lDETAILED ACTION
The present office action represents a final action on the merits.
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 .
Priority
This application claims the priority date of August 26, 2022.
Status of Claims
Claims 1-3, 13, and 17 are amended and Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-12 are drawn to a method for hearing assistance device model prediction, which is within the four statutory categories (i.e., process), 13-16 are drawn to a system for hearing assistance device model prediction, which is within the four statutory categories (i.e., machine), 17-20 are drawn to a method for hearing assistance device model prediction, which is within the four statutory categories (i.e., process).
Claim 1 recites a system, comprising:
a processor; and
a memory that stores executable instructions that, when executed by the processor of the system, facilitate performance of operations, the operations comprising:
obtaining baseline data describing a medical patient wherein the obtaining the baseline data comprises:
detecting video information of the medical patient using one or more video sensors and collecting audio information of the medical patient using one or more audio sensors;
calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, forming calibrated sensors, wherein the calibrating comprises identifying required corrections to the video information and the audio information; and
correcting the video information and the audio information, forming corrected baseline data, wherein the correcting is done according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors for comparisons between a time of collecting the baseline data and a subsequent collecting of data describing the medical patient;
determining interaction data representing an interaction between a medical practitioner and the medical patient, wherein the interaction data is received using at least one of the calibrated sensors, wherein the determining the interaction data comprises collecting data describing the medical patient;
determining a current state of the medical patient during the interaction between the medical practitioner and the medical patient, wherein the determining the current state of the medical patient is based on the interaction data;
comparing the current state of the medical patient with the corrected baseline data;
identifying variations in the current state of the medical patient from the corrected baseline data;
developing coaching assistance data highlighting the variations;
presenting, via a device associated with the medical practitioner, coaching assistance data to a medical practitioner, during the interaction between the medical practitioner and the medical patient, wherein the presenting the coaching assistance data to the medical practitioner comprises providing information to the medical practitioner about the current state of the medical patient which the medical practitioner may not discern during the interaction between the medical practitioner and the medical patient to facilitate communication between the medical practitioner and the medical patient;
sensing practitioner data associated with the medical practitioner;
analyzing, by an artificial intelligence or machine learning process, the practitioner data and, based on the analyzing the practitioner data, receiving additional coaching assistance data for the medical practitioner from the artificial intelligence or machine learning process;
presenting, via the device associated with the medical practitioner, the additional coaching assistance data to the medical practitioner, the additional coaching assistance data for use by the medical practitioner to adjust conveying by the medical practitioner of information to the medical patient to improve reception of the information by the medical patient;
sensing additional practitioner data, forming updated practitioner data;
updating the artificial intelligence or machine learning process based on the additional practitioner data; and
updating and revising the additional coaching assistance data based on the updated practitioner data throughout the interaction between the medical practitioner and the medical patient.
Claim 13 recites a method, comprising:
obtaining, by a system comprising a processor, first baseline data describing a patient, wherein the obtaining the first baseline data comprises:
detecting video information of the patient using one or more video sensors and collecting audio information of the patient using one or more audio sensors;
calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, wherein the calibrated comprises identifying required corrections to the video information and the audio information; and
correcting the video information and the audio information of the first baseline data, forming corrected first baseline data, wherein the correcting the video information and the audio information is according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors between collecting the first baseline data and a subsequent collecting of data describing the patient;
determining, by the system, second baseline data describing the patient based on data obtained during a conversation with the patient;
comparing, by the system, the corrected first baseline data and the second baseline data;
identifying variations between the corrected first baseline data and the second baseline;
developing coaching assistance data highlighting the variations
presenting, by the system via a device associated with practitioner identity associated with a medical practitioner, the coaching assistance data, rendering of which provides coaching assistance to a medical practitioner during the conversation with the patient the coaching assistance data including information about the patient which the medical practitioner may not discern;
obtaining, by the system, practitioner data of the medical practitioner during the conversation with the patient; and
presenting, by the system, additional coaching assistance data, the additional coaching assistance data based on the practitioner data of the medical practitioner, the additional coaching assistance data for use by the medical practitioner to adjust conveying by the medical practitioner of information to the patient during the conversation with the patient to improve reception of the information by the patient.
