DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/12/2026 is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/12/2026 has been entered.
Respond to Arguments
35 U.S.C. § 112(b)
Applicant’s arguments, see page 10 filed 03/12/2026, with respect to 35 U.S.C. § 112(b)
rejection has been fully considered and is persuasive. The rejection is withdraw amendments remove 112(b) previous action rejection.
Double Patenting
Applicant’s arguments, see page 10 filed 03/12/2026, with respect to the provisional nonstatutory double patenting rejection have been fully considered and is persuasive. The rejection of claims 1-26 on the ground of nonstatutory double patenting sufficiently diverge from the claims of the ‘921 application such that the double patenting rejections are overcome..
35 U.S.C. § 101 - Subject Matter Eligibility
Applicant’s arguments, see page 10-16 filed 03/12/2026, with respect to pending Claims 2, 4-19, and 21-29 have been fully considered and are not persuasive. The rejection of claims 1-26 under 35 U.S.C. § 101 is maintained.
The applicant argues that elements of claim 21 in combination with the other recited elements provide a technical solution to various technical problems.
Examiner respectfully disagrees because the classifier recited in claim 21 is used only to determine long COVID and does not improve the underlying technology. Claim 21 relies on a generic computer system to access physiological data from at least one sleep session and, based on a threshold, determine a probability of COVID-19. Considered as a whole, the additional elements merely apply the abstract idea and do not provide a particular technical solution or improve computer functioning, data processing, or machine performance.
The applicant argues that elements of claim 22 in combination with the other recited elements provide a technical solution to various technical problems.
Examiner respectfully disagrees because claim 22 merely recites a more specific abstract idea implemented on a computer, not a technical solution. Using an intensity value as a threshold to determine an action is analogous to a clinician applying reference values and corresponding instructions when a value falls above or below a threshold. This may refine the abstract decision-making process, but it does not improve the underlying technology. Claim 22 does not recite a particular technical solution; it merely states the goal of determining a medical intervention and does not improve computer functioning, data processing, or machine performance.
Applicant argues that New Claim 27, “first data signals and second data signals... an acute COVID classifier... a long COVID classifier... wherein the classified physical state... comprises at least one long COVID intensity value,” provides a unique technological structure that integrates separate acute and chronic condition models with fluid-communicating mattress sensors to achieve automated health monitoring.
The Examiner respectfully disagreed because under proper BRI, New Claim 27, “classify, using an acute COVID classifier... receive the second data signals... and classify, using a long COVID classifier... into one of a plurality of classified physical states of long COVID” means applying mathematical diagnostic models sequentially to separate datasets derived from physical sensor signals. The record shows that the hardware environment consists of generic pneumatic mattress components and generic computer processors. Thus, the applicant’s position is not persuasive because the inclusion of physical sensor elements and generic computing devices merely acts as an incidental environment or data-gathering mechanism for the underlying abstract mathematical concepts and mental processes under MPEP § 2106.04(d)(1) and MPEP § 2106.05(g). The combination of an acute classifier and a long-term condition classifier is an algorithmic modification that narrows the mathematical calculations to a specific medical field of use without introducing an unconventional physical configuration or an explicit hardware modification to the computer itself. Therefore, the claim is directed to an ineligible abstract idea and the rejection is maintained.
Applicant argues that New Claim 28, “first data signals and second data signals... an acute condition classifier... a chronic condition classifier... wherein the classified illness state... comprises at least one chronic condition intensity value,” integrates independent data models with a mattress air bladder sensor network to track disease progression over separate sleep sessions.
The Examiner respectfully disagreed because under proper BRI, New Claim 28, “classify, using an acute condition classifier... and classify, using a chronic condition classifier... into an illness state of a chronic condition” means generating multi-step diagnostic classifications through separate algorithms using values representing sleep duration, breathing rate, gross-body motion, and heart rate. The record shows that the mathematical framework is executed on generic computing processors connected to generic pressure transducers. Thus, the applicant’s position is not persuasive because the steps of converting pressure waves into distinct feature vectors and processing them through consecutive data classifiers represent result-oriented abstractions that lack the specific structural implementation required to demonstrate an improvement to computer technology under MPEP § 2106.05(a) and MPEP § 2106.05(f). An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment; the claim merely restricts the deployment of diagnostic logic to a bed infrastructure, which does not alter the fundamental operation of the computer or the physical elements of the mattress. Therefore, the claim is directed to an ineligible abstract idea and the rejection is maintained.
35 U.S.C. § 103 - Obviousness
Applicant’s arguments, see page 16-20 filed 03/12/2026, with respect to pending Claims 2, 4-19, and 21-29 have been fully considered and are not persuasive. The rejection of claims 2, 4-19, and 21-29 under 35 U.S.C. § 103 is maintained.
Under MPEP § 2111, the claimed long COVID classifier, classified physical states of long COVID, and long COVID intensity value are therefore read as requiring the recited classification function and output, not an unclaimed classifier architecture, training protocol, or specific clinical implementation.
Under MPEP § 2141.02, the § 103 inquiry considers the claimed invention as a whole, and under MPEP § 2143/KSR, familiar elements combined by known methods are obvious when they yield predictable results. The rejection properly relies on Garcia Molina for the smart-bed physiological sensing and classifier platform, Visco for the recognized long-COVID/post-COVID disease state, and Kehr for intervention-history and symptom-severity correlation.
Applicant’s argument that Garcia Molina does not expressly disclose long COVID does not overcome the rejection because Garcia Molina is not relied upon alone for that feature.
Examiner respectfully disagreed because Garcia Molina teaches a feature-vector classifier that generates classifications for a predefined set of possible physical states and produces a single probability value for each sleep session (Garcia 0193). Garcia further teaches COVID-19 positive/negative training and expressly states that Other states and other outcomes may be used and that classifiers produce p values on new sensor readings (Garcia 0196). Visco supplies the missing post-COVID target by teaching post-COVID-19 syndrome, long COVID, shortness of breath, increased heart rate, sleep disorders, and that long COVID is a complex syndrome with protracted heterogeneous symptoms requiring personalized treatment as well as ongoing support (Visco Abstract; p. 1).
Accordingly, for claims 21, 27, and 28, a person of ordinary skill would have configured Garcia repeated COVID-state classifier framework to classify Visco’s long-COVID/post-COVID condition after an acute COVID-positive state. The modification uses Garcia Molina’s same sensor hardware, same sleep-session feature vectors, and same p-value/state-transition classifier process for a known later COVID-related condition. This is a predictable application of a known classification platform to a known related disease state, not bodily incorporation of Visco into Garcia Molina.
