DETAILED CORRESPONDENCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This non-final office action on merits is in response to the Patent Application filed on 14 January 2025. Claims 1-20 are pending and considered below.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Applicants claim of priority to previously filed provisional application 63/621694 filed 17 January 2024 is recognized and therefore the instant invention is afforded a priority date of 17 January 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claimed limitations, as per Claims 1, 13, and 16 include the steps of:
accessing a first set of characteristics for a first healthcare provider;
generating a first probability of transmission, with respect to a first disease, using a first machine learning model corresponding to the first disease and based on the first set of characteristics and a first community comprising a plurality of individuals;
determining a current configuration of a first healthcare facility in the first community with respect to the first disease; and
generating an updated configuration for the first healthcare facility with respect to the first disease based on the first probability of transmission
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
Under Step One of the analysis under the Mayo framework, claims 1-15 is/are drawn to methods (i.e., a process), and claims 16-20 is/are drawn to a storage medium (i.e., a machine/manufacture). As such, claims 1-20 is/are drawn to one of the statutory categories of invention.
Under Step 2A Prong One of the analysis under the Mayo framework the claim(s) are determined to recite(s) the judicial exception of accessing a first set of characteristics for a first healthcare provider; generating a first probability of transmission, with respect to a first disease, corresponding to the first disease and based on the first set of characteristics and a first community comprising a plurality of individuals; determining a current configuration of a first healthcare facility in the first community with respect to the first disease; and generating an updated configuration for the first healthcare facility with respect to the first disease based on the first probability of transmission.
This judicial exception is similar to abstract ideas related to certain methods of organizing human activity such as managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions as well as mental processes including concepts performed in the human including observation, evaluation, judgement, or opinion.
Under Step 2A Prong Two of the analysis under the Mayo Framework, the judicial exception expressed as the steps of the instant claims is not integrated into a practical application because the claims only recite one additional element, the element of using a processor or computing system including a local registry or memory to perform the steps of the claimed abstract idea. The processor is recited at a high-level of generality (i.e., as a generic processor performing generic computer functions to perform the claimed steps of the invention), and therefore the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element of performing the inventive steps with a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus the claimed invention is directed to an abstract idea without a practical application.
Under step 2B of the Mayo analysis framework the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of performing the steps with a computer processor, a display module, and a memory storing machine executable instructions represents insignificant data gathering and data processing steps requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. Applicant’s published written description paragraph [184] recites “computing device 1500 may be implemented using virtual device(s), and/or across a number of devices (e.g., in a cloud environment). In some embodiments, the computing device 1500 corresponds to or implements a transmission prediction system, such as the transmission prediction system 125 of FIG. 1. In some embodiments, the computing device 1500 corresponds to or implements a feedback system, such as the feedback component 440 of FIG. 4. In some embodiments, the computing device 1500 corresponds to or implements a machine learning system, such as the machine learning system,” written description paragraph [185] recites “computing device 1500 includes a CPU 1505, memory 1510, a network interface 1525, and one or more I/O interfaces 1520. Though not included in the depicted example, in some embodiments, the computing device 1500 also includes one or more storages. In the illustrated embodiment, the CPU 1505 retrieves and executes programming instructions stored in memory 1510, as well as stores and retrieves application data residing in memory 1510 and/or storage (not depicted). The CPU 1505 is generally representative of a single CPU and/or GPU, multiple CPUs and/or GPUs, a single CPU and/or GPU having multiple processing cores,” written description paragraph [187] recites “memory 1510 includes a feature component 1550, a training component 1555, an inferencing component 1560, and a feedback component 1565, which may perform one or more embodiments discussed above. Although depicted as discrete components for conceptual clarity, in embodiments, the operations of the depicted components (and others not illustrated) may be combined or distributed across any number of components. Further, although depicted as software residing in memory 1510, in embodiments, the operations of the depicted components (and others not illustrated) may be implemented using hardware, software, or a combination of hardware and software,” written description paragraph [188] recites “feature component 1550 may generally be used to identify, extract, and/or generate feature data from transmission and/or vaccination data, as discussed above. For example, the feature component 1550 may evaluate provider data and/or community data (e.g., provider data 110 and/or community data 115 of FIG. 1, input data 205 of FIG. 2, input data 305 of FIG. 3, input data 405 of FIG. 4, and/or training data 505 of FIG. 5) to generate model input (e.g., a feature tensor) that encodes this information for more efficient machine learning,” and written description paragraph [189] recites “training component 1555 may be used to train one or more machine learning models (such as machine learning models 1585) based on historic transmission and vaccination data, as discussed above. The inferencing component 1560 may be used to generate transmission predictions by processing provider and/or community data using one or more machine learning models (such as machine learning models 1585), as discussed above. The feedback component 1565 may be used to collect and/or generate feedback based on transmission outcomes, allowing the feedback component 1565 and/or the training component 1555 to update the machine learning models continuously or periodically.” Thus the claimed inventive steps are performed by generic or general purpose computing systems executing well known and understood instructions and processes which do not comprise significantly more than a known computing system, or comprise improvements to another technological field.
Further, as per MPEP 2106, and TLI Communications LLC v. AV Automotive LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) ("It is well-settled that mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea") and as per Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) ("An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer") simply performing the steps of an abstract idea by a computing apparatus does not make an inventive concept statutorily eligible. Therefore, it is clear from Applicants’ specification that the elements and modules in the claims require no more than a generic computer (e.g., a general-purpose computing device) to perform generic computer functions (e.g., accessing, transmitting/receiving, sorting, and storing data) that are well-understood, routine and conventional activities previously known in the industry. None of the limitations, considered as a whole and as an ordered combination provide eligibility, because the steps of the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer, generally link the abstract idea to a particular technological environment or field of use, and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering).
Dependent claims 2-12, 14-15, and 17-20 are directed to the judicial exception as explained above for independent Claims 1, 13, and 16 and are further directed to limitations directed to the implementation of transmission rate determinations relative to vaccination rates, historical transmission rates, updating staffing configurations, determining transmission probabilities and associated comorbidity conditions, determining health care provider elements as well as allocation of supplies, healthcare facility configurations. These limitations or processes are considered to be executed by the general purpose computing system as explained above, and therefore do not result in the claimed invention being directed to a practical application or comprise significantly more than the identified abstract idea.
Dependent claims 2-12, 14-15, and 17-20 do not add more to the abstract idea of independent Claims 1, 13, and 16 and therefore are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to the claims from which they depend.