Claim 17 recites a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, the operations comprising:
obtaining, based on a first visit of a medical patient with a first medical practitioner, health- related state data of the medical patient, wherein the obtaining the health-related state data of the medical patient comprises:
detecting video information of the medical patient using one or more video sensors and collecting audio information of the medical patient using one or more audio sensors;
calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, wherein the calibrated comprises identifying required corrections to the video information and the audio information; and
correcting the video information and the audio information, forming corrected patient information, wherein the correcting is done according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors for comparisons between a time of obtaining the health-related state data of the medical patient a subsequent obtaining of subsequent health-related state data of the medical patient;
during a second visit of the medical patient with a second medical practitioner, determining a current state of the medical patient;
comparing the current state of the medical patient with the corrected patient information;
identifying variations in the current state of the medical from the corrected patient information;
developing coaching assistance data based on the variations;
presenting, via a device associated with the second medical practitioner, the coaching assistance data to the second medical practitioner, that facilitates an interaction between the medical patient and the second medical practitioner providing information to the medical practitioner about the current state of the medical patient which the medical practitioner may not discern;
during the second visit, sensing practitioner data of the second medical practitioner, including sensing one or more of practitioner gestures, practitioner excitement, practitioner agitation or practitioner apprehension;
presenting, via the device, to the second medical practitioner, additional coaching assistance data, the additional coaching assistance data based on the practitioner of the second medical practitioner, the additional coaching assistance data for use by the second medical practitioner to adjust conveying by the second medical practitioner of information to the medical patient to improve reception of the information by the medical patient; and
continuously revising the additional coaching assistance data for presentation to the medical practitioner during the interaction with the medical patient, wherein the revising the additional coaching data is based on updated practitioner data sensed during the interaction with the medical patient.
The bolded limitations, given the broadest reasonable interpretation, cover a certain method of organizing human activity because it recites limitations that are managing personal behavior or relationships or interactions between people (e.g., obtaining patient information; managing patient information, in this case providing intelligent assistance for the communication of medical information). The underlined limitations are not part of the identified abstract idea (the method of organizing human activity) and are deemed “additional elements,” and will be discussed in further detail below. If a claim limitation, under its broadest reasonable interpretation, is managing personal behavior or interactions between people but for the recitation of generic computer components, then it fails within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Dependent claims 2-12, 14-16, and 18-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
The additional elements from claims 1, 13 and 17 include:
a system (apply it, MPEP 2106.05(f)).
a processor (apply it, MPEP 2106.05(f)).
one or more video sensors (apply it, MPEP 2106.05(f)).
one or more audio sensors (apply it, MPEP 2106.05(f)).
calibrated sensors (apply it, MPEP 2106.05(f)).
via a device associated with the medical practitioner (apply it, MPEP 2106.05(f)).
The additional elements from claim 1 include:
a memory that stores executable instructions that, when executed by the processor of the system, facilitate performance of operations, the operations comprising (apply it, MPEP 2106.05(f)).
The independent claims include additional elements not recited in the independent claims, including:
outputting, via the device, augmented reality data in association with image data representing the medical patient during the interaction between the medical practitioner and the medical patient (apply it, MPEP 2106.05(f)); (insignificant extra-solution activity, MPEP 2106.05(g).
outputting, via the device, audio data during the interaction between the medical practitioner and the medical patient (apply it, MPEP 2106.05(f)); (insignificant extra-solution activity, MPEP 2106.05(g).
outputting at least one of augmented reality data, or audio data (apply it, MPEP 2106.05(f)).; (insignificant extra-solution activity, MPEP 2106.05(g).
wherein the device is a first device (apply it, MPEP 2106.05(f)).
via a second device associated with the third medical practitioner (apply it, MPEP 2106.05(f)).
The additional elements from claim 17 include:
a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, the operations comprising (apply it, MPEP 2106.05(f)).
