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 .
DETAILED ACTION
In the amendment filed 1 April 2026:
Claims 69-70 are new
Claims 48, 62-63 are cancelled
Claims 46-47,49,51-,56-59, 61, 65-68 are amended
Claims 46-47,49-61, 64-70 are pending
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 46-47,50-51,53,55,60-61,65,70 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over Tran et al (US Publication No. 20090318779) in view of Simon et al (US Publication No. 20130310909) in view of NAGALE et al (Foreign Publication WO-2018102579-A1).
Regarding Claim 46
Tran teaches a medical device comprising:
sensors comprising;
one or more cameras configured to capture image and/or video data, and one or more microphones a microphone configured to capture audio [Tran at Para. 0019 teaches the system can include one or more cameras positioned to capture three dimensional (3D) video of the patient; Tran at Para. 0246 teaches the CPU is a preferably low power 16-bit or 32-bit microprocessor and the memory is preferably a high density, low-power RAM. The CPU is coupled via the bus to processor wake-up logic, one or more accelerometers to detect sudden movement in a patient, an ADC 102 which receives speech input from the microphone.];
at least one non-transitory memory storing instructions [Tran at Para. 0246],
at least one database comprising a set of reference values obtained based on prior data [Tran at Para. 0137 teaches a module 62 analyzes facial changes such as facial asymmetries. The change will be detected by superimpose a newly acquired 3D anatomy structure to a historical (normal) 3D anatomy structure to detect face/eye sagging or excess stretch of facial muscles; Tran at Para. 0141 teaches next, the system super-imposes two 3D facial shapes (historical or normal facial shapes and current facial shapes). By matching features and geometry of changing areas on the face, closely blended shapes can be matched and facial shape change detection can be performed. By overlaying the two shapes, the abnormal facial change such as sagging eyes or mouth can be detected],
and a processor communicably coupled to the at least one non-transitory memory, the sensors, and the output device, wherein the processor is configured to access the at least one non-transitory memory and execute the instructions to [Tran at Para. 0246]:
receive data inputs, wherein the data inputs comprise [Tran at Para. 0246]:
first data from the one or more cameras at a first time, wherein the first data comprises image and/or video data of a patient prior to arrival at a doctor's office and/or a hospital [Tran at Para. 0025 teaches Data measured several times each day provide a relatively comprehensive data set compared to that measured during medical appointments separated by several weeks or even months];
second data from the one or more microphones at a second time, wherein the second data comprises audio data related to speech of the patient prior to arrival at the doctor's office and/or the hospital [Tran at Para. 0025 (interpreted as prior to arrival at the doctor’s office and/or the hospital)];
and third data comprising at least one medical history element of the patient [Tran at Para. 0314 teaches the data may further include the health history data of the patient, including alcohol intake, autoimmune diseases, caffeine intake, carbohydrate intake, carotid artery disease, coronary disease, diabetes, drug abuse, fainting, glaucoma, head injury, hypertension, lupus, medications, smoking, stroke, family history of stroke, surgery history, for example];
Tran does not teach an output device comprising a nerve stimulator;
and at least one computational algorithm to estimate a diagnosis of a neurological disease or a mimic disease trained based on the set of reference;
determine at least one diagnostic score for the patient based on applying the trained at least one computational algorithm to at least a portion of the data inputs;
determine a diagnosis of the patient as having the neurological disease or the mimic disease based on a consensus and/or a majority of the at least one diagnostic score;
and provide the diagnosis of the patient as having the neurological disease or the mimic disease as an output via the output device to control the nerve stimulator to provide a treatment to the patient to treat a condition based on the diagnosis prior to arrival at the physician's office and/or the hospital.
