DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed December 12, 2025.
Claims 1-13 are pending.
Response to Amendment(s)
Claims 1-13 are pending. The 112(b) rejections have been overcome.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1 and 13:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method.
Independent claim 13 falls within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1 and 13 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claim 1 are:
A method to optimize human resource management within emergency medical services (EMS), comprising the steps of:
receiving inputs of one or more of the group selected from the list of traffic conditions, weather, incident location of emergency or non emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, available medical consumables, available medical non consumables, available staff, Cellular triangulation of staff, Cellular triangulation of ambulance or other mobile EMS equipment, Cellular triangulation of service base sites, GPS location of staff, GPS location of the service base sites, GPS location of ambulance or other mobile EMS equipment, identified location of needed services, dispatch requests, time of dispatch requests, latitude and longitude of dispatch requests, hospital census counts, duration of patient admittance in hospital, admitting diagnosis in hospital, discharge diagnosis in hospital, unit transition patterns of patients, unit transition date and time, unit admitting date and time, admitting unit in hospital, on-site manufacturing capabilities,
…[…]…building one or more predictive assessment values;
outputting automatically a suggested scheduling template which matches EMS call volume appropriately to provide the maximum ambulance per call ratio per shift and is configured to ensure maximum revenue for an EMS agency;
and employing …[…]…to generate the one or more predictive assessment values that relate to the comparison of the inputs of the evolution of data over a time interval…[…]…;
Employing…[…]… provide geo- location information …[…]…in order to assess availability;
and generating and transmitting…[…]…a report to a user the results of the comparison, including the one or more comparison outcomes, and one or more predicted assessment values that relate to the comparison of inputs or to the predicted evolution of data over a time interval…[…]…
The claim elements which set forth the abstract idea in the independent claim 13 are:
A system to optimize human resource management within emergency medical services comprising:
…[…]…provide functions to distribute data …[…]…to each stakeholder in real-time, to redistribute data in real-time,
provides a specific application for a targeted solution, …[…]…
provides …[…]…data gathering in real-time within the environment in which it is implemented and redistribute its data accordingly, either to the stakeholder, back to itself, …[…]…
…[…]…generates and transmits…[…]…a report to a user the results of the comparison, including the one or more comparison outcomes, and one or more predicted assessment values
This abstract idea is “certain methods of organizing human activity” as it is merely managing personal behavior by following rules or instructions to judge what call volume might be for services and determining resource allocation based on received data MPEP § 2106.04(a)(2), subsection II)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1 and 13 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional claim elements below:
the scheduling module
one or more machine learning models and machine learning
a global positioning systems (GPS) device
EMS equipment and personal communication devices
a display
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
No additional element is performing the abstract idea.
The additional element, the scheduling module, is merely “apply-it” used as a tool to gather data
The additional elements, one or more machine learning models and machine learning, are merely generally linking the abstract idea to the technological field of machine learning
The additional element, a global positioning systems (GPS) device, is merely “apply-it” as a tool to gather data
The additional element, EMS equipment and personal communication devices, ais merely generally linking the abstract idea to the environment of emergency services
The additional element, a display, is merely “apply-it” as a tool to output data
Independent claim 13 recites the additional claim elements below:
A plurality of modules
Trained ML models
A system
Sensors
specific healthcare systems
another module
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
No additional element is performing the abstract idea.
The additional elements, (a), (c), (d), and (f), are merely “apply-it” used as a tool to manipulate data
The additional element, (b), is merely generally linking the abstract idea to the technological field of machine learning
The additional element, (e), is merely “apply-it” is merely generally linking the abstract idea to the environment of healthcare
Accordingly, independent claims 1 and 13 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer element as analyzed above in step 2A prong 2, is merely generally linking or applying the abstract idea and therefore, does not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2-12:
Eligibility Step 1 (does the subject matter fall within a statutory category?):The dependent claims 2-12 fall within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2-12 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claim 1 abstract idea by (1) further limiting the data input and output into determining scheduling, (2) further limiting the patient condition and profile, and (3) further limiting the aligning of healthcare resources. This abstract idea is “certain methods of organizing human activity” as it is merely managing personal behavior by following rules or instructions to judge what call volume might be for services and determining resource allocation based on received data MPEP § 2106.04(a)(2), subsection II)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2-12 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional elements not already recited in the independent claims
Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms
a digital computing device
a Patient Condition Profile engine
a Patient Condition Profile Database
an EMS Response Module
an EMS Unit Provisioning Module
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is recited in the manner of generally linking to the technological environment of deep learning
The additional elements, (b)-(f), are merely “apply-it” as they are used as tools to manipulate data, data gather, and data output.
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in Prong 2. The claims are patent ineligible.
Claim Interpretation - 35 USC § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a plurality of modules which provide functions to distribute data which is obtained from the plurality of modules to each stakeholder in real-time…[…]…” in claim 13
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
Claim 13 recites the limitation “the specific healthcare systems environment” There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the group selected from” There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the list of traffic conditions” There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “the service base sites” There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 3, 10, 11, 12, and 13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Martin et. al (hereinafter Martin) (US20210233388A1)
As per claim 1, Martin teaches:
A computer implemented method to optimize human resource management within emergency medical services (EMS), comprising the steps of: providing a platform to two or more users, the platform configured to facilitate an on- demand sessions between two or more computers having a readable storage medium embodying a computer program product: (abstract discloses, “Disclosed are systems , methods , and media capable of generating emergency predictions . The systems , methods , and media generate spatiotemporal emergency communication predictions , carry out data augmentation , detect emergency anomalies , optimize emergency resource allocation , or any combination thereof” and see [0346] discloses, “In further embodiments , software mod ules are hosted on cloud computing platforms . In some embodiments , software modules are hosted on one or more machines in one location . In other embodiments , software modules are hosted on one or more machines in more than one location” And see [0337] discloses, “In some embodiments , the platforms , media , methods and applications described herein include one or more non - transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked digital processing device” and see [0131] discloses, “In some embodiments , the systems , methods , and media described herein provide for enhanced resource allocation.” And see [0078] discloses, “As referenced herein , “ emergency service providers ” may include organizations and institutions that may provide assistance in an emergency . For example , law enforcement , fire, emergency medical services commonly handle many emergency requests.”)
receiving inputs of one or more of the group selected from the list of traffic conditions, weather, incident location of emergency or non emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, available medical consumables, available medical non consumables, available staff, Cellular triangulation of staff, Cellular triangulation of ambulance or other mobile EMS equipment, Cellular triangulation of service base sites, GPS location of staff, GPS location of the service base sites, GPS location of ambulance or other mobile EMS equipment, identified location of needed services, dispatch requests, time of dispatch requests, latitude and longitude of dispatch requests, hospital census counts, duration of patient admittance in hospital, admitting diagnosis in hospital, discharge diagnosis in hospital, unit transition patterns of patients, unit transition date and time, unit admitting date and time, admitting unit in hospital, on-site manufacturing capabilities, ([0011] discloses, “In one aspect , disclosed herein are methods of creating a prediction model for generating at least one spatiotemporal emergency prediction , the methods comprising : a ) obtaining , by an emergency prediction system ( EPS ) , emergency data comprising emergency type , emergency location , and emergency time for a plurality of emergency communications” and see [0136] discloses, “Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency (such as medical , fire , police or car crashes) , the location of the emergency (e.g. , GPS coordinates , altitude , etc. ) , the time of the emergency ( e.g. , date and time ) , or any combination thereof . In some embodiments , additional information regarding the emergency is obtained including , but not limited to , fatalities , types of injuries , proximity to landmarks ( such as sports stadiums ) , signal strength for emergency call , whether the subject was in a vehicle during the emergency , information about road conditions , number and effectiveness of emergency service providers involved , time for emergency response , etc. Emergency data may comprise historical data or current data.”)
providing a scheduling module for building one or more predictive assessment values; ([0252] discloses, “In some embodiments , the primary output of an emergency anomaly detection module is the detected clusters of emergency calls . In some embodiments , clusters are updated either at the time of each incoming call or on a discrete schedule such as a time block or other time period ( e.g. , every 5 minutes , 15 minutes , etc. ) depending on computational demand . In some embodiments , the output includes the center of the cluster , radius , start / end time , p - value , number of calls , expected number of calls , or any combination thereof.”)
the scheduling module outputting automatically a suggested scheduling template which matches EMS call volume appropriately to provide the maximum ambulance per call ratio per shift and is configured to ensure maximum revenue for an EMS agency; ([0267] discloses, “Various techniques and methods are capable of being used for evaluation of simulations . An exemplary method is the greedy allocation algorithm , for example , for providing a recommendation for allocating ERVs to known base locations to respond to upcoming call volumes . Here , the objective is to allocate vehicles in such a way that response time is minimized . In some embodiments , risk based allocation is considered , which optimizes allocation to ensure a threshold of calls meet a target response time ( e.g. , less than 10 % of calls have a response time greater than 10 minutes )” and see [0268] discloses, “In some embodiments , the availability and frequency of data on emergency resources limits the resolution of this model to shift ( 8 - hours ) or daily . In some embodiments , specialized models for higher resolution are developed. In some embodiments , various models are capable of being used for anomalous cluster detection including unsupervised anomaly detection , supervised anomaly detection , or semi - supervised anomaly detection.” And see [0269] discloses, “In some embodiments , in addition to daily and weekly allocation 779 optimization and recommendations , dynamic reallocation of resources 778 is recommended based on predicted near - term call volumes ( in the next 2-4 hours ) . For example , the current allocation of vehicles is considered and the impact on response time for reallocating them to meet upcoming demand . For example , in some embodiments , vehicles are identified for moving to optimally meet the projected demand in 2 hours.” And see [0270] discloses, “In some embodiments , anomalous cluster detection is carried out for a city , such as City X in FIG . 5B . In some embodiments , there are several PSAP jurisdictions within City X , which handle emergency calls from different areas of the city . In some embodiments , when a collective emergency such as an earthquake or a terrorist attack occurs , there is a sudden increase in emergency calls from certain sections of each PSAP . Using anomalous cluster detection , the clusters within different PSAPs are monitored in real time or near real - time . In some embodiments , clusters of calls are detected in different PSAP areas . In some embodiments , notifications and / or recommendations regarding diverting emergency resources to affected locations are sent to the associated PSAPs . In some embodiments , dynamic reallocation of emergency resources is carried out within different PSAP areas to respond to the group emergency event.” And see see [0189] discloses, “In some embodiments , short - term ( e.g. , 1 hour , 1 day , 1 week ) recommendations for emergency allocation are of emergency personnel or emergency vehicles ) . In some embodiments , long - term predictions ( e.g. , a month , half year or a year ) are used for long - term recommendations ( e.g. , budgeting and planning for hiring emergency personnel and purchasing emergency equipment ) . / examiner notes the configured to ensure maximum revenue claim construction is recited as the intended use of the claimed invention not affirmative recitation that it does thus examiner notes th disclosure teaches long term predictions for budgeting and planning as well as target response time which one of ordinary skill would understand to be ensuring revenue )
and employing one or more machine learning models to determine and generate the one or more predictive assessment values that relate to the comparison of the inputs of the evolution of data over a time interval using machine learning; ([0252] discloses, “In some embodiments , the primary output of an emergency anomaly detection module is the detected clusters of emergency calls . In some embodiments , clusters are updated either at the time of each incoming call or on a discrete schedule such as a time block or other time period ( e.g. , every 5 minutes , 15 minutes , etc. ) depending on computational demand . In some embodiments , the output includes the center of the cluster , radius , start / end time , p - value , number of calls , expected number of calls , or any combination thereof.” And see [0088] discloses, “The algorithm may also comprise machine learning methods in generating the prediction model . In some embodiments , a prediction model is a formula comprising parameters that determine the likelihood of a defined emergency . For example , in some embodiments , a prediction model is a multiple linear regression model or formula that generates a risk prediction for the total number of all emergency calls ( including emergency incidents ) within the city limits of city B for next Friday when data corresponding to environmental condition ( s ) ( e.g. , expected rainfall ) and / or event ( s ) ( e.g. , grand opening of a museum downtown ) inside city B next Friday is entered into the model . In some embodiments , a prediction model is a classifier or trained algorithm generated by the application of a machine learning algorithm to a dataset comprising emergency , environmental , and event data.” And see [0186] discloses, “As an illustrative example , as a call comes in , clusters of calls are detected within a radius of the location of the call ( e.g. , within 1 km , 2 km , or 5 km , including increments therein ) and within a near real - time period ( e.g. , 1 min , 2 min , 5 min , 10 min , or 15 min , including increments therein ) . Examples of near - real time include no more than 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 25 , 30 , 35 , 40 , 45 , 50 , 55 , or 60 minutes from the time of a call or communication . For example , in some embodiments , the analysis of a communication in near real - time includes analysis of the communication within the recited time range of no more than 1 to 60 minutes from the time of sending , receiving , and / or detecting the communication . In some embodiments , a cluster is detected by comparing the recent and on - going calls with historical or predicted call volume or density and detecting abnormal cluster of large number of emergency calls . In some embodiments , the expected number of calls is calculated by sampling the predicted call density in an area , or in the case of the space - time permutation scan statistic model , the expected number of calls is the proportion of calls that occur at a grid point times the total number of calls that occur in the time slot ( see FIG . 5A ).” And see [0187] discloses, “In some embodiments , using the anomaly detection algorithm , a cluster is detected when the actual number of calls exceeds the expected number of calls by a certain threshold . In some embodiments , after anomalies are detected , one or more of users , customers and / or administrators of the system 200 or subjects who are likely to be affected by the emergency event are sent notifications 293 . In some embodiments , subjects who are affected by the emergency event are identified based on proximity or location within a detected anomaly . In some embodiments , the location of a subject is obtained from a communication device of the subject such as a smart phone . Detailed description regarding the anomaly detection module 240 are found in reference to FIGS . 6A and 6B.” and see [0188] discloses, “As shown in FIG . 2 , in some embodiments , emergency resource data 242 enters the Emergency Resource Allocation module 270 for optimizing emergency resource allocation for responding to emergency calls within a dis patch jurisdiction . In some embodiments , the emergency resource data 242 is a local emergency resource data ( e.g. , for a PSAP jurisdiction ) . In some embodiments , an emergency resource recommendation ( e.g. , a recommendation allocating ERVs to particular base locations to minimize response times ) is generated based on the emergency data 222 and one or more spatiotemporal call prediction from the spatiotemporal call prediction module 220. In some embodiments , an estimated response time prediction is generated using a prediction model . In some embodiments , the pre diction model is generated by analyzing labeled emergency data including response times . In some embodiments , using an allocation algorithm , the allocation of the emergency resource is optimized by using an allocation simulator . In some embodiments , current or forecast additional data 232 ( e.g. , weather and traffic data ) is considered for the emergency allocation recommendation.” And see [0189] discloses, “In some embodiments , short - term ( e.g. , 1 hour , 1 day , 1 week ) recommendations for emergency allocation are of emergency personnel or emergency vehicles ) . In some embodiments , long - term predictions ( e.g. , a month , half year or a year ) are used for long - term recommendations ( e.g. , budgeting and planning for hiring emergency personnel and purchasing emergency equipment ) . description regarding the anomaly detection module 240 are found in reference to FIG . 7.” And see [0245]-[0247)
employing a global positioning systems (GPS) device to provide geo- location information of EMS equipment and personal communication devices in order to assess availability; ([0134] discloses, “In some embodiments , data comprises electronic information stored on a server . In some embodiments , data comprises information obtained from communication devices such as , for example , a landline phone . In some embodiments , data comprises information obtained from wireless mobile devices such as , for example , a smart phone.” And see [0136] discloses, “Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency ( such as medical , fire , police or car crashes ) , the location of the emergency ( e.g. , GPS coordinates , altitude , etc. ) , the time of the emergency ( e.g. , date and time ) , or any combination thereof . In some embodiments , additional information regarding the emergency is obtained including , but not limited to , fatalities , types of injuries , proximity to landmarks ( such as sports stadiums ) , signal strength for emergency call , whether the subject was in a vehicle during the emergency , information about road conditions , number and effectiveness of emergency service providers involved , time for emergency response , etc. Emergency data may comprise historical data or current data.” And see [0138] discloses, “In some cases , the emergency data comprises an emergency call log with basic information such as a time stamp , GPS coordinates , and type of emergency ( e.g. , as indicated by the subject ) . In other embodiments , the emergency data is the content of multi - media alerts sent by the subjects to the EMS within a time period . In some embodiments , the emergency data is the content of the emergency session with the EDC including details regarding the emergency.” And see [0139] discloses, “In some embodiments , emergency data is sourced from one or more EMS that receives , routes , monitors , or bridges emergency calls.” And see see [0209] discloses, “In some embodiments , when emergency call data from other sources ( e.g. , a PSAP ) arrives , the data comprises metadata or information for each call that reflects the ground truth of the situation such as , for example , the actual nature ( e.g. , emergency type ) and / or priority of the call . In some embodiments , the metadata or information comprises the actual response time , origin and identification of the response vehicle , final destination of the response vehicle , description of the emergency , or any combination thereof . In some embodiments , the labeled emergency call data and the proprietary call data are matched using the call time and location in the matching module 316. A sample of matched emergency data is shown in Table 4 , which is optionally utilized for training the predictive models applied to the incoming call data stream.” / examiner notes the smart device taught is interpreted as a device having GPS and providing geolocation information and communication of EMS personal and equipment from personal communication devices bridged to receive route and monitor calls.)