Furthermore, claims 1-20 are not integrated into a practical application because the additional elements (i.e., the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of “system”, “processor”, “memory”, “device”, “non-transitory machine-readable medium”, and “sensor”, which amounts to merely invoking a computer as a tool to perform the abstract idea e.g. see, Specification Paragraphs [0030], [0041], and [0047]-[0052]. (See MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea – for example, the recitation of outputting at least one of augmented reality data, or audio data or outputting, via the device, augmented reality data in association with image data representing the medical patient during the interaction between the medical practitioner and the medical patient, which amounts to mere data outputting, which amounts to an insignificant application, see MPEP 2106.05(g).
Furthermore, the claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because, the additional elements (i.e., the elements other than the abstract idea) amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification discloses that the additional elements are well-understood, routine, and conventional in nature (i.e., Specification Paragraphs [0030], [0041], and [0047]-[0052] disclose that the additional elements (i.e., system, computer, processor, device, non-transitory machine-readable medium) comprise a plurality of different types of generic computing systems that are configured to perform generic computer that are well understood routine, and conventional activities previously known to the pertinent industry (i.e., healthcare).
Insignificant extra-solution activity where the following is an example of court decisions demonstrating well understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): The additional element of receiving or transmitting data over a network and does not provide a practical application or significantly more, e.g. see Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) – similarly, Claims 6, 7, and 16 of the current invention outputs the patient data via a device.
Dependent claims 2-12, 14-16, and 18-20 include other limitations, but none of these functions are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly represent no more than those found in the independent claims.
Thus, taken alone, the additional elements do not amount to “significantly more” than the above identified abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the communication of medical information or improves any other technology, and their collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2 and 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Rusak (U.S. Pub. No. 2021/0225495 A1) in view of Shears (U.S. Pat. No. 7821407 B2) and Bardy (U.S. Pub. No. 2002/0026104 A1).
Regarding claim 1, Rusak discloses a system, comprising:
a processor (Paragraph [0006] discusses a processor); and
a memory that stores executable instructions that, when executed by the processor of the system, facilitate performance of operations, the operations comprising (Paragraph [0007] discusses a non-transitory memory storing code for execution, including instructions.):
obtaining baseline data describing a medical patient wherein the obtaining the baseline data comprises (Paragraphs [0027] and [0058] discuss monitor patient and obtain patient parameters, for example, physiological measurements obtained by sensors (e.g., blood pressure, temperature, intracranial pressure), and/or other stored data (e.g., patient identity, profile of the healthcare provider, medical images, medical diagnosis stored in the EMR).):
detecting video information of the medical patient using one or more video sensors and collecting audio information of the medical patient using one or more audio sensors (Paragraphs [0026], [0136], [0151], [0153] discuss patient parameter outputted by the plurality of physiological sensors are selected from the group consisting of: medical images, blood pressure measurement devices, arterial line, respiration devices, resuscitation devices, monitors, body temperature measurement devices, patient monitors, intracranial pressure sensors, Cerebral perfusion pressure sensors and capturing patient events from data sources, for example, sound and/or images (e.g., video);
one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors (Paragraphs [0026], [0136], [0141], [0151], [0153] discuss patient parameter outputted by the plurality of physiological sensors are selected from the group consisting of: medical images, blood pressure measurement devices, arterial line, respiration devices, resuscitation devices, monitors, body temperature measurement devices, patient monitors, intracranial pressure sensors, Cerebral perfusion pressure sensors and capturing patient events from data sources, for example, sound and/or images (e.g., video) and calibration to different parameters of the data set may be performed.); and
correcting the video information and the audio information, according to the required corrections to ensure accurate video information from the one or more video sensors and among the audio information from the one or more audio sensors (Paragraphs [0150]-[0151], [0155], and [0163] discuss medical image, patient monitor, anesthesiology monitor, physiological sensor or monitor, intracranial pressure sensor, cerebral perfusion pressure sensor, arterial line, respiration device, blood pressure sensor, temperature sensor, and pulse oximeter, capturing patient events using camera, microphone, video, etc.; further, the interaction journey may include use of different information sources, for example, did a physician correct a certain parameter recorded.);