Simon teaches an output device comprising a nerve stimulator [Simon at Para. 0187 teaches methods of treating a patient comprise stimulating the vagus nerve as indicated in FIGS. 6 and 7, using the electrical stimulation devices that are disclosed herein];
and at least one computational algorithm to estimate a diagnosis of a neurological disease or a mimic disease trained based on the set of reference values [Simon at Para. 0239 teaches for a patient who is not experiencing a stroke or transient ischemic attack, the SVM is trained to forecast the imminence of a stroke or transient ischemic attack, Δ time units into the future, and the training set includes the above-mentioned physiological signals; Tran at Para. 0235 teaches a training set of physiological data will have been acquired that includes whether or not a stroke or transient ischemic attack is in progress (svm interpreted as computational algorithm; interpret to combine with video and speech data of Tran)];
and provide the diagnosis of the patient as having the neurological disease or the mimic disease as an output via the output device to control the nerve stimulator to provide a treatment to the patient to treat a condition based on the diagnosis prior to arrival at the physician's office and/or the hospital [Simon at Para. 0239 teaches the controller may apply the vagus nerve stimulation as a prophylactic whenever there is a forecast of imminent stroke or transient ischemic attack. The controller may also be programmed to turn off the vagaus nerve stimulation when it forecasts or detects the termination of a transient ischemic attack. Itis understood that in any event, the patient should treat any in-progress stroke or transient ischemic attack as a medical emergency and seek immediate emergency medical attention, notwithstanding the use of vagus nerve stimulation as a prophylactic. If the stroke or transient ischemic attack is only forecasted, the patient should immediately seek transportation to the waiting room of the nearest acute stroke treatment center or emergency room and wait at that location to see whether the predicted stroke or transient ischemic attack happens, notwithstanding the use of vagus nerve stimulation as a prophylactic that may have prevented the event].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine data of Tran with the stimulator of Simon with the motivation to improve treatment of individual patients.
Tran/Simon do not teach determine at least one diagnostic score for the patient based on applying the trained at least one computational algorithm to at least a portion of the data inputs;
determine a diagnosis of the patient as having the neurological disease or the mimic disease based on a consensus and/or a majority of the at least one diagnostic score;
NAGALE teaches teach determine at least one diagnostic score for the patient based on applying the trained at least one computational algorithm to at least a portion of the data inputs [NAGALE at Para. 0060 teaches a stroke risk indicator may be generated when the composite risk score exceeds the threshold. In some examples, the stroke detector 224 may generate the stroke risk indicator using patient demographic information including to age, race and sex, and acquired risk factors include cigarette smoking, hypertension, diabetes, or obesity, among others. n some examples, the stroke detector 224 may generate the stroke risk indicator further using likelihood of an epileptic event further based upon information from patient medical history, such as specific risk factors, conditions, or procedures or treatment that would influence functional or physiological parameters];
determine a diagnosis of the patient as having the neurological disease or the mimic disease based on a consensus and/or a majority of the at least one diagnostic score [NAGALE at Para. 0060; NAGALE at Para. 0084 teaches at 560, a stroke risk indicator may be generated if the functional abnormality is detected at 540, or the behavioral cognitive impairment is detected at 550. A stroke is detected when the initial detection based on the physiological symptoms at 511 is confirmed by the cognitive or behavioral impairment and the functional abnormality. The stroke risk indicator, optionally along with the physiological, the functional, and the cognitive or behavioral signals, may be output to a user or a process at 440];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon with the output of NAGALE with the motivation to improve medical diagnostics of stroke.
Regarding Claim 47
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the first data comprises at least one of facial asymmetry, limb movement, or gait cadence, or the second data comprises a speech abnormality [Tran at Para. 0137 teaches a module 62 analyzes facial changes such as facial asymmetries].
Regarding Claim 50
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach further comprising one or more straps or fastening elements configured to allow the medical device to be worn by the patient [Tran at Para. 0455 teaches in one embodiment, a housing (such as a strap, a wrist-band, or a patch) provides a plurality of sensor contacts for EKG and/or EMG.].
Regarding Claim 51
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the at least one medical history element comprises at least one of demographic data, symptoms, and diagnostic testing results, or some combination thereof [Tran at Para. 0454 teaches the system can detect dominant symptoms of stroke can include weakness or paralysis of the arms and/or legs, incoordination (ataxia), numbness in the arms/legs using accelerometers or EMG sensors].
Regarding Claim 53
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the processor is further configured to execute the instructions to initiate interaction with the patient with a spoken prompt to automatically obtain the at least one medical history element [Tran at Para. 0442 teaches the system can also detect dysarthria, a disorder of speech articulation (e.g., slurred speech), by prompting the user to say a word or phrase that is recorded for subsequent comparison by voice pattern recognition or evaluation by medical personnel].