and generating and transmitting to an electronic visual display system of a computing device, by the processor, a report to a user the results of the comparison, including the one or more comparison outcomes, and one or more predicted assessment values that relate to the comparison of inputs or to the predicted evolution of data over a time interval using machine learning. ([0186] discloses, “In some embodiments , a cluster is detected by comparing the recent and on - going calls with historical or predicted call volume or density and detecting abnormal cluster of large number of emergency calls .” and see [0156] discloses, “In some embodiments , visualizations of the emergency prediction are provided to a user or administrator ( e.g. , used to generate a virtual map for displaying emergency predictions , emergency resource allocations , and / or emergency anomalies ) . In some embodiments , the visualization is a spatiotemporal emergency pre diction visualization . In embodiments , spatiotemporal emergency prediction visualization provides a visual representation of the prediction such as , for example , a density map of emergency events , emergency communications , emergency communications , emergency signals , or any combination thereof.” And see [0353] discloses, “Filters and selection options are available to display the desired layers with options for viewing detailed or aggregated information and can be used to conduct analysis and produce reports”)
As per claim 2, Martin teaches:
The method of claim 1, wherein scheduling module uses incident time, location, and type to predict and forecast future call volume type and location in real-time. ([0197] discloses, “As shown in FIG . 3A , several inputs 312 , 322 , 332 enter the data augmentation module 310 and an output is generated including emergency call data with predicted type or nature , priority and estimated response time ( referred to as “ Emergency Data with predicted labels ” 389 ) . In some embodiments , the call data stream 312 is a proprietary data stream . In some embodiments , the call data stream is a real - time or near real - time call stream from a dispatch center or PSAP .” and see [0198] discloses, “In many cases , the call data stream 312 does not include determinations regarding the type or nature , priority and / or estimated response times ( referred to as “ labels ” ) . Accordingly , in some embodiments , these labels are used to apply the right predictive model and get accurate emergency predictions . In some embodiments , the data augmentation module 310 predicts labels to the call data stream 312 , which is optionally used in the anomaly detection module 240 , spatiotemporal call prediction 220 , emergency resource allocation module 270 , or any combination thereof ( see FIG . 2 ).” And see [0199] discloses, “In some embodiments , for predicting labels , labeled emergency data from various sources such as PSAPs , EDCs and public and private sources is included in the emergency data 322. In some embodiments , the emergency data 322 includes unlabeled emergency data with or without additional information ( see Table 1 ).” And see [0200] discloses, “In some embodiments , the data stream 312 comprises the emergency call time and the emergency call location ( e.g. , latitude / longitude / elevation / address ) . In some embodiments , the data stream includes a calling device identifier such as a subject's phone number , account number , name or log - in ID , universal ID ( uid ) , or any combination thereof . In some embodiments , one or more additional fields are available depending on the source of the data stream , such as the call device and network , accuracy of location information , and call duration . In some embodiments , one or more of these fields are included as features in the predictive models for improving prediction accuracy. Exemplary raw or unlabeled data stream is shown in Table 1.” And see [0206] discloses, “Next , predicted labels are generated for each call record in one or more prediction models . In some embodiments , the predicted labels are generated by a multi - class classifier 334. In some embodiments , the matched emergency data from the matching module 316 ( with or without additional features ) is used as the input to a multiclass classifier 334. In some embodiments , the nature and priority of calls have different criteria for each region or PSAP , and a model for each distinct region is generated . In some embodiments , several classifier models are assessed with the best performing model being selected using model selection and validation 318. Sample output from the classifier 334 is shown in Table 3. And see [0209] discloses, “In some embodiments , when emergency call data from other sources ( e.g. , a PSAP ) arrives , the data comprises metadata or information for each call that reflects the ground truth of the situation such as , for example , the actual nature ( e.g. , emergency type ) and / or priority of the call . In some embodiments , the metadata or information comprises the actual response time , origin and identification of the response vehicle , final destination of the response vehicle , description of the emergency , or any combination thereof . In some embodiments , the labeled emergency call data and the proprietary call data are matched using the call time and location in the matching module 316. A sample of matched emergency data is shown in Table 4 , which is optionally utilized for training the predictive models applied to the incoming call data stream.” And see [0247] discloses, “Referring to FIG . 5C , an exemplary kernel density map 558 is shown . In some embodiments , the output from kernel warping is a set of predicted kernel density estimates ( KDE ) for calculated components in each region within a time window / time block / defined time period ( e.g. , 1 hr . ) . In some embodiments , the output includes date , hour , region , category , component , KDE , or any combination thereof . In some embodiments , predicted call volume in a given region or PSAP is extracted from these estimated by computing the aggregate KDE sum over the area of interest .”)
As per claim 3, Martin teaches:
The method of claim 1, wherein the scheduling model outputs one or more of automatic scheduling, tracking of epidemiological data for research, and resource/supply management. ([0191] discloses, “In some embodiments , the emergency predictions are accessible through an API 280 using one or more output services 290. In some embodiments , output services 190 comprise one or more of a query service 291 , emergency event detection and notification 293 , call prioritization and routing 295 , hourly or weekly call density projections 296 , emergency resource allocation and planning 297 , and dynamic reallocation 298 .” and see [0360] discloses, “An operations officer for a City D fire department is tasked with developing a resource allocation plan for the upcoming week . The purpose of this plan is to report on the available fire resources in the city , identify any possible gaps or risks , and provide recommendations for solutions to cover identified gaps and mitigate risks . Fire resources include the number and type of fire vehicles available in the city and their base locations.” And see [0363] discloses, “The operations officer selects an option on the application to produce a recommended allocation of fire response vehicles for the next week , from 00:00 hours on Monday until 23:59 hours on the next Sunday , and to display the recommended allocations on the map and in associated tables for each day of the week . The emergency prediction system has already calculated ahead of time the predicted density of fire - related emergency calls for every hour of the next week based on the augmented emergency call data . The emergency prediction system runs on a daily schedule providing new predictions and updating current predictions . The application makes an API call to the PSAP system to request the call density data for the week . The results are then sampled and aggregated for each fire station's area of responsibility to give a daily estimate of calls , which is shown on the map as a layer.”)
As per claim 10, Martin teaches:
The method of claim 1, wherein the system comprises an EMS Unit Provisioning Module that aligns healthcare resources, including supply and personnel distribution; wherein the system aligns acquisition and provisioning to ensure the supplies needed to respond to the patient condition are present on the responding EMS unit; and wherein supplies may comprise at least one of medications, equipment, and consumable and non- consumable supplies. ([0016] discloses, “In another aspect , disclosed herein are methods for optimizing emergency resource allocation using emergency data , comprising : a ) obtaining , by an emergency resource management system , at least one spatiotemporal emergency prediction ; b ) obtaining , by the emergency resource management system , at least one estimated response time pre diction corresponding to the at least one spatiotemporal emergency prediction ; c ) obtaining , by the emergency resource management system , local emergency resource allocation data ; and d ) using , by the emergency resource management system , an allocation algorithm to generate a recommendation for optimal allocation of local emergency resources based on the at least one spatiotemporal emergency prediction , the at least one estimated response time prediction , and the local emergency resource allocation data . In some embodiments , the allocation algorithm comprises a greedy allocation algorithm . In some embodiments , the optimal allocation minimizes a predicted overall emergency response time . In some embodiments , the optimal allocation minimizes a number of emergency communications having an emergency response time exceeding a threshold time . In further embodiments , the threshold time is no more than about 10 minutes . In further embodiments , the threshold time is no more than about 20 minutes . In further embodiments , the local emergency resources comprise emergency response vehicle , emergency response personnel , emergency response equipment , emergency response base , or any combination thereof.”)
As per claim 11, Martin teaches:
The method of claim 1, wherein the system comprises coordinated and Intelligent Predictive Analytics with visual outputs from analytics of a Visualization Function Map which includes the step of displaying forecasting of events for patient demand and supports quantitative reasoning; wherein the events can be filtered by at least one of date, time, type of call, disease, and diagnosis. (see Fig. 5C and see [0165] discloses, “In the batch layer 150 , in some embodiments , the Predictive Model module 156 analyzes the data and generates a model or algorithm for making emergency predictions . Various techniques , models or algorithms are used in the Predictive Model module 156. In some embodiments , after model generation or training of the algorithm , the Predictive Model module 156 queries the model with input data ( e.g. , an emergency data set ) for generating an emergency prediction . In some embodiments , emergency predictions are saved in the batch serving database 158 and is made accessible using one or more output services 190. In some embodiments , output services 190 includes one or more of a query services 191 , visualization / mapping 192 , analytics 194 , web applications 187 , and mobile applications 189.” And see [0015] discloses, “In further embodiments , the emergency anomaly is provided as the cluster of emergency communications . In yet further embodiments , the emergency prediction system further provides information about the cluster comprising a center , a radius , a start time , an end time , p - value , number of calls , expected number of calls , or any combination thereof . In further embodiments , providing the emergency anomaly comprises displaying the cluster of emergency communications on a digital map . In yet further embodiments , the emergency prediction system provides the emergency anomaly in response to a request from the emergency dispatch center . In yet further embodiments , the emergency prediction” and see [0247] discloses, “Referring to FIG . 5C , an exemplary kernel density map 558 is shown . In some embodiments , the output from kernel warping is a set of predicted kernel density estimates ( KDE ) for calculated components in each region within a time window / time block / defined time period ( e.g. , 1 hr . ) . In some embodiments , the output includes date , hour , region , category , component , KDE , or any combination thereof . In some embodiments , predicted call volume in a given region or PSAP is extracted from these estimated by computing the aggregate KDE sum over the area of interest . In some embodiments , in order to maintain fine resolution of pre dictions , the KDEs for each component are stored.”)
As per claim 12, Martin further teaches:
The method of claim 11 wherein the system comprises the step of simulating emergency responses to support decision making at all levels, including municipalities. ([0016] discloses, “In some embodiments , the method comprises providing , by the emergency resource management system , a simulation platform for an administrator to simulate a local emergency resource allocation . In some embodiments , an estimated response time is calculated for the local emergency resource allocation. In some embodiments , an estimated response time is calculated for the local emergency.” And see [0192] discloses, “In some embodiments , a platform for simulation is provided for allocating emergency resources ( such as police cars ) . For example , in some embodiments , an administrator of the emergency predictions system or of the EMS or a customer logging in at the PSAP system is able to access output services such as the simulation platform for providing estimated response times for responding to predicted emergencies. In some embodiments , the administrator or customer is able to adjust the allocation ( such as location of police cars ) to see the predicted effect on response times and / or other results.” And see [0260] discloses, “The predicted call response time ( s ) and sample requests as well as local emergency resource avail ability data 742 may then be used by the allocation simulator in step 776 .”)