determining interaction data representing an interaction between a medical practitioner and the medical patient, wherein the interaction data is received using at least one of the sensors, wherein the determining the interaction data comprises collecting data describing the medical patient (Paragraphs [0058], [0150]-[0151] discuss a user interface (UI) for presenting medical data of a patient for assisting the healthcare provider using the UI in treating the patient, the interaction journey of the healthcare provider with a medical device is monitored, for example, blood pressure, temperature, etc. from sensors.);
determining a current state of the medical patient during the interaction between the medical practitioner and the medical patient, wherein the determining the current state of the medical patient is based on the interaction data (Paragraphs [0018] and [0058] discuss patient parameter obtained is indicative of a current medical state; adapting a user interface (UI) for presenting medical data of a target patient, for example, for assisting the healthcare provider using the UI in treating the patient, planning treatment of the patient, and/or monitoring the health status of the patient. An interaction journey of the healthcare provider with one or more medical devices is monitored and/or are used for treatment of the patient, for example, blood pressure sensor, computing device presenting an electronic medical record, viewer application installed on a computer device accessing medical images stored on an imaging server and/or software system, patient room monitor, operating room monitor, and ventilation machine.);
the current state of the medical patient with the baseline data (Paragraphs [0018]-[0019] and [0201] discuss the at least one patient parameter is indicative of a current medical state relative to a target medical outcome that includes a target value, range, or threshold of the target patient.);
identifying variations in the current state of the medical patient from the baseline data (Paragraphs [0019] and [0191] discuss the current medical state comprises a current value of the monitored patient parameter, and the target medical outcome comprises a target value or target range or target threshold; the adaptation to the UI outputted by the model is computed for increasing likelihood of the current medical state reaching the target medical outcome, for example, when the current medical state is a current value of the monitored patient parameter, and the target medical outcome is a target value or target range or target threshold, the adaptation to the UI is selected to increase likelihood of reaching the target value, the UI is dynamically updated with real time patient medical data.);
developing coaching assistance data highlighting the variations (Paragraphs (0068] and [0095] discuss collecting patient parameters from various sources, monitoring an interaction journey of the health care provider(s) with one or more medical devices, analyzing the data in real time by the model that receives the interaction journey and patient parameters, and outputting by the model instructions for adapting a UI for visualizing data in a clear and readable way in front of the medical team (e.g., nurses, doctors, clinician doctors, surgeons, pharmacists, etc.) so the medical personnel is able to provide the best possible treatment to the patient on hand, and develop a real time battle plan for immediate medical needs as well as for long term need and for example, zoom in on previous treatment processes and/or check which measures were used and by whom: a physician, clinician, nurse, etc., and this person's experience, to analyze the specific medical condition of the patient. Based on this specific condition, the model may learn which are the necessary steps needed to be performed for such condition and present the results (by adapting the UI) to the current caregivers. In turn, the caregivers on hand may check themselves, visualize by looking at the screen's presentation of the UI which steps are taken for this condition in previous cases, for example, but not necessarily limited to what medications are prescribed, where are the checkpoints, etc. and thus provide more efficient treatment to the patients on hand.);
presenting, via a device associated with the medical practitioner, coaching assistance data to a medical practitioner, during the interaction between the medical practitioner and the medical patient wherein the presenting the coaching assistance data to the medical practitioner comprises providing information to the medical practitioner about the current state of the medical patient which the medical practitioner may not discern during the interaction between the medical practitioner and the medical patient to facilitate communication between the medical practitioner and the medical patient (Paragraphs [0008], [0015], [0058], [0061], [0068], [0081], [0151], and [0205] discuss collecting patient parameters from various sources, physiological sensor or monitor, intracranial pressure sensor, cerebral perfusion pressure sensor, arterial line, respiration device, blood pressure sensor, temperature sensor, and pulse oximeter, analyzing the data in real time and outputting a UI for visualizing data to the medical team and develop a real time battle plan for immediate medical needs and an interaction journey of the healthcare provider and patient and automatically and dynamically adjust the UI, for example, to present the most relevant data for the current patient based on monitored patient parameter(s) and presenting the medical information that is most relevant to planning treatment of the target patient improving the ability of the physician to plan the treatment and the medical data is collected by processes that may enable a real time analysis and/or display of results in different views for analysis of patient data in front of the caregiver and/or patient.);
