processor 1205. In some situations, the electronic storage unit 1215 can be precluded, and machine-executable instructions are stored on memory 1210…”)
receive at the first processing resource (the system), the memory resource, or both, patient image data (patient video), patient audio data (patient audio), , via first signaling (data and metadata related to a patient) configured to monitor the patient (monitoring …behavior of patients) including a personal medical device to monitor real time patient health data; (See at least [0185] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio..”; in addition see at least [0171] via: “… Data sources can comprise either active or passive sources, in digital format via one or more digital devices such as mobile phones, video capture, audio capture, activity monitors, or wearable digital monitors. Examples of active data collection comprise devices, systems or methods for tracking eye movements, recording body or appendage movement, monitoring sleep patterns, recording speech patterns. In some instances, the active sources can include audio feed data source such as speech patterns, lexical/syntactic patterns (for example, size of vocabulary, correct/incorrect use of pronouns, correct/incorrect inflection and conjugation, use of grammatical structures such as active/passive voice etc., and sentence flow), higher order linguistic patterns (for example, coherence, comprehension, conversational engagement, and curiosity), touch-screen data source (for example, fine-motor function, dexterity, precision and frequency of pointing, precision and frequency of swipe movement, and focus/attention span), and video recording of subject's face during activity (for example, quality/quantity of eye fixations vs saccades, heat map of eye focus on the screen, focus/attention span, variability of facial expression, and quality of response to emotional stimuli)…”; in addition see at least [0187] via: “… a mobile device such as a smart phone, an activity monitors, or a wearable digital monitor—records data and metadata related to a patient. Data may be collected based on interactions of the patient with the device, as well as based on interactions with caregivers and health care professionals. The data may be collected actively, such as by administering tests, recording speech and/or video, and recording responses to diagnostic questions. The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder..”)
receive, at the first processing resource (the system), second signaling from a second source configured to monitor environmental data (noise or loud music) associated with the patient (See at least [0185] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio..”; in addition see at least [0215] via: “... Passively collected data can comprise data collected continuously from a variety of environments..”; in addition see at least [0258] via: “…features of interest in a subject may be evaluated with observation of the subject's behaviors, for example with videos of the subject. The user interface may be configured to allow a subject or the subject's caretaker to record or upload one or more videos of the subject. ….. In some cases, the analysis may further infer the intention of the behaviors, for example, a child being upset by noise or loud music, … The sounds and/or voices recorded in the video files may also be analyzed. The analysis may infer a context of the subject's behavior. The sound/voice analysis may infer a feeling of the subject..”
receive at the first processing resource (the system), the memory resource, or both, patient health data (mental health disorders) and patient environmental data (noise or loud music) via second signaling configured to receive input from the patient (videos of the subject), a health care provider (interactions with caregivers), a sensor, or any combination thereof; (See at least [0185] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio..”; in addition see at least [0258] via: “…features of interest in a subject may be evaluated with observation of the subject's behaviors, for example with videos of the subject. The user interface may be configured to allow a subject or the subject's caretaker to record or upload one or more videos of the subject. ….. In some cases, the analysis may further infer the intention of the behaviors, for example, a child being upset by noise or loud music, … The sounds and/or voices recorded in the video files may also be analyzed. The analysis may infer a context of the subject's behavior. The sound/voice analysis may infer a feeling of the subject..”; in addition see at least [0187] via: “…. FIG. 1A illustrates a system diagram for a digital personalized medicine platform 100 for providing diagnosis and therapy related to behavioral, neurological or mental health disorders. The platform 100 can provide diagnosis and treatment of pediatric cognitive and behavioral conditions associated with developmental delays, for example. A user digital device 110—for example, a mobile device such as a smart phone, an activity monitors, or a wearable digital monitor—records data and metadata related to a patient. Data may be collected based on interactions of the patient with the device, as well as based on interactions with caregivers and health care professionals. The data may be collected actively, such as by administering tests, recording speech and/or video, and recording responses to diagnostic questions. The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder..”)
write (stored, … in a database) from the first processing resource to the memory resource data that is based at least in part on a combination of the first signaling, the second signaling, and the third signaling, wherein in the data includes the patient health data, the patient environmental data and the patient image data, and the patient audio data,; (See at least [0192] via: “… FIG. 1B illustrates a detailed diagram of diagnosis module 132. … Diagnostic data relating to each treated patient are stored, for example in a database, to form a library of diagnostic data for pattern matching and machine learning. A large number of subject profiles can be simultaneously stored in such a database, for example 10,000 or more..”). The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187].
establishing an individual developmental baseline based on the data (fitting of the new data) including an estimated threshold (specific disorder .. exceeding a threshold value) for the patient at which a developmental delay becomes more likely; (See at least [0204] via: “...In step 302 an initial assessment is determined by diagnosis module 132. The initial assessment can assess the patient's performance in one or more domains, such as speech and language use, and assess a degree and type of developmental delay along a number of axes, as disclosed herein. The assessment can further place the subject into one of a plurality of overall tracks of progress; for example, the subject can be assessed as verbal or nonverbal...”; in addition see at least [0280] via: “...Diagnostic tests (for example, a set of tests and questions) as generated from the diagnosis module 132 can be provided to the patient or caregiver via the digital device 110. The patient's answers to the diagnostic tests can be received by the diagnosis module 132. The diagnosis module 132 can generate an initial diagnosis based on the patient's answers. For example, the diagnostic module may diagnose autism-related speech delay based on questions asked to the caregiver and tests administered to the patient such as vocabulary or verbal communication tests..”; in addition see at least [0078] via: “...In some embodiments, the prediction module is configured to generate the predicted classification of the subject by fitting new data to the assessment model, the new data being standardized by the preprocessing module. The prediction module may check whether the fitting of the new data generates a prediction of a specific disorder within a confidence interval exceeding a threshold value..”)