Applicant’s argument regarding future scheduling in claim 21.
Examiner respectfully disagreed because Garcia Molina teaches that sleep sessions may define the end of one sleep session and the beginning of the next sleep session (Garcia 0194), that each state identifies a probability for the next sleep session (Garcia 0196), and that the classifier operates on new sensor readings (Garcia 0196).
Applicant’s argument regarding the long-COVID intensity value is not persuasive.
Examiner respectfully disagreed because Garcia teaches a numerical score of risk or probability of illness (Garcia 0027), physiological changes proportional to illness features such as symptom severity (Garcia 0028), and reports including respiratory feature values that help the sleeper understand how serious their symptoms may be (Garcia 0219). When Garcia Molina’s numerical illness/progression output is applied to Visco’s long-COVID symptom set, the combination teaches or suggests the claimed symptom-corresponding long-COVID intensity value.
Applicant’s arguments regarding claims 22 and 23
Examiner respectfully disagreed because they attacked the references separately. Garcia Molina teaches longitudinal illness monitoring, storage for long-term storage and access (Garcia 0215), and state-progression data including symptom onset, peak-intensity, symptom regression, and virus-free (Garcia 0217). Kehr supplies the intervention/effectiveness feature by teaching clinical information correlated with when medication was taken or missed, symptom severity over time, and medication effect on the disease process, including a severity index on the Y-axis and dates/times on the X-axis (Kehr Abstract, Figs. 2C, 2E, 19; cols. 21-22). A person of ordinary skill would have applied Kehr’s known treatment-response tracking to Garcia Molina’s longitudinal monitoring system, as modified by Visco, to determine whether medical or wellness interventions correspond to improvement, worsening, or no change in long-COVID intensity.
Applicant’s arguments regarding claim 29 and the dependent claims are likewise not persuasive.
Examiner respectfully disagreed because Garcia Molina teaches reports, feature-value records, recovery recommendations, medically generated rule sets, automated clinical processes, EMR updating, and scheduling medical tests, including schedules, for the sleeper, a medical test to confirm the sleeper is in the classified physical state (Garcia 0221). Those functions remain applicable when the classified state is Visco’s long-COVID/post-COVID condition.
The hindsight argument is not persuasive. The rejection is based on Garcia Molina’s disclosed COVID monitoring architecture, Visco’s disclosed long-COVID condition, and Kehr’s disclosed intervention/symptom-severity tracking. Under MPEP § 2141/2145, the examiner must put Applicant’s disclosure aside and rely on the prior-art facts and articulated reasoning; here, the record supplies both.
For these reasons, the rejections of claims 2, 4-29 under 35 U.S.C. § 103 are maintained.
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 2, 4-19, and 21-29 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception, namely an abstract idea, without additional elements that integrate the exception into a practical application or amount to significantly more than the exception.
Step 1: Statutory Categories Analysis
The claims are directed to statutory subject matter, encompassing the following statutory categories:
Machine (Claims 2, 4-19, 21-29): The language reciting “A system comprising: a bed... sensors... computing system” and “A computer system... comprising: one or more processors; and memory” describes a concrete thing consisting of parts, aligning with the definition of a machine in MPEP § 2106.03.
Having confirmed the claims are directed to statutory subject matter, the analysis proceeds to Step 2A Prong One.
Step 2A, Prong One: Judicial Exception Analysis
Step 2A, Prong One determines whether the claim recites a judicial exception.
The following non-bold language recite abstract ideas, and the bold language additional elements for the purpose of being further evaluated under prong two and step 2b.
Independent Claims Analysis
Claim 21. A computer system for classifying a subject based on sensor data, the computer system comprising:
one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to perform operations comprising:
accessing, from a first sensors, first sensor data of a subject that records physiological measures of the subject in at least one sleep session, the first sensor data comprising at least pressure data collected by the first sensor from an air bladder of a mattress of a bed supporting a subject in the sleep session; determining, based on the first sensor data, the subject has at least a threshold probability of being positive for COVID-19;
scheduling in the future a determination for the subject to determine if the subject begins to demonstrate symptoms of long COVID, wherein the determination for the subject is performed using a long COVID classifier of the computer system;
and later, according to the scheduling, determine, using the long COVID classifier and second sensor data from the first sensor, if the subject begins to demonstrate symptoms of long COVID
Claim 22.
A computer system for analysis of a progression for long COVID, the computer system comprising:
one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to perform operations comprising:
periodically:accessing sensor data of a subject that records physiological measures of the subject in at least one sleep session, the sensor data comprising at least pressure data collected from an air bladder of a mattress of a bed supporting a subject in the sleep session;
and determining, based on the sensor data, a new intensity value for severity of symptoms of long COVID for the subject, the new intensity value provided on a numerical scale representative of symptom severity;
adding the new intensity value of long COVID for the subject to acollection of intensity values of long COVID for the subject, each of the collection of intensity values provided on the numerical scale representative of symptom severity;
accessing a collection of medical or wellness interventions to determine dates and types of one or more medical or wellness interventions undertaken by the subject; and
determining, based on the collection of intensity values of long COVID for the subject and the collection of medical or wellness interventions, changes in the intensity values of long COVID for the subject and effectiveness of the one or more medical or wellness interventions undertaken by the subject.
Claim 27.
A system comprising:
a bed having a mattress having at least one air bladder;one or more sensors in fluid communication with the air bladder, the one or more sensors configured to:
sense one or more physical phenomena of a subject on the bed comprising at least air pressure of the at least one air bladder caused by weight of the subject on the bed; generate first data signals and second data signals based on the one or more physical phenomena sensed by the one or more sensors; and
send, to a computing system, the first data signals and the second data signals;
the computing system comprising one or more processors and computer memory,the computing system configured to:
receive the first data signals;
generate, from the first data signals of a sleep session of the subject, a first feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the first feature vector comprises four features, the four features being: sleep duration, breathing rate, gross-body motion, and heart rate;
classify, using an acute COVID classifier, the subject into one of a plurality of classified physical states of acute COVID for the sleep session based on the first feature vector;receive the second data signals;
generate, from the second data signals of a later sleep session of the subject, a second feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the second feature vector comprises four features, the four features being: sleep duration, breathing rate, gross-body motion, and heart rate;
and classify, using a long COVID classifier and in response to the classified physical state of acute COVID being acute COVID positive, the subject into one of a plurality of classified physical states of long COVID for a later sleep session based on the second feature vector, wherein the classified physical state of long COVID comprises at least one long COVID intensity value corresponding to at least one long COVID symptom for the classified physical state.Claim 28.