sensing practitioner data (Examiner is interpreting practitioner data to include collecting data from monitoring an interaction journey of the health care provider with a medical device and patient room monitor data using a microphone, VR, camera capturing images of the interaction. See Specification Paragraph [0044].) associated with the medical practitioner (Paragraph [0097] discusses tracking the interaction and/or the contextual data and/or in the basic data and/or tracing and/or tracking the caregiver activities, order of operation, and/or their train of thoughts. For example, but not necessarily limited to, where and/or on what images the caregiver clicked, which measurement were essential to a treatment decision, which lab test were ordered in cases similar to the given case, identity and/or experience of the caregiver, demographic note of the patients, etc. (“the interaction journey”).);
analyzing, by an artificial intelligence or machine learning process, the practitioner data and, based on the analyzing the practitioner data, receiving additional coaching assistance data for the medical practitioner from the artificial intelligence or machine learning process (Paragraphs [0068] and [0196]-[0199] discuss collecting patient parameters from various sources, monitoring an interaction journey of the health care provider(s) with one or more medical devices, analyzing the data in real time by the model that receives the interaction journey and patient parameters, and outputting by the model instructions for adapting a UI for visualizing data in a clear and readable way in front of the medical team (e.g., nurses, doctors, clinician doctors, surgeons, pharmacists, etc.) so the medical personnel is able to provide the best possible treatment to the patient on hand, and develop a real time battle plan for immediate medical needs as well as for long term needs.);
presenting, via the device associated with the medical practitioner, the additional coaching assistance data to the medical practitioner, the additional coaching assistance data for use by the medical practitioner to adjust conveying by the medical practitioner of information to the medical patient to improve reception of the information by the medical patient (Paragraphs [0096]-[0097] discuss displaying on a touch screen and/or other screens includes but is not limited to the caregiver's zoom in preferences, checkup, and medication preferences, tests and images, etc. and the model learns from the past from data available and/or shows, for example but not necessarily limited to treatments, medical thoughts and/or notes, medication to the on hand caregiver for the benefit of the on hand patient and tracking the interaction and/or the contextual data and/or in the basic data and/or tracing and/or tracking the caregiver activities, order of operation, and/or their train of thoughts. For example, but not necessarily limited to, where and/or on what images the caregiver clicked, which measurement were essential to a treatment decision, which lab test were ordered in cases similar to the given case, identity and/or experience of the caregiver, demographic note of the patients, etc. (“the interaction journey”).);
sensing additional practitioner data, forming updated practitioner data (Paragraphs [0097] and [0112] discuss tracking the interaction and/or the contextual data and/or in the basic data and/or tracing and/or tracking the caregiver activities, order of operation, and/or their train of thoughts. For example, but not necessarily limited to, where and/or on what images the caregiver clicked, which measurement were essential to a treatment decision, which lab test were ordered in cases similar to the given case, identity and/or experience of the caregiver, demographic note of the patients, etc. (“the interaction journey”) and data sources may be dynamically updated continuously or per event.);
updating the artificial intelligence or machine learning process based on the additional practitioner data (Paragraphs [0100], [0131], and [0137] discuss collecting and/or storing the data from several medical devices (i.e., the interaction journey and/or patient parameters) that fits an individual target patient, the data may be analyzed by applying the model (e.g., a state of the art analysis and/or insights process) to give a real time overview of the individualized patients conditions and/or needs and the model is trained based on the interaction journey of the provider and the patient and updating the model as new interaction journeys and new patient parameters are available.); and
updating and revising the additional coaching assistance data based on the updated practitioner data throughout the interaction between the medical practitioner and the medical patient (Paragraphs [0112] discuss data sources may be dynamically updated continuously and/or per event, as new interaction journeys and new patient parameters are available.).