Determine and output, at the first processing resource or a second processing resource, how much and what data (data inputs) is needed to determine a health risk for the patient (likely to have a tested disorder (e.g. Autism Spectrum Disorder)), using a plurality of trained machine learning models (machine learning algorithm) to improve a performance of the computing device through iterations (iterative process) by continuously incorporating new data including the real time patient health data into the plurality of trained machine learning models, ; (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525)..”; in addition see at least [0235] via: “… To further reduce the contribution of training data sample bias to the generation of an assessment model, a boosting technique may be implemented during the training process. Boosting comprises an iterative process, wherein after one iteration of training, the weighting of each sample data point is updated. For example, samples that are misclassified after the iteration can be updated with higher significances. The training process may then be repeated with the updated weightings for the training data...”)
determine a developmental delay risk of the patient based on the output of the plurality of trained machine learning models, input data representative of the written patient health data and patient environmental data and the written patient image data, patient audio data, (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525). ..”; in addition see at least [0218] via: “…If the digital personalized medicine system predicts that the user is likely to have a diagnosable condition (e.g. Autism Spectrum Disorder), then a therapy module can provide a behavioral treatment (530) which can comprise behavioral interventions; prescribed activities or trainings; interventions with medical devices or other therapeutics for specific durations or, at specific times or instances. As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve. Procedures for assessing the performance of an individual subject may be repeated or iterated (535) until an appropriate behavioral treatment is identified..”) The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187]
wherein determining the developmental delay risk comprises:
compiling the data, wherein the data comprises different types of data (subject data are received, such as test results; caregiver feedback; meta-data from patient and caregiver interactions) and different weights to increase an accuracy of the health risk determination (apply weights to diagnosis data in order to improve diagnostic accuracy and consistency), and determining the health risk within a health risk range based on the compiled data; (See at least [0192] via: “...FIG. 1B illustrates a detailed diagram of diagnosis module 132. The diagnosis module 132 comprises a test administration module 142 that generates tests and corresponding instructions for administration to a subject. The diagnosis module 132 also comprises a subject data receiving module 144 in which subject data are received, such as test results; caregiver feedback; meta-data from patient and caregiver interactions with the system; and video, audio, and gaming interactions with the system, for example. A subject assessment module 146 generates a diagnosis of the subject based on the data from subject data receiving module 144, as well as past diagnoses of the subject and of similar subjects. A machine learning module 148 assesses the relative sensitivity of each input to the diagnosis to determine which types of measurement provide the most information regarding a patient's diagnosis. These results can be used by test administration module 142 to provide tests which most efficiently inform diagnoses and by subject assessment module 146 to apply weights to diagnosis data in order to improve diagnostic accuracy and consistency. ...”; in addition see at least [0235] via: “... sample weighting may be applied in training the assessment model. Sample weighting can comprise lending a relatively greater degree of significance to a specific set of samples during the model training process. For example, during model training, if the training data is skewed towards individuals diagnosed with autism, higher significance can be attributed to the data from individuals not diagnosed with autism (e.g., up to 50 times more significance than data from individuals diagnosed with autism). Such a sample weighting technique can substantially balance the sample bias present in the training data, thereby producing an assessment model with reduced bias and improved accuracy in classifying data in the real world. )
tracking regression of the patient based on the individual developmental baseline, (Therapy tracking module 158 then tracks the progress of the recommended therapies) output of the plurality of trained machine learning (analysis using machine learning) models, the first signaling, and the second signaling; (See at least [0203] via: “... In step 302 an initial assessment is determined by diagnosis module 132. ... The assessment can further place the subject into one of a plurality of overall tracks of progress; for example, the subject can be assessed as verbal or nonverbal..”; in addition see at least [0193] via: “...FIG. 1C illustrates a detailed diagram of therapy module 134. Therapy module 134 comprises a therapy assessment module 152 that scores therapies based on their effectiveness. A previously suggested therapy is evaluated based on the diagnoses provided by the diagnostic module both before and after the therapy, and a degree of improvement is determined. This degree of improvement is used to score the effectiveness of the therapy. The therapy may have its effectiveness correlated with particular classes of diagnosis; for example, a therapy may be considered effective for subjects with one type of diagnosis but ineffective for subjects with a second type of diagnosis. A therapy matching module 154 is also provided that compares the diagnosis of the subject from diagnosis module 132 with a list of therapies to determine a set of therapies that have been determined by the therapy assessment module 152 to be most effective at treating diagnoses similar to the subject's diagnosis. Therapy recommendation module 156 then generates a recommended therapy comprising one or more of the therapies identified as promising by the therapy matching module 154, and sends that recommendation to the subject with instructions for administration of the recommended therapies. Therapy tracking module 158 then tracks the progress of the recommended therapies, and determines when a new diagnosis should be performed by diagnosis module 132, or when a given therapy should be continued and progress further monitored. ... The therapeutic data can be correlated to the diagnostic data of the diagnostic module 132 to allow a matching of effective therapies to diagnoses...”; in addition see at least [0218] via: “... As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve. Procedures for assessing the performance of an individual subject may be repeated or iterated (535) until an appropriate behavioral treatment is identified...”; in addition see at least [0227] via: “...The training module 610 can utilize a machine learning algorithm or other algorithm to construct and train an assessment model to be used in the diagnostic tests,..”)