Regarding Claim 55
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach where the neurological disease comprises stroke, a transient ischemic attack, or both a stroke and a transient ischemic attack [Tran at Para. 0012 teaches in one aspect, a monitoring system for a person includes one or more wireless nodes and a stroke sensor coupled to the person and the wireless nodes to determine a stroke attack].
Regarding Claim 60
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the at least one computational algorithm comprises one or more of an artificial neural network, a support-vector machine (SVM), a Nu-SVM, a linear SVM, a Naive Bayes (NB) algorithm, a Gaussian NB, a multinomial NB computation algorithm, a multiclass SVM, a directed acyclic graph SVM (DAGSVM), a structured SVM, a least-squares support-vector machine (LS-SVM), a Bayesian SVM, a transductive support-vector machine, a support-vector clustering (SVC), a classification SVM Type 1 (C-SVM classification), a classification SVM Type 2 (nu-SVM classification), a regression SVM Type 1 (epsilon-SVM regression), and a regression SVM Type 2 (nu-SVM regression) [Tran at Para. 0066 teaches in one embodiment, the server 20 feeds the data to a statistical analyzer such as a neural network which has been trained to flag potentially dangerous conditions].
Regarding Claim 61
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the processor is further configured to execute the instructions to provide the diagnosis as an output to a medical facility via a wired or wireless network [Tran at Para. 0188 teaches the database tracks typical arm and leg movements to determine whether the user is experiencing muscle weakness reflective of a stroke. If muscle weakness is detected, the system presents the user with additional tests to confirm the likelihood of a stroke attack. If the information indicates a stroke had occurred, the system stores the time of the stroke detection and calls for emergency assistance to get timely treatment for the stroke. The user's habits and movements can be determined by the system for stroke detection; Tran at Para. 0375 teaches FIG. 15A shows a system block diagram of the network-based patient monitoring system in a hospital or nursing home setting. The system has a patient component 215, a server component 216, and a client component 217. The patient component 215 has one or more mesh network patient transmitters 202 for transmitting data to the central station], and the medical device further comprises a transmitter configured to communicate the diagnosis via the network [Tran at Para. 0013 teaches in another aspect, heart monitoring system for a patient includes one or more wireless nodes forming a wireless mesh network; a wearable appliance having a wireless transceiver adapted to communicate with the one or more wireless nodes; and a statistical analyzer to determine heart attack or stroke attack, the statistical analyzer coupled to the wireless transceiver to communicate patient data over the wireless mesh network].
Regarding Claim 65
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the processor is further configured to execute the instructions to automatically obtain updated at least one of the data inputs in response to activation in an EMS context [Tran at Para. 0216 teaches in one embodiment, if the wearable appliance detects that the patient needs help, or if the patient decides help is needed, the system can call his or her primary care physician. If the patient is unable to access his or her primary care physician (or another practicing physician providing care to the patient) a call from the patient is received, by an answering service or a call center associated with the patient or with the practicing physician. The call center determines whether the patient is exhibiting symptoms of an emergency condition by polling vital patient information generated by the wearable device, and if so, the answering service contacts 911 emergency service or some other emergency service. The call center can review falls information, blood pressure information, and other vital information to determine if the patient is in need of emergency assistance (fall information, blood pressure information, and other vital information interpreted as updated data inputs)].
Regarding Claim 70
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE further teach wherein the nerve stimulator is configured to deliver at least one therapeutic transcranial magnetic stimulation (TMS) [Simon at Para. 0150 teaches noninvasive techniques for probing neurocircuitry and treating illness: vagus nerve stimulation (VNS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS)].
Claim 49 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of VAUGHAN et al (Foreign Publication WO-2018090009-A1).
Regarding Claim 49
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to determine an inconclusive diagnosis when the consensus and/or the majority of the at least one diagnostic score is between the first value and the second value.