Rusak does not explicitly disclose:
calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, forming calibrated sensors, wherein the calibrating comprises identifying required corrections to the video information and the audio information;
forming corrected baseline data, wherein the correcting is done according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors for comparisons between a time of collecting the baseline data and a subsequent collecting of data describing the medical patient;
wherein the interaction data is received using at least one of the calibrated sensors;
comparing the current state of the medical patient with the corrected baseline data.
Shears teaches:
calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, forming calibrated sensors, wherein the calibrating comprises identifying required corrections to the video information and the audio information; (Column 3 lines 64-67, Column 4 lines 1-11, Column 5 lines 2-3, and Column 23 lines 25-36 discuss calibrating the plurality of sensors to provide a calibration position, analyze the sensor information, compare it to reference information; and correct alignment of the sensor when the proper position is achieved.);
ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors (Column 4 lines 5-11 discuss analyze sensor information, compare it to reference information.);
wherein the interaction data is received using at least one of the calibrated sensors (Column 3 lines 64-67, Column 4 lines 1-11, Column 5 lines 2-3, and Column 23 lines 25-36 discuss calibrating the plurality of sensors to provide a calibration position, analyze the sensor information, compare it to reference information; and correct alignment of the sensor when the proper position is achieved.); and
comparing the current state of the medical patient with the corrected baseline data (Column 4 lines 5-11 discuss analyze sensor information, compare it to reference information.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Rusak to include calibrating the one or more video sensors and the one or more audio sensors to account for variation among the one or more video sensors and the one or more audio sensors, forming calibrated sensors, wherein the calibrating comprises identifying required corrections to the video information and the audio information, ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors, wherein the interaction data is received using at least one of the calibrated sensors, and comparing the current state of the medical patient with the corrected baseline data, as taught by Shears, in order to conveniently and easily communicate sensor measurements to healthcare providers to prevent delay in diagnosis. (Shears Column 1 lines 66-67 and Column 2 line 1).
Bardy teaches:
forming corrected baseline data, wherein the correcting is done according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors for comparisons between a time of collecting the baseline data and a subsequent collecting of data describing the medical patient (Paragraphs [0013], [0038], and [0055] discuss a reference baseline of observed parameters is maintained in the database and can be reassessed as needed and the analysis module analyzes subsequently collected data of the patient and compared to the reference physiological measures in the reference baseline.);
the corrected baseline data (Paragraph [0013] discusses one or more reference physiological measures relating to individual patient information recorded during an initial observation period are processed into a reference baseline. One or more updated physiological measures recorded subsequent to the initial observation period are periodically received. The updated physiological measures are compared to the reference physiological measures in the reference baseline to generate a patient status indicator identifying any such substantially non-conforming updated physiological measure.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Rusak to include forming corrected baseline data, wherein the correcting is done according to the required corrections to ensure accurate comparison among the video information from the one or more video sensors and among the audio information from the one or more audio sensors for comparisons between a time of collecting the baseline data and a subsequent collecting of data describing the medical patient and the corrected baseline data, as taught by Bardy, in order to provide a meaningful, quantitative measure of patient wellness. (Bardy Paragraph [0015].
Regarding claim 2, Rusak discloses wherein the sensing additional practitioner data comprises:
sensing one or more of practitioner eye gaze by the medical practitioner, practitioner eye contact by the medical practitioner, complexity of content of information presented by the medical practitioner to the medical patient, and speed and volume of speech of the medical practitioner spoken to the medical patient (Paragraphs [0017], [0154], and [0168] discuss the interaction journey is computed based on data from a camera capturing images of the healthcare provider treating the patient, a camera capturing images of healthcare provider actions when not directly treating the patient, a camera capturing images of healthcare provider washing hands, a camera capturing images of patient events including cough, seizure, sneeze, and/or fall and data from a microphone recording sounds captured during the patient events, a microphone recording sound captured during activities taking place in proximity to the patient, a microphone recording sound of the healthcare provider and interaction with patient.).