identify, at the first processing resource (digital personalized medicine system) or the second processing resource, output data representative of a developmental delay plan (therapy module can provide a behavioral treatment (530)) for the patient using the determination and output of the plurality of trained machine learning models (machine learning algorithm), input data representative of the written patient health data and patient environmental data, the written patient image data, patient audio data, , the tracked regression, the individual developmental baseline and associated threshold and input data representative of the developmental delay risk; (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525). ..”; in addition see at least [0218] via: “…If the digital personalized medicine system predicts that the user is likely to have a diagnosable condition (e.g. Autism Spectrum Disorder), then a therapy module can provide a behavioral treatment (530) which can comprise behavioral interventions; prescribed activities or trainings; interventions with medical devices or other therapeutics for specific durations or, at specific times or instances. As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve. Procedures for assessing the performance of an individual subject may be repeated or iterated (535) until an appropriate behavioral treatment is identified..”) The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187].
transmit the output data (informative display) representative of the developmental delay treatment plan (provide a behavioral treatment) to the patient, a health care provider (medical practitioner), a caregiver (parent), or any combination thereof. (See at least [0217] via: “…In instances where the subject is likely to have the disorder (520), a secondary party (e.g. medical practitioner, parent, guardian or other individual) may be presented with an informative display. An informative display can provide symptoms of the disorder that can be displayed as a graph depicting covariance of symptoms displayed by the subject and symptoms displayed by the average population. A list of characteristics associated with a particular diagnosis can be displayed with confidence values, correlation coefficients, or other means for displaying the relationship between a subject's performance and the average population or a population comprised of those with a similar disorders..”; in addition see at least [0218] via: “… If the digital personalized medicine system predicts that the user is likely to have a diagnosable condition (e.g. Autism Spectrum Disorder), then a therapy module can provide a behavioral treatment (530) which can comprise behavioral interventions; prescribed activities or trainings; interventions with medical devices or other therapeutics for specific durations or, at specific times or instances. As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve.).
generating a dashboard (electronic display 1235 ) including health risk trend charts associated with different metrics associated with the health risk using the output data. (See at least [0217] via: “...An informative display can provide symptoms of the disorder that can be displayed as a graph depicting covariance of symptoms displayed by the subject and symptoms displayed by the average population. A list of characteristics associated with a particular diagnosis can be displayed with confidence values, correlation coefficients, or other means for displaying the relationship between a subject's performance and the average population or a population comprised of those with a similar disorders..”; in addition see at least [0273] via: “...The computer system 1201 can include or be in communication with an electronic display 1235 that comprises a user interface (UI) 1240 for providing, for example, questions and answers, analysis results, recommendations. Examples of UI's include, without limitation, a graphical user interface (GUI) and web-based user interface...”)
However Vaughan is silent the following limitations that are taught by Cemgil:
a first one of the plurality of machine learning models uses the patient health data (a user's health) and at least a portion of the environmental data (state of the environment) to perform multi-class classification; (See at least [0051] via: “…In general, the classification machine-learning model 120 is configured to perform a classification task, i.e., to process input data to generate output data that specifies predicted classification information of the input data. The input data can specify any type of data to be analyzed… The output data can specify classification information for the input data, e.g., … one or more health condition categories of a person, …”; in addition see at least [0015] via: “…the classification model and the conformal predictor are used for a medical screening and diagnosis task, in which the input to the model is medical data characterizing a user's health and the model output is a prediction over a plurality of categories that each represents a different diagnosis…The classification model can be configured to process the model input to predict probability scores for multiple diagnostic categories. For example, when the medical diagnosis task is cancer, e.g., breast cancer, screen, and detection, the diagnostic categories can include different diagnoses for breast cancer screening and detection, including, for example, “Normal”, “Adenosis”, “Fibroadenoma”, “Ductal Carcinoma”, “Tubular Carcinoma”, “Lobular Carcinoma”, etc…”; in addition see at least [0026] via: “…the model input may comprise observations of the environment and the classifications each correspond to a different state of the environment..”;
a second one of the plurality of machine learning models comprises a convolutional neural network machine learning model to perform multi-class classification on the patient image data (medical images); (See at least [0008] via: “…The classification model can be a classifier neural network configured to perform any of a variety of classification tasks. As used in this specification, a classification task is any task that requires the model to generate an output that includes a respective score (e.g., the predicted probability) for each of a set of multiple categories. The respective scores can be used to select one or more of the categories as a “classification” for the model input using the respective scores..”; in addition see at least [0009] via: “…One example of a classification task is image classification, where the input to the classification model is an image, e.g., the intensity values of the pixels of the image, the categories are object categories, and the task is to classify the image as depicting an object from one or more of the object categories. That is, the classification output for a given input image indicates a prediction of one or more object categories that are depicted in the input image. As used herein, an image may refer to a still image or a moving image (e.g. video). The input to the classification model (i.e. image data) may comprise pixels of the image or another representation of the image, such as may have been produced by an encoder, e.g. an encoder neural network. An image may comprise color or monochrome pixel value data. Such images may be captured from an image sensor such as a camera or LIDAR sensor…”; in addition see at least [0015] via: “…. The model input can include medical images, e.g., one or more medical scan images, X-ray, CT, MRI, or ultrasound images of the subject. Alternatively or additionally, the model input can include microscopic histology images from biopsied tissues…”)