As per claim 13, Martin teaches:
A computer implemented system to optimize human resource management within emergency medical services comprising: (abstract discloses, “Disclosed are systems , methods , and media capable of generating emergency predictions . The systems , methods , and media generate spatiotemporal emergency communication predictions , carry out data augmentation , detect emergency anomalies , optimize emergency resource allocation , or any combination thereof.”)
a plurality of modules which provide functions to distribute data which is obtained from the plurality of modules to each stakeholder in real-time, to redistribute data which is obtained from the plurality of modules in real-time, ([0288] discloses, “The device 906 may include a touchscreen 913 ( which may function as a display and user interface ) . The device 906 may also include a computer program 908 , which may include one or more modules of an emergency prediction program . Thus , the program 908 may detect or collect information about the user through device 906 and provide notification to the user 900 about pertinent threats and may also connect the user 900 to a dispatch center ( e.g. , EDC 950 or a private dispatch center ) for emergency assistance.” And see [0289] discloses, “In some embodiments , the emergency prediction system includes an EMS 930 , which optionally connects the user 900 through devices 906 , 912 , 946 when there is an on - going emergency or a possible emergency . As shown , in some embodiments , the devices 906 , 912,946 connect to the EMS 930 through various wired or wireless connections such as cellular voice network , cellular data network , Wi - Fi , Bluetooth® , Internet - based networks , etc. For example , in one embodiment , the communication link 924 connects device 906 to the EMS 930 , while the communication links 926 or 945 and 947 connect to the EDC 950 via a gateway 944. In some embodiments , the devices 906 , 912 , 946 collectively analyze the information about the user and environment to determine whether there is an on - going or possible emergency . In some embodiments , one device ( e.g. , device 906 ) is a master device that may determine whether there is an emergency and autonomously decide to send an emergency alert to a dispatch center for assistance.” And see [0290] discloses, “In some embodiments , the emergency prediction modules on the device 906 communicate with an emergency prediction server 985 in the EMS 930 where the analysis for emergency predictions are conducted . In other embodiments , the emergency predictions are generated on a pre diction server 1485 ( as depicted in FIG . 14A ) . In some embodiments , the emergency predictions are generated on the device 906. Databases 995 ( e.g. , Master DB , Batch Serving DB , Real - time Serving DB ) and other components of the emergency prediction system are housed in the EMS 930”
and to retrain its own ML models in real-time; see [0211] discloses, “The validation process may be executed in batch either when emergency call data is added to the database or at a set time interval . FIG . 8 shows exemplary validation data . The shaded values indicate where the predicted and actual features were inconsistent . For rows 7 and 9 , the call nature prediction may be incorrect . For row 3 , a prediction has 74.7 % probability that it was a high priority call . However , the actual call was not high priority . For rows 4 , 5 , and 7 , the predicted response times are smaller than the actual response time by a threshold percentage ( greater than 50 % difference ) . In some embodiments , when validation is unsuccessful retraining or recalculation of the model is carried out.” And see [0013] discloses, “In further embodiments , the prediction algorithm is re - trained at least once a week.”)
wherein each module provides a specific application for a targeted solution, provides the system a source of sensors for data gathering for the system to train itself in real-time within a specific healthcare systems environment in which it is implemented and redistribute its data accordingly, either to the stakeholder, back to the system itself, or another module ([0010] discloses, “Another advantage of the systems , methods , and media provided herein is auto - detection of emergency ( and non - emergency ) events . In some scenarios , when a user is unable or unaware of a potential emergency situation , a device or a group of devices monitors various forms of information such as sensor data to detect emergency or possible emergency events . For example , systems , methods , and media provided herein may associate certain sensor data such as health indicators from a wearable with environmental data such as temperature and humidity to predict an increased risk for a health emergency . In many instances , the device or group of devices then sends emergency alerts and / or requests for assistance to a recipient such as , for example , a user , an emergency dispatch enter , operations center , a mapping software, a connected device or healthcare system.” And see or any combination thereof.” And see [0326] discloses, “FIG . 14C also shows a schematic diagram of one embodiment of an emergency prediction program 1488 installed on a server ( e.g. , a server in an EMS ) . In some embodiments , the server application 1488 comprises one or more emergency prediction software modules selected from a spatiotemporal communication module 1420 , an augmenting module 1410 , an anomaly module 1440 , a resource allocation module 1470 , or any combination thereof.” And see [0014] discloses, “d ) training , by the emergency prediction system , a prediction algorithm using the matched emergency data ;”)
Wherein the system generates and transmits to an electronic visual display system of a computing device, by the processor, a report to a user the results of the comparison, including the one or more comparison outcomes, and one or more predicted assessment values ([0186] discloses, “In some embodiments , a cluster is detected by comparing the recent and on - going calls with historical or predicted call volume or density and detecting abnormal cluster of large number of emergency calls .” and see [0156] discloses, “In some embodiments , visualizations of the emergency prediction are provided to a user or administrator ( e.g. , used to generate a virtual map for displaying emergency predictions , emergency resource allocations , and / or emergency anomalies ) . In some embodiments , the visualization is a spatiotemporal emergency pre diction visualization . In embodiments , spatiotemporal emergency prediction visualization provides a visual representation of the prediction such as , for example , a density map of emergency events , emergency communications , emergency communications , emergency signals , or any combination thereof.” And see [0353] discloses, “Filters and selection options are available to display the desired layers with options for viewing detailed or aggregated information and can be used to conduct analysis and produce reports”)
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.
Claims 4, 8, and 9 are rejected to under 35 U.S.C. 103 as being unpatentable over Martin et. al (hereinafter Martin) (US20210233388A1) in view of Newton et. al (hereinafter Newton) (US11838112B1)
As per claim 4, Martin does not explicitly teach:
The method of claim 1, wherein the scheduling model outputs predicted medical consumable needs by agency.
However, Newton does teach:
The method of claim 1, wherein the scheduling model outputs predicted medical consumable needs by agency. (Col. 19 lines 48-60 discloses, “For example, collected data may be utilized to anticipate the needs of a community and to manage the flow of patients, caregivers, supplies and 50 facilities accordingly. As shown by arrow 205, exemplary system 100 may coordinate these resources to meet anticipated needs, such as a spreading illness within the community, while minimizing costs associated with wasted time and resources. For example, system 100 may contact a 55 healthcare provider to initiate a perimeter vaccination of an area, request shipment of consumable supplies, medicines, and vaccines to an area, adjust staffing and intake procedures at a healthcare facility and/or generate an advisory for the area.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area as previously cited with Newton’s teachings of predicting when consumables are needed as previously cited, the motivation being Martin teaches a predication and notification system for determining status of a patient and their environment such as oxygen levels and any threats (see [0303]), therefore it would be predictable to combine the prediction of explicit environment and needs such as anticipated consumables with the scheduling and prediction service in Martin to increase efficiency and decrease resource waste and decrease cost for EMS.
As per claim 8, Martin does not teach:
The method of claim 1, wherein the system has components for a Patient Condition Profile engine and a Patient Condition Profile Database, which receives information gathered from an emergency dispatch caller and generates, stores and updates the Patient Condition Profile in real time; wherein data of the Patient Condition Profile is derived from a caller's answers to a 911 call taker's questions according to predetermined scripts; wherein the Patient Condition Profile output assists the system in determining a response based on predetermined characteristics, including optimal outcome.
However, Newton does teach:
The method of claim 1, wherein the system has components for a Patient Condition Profile engine and a Patient Condition Profile Database, which receives information gathered from an emergency dispatch caller and generates, stores and updates the Patient Condition Profile in real time; wherein data of the Patient Condition Profile is derived from a caller's answers to a 911 call taker's questions according to predetermined scripts; wherein the Patient Condition Profile output assists the system in determining a response based on predetermined characteristics, including optimal outcome. (Col. 16 lines 45-67 and Col. 17 lines 1-67 discloses, “The voice print and the associated data may be linked with one or more electronic records for later access and review. For example, when a patient places a 911 call, that call may enter the healthcare command center computer aided dispatch (CAD) system. The CAD may auto-populate the phone number, address, cross-street, local police department, first responders, and any other relevant information about the call source, and save the data with the unique identifier associated with that source. Alternatively, if the CAD receives a call from a source already associated with a unique identifier, the CAD may access that unique identifier and associated information, view historical data, and update the data if desired. In some embodiments, different call sources may each have a unique identifier with associated data and with which calls may be associated and stored. For example, individual patients and homes may have unique identifiers, as may other healthcare venues, systems. and healthcare command centers. As a result, when the healthcare command center receives a call from one of these sources, the dispatcher within the healthcare command center may view the information associated with the call to determine who is calling. The information may include historical data about the frequency of calls and the frequency of admissions. According to embodiments in which a call is received at the healthcare command center from a different healthcare venue, the call may include a unique identifier for the source of the call. The identifier may include, for example, the hospital from which the call is coming, as well as the floor number, room number, and optionally the individual placing the call. As a result, the staff member in the healthcare command center who receives the call may know the source of the call, may view data from past calls, and may log the current call into the history associated with the unique identifier. As a result, if resources are distributed as a result of the call, or if patients are transferred between healthcare venues as a result of the call, the healthcare command center may ensure that the resources and/or patients are sent to the correct venue. Alternatively or additionally, in some embodiments, a call received at the healthcare command center may be assigned a unique patient identifier which may identify the patient or patients about whom the call concerns. As a result, the patient's record may include a history of calls relating to that patient, and the voice prints of those calls. This voice print data may be shared when the patient's record is shared between caregivers and/or between healthcare venues or systems. In some embodiments, the patient's medical records, lab result data, transfer history, call history, and transaction case number may be associated using one or more electronic record links, for ease of access. In some embodiments, the aforementioned data may be associated with the patient's medical records, so that the medical records include historical lab result data, histories of calls about the patient, the patient's transfer history, and a history of patient's admissions across venues. This data may be maintained and updated over time. …[…]…In some embodiments, a healthcare command center may be integrated with an automated capacity operations status board. The board may be mounted within a healthcare venue and may provide visual indications of available capacities in specialty service units. These units may include trauma units, pediatrics, STEMI, stroke units, and neurosurgical units. Data depicted on the board may be collected, analyzed, and provided by the healthcare command center. The board may indicate the number of beds available in each unit, as well as available staff and resources.” And see Col. 28 lines2-5 discloses, “During the trip, the electronic device may provide questions” and see Col. 10 lines 53-54 discloses, “A variety of services may also be implemented during the development and delivery of a healthcare command center.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Newton’s explicit teachings of utilizing patient data through the 911 calls to generate the best course of action to determine patient trajectory, the motivation being Martin already receives various labelled and unlabeled emergency data such as response type and nature and priority to determine predicted response conditions thus it would only improve the machine learnings ability to make precise response predications by providing additional choice data such as scripted answered questions and patient profiles to increase efficiency, decrease resource strain, and improve the overall prediction density maps in Martin while utilizing the same computer implementation as disclosed in Martin.
As per claim 9, Martin teaches the underlined portion:
The method of claim 1, wherein the system comprises an EMS Response Module that continuously analyzes and updates a Patient Condition Profile to provide intelligent EMS response to the patient's condition; wherein the system adapts to input of a changing patient condition; wherein an optimal choice of EMS unit response is determined by analyzing the patient, EMS unit, and receiving entity profiles for the most optimal patient outcome. ([0325] discloses, “FIG . 14C also shows a schematic diagram of one embodiment of a server application 1448 installed on a server ( e.g. , a server in an EMS ) . In some embodiments , the server application 1448 comprises one or more server software modules from an emergency communication module 1441 , a proxy determination module 1443 , an emergency management module 1445 , a group location module 1447 , or any combination thereof.”) ([0090] discloses, “For instance , the task identification component 304 can determine , based on monitored physiological data for a patient , that emergency services are needed for dispatch to the patient and generate task information that identifies the one or more discrete tasks involved with provision of the emergency services to the patient.” And see [0165] discloses, “ In the batch layer 150 , in some embodiments , the Predictive Model module 156 analyzes the data and generates a model or algorithm for making emergency predictions.”)
However, Martin does not teach the underlined portion:
The method of claim 1, wherein the system comprises an EMS Response Module that continuously analyzes and updates a Patient Condition Profile to provide intelligent EMS response to the patient's condition; wherein the system adapts to input of a changing patient condition; wherein an optimal choice of EMS unit response is determined by analyzing the patient, EMS unit, and receiving entity profiles for the most optimal patient outcome.
However, Newton does teach:
The method of claim 1, wherein the system comprises an EMS Response Module that continuously analyzes and updates a Patient Condition Profile to provide intelligent EMS response to the patient's condition; wherein the system adapts to input of a changing patient condition; wherein an optimal choice of EMS unit response is determined by analyzing the patient, EMS unit, and receiving entity profiles for the most optimal patient outcome. (Col. 19 lines 61-67 and Col. 20 lines 1-11 discloses, “In various embodiments, exemplary system 100 may collect information about a given patient and their medical history, and may utilize the information to route the patient to the best venue of care. For example, a patient experiencing a chronic back injury may call 911 because he is experiencing pain. In such circumstances, it may be unnecessary for the patient to come into the emergency department, given the high cost of emergency care and because a more appropriate care venue is available. System 100 may access the patient's medical history and analyze factors such as the patient's plan of care, appointment history, medications, and his physical location, and may use these factors to determine the best treatment option. For example, system 100 may determine that the patient has recently missed a physiotherapy appointment and has not filled his recent prescription for pain medication. System 100 may schedule an appointment for the patient at a physiotherapy clinic, such as a clinic where the patient was previously treated. System 100 may schedule an appointment for the patient at a physiotherapy clinic, such as a clinic where the patient was previously treated. Optionally, system 100 may coordinate with a transportation service, such as taxi service, or a ride-share service such as Lyft®, or Uber®, to transport the patient to the clinic at the appropriate time.” And see Col.14 lines 10-29 discloses, “In some embodiments, a healthcare command center may be integrated with emergency medical services (EMS) to gain visibility into community needs. This integration may include routine medical transportation services such as ambulances and wheelchair vans, as well as integration with non-medical transportation services, such as Uber, Lyft, and taxis or other similar ride-share programs. Integration with EMS may occur at both the front-end (transportation of patients to a healthcare venue for treatment) and the backend (transportation to post-care facilities after patient discharge), and may provide data to predict future transportation demands. In some embodiments, front-end EMS may be integrated with the healthcare command center to provide real-time data about current demands for service and about capacities of different healthcare venues across a healthcare system. Instead of EMS services transporting all patients to an acute-care setting such as an emergency department, exemplary healthcare command centers may coordinate delivery of patients to the appropriate healthcare venue via EMS.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Newton’s explicit teachings of utilizing patient data through the 911 calls to generate the best course of action to determine patient trajectory, for the same reasons given in claim 8.
Claims 5, 6, and 7are rejected to under 35 U.S.C. 103 as being unpatentable over Martin et. al (hereinafter Martin) (US20210233388A1) in view of Brown et. al (hereinafter brown) (US20200411170A1)
As per claim 5, Martin teaches:
The method of claim 1, wherein the machine learning models utilize …[…]…+ neural networks, …[…]…and deep reinforcement learning via call simulations to create Model-free algorithms. ([0109] discloses, “In some embodiments , the systems , methods , and media described herein use machine learning algorithms for training prediction models and / or making predictions . Machine learning explores the study and construction of algorithms that are capable of learning from and making predictions on data . In some embodiments , techniques used for generating models and / or making predictions include machine learning , neural networks , multilayer perceptron ( MLP ) , support vector machines ( SVM ) , radial basis function , Naïve Bayes , nearest neighbor , or geospatial predictive modeling.” And see [0113] discloses, “In some embodiments , a machine learning algorithm uses a reinforcement learning approach . In reinforcement learning , the algorithm learns a policy of how to act given an observation of the world . Every action has some impact in the environment , and the environment provides feedback that guides the learning algorithm.”
However, Martin does not teach the underlined portions:
The method of claim 1, wherein the machine learning models utilize Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms.