VAUGHAN teaches wherein the processor is further configured to execute the instructions to determine an inconclusive diagnosis when the consensus and/or the majority of the at least one diagnostic score is between the first value and the second value [VAUGHAN at Para. 00186 teaches in particular, as shown in FIG. 14, these thresholds are indicated by the dashed regions that partition the range of numerical scores 1460 into three segments corresponding to a negative determination output 1470, an inconclusive determination output 1480, and a positive determination output 1490. This effectively maps the combined numerical score to a categorical determination, or to an inconclusive determination if the output is within the predetermined inconclusive range].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE, with the inconclusive diagnosis of VAUGHAN with the motivation to improve the accuracy and consistency of the identification outcomes across subjects.
Claim 52,56-57 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of An et al (US Publication No. 20180192894).
Regarding Claim 52
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to automatically obtain the at least one medical history element from one of the patient, one or more third party, and both the patient and the one or more third party.
An teaches wherein the processor is further configured to execute the instructions to automatically obtain the at least one medical history element from one of the patient, one or more third party, and both the patient and the one or more third party [An at Para. 0086 teaches at 802, two or more occurrences of different patient responses are detected. Detection may be automatic or manual. Examples of an automatically detected patient response includes using a software program or other programmable device to telephone or email a patient daily at a particular time and detect a patient response. Other examples include sensors in implanted or external devices to detect things, such as physical activity levels of the patient, physical location of the patient (e.g., using a GPS device to detect whether the patient has left their house in a particular time period), or the like. Examples of manual detection include requesting that a patient measure themselves daily, such as by using a network-enabled weight scale connected to a centralized patient management system, or having a live operator or other personnel call or visit the patient daily to determine whether the patient was compliant that day].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the inputs of An with the motivation to reduce the number of false indications and improve accuracy.
Regarding Claim 56
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to evaluate the data inputs for an initial sign of the neurological disease, wherein the processor is further configured to automatically trigger a more comprehensive evaluation when the initial sign is detected.
An teaches wherein the processor is further configured to execute the instructions to evaluate the data inputs for an initial sign of the neurological disease, wherein the processor is further configured to automatically trigger a more comprehensive evaluation when the initial sign is detected [An at Para. 0086 teaches 0156 teaches a composite alert score is evaluated and compared to a threshold value (Th). If the composite alert score is greater than the threshold (Th), then the status is presented to a physician interface 2010, such as for display. In examples, the physician interface 2010 may include a computer terminal, an electronic medical records system, or other input mechanism. A physician may make an independent determination of the patient's status, for example during an office visit or during a telephonic patient interview. The physician may then provide the independent determination using the interface, such as an interface illustrated in FIG. 19. The independent determination may be performed asynchronously with contemporaneous evaluations performed by the control system 2004 or other systems, such that, for example, the independent determination may occur before, during, or after a particular within-patient analysis 2008 has been evaluated. The independent evaluation may rely on, at least in part, data similar to that received by the control system 2004, such as data 2006, or may use independently obtained data, such as data obtained during a patient examination, or may use a combination of data sources. Whatever the source of data, the independent evaluation is typically made without reference to automatically determined results, such as results of within-patient analysis 2008].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the inputs of An with the motivation to reduce the number of false indications and improve accuracy.
Regarding Claim 57
Tran/Simon/NAGALE/An teach the medical device of claim 56,
Tran/Simon/NAGALE/An further teach wherein the first data comprises in the image and/or video data of the patient alterations in gait, speech, and extremity movements, or some combination thereof as initial signs of the neurological disease [Tran at Para. 0143 (see Claim 46 for explanation)].
Claim 54 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of Bates et al (US Publication No. 20170323064) in view of Barr et al (US Publication No. 20130189243).
Regarding Claim 54
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute instructions to increase the likelihood of a diagnosis of the neurological disease when a positive symptom of the neurological disease is present,
and to decrease the likelihood of the diagnosis of the neurological disease when a symptom of the mimic disease, comprising a condition that is clinically similar but distinct in terms of causation including seizure, migraine, or hypoglycemia, is present.