a third one of the plurality of machine learning models comprises a speech recognition machine learning model to perform multi-class classification on the patient audio data (audio data representing speech). (See at least [0006] via: “…training a classification machine-learning model to be used with a conformal predictor to predict confidence sets..”; in addition see at least [0007] via: “…the classification model is configured to process a model input to generate a classification output that indicates, for each particular classification in a set of classifications, a predicted probability for the model input for the particular classification..”; in addition see at least [0011] via: “… examples of classification tasks include audio classification tasks, such as speech processing tasks, where the input to the classification model is audio data representing speech. Examples of speech processing tasks include language identification (where the categories are different possible languages for the speech), hotword identification (where the categories indicate whether one or more specific “hotwords” are spoken in the audio data), and so on. …The classification task may include a speech or sound recognition task (e.g. the category scores may be indicative of a likelihood of different respective words being present in the audio data), a phone or speaker classification task (e.g. the category scores may each be indicative of a likelihood that different respective speakers were speaking in the audio data..”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Vaughan with Cemgil. Vaughan teaches digital diagnostics and digital therapeutics to patients that uses digital data to assess or diagnose symptoms of a patient, and feedback from the patient response to treatment is considered to update the personalized therapeutic interventions. The methods and apparatus disclosed herein can also diagnose and treat cognitive function of a subject, with fewer questions, decreased amounts of time, and determine a plurality of behavioral, neurological or mental health disorders, and provide clinically acceptable sensitivity and specificity in the diagnosis and treatment. However, Vaughan fails to disclose machine learning models for the classification of health, environmental, image, and audio data and speech recognition as taught by Cemgil. Combining Vaughan and Cemgil is helpful such that a “classification model and the conformal predictor are used for a medical screening and diagnosis task, in which the input to the model is medical data characterizing a user's health and the model output is a prediction over a plurality of categories that each represents a different diagnosis…The classification model can be configured to process the model input to predict probability scores for multiple diagnostic categories” [Cemgil 0015]
Regarding claim 2: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein identifying the output data representative of the health risk response plan comprises utilizing the plurality of trained machine learning models to identify the output data representative of the health risk response plan based on data associated with the first signaling, the second signaling, the health risk, and previously received signaling and associated data associated with previous health risk response plans. (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525). ..”; in addition see at least [0218] via: “…If the digital personalized medicine system predicts that the user is likely to have a diagnosable condition (e.g. Autism Spectrum Disorder), then a therapy module can provide a behavioral treatment (530) which can comprise behavioral interventions; prescribed activities or trainings; interventions with medical devices or other therapeutics for specific durations or, at specific times or instances. As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve. Procedures for assessing the performance of an individual subject may be repeated or iterated (535) until an appropriate behavioral treatment is identified..”) The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187]
Regarding claim 3: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein determining the health risk comprises utilizing the plurality of trained machine learning models to perform multi-class classification on tabular data, image data, and language data (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525)...”; in addition see at least [0223] via: “…The training data 650, used by the training module to construct the assessment model, can comprise a plurality of datasets from a plurality of subjects, each subject's dataset comprising an array of features and corresponding feature values, and a classification of the subject's developmental disorder or condition. As described herein, the features may be evaluated in the subject via one or more of questions asked to the subject, observations of the subject, or structured interactions with the subject. Feature values may comprise one or more of answers to the questions, observations of the subject such as characterizations based on video images, or responses of the subject to a structured interaction, for example..”)
Regarding claim 4: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein determining the health risk comprises determining a likelihood that the patient is at risk of a particular health condition or currently has the particular health condition (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525). In instances where the subject is likely to have the disorder (520), a secondary party (e.g. medical practitioner, parent, guardian or other individual) may be presented with an informative display. An informative display can provide symptoms of the disorder that can be displayed as a graph depicting covariance of symptoms displayed by the subject and symptoms displayed by the average population. A list of characteristics associated with a particular diagnosis can be displayed with confidence values, correlation coefficients, or other means for displaying the relationship between a subject's performance and the average population or a population comprised of those with a similar disorders.. .”)
Regarding claim 5: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein identifying the output data representative of the health risks response plan comprises:
identifying an alert to transmit to a computing device of the patient; (See at least [0217] via: “…In instances where the subject is likely to have the disorder (520), a secondary party (e.g. medical practitioner, parent, guardian or other individual) may be presented with an informative display. An informative display can provide symptoms of the disorder that can be displayed as a graph depicting covariance of symptoms displayed by the subject and symptoms displayed by the average population. A list of characteristics associated with a particular diagnosis can be displayed with confidence values, correlation coefficients, or other means for displaying the relationship between a subject's performance and the average population or a population comprised of those with a similar disorders..”) and
identifying a proposed action and associated instructions to address the health risk of the patient (See at least [0218] via: “… If the digital personalized medicine system predicts that the user is likely to have a diagnosable condition (e.g. Autism Spectrum Disorder), then a therapy module can provide a behavioral treatment (530) which can comprise behavioral interventions; prescribed activities or trainings; interventions with medical devices or other therapeutics for specific durations or, at specific times or instances. As the subject undergoes the therapy, data (e.g. passive data and diagnostic question data) can continue to be collected to perform follow-up assessments, to determine for example, whether the therapy is working. Collected data can undergo data analysis (540) (e.g. analysis using machine learning, statistical modeling, classification tasks, predictive algorithms) to make determinations about the suitability of a given subject. A growth curve display can be used to show the subject's progress against a baseline (e.g. against an age-matched cohort). Performance or progress of the individual may be measured to track compliance for the subject with a suggested behavioral therapy predicted by the therapy module may be presented as a historic and predicted performance on a growth curve.)