Eligibility Under 35 USC 101
As noted above in the 101 rejection the instant claims as currently rendered result in the instant invention being determined to not be eligible under 35 USC 101. However, Examiner has examined and analyzed the written description in detail and the written description is replete with a wide variety of technically specific operations, which if incorporated into the claims in detail, will result in the 101 being removed. Paragraphs [34]-[46] disclose a range of procedures related to the prediction of diseases including the identification of alternative providers, [56]-[64] include a wide range of transmission prediction processes, [154]-[163] include a wide range of model refinement processes, [164]-[173] include a wide range of transmission prediction operations and as well the written description is replete with technically related information. Examiner recommends applicants include specifically detailed and technically related processes as extracted from the written description and as a result the rejection of all pending claims under 35 USC 101 will be withdrawn.
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) 1, 2, 6, 11, 12-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Son et al. (20220102012) in view of Macoviak (20250378923) and Gopalakrishnan et al. (20220384048).
Claims 1 and 16: Son discloses a method and storage medium comprising:
generating a first probability of transmission ([93 “infectious risk for each agent is calculated via the disease propagation model with droplet transmission model, airborne transmission model, residential transmission model and off-campus transmission model during departure from the current indoor facility,”]), with respect to a first disease ([89-93]), using a first machine learning model ([8-10, 91 “disease propagation module 708 is comprised of modules that calculate infectious risk probability 724, the current disease status 726, and a symptom onset analyzer 728. Whenever an agent leaves for a new destination 722, the disease propagation module 708 calculates the infectious risk probability 724 and determines the agent's current disease status,” 93 “Anylogic simulation model,” 98 “Indoor space model,” 101 “Droplet transmission model,” 105 “Airborne Transmission Model,” 112 Test Accuracy Model,”]) corresponding to the first disease and based on the first set of characteristics and a first community comprising a plurality of individuals ([75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 95 “latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,” 99 “indoor space contact model to mimic student agents' contracts, exposure, and physical distance violation behavior during the entrance and exit operations from a classroom-type indoor facility. The pedestrian dynamics in classroom-like facilities (e.g., classroom, meeting room, office room, auditorium, dance class) were modeled and analyzed using an agent-based modeling approach that took into account physical distancing, seat assignment, and entrance and exit policies,” 101, 102, 145 “estimating the infectious rate based on every agent's interaction rather than group likelihood. In addition to the internal disease propagation cycle of the university community (e.g., cohorts, roommates), the external infectious risk based on zip code specific R.sub.0 value was taken into account since this university community also interacts with the local population, which is difficult to trace but can have a significant impact on campus transmission,”]);
Son does not explicitly disclose however Macoviak discloses:
accessing a first set of characteristics for a first healthcare provider ([82 “human provider may include, but is not limited to, a primary care physician, a physician specialist, a nurse, a physician's assistant, a pharmacist, a healthcare insurer, a telemedicalist, a telemedicologist, an EMT, a community public health specialist,” 86 “computer program includes a module for telecommunications between the device, or a user thereof, and a live, licensed healthcare provider. In some embodiments, a computer program includes a module for applying a diagnostic or therapeutic analysis,” 92 “patient initiates contact with a remotely located telemedical care provider (including, for example, a telemedicologist or telemedicalist) 1. The telemedical care provider interviews the subject via a telecommunications ling to determine if a medical emergency exists 2 and whether or not to activate EMS,” 149 “system for providing remote medical diagnosis and therapy to a subject includes a stationary device for collecting biosensor data 84 and wirelessly transmitting the data to a telemedical care provider,” 150, 151, 299 “administering telemedical, outpatient healthcare to a subject comprising: receiving a request for care for the subject, wherein the subject is under the care of a licensed primary healthcare payer, or healthcare provider facility, group, or individual maintaining an electronic health record for the subject,” 376 “risk assessments may be performed by a risk profiling module. The risk profiling module may be a processor-executed digital media-containing software using machine-learning techniques to predict a health or economic risk for the subject or the population. The machine learning techniques may include those techniques previously described, such as nearest neighbor, k-nearest neighbor, decision trees, additive logistics, multivariant adaptive regression splines, support vector machines,”]).
Therefore it would be obvious for Son wherein accessing a first set of characteristics for a first healthcare provider as disclosed by Macoviak results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Son does not explicitly disclose however Gopalakrishnan discloses
determining a current configuration of a first healthcare facility in the first community with respect to the first disease ([26 “predict the dynamics of infectious diseases, especially in the case of epidemics or pandemics, and characterize risks for populations and risks of changes in governmental and private organization policies with regard to the disease, is of utmost importance from a health safety and economics perspective,” 29 “modeling seasonality of an infectious disease spread, they do not explicitly model localized information that adds contextual knowledge vital to modeling the spread of the disease through the population. What is meant by contextual knowledge is the context of a particular locality relative to other neighboring localities,” 34 “provide mechanisms for evaluating the input data with an epidemiological computer model that assumes a community spread of the infectious disease, and a second model that assumes no community spread, or a fixed number of instances of the infectious disease, and determining which hypothesis is most accurate to the real-world data to thereby eliminate modeling assuming community spread when the data points are merely noise due to imported cases,” 38, 39, 40, 41, 66 “regions are defined on a level smaller than country or national levels of geographic size and thus, facilitate a local evaluation of infectious disease dynamics. The regions may be predetermined according to geographic and/or political borders, e.g., counties, cities, states, territories, or the like. By organizing the data according to predetermined local region, hyperlocal infectious disease modeling is made possible that facilitates identifying similarities between relatively smaller populations,” 71, 84 “target region may not have experienced a community spread of the infectious disease but may have incidents reported due to individuals from other regions temporarily traveling to the target region, e.g., vacationers, individuals “just passing through”, or the like. These incidents will get reported in the target region but will not represent a community spread,”]); and
generating an updated configuration for the first healthcare facility ([34]) with respect to the first disease based on the first probability of transmission ([5 “multiplied by the probability of disease transmission in a contact between as susceptible and infections individual. The transmission rate between compartment I and compartment R is proportional to the number of infectious individuals such that the probability of an infectious individual recovering y in any time interval t is simply ydt, e.g., if an individual is infectious for an average time period D, then y=I/D,” 26 “predict the dynamics of infectious diseases, especially in the case of epidemics or pandemics, and characterize risks for populations and risks of changes in governmental and private organization policies with regard to the disease, is of utmost importance from a health safety and economics perspective,” 29 “modeling seasonality of an infectious disease spread, they do not explicitly model localized information that adds contextual knowledge vital to modeling the spread of the disease through the population. What is meant by contextual knowledge is the context of a particular locality relative to other neighboring localities,” 40, 41, 66 “regions are defined on a level smaller than country or national levels of geographic size and thus, facilitate a local evaluation of infectious disease dynamics. The regions may be predetermined according to geographic and/or political borders, e.g., counties, cities, states, territories, or the like. By organizing the data according to predetermined local region, hyperlocal infectious disease modeling is made possible that facilitates identifying similarities between relatively smaller populations,” 138 “statistical significance computer model 260 on the predictions of the epidemiological computer model 110 and the null hypothesis model 252 is to perform a counterfactual analysis based on the observation that infectious disease dynamics are stochastic, i.e., have a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely. Before the start of a community spread in a region, imported cases from neighboring regions will show up and be reported,” 178 “appreciated that this granular and temporal modeling of isolation features may be performed for various levels of geographic regions. For example, different AI-ML computer models 680 may be provided for different populations for different geographical regions, such as cities, counties, states, countries, territories, continents, etc., i.e., any desired population,”]).