Bates teaches wherein the processor is further configured to execute instructions to increase the likelihood of a diagnosis of the neurological disease when a positive symptom of the neurological disease is present [Bates at Para. 0056 teaches in embodiments, an elimination process may be repeated until a relatively small number of potential illnesses (e.g., five illnesses), each having been assigned a relatively high diagnosis probability (e.g., 90%), is identified. Then, in embodiments, subsequent illness-specific questions or measurements are selected to further increase the statistical weight (i.e., the probability) of one of the identified illnesses to an even higher level prior to determining that the selected illness is a correct diagnosis], and to decrease the likelihood of the diagnosis of the neurological disease when a symptom of the mimic disease [Bates at Para. 0053 teaches FIG. 8 illustrates a process for generating a diagnosis probability according to embodiments of the present disclosure. In embodiments, machine learning may be applied to patient-provided and other data to improve the data weights used in the algorithm to eliminate a relatively large number of potential illnesses and narrow the list of potential illnesses. For example, by using a self-learning and medical database decision vectors combined with an algorithm, after a relatively low number of iterations of patient answers and measurements, a diagnosis that has a likelihood greater than a certain percentage, e.g., 50%, may be generated; Bates at Para. 0054 teaches in embodiments, reducing the number of questions to a set of questions that may identify a particular illness comprises selecting answers based on symptom, illness, measurements or patient history data weights. A data weight may be used by the algorithm by comparing or matching, for example, the patient's input that may be compared to data in the database that are related to the particular illness. Based on the match a probability of the illness reflecting an accurate diagnosis may be calculated], … [ … ]
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the probability of Bates with the motivation to provide better patient experience and increase the doctor to patient throughput per hour, while simultaneously reducing cost.
Tran/Simon/NAGALE/Bates do not teach [ … ] … comprising a condition that is clinically similar but distinct in terms of causation including seizure, migraine, or hypoglycemia, is present.
Barr teaches [ … ] … comprising a condition that is clinically similar but distinct in terms of causation including seizure, migraine, or hypoglycemia, is present [Barr at Para. 0164 teaches specific neurologic dysfunctions or “stroke-associated symptoms” or “stroke-mimicking symptoms” may include, but are not limited to, pain, headache, aphasia, apraxia, agnosia, amnesia, stupor, confusion, vertigo, coma, delirium, dementia, seizure, migraine insomnia, hypersomnia, sleep apnea, tremor, dyskinesia, paralysis, visual disturbances, diplopia, paresthesias, dysarthria, hemiplegia, hemianesthesia, hemianopia, etc. Patients exhibiting one or more of these symptoms but that have not suffered from a stroke are referred to herein as “stroke mimics.”].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE, Bates with the similar conditions of Barr with the motivation to improve acute stroke diagnosis.
Claims 58, 68 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of Bates et al (US Publication No. 20170323064).
Regarding Claim 58
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein in response to diagnosing the patient, the processor is further configured to execute instructions to automatically determine or recommend an appropriate medication for the patient, based on the diagnosis of having the neurological disease or the mimic disease.
Bates teaches wherein in response to diagnosing the patient, the processor is further configured to execute instructions to automatically determine or recommend an appropriate medication for the patient, based on the diagnosis of having the neurological disease or the mimic disease [Bates at Para. 0091 teaches returning to FIG. 1, in embodiments, upon identifying a diagnosis, system 100 generates one or more recommendations/suggestions/options for a particular treatment. In embodiments, system 100 may generate a prescription/lab test request and considers factors, such as recent research results, available drugs and possible drug interactions, the patient's medical history, traits of the patient, family history and any other factors that may affect treatment to provide treatment information for a doctor].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the recommendation of Bates with the motivation to provide better patient experience and increase the doctor to patient throughput per hour, while simultaneously reducing cost.
Regarding Claim 68
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to compare the data inputs to a library of classic syndromes and reduce possible diagnoses based on presence or absence of syndrome elements in the data inputs.