Regarding claim 6: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
further comprising updating the health risk in response to receiving at the first processing resource additional first signaling, second signaling, or any combination thereof and based at least in part on feedback received at the first processing resource associated with outcomes of the output data representative of the health risk response plan (See at least [0019] via: “… digital therapeutic systems to treat a subject with a personal therapeutic treatment plan. An exemplary system may comprise one or more processors comprising software instructions for a diagnostic module and a therapeutic module. The diagnostic module may receive data from the subject and output diagnostics data for the subject. The diagnostic module may comprise one or more of machine learning, a classifier, artificial intelligence, or statistical modeling based on a subject population to determine the diagnostic data for the subject. The therapeutic module may receive the diagnostic data and output the personal therapeutic treatment plan for the subject. The therapeutic module may comprise one or more of machine learning, a classifier, artificial intelligence, or statistical modeling based on at least a portion the subject population to determine and output the personal therapeutic treatment plan of the subject. The diagnostic module may be configured to received updated subject data from the subject in response to the therapy of the subject and generate updated diagnostic data from the subject. The therapeutic module may be configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data...”; in addition see at least [0049] via: “…diagnostic process further comprises receiving updated subject data from the subject in response to a feedback data of the subject and generating updated diagnostic data, and therapeutic process further comprises receiving the updated diagnostic data and outputting an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data. The updated subject data may be received in response to a feedback data that identifies relative levels of efficacy, compliance, and response resulting from the personal therapeutic treatment plan..”)
Regarding claim 7: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
further comprising:
receiving at the first processing resource via an application of a computing device accessible by the patient or a different mobile device of the patient, manual input from the patient comprising personal patient data, patient health data, environmental data, health care provider data, or a combination thereof; (See at least [0185] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio..”; in addition see at least [0258] via: “…features of interest in a subject may be evaluated with observation of the subject's behaviors, for example with videos of the subject. The user interface may be configured to allow a subject or the subject's caretaker to record or upload one or more videos of the subject. ….. In some cases, the analysis may further infer the intention of the behaviors, for example, a child being upset by noise or loud music, … The sounds and/or voices recorded in the video files may also be analyzed. The analysis may infer a context of the subject's behavior. The sound/voice analysis may infer a feeling of the subject..”; in addition see at least [0187] via: “…. FIG. 1A illustrates a system diagram for a digital personalized medicine platform 100 for providing diagnosis and therapy related to behavioral, neurological or mental health disorders. The platform 100 can provide diagnosis and treatment of pediatric cognitive and behavioral conditions associated with developmental delays, for example. A user digital device 110—for example, a mobile device such as a smart phone, an activity monitors, or a wearable digital monitor—records data and metadata related to a patient. Data may be collected based on interactions of the patient with the device, as well as based on interactions with caregivers and health care professionals. The data may be collected actively, such as by administering tests, recording speech and/or video, and recording responses to diagnostic questions. The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder..”) and
writing from the first processing resource to the memory resource coupled to the first processing resource data that is based at least in part on a combination of the first signaling, the second signaling, and the manual input (See at least [0192] via: “… FIG. 1B illustrates a detailed diagram of diagnosis module 132. … Diagnostic data relating to each treated patient are stored, for example in a database, to form a library of diagnostic data for pattern matching and machine learning. A large number of subject profiles can be simultaneously stored in such a database, for example 10,000 or more..”). The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187].
Regarding claim 8: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein determining the health risk comprises determining a developmental delay risk (See at least [0217] via: “…As illustrated in FIG. 4, data inputs can be fed into a diagnostic module which can comprising data analysis (515) using for example a classifier, algorithm (e.g. machine learning algorithm), or statistical model, to make a diagnosis of whether the subject is likely to have a tested disorder (e.g. Autism Spectrum Disorder) (520) or is unlikely to have the tested disorder (525)..”)
Regarding claim 9: Vaughan teaches the invention as claimed and detailed above with respect to claim 1. Vaughan also teaches:
wherein the first signaling, the second signaling, or both, comprise image sequences (See at least [0015] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio, responses to questions or activities, and active or passive data streams from user interaction with activities, games or software features of the system, for example. Such data can also include meta-data from patient or caregiver interaction with the system, for example, when performing recommended activities. Specific meta-data examples include data from a user's interaction with the system's device or mobile app that captures aspects of the user's behaviors, profile, activities, interactions with the software system, interactions with games, frequency of use, session time, options or features selected, and content and activity preferences. Data may also include data and meta-data from various third party devices such as activity monitors, games or interactive content.