However, Brown does teach the underlined portions:
The method of claim 1, wherein the machine learning models utilize Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms. ([0147] discloses, “The one or more task optimization models can employ various machine learning techniques ( e.g. , developed based on based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system ) and / or statistical techniques to facilitate determining / inferring the optimal task scheduling and resource assignment information ( e.g. , SVM classification , neural networks ( e.g. , including deep neural networks , deep adversarial neural networks , convolutional neural networks , and the like ) , Bayesian networks , decision trees , a nearest neighbor algorithms , boosting algorithm , gradient boosting algorithms , linear regression algorithms , k - means clustering algorithms , association rules algorithms , q - learning algorithms , temporal difference algorithm , and probabilistic classification models providing different pat terns of independence , and the like).”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees as previously cited, the motivation being Martin’s prediction’s would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take utilizing patient data within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling.
As per claim 6, Martin does not explicitly teach:
The method of claim 1, wherein the system machine learning models learns and analyzes a patient condition profile to determine a patient trajectory based on patient outcome and time to treatment.
However, Brown does teach:
The method of claim 1, wherein the system machine learning models learns and analyzes a patient condition profile to determine a patient trajectory based on patient outcome and time to treatment. ([0030] discloses, “The disclosed subject matter is directed to systems , computer - implemented methods , apparatus and / or computer program products that provide facilitate coordinating and optimizing resource utilization and delivery of healthcare services across an integrated healthcare system using a machine learning framework . An integrated health care delivery system is one in which all the providers whose services affect a patient work together in a coordinated fashion , sharing relevant medical information , sharing aims or goals , sharing responsibility for patient outcomes , and for resource use . For example , an integrated healthcare system can include many different operating entities that provide a variety of different healthcare services to patients , including hospitals , specialized hospital units , specialized physician clinics / offices , outpatient facilities , ambulatory services , nursing home facilities , surgery centers , imaging / diagnostic providers , pharmacy providers , traveling / in - home patient care , rehabilitation providers , telemedicine providers , and the like.” And see [0032] discloses, “The second dimension of this model is the discrete patient that has a prescribed number of activities or services to be rendered . Taking into account unique patient's acuity or need , list of discrete services or activities to be completed , and any requires sequencing , the disclosed techniques can determine how to schedule the patient for service to optimize the time required to complete all activities.” And see [0034] discloses, “In one or more embodiments , the system collects and combines real - time and historical data from various integrated healthcare provider systems and sources regarding patient needs and all aspects of operations of the different healthcare providers that are available to provide access and retrieve or receive operating information from different operating entities in real - time over a course of operating of the one or more operating entities regarding what task needs to be done ( e.g. , clinical tasks and non clinical task for performance by a wide range of clinicians , healthcare workers and the like , when and where within the healthcare system at a current point in time and / or over a defined , upcoming period of time . The system can further extract and receive up - to - date information from the different operating entities regarding who or whom is available to perform the tasks , and who is the best person / persons to perform the tasks . The system can further evaluate the information using various machine learning models and / or optimization models / algorithms to determine how to sched ule performance of the tasks with respect to time and location and how to assign resources ( e.g. , workers and optionally non - human resources ) to the tasks in a manner that results in performing the tasks in the most efficient and effective manner , using the right resources at the right time for the right patient in the right place.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited, the motivation being Martin’s prediction’s would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take even further improving precision and accuracy by utilizing choice patient data such as outcomes and trajectories within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling.
As per claim 7, Martin does not teach:
The method of claim 6, wherein the system analyzes a receiving entities profile for characteristics including at least one of bed availability, staff availability, supply availability and predicted patient load by patient condition for that facility; wherein the profiles are encoded to enable storage and processing using a digital computing device; wherein the system analyzes the encoded profiles to determine optimal outcome and coordinates EMS unit response and patient trajectory based on current and forecasted conditions.
However, Brown does teach:
The method of claim 6, wherein the system analyzes a receiving entities profile for characteristics including at least one of bed availability, staff availability, supply availability and predicted patient load by patient condition for that facility; ([0054] discloses, “Although the embodiments , described above are directed to evaluating and determining availability of health care workers ( e.g. , humans ) , the resource assessment mod ule 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources . For example , the other system resources can include supplies , instruments , equipment , machines , technology , and the like that are needed to per form and / or facilitate performance of the healthcare tasks . Thus , in some embodiments , the resources availability data 116 can also include information regarding availability of other resources , such as current availability status of the resources ( e.g. , whether they are in - use , clean , dirty , in repair , offline , overloaded , power levels , etc. ) , expected availability status of the resources ( e.g. , and the like .”)
wherein the profiles are encoded to enable storage and processing using a digital computing device; ([0063] discloses, “In the embodiment shown , the healthcare information systems / sources 102 include one or more databases that provide static / semi - static system data 106 for an operating entity or group of operating entities , including task definitions / requirement data 202 , worker information 204 , system geospatial data 206 , finance data 210 and patient information 252.” And see [ 0073 ] The patient information 252 can include patient information regarding current patients of one or more healthcare systems ( e.g. , patients that have entered the healthcare system via at least one entry point ) and their medical needs . For example , the patient information 252 can include care plan information 240 that describes or defines care plans for the patients ( if available ) . For example , the care plan can include information a list or timeline of the various prescribed clinical treatment to for the patient in association with a course of patient care . In some implementations , the care plan information can also be associated with information identifying patient rest and recovery periods / times , such as amounts of time and / or periods of time during which the patient is required or preferred to rest ( e.g. , between procedures or appointments and the like ) . In some implementations , the care plan information can be automatically generated and provided by an artificial intelligence ( AI ) system configured to evaluate a patient's condition , diagnosis , needs and medical history and generate a care plan accordingly . In some implementations , the AI system can also evaluate information regarding the patient's insurance plan / carrier and / or form of payment in association with determining the type of services that are available to the patient for including in the patient's care plan ( e.g. , only those services that are approved or anticipated for approval by the patient's insurance provider ) . The patient information 252 can also include clinical order data 242 providing clinical order prescribed for a patient . The patient information can also include medical history information for current and past patients , such as that provided in the EHR data 244.”)
wherein the system analyzes the encoded profiles to determine optimal outcome and coordinates EMS unit response and patient trajectory based on current and forecasted conditions. ([0087] discloses, “The task identification component 304 can further process the extracted / received task information ( e.g. , the newly reported tasks data 214 , the task scheduling data 218 , and / or the forecasted task data 222 ) to identify all ( or defined subsets ) of the discrete tasks reflected in the task information for performance over the defined , upcoming timeframe” and see [0040] discloses, “The healthcare delivery optimization server device 108 can provide various features and functionalities that facilitate optimizing utilization of healthcare resources and delivery of healthcare services . In one or more embodiments , the healthcare delivery optimization server device 108 can facilitate optimizing scheduling of different health care tasks and assigning resources to the different healthcare tasks in real - time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment . The healthcare environment can include individual operating entities , as well as a macro ecosystem that combines the individual operating entities into a unified integrated healthcare system . For example , the operating entities can include various types of healthcare facilities that provide healthcare services , including ( but not limited to ) , hospitals , clinics , ambulatory surgical centers , birth centers , blood banks , specialty clinics or medical offices , dialysis centers , hospice homes , imaging and radiology centers , therapy centers , mental health treatment centers , nursing homes , orthopedic and other rehabilitation centers , urgent care facilities , and the like . The operating entities can also include healthcare entities that provide mobile or in - home care services patients ( e.g. , traveling nurses , emergency medical services ).” And see [0090] discloses, “For instance , the task identification component 304 can determine , based on monitored physiological data for a patient , that emergency services are needed for dispatch to the patient and generate task information that identifies the one or more discrete tasks involved with provision of the emergency services to the patient.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited for the same reasons given in claim 6.
Prior Art not cited but made of record
US6058370A– Church et. al
A method of forecasting the demand for ambulance Services based upon a determination from the historical record of the number of currently active calls at the time each new call is received. The method may utilize records of Specific calls history including the time each call was received and the time each call was completed, or it may use information on the number of calls received each hour to Simulate specific calls history. The method may be extended to a “third dimension” to better account for the effect of Service demands and Staffing levels in preceding hours. The method has application to other Services having the characteristics of limited available Service resources, randomly occurring Ser Vice requests but historically-repeating levels of demand, and relatively long Service times.
Response to Arguments Regarding 35 U.S.C § 101 Rejections
Applicant’s arguments on pages 1-5 of remarks have been considered. Applicant argues the 35 U.S.C § 101 rejection should be withdrawn for the following reasons. The claims are directed to a practical application of Al-based optimization for human resource management in emergency medical services (EMS), providing a technical solution to the technical problem of dynamic resource allocation and scheduling in response to real-time and predicted demand. The claims recite specific steps for receiving diverse data inputs, generating predictive assessment values using machine learning, and outputting actionable scheduling templates, which are not abstract ideas but rather improvements to the functioning of computer-based EMS management systems.
Applicant submits that the Manual of Patent Examining Procedure [hereinafter "MPEP"] describes a two-part test for determining subject matter eligibility. MPEP 2106. Eligibility Step 1 determines whether the claimed solution is directed to one of the four categories of statutory subject matter - a process, a machine, a manufacture, and a composition of matter. Id at 2106.03 I. Applicant submits that claims 1 and 13, as currently amended, are directed to a process and therefore the claimed solution is directed to one of the four categories of statutory subject matter. Eliqibility Step 2A - Whether Claim is Directed to a Judicial Exception
The MPEP defines a judicial exception as an abstract idea, a law of nature, or a natural phenomenon. Id at 2106.04 1. Here, the issue is whether the claimed solution would preempt or monopolize "the basic tools of scientific and technological work. "Id Step 2A provides a two- prong inquiry - (1) whether the claimed solution recites a judicial exception and if so (2) whether the recited judicial exception is integrated into a practical application. Id at 2106.04 II Prong One - Does the claimed solution recite an abstract idea? Under Prong One, a claim is examined to determine whether an abstract idea is set forth or described in the claim. Id at 2106.04 II A 1. The MPEP explicitly explains that examiners should be "careful to distinguish claims that recite an exception (which require further eligibility analysis) and claims that merely involve an exception (which are eligible and do not require further eligibility analysis)."Id (emphasis in original).
The MPEP enumerates groupings of abstract ideas such that if a claim is determined to recite an abstract idea and the determined abstract idea falls within at least one of the enumerated groupings of abstract ideas, then "it is reasonable to conclude that the claim recites an abstract idea."Id at 2106.04(a). The enumerated groupings include mathematical concepts, certain methods of organizing human activity, and mental processes. The Office Action states that the claims Independent claims 1 and 13 claimed invention is directed to an abstract idea without significantly more. (See P.A., page 13, lines 7-9)
The MPEP defines a mental process as "thinking that 'can be performed in the human mind or by a human using a pen and paper," which includes "observations, evaluations, judgements, and opinions." Id at 2106.04(a)(2) II. In addition, claims "do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." Id (emphasis added)
Accordingly, the claimed solution, as in claims 1 and 13, recites steps that are transformational by receiving diverse data inputs, generating predictive assessment values using machine learning, and outputting actionable scheduling templates. Accordingly, the claimed solution requires complex computing technology to perform, such as receiving diverse and complicated data inputs, generating predictive assessment values using machine learning, and outputting actionable scheduling templates.
Furthermore, the claimed solution is not directed to a mental process because the recited claim elements cannot be practically performed by "thinking" or using "pen and paper." In other words, the claimed Al-based optimization for human resource management in emergency medical services (EMS), providing a technical solution to the technical problem of dynamic resource allocation and scheduling in response to real-time and predicted demand are all claim elements that cannot be practically performed in the human mind or on paper, individually and/or as a combination. The human mind is simply not equipped to practically perform the recited claim elements.
Prong Two - Does the claim recite additional elements that inteqrate the judicial exception into a practical application? Under Prong Two, claims are evaluated to determine whether a claim as a whole integrates the exception into a practical application of that exception. MPEP 2106.04 IIA. For a claim reciting a judicial exception to be eligible, the additional elements in the claim must transform the nature of the claim into a patent-eligible application of the judicial exception. Id The Federal Circuit "has distinguished between claims that are 'directed to' a judicial exception (which require further analysis to determine their eligibility) and those that are not (which are therefore patent eligible), e.g., claims that improve the functioning of a computer or other technology or technological field. "Id at 2106.04(d).
The Prong Two analysis considers the claim as a whole - "limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception need to be evaluated together to determine whether the claim integrates the judicial exception into a practical application. "Id at 2106.04(d) III. In this case, looking at the claims as a whole, including the previous and current amendments, Applicant respectfully submits that the claimed limitations integrated into a practical application by improving the functioning of the computing technology, e.g., improving by Al-based optimization for human resource management in emergency medical services (EMS), providing a technical solution to the technical problem of dynamic resource allocation and scheduling in response to real-time and predicted demand.
Thus, the instant amendments further define the scope of the claimed solution. In particular, the amendments clarify that the process can use machine leaning to improve its overall ability to more accurately use dynamic resource allocation and scheduling in response to real-time and predicted demand. Accordingly, since the claimed solution, therefore, is integrated into a practical application, Applicant respectfully asserts that the claimed solution recites eligible subject matter because the claimed solution is integrated into a practical application.
Based on the foregoing, the Applicant respectfully asserts that the claimed solution is not directed to an abstract idea and, even if it was, the claimed solution integrates the abstract idea into a practical application.
Eligibility Step 2B - Whether Claim Adds Significantly More
Applicants respectfully assert that the claimed process provides an Al-based optimization for human resource management in emergency medical services (EMS), providing a technical solution to the technical problem of dynamic resource allocation and scheduling in response to real-time and predicted demand using computing technology in a way that is not well understood, conventional, or routine. Thus, the focus of the claims is on improving the computer-related technology, in particular, utilizing and improving upon the computing technology as it relates to receiving diverse data inputs, generating predictive assessment values using machine learning, and outputting actionable scheduling templates in such a way that the computer is not merely being used as a tool. Nevertheless, in order to further the prosecution of this application, and without acquiescing to the Examiner's rejection and while reserving the right to prosecute the original claims (or similar claims) in the future, Applicant has amended claims 1 and 13 to clarify and address the allegations of the Present Action. No new matter has been added.
Accordingly, based on the foregoing, the Applicant respectfully submits that the claimed solution of claims 1 and 13, as currently amended, is directed to patentable subject matter and requests removal of this rejection under 35 USC 101.