Bates teaches wherein the processor is further configured to execute the instructions to compare the data inputs to a library of classic syndromes and reduce possible diagnoses based on presence or absence of syndrome elements in the data inputs [Bates at Para. 0054 teaches in embodiments, reducing the number of questions to a set of questions that may identify a particular illness comprises selecting answers based on symptom, illness, measurements or patient history data weights. A data weight may be used by the algorithm by comparing or matching, for example, the patient's input that may be compared to data in the database that are related to the particular illness. Based on the match a probability of the illness reflecting an accurate diagnosis may be calculated; Bates at Para. 0055 teaches in detail, in embodiments, based on the patient's answers, such as self-identified zones of pain or problem zones or conditions, the patient is provided with a number of questions or symptoms or request for medical device measurements that relate to that problem zone. The patient may be prompted to identify a first symptom (e.g., headache) from the set of symptoms and, based on keywords in the patient's response that match keywords in a database, a set of questions and measurement requests may be generated to identify additional symptoms (e.g., fever). In embodiments, a question or measurement regarding a second symptom may be selected based on a highest weighted symptom related to the first symptom, such that, for example, based on medical database decision vectors, as many diagnoses as possible may be eliminated from a probability matrix].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the recommendation of Bates with the motivation to provide better patient experience and increase the doctor to patient throughput per hour, while simultaneously reducing cost.
Claim 59 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of GRAS et al (US Publication No. 20170258410).
Regarding Claim 59
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to evaluate the patient for a likelihood of the mimic disease that is a mimic to the neurological disease.
GRAS teaches wherein the processor is further configured to execute the instructions to evaluate the patient for a likelihood of the mimic disease that is a mimic to the neurological disease [GRAS at Para. 0068 teaches 11. A system and/or method in accordance with any of the aforementioned aspect, wherein the processor is operable to provide the wearer with a visual and/or audible indication which indicates either a percentage or relative likelihood and/or severity of the project seizure event].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the likelihood of GRAS with the motivation to improve reliability for chaotic or event predictions.
Claim 66 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of Kabir et al (US Publication No. 9536051).
Regarding Claim 66
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the processor is further configured to execute the instructions to determine whether a classic syndrome definition is fulfilled based on a point-based weighting of syndrome elements, the weighting being proportionate to a prevalence of the syndrome elements in a population of patients confirmed to have the classic syndrome.
Kabir teaches wherein the processor is further configured to execute the instructions to determine whether a classic syndrome definition is fulfilled based on a point-based weighting of syndrome elements, the weighting being proportionate to a prevalence of the syndrome elements in a population of patients confirmed to have the classic syndrome [Kabir at Para. 47 teaches for example, in one embodiment, a specific weight can be assigned to each sign, symptom or finding during diagnostic decision making. In the present invention, to satisfy the rule of addition, a universal weight of one (1) is assigned to any sign and symptom or finding during generating the high priority differential diagnoses; Kabir at Para. 49 teaches in alternative embodiments, the present invention may also assign a higher weight to potential diseases or differential diagnoses that are highly prevalent in a certain group of population so that the corresponding diagnosis is ranked higher among other high probability differential diagnoses that may not be as prevalent in the certain group of population].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the weights of Kabir with the motivation to improve patient safety.
Claim 67 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE, An as applied to claim 56 above, and further in view of Bates et al (US Publication No. 20170323064).
Regarding Claim 67
Tran/Simon/NAGALE/An teach the medical device of claim 56,
Tran/Simon/NAGALE/An do not teach wherein the processor is further configured to execute the instructions to initiate a full syndrome evaluation only when at least a predetermined number or proportion of syndrome elements associated with the classic syndrome are present in the patient data inputs.
Bates teaches wherein the processor is further configured to execute the instructions to initiate a full syndrome evaluation only when at least a predetermined number or proportion of syndrome elements associated with the classic syndrome are present in the patient data inputs [Bates at Para. 0060 teaches in embodiments, when two or more potential illness are identified, questions about, e.g., symptoms, may be tailored to identify which of the two or more potential illness exist. In embodiments, tailored questions are asked until the likelihood of further questions would not increase the likelihood of that illness by more than a certain percentage (e.g., 1%). In embodiments, based on the diagnosis probabilities, a list of potential illness is ranked by probability and output. In embodiments, if a final probability is less than a certain threshold (e.g., 90%), additional testing, e.g., lab testing, may be initiated or suggested].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE, An with the recommendation of Bates with the motivation to provide better patient experience and increase the doctor to patient throughput per hour, while simultaneously reducing cost.