Therefore it would be obvious for Son determining a current configuration of a first healthcare facility in the first community with respect to the first disease and generating an updated configuration for the first healthcare facility with respect to the first disease based on the first probability of transmission as disclosed by Gopalakrishnan results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claims 2, 14, and 17: Son discloses the method of claims 1 and 14 and storage medium claim 17 and Son further discloses:
determining a vaccination rate of the plurality of individuals with respect to the first disease ([139, 140 “disease propagation would be under control with a vaccination rate of 40% under 0.5% initial infected case and a vaccination rate of 70% under 5% initial infectious case. And for the re-open stage 2 (FIG. 10B) suggests that 70% to 80% vaccination rate is necessary to minimize the disease transmission risk,” 142-146]); and
determining a historical transmission rate of the first community with respect to the first disease ([9 “disease propagation model represents students' health status (viz. susceptible, pre-symptomatic, asymptomatic, quarantine, isolation, and recovered) based on different factors such as the number of infected students attending the class or living in a dorm, classroom/dorm features (e.g. size, humidity, ventilation), probabilities of disease transmissions (e.g. droplet, airborne) in classrooms based on a dose-response model, probabilities of disease transmissions in dorms based on cohort studies, and mask wearing condition and effectiveness. The airborne transmission model employed in the analysis is based on models that consider classroom volume, mask effectiveness, and ventilation condition as variables. The droplet transmission model employed in the analysis considers the contact times and frequencies in 0-3 feet and 3-6 feet,” 110 “basic reproduction number, R.sub.0, is widely used as a public health index representing the average number of secondary cases introduced by one infectious agent in a community,” 139 “value based on the secondary transmission rate combining with the potential infection to external community during the off-campus activity,” 145 “internal disease propagation cycle of the university community (e.g., cohorts, roommates), the external infectious risk based on zip code specific R.sub.0 value was taken into account since this university community also interacts with the local population, which is difficult to trace but can have a significant impact on campus transmission,”]).
Claims 6 and 20: Son in view of Macoviak and Gopalakrishnan disclose the method of claims 5 and 19 above, and Son further discloses:
generating a second probability of transmission for the second healthcare provider with respect to the first disease using the first machine learning model ([8-10, 91 “disease propagation module 708 is comprised of modules that calculate infectious risk probability 724, the current disease status 726, and a symptom onset analyzer 728. Whenever an agent leaves for a new destination 722, the disease propagation module 708 calculates the infectious risk probability 724 and determines the agent's current disease status,” 93 “Anylogic simulation model,” 98 “Indoor space model,” 101 “Droplet transmission model,” 105 “Airborne Transmission Model,” 112 Test Accuracy Model,” 75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 95 “latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,” 99 “indoor space contact model to mimic student agents' contracts, exposure, and physical distance violation behavior during the entrance and exit operations from a classroom-type indoor facility. The pedestrian dynamics in classroom-like facilities (e.g., classroom, meeting room, office room, auditorium, dance class) were modeled and analyzed using an agent-based modeling approach that took into account physical distancing, seat assignment, and entrance and exit policies,” 101, 102, 145 “estimating the infectious rate based on every agent's interaction rather than group likelihood. In addition to the internal disease propagation cycle of the university community (e.g., cohorts, roommates), the external infectious risk based on zip code specific R.sub.0 value was taken into account since this university community also interacts with the local population, which is difficult to trace but can have a significant impact on campus transmission,”]); and
determining that the second probability of transmission is lower than the first probability of transmission ([75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 82, 84 “classroom disease transmission function is configured to detect if there is any infectious agent presenting in the classroom and calculate the infectious risk p for other agents in Susceptible State after attending the class,” 87-93, 117 “GIS information for routing algorithm calculation, the physical size of indoor space, ventilation rate for airborne transmission calculation, building capacity for occupancy visualization on the GIS map, and it also stores the disease propagation information of human agents who present at this location,” 145 “agent-based campus-wide disease propagation simulation model that could be utilized as a tool for policy analysis and prediction. The model focused on the three critical factors that affect the university's re-opening stage: the current infectious case, student engagement and interaction, and vaccination status,”]) Examiner Note: Examiner under a broadest reasonable interpretation interprets the disclosures of Son to disclose the collection of a wide variety of disease propagation information which relates to the transmission of infection information and the resulting provision of information related to the issue.