Examiner appreciates applicant’s arguments but respectfully does not find them persuasive. The MPEP states The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. Furthermore, the MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
The positively recited in claim 1 (as representative) are directed to a judicial exception (i.e. certain methods of organizing human activity) as merely managing personal behavior by following rules or instructions to judge what call volume might be for services and determining resource allocation based on received data this is abstract in substance as a human can analyze data to judge and report what resources may be needed for services like emergency services by following rules and instructions. Being implemented by a computer environment or even in real time does not make the recited claim dispositive of being certain methods of organizing human activity.
Further responding to applicants arguments, the judicial exception (abstract idea) cannot integrate itself into a practical application but identification of any additional elements recited in the claim can be evaluated to determine if the additional elements integrate the exception into a practical application. The claims additional elements are not recited as being an improvement to a technology field or a technology confined to the computer environment in which the claims recite. A technical problem must first be identified in instant application specification and reflected in the claims. Problems recited in the arguments are abstract problems related to human resource management. The recitation of machine learning in the claim even considered an additional element is merely apply it level as there is no improvement to the machine learning itself but rather improvement to the abstract idea of resource allocation and reporting. The additional elements are apply it or generally linking and the claims do not recite technical improvements whether alone or in combination with the abstract idea. The abstract idea cannot bring forth the practical application. If applicant’s line of reasoning were correct Alice corp. would have been deemed eligible. Examiner maintains the claims are directed to an abstract idea and do not integrate into a practical application. Therefore, they also do not amount to significantly more.
Examiner maintains the 35 U.S.C § 101 rejection
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant’s arguments on pages 7-22 of remarks have been considered. Applicant argues the 35 U.S.C § 102/103 rejection should be withdrawn for the following reasons
Accordingly, Applicant respectfully traverses these rejections. The present invention also provides technical improvements in EMS operations, including reduced response times, optimized staff utilization, minimized supply waste, and enhanced patient outcomes through continuous, real-time machine learning optimization. These improvements are not taught or suggested by the cited references. For example, the present claims require receiving a comprehensive set of real-time and historical data inputs, including but not limited to traffic, weather, incident location, call type, dispatch type, patient demographics, hospital census, and resource availability. While US20210233388A1 Martin discloses predictive analytics for emergency detection and response, it does not teach or suggest the specific combination of data sources, nor the integration of hospital census, staff triangulation, and on-site manufacturing capabilities as recited in the present claims.
Examiner appreciates applicant’s arguments but does not find them persuasive. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). The claim 1 recites “receiving inputs of one or more of the group selected from the list of traffic conditions, weather, incident location of emergency or non-emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, available medical consumables, available medical non consumables, available staff, Cellular triangulation of staff, Cellular triangulation of ambulance or other mobile EMS equipment, Cellular triangulation of service base sites, GPS location of staff, GPS location of the service base sites, GPS location of ambulance or other mobile EMS equipment, identified location of needed services, dispatch requests, time of dispatch requests, latitude and longitude of dispatch requests, hospital census counts, duration of patient admittance in hospital, admitting diagnosis in hospital, discharge diagnosis in hospital, unit transition patterns of patients, unit transition date and time, unit admitting date and time, admitting unit in hospital, on-site manufacturing capabilities,” Therefore only requiring one or more of the recited options for inputs and Martin clearly teaches one or more of these groups by disclosing in [0011] which discloses, “In one aspect , disclosed herein are methods of creating a prediction model for generating at least one spatiotemporal emergency prediction , the methods comprising : a ) obtaining , by an emergency prediction system ( EPS ) , emergency data comprising emergency type , emergency location , and emergency time for a plurality of emergency communications” and see [0136] discloses, “Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency (such as medical , fire , police or car crashes) , the location of the emergency (e.g. , GPS coordinates , altitude , etc. ) , the time of the emergency ( e.g. , date and time ) , or any combination thereof . In some embodiments , additional information regarding the emergency is obtained including , but not limited to , fatalities , types of injuries , proximity to landmarks ( such as sports stadiums ) , signal strength for emergency call , whether the subject was in a vehicle during the emergency , information about road conditions , number and effectiveness of emergency service providers involved , time for emergency response , etc. Emergency data may comprise historical data or current data.”. For example location of emergency incident is taught. Examiner maintains the 102 rejection for this argument.
Further, the claims require a scheduling module that outputs a suggested scheduling template based on predictive assessment values generated by machine learning models, including ensemble learning, neural networks, decision trees, and reinforcement learning. US20210233388A1 Martin focuses on emergency prediction and resource allocation but does not disclose or suggest an Al-driven scheduling module that automatically generates actionable staff schedules for EMS based on predicted call volume, incident type, and location, nor does it teach the specific use of reinforcement learning for this purpose.
Examiner appreciates applicant’s arguments but does not find them persuasive. Examiner must provide prior art that reads on the recited claim language only in light of the specification but not reading the specification into the claims and under broadest reasonable interpretation as one of ordinary skill would reasonably understand. The claim 1 recites “providing a scheduling module for building one or more predictive assessment values;” and Martin clearly teaches in paragraph [0252] which discloses, “In some embodiments , the primary output of an emergency anomaly detection module is the detected clusters of emergency calls . In some embodiments , clusters are updated either at the time of each incoming call or on a discrete schedule such as a time block or other time period ( e.g. , every 5 minutes , 15 minutes , etc. ) depending on computational demand . In some embodiments , the output includes the center of the cluster , radius , start / end time , p - value , number of calls , expected number of calls , or any combination thereof.” That a software module provides predictive values of scheduling information such as expected number of calls in clusters. There is no requirement of the claim limitation for specifically what the predictive value needs to be or what exactly makes the module a “scheduling” module. Further claim 1 recites “the scheduling module outputting automatically a suggested scheduling template which matches EMS call volume appropriately to provide the maximum ambulance per call ratio per shift and is configured to ensure maximum revenue for an EMS agency;” and Martin clearly teaches in paragraph [0267] which discloses, “Various techniques and methods are capable of being used for evaluation of simulations . An exemplary method is the greedy allocation algorithm , for example , for providing a recommendation for allocating ERVs to known base locations to respond to upcoming call volumes . Here , the objective is to allocate vehicles in such a way that response time is minimized . In some embodiments , risk based allocation is considered , which optimizes allocation to ensure a threshold of calls meet a target response time ( e.g. , less than 10 % of calls have a response time greater than 10 minutes )” and see [0268] discloses, “In some embodiments , the availability and frequency of data on emergency resources limits the resolution of this model to shift ( 8 - hours ) or daily . In some embodiments , specialized models for higher resolution are developed. In some embodiments , various models are capable of being used for anomalous cluster detection including unsupervised anomaly detection , supervised anomaly detection , or semi - supervised anomaly detection.” And see [0269] discloses, “In some embodiments , in addition to daily and weekly allocation 779 optimization and recommendations , dynamic reallocation of resources 778 is recommended based on predicted near - term call volumes ( in the next 2-4 hours ) . For example , the current allocation of vehicles is considered and the impact on response time for reallocating them to meet upcoming demand . For example , in some embodiments , vehicles are identified for moving to optimally meet the projected demand in 2 hours.” And see [0270] discloses, “In some embodiments , anomalous cluster detection is carried out for a city , such as City X in FIG . 5B . In some embodiments , there are several PSAP jurisdictions within City X , which handle emergency calls from different areas of the city . In some embodiments , when a collective emergency such as an earthquake or a terrorist attack occurs , there is a sudden increase in emergency calls from certain sections of each PSAP . Using anomalous cluster detection , the clusters within different PSAPs are monitored in real time or near real - time . In some embodiments , clusters of calls are detected in different PSAP areas . In some embodiments , notifications and / or recommendations regarding diverting emergency resources to affected locations are sent to the associated PSAPs . In some embodiments , dynamic reallocation of emergency resources is carried out within different PSAP areas to respond to the group emergency event.” And see [0189] which discloses, “In some embodiments , short - term ( e.g. , 1 hour , 1 day , 1 week ) recommendations for emergency allocation are of emergency personnel or emergency vehicles ) . In some embodiments , long - term predictions ( e.g. , a month , half year or a year ) are used for long - term recommendations ( e.g. , budgeting and planning for hiring emergency personnel and purchasing emergency equipment ).” therefore examiner notes the configured to ensure maximum revenue claim construction is recited as the intended use of the claimed invention not affirmative recitation that it does thus examiner notes the disclosure teaches long term predictions for budgeting and planning as well as target response time which one of ordinary skill would understand to be ensuring revenue. Further Martin clearly teaches the machine learning automatically suggesting whether long term or short term scheduling by matching EMS call volume such as diverting the max EMS when disaster strikes to provide the max allocation available per the shift to meet the demand and someone of ordinary skill in the art would understand Martins disclosure of optimizing the resources and taking into account shift times to meet a low response time to teach this as its not an ipsissima verba test but rather what one of ordinary skill under BRI would understand of dynamic resource allocation and responding. No recitation of ensemble learning, neural networks, decision trees, and reinforcement learning is constructed in the claim 1.However, Claim 5 recites the use of these types of machine learning but Examiner only relied on Martin for a portion of this claim and also added a secondary reference of Brown therefore applicant’s argument is viewed as moot however for sake of response Martin does teach neural network and reinforcement learning in paragraph [0109] which discloses, “In some embodiments , techniques used for generating models and / or making predictions include machine learning , neural networks , multilayer perceptron ( MLP ) , support vector machines ( SVM ) , radial basis function , Naïve Bayes , nearest neighbor , or geospatial predictive modeling.” And see paragraph [0113] which discloses, “In some embodiments , a machine learning algorithm uses a reinforcement learning approach . In reinforcement learning , the algorithm learns a policy of how to act given an observation of the world . Every action has some impact in the environment , and the environment provides feedback that guides the learning algorithm.” Examiner maintains the 102 rejection for this argument.
Further still, the present application introduces the concept of a Patient Condition Profile (PCP) engine and database, which continuously updates and analyzes patient status throughout the EMS and hospital trajectory, optimizing resource allocation and care delivery in real time. US20210233388A1 Martin does not disclose a system that generates, stores, and updates a Patient Condition Profile for each incident, nor does it coordinate EMS and hospital resources based on such profiles.
Examiner appreciates applicant’s arguments but does not find them persuasive. Examiner did not cite to Martin for the patient condition profile in dependent claims therefore finds this argument moot. Examiner maintains the 102 rejection.
And still further, the claims recite modules for predictive supply management, including on-site manufacturing (OSM) of medical consumables and equipment based on forecasted demand. US20210233388A1 Martin does not teach or suggest the integration of predictive analytics with automated supply chain management and on-site manufacturing capabilities.
Examiner appreciates applicant’s arguments but does not find them persuasive. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). The claim 1 recites “receiving inputs of one or more of the group selected from the list of traffic conditions, weather, incident location of emergency or non-emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, available medical consumables, available medical non consumables, available staff, Cellular triangulation of staff, Cellular triangulation of ambulance or other mobile EMS equipment, Cellular triangulation of service base sites, GPS location of staff, GPS location of the service base sites, GPS location of ambulance or other mobile EMS equipment, identified location of needed services, dispatch requests, time of dispatch requests, latitude and longitude of dispatch requests, hospital census counts, duration of patient admittance in hospital, admitting diagnosis in hospital, discharge diagnosis in hospital, unit transition patterns of patients, unit transition date and time, unit admitting date and time, admitting unit in hospital, on-site manufacturing capabilities,” Therefore only requiring one or more of the recited options for inputs and Martin clearly teaches one or more of these groups by disclosing in [0011] which discloses, “In one aspect , disclosed herein are methods of creating a prediction model for generating at least one spatiotemporal emergency prediction , the methods comprising : a ) obtaining , by an emergency prediction system ( EPS ) , emergency data comprising emergency type , emergency location , and emergency time for a plurality of emergency communications” and see [0136] discloses, “Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency (such as medical , fire , police or car crashes) , the location of the emergency (e.g. , GPS coordinates , altitude , etc. ) , the time of the emergency ( e.g. , date and time ) , or any combination thereof . In some embodiments , additional information regarding the emergency is obtained including , but not limited to , fatalities , types of injuries , proximity to landmarks ( such as sports stadiums ) , signal strength for emergency call , whether the subject was in a vehicle during the emergency , information about road conditions , number and effectiveness of emergency service providers involved , time for emergency response , etc. Emergency data may comprise historical data or current data.”. For example location of emergency incident is taught therefore the scope of the claim is taught and does not require the on site manufacturing. Examiner maintains the 102 rejection for this argument.
Other features are that the present invention enables real-time, bidirectional communication between EMS, hospitals, and other stakeholders, including automated documentation, device integration, and compliance with HIPAA and GDPR. US20210233388A1 Martin does not disclose such comprehensive interoperability or automated compliance features.
Examiner appreciates applicant’s arguments but does not find them persuasive. No specific argument is made to any element of a claim construction limitation rather just narrative conclusive argument to Martin overall which examiner finds moot. However, to try and respond Martin does teach overall communication between EMS resources and device integration as well as automated documentation. Examiner maintains the 102 rejection for this argument.
As to claim 1, the present action alleges that Martin states [0078], "As referenced herein," emergency service providers" may include organizations and institutions that may provide assistance in an emergency. For example, law enforcement, fire, emergency medical services commonly handle many emergency requests. In contrast though the present invention uses multiple forms of patient data to predict the type and location, and resources such as supplies, personnel, and specialty resources along with forecasted trajectory through the healthcare system and provides suggestions to ensure adequate resources are available in advance for emergencies. The present action also states Martin's definitions of emergency types to medical, fire, police or car crashes, ("Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency (such as medical, fire, police or car crashes), the location of the emergency (e.g., GPS coordinates, altitude, etc. ), the time of the emergency (e.g., date and time ), or any combination thereof). However, Applicant submits that Martin disregards in his analysis patient data such as age, sex, chief complaint (with the exception of mentioning injury type) as well as medical diagnostics such as admitting diagnosis in hospital, lab diagnostics, discharge diagnosis in hospital, unit transition patterns of patients, on-site manufacturing capabilities.