A system for classifying a subject on a bed into an illness state, the system comprising:
a bed having a mattress having at least one air bladder;one or more sensors in fluid communication with the air bladder, the one or more sensors configured to:
sense one or more physical phenomena of a subject on the bed comprising at least air pressure of the at least one air bladder caused by weight of the subject on the bed;
generate first data signals and second data signals based on the one or more physical phenomena sensed by the one or more sensors;
and send, to a computing system, the first data signals and the second data signals;
the computing system comprising one or more processors and computer memory,the computing system configured to:
receive the first data signals;
generate, from the first data signals of a sleep session of the subject, a first feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the first feature vector comprises four features,the four features being: sleep duration, breathing rate, gross-body motion, and heart rate;
classify, using an acute condition classifier, the subject into an illness state of an acute condition for the sleep session based on the first feature vector;receive the second data signals;
generate, from the second data signals of a later sleep session of the subject, a second feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the second feature vector comprises four features, the four features being: sleep duration,breathing rate, gross-body motion, and heart rate;
and classify, using a chronic condition classifier and in response to the classified illness state of the acute condition being acute condition positive, the subject into an illness state of a chronic condition for a later sleep session based on the second feature vector, wherein the classified illness state of the chronic condition comprises at least one chronic condition intensity value corresponding to at least one symptom of the chronic condition for the classified illness state.
Claim Abstract Classification Rational
Claims 21, 22, 27, and 28 recite an abstract idea under Step 2A, Prong One because the claims set forth evaluation, scoring, scheduling, and classification of health information.
Claim 21 recites a mental process and mathematical concept because limitation 2 requires determining, based on the first sensor data, the subject has at least a threshold probability of being positive for COVID-19, limitation 3 requires scheduling in the future a determination... to determine if the subject begins to demonstrate symptoms of long COVID, and limitation 4 requires later determining if the subject begins to demonstrate symptoms of long COVID. These limitations recite observing physiological information, evaluating that information, making a probability-based judgment about COVID-19 status, and planning a later long-COVID evaluation.
The threshold probability language also recites mathematical-concept activity because the specification describes a classifier output as a probability value compared to a threshold at [0005], [00199], and [00213]-[00214]. MPEP 2106 states that determining a variable or number using mathematical methods may be a mathematical calculation even when the claim does not use the word calculating.
Claim 22 recites a mental process and mathematical concept because limitation 3 requires determining... a new intensity value for severity of symptoms of long COVID... on a numerical scale, limitation 4 requires adding that value to a collection of intensity values, limitation 5 requires accessing medical or wellness intervention information to determine intervention dates and types, and limitation 6 requires determining changes in intensity values and intervention effectiveness. These limitations recite evaluating medical information over time, organizing symptom-severity values, comparing those values with intervention information, and judging treatment effectiveness. The numerical-scale severity value is also a mathematical concept. The specification supports this reading because [0009] describes determining treatment efficacy based on changes in long-COVID intensity value, [00212] describes long-COVID intensity values on a predetermined scale such as 0 to 1 or another scale, and [00234]-[00235] describe periodically determining long-COVID intensity, adding it to a collection, creating a time series, recording dates and types of interventions, and determining effectiveness.
Claims 27 and 28 recite a mental process and mathematical concept because the claimed sensing, feature-vector generation, and classification limitations set forth monitoring physiological information, organizing that information into selected feature values, and evaluating the values to classify a subject’s illness state.
For claim 27, the abstract-idea language includes limitation 2, sense one or more physical phenomena of a subject on the bed comprising at least air pressure of the at least one air bladder caused by weight of the subject on the bed; limitation 6, generate... a first feature vector... sleep duration, breathing rate, gross-body motion, and heart rate; limitation 7, classify, using an acute COVID classifier; limitation 8, generate... a second feature vector; and limitation 9, classify, using a long COVID classifier... into one of a plurality of classified physical states of long COVID... comprising at least one long COVID intensity value.
For claim 28, the corresponding abstract-idea language includes limitation 2, sense one or more physical phenomena of a subject on the bed comprising at least air pressure of the at least one air bladder caused by weight of the subject on the bed; limitation 7, generate... a first feature vector; limitation 8, classify, using an acute condition classifier; limitation 9, generate... a second feature vector; and limitation 10, classify, using a chronic condition classifier... into an illness state of a chronic condition... comprising at least one chronic condition intensity value. Under the broadest reasonable interpretation, sense in this context includes monitoring a subject’s physiological phenomena from pressure information, such as reading air-pressure changes caused by the subject on the mattress; generate... feature vector includes organizing the monitored physiological information into selected values; and classify includes evaluating those values to identify acute and later long/chronic illness states. These acts fall within the mental-process grouping because they recite observation, evaluation, judgment, and opinion applied to health information, even though the claims also recite sensors and a computing system as additional elements.
Manual Replication Scenario (Human Equivalence)
The abstract nature of the claims is reinforced because the entire process is analogous to fundamental human activities. A clinician could perform the same information analysis by reading the patient’s sleep/vital-sign data, writing selected values in a chart, comparing them to illness indicators, assigning a symptom-severity score, and scheduling a later check. Thus, the claimed determinations, classifications, scheduling, and progression tracking fall within the mental-process grouping. The claimed threshold probabilities, numerical intensity values, feature vectors, and classifier scores also implicate mathematical concepts.
This demonstrates that the claims are directed to disembodied professional practices and administrative workflows, automated by generic computer components.
Dependent Claims Analysis
Dependent claims recite narrowed fundamental concepts of the independent claims as follows:Groups:
Claim 2 recite mental process since only classify physical state into positive or negative.
Claims 4-7
This group is categorized as mental processes because, under BRI, it involves evaluating and judging the sleeper’s state (e.g., compare the probability value or analyze historical data).
Claims 8-11
This group is categorized as mental processes because, under BRI, selecting and organizing data (e.g., “physical measure” or “environmental measure”) involves observation and evaluation, fitting mental processes.
Claim 24 recite scheduling and classify a physical state a method of organization activity processed in the sub-category of Managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, following rules or instructions).
Claims 12-19, 23, 25 and 29
This group is categorized as Certain methods of organizing human activity because, under BRI, it involves managing personal behavior or healthcare interactions (e.g., recovery recommendation or schedule a medical test).
Claims 25, 26
This group is categorized as Mental process because, under BRI, collecting and comparing data over time (e.g., individualized baseline) involves observation and judgment.