Son does not explicitly disclose, however Macoviak discloses:
accessing a second set of characteristics for the second healthcare provider ([82 “Healthcare data may include data and information originating from an encounter with a healthcare service provider, either in person or remotely using telecommunications systems, and either with a human provider and/or an autonomous system including an artificial intelligence-powered apparatus or system. A human provider may include, but is not limited to, a primary care physician, a physician specialist, a nurse, a physician's assistant, a pharmacist, a healthcare insurer, a telemedicalist, a telemedicologist, an EMT, a community public health specialist,” 86 “includes a module for identifying and/or verifying the credentials of healthcare providers,” 149 “system for providing remote medical diagnosis and therapy to a subject includes a stationary device for collecting biosensor data 84 and wirelessly transmitting the data to a telemedical care provider,” 150, 151 “system for providing remote medical diagnosis and therapy to a subject includes an ultraportable device for collecting biosensor data 92 and wirelessly transmitting the data to a telemedical care provider 94. In this embodiment, the device for collecting biosensor data is in communication with a portable diagnostic module 93 selected for its utility in addressing the risks facing a particular subject, family, or population,”]);
Therefore it would be obvious for Son to access a second set of characteristics for the second healthcare provider as disclosed by Macoviak results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claims 11 and 15: Son in view of Macoviak and Gopalakrishnan disclose the method of claims 1 and 13 above and Gopalakrishnan further discloses wherein the first set of characteristics comprise an indication as to whether the first healthcare provider is vaccinated against the first disease ([41 “evaluations of risk on a hyperlocal level allow community leaders, political leaders, healthcare authorities, or the like, to make more informed decisions regarding the populations over which they have authority based on local needs,” 42 “advantages include hyperlocal level accurate predictions of infectious disease state, dynamics, and predicted risk. In addition, the illustrative embodiments provide mechanism for automatic intervention detection to detect changes in disease dynamics, such as due to government policies, changes in human behavior, and changes in therapeutic usage. The illustrative embodiments provide mechanism for automatically learning and tuning parameters and hyperparameters of the compartmental computer model,” 57 “present invention may provide an output that ultimately assists human beings in evaluating and predicting the spread of infectious diseases and provides assistance for evaluating various interventions that may be implemented and predictions of their efficacy in reducing the spread of the infectious disease,” 85 “perform identification of inflection points in the data that are more representative of actual changes in the dynamics of the infectious disease spread, which may correspond to instituted policies, lifting of restrictions, distribution of a vaccine, implementation of other types of interventions, or other outside influences that change the number of incidents and/or fatalities,” 100 “interventions, where “interventions” are any purposeful manipulations of the infectious disease state or characteristics of the population, e.g., mobility, to change the dynamics of the infectious disease, e.g., government mandates, self-isolation measures, distribution of a vaccine,” 104 “user may wish to determine what the predicted disease state and population state may be if particular interventions are implemented and/or lifted, e.g., implementing a shelter-in-place order, lifting a restriction on bar/restaurant capacity, increasing a roll-out of a vaccine,”]) .
Therefore it would be obvious for Son wherein the first set of characteristics comprise an indication as to whether the first healthcare provider is vaccinated against the first disease as disclosed by Gopalakrishnan results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim 12: Son in view of Macoviak and Gopalakrishnan disclose the method of claims 1 above and Son further discloses:
generating a second probability of transmission for the first healthcare provider with respect to a second disease using a second machine learning model corresponding to the second disease ([8-10, 91 “disease propagation module 708 is comprised of modules that calculate infectious risk probability 724, the current disease status 726, and a symptom onset analyzer 728. Whenever an agent leaves for a new destination 722, the disease propagation module 708 calculates the infectious risk probability 724 and determines the agent's current disease status,” 93 “Anylogic simulation model,” 98 “Indoor space model,” 101 “Droplet transmission model,” 105 “Airborne Transmission Model,” 112 Test Accuracy Model,” 75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 95 “latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,” 99 “indoor space contact model to mimic student agents' contracts, exposure, and physical distance violation behavior during the entrance and exit operations from a classroom-type indoor facility. The pedestrian dynamics in classroom-like facilities (e.g., classroom, meeting room, office room, auditorium, dance class) were modeled and analyzed using an agent-based modeling approach that took into account physical distancing, seat assignment, and entrance and exit policies,” 101, 102, 145 “estimating the infectious rate based on every agent's interaction rather than group likelihood. In addition to the internal disease propagation cycle of the university community (e.g., cohorts, roommates), the external infectious risk based on zip code specific R.sub.0 value was taken into account since this university community also interacts with the local population, which is difficult to trace but can have a significant impact on campus transmission,”]); and
generating an updated configuration for the first healthcare facility with respect to the second disease based on the second probability of transmission ([75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 82, 84 “classroom disease transmission function is configured to detect if there is any infectious agent presenting in the classroom and calculate the infectious risk p for other agents in Susceptible State after attending the class,” 87-93, 117 “GIS information for routing algorithm calculation, the physical size of indoor space, ventilation rate for airborne transmission calculation, building capacity for occupancy visualization on the GIS map, and it also stores the disease propagation information of human agents who present at this location,” 145 “agent-based campus-wide disease propagation simulation model that could be utilized as a tool for policy analysis and prediction. The model focused on the three critical factors that affect the university's re-opening stage: the current infectious case, student engagement and interaction, and vaccination status,”]).
Claim 13: Son discloses a method, comprising:
generating a first probability of transmission ([93 “infectious risk for each agent is calculated via the disease propagation model with droplet transmission model, airborne transmission model, residential transmission model and off-campus transmission model during departure from the current indoor facility,”]), with respect to a first disease ([89-93]), using a first machine learning model ([8-10, 91 “disease propagation module 708 is comprised of modules that calculate infectious risk probability 724, the current disease status 726, and a symptom onset analyzer 728. Whenever an agent leaves for a new destination 722, the disease propagation module 708 calculates the infectious risk probability 724 and determines the agent's current disease status,” 93 “Anylogic simulation model,” 98 “Indoor space model,” 101 “Droplet transmission model,” 105 “Airborne Transmission Model,” 112 Test Accuracy Model,”]) corresponding to the first disease and based on the first set of characteristics and a first community comprising a plurality of individuals ([75 “disease propagation dataset is configured to store the different states of disease (transition between each health status) and parameters (incubation period and viral shedding amount). In an exemplary embodiment, the disease propagation dataset is described with respect to FIG. 6. The disease propagation state-chart determines the state of each data point in the set as follows: At “Initialization” 610, the dataset analysis of the disease propagation state commences,” 95 “latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,” 99 “indoor space contact model to mimic student agents' contracts, exposure, and physical distance violation behavior during the entrance and exit operations from a classroom-type indoor facility. The pedestrian dynamics in classroom-like facilities (e.g., classroom, meeting room, office room, auditorium, dance class) were modeled and analyzed using an agent-based modeling approach that took into account physical distancing, seat assignment, and entrance and exit policies,” 101, 102, 145 “estimating the infectious rate based on every agent's interaction rather than group likelihood. In addition to the internal disease propagation cycle of the university community (e.g., cohorts, roommates), the external infectious risk based on zip code specific R.sub.0 value was taken into account since this university community also interacts with the local population, which is difficult to trace but can have a significant impact on campus transmission,”]);
Son does not explicitly disclose however Makoviak discloses:
accessing a first set of characteristics for a first healthcare provider ([82 “human provider may include, but is not limited to, a primary care physician, a physician specialist, a nurse, a physician's assistant, a pharmacist, a healthcare insurer, a telemedicalist, a telemedicologist, an EMT, a community public health specialist,” 86 “computer program includes a module for telecommunications between the device, or a user thereof, and a live, licensed healthcare provider. In some embodiments, a computer program includes a module for applying a diagnostic or therapeutic analysis,” 92 “patient initiates contact with a remotely located telemedical care provider (including, for example, a telemedicologist or telemedicalist) 1. The telemedical care provider interviews the subject via a telecommunications ling to determine if a medical emergency exists 2 and whether or not to activate EMS,” 149 “system for providing remote medical diagnosis and therapy to a subject includes a stationary device for collecting biosensor data 84 and wirelessly transmitting the data to a telemedical care provider,” 150, 151, 299 “administering telemedical, outpatient healthcare to a subject comprising: receiving a request for care for the subject, wherein the subject is under the care of a licensed primary healthcare payer, or healthcare provider facility, group, or individual maintaining an electronic health record for the subject,” 376 “risk assessments may be performed by a risk profiling module. The risk profiling module may be a processor-executed digital media-containing software using machine-learning techniques to predict a health or economic risk for the subject or the population. The machine learning techniques may include those techniques previously described, such as nearest neighbor, k-nearest neighbor, decision trees, additive logistics, multivariant adaptive regression splines, support vector machines,”]).