Regarding claim 11: Vaughan teaches the invention as claimed and detailed above with respect to claim 10. Vaughan also teaches:
further comprising the instructions executable to identify the output data representative of the developmental delay plan based at least in part on generic developmental patient information and generic developmental delay treatment information stored in a portion of the memory resource or other storage accessible by the processing resource (See at least [0019] via: “…Aspects of the present disclosure provide digital therapeutic systems to treat a subject with a personal therapeutic treatment plan. An exemplary system may comprise one or more processors comprising software instructions for a diagnostic module and a therapeutic module. .. The diagnostic module may comprise one or more of machine learning, a classifier, artificial intelligence, or statistical modeling based on a subject population to determine the diagnostic data for the subject. The therapeutic module may receive the diagnostic data and output the personal therapeutic treatment plan for the subject. The therapeutic module may comprise one or more of machine learning, a classifier, artificial intelligence, or statistical modeling based on at least a portion the subject population to determine and output the personal therapeutic treatment plan of the subject..”; in addition see at least [0020] via: “.. In some embodiments, the diagnostic module comprises a diagnostic machine learning classifier trained on the subject population and the therapeutic module comprises a therapeutic machine learning classifier trained on the at least the portion of the subject population. The diagnostic module and the therapeutic module may be arranged for the diagnostic module to provide feedback to the therapeutic module based on performance of the treatment plan. The therapeutic classifier may comprise instructions trained on a data set comprising a population of which the subject is not a member. The subject may comprise a person who is not a member of the population...”)
Regarding claim 12: Vaughan teaches the invention as claimed and detailed above with respect to claim 10. Vaughan also teaches:
further comprising the instructions executable to identify the output data representative of the developmental delay plan based at least in part on patient medical history information stored in a portion of the memory resource or other storage accessible by the processing resource (See at least [0019] via: “…Aspects of the present disclosure provide digital therapeutic systems to treat a subject with a personal therapeutic treatment plan. An exemplary system may comprise one or more processors comprising software instructions for a diagnostic module and a therapeutic module. The diagnostic module may receive data from the subject and output diagnostics data for the subject. .... The diagnostic module may be configured to received updated subject data from the subject in response to the therapy of the subject and generate updated diagnostic data from the subject. The therapeutic module may be configured to receive the updated diagnostic data and output an updated personal treatment plan for the subject in response to the diagnostic data and the updated diagnostic data...”)
Regarding claim 13: Vaughan teaches the invention as claimed and detailed above with respect to claim 10. Vaughan also teaches:
wherein the plurality of input data comprises patient health data, environmental data, or any combination thereof (See at least [0187] via: “… a mobile device such as a smart phone, an activity monitors, or a wearable digital monitor—records data and metadata related to a patient. Data may be collected based on interactions of the patient with the device, as well as based on interactions with caregivers and health care professionals. The data may be collected actively, such as by administering tests, recording speech and/or video, and recording responses to diagnostic questions. The data may also be collected passively, such as by monitoring online behavior of patients and caregivers, such as recording questions asked and topics investigated relating to a diagnosed developmental disorder..”; in addition see at least [0258] via: “…features of interest in a subject may be evaluated with observation of the subject's behaviors, for example with videos of the subject. The user interface may be configured to allow a subject or the subject's caretaker to record or upload one or more videos of the subject. ….. In some cases, the analysis may further infer the intention of the behaviors, for example, a child being upset by noise or loud music, … The sounds and/or voices recorded in the video files may also be analyzed. The analysis may infer a context of the subject's behavior. The sound/voice analysis may infer a feeling of the subject..”)
Regarding claim 18: Vaughan teaches the invention as claimed and detailed above with respect to claim 15. Vaughan also teaches:
wherein the instructions executable to receive the patient image data, patient audio data, or both via first signaling from the first source configured to monitor patient health data comprise instructions executable to receive the patient image data, patient audio data, or both via signaling from at least one of a health sensor, health monitor, wearable device, camera, audio collection device, or mobile device of the patient. (See at least [0146] via: “…instructions, that when executed cause a processor to: receive updated subject data..”; in addition see at least [0014] via: “…Types of data collected and utilized by the system can include patient and caregiver video, audio..”; in addition see at least [0171] via: “… Data sources can comprise either active or passive sources, in digital format via one or more digital devices such as mobile phones, video capture, audio capture, activity monitors, or wearable digital monitors. Examples of active data collection comprise devices, systems or methods for tracking eye movements, recording body or appendage movement, monitoring sleep patterns, recording speech patterns. In some instances, the active sources can include audio feed data source such as speech patterns, lexical/syntactic patterns (for example, size of vocabulary, correct/incorrect use of pronouns, correct/incorrect inflection and conjugation, use of grammatical structures such as active/passive voice etc., and sentence flow), higher order linguistic patterns (for example, coherence, comprehension, conversational engagement, and curiosity), touch-screen data source (for example, fine-motor function, dexterity, precision and frequency of pointing, precision and frequency of swipe movement, and focus/attention span), and video recording of subject's face during activity (for example, quality/quantity of eye fixations vs saccades, heat map of eye focus on the screen, focus/attention span, variability of facial expression, and quality of response to emotional stimuli)…”; in addition see at least [0214] via: “… FIG. 4 illustrates an overall of data processing flows for a digital personalized medical system comprising a diagnostic module and a therapeutic