Claim 69 rejected under 35 U.S.C. 103(a) as being unpatentable over Tran, Simon, NAGALE as applied to claim 46 above, and further in view of POPOVIC et al (US Publication No. 20140277309).
Regarding Claim 69
Tran/Simon/NAGALE teach the medical device of claim 46,
Tran/Simon/NAGALE do not teach wherein the nerve stimulator is a facial nerve stimulator that is configured to provide the treatment to the patient.
POPOVIC teaches wherein the nerve stimulator is a facial nerve stimulator that is configured to provide the treatment to the patient [POPOVIC at Para. 0027 teaches in another aspect, there is provided an apparatus comprising a multi-channel stimulator for use in performing the method as hereinabove defined. In some exemplary embodiments, the multi-channel stimulator is preprogramed to automatically and repeatedly provide steps a) to d) of the method in a sequential manner to the individual having the plurality of electrodes in operable communication with a their facial regions].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of Tran, Simon, NAGALE with the stimulator of POPOVIC with the motivation to improve the emotional state of the subject.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claim 46-47,49-61, 64-70, the Examiner has reconsidered the rejection in light of the 2019 Revised Patent Subject Matter Eligibility Guidance dated January 7, 2019 and withdraws the rejection. The claimed invention is subject matter eligible because the controlling of the neural stimulator from the diagnosis improves the technological environment and therefore, provides a practical application.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 46-47,49-61, 64-70, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Applicant argues:
Tran, Barnhill, and Nagale, taken alone or in combination, fail to teach or suggest each and every element recited in amended claim 46. Accordingly, Tran, Barnhill, and Nagale, taken alone or in combination, fail to render amended claim 46 (or any of the pending associated dependent claims) obvious to one having ordinary skill in the art.
Claims 69 and 70 have been newly added and depend from claim 46. Claims 69 and 70 are allowable for at least the same reasons as claim 46 and for the specific elements recited therein. A prompt action allowing claims 69 and 70 is respectfully requested.
Regarding (a, e), the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as necessitated by amendment.
Claim 66 recites that the processor is configured to execute the instructions to determine whether the classic syndrome definition is fulfilled based on a point-based weighting of syndrome elements proportionate to prevalence in a patient population. The art of record does not appear to disclose syndrome-based point weighting, nor does it appear to disclose population prevalence weighting. This feature appears to be absent from the cited art. Claim 66 is therefore allowable for these additional reasons.
Regarding (c), the Examiner respectfully disagrees. The syndrome-based weighing and population prevalence weighing is not defined in the claim and is therefore given its broadest reasonable interpretation, which is interpreted as weights. Kabir teaches how weights are affected for determining a diagnosis. Which is based on prevalence in population groups and weights based on symptoms.
Claim 67 recites that the processor is configured execute the instructions to initiate a full syndrome evaluation only when at least a predetermined number or proportion of syndrome elements are present in the patient data inputs. The art of record does not appear to disclose this functionality. Claim 67 is therefore allowable for these additional reasons.
Regarding (d), the Examiner respectfully disagrees. Additional testing being initiated of Bates is interpreted as initiating full syndrome evaluation. There is no defined number of “predetermined number of syndrome elements” which is interpreted as 1. Therefore, Bates teaches the limitation.
Claim 68 recites that the processor is configured to execute the instructions to compare patient data inputs to a library of classic syndromes and reduce possible diagnoses based on syndrome element presence or absence. The art of record does not appear to disclose this functionality. Claim 68 is therefore allowable for these additional reasons.
Regarding (e), the Examiner respectfully disagrees. Syndrome elements are interpreted as symptoms and therefore, Bates at Para. 0054 and 055 teaches selecting questions based on symptoms and eliminating potential diagnoses by comparing to database of particular illness, which covers the limitation in Clam 68.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Howard e at (US Publication No. 20170258390) discloses systems and methods for the early detection of neurodegenerative disease.
GIROUARD et al (Foreign Publication WO-2015035413-A1) discloses a method of monitoring a patient for seizures with motor manifestations.
THIS ACTION IS MADE FINAL, as necessitated by amendment. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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