Therefore it would be obvious for Son wherein accessing a first set of characteristics for a first healthcare provider as disclosed by Makoviak results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Son does not explicitly disclose however Gopalakrishnan discloses:
determining updated transmission data indicating whether the first healthcare provider contracted the first disease in the first community ([26 “predict the dynamics of infectious diseases, especially in the case of epidemics or pandemics, and characterize risks for populations and risks of changes in governmental and private organization policies with regard to the disease, is of utmost importance from a health safety and economics perspective,” 29 “modeling seasonality of an infectious disease spread, they do not explicitly model localized information that adds contextual knowledge vital to modeling the spread of the disease through the population. What is meant by contextual knowledge is the context of a particular locality relative to other neighboring localities,” 34 “provide mechanisms for evaluating the input data with an epidemiological computer model that assumes a community spread of the infectious disease, and a second model that assumes no community spread, or a fixed number of instances of the infectious disease, and determining which hypothesis is most accurate to the real-world data to thereby eliminate modeling assuming community spread when the data points are merely noise due to imported cases,” 38, 39, 40, 41, 66 “regions are defined on a level smaller than country or national levels of geographic size and thus, facilitate a local evaluation of infectious disease dynamics. The regions may be predetermined according to geographic and/or political borders, e.g., counties, cities, states, territories, or the like. By organizing the data according to predetermined local region, hyperlocal infectious disease modeling is made possible that facilitates identifying similarities between relatively smaller populations,” 71, 84 “target region may not have experienced a community spread of the infectious disease but may have incidents reported due to individuals from other regions temporarily traveling to the target region, e.g., vacationers, individuals “just passing through”, or the like. These incidents will get reported in the target region but will not represent a community spread,”]); and
updating one or more parameters of the first machine learning model based on comparing the first probability of transmission and the updated transmission data ([5 “multiplied by the probability of disease transmission in a contact between as susceptible and infections individual. The transmission rate between compartment I and compartment R is proportional to the number of infectious individuals such that the probability of an infectious individual recovering y in any time interval t is simply ydt, e.g., if an individual is infectious for an average time period D, then y=I/D,” 26 “predict the dynamics of infectious diseases, especially in the case of epidemics or pandemics, and characterize risks for populations and risks of changes in governmental and private organization policies with regard to the disease, is of utmost importance from a health safety and economics perspective,” 29 “modeling seasonality of an infectious disease spread, they do not explicitly model localized information that adds contextual knowledge vital to modeling the spread of the disease through the population. What is meant by contextual knowledge is the context of a particular locality relative to other neighboring localities,” 40, 41, 66 “regions are defined on a level smaller than country or national levels of geographic size and thus, facilitate a local evaluation of infectious disease dynamics. The regions may be predetermined according to geographic and/or political borders, e.g., counties, cities, states, territories, or the like. By organizing the data according to predetermined local region, hyperlocal infectious disease modeling is made possible that facilitates identifying similarities between relatively smaller populations,” 138 “statistical significance computer model 260 on the predictions of the epidemiological computer model 110 and the null hypothesis model 252 is to perform a counterfactual analysis based on the observation that infectious disease dynamics are stochastic, i.e., have a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely. Before the start of a community spread in a region, imported cases from neighboring regions will show up and be reported,” 178 “appreciated that this granular and temporal modeling of isolation features may be performed for various levels of geographic regions. For example, different AI-ML computer models 680 may be provided for different populations for different geographical regions, such as cities, counties, states, countries, territories, continents, etc., i.e., any desired population,”]).
Therefore it would be obvious for Son determining updated transmission data indicating whether the first healthcare provider contracted the first disease in the first community and updating one or more parameters of the first machine learning model based on comparing the first probability of transmission and the updated transmission data as disclosed by Gopalakrishnan results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim(s) 3, 7, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Son et al. (20220102012) in view of Macoviak (20250378923) and Gopalakrishnan et al. (20220384048) and in further view of Jain (11,127,506).
Claim 3: Son in view of Macoviak and Gopalakrishnan disclose the method of claim 1, and Son does not disclose, however Jain discloses wherein the first machine learning model was trained based on historical transmission data for the first disease (52:52-67, 53:1-37 “predictive agent 640 can also predict whether the user 102a is currently infected or, in some cases, whether the user 102a has previously been infected. To do this, the predictive agent 640 can use the collected data from various sources (e.g., health records 606, user input such as EMA data 602, sensor data 604, treatment data 608, user demographic data, and so on), using collected data collected for a current time period (e.g., the current day, week, etc.) as well as collected data for prior time periods and/or long-term historical data,” 124:38-67 “computer system 110 can predict a user's future activities based on various inputs, such as historical behavior patterns for the user, context data for the user, behavior of other users in the community, calendar data for the user….computer system 110 can also predict a potential future action by the user using historical information about the user, such as prior tracked actions showing prior visits to a particular location or other information indicating a behavior pattern for the user. For example, a user's location history may show that the user has visited a shopping mall several times in the last month,” 125:1-5).