The claims recite fundamental mental processes tasks and certain methods of organizing human activity thus are directed to abstract ideas under Step 2A Prong One Next, we evaluate whether these limitations integrate the abstract ideas into a practical application under Step 2A Prong 2.
Step 2A, Prong Two: Integration into a Practical Application
Prong Two therefore evaluates only the additional elements recited beyond that abstract idea.
Claims 21 and 22. The additional elements are claim 21, limitation 1, one or more processors and memory storing instructions; claim 21, limitation 2, accessing, from a first sensor, first sensor data... comprising at least pressure data collected by the first sensor from an air bladder of a mattress of a bed; claim 22, limitation 1, one or more processors and memory storing instructions; and claim 22, limitation 2, accessing sensor data... comprising at least pressure data collected from an air bladder of a mattress of a bed. These elements place the abstract medical evaluation in a computer and bed-sensor environment. They do not improve computer operation, pressure sensing, air-bladder operation, or mattress structure. The specification describes generic processor, memory, computer-readable media, input/output, client/server, and communication-network components at 0183]-[0193], including implementation on general programmable processors and computer systems connected by LAN, WAN, or Internet communication. These elements therefore amount to generic computer implementation and pre-solution data gathering, not integration into a practical application.
Claims 27 and 28. The additional elements are claim 27, limitation 1, a bed having a mattress having at least one air bladder and one or more sensors in fluid communication with the air bladder; claim 27, limitation 3, send, to a computing system, the first data signals and the second data signals; claim 27, limitation 4, the computing system comprising one or more processors and computer memory; claim 27, limitation 5, receive the first data signals and the later recited receive the second data signals; and the corresponding claim 28 limitations 1 and 3-6. These elements collect, transmit, and receive the information used by the abstract classification. The specification describes the bed, air chambers, pressure transducer, processor, and remote/cloud analysis as general hardware for detecting pressure, heart rate, respiration, motion, and sleep state at [0039]-[0056], and describes communication through generic wired/wireless interfaces and computer networks at [0183]-[0193]. The claim does not recite a new sensor structure, a new air-bladder arrangement, a new pressure-measurement technique, or a new communication protocol. Under MPEP 2106.05(g), gathering data for use in the abstract analysis is insignificant extra-solution activity; sending and receiving data signals likewise move the collected data to the computer for analysis, but do not meaningfully limit the abstract idea.
The send/receive limitations must be evaluated at Prong Two because they were not the abstract idea identified in Prong One. Under their broadest reasonable interpretation, claim 27 limitation 3 and claim 28 limitation 4 merely require sending sensor data to a computing system, while claim 27 limitation 5 and claim 28 limitation 6 merely require receiving that data. The claims do not specify how the data is sent or received beyond generic communication. The specification confirms this generic reading by describing wired or wireless communication, Bluetooth, WiFi, radio-frequency communication, LAN, WAN, and Internet communication as interchangeable communication media at [0183]-[0193]. These limitations therefore do not integrate the abstract idea into a practical application; they are ordinary data-transfer steps used to supply inputs to the abstract classification.
The additional elements also do not provide a particular treatment or prophylaxis. The claims classify or track illness states and, in dependent claims, may generate reports, recommendations, test scheduling, or progression information. They do not require administering medicine, changing a dosage, performing a physical treatment step, or otherwise applying the classification to treat the subject.
The dependent claims do not integrate the abstract idea. Claims 2, 4-11, and 23-26 narrow the medical information, scores, baselines, or comparisons used in the abstract analysis. Claim 12 adds generic storage, data-network transmission, or automated action. Claims 13-19 add reports, recommendations, rule-set comparison, medical-test scheduling, and future milestones. These are output, communication, scheduling, prediction, or recordkeeping details, not improvements to the computer, bed, air bladder, sensors, or network, and they do not transform an article or require a particular treatment. Accordingly, claims 2, 4-19, and 23-26 and 29 do not integrate the abstract idea into a practical application.
When the claims are considered as a whole, the additional elements amount to a bed-based data source, generic pressure/communication hardware, and generic computer components used to automate the abstract medical evaluation identified in Prong One. The claimed hardware supplies, moves, stores, and outputs information; it does not change how the sensor measures pressure, how the computer operates, or how the communication system functions. Accordingly, claims 21, 22, 27, and 28, and dependent claims 2-19 and 23-29, do not integrate the judicial exception into a practical application under Step 2A, Prong Two.
Step 2B: Inventive Concept Analysis
Since the claims are directed to an abstract idea, we must evaluate whether the additional elements, individually or in combination, provide an inventive concept that amounts to "significantly more" than the abstract idea itself.
The additional elements are: processors and memory in claims 21 and 22; pressure data collected from an air bladder of a mattress of a bed in claims 21 and 22; and, in claims 27 and 28, a bed, mattress air bladder, sensors in fluid communication with the air bladder, data-signal generation, sending data signals, receiving data signals, and processors/computer memory.
These elements do not provide an inventive concept. The processors and memory are used to execute the abstract determinations, classifications, scheduling, periodic access, and tracking steps. The specification describes general computing implementation using processors, memory, storage, input/output devices, mobile devices, servers, clients, and communication networks at [0175]-[0193]. The claims do not recite a new computer architecture, a new memory structure, a new processor operation, or a technical improvement to computer functionality. Thus, the computer components merely apply the abstract idea using generic computer implementation, which does not amount to significantly more under MPEP 2106.05(f).
The air bladder and pressure sensors also do not provide an inventive concept. The claims use pressure data from the mattress air bladder as input to the abstract health-status evaluation. The specification describes pressure-based monitoring of heart rate, respiration, motion, presence, sleep state, and biometric signals at 0049]-[0056], and [0054] states that techniques for monitoring sleep using heart-rate information, respiration-rate information, and other user information were already disclosed in US 20100170043 A1, directed to a sleep monitoring system with a fluid bladder to detect pressure of a subject for health-based determinations. This supports that the claimed pressure-sensing arrangement is used in its ordinary capacity to gather physiological data for later analysis. This data-gathering activity remains well-understood, routine, and conventional and does not supply an inventive concept.
The send/receive limitations do not provide an inventive concept. Under BRI, claim 27, limitations 3 and 5, and claim 28, limitations 4 and 6, merely require sensors to send data signals to a computing system and the computing system to receive those data signals, without reciting any specific protocol, packet structure, network architecture, synchronization, compression, or security mechanism. This is the same generic communication activity held insufficient in buySAFE, where the Federal Circuit stated: That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive. It is also consistent with TLI Communications, where the court found no inventive concept when the physical components performed ordinary functions and the specification described the server in terms of storing, receiving, and extracting data without meaningful technical limitations. Accordingly, the claimed sending and receiving are WURC data-transfer functions, not an inventive communication arrangement, and they do not amount to significantly more than the judicial exception.