Examiner appreciates applicant’s arguments but does not find them persuasive. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). The claim 1 recites “receiving inputs of one or more of the group selected from the list of traffic conditions, weather, incident location of emergency or non-emergency call, call type, dispatch type, latitude and longitude of incident location, age, sex, chief complaint, incident date and time, holiday, day of the week, call classification, emergency department population status, incoming EMS service requests, available medical consumables, available medical non consumables, available staff, Cellular triangulation of staff, Cellular triangulation of ambulance or other mobile EMS equipment, Cellular triangulation of service base sites, GPS location of staff, GPS location of the service base sites, GPS location of ambulance or other mobile EMS equipment, identified location of needed services, dispatch requests, time of dispatch requests, latitude and longitude of dispatch requests, hospital census counts, duration of patient admittance in hospital, admitting diagnosis in hospital, discharge diagnosis in hospital, unit transition patterns of patients, unit transition date and time, unit admitting date and time, admitting unit in hospital, on-site manufacturing capabilities,” Therefore only requiring one or more of the recited options for inputs and Martin clearly teaches one or more of these groups by disclosing in [0011] which discloses, “In one aspect , disclosed herein are methods of creating a prediction model for generating at least one spatiotemporal emergency prediction , the methods comprising : a ) obtaining , by an emergency prediction system ( EPS ) , emergency data comprising emergency type , emergency location , and emergency time for a plurality of emergency communications” and see [0136] discloses, “Emergency data refers to information about emergencies that have occurred or are on - going and optionally includes the type of emergency (such as medical , fire , police or car crashes) , the location of the emergency (e.g. , GPS coordinates , altitude , etc. ) , the time of the emergency ( e.g. , date and time ) , or any combination thereof . In some embodiments , additional information regarding the emergency is obtained including , but not limited to , fatalities , types of injuries , proximity to landmarks ( such as sports stadiums ) , signal strength for emergency call , whether the subject was in a vehicle during the emergency , information about road conditions , number and effectiveness of emergency service providers involved , time for emergency response , etc. Emergency data may comprise historical data or current data.”. For example location of emergency incident is taught therefore the scope of the claim is taught and does not require the remaining groups such as age, sex, chief complaint (with the exception of mentioning injury type) as well as medical diagnostics such as admitting diagnosis in hospital, lab diagnostics, discharge diagnosis in hospital, unit transition patterns of patients, on-site manufacturing capabilities. Examiner maintains the 102 rejection for this argument.
In the present action Martin discloses that the primary output of the emergency anomaly detection module is the detection of clusters of emergency calls, further defining them as time intervals or time periods, and using additional data to estimate response time for a dispatcher to access. However, this is not what is presently claimed. Claim 1 recites that a user can provide automated scheduling template for EMS agencies to use to allow to the best distribution of EMS ambulances matching the call ration per shift is configured.
Examiner does not find applicant’s arguments persuasive. claim 1 recites “the scheduling module outputting automatically a suggested scheduling template which matches EMS call volume appropriately to provide the maximum ambulance per call ratio per shift and is configured to ensure maximum revenue for an EMS agency;” and Martin clearly teaches in paragraph [0267] which discloses, “Various techniques and methods are capable of being used for evaluation of simulations . An exemplary method is the greedy allocation algorithm , for example , for providing a recommendation for allocating ERVs to known base locations to respond to upcoming call volumes . Here , the objective is to allocate vehicles in such a way that response time is minimized . In some embodiments , risk based allocation is considered , which optimizes allocation to ensure a threshold of calls meet a target response time ( e.g. , less than 10 % of calls have a response time greater than 10 minutes )” and see [0268] discloses, “In some embodiments , the availability and frequency of data on emergency resources limits the resolution of this model to shift ( 8 - hours ) or daily . In some embodiments , specialized models for higher resolution are developed. In some embodiments , various models are capable of being used for anomalous cluster detection including unsupervised anomaly detection , supervised anomaly detection , or semi - supervised anomaly detection.” And see [0269] discloses, “In some embodiments , in addition to daily and weekly allocation 779 optimization and recommendations , dynamic reallocation of resources 778 is recommended based on predicted near - term call volumes ( in the next 2-4 hours ) . For example , the current allocation of vehicles is considered and the impact on response time for reallocating them to meet upcoming demand . For example , in some embodiments , vehicles are identified for moving to optimally meet the projected demand in 2 hours.” And see [0270] discloses, “In some embodiments , anomalous cluster detection is carried out for a city , such as City X in FIG . 5B . In some embodiments , there are several PSAP jurisdictions within City X , which handle emergency calls from different areas of the city . In some embodiments , when a collective emergency such as an earthquake or a terrorist attack occurs , there is a sudden increase in emergency calls from certain sections of each PSAP . Using anomalous cluster detection , the clusters within different PSAPs are monitored in real time or near real - time . In some embodiments , clusters of calls are detected in different PSAP areas . In some embodiments , notifications and / or recommendations regarding diverting emergency resources to affected locations are sent to the associated PSAPs . In some embodiments , dynamic reallocation of emergency resources is carried out within different PSAP areas to respond to the group emergency event.” And see [0189] which discloses, “In some embodiments , short - term ( e.g. , 1 hour , 1 day , 1 week ) recommendations for emergency allocation are of emergency personnel or emergency vehicles ) . In some embodiments , long - term predictions ( e.g. , a month , half year or a year ) are used for long - term recommendations ( e.g. , budgeting and planning for hiring emergency personnel and purchasing emergency equipment ).” therefore examiner notes the configured to ensure maximum revenue claim construction is recited as the intended use of the claimed invention not affirmative recitation that it does thus examiner notes the disclosure teaches long term predictions for budgeting and planning as well as target response time which one of ordinary skill would understand to be ensuring revenue. Further Martin clearly teaches the machine learning automatically suggesting whether long term or short term scheduling by matching EMS call volume such as diverting the max EMS when disaster strikes to provide the max allocation available per the shift to meet the demand and someone of ordinary skill in the art would understand Martins disclosure of optimizing the resources and taking into account shift times to meet a low response time to teach this as its not an ipsissima verba test but rather what one of ordinary skill under BRI would understand of dynamic resource allocation and responding. No recitation of ensemble learning, neural networks, decision trees, and reinforcement learning is constructed in the claim 1.However, Claim 5 recites the use of these types of machine learning but Examiner only relied on Martin for a portion of this claim and also added a secondary reference of Brown therefore applicant’s argument is viewed as moot however for sake of response Martin does teach neural network and reinforcement learning in paragraph [0109] which discloses, “In some embodiments , techniques used for generating models and / or making predictions include machine learning , neural networks , multilayer perceptron ( MLP ) , support vector machines ( SVM ) , radial basis function , Naïve Bayes , nearest neighbor , or geospatial predictive modeling.” And see paragraph [0113] which discloses, “In some embodiments , a machine learning algorithm uses a reinforcement learning approach . In reinforcement learning , the algorithm learns a policy of how to act given an observation of the world . Every action has some impact in the environment , and the environment provides feedback that guides the learning algorithm.” Examiner maintains the 102 rejection for this argument.
In the present action Martin discloses that its invention uses prediction models to determine the likelihood of a defined emergency (a prediction model is a multiple linear regression model or formula that generates a risk prediction for the total number of all emergency calls (including emergency incidents) within the city limits of city B for next Friday when data corresponding to environmental condition (s) (e.g., expected rainfall) and/ or event(s) (e.g., grand opening of a museum downtown) inside city B next Friday is entered into the model). However the present invention of claim 1 employes ensembles learning and neural networks, decision tree and deep reinforcement learning via call simulations to create model-free algorithms, as linear regression does not work for this type of analysis, because incident patterns are not linear.
Examiner does not find applicant’s argument persuasive. No recitation of ensemble learning, neural networks, decision trees, and reinforcement learning is constructed in the claim 1.However, Claim 5 recites the use of these types of machine learning but Examiner only relied on Martin for a portion of this claim and also added a secondary reference of Brown therefore applicant’s argument is viewed as moot however for sake of response Martin does teach neural network and reinforcement learning in paragraph [0109] which discloses, “In some embodiments , techniques used for generating models and / or making predictions include machine learning , neural networks , multilayer perceptron ( MLP ) , support vector machines ( SVM ) , radial basis function , Naïve Bayes , nearest neighbor , or geospatial predictive modeling.” And see paragraph [0113] which discloses, “In some embodiments , a machine learning algorithm uses a reinforcement learning approach . In reinforcement learning , the algorithm learns a policy of how to act given an observation of the world . Every action has some impact in the environment , and the environment provides feedback that guides the learning algorithm.” Examiner maintains the 102 rejection for this argument.
In the present action Martin discloses that the type of data that is used for the analysis. However, present claim 1 specifies the method of data collection rather than simply stating that the data is collected and used, which in real life is an important issue to address. In the present action Martin discloses that that cluster of calls are detected and compared with each other, and a spatiotemporal emergency prediction visualization is created. However, this is not what is presently claimed, which instead describes how predictions of patient conditions are used to detect emergencies, building a schedule around those prediction and relaying this to the EMS agency, so that they can adjust their staffing and ambulances accordingly to match best patient per staff per ambulance ratio.
Examiner does not find applicant’s arguments persuasive. as aforementioned the input groups are one or more of a group selected from and examiner is not sure what additional claim limitation specific recitation is being argued here to be able to respond appropriately therefore please see aforementioned arguments for claim 1 as Martin does disclose predictive analysis to staff EMVs and staff per situation warranted such as emergency types like e.g. stroke in [0137] of Martin. Examiner maintains 102 rejection.
Martins' invention seems to be related to traffic incidences and trauma incidences in public areas (e.g. grand museum opening) and disregards medical emergencies such heart attacks, strokes, allergic reaction etc. and the type of resource allocation for these kinds of emergencies. The present invention's analysis and prediction are derived from the patient condition and is trained on the type of incidences and patient condition to estimate a Patient Condition Profile, which is then used to predict upcoming problems which will require intervention.
Examiner does not find applicant’s arguments persuasive. as aforementioned the patient condition profile is taught by a secondary reference not claimed to be taught by Martin. Further again no specific claim limitation is argued here but rather examiner notes to expand upoin the already cited portions of Martin, Martin does disclose predictive analysis to staff EMVs and staff per situation warranted such as emergency types like e.g. stroke in [0137] of Martin. Examiner maintains 102 rejection.
As to claim 2. The present action discloses that Martin states that the data stream comprises the emergency call time and the emergency call location and use a set of predicted kernel density estimates ( KDE) for calculated components in each region within a time window / time block/ defined time period. However claim 2 relates to the type of incidence, referring to the specific condition of a patient, for example STEMI, Stroke, allergic reaction, trauma, renal failure, etc.
Examiner does not find applicant’s arguments persuasive. Martin clearly teaches the recited claim construction. The claim limitation states no specific incidence type in the claim itself of claim 2. ([0197] discloses, “As shown in FIG . 3A , several inputs 312 , 322 , 332 enter the data augmentation module 310 and an output is generated including emergency call data with predicted type or nature , priority and estimated response time ( referred to as “ Emergency Data with predicted labels ” 389 ) . In some embodiments , the call data stream 312 is a proprietary data stream . In some embodiments , the call data stream is a real - time or near real - time call stream from a dispatch center or PSAP .” and see [0198] discloses, “In many cases , the call data stream 312 does not include determinations regarding the type or nature , priority and / or estimated response times ( referred to as “ labels ” ) . Accordingly , in some embodiments , these labels are used to apply the right predictive model and get accurate emergency predictions . In some embodiments , the data augmentation module 310 predicts labels to the call data stream 312 , which is optionally used in the anomaly detection module 240 , spatiotemporal call prediction 220 , emergency resource allocation module 270 , or any combination thereof ( see FIG . 2 ).” And see [0199] discloses, “In some embodiments , for predicting labels , labeled emergency data from various sources such as PSAPs , EDCs and public and private sources is included in the emergency data 322. In some embodiments , the emergency data 322 includes unlabeled emergency data with or without additional information ( see Table 1 ).” And see [0200] discloses, “In some embodiments , the data stream 312 comprises the emergency call time and the emergency call location ( e.g. , latitude / longitude / elevation / address ) . In some embodiments , the data stream includes a calling device identifier such as a subject's phone number , account number , name or log - in ID , universal ID ( uid ) , or any combination thereof . In some embodiments , one or more additional fields are available depending on the source of the data stream , such as the call device and network , accuracy of location information , and call duration . In some embodiments , one or more of these fields are included as features in the predictive models for improving prediction accuracy. Exemplary raw or unlabeled data stream is shown in Table 1.” And see [0206] discloses, “Next , predicted labels are generated for each call record in one or more prediction models . In some embodiments , the predicted labels are generated by a multi - class classifier 334. In some embodiments , the matched emergency data from the matching module 316 ( with or without additional features ) is used as the input to a multiclass classifier 334. In some embodiments , the nature and priority of calls have different criteria for each region or PSAP , and a model for each distinct region is generated . In some embodiments , several classifier models are assessed with the best performing model being selected using model selection and validation 318. Sample output from the classifier 334 is shown in Table 3. And see [0209] discloses, “In some embodiments , when emergency call data from other sources ( e.g. , a PSAP ) arrives , the data comprises metadata or information for each call that reflects the ground truth of the situation such as , for example , the actual nature ( e.g. , emergency type ) and / or priority of the call . In some embodiments , the metadata or information comprises the actual response time , origin and identification of the response vehicle , final destination of the response vehicle , description of the emergency , or any combination thereof . In some embodiments , the labeled emergency call data and the proprietary call data are matched using the call time and location in the matching module 316. A sample of matched emergency data is shown in Table 4 , which is optionally utilized for training the predictive models applied to the incoming call data stream.” And see [0247] discloses, “Referring to FIG . 5C , an exemplary kernel density map 558 is shown . In some embodiments , the output from kernel warping is a set of predicted kernel density estimates ( KDE ) for calculated components in each region within a time window / time block / defined time period ( e.g. , 1 hr . ) . In some embodiments , the output includes date , hour , region , category , component , KDE , or any combination thereof . In some embodiments , predicted call volume in a given region or PSAP is extracted from these estimated by computing the aggregate KDE sum over the area of interest .”) Examiner maintains 102 rejection.
As to claim 3. The present action discloses that Martin states the application produces recommended resource allocation for operations officer to view on a map and in associated tables for each day of the week. In contrast the present claim recites that the output not only includes schedules for the purpose of resource allocation but also epidemiological data, for example to track and predict future transmission of communicable diseases, and other patient conditions, to be used for research, public health planning, resource planning, policy making, and medical interventions needed to prepare EMS units, and hospitals to deal with the surge of demand of an endemic, or pandemic with the proper number of personnel, supplies, equipment, and connect to on-site manufacturing.
Examiner does not find applicant’s arguments persuasive. The claim recites “or” between the series of elements. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). Therefore art does not need to teach the epidemiological data. Examiner maintains 102 rejection.
As to claim 10. The present action discloses that Martin states that an emergency resource management system is used to allocate EMS units accordingly to optimize response times. However, present claim 10 recites that predictions are used to not only allocate personnel accordingly but also accounts for physical provisions and supplies including medications, equipment, consumable and non-consumables.