Therefore it would be obvious for Son wherein the first machine learning model was trained based on historical transmission data for the first disease as disclosed by Jain results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim 7: Son in view of Macoviak and Gopalakrishnan disclose the method of claim 5, and Son does not disclose, however Jain discloses:
determining that the first probability of transmission satisfies a minimum threshold ([105:37-67 “disease transmission score for a location, where the score quantifies a risk level for or predicted level of disease transmission at the location, given the characteristics of the location, the characteristics and disease prevalence in the community, and the behaviors of individuals in the community and/or at the location. The computer system 110 can then evaluate the disease transmission scores for different locations by comparing them with a predetermined threshold. Disease transmission scores having values that exceed a threshold (e.g., indicating disease transmission risk above a certain level) can be identified as disease transmission hotspots,” 106:12-36 “process 1900 includes providing output indicating the one or more disease-related predictions for the community (step 1910). For example, the predictions can be provided to individuals that live in the community, to warn them of disease transmission hotspots, to indicate which disease preventions measures are most likely to be effective in their community or in the locations they visit,”) ;
determining a first set of one or more comorbidities of the first healthcare provider with respect to the first disease (49:12-54 “EMR/EHR data 606 can be used for various purposes, including to determine baseline measures for a user and to determine existing conditions and comorbidities, which the computer system 110 can use to make predictions and estimates (e.g., to estimate the susceptibility or risk that a disease poses for the user 102a) and to adjust decisions for the user (e.g., about which testing kit, vaccine, module 112, medication, digital therapeutics, monitoring procedures, or other intervention is recommended or provided by the computer system,” 49:39-54 “community data 610 can include data indicating population levels, demographic information, types of locations present in the region, mapping data, and so on. The community data can also include measures that aid and assist in determining exposure risks, infection or exposure hotspots, and general disease outbreak related conditions,” 54:5-44 “determine a user's susceptibility to the disease, the appropriate model 612 may use (e.g., receive inputs indicating) various types of data about a user, such as a user's demographic characteristics (e.g., age, sex, etc.), the user's physiological data (e.g., blood pressure, height, weight, respiration rate, resting heart rate, etc.), the user's behavioral data (e.g., current and historical records for exercise, sleep, nutrition, the user's medical records (e.g., data indicating comorbidities and chronic conditions, such as diabetes, heart disease, lung disease, COPD, cancer, etc.),” 58:5-12 “detecting signs and symptoms of viral infection worsening or Improving; (2) detecting signs of vaccines being effective or ineffective; and (3) detecting signs of morbidities and co-morbidities,”);
determining a second set of one or more comorbidities of the second healthcare provider with respect to the first disease (49:12-54 “EMR/EHR data 606 can be used for various purposes, including to determine baseline measures for a user and to determine existing conditions and comorbidities, which the computer system 110 can use to make predictions and estimates (e.g., to estimate the susceptibility or risk that a disease poses for the user 102a) and to adjust decisions for the user (e.g., about which testing kit, vaccine, module 112, medication, digital therapeutics, monitoring procedures, or other intervention is recommended or provided by the computer system,” 49:39-54 “community data 610 can include data indicating population levels, demographic information, types of locations present in the region, mapping data, and so on. The community data can also include measures that aid and assist in determining exposure risks, infection or exposure hotspots, and general disease outbreak related conditions,” 54:5-44 “determine a user's susceptibility to the disease, the appropriate model 612 may use (e.g., receive inputs indicating) various types of data about a user, such as a user's demographic characteristics (e.g., age, sex, etc.), the user's physiological data (e.g., blood pressure, height, weight, respiration rate, resting heart rate, etc.), the user's behavioral data (e.g., current and historical records for exercise, sleep, nutrition, the user's medical records (e.g., data indicating comorbidities and chronic conditions, such as diabetes, heart disease, lung disease, COPD, cancer, etc.),” 58:5-12 “detecting signs and symptoms of viral infection worsening or Improving; (2) detecting signs of vaccines being effective or ineffective; and (3) detecting signs of morbidities and co-morbidities,”); Examiner Note: Examiner under a broadest reasonable interpretation interprets the disclosures of Jain with respect to the implementation of a wide variety of processes for detecting comorbidities to disclose the collection of first and second sets of data to determine comorbidities with respect to diseases.
determining to add the second healthcare provider to the set of healthcare providers based on the first and second sets of comorbidities (13:37-50 “determined disease exposure score and (ii) sensor data indicating one or more physiological measurements for the second user; and providing the infection likelihood metric for the second user to a device associated with the second user or a device associated with a healthcare provider for the second user,” 49:12-54 “EMR/EHR data 606 can be used for various purposes, including to determine baseline measures for a user and to determine existing conditions and comorbidities, which the computer system 110 can use to make predictions and estimates (e.g., to estimate the susceptibility or risk that a disease poses for the user 102a) and to adjust decisions for the user (e.g., about which testing kit, vaccine, module 112, medication, digital therapeutics, monitoring procedures, or other intervention is recommended or provided by the computer system,” 49:39-54 “community data 610 can include data indicating population levels, demographic information, types of locations present in the region, mapping data, and so on. The community data can also include measures that aid and assist in determining exposure risks, infection or exposure hotspots, and general disease outbreak related conditions,” 54:5-44 “determine a user's susceptibility to the disease, the appropriate model 612 may use (e.g., receive inputs indicating) various types of data about a user, such as a user's demographic characteristics (e.g., age, sex, etc.), the user's physiological data (e.g., blood pressure, height, weight, respiration rate, resting heart rate, etc.), the user's behavioral data (e.g., current and historical records for exercise, sleep, nutrition, the user's medical records (e.g., data indicating comorbidities and chronic conditions, such as diabetes, heart disease, lung disease, COPD, cancer, etc.),” 58:5-12 “detecting signs and symptoms of viral infection worsening or Improving; (2) detecting signs of vaccines being effective or ineffective; and (3) detecting signs of morbidities and co-morbidities,”) Examiner Note: As cited to above Examiner included relative to 13:37-50 a reference to a second healthcare provider as related to providing medical care to the patient..