Regarding claim 6, Rusak discloses wherein the presenting of the coaching assistance data comprises outputting, via the device, augmented reality data in association with image data representing the medical patient during the interaction between the medical practitioner and the medical patient (Paragraphs [0021], [0114], [0153], [0192] discuss interface used for monitoring the interaction journey include augmented reality and presenting a suggested treatment plan in the UI, and presenting one or more parameter of a customizable period of time in the UI, playing an audio message on speakers, presenting an augmented reality image on an augmented reality headset, and presenting a virtual reality image on virtual reality glasses.).
Regarding claim 7, Rusak discloses wherein the presenting of the coaching assistance data comprises outputting, via the device, audio data during the interaction between the medical practitioner and the medical patient (Paragraphs [0005], [0021], [0058] and [0151] discuss and interaction journey of the healthcare provider and patient interaction and marking a certain monitored patient parameter to attract attention of the healthcare provider, presenting suggested diagnoses in the UI, presenting a suggested treatment plan in the UI, and presenting one or more parameter of a customizable period of time in the UI, playing an audio message on speakers.).
Regarding claim 8, Rusak discloses wherein the presenting of the coaching assistance data comprises presenting, via the device, medical patient trend data during the interaction between the medical practitioner and the medical patient (Paragraphs [0092[, [0135], [0177], and [0213] discuss physician obtains patient’s medical history, current diagnosis and get a full scope of the patient's condition based on many physiological conditions with high frequency collection, and in real time display a treatment plan for the physicians, clinicians, surgeons, nurses, and other caregivers and a workflow pattern represented as the interaction journey and one or more patient parameters, for example, a urinary catheter is changed on a regular basis at a defined frequency (e.g., obtained from digital, analog, or voice nursing records) and the model may output an alert when the urinary catheter has not been changed according to the learned frequency and when the certain diagnosis and/or physician orders still stand and looks at the interaction journey.).
Regarding claim 9, Rusak discloses wherein the presenting of the coaching assistance data comprises presenting, via the device, current status data relative to the baseline data during the interaction between the medical practitioner and the medical patient (Paragraphs [0005], [0018], [0213], and FIGS. 12A-12E discuss monitoring an interaction journey of a healthcare provider with at least one medical device that monitors the target patient, patient parameter is indicative of a current medical state relative to a target medical outcome of the target patient, and wherein the adaptation to the UI outputted by the model is computed for increasing likelihood of the current medical state reaching the target medical outcome.).
Regarding claim 10, Rusak discloses wherein the presenting of the coaching assistance data comprises presenting, via the device, current status data relative to the baseline data, the current status data comprising at least one of heart rate data, respiration data, perspiration data, pallor data, posture data, or expression data (Paragraphs [0026], [0085], [0110], [02210], and FIG. 2 discuss patient parameter outputted from a plurality of physiological sensors including, medical images, blood pressure, arterial lines, respiration, body temperature, intracranial pressure, cerebral perfusion, non-physiological data such as patient identify, demographic and provide an insight, overall scope, from clinical and/or biomedical data and/or information collected for a specific patient and provide personalized medicine and/or healthcare specified to the benefit of each patient and the UI is dynamically updated with a personalized treatment plan for the patient, real time information based on the analyzed data and real time decision analysis.).
Claims 3-5 and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Rusak in view of Shears and Bardy and in further view of Hanina (U.S. Pub. No. 2018/0052971 A1).
Regarding claim 3, Rusak discloses wherein the sensing additional practitioner data associated with the medical practitioner comprises:
collecting data for a conversation between the medical practitioner and the medical patient during the interaction between the medical practitioner and the medical patient (Paragraphs [0017] and [0154] discuss the interaction journey is computed based on data from a microphone recording sounds captured during the patient events, a microphone recording sound captured during activities taking place in proximity to the patient, a microphone recording sound captured of the healthcare provider, and interactions of the healthcare provider with an input device.).
Rusak does not explicitly disclose:
conversation between the medical practitioner and the medical patient.
Hanina teaches:
conversation between the medical practitioner and the medical patient (Paragraphs [0107] discuss track markers while conversing.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Rusak to include conversation between the medical practitioner and the medical patient, as taught by Hanina, in order to balance the need to secure video data while allowing for analysis of the data to diagnose and monitor disease and symptoms of disease as related to particular disease states and patient populations. (Hanina Paragraphs [0007] and [0014]).