The ordered combination does not provide an inventive concept. The claims use each additional element in its ordinary role: the bed and air bladder support the subject, the sensors collect pressure-based physiological data, the communication elements send and receive that data, and the computer stores and processes it to perform the abstract classification, scoring, scheduling, and tracking. The claims do not recite a non-conventional component arrangement, a technical improvement to the bed, sensor, computer, or network,. The combination therefore merely automates the abstract medical evaluation identified in Prong One.
The dependent claims do not change the result. Claims 2, 4-11, and 23-26 narrow the probabilities, thresholds, historical data, baselines, feature types, or other information used in the abstract evaluation. Claim 12 adds generic storing, data-network transmission, or automated post-classification action. Claims 13-19 and 29 add reports, recommendations, rule-set comparison, medical-test scheduling, state-progression data, future milestones, and record-updating or follow-up activity. These limitations add information details, output, communication, advice, scheduling, prediction, or recordkeeping, but do not improve the computer, bed, air bladder, sensors, or network, and do not require a particular treatment.
Accordingly, claims 2, 4-19, and 21-29, individually and as an ordered combination, add only generic computer implementation, ordinary pressure-sensor data gathering, generic data transmission/reception, and post-classification output or administrative activity. They do not amount to significantly more than the judicial exception and are not patent eligible under 35 U.S.C. § 101.
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 (i.e., changing from AIA to pre-AIA ) 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2, 4-21 and 24-29 are rejected under 35 U.S.C. 103 as being unpatentable over US20220175600A1- Gary N. Garcia, and further in view of Visco, V., (2022). Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems. Journal of clinical medicine, 11(3), 524. https://doi.org/10.3390/jcm11030524
Gary N (see PTO-892, Ref. U).
Claim 27.
Gracia teaches, A system comprising:
a bed having a mattress having at least one air bladder; one or more sensors in fluid communication with the air bladder, the one or more sensors configured to:(Garcia, A bed has a mattress; pressure transducer 146 can sense pressure within the pump manifold 143; the pressure sensed within the pump manifold 143 can provide an approximation of the pressure within the respective air chamber that is in fluid communication with the pump manifold 143; Garcia Molina, Abstract, 0041-0042)sense one or more physical phenomena of a subject on the bed comprising at least air pressure of the at least one air bladder caused by weight of the subject on the bed;(Garcia, monitor fluctuations in pressure of the chamber 114A; determine the user's heart rate and/or respiration rate; motion of the user, presence of the user on a surface of the bed 112, weight of the user; a simple pressure detection process can identify an increase in pressure as an indication that the user is present on the bed 112; Garcia, 0043-0044.)
generate first data signals and second data signals based on the one or more physical phenomena sensed by the one or more sensors; and send, to a computing system, the first data signals and the second data signals;(generate data signals based on the sensed physical phenomena; and send, to a computing system, the data signals; data collected by the pressure transducer could be sent to a cloud-based computing system for remote analysis; the classifiers operate to produce p values on new sensor readings; Garcia, Abstract, 0050, 0197)
the computing system comprising one or more processors and computer memory,the computing system configured to:receive the first data signals;(A computing system comprising one or more processors and computer memory; configured to: receive the data signals; Garcia, Abstract)
generate, from the first data signals of a sleep session of the subject, a first feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the first feature vector comprises four features, the four features being: sleep duration, breathing rate, gross-body motion, and heart rate; (generate, from data signals of a sleep-session of the sleeper, a feature vector of features, each feature having a feature value that represents one of the physical phenomena; respiration rate, heart rate, gross-body motion, sleep quality, sleep duration, restful-sleep duration, and time-to-fall-asleep; Garcia, Abstract, 0191, 0203-0204.)
classify, using an acute COVID classifier, the subject into one of a plurality of classified physical states of acute COVID for the sleep session based on the first feature vector; (classifier may use the prediction model 1914 to use the feature vector... to generate a classification of the sleeper... relative to a pre-defined plurality of possible physical states; COVID-19 positive and COVID-19 negative; classifier trained specifically to classify relative to COVID-19 state; Garcia, 0193, 0196, 0206.)
Garcia teaches a COVID-19 classifier using the feature vector to classify the sleeper into COVID-19 positive/negative states.
receive the second data signals; (the classifiers operate to produce p values on new sensor readings; with one p value for each sleep session; Garcia, 0193, 0197)
Garcia’s new sensor readings and one p-value for each sleep session indicate that later sensor data was received for additional classification.
generate, from the second data signals of a later sleep session of the subject, a second feature vector of features, each feature having a feature value that represents one of the one or more physical phenomena, wherein the second feature vector comprises four features, the four features being: sleep duration, breathing rate, gross-body motion, and heart rate;(Sleep sessions can be organized in a number of different ways; the end of one sleep session and the beginning of the next sleep session; generate feature values for the feature vector; respiration rate, heart rate, gross-body motion... sleep duration; Garcia, 0191, 0194, 0203-0204.)and classify, using a . (COVID-19 infections are being tracked, and two states are established by the training COVID-19 positive and COVID-19 negative. Other states and other outcomes may be used; Each state is trained to identify a probability of remaining constant for the next sleep session, or transitioning to the other state; the classifiers operate to produce p values on new sensor readings; Information about symptom progression... expected milestones for exacerbation, duration, and recovery; Garcia, 0196-0197, 0217.)
Garcia teaches the later-classification framework because it trains COVID states, permits other states/outcomes, evaluates next-sleep-session transitions, and produces p-values on new sensor readings. Garcia also teaches symptom-progression information, including exacerbation, duration, recovery, and peak intensity.
Garcia teaches all the limitations above.
However, Garcia does not expressly teach long COVID classifier, classified physical states of long COVID, or long COVID intensity value corresponding to at least one long COVID symptom.
Visco teaches the missing long-COVID disease state and symptom target, as shown by post-COVID-19 syndrome, long COVID, long COVID is a complex syndrome with protracted heterogeneous symptoms, and the WHO definition requiring symptoms usually 3 months from the onset of COVID-19 that last for at least 2 months. Visco identifies symptom classes corresponding to Garcia’s sensed sleep/cardiorespiratory features, including shortness of breath, increased heart rate, sleep disorders, and insomnia. Visco, Abstract and pp. 1-2.