Therefore it would be obvious for Son determining that the first probability of transmission satisfies a minimum threshold, determining a first set of one or more comorbidities of the first healthcare provider with respect to the first disease, determining a second set of one or more comorbidities of the second healthcare provider with respect to the first disease, and determining to add the second healthcare provider to the set of healthcare providers based on the first and second sets of comorbidities as disclosed by Jain results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim 8: Son in view of Macoviak and Gopalakrishnan disclose the method of claim 5, and Son does not disclose, however Jain discloses:
generating a second probability of transmission for the first healthcare provider using the first machine learning model based on the first set of characteristics and a second community (2:48-67 “machine learning techniques can train a model to incorporate relationships among travel patterns, location types, environmental factors, geographical factors, and other factors with disease transmission. For example, the model can learn to identify the relative risk of transmission posed by different types of locations when combined with different community characteristics and behavior or travel patterns,” 9:30-67 “indication of one or more regions of elevated potential for disease transmission based on data, derived from the monitoring data for the community, that is indicative of behavior patterns of the individuals in the community; and providing output indicating the one or more regions identified using the one or more predictive models; wherein the medical treatment device is configured to provide the one or more therapies to the user based on (i) physiological measurements for the user using the one or more sensors and (ii) determining that the medical treatment device was located in the one or more regions of elevated potential for disease transmission,”); and
generating an updated configuration for a second healthcare facility in the second community comprising adding the first healthcare provider to a set of healthcare providers staffing the second healthcare facility (94:23-31 “provide location classification and business context data management. The computer system 110 can provide user interfaces and APIs for managing building information and traffic-related information. This can include entering and retrieving information about locations, such as a number of individuals allowed (e.g., maximum occupancy), typical occupancy, business size, operating hours, environment, air conditioning, outdoor seating capacity, and type of business,” 94:32-67, 95:10-26 “computer system 110 can provide case management tools. The computer system 110 can provide user interfaces and APIs for managing appointment scheduling for individuals and health care systems. The system 110 can facilitate management of healthcare staff schedules, automatically assign specific cases to staff members to promote efficient staff management, provide additional information about individuals when available from data from previous interviews, and provide elements to escalate a case to emergency services in situations where interviewees require immediate assistance,” 95:47-67 “computer system 110 can provide contact tracing script management. As public health staff begin contact tracing by notifying exposed individuals of their potential exposure as quickly and sensitively as possible, the system 110 improves the quality and efficiency of these engagements,”).
Therefore it would be obvious for Son generating a second probability of transmission for the first healthcare provider using the first machine learning model based on the first set of characteristics and a second community and generating an updated configuration for a second healthcare facility in the second community comprising adding the first healthcare provider to a set of healthcare providers staffing the second healthcare facility as disclosed by Jain results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim(s) 4, 5, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Son et al. (20220102012) in view of Macoviak (20250378923) and Gopalakrishnan et al. (20220384048) and in further view of Neubauer (20250014451).
Claims 4 and 18: Son in view of Macoviak and Gopalakrishnan disclose the method of claims 4 and 18 above, and Son further discloses:
generating the updated configuration comprises adding the first healthcare provider to the set of healthcare providers based on the first probability of transmission ([93 “infectious risk for each agent is calculated via the disease propagation model with droplet transmission model, airborne transmission model, residential transmission model and off-campus transmission model during departure from the current indoor facility. Finally, the testing component model is introduced to simulate the test and treat policy of the university with the test accuracy model,” 95 “Anylogic's GIS functionality was used to mimic the agents' movement from one location to another. Based on the latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,”]).
Son does not explicitly disclose, however Neubauer discloses:
the current configuration comprises a set of healthcare providers staffing the first healthcare facility ([44, 45 “system defines multiple staff roles associated with levels of training and/or specific skills. Table 2 is an exemplary embodiment of defined staff roles. In this example, there are four clinical roles S1, S2, S3, S4 and a technician role S5. Primary care nurses, e.g., S2, perform most of the care of the patient, program medical devices, and address most alarms. In some settings, e.g., a surgical suite, specialists such as anesthesiologists, e.g., S1, program infusion pumps to administer anesthetics and controlled substances and will also address alarms. For ventilators. S1 may include respiratory therapists to troubleshoot complex situations and change ventilator and alarm settings,” 46 “small hospital or clinic, there may be a limited number of staff and a single staff role, e.g., S2, may receive alerts for all sub-priorities. In larger facilities with a greater number and range of staff, the system of the present disclosure implements a risk-based approach to responding to alarms and associates different roles with different sub priorities,” Table 1]),
Therefore it would be obvious for Son wherein the current configuration comprises a set of healthcare providers staffing the first healthcare facility as disclosed by Neubauer results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claims 5 and 19: Son in view of Macoviak and Gopalakrishnan disclose the method of claims 4 and 18 above, and Son further discloses:
generating the updated configuration comprises adding a second healthcare provider to the set of healthcare providers based on the first probability of transmission ([93 “infectious risk for each agent is calculated via the disease propagation model with droplet transmission model, airborne transmission model, residential transmission model and off-campus transmission model during departure from the current indoor facility. Finally, the testing component model is introduced to simulate the test and treat policy of the university with the test accuracy model,” 95 “Anylogic's GIS functionality was used to mimic the agents' movement from one location to another. Based on the latitude and longitude coordinates, we defined all facilities (e.g., academic buildings, dorms, recreational facilities, and healthcare facilities) as GIS Points,”]).
Son does not explicitly disclose, however Neubauer discloses:
the current configuration comprises a set of healthcare providers staffing the first healthcare facility ([44, 45 “system defines multiple staff roles associated with levels of training and/or specific skills. Table 2 is an exemplary embodiment of defined staff roles. In this example, there are four clinical roles S1, S2, S3, S4 and a technician role S5. Primary care nurses, e.g., S2, perform most of the care of the patient, program medical devices, and address most alarms. In some settings, e.g., a surgical suite, specialists such as anesthesiologists, e.g., S1, program infusion pumps to administer anesthetics and controlled substances and will also address alarms. For ventilators. S1 may include respiratory therapists to troubleshoot complex situations and change ventilator and alarm settings,” 46 “small hospital or clinic, there may be a limited number of staff and a single staff role, e.g., S2, may receive alerts for all sub-priorities. In larger facilities with a greater number and range of staff, the system of the present disclosure implements a risk-based approach to responding to alarms and associates different roles with different sub priorities,” Table 1]).