Regarding claim 4, Rusak discloses further comprising:
sensing the medical patient during the conversation between the medical practitioner and the medical patient (Paragraphs [0017] and [0154] discuss the interaction journey is computed based on data from a camera capturing images of the healthcare provider treating the patient, a camera capturing images of healthcare provider actions when not directly treating the patient, a camera capturing images of healthcare provider washing hands, a camera capturing images of patient events including cough, seizure, sneeze, and/or fall and data from a microphone recording sounds captured during the patient events, a microphone recording sound captured during activities taking place in proximity to the patient, a microphone recording sound captured of the healthcare provider, and interactions of the healthcare provider with an input device.); and
presenting, to the medical practitioner, the additional coaching assistance data based on the data of the medical patient (Paragraph [0068] discusses monitoring an interaction journey of the health care provider(s) with one or more medical devices, analyzing the data in real time by the model that receives the interaction journey and patient parameters, and outputting by the model instructions for adapting a UI for visualizing data in a clear and readable way in front of the medical team (e.g., nurses, doctors, clinician doctors, surgeons, pharmacists, etc.) so the medical personnel is able to provide the best possible treatment to the patient on hand, and develop a real time battle plan for immediate medical needs as well as for long term needs.).
Rusak does not explicitly disclose:
reactions or responses of the medical patient.
Hanina teaches: reactions or responses of the medical patient (Paragraphs [0015] and [0064] discuss a video sequence is analyzed to determine a number of features that may be representative of images in a video sequence, such landmarks of the face, a current health or other state of the user, or other visual features and allow an in-depth analysis of facial features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Rusak to include reactions or responses of the medical patient, as taught by Hanina, in order to balance the need to secure video data while allowing for analysis of the data to diagnose and monitor disease and symptoms of disease as related to particular disease states and patient populations. (Hanina Paragraphs [0007] and [0014]).
Regarding claim 5, Rusak discloses wherein the sensing the medical patient comprises:
sensing one or more of patient expression, patient posture or patient gestures of the medical patient during the conversation between the medical practitioner and the medical patient (Paragraphs [0017] and [0154] discuss the interaction journey is computed based on data from a camera capturing images of the healthcare provider treating the patient, a camera capturing images of healthcare provider actions when not directly treating the patient, a camera capturing images of healthcare provider washing hands, a camera capturing images of patient events including cough, seizure, sneeze, and/or fall.).
Rusak does not explicitly disclose:
reactions or responses of the medical patient.
Hanina teaches: reactions or responses of the medical patient (Paragraphs [0015] and [0064] discuss a video sequence is analyzed to determine a number of features that may be representative of images in a video sequence, such landmarks of the face, a current health or other state of the user, or other visual features and allow an in-depth analysis of facial features.).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Rusak to include reactions or responses of the medical patient, as taught by Hanina, in order to balance the need to secure video data while allowing for analysis of the data to diagnose and monitor disease and symptoms of disease as related to particular disease states and patient populations. (Hanina Paragraphs [0007] and [0014]).
Regarding claim 11, Rusak discloses wherein the obtaining of the baseline data describing the medical patient comprises obtaining information representative of patient data (Paragraphs [0026], [0085], [0110], [02210] , and FIG. 2 discuss patient parameter outputted from a plurality of physiological sensors including, medical images, blood pressure, arterial lines, respiration, body temperature, intracranial pressure, cerebral perfusion, non-physiological data such as patient identify, demographic and provide an insight, overall scope, from clinical and/or biomedical data and/or information collected for a specific patient and provide personalized medicine and/or healthcare specified to the benefit of each patient and the UI is dynamically updated with a personalized treatment plan for the patient, real time information based on the analyzed data and real time decision analysis.).
Rusak does not explicitly disclose:
information representative of at least one of medical patient skin color level data, medical patient body posture, medical patient gait, medical patient facial expression, medical patient perspiration level, or medical patient eye gaze data.
Hanina teaches:
information representative of at least one of me