module, configured to integrate information from multiple sources. Data can include passive data sources (501), passive data can be configured to provide more fine grained information, and can comprise data sets taken over longer periods of time under more nature conditions. Passive data sources can including for example, data collected from wearable devices, data collected from video feed (e.g. video feed collected from a video-enable toy, a mobile device, eye tracking data from video footage, information on the dexterity of a subject based on information gathered from three-axis sensors or gyroscopes (e.g. sensors embedded in toys or other devices that the patient may interact with for example at home, or under normal conditions outside of a medical setting), smart devices that measure any single or combination of the following: subject's speech patterns, motions, touch response time, prosody, lexical analysis, facial expressions, and other characteristic expressed by the subject. Passive data can comprise data on the motion or motions of the user, and can include subtle information that may or may not be readily detectable to an untrained individual. In some instances, passive data can provide information that can be more encompassing..” )
Regarding claim 19: Vaughan teaches the invention as claimed and detailed above with respect to claim 15. Vaughan also teaches:
wherein the patient health data, the patient environmental data, the patient image data, the patient audio data, or any combination thereof is received in real time (data collected from devices configured for tracking eye movement, or measuring or analyzing speech patterns.). (See at least [0214] via: “…FIG. 4 illustrates an overall of data processing flows for a digital personalized medical system comprising a diagnostic module and a therapeutic module, configured to integrate information from multiple sources. Data can include passive data sources (501), .. Passive data sources can including for example, data collected from wearable devices, data collected from video feed (e.g. video feed collected from a video-enable toy, a mobile device, eye tracking data from video footage, information on the dexterity of a subject based on information gathered from three-axis sensors or gyroscopes (e.g. sensors embedded in toys or other devices that the patient may interact with for example at home, or under normal conditions outside of a medical setting), smart devices that measure any single or combination of the following: subject's speech patterns, motions, touch response time, prosody, lexical analysis, facial expressions, and other characteristic expressed by the subject. Passive data can comprise data on the motion or motions of the user, and can include subtle information that may or may not be readily detectable to an untrained individual. In some instances, passive data can provide information that can be more encompassing..”; in addition see at least [0215] via: “…Passively collected data can comprise data collected continuously from a variety of environments..”; in addition see at least [0216] via: “…Data can include active data sources (510), for example data collected from devices configured for tracking eye movement, or measuring or analyzing speech patterns..”) The Examiner interprets that the input data comprising image data and audio data are taught in [0015], the environmental data taught in [0258], and the health data taught in [0187]
Regarding claim 20: Vaughan teaches the invention as claimed and detailed above with respect to claim 15. Vaughan also teaches:
wherein the patient environmental data comprises lighting, screen time, diet, humidity, temperature, sound (noise or loud music), caregiver actions, community traits, socioeconomic status, caregiver traits, social interactions, or any combination thereof. (See at least [0258] via: “…features of interest in a subject may be evaluated with observation of the subject's behaviors, for example with videos of the subject. The user interface may be configured to allow a subject or the subject's caretaker to record or upload one or more videos of the subject. ….. In some cases, the analysis may further infer the intention of the behaviors, for example, a child being upset by noise or loud music, … The sounds and/or voices recorded in the video files may also be analyzed. The analysis may infer a context of the subject's behavior. The sound/voice analysis may infer a feeling of the subject..”)
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Henschke (US 20060059145 A1) – System And Method For Analyzing Medical Data To Determine Diagnosis And Treatment- Teaches: generating an action plan for diagnosis and treatment of a patient. In particular, a historical database is complied which includes a plurality of records. Each record includes a personal profile and diagnosis data for a person. A plurality of characterizations and corresponding weighting coefficients are derived based on the records in the historical database. Pre-diagnostic patient profile data for a selected patient is obtained for the selected patient. One or more computing modules generate output data for the selected patient as a function of (i) the pre-diagnostic patient profile data, along with the physician's modifications, if any and (ii) the plurality of characterizations and corresponding weighting coefficients. The output data includes at least one of a diagnostic action plan, a confirmation action plan, a confirmation patient profile data and a therapeutic action plan.
Mazar (US 20040103001 A1) - System And Method For Automatic Diagnosis Of Patient Health- teaches: providing a clinically modeled automatic diagnosis of patient health. A preferred embodiment uses a medical device and network to analyze patient data in a manner consistent with a standard of medical care. The system can be configured as an Advanced Patient Management System that helps better monitor, predict and manage chronic diseases.
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered but they are not persuasive.
Claims 1-13, 15, 18-20 are pending. Applicant amended independent claims 1, 10 and 15 as posted in the above analysis with additions underlined
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101:
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 10 and 15 since the claims recite a method, and a non-transitory machine-readable medium comprising a processing resource in communication with a memory resource having instructions executable to determine health risk for creating, training and reconfiguring a cross engine for financial trading.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as belonging to the grouping of mental processes under concepts performed in the human mind as it recites “determining a health risk”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “determining a health risk”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea.
Regarding Step 2A Prong One:
The Applicant argues that the independent claims 1, 10 and 15 are not directed to an abstract idea without further analysis..
The Examiner disagrees since the Applicant’s argument is not persuasive.