Therefore it would be obvious for Son wherein the current configuration comprises a set of healthcare providers staffing the first healthcare facility as disclosed by Neubauer results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Son et al. (20220102012) in view of Macoviak (20250378923) and Gopalakrishnan et al. (20220384048) and in further view of Gnanasambandam et al. (20240177846).
Claim 9: Son in view of Macoviak and Gopalakrishnan disclose the method of claim 1 above, and Son does not explicitly disclose, however Gnanasambandam discloses:
the current configuration comprises a set of supplies allocated to the first healthcare facility with respect to the first disease ([38, 39 “attribute data, disease progression level data, treatment plan data, results data, utilization type data, resource data, and costs data may be obtained from and/or computing devices over time and stored, for example, in a data store 108. The attribute data, disease progression level data, treatment plan data, and results data may be correlated in patient-cohort databases,” 40, 212 “determine based on the disease progression level of the one or more patients that 50 of the patients are going to need laboratory diagnostic test supplies for testing blood glucose levels (e.g., diabetics) at a certain time period (e.g., in a week), so the artificial intelligence engine 100 may place an electronic order for 50 laboratory diagnostic tests,”]), and
generating the updated configuration comprises adding one or more additional supplies to the set of allocated supplies based on the first probability of transmission ([44 “may also be used by the healthcare professional to obtain, monitor, and adjust resource utilization plans for patients,” 201 “training a machine learning model to output, based on medical data pertaining to a patient, a resource utilization plan 1702 for a medical condition of one or more patients….details of the treatment plans performed by the other patients, the results of performing the treatment plans, utilization types (e.g., admit, emergency, specialist, specialist follow-up, lab, etc.), resources (e.g., number of healthcare professionals available, number of healthcare facility rooms available, number of laboratory testing supplies available, number of medical imaging devices available, etc.), and costs associated with each of the resources,” 209]).
Therefore it would be obvious for Son wherein the current configuration comprises a set of supplies allocated to the first healthcare facility with respect to the first disease and generating the updated configuration comprises adding one or more additional supplies to the set of allocated supplies based on the first probability of transmission as disclosed by Gnanasambandam results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Son et al. (20220102012) in view of Macoviak (20250378923) and Gopalakrishnan et al. (20220384048) and in further view of Lamb et al. (20190355481).
Claim 10: Son in view of Macoviak and Gopalakrishnan disclose the method of claim 1 above, and Son does not explicitly disclose, however Lamb discloses wherein:
the current configuration comprises a set of protocols used by the first healthcare facility with respect to the first disease ([28 “patient-related information, including but not limited to patient admission information (including date of admission and location of the patient within the medical facility), patient care protocols and workflows,” 31, 33 “interpret questions by the human care providers regarding the patient and allows querying the EMR database for relevant information regarding the patient (e.g. “what was the average systolic blood pressure in the last four hours?” or “show me the trend of the O2 saturation”),” 35 “care guidelines obtained from external guideline service 124 may be preconfigured by protocols and guidelines that are specific to the medical facility that the collaborative space server system 102 services. Further, external guideline service 124 may include differential diagnoses trees that guideline VHA 112 may access to determine potential diagnoses based on a patient condition or state,”]), and
generating the updated configuration comprises adding one or more additional protocols to the set of protocols based on the first probability of transmission ([36 “guideline VHA may also serve as a source for generating reminders for treatments that are part of a care protocol or to keep track of what decision-driving tests have been completed and what are still needed to complete the protocol. A change in patient status may be a trigger for automatic notification of relevant guidelines. The guideline VHA may also be used to plan a trajectory for the patient, of both disease progression and a care path. A patient trajectory may be determined based on the combined trajectories of vital signs, laboratory test results or other data for that specific patient. In defining a patient trajectory, the guideline VHA may assist care providers to adjust care pathways or to stay the course and give early warning if the patient deviates from the planned trajectory,” 37 “Predictive VHA 114 is configured to retrieve predictions of future patient states from an external prediction service 126. Predictive VHA 114 may detect and issue alerts on relevant changes in the patient's state (e.g., small but worrying changes in vital signs, changes in qSOFA score). Predictive VHA 114 may also predict future events (e.g., a prediction of sepsis being developed in the coming four hours) by connecting to external prediction service 126. Predictive VHA 114 may query external prediction service 126 with search terms indicating current and/or past patient state (e.g., blood pressure trend, glucose level trend, etc.). If prediction service 126 outputs a possible future condition, the predictive VHA 114 may send an alert into the communication thread]).
Therefore it would be obvious for Son wherein the current configuration comprises a set of protocols used by the first healthcare facility with respect to the first disease and generating the updated configuration comprises adding one or more additional protocols to the set of protocols based on the first probability of transmission as disclosed by Lamb results in a more precise allocation of health care providers based upon performance characteristics of the health care providers and therefore results in a more relevant allocation of healthcare providers as related to patient related medical conditions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Please see attached References Cited form 892.
See Basu et al (20240274291) for disclosures related to the optimization of risk related events with respect to patient engagement data and the collection of related information and determination of prioritized interventions. See at least paras. [35]-[55].
See Talvola et al (11,238,469) for disclosures related to making risk adjusted performance rankings of healthcare service providers and generate and transmit reports of the rankings, and may generate such rankings either occasionally on demand, or periodically. See pages 1-4.
See Joao et al. (20160125549) for disclosures related to the reception of healthcare provider information related to a variety of treatment related information and the determination of risk levels and risk scores. See at least paras. [141]-[170].
See Amarasingham et al (20150213206) for disclosures related to a holistic hospital patient care and management system to collect and process medical staff related tags and associated tracking and location of medical staff including availability. See at least paras. [29]-[58].
See Fotsch et al (20060229918) for disclosures related to the provision of access to patient information associated with a patient’s health record with access provided to providers, patients, payors, and other third parties. See at least paras. [45]-[70].
See Krishnan et al. (20050234740) for disclosures related to the implementation of expert systems for the extraction of structured and unstructured clinical data for the purpose of coordinating patient care processes. See at least paras. [13]-[36]
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David Stoltenberg whose telephone number is (571) 270-3472.
The examiner can normally be reached on Monday-Friday 8:30AM to 5:00PM EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi, can be reached on (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300, or the examiner’s direct fax phone number is (571) 270 4472.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published application may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center at (866) 217-9197 (toll free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (IN USA OR CANADA) or (571) 272-1000.
/DAVID J STOLTENBERG/Primary Examiner, Art Unit 3685