A person of ordinary skill in the art would have combined Garcia with Visco before the effective filing date by configuring Garcia Molina’s disclosed COVID classifier framework to include long COVID as one of Garcia’s Other states and other outcomes, triggering the later long-COVID classification after a COVID-positive state, and using Garcia’s later sleep-session feature vector and p-value/progression outputs to classify Visco’s known post-COVID symptom condition. The reason to combine is that Garcia already performs longitudinal COVID illness monitoring of sleep duration, respiration rate, heart rate, and motion, while Visco identifies long COVID as a post-COVID condition with respiratory, cardiovascular, neurological, and sleep-related symptoms that require personalized treatment and ongoing support. The modification would have predictably resulted in the claimed system because it applies a known clinical post-COVID target to Garcia’s known repeated sleep-session classifier architecture, with no change to Garcia’s sensor hardware or principle of operation.
Note: Claim 28 is rejected for the same reasons as claim 27, as it is very similar and does not present any substantial differences beyond the obvious rationale already applied to claim 27.
Claim 2. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the classified physical state of long COVID comprises positive and negative; (See at least, par. 0005, par. 0037 and par. 0027 Gary N. Garcia)
Claim 4. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the classified physical state of long COVID includes a probability value that the subject in a particular state in the later sleep session or after the later sleep session. (See at least, par. 0005, 0193-0197, 0207)
Claim 5. Garcia and in further view of Visco, V teaches, The system of claim 4, wherein to classify the subject into a physical state for the later sleep session, the computing system is further configured to: compare the probability value against at least one threshold value; (See at least, par. 0005, 0193, 0207)
and select the classified physical state of long COVID based on the comparison of the probability value against the at least one threshold value. (See at least, par. 0005, 0193, 0207)
Claim 6. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein to classify the subject into a classified physical state of long COVID for the sleep session based on the feature vector, the computing system is further configured to analyze historical data for the subject. (See at least, par. 0212)
Claim 7. Garcia and in further view of Visco, V teaches, The system of claim27, wherein the classified physical state of long comprises one of healthy and not-healthy. (See at least, par. 0005)
Claim 8. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein each feature of the first feature vector and the second feature vector is a physical measure of the subject. (See at least, par. 0204, 0005)
Claim 9. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein at least one of the first feature vector and the second feature vector comprises at least five features.. (See at least, par. 0005, 0191, 0204)
Claim 10. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein at least one of the features in one or both of the first feature vector and the second feature vector is an environmental measure of the environment around the subject. (See at least, par. 0005, 0191 and 0204-0205)
Claim 11. Garcia and in further view of Visco, V teaches, The system of claim 10, wherein the environmental measure is a measure of one of the group consisting of ambient temperature, bed temperature, air-quality, and ambient illumination. (See at least, par. 0005, 0191, 0204)
Claim 12. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the computing system is further configured to, responsive to classifying the subject into one of a plurality of classified physical states of long COVID for the sleep session based on the feature vector, perform at least one of the group consisting of storing the classified physical state of long COVID to the computer memory, transmitting the classified physical state of long COVID over a data network, and initiating an automated process based on the classified physical state of long COVID without specific user input. (See at least, par. 0005)
Claim 13. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the computing system is further configured to generate a report of the sleep session and a report of the later sleep session, the report of the sleep session comprising a record of the classified physical state of acute COVID, the report of the later sleep session comprising a record of the classified physical state of long COVID. (See at least, par. 0005, 0193, 0197, 0218-0219, claim 13)
Garcia discloses a report for a sleep session containing a record of the classified physical state, and its repeated monitoring framework produces classifier outputs for each sleep session from new sensor readings. A POSITA would reasonably read Garcia’s report function as applicable to later sleep sessions because Garcia’s classifier produces one p-value per sleep session and operates on new sensor readings, so the same disclosed report-generation function would record the classified state for each later classified session.
Claim 14. Garcia and in further view of Visco, V teaches, The system of claim 13, wherein the report of the sleep session further comprises a record of at least some of the feature values of the first feature vector. (See at least, par. 0005, 0196-0197, claim 14, 0219)
Claim 15. Garcia and in further view of Visco, V teaches, The system of claim 14, wherein: the computing system is further configured to generate, based on the classified physical state of long COVID, a recovery recommendation, the recovery recommendation including human-readable text; (See at least, par. 0005, par. 0197)
, and the report further comprises the human-readable text of the recovery recommendation. (See at least, par. 0005, par. 0197, 0216, 0218)
Claim 16. Garcia and in further view of Visco, V teaches, The system of claim 15, wherein to generate, based on the classified physical state of long COVID, a recovery recommendation, the computing system is further configured to compare the classified physical state of long COVID against a rule-set of recovery recommendations generated by medically-expert users. (See at least, par. 0005, 0218)
Claim 17. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the computing system is further configured to schedule, for the subject, a medical test to confirm the subject is in the classified physical state of long COVID. (See at least, par. 0005, 0221)
Claim 18. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the computing system is further configured to generate state-progression data that include at least one estimation of a future milestone of progression of the physical state of the subject. (See at least, par. 0005, 0217)
Claim 19. Garcia and in further view of Visco, V teaches, The system of claim 18, wherein at least one of the future milestones is from the group consisting of symptom onset, peak-intensity, symptom regression, and virus- free. (See at least, par.0005, 0217)
Claim 21. Garcia teaches, A computer system for classifying a subject based on sensor data, the computer system comprising: (See at least, par. 0004-0005)
one or more processors; (See at least, par. 0004-0005)
and memory storing instructions that, when executed by the processors, cause the processors to perform operations comprising:
accessing, from a first sensor, first sensor data of a subject that records physiological measures of the subject in at least one sleep session, the first sensor data comprising at least pressure data collected by the first sensor from an air bladder of a mattress of a bed supporting a subject in the sleep session; (Garcia Molina, paragraphs 0004, 0027, 0200, 0046, 0047, 0123, 0191, 0203)
Garcia Molina describes a computing system that receives and processes electronic signals representing a user's biological metrics during sleep, where these signals are derived from pneumatic force readings within the bed's air chamber. Garcia Molina discloses a "computing system" configured to "receive the data signals" of a "sleep-session" (accessing... sensor data... in at least one sleep session), where the signals include "biometric signals" such as "heart rate" (records physiological measures) derived from a "pressure signal" generated by a "pressure sensor" detecting force in the "chamber 114A" (comprising at least pressure data... from an air bladder).