Examiner does not find applicant’s arguments persuasive. Claim 10 recites, “The method of claim 1, wherein the system comprises an EMS Unit Provisioning Module that aligns healthcare resources, including supply and personnel distribution; wherein the system aligns acquisition and provisioning to ensure the supplies needed to respond to the patient condition are present on the responding EMS unit; and wherein supplies may comprise at least one of medications, equipment, and consumable and non- consumable supplies. ([0016] discloses, “In another aspect , disclosed herein are methods for optimizing emergency resource allocation using emergency data , comprising : a ) obtaining , by an emergency resource management system , at least one spatiotemporal emergency prediction ; b ) obtaining , by the emergency resource management system , at least one estimated response time pre diction corresponding to the at least one spatiotemporal emergency prediction ; c ) obtaining , by the emergency resource management system , local emergency resource allocation data ; and d ) using , by the emergency resource management system , an allocation algorithm to generate a recommendation for optimal allocation of local emergency resources based on the at least one spatiotemporal emergency prediction , the at least one estimated response time prediction , and the local emergency resource allocation data . In some embodiments , the allocation algorithm comprises a greedy allocation algorithm . In some embodiments , the optimal allocation minimizes a predicted overall emergency response time . In some embodiments , the optimal allocation minimizes a number of emergency communications having an emergency response time exceeding a threshold time . In further embodiments , the threshold time is no more than about 10 minutes . In further embodiments , the threshold time is no more than about 20 minutes . In further embodiments , the local emergency resources comprise emergency response vehicle , emergency response personnel , emergency response equipment , emergency response base , or any combination thereof.”
Examiner does not find applicant’s argument persuasive. Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). “comprise at least one of” physical provisions and supplies including medications, equipment, consumable and non-consumables does not need to be taught rather Martin teaches atleast one of which is emergency response equipment.. Examiner maintains 102 rejection.
As to claim 11. The present action discloses that Martin states that a predictive Module is used to out a series of queries, visualization/ mapping, analytics, information about the cluster comprising a center, a radius, a start time, an end time, p - value, number of calls, expected number of calls, or any combination thereof. In contrast, present claim 11 extends the information provides, as information is centered around that patient's condition and condition profile, providing information about the diseases and diagnosis of the expected calls.
Examiner does not find applicant’s argument persuasive. Claim 11 recites, “The method of claim 1, wherein the system comprises coordinated and Intelligent Predictive Analytics with visual outputs from analytics of a Visualization Function Map which includes the step of displaying forecasting of events for patient demand and supports quantitative reasoning; wherein the events can be filtered by at least one of date, time, type of call, disease, and diagnosis.” There is no recitation of providing information about disease and diagnosis rather just that events can be filtered by atleast on of these and Martin teaches at least one of these such as date see Fig. 5C and see [0165] discloses, “In the batch layer 150 , in some embodiments , the Predictive Model module 156 analyzes the data and generates a model or algorithm for making emergency predictions . Various techniques , models or algorithms are used in the Predictive Model module 156. In some embodiments , after model generation or training of the algorithm , the Predictive Model module 156 queries the model with input data ( e.g. , an emergency data set ) for generating an emergency prediction . In some embodiments , emergency predictions are saved in the batch serving database 158 and is made accessible using one or more output services 190. In some embodiments , output services 190 includes one or more of a query services 191 , visualization / mapping 192 , analytics 194 , web applications 187 , and mobile applications 189.” And see [0015] discloses, “In further embodiments , the emergency anomaly is provided as the cluster of emergency communications . In yet further embodiments , the emergency prediction system further provides information about the cluster comprising a center , a radius , a start time , an end time , p - value , number of calls , expected number of calls , or any combination thereof . In further embodiments , providing the emergency anomaly comprises displaying the cluster of emergency communications on a digital map . In yet further embodiments , the emergency prediction system provides the emergency anomaly in response to a request from the emergency dispatch center . In yet further embodiments , the emergency prediction” and see [0247] discloses, “Referring to FIG . 5C , an exemplary kernel density map 558 is shown . In some embodiments , the output from kernel warping is a set of predicted kernel density estimates ( KDE ) for calculated components in each region within a time window / time block / defined time period ( e.g. , 1 hr . ) . In some embodiments , the output includes date , hour , region , category , component , KDE , or any combination thereof . In some embodiments , predicted call volume in a given region or PSAP is extracted from these estimated by computing the aggregate KDE sum over the area of interest . In some embodiments , in order to maintain fine resolution of pre dictions , the KDEs for each component are stored.”). Per MPEP § 2143.03, Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009). Examiner maintains 102 rejection.
As to claim 12. The present action discloses this in some embodiments, the method comprises providing, by the emergency resource management system, a simulation platform for an administrator to simulate a local emergency resource allocation. When describing resources meaning police cars, number of police cars, in order to estimating response time, to improve local emergency resource allocation and response time and platform for simulation is provided for allocating emergency resources (such as police cars). However, claim 11 recites taking the outputs of our previous claims, predicting patient type, age, location, incident/illness type, disease, and epidemiological mapping and research of disease spread, including communicable disease transmission, to simulate transmission of communicable disease, to understand the rate, and path of transmission and how it will effect the local healthcare system and distribute resources/supply provisions personnel, accordingly, it includes simulation of mass casualty incidents and its impact on a local healthcare system to identify bottle necking, improve time-to- treatment and other essential KPIs to improve the patients life, and better prepare in advance to distribute personnel/supplies and other resources. It is used to simulate the economics stressing the healthcare setting under these events, pandemic, future, events, potential budgets for aid and support for federal purposes, for federal agencies to understand optimal amount and type of resources including financial to send after a disaster to support the local healthcare system. To simulate the cost effected of chronic diseases on specified area, and how distribution of resources, public health solutions, can improve the cost of treatment, and population health of an area.
Examiner does not find applicant’s argument persuasive. Claim merely 12 recites, “The method of claim 11 wherein the system comprises the step of simulating emergency responses to support decision making at all levels, including municipalities. And Maritn teaches in [0016] which discloses, “In some embodiments , the method comprises providing , by the emergency resource management system , a simulation platform for an administrator to simulate a local emergency resource allocation . In some embodiments , an estimated response time is calculated for the local emergency resource allocation. In some embodiments , an estimated response time is calculated for the local emergency.” And see [0192] discloses, “In some embodiments , a platform for simulation is provided for allocating emergency resources ( such as police cars ) . For example , in some embodiments , an administrator of the emergency predictions system or of the EMS or a customer logging in at the PSAP system is able to access output services such as the simulation platform for providing estimated response times for responding to predicted emergencies. In some embodiments , the administrator or customer is able to adjust the allocation ( such as location of police cars ) to see the predicted effect on response times and / or other results.” And see [0260] discloses, “The predicted call response time ( s ) and sample requests as well as local emergency resource avail ability data 742 may then be used by the allocation simulator in step 776 .” Therefore teaches local emergency resource allocation which is a municipality. Examiner maintains 102 rejection.
Since each and every element as set forth in claims 1 and 13, as currently amended, are not found, either expressly or inherently, in a single prior art reference, i.e., Martin, it is insufficient to support a rejection under 35 USC 102 (and even 35 USC 103) and should be removed. Accordingly, Applicant submits claims 1 and 13, as currently amended, are in condition for allowance and respectfully requests such allowance. Similarly, claims which depend from claims 1 and 13, directly or indirectly should be allowed for at least these same reasons. No new matter has been added.
Examiner maintains the 102 rejection for the aforementioned arguments.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows: Determining the scope and contents of the prior art. Ascertaining the differences between the prior art and the claims at issue. Resolving the level of ordinary skill in the pertinent art. Considering objective evidence present in the application indicating obviousness or non-obviousness.
First, there must be some suggestion or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings with the claimed specific properties. Second, there must be some reasonable expectation of success. Finally, the prior art reference must teach or suggest all the claim limitations. The teaching or suggestion to make the claimed combination and the reasonable expectation of success must be both found in the prior art, and not based on Applicant's disclosure. In re Vaeck, 947 F.2d 488, 20 USPQ2d 1438 (Fed. Cir. 1991). See MPEP 2142. Accordingly, Applicant traverses these rejections. Applicant respectfully traverses these rejections and based on the remarks presented above believe that claim 1, as amended, is allowable.
As to claim 4. The present action discloses collected data may be utilized to anticipate the needs of a community and to manage the flow of patients, caregivers, supplies and 50 facilities accordingly while minimizing costs associated with wasted time and resources. For example, to initiate a perimeter vaccination of an area, request shipment of consumable supplies, medicines, and vaccines to an area, adjust staffing and intake procedures at a healthcare facility and/or generate an advisory for the area. However, that is not what the claim is about. The method of claim 1, wherein the scheduling model outputs predicted medical consumable needs by agency. The system predicts consumable needs by agency from the outputs of claim 1, and provides feedback to the manufacturer to ensure production of needed consumables, and feedback to the EMS agency to prepare for predicted patient conditions, types, diseases, illness/injuries, described in the previous claim.
Further, the present action discloses It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin's teachings of scheduling and predicting service calls in an area as previously cited with Newton's teachings of predicting when consumables are needed as previously cited, the motivation being Martin teaches a predication and notification system for determining status of a patient and their environment such as oxygen levels and any threats therefore it would be predictable to combine the prediction of explicit environment and needs such as anticipated consumables with the scheduling and prediction service in Martin to increase efficiency and decrease resource waste and decrease cost for EMS. However, that is not what claim 4 recites. Predicted geolocation patient incident, type, age, disease, and diagnosis, to predict forecasted supplies needed for the EMS agency including consumables, non consumables, and medications to the manufacturer to produce adequate supplies to ensure EMS agencies are stocked with proper supplies for predicted demand.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 4 that is recited is argued but however examiner notes claim 4 actually recites “The method of claim 1, wherein the scheduling model outputs predicted medical consumable needs by agency.” And Newton in combination with Martin teaches in Col. 19 lines 48-60 discloses, “For example, collected data may be utilized to anticipate the needs of a community and to manage the flow of patients, caregivers, supplies and 50 facilities accordingly. As shown by arrow 205, exemplary system 100 may coordinate these resources to meet anticipated needs, such as a spreading illness within the community, while minimizing costs associated with wasted time and resources. For example, system 100 may contact a 55 healthcare provider to initiate a perimeter vaccination of an area, request shipment of consumable supplies, medicines, and vaccines to an area, adjust staffing and intake procedures at a healthcare facility and/or generate an advisory for the area.”. It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area as previously cited with Newton’s teachings of predicting when consumables are needed as previously cited, the motivation being Martin teaches a predication and notification system for determining status of a patient and their environment such as oxygen levels and any threats (see [0303]), therefore it would be predictable to combine the prediction of explicit environment and needs such as anticipated consumables with the scheduling and prediction service in Martin to increase efficiency and decrease resource waste and decrease cost for EMS such as areas who might be having major illness spreading. This is a clear TSM obviousness example as the claim is broad and just merely states predicted consumables needed by agency it is obvious this is needed in EMS situations of all types. Examiner maintains the 103 rejection.
As to claim 8. The present action states that Newtons teaches that data around the patient may be recorded and stored for future access by an identifier and using this identifier additional information, including medical records such as historical lab result data, histories of calls about the patient, the patient's transfer history, and a history of patient's admissions across venues, to provide quick access for information about the patient. In contrast to the present claim 4 in which the system is learning, and updated the predicted Patient Condition Profile, from time of dispatch call which includes using AI/ML, NLP to capture the data from 911 call taking throughout the emergency call, with the data collected in method 1 and continuously updates the PCP to the system throughout the entire patient trajectory, and provides the new inputs to the predictive values in the system.
The present action also states it would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin's teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Newton's explicit teachings of utilizing patient data through the 911 calls to generate the best course of action to determine patient trajectory, the motivation being Martin already receives various labelled and unlabeled emergency data such as response type and nature and priority to determine predicted response conditions thus it would only improve the machine learnings ability to make precise response predications by providing additional choice data such as scripted answered questions and patient profiles to increase efficiency, decrease resource strain, and improve the overall prediction density maps in Martin while utilizing the same computer implementation as disclosed in Martin. However, that is not what claim 8 recites. Claim 8 recites combining the data points outlined in the method of claim 1, and from the start of an emergency call all information is sent directly to the system with AI/ML, NLP and other deep learning techniques to generate a Patient Condition Profile, which is of patient type, age, suspected disease/diagnosis, injury, and the PCP continuously updates through more data which is inputted into the system throughout the entire patient trajectory, for example when the EMS unit meets the patient, the vitals, patient assessment findings, ekg, and other diagnostics are updated into the system to update the PCP, and optimize the forecasted trajectory, and resources/supply provisions through the suspected trajectory. Claim 8 is centered around the Patient condition profile and the patient state space, two metrics which derive the diagnosis incorporating the history of the patient which directly translate the before mentioned information received by the identifier as cited by Newton into a usable information package that follows the patient around and represent a digital format/digital twin of the patients current state of the patient. Such a usable data format is not mentioned in the claims by Newton or Martin, and it solves important problems in the transfer of patient data between facilities, so information about the patient is not isolated on different servers at different medical facilities, but attach to the patient and its trajectory through the healthcare filed. It is thus not an obvious improvement as stated by the examiner to simply identify and link information of a patient to an incoming call as stated by combining Newton and Martins art, but 8 is novel and non-obvious in building a metric such as the Patient Condition Profile continuously being updated through the patient state space, with the feedback to the system to shorten the patient trajectory through optimization of patient care that is dynamic and not static in its nature.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 8 that is recited is argued but however examiner notes claim 8 actually recites “The method of claim 1, wherein the system has components for a Patient Condition Profile engine and a Patient Condition Profile Database, which receives information gathered from an emergency dispatch caller and generates, stores and updates the Patient Condition Profile in real time; wherein data of the Patient Condition Profile is derived from a caller's answers to a 911 call taker's questions according to predetermined scripts; wherein the Patient Condition Profile output assists the system in determining a response based on predetermined characteristics, including optimal outcome.” And Newton in combination with Martin discloses, Col. 16 lines 45-67 and Col. 17 lines 1-67 discloses, “The voice print and the associated data may be linked with one or more electronic records for later access and review. For example, when a patient places a 911 call, that call may enter the healthcare command center computer aided dispatch (CAD) system. The CAD may auto-populate the phone number, address, cross-street, local police department, first responders, and any other relevant information about the call source, and save the data with the unique identifier associated with that source. Alternatively, if the CAD receives a call from a source already associated with a unique identifier, the CAD may access that unique identifier and associated information, view historical data, and update the data if desired. In some embodiments, different call sources may each have a unique identifier with associated data and with which calls may be associated and stored. For example, individual patients and homes may have unique identifiers, as may other healthcare venues, systems. and healthcare command centers. As a result, when the healthcare command center receives a call from one of these sources, the dispatcher within the healthcare command center may view the information associated with the call to determine who is calling. The information may include historical data about the frequency of calls and the frequency of admissions. According to embodiments in which a call is received at the healthcare command center from a different healthcare venue, the call may include a unique identifier for the source of the call. The identifier may include, for example, the hospital from which the call is coming, as well as the floor number, room number, and optionally the individual placing the call. As a result, the staff member in the healthcare command center who receives the call may know the source of the call, may view data from past calls, and may log the current call into the history associated with the unique identifier. As a result, if resources are distributed as a result of the call, or if patients are transferred between healthcare venues as a result of the call, the healthcare command center may ensure that the resources and/or patients are sent to the correct venue. Alternatively or additionally, in some embodiments, a call received at the healthcare command center may be assigned a unique patient identifier which may identify the patient or patients about whom the call concerns. As a result, the patient's record may include a history of calls relating to that patient, and the voice prints of those calls. This voice print data may be shared when the patient's record is shared between caregivers and/or between healthcare venues or systems. In some embodiments, the patient's medical records, lab result data, transfer history, call history, and transaction case number may be associated using one or more electronic record links, for ease of access. In some embodiments, the aforementioned data may be associated with the patient's medical records, so that the medical records include historical lab result data, histories of calls about the patient, the patient's transfer history, and a history of patient's admissions across venues. This data may be maintained and updated over time. …[…]…In some embodiments, a healthcare command center may be integrated with an automated capacity operations status board. The board may be mounted within a healthcare venue and may provide visual indications of available capacities in specialty service units. These units may include trauma units, pediatrics, STEMI, stroke units, and neurosurgical units. Data depicted on the board may be collected, analyzed, and provided by the healthcare command center. The board may indicate the number of beds available in each unit, as well as available staff and resources.” And see Col. 28 lines2-5 discloses, “During the trip, the electronic device may provide questions” and see Col. 10 lines 53-54 discloses, “A variety of services may also be implemented during the development and delivery of a healthcare command center.” It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Newton’s explicit teachings of utilizing patient data through the 911 calls to generate the best course of action to determine patient trajectory, the motivation being Martin already receives various labelled and unlabeled emergency data such as response type and nature and priority to determine predicted response conditions thus it would only improve the machine learnings ability to make precise response predications by providing additional choice data such as scripted answered questions and patient profiles to increase efficiency, decrease resource strain, and improve the overall prediction density maps in Martin while utilizing the same computer implementation as disclosed in Martin. Examiner maintains the 103 rejection.