The method used to select the abstract idea is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claim 1:
receiving, at a first processing resource, first signaling from a first source configured to monitor behavior of a patient including a personal medical device to monitor real time patient health data;
receiving, at the first processing resource, second signaling from a second source configured to monitor environmental data associated with the patient;
writing from the first processing resource to a memory resource coupled to the first processing resource data that is based at least in part on a combination of the first signaling and the second signaling including data comprising at least in part the real time patient health data associated with the patient and real time environmental data associated with the patient;
establishing an individual developmental baseline based on the data including an estimated threshold for the patient at which a developmental delay becomes more likely;
determining and outputting how much and what data is needed to determine a health risk for the patient using a plurality of trained machine learning models to improve a performance of the computing device through iterations by continuously incorporating new data including the real time patient health data and the real time environmental data into the plurality of trained machine learning models,
wherein a first one of the plurality of machine learning models uses the real time patient health data and at least a portion of the real time environmental data to perform multi-class classification;
a second one of the plurality of machine learning models comprises a convolutional neural network machine learning model to perform multi-class classification on patient image data; and
a third one of the plurality of machine learning models comprises a speech recognition machine learning model to perform multi-class classification on patient audio data;
determining, at the first processing resource or a different, second processing resource, a health risk for the patient based on the output of the plurality of trained machine learning models, the first signaling, and the second signaling, wherein determining the health risk comprises;
compiling the data, wherein the data comprises different types of data and different weights to increase an accuracy of the health risk determination, and determining the health risk within a health risk range based on the compiled data;
tracking regression of the patient based on the individual developmental baseline, output of the plurality of trained machine learning models, the first signaling, and the second signaling; and
identifying, at the first processing resource or the different, second processing resource, output data representative of a health risk response plan for the patient based at least in part on input data representative of the health risk, the tracked regression, the individual developmental baseline and associated threshold and additional patient data stored in a portion of the memory resource or other storage accessible by the first processing resource; and
transmitting the output data representative of the health risk response plan via third signaling;
generating a dashboard including health risk trend charts associated with different metrics associated with the health risk using the output data..
Similar analysis pertains to claims 10 and 15. As a result as listed in the 2A Prong One analysis claims 1, 10 and 15 fall within one of the groupings of abstract ideas. Specifically they belong to the grouping of mental processes under concepts performed in the human mind as it recites “determining a health risk”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “determining a health risk”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
Regarding Step 2A Prong Two and Step 2B:
The Applicant argues that even assuming the arguendo that the claims are directed to an abstract idea, the Applicant submits that the claims recite additional elements that integrate the judicial exception into a practical since they are directed to an improvement in the functioning of a computer.
The Applicant cites several examples based on the amendments to demonstrate the improvement in technology including: the present disclosure can allow for customized, individual early health risk detection to determine the health risk and a health risk response plan to potentially prevent harm or adverse health effects including, for instance, developmental delays. Early detection can allow for timely intervention and improved treatment and outcomes. Further, a non- mobile (mechanical) device can be improved by becoming an alert device for a patient. Output data, can also include warning transmissions to a computing device of a patient, healthcare provider, or caregiver to alert the patient or caregiver of a health risk (e.g., potential stutter, potential allergy, etc.) and provide a health risk response plan. In some examples, the output data, can include an alert sent to a non-mobile device such as a television screen, personal computer, refrigerator display, or smart device (e.g., smart speaker), among others. Furthermore, the Applicant's claim elements allow for a continuous update to the machine learning models (e.g., Paragraph 0024). As such, through improvements of the performance of the computing device by automatically learning (i.e. through the utilization of a machine learning model) and adjusting the health risk/developmental delay risk based on newly received data, a risk and plan customized for the individual patient may be provided without the need of human intervention or assistance. As a result, this improvement in the determination of the health risk, and in turn, the plan for the individual, can provide the individual patient with a timely and improved management plan and as opposed to such performance with a need for human intervention and/or assistance. Additionally, Applicant submits that it is not logical that the elements of Applicant's independent claims can be performed without the use of a computing device. For instance, because of the different types and combinations and classifications of data and other collected data, it would be nonsensical for a human to decipher the different types of data (e.g., health data, audio data, image data, environmental data, etc.) and combine them in order to determine a likelihood that a particular patient is at a particular risk for a developmental delay.
The additional elements considered individually and in combination with the other claim elements reflect an improvement to the technological field of health/developmental delay risk determination. As such, Applicant submits that the claims are allowable under § 101
The Examiner disagrees since the Applicant’s arguments are not persuasive. The above cited “alleged technological improvements” are all performed by a generic computer or processor. The Applicant fails to show otherwise.
Neither claims 1, 10 or 15 recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites the following additional elements:
computing device;
first processing resource;
a personal medical device to monitor real time patient health data;
second processing resource;
memory resource;
trained machine learning models;
improve a performance of the computing device through iterations;
convolutional neural network machine learning model;
plurality of trained machine learning models
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly the claim as a whole does not integrate the abstract idea into a practical application, nor does it provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible and hence the claims remain rejected under 35 U.S.C. 101
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate that at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
The analysis of Step 2B is similar to Step 2A Prong Two in that the additional elements as recited in claims 1, 10 and 15 amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103:
The Applicant argues that the following emphasized amended limitations of claims 1, 10 and 15 are not taught by Vaughan or Cemgil.
establish an individual developmental baseline based on the data including an estimated threshold for the patient at which a developmental delay becomes more likely;
compiling the data, wherein the data comprises different types of data and different weights to increase an accuracy of the development delay, and determining the developmental delay risk within a health risk range based on the compiled data;
track regression of the patient based on the individual developmental baseline, output of the plurality of trained machine learning models, the first signaling, and the second signaling;
generate a dashboard including health risk trend charts associated with different metrics associated with the developmental delay risk.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The following amended limitations are at taught by Vaughan.
Regarding the first limitation: See Vaughan paragraphs [0078] , [0204], [0280].
Regarding the second limitation: See Vaughan [0192], [0235].
Regarding the third limitation: See Vaughan [0203], [0193], [0218], [0227].
Regarding the fourth limitation: See Vaughan [0217], [0273] .
In conclusion for reasons of record and as set forth above, the examiner maintains the rejection of claims 1-13, 15, 18-20 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101 in addition to being rejected under 35 USC §103. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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