determining, based on the first sensor data, the subject has at least a threshold probability of being positive for COVID-19; (Garcia Molina, paragraphs 0005, 0193, 0207, 0208, 0223)
Garcia Molina describes a system that utilizes sensor-derived sleep metrics to calculate a probability score regarding a specific viral infection and compares that score to a limit to classify the user. Garcia Molina discloses a "predictive model" that generates a "probability value" (probability) that the sleeper is ""COVID-19 positive"" (being positive for COVID-19), and the system is configured to "compare the probability value against at least one threshold value" to determine the state (determining... has at least a threshold probability).
scheduling in the future a determination for the subject to determine if the subject begins to demonstrate symptoms of , wherein the determination for the subject is performed using a ; (See at least, par. 0005, 0193-0197,0203, 0217, 0221-0222, 0023, 0223, 0004-0005, 0026), Garcia Molina discloses a state-classifier that classifies the sleeper as COVID-19 positive based on feature vectors and probability thresholds, organizes sleep sessions by future time or bed-event boundaries, trains COVID-19 positive/negative states to determine transition probability for the next sleep session, and operates classifiers on new sensor readings to produce later p-values indicating COVID-19 positive/possible status and symptom progression.
andlater, according to the scheduling, determine, using second sensor data from the first sensor, if the subject begins to demonstrate symptoms of (Garcia, paragraphs 0217, 0223, 0227, 0229, 0231)
Garcia describes a longitudinal monitoring system that schedules daily predictions to track the progression and duration of symptoms associated with a viral infection. Garcia Molina discloses a system that monitors "longitudinal sleep metrics" and calculates a "daily probability" (scheduling in the future a determination), specifically to "track the development of symptoms" and estimate "symptom regression" and "duration" (determine if the subject begins to demonstrate symptoms of long COVID). Furthermore, the system utilizes the "daily" schedule to analyze new data regarding "symptom exacerbation" (later according to the scheduling, determine, using second sensor data... if the subject begins to demonstrate symptoms of long COVID).
Garcia teaches the future scheduled classifier framework because the classifier generates a probability with one p value for each sleep session, sleep sessions may be organized by bed-event boundaries or bounded by a particular time, COVID states are trained for the next sleep session, and the classifiers operate to produce p values on new sensor readings 1908. Garcia Molina further teaches COVID symptom-status output because the report may provide COVID-19 positive or possible and Information about symptom progression... expected milestones for exacerbation, duration, and recovery. Garcia Molina, 0193-0197, pp. 21-22.
However, Garcia Molina does not expressly teach long COVID or a long COVID classifier.
Visco teaches the missing long-COVID disease state and symptom target because it identifies post-COVID-19 syndrome, long COVID, and long COVID is a complex syndrome with protracted heterogeneous symptoms, including shortness of breath, increased heart rate, and sleep disorders. Visco also defines post-COVID-19 syndrome as occurring in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms and that last for at least 2 months. Visco, Abstract and pp. 1-2.
A person of ordinary skill in the art would have combined Garcia with Visco before the effective filing date by configuring Garcia Molina’s classifier to include long COVID as one of Garcia Molina’s disclosed Other states and other outcomes and applying Garcia Molina’s future sleep-session/new-sensor-reading classifier process to Visco’s recognized post-COVID condition. The motivation is that Garcia Molina already uses smart-bed sleep and cardiorespiratory metrics for surveillance and monitoring of infectious illnesses and a COVID-19 predictive model, while Visco identifies persistent post-COVID symptoms needing ongoing monitoring and support; the modification would have predictably produced the claimed long-COVID classifier determination because Garcia Molina already collects sleep duration, respiration rate, heart rate, and motion, which correspond to Visco’s respiratory, cardiovascular, and sleep-disorder manifestations. Garcia Molina, 0223, 0226-0227, p. 24; Visco, Abstract, p. 1.
Claim 24. Garcia and in further view of Visco, V teaches, The system of claim 27, wherein the computing system is further configured to schedule, for the subject, a medical test at a threshold time in the future responsive to classifying the subject into one of the plurality of classified physical states of long COVID for the sleep session. (See at least, 0221)
Claim 25. Garcia and in further view of Visco, V teaches, The system of claim 1, wherein the subject is a user of the system at a time of classification and also a user of the system at previous times when baseline data is collected. (See at least, par. 0029-0030), Garcia directly discloses establishing baseline readings for individual users on a regular basis and then comparing current sensor inputs to this historic information to identify physical state.
Claim 26. Garcia and in further view of Visco, V teaches, The system of claim 25, wherein the baseline data is individualized for the subject and different than other baseline data for other users. (See at least, par. 0030 and 0213), Garcia describes the creation and use of individualized baselines that are specific to each user and different from other users ‘baselines.
Claim 29.
Garcia teaches, The system of claim 21, the operations further comprising: scheduling, for the subject, a medical test to confirm the determination of the long COVID classifier; (Garcia Molina, 0221)
and updating an electronic medical record for the subject with at least one of the first sensor data, the second sensor data, and the determination of the long COVID classifier. (Garcia Molina, 0220)
Claim 22-23 rejected under 35 U.S.C. 103 as being unpatentable over US20220175600A1- Gary N. Garcia, in combination with Visco, V., (2022). Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems. Journal of clinical medicine, 11(3), 524. (see PTO-892, Ref. U). https://doi.org/10.3390/jcm11030524
and further in view of US5642731 – Kehr et al
Claim 22.
Garcia teaches, A computer system for analysis of a progression for (Garcia, par. 0004)
and memory storing instructions that, when executed by the processors, cause the processors to perform operations comprising: periodically: (Gary, par. 0004-0006,0190)
accessing sensor data of a subject that records physiological measures of the subject in at least one sleep session, the sensor data comprising at least pressure data collected from an air bladder of a mattress of a bed supporting a subject in the sleep session; (Gary see at least par.0004- 0005, 0191, 0200, figure 1)
Gary clear teaches physiological measure of the sleeper in sleep session, and sensor data collected by air bladder of a mattress of a bed.
determining, based on the sensor data, a new intensity value for severity of symptoms of long COVID for the subject; (See at least, par. 0024, 0028, 0197)
Gary describe process can alter the user's, using, various features values for the sleeper for example, risk awareness information and symptom severity.
adding the new intensity value of (see at least, par. 0005, 0197, 0213-0214), Garcia discloses storing (adding) each newly determined classification state (intensity value) to computer memory, creating a stored collection of values over time. The system specifically mentions “long-term storage and access,” indicating the values are retained as collection for subsequent analysis, rather than being discarded after use.
and determining, based on the collection of intensity values of (See at least, par. 0217 and 0067), Garcia discloses determining changes in physical state (intensity values) by generating “state-progression data” and specifically determining “where in the progression the sleeper currently is” by comparing current values with historical data.
the new intensity value provided on a numerical scale representative of symptom severity; (Garcia, par. 0027-0028),
Garcia provides numerical data on illness risk and severity, including risk scores, symptom severity, physiological changes, and sleep or respiratory values, to help users assess symptom severity.
adding the new intensity value of subject, each of the collection of intensity values provided on the numerical scale representative of symptom severity;(Garcia, 0213, 0215, 0219)
Garcia provides numerical data, such as risk scores, symptom severity, physiological changes, and sleep or respiratory values.
accessing a collection of medical or wellness interventions Garcia Molina, 0147-0152, 0216, 0220-0221)
Garcia Molina discloses storing and accessing user-related histories (such as purchases, engagement, and app usage) and recording medical or wellness actions (like sleep improvement or scheduling appointments).
and determining, based on (Garcia Molina, 0193, 0213)
Garcia’s prior p-values/feature values are reasonably read as the claimed intensity values.
and the collection of medical or wellness interventions, changes in the intensity values of (Garcia Molina, 0193, 0213-0219)
and Garcia, 0193, 0197, 0213-0219)Garcia discloses offering wellness recommendations and tracking illness progress, including symptom changes and recovery.
While Garcia teaches many limitations see above, however it does not disclose one of the states being long COVID. However, Visco, V, teaches the concept of long-Covid in the following quotation: …the WHO has established a clinical case definition of post COVID-19 syndrome: “it occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms and that last for at least 2 months and cannot be explained by an alternative diagnosis.
A skilled person in the art would combine these teachings to enhance the bed system of Gary N. Garcia with Visco, V long Covid concept as have been defined as recognized sub-condition.
Visco teaches the Missing Element of long COVID, describing it as Post-COVID-19 syndrome or “long hauler” syndrome where Long COVID is a complex syndrome with protracted heterogeneous symptoms including sleep disorders and respiratory manifestations (Abstract).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Garcia with Visco because both references relate to the surveillance and management of COVID-19 and its physiological effects; Garcia provides the methods to monitor longitudinal sleep metrics (para. 0223) while Visco establishes the clinical necessity to develop broad strategies to manage post-COVID-19 symptoms (Abstract). The combination makes the full limitation obvious because Visco’s Post-COVID-19 syndrome is the direct sequelae (Abstract) of the COVID-19 positive state (Garcia, para. 0005) Garcia is already configured to monitor, making long COVID a logical future milestone of progression (Garcia, para. 0005).
A person of ordinary skill in the art would have been motivated to integrate the long COVID symptoms from Visco into the system of Garcia to achieve the benefit of ensuring the system addresses the full lifecycle of the disease, as Visco teaches that patients who experience post-COVID-19 sequelae require personalized treatment as well as ongoing support (Abstract).
Furthermore, the proposed combination is obvious under the flexible approach mandated by KSR because it represents Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results. The system of Garcia was recognized as ready for improvement to address the limitation of monitoring specific long-term outcomes of COVID-19 beyond just acute symptom regression (para. 0005). Applying the known clinical definition and symptoms of long COVID from Visco (as evidenced by the listing of sleep disorders and cardiovascular events in the Abstract) to this known monitoring system yields the predictable result of a bed system that can detect the protracted heterogeneous symptoms (Visco, Abstract) using the existing sensors and feature vector (Garcia, para. 0004).
A PHOSITA would have had a reasonable expectation of success in combining the references because the modification required only ordinary skill and routine experimentation; Garcia's system is already fully enabled to sense respiration rate and heart rate (para. 0226), which are the exact data points required to identify the respiratory manifestations and arrhythmias (Visco, Abstract) associated with Long COVID.
Garcia teaches determining illness/symptom progression from stored subject-specific values, as shown by the remote server 2406 stores the classified physical state to computer memory... for long-term storage and access, using historical data of illness progression... determine where in the progression the sleeper currently is, symptom onset, peak-intensity, symptom regression, and virus-free, and reports including feature values that will help the sleeper understand how serious their symptoms may be. Garcia also teaches recovery actions, including suggestions to increase sleep, eat sufficient food, lower stress, reduce physical activity, using a humidifier, and consider fever-reducing medicine. Garcia Molina, 0215-0217, 0219. However, Garcia Molina does not teach accessing a collection of medical or wellness interventions to determine dates and types of one or more medical or wellness interventions undertaken by the subject.
Kehr teaches the missing intervention-history and effectiveness analysis, as shown by the date and time of entry of the specific clinical information is entered, and correlated with the date and time when the patient has taken or missed a particular medication, recording a variety of clinical information data; such as illness symptoms, side effects, general health ratings, and the effects of the presumptive blood levels of one or more medications can then be assessed, as to their impact on the disease process. Kehr further teaches symptom-specific intensity values over time because the patient records symptom severity and the system displays a graph of how symptom severity is rated over time, where the Y axis can be an index of severity for a particular symptom, and the X axis could be dates and times. Kehr, Abstract, Figs. 2C, 2E, 19, and col. 21-22.
A person of ordinary skill in the art would have combined Garcia and Visco with Kehr by applying Kehr’s known medication/intervention-history and symptom-severity correlation technique to Garcia Molina’s long-term illness-progression monitoring system, as modified by Visco’s long-COVID disease target, so that the system stores long-COVID symptom-intensity values over time, accesses undertaken medical or wellness interventions by date and type, and determines whether those interventions are effective based on changes in the long-COVID intensity values. The reason to combine is that Garcia already generates illness-progression and recovery-recommendation data, Visco teaches that long-COVID patients require personalized treatment as well as ongoing support, and Kehr expressly teaches correlating symptom severity and medication-taking behavior to identify how the patient is responding to medication and whether noncompliance explains lack of symptom improvement. Kehr, col. 21-22; Visco, Abstract. Doing so would have predictably produced the claimed determination because it uses Kehr’s established clinical-response tracking for the same purpose in Garcia’s monitoring environment: determining whether undertaken interventions correspond to improvement, worsening, or lack of improvement in symptom intensity.
Claim 23. Gary N. Garcia and in further view of Visco, in view of Kehr teaches, The computer system of claim 22, the operations further comprising determining the efficacy of a long COVID treatment for the subject based on the changes in the intensity value of long COVID for the subject. (See at least par. 0005, 0023, 0026, 0028, refer claim 22 obvious rejection), Garcia teaches a determination of physical states, and provide recommendation that it is compare against rule-set of recovery recommendation.
Conclusion
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/JOSHUA DAMIAN RUIZ/Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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