As to claim 9. The present states that Martin discloses through a combination of modules, and physiologic data of a patient, they will determine what emergency services are needed to be dispatched to the patient and generate task information that identifies the one or more discrete tasks involved with provision of the emergency services to the patient and make emergency predictions. In contrast, the present claim 9 recites that through analyzing and continuously updating the Patient Condition Profile with respect to predicted patient disease and diagnosis outcome, not only the optimal choice of EMS unit response is determined by analyzing the patient, EMS unit, but also predict which facility is best for the patient, as stated in the claim 'receiving entity profiles' based on the most optimal patient outcome.
The present action discloses that Newton states that they take real time patient data to determine proper destination, transport method to destination to reduce cost, and transport destination based on current capacity. However, that is not what claim 9 recites, which is about ensuring optimal resources, such as personnel supply provisions, and the like are in place from the predicted Patient Condition Profile and trajectory, from EMS pick up to hospital discharge ensuring optimal treatment outcomes, and reducing bottlenecks throughout the patient trajectory, and our system continuously analyzes the Patient Condition Profile, to determine the most appropriate patient trajectory while accounting for predicted future demand throughout the hospital destinations.
The present action also states that Newtons teaches that patient information is accessed by the system to determine the most appropriate course of action. However, claim 9 describes a smart system that continuously monitors changes in the current Patient Condition Profile in the patient state space, predicting the patient trajectory, and continuously optimizes itself with the aim to shorten the patient trajectory by improving patient outcomes through improving time to treatment, care access, and system wide efficiency. Our claim describes an Intelligent system that continuously optimizes itself, including personnel, provision, resources, supplies, consumables, non-consumables, medications, on-site manufacturing capabilities, and is also able to determine, alert and schedule appropriate response units, including EMS units to meet and check up on the patient, regardless of an emergency call has been received or not, while factoring in predicted surges in demand such as mass casualty incidents. The Patient Condition Profile and the patient state space are two metrics that are derives from the diagnosis and history of the patients sit in the cyber space as a digital representation or digital twin of the patient.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 9 that is recited is argued but however examiner notes claim 9 actually recites “The method of claim 1, wherein the system comprises an EMS Response Module that continuously analyzes and updates a Patient Condition Profile to provide intelligent EMS response to the patient's condition; wherein the system adapts to input of a changing patient condition; wherein an optimal choice of EMS unit response is determined by analyzing the patient, EMS unit, and receiving entity profiles for the most optimal patient outcome.” And Newton in combination with Martin discloses in Col. 19 lines 61-67 and Col. 20 lines 1-11 discloses, “In various embodiments, exemplary system 100 may collect information about a given patient and their medical history, and may utilize the information to route the patient to the best venue of care. For example, a patient experiencing a chronic back injury may call 911 because he is experiencing pain. In such circumstances, it may be unnecessary for the patient to come into the emergency department, given the high cost of emergency care and because a more appropriate care venue is available. System 100 may access the patient's medical history and analyze factors such as the patient's plan of care, appointment history, medications, and his physical location, and may use these factors to determine the best treatment option. For example, system 100 may determine that the patient has recently missed a physiotherapy appointment and has not filled his recent prescription for pain medication. System 100 may schedule an appointment for the patient at a physiotherapy clinic, such as a clinic where the patient was previously treated. System 100 may schedule an appointment for the patient at a physiotherapy clinic, such as a clinic where the patient was previously treated. Optionally, system 100 may coordinate with a transportation service, such as taxi service, or a ride-share service such as Lyft®, or Uber®, to transport the patient to the clinic at the appropriate time.” And see Col.14 lines 10-29 discloses, “In some embodiments, a healthcare command center may be integrated with emergency medical services (EMS) to gain visibility into community needs. This integration may include routine medical transportation services such as ambulances and wheelchair vans, as well as integration with non-medical transportation services, such as Uber, Lyft, and taxis or other similar ride-share programs. Integration with EMS may occur at both the front-end (transportation of patients to a healthcare venue for treatment) and the backend (transportation to post-care facilities after patient discharge), and may provide data to predict future transportation demands. In some embodiments, front-end EMS may be integrated with the healthcare command center to provide real-time data about current demands for service and about capacities of different healthcare venues across a healthcare system. Instead of EMS services transporting all patients to an acute-care setting such as an emergency department, exemplary healthcare command centers may coordinate delivery of patients to the appropriate healthcare venue via EMS.” It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Newton’s explicit teachings of utilizing patient data through the 911 calls to generate the best course of action to determine patient trajectory, for the same reasons given in claim 8.”
Examiner maintains the 103 rejection.
As to claim 5. The present action states that Brown says the one or more task optimization models can employ various machine learning techniques (e.g., developed based on based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system) and/ or statistical techniques to facilitate determining / inferring the optimal task scheduling and resource assignment information. Brown is modeling task assignments, and down time of healthcare providers to provide AI/ML models to optimize providers tasks in the hospital. In contrast present claim 9 recites that through predicting the patient type, disease, diagnosis, geolocation, and trajectory, and pre-emotively providing scheduling suggestions to ensure optimal coverage, patient staff ratios and the like, machine learning models utilize Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms.
The present action further states that combining Martin's teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown's explicit teachings of predicting using ensemble methods and decision trees as previously cited, the motivation being Martin's prediction's would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take utilizing patient data within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling. In contrast, present claim 9 recites that through predicting the patient type, diagnosis, and trajectory to ensure optimal personnel, supply provision through out the entire trajectory from EMS pick to hospital discharge, multiple machine learning, ensemble learning, and deep learning techniques are used to simulate different scenarios, outcomes in the healthcare area and use this data to train and create model free algorithms.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 5 that is recited is argued but however examiner notes claim 5 actually recites “The method of claim 1, wherein the machine learning models utilize Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms. “ and Martin teaches the neural network and deep reinforcement learning in [0109] discloses, “In some embodiments , the systems , methods , and media described herein use machine learning algorithms for training prediction models and / or making predictions . Machine learning explores the study and construction of algorithms that are capable of learning from and making predictions on data . In some embodiments , techniques used for generating models and / or making predictions include machine learning , neural networks , multilayer perceptron ( MLP ) , support vector machines ( SVM ) , radial basis function , Naïve Bayes , nearest neighbor , or geospatial predictive modeling.” And see [0113] discloses, “In some embodiments , a machine learning algorithm uses a reinforcement learning approach . In reinforcement learning , the algorithm learns a policy of how to act given an observation of the world . Every action has some impact in the environment , and the environment provides feedback that guides the learning algorithm.” However, Martin does not teach the underlined portions: “The method of claim 1, wherein the machine learning models utilize Ensemble learning + neural networks, Decision tree and deep reinforcement learning via call simulations to create Model-free algorithms. “ However, Brown in combination with Martin does teach the underlined portions see Brown [0147] which discloses, “The one or more task optimization models can employ various machine learning techniques ( e.g. , developed based on based on analysis of historical operations data regarding historical performance of various healthcare tasks by the healthcare workers under different operating conditions of the healthcare system ) and / or statistical techniques to facilitate determining / inferring the optimal task scheduling and resource assignment information ( e.g. , SVM classification , neural networks ( e.g. , including deep neural networks , deep adversarial neural networks , convolutional neural networks , and the like ) , Bayesian networks , decision trees , a nearest neighbor algorithms , boosting algorithm , gradient boosting algorithms , linear regression algorithms , k - means clustering algorithms , association rules algorithms , q - learning algorithms , temporal difference algorithm , and probabilistic classification models providing different pat terns of independence , and the like).” It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees as previously cited, the motivation being Martin’s prediction’s would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take utilizing patient data within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling.
Examiner maintains the 103 rejection.
As to claim 6. The present action states that to combine Martin's teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown's explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited, the motivation being Martin's prediction's would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take even further improving precision and accuracy by utilizing choice patient data such as outcomes and trajectories within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling. However, claim 9 recites optimizing the patient trajectory to improve patient outcome through improving metrics such as time to treatment to shorten the patient trajectory, meaning improving time to treatment resulting in improved recovery time and shortened hospital stays. The system does this through optimizing personnel, reducing bottlenecks, reducing wait times, and proactively optimizes the patient trajectory based on predicted surges and needs of resources, while also monitoring real time patient condition changes.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 6 that is recited is argued but however examiner notes claim 6 actually recites “The method of claim 1, wherein the system machine learning models learns and analyzes a patient condition profile to determine a patient trajectory based on patient outcome and time to treatment.” And Brown in combination with Martin teaches in [0030] discloses, “The disclosed subject matter is directed to systems , computer - implemented methods , apparatus and / or computer program products that provide facilitate coordinating and optimizing resource utilization and delivery of healthcare services across an integrated healthcare system using a machine learning framework . An integrated health care delivery system is one in which all the providers whose services affect a patient work together in a coordinated fashion , sharing relevant medical information , sharing aims or goals , sharing responsibility for patient outcomes , and for resource use . For example , an integrated healthcare system can include many different operating entities that provide a variety of different healthcare services to patients , including hospitals , specialized hospital units , specialized physician clinics / offices , outpatient facilities , ambulatory services , nursing home facilities , surgery centers , imaging / diagnostic providers , pharmacy providers , traveling / in - home patient care , rehabilitation providers , telemedicine providers , and the like.” And see [0032] discloses, “The second dimension of this model is the discrete patient that has a prescribed number of activities or services to be rendered . Taking into account unique patient's acuity or need , list of discrete services or activities to be completed , and any requires sequencing , the disclosed techniques can determine how to schedule the patient for service to optimize the time required to complete all activities.” And see [0034] discloses, “In one or more embodiments , the system collects and combines real - time and historical data from various integrated healthcare provider systems and sources regarding patient needs and all aspects of operations of the different healthcare providers that are available to provide access and retrieve or receive operating information from different operating entities in real - time over a course of operating of the one or more operating entities regarding what task needs to be done ( e.g. , clinical tasks and non clinical task for performance by a wide range of clinicians , healthcare workers and the like , when and where within the healthcare system at a current point in time and / or over a defined , upcoming period of time . The system can further extract and receive up - to - date information from the different operating entities regarding who or whom is available to perform the tasks , and who is the best person / persons to perform the tasks . The system can further evaluate the information using various machine learning models and / or optimization models / algorithms to determine how to sched ule performance of the tasks with respect to time and location and how to assign resources ( e.g. , workers and optionally non - human resources ) to the tasks in a manner that results in performing the tasks in the most efficient and effective manner , using the right resources at the right time for the right patient in the right place.”) It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited, the motivation being Martin’s prediction’s would be more task specific and combine multiple models improving accuracy thus improving the best course of action to take even further improving precision and accuracy by utilizing choice patient data such as outcomes and trajectories within EMS and would not be unpredictable to implement as Martin contains the technological components to implement the substituted machine modeling.
Examiner maintains the 103 rejection.
As to claim 7. The present action states that Brown determines and evaluated personnel and resources available at an appropriate facility destination. However, claim 9 recites limitations that measure metrics such as time to treatment, based on available resources and while taking into account predicted future surges in the same time frame. The present action states Brown states wherein the profiles are encoded to enable storage and processing using a digital computing device; integrating static and semi static data, Al models evaluate a patient's condition, diagnosis, needs and medical history and generate a care plan accordingly. In some implementations, the Al system can also evaluate information regarding the patient's insurance plan / carrier and/ or form of payment in association with determining the type of services that are available to the patient for including in the patient's care plan. However, claim 7 recites limitations related to a dynamic digital twin which follows the patient within the healthcare system, but also captures health data not from the healthcare system and not from medical records, for example daily health habits. This dynamic digital twin is incorporated into the Patient Condition Profile which provides positive feedback to the healthcare system to optimize the patient care journey based on metrics such as time to treatment.
The present action states wherein the system analyzes the encoded profiles to determine optimal outcome and coordinates EMS unit response and patient trajectory based on current and forecasted conditions. ([0087] discloses, "The task identification component 304 can further process the extracted I received task information (e.g., the newly reported tasks data 214, the task scheduling data 218, and/ or the forecasted task data 222) to identify all (or defined subsets) of the discrete tasks reflected in the task information for performance over the defined, upcoming timeframe". However, claim 7 recites future forecasted conditions, including on-site manufacturing capabilities, predicted mass casualty incidents and large-scale fluctuations in demand, and continuously optimizes the system, around the predicted need, to optimize patient care. Claim 7 builds upon Browns ability to use machine learning techniques to predict task work flows, while also combining the predictive capability of surges in demand, future incidents and emergencies, other patient trajectories and mapping them through the entire healthcare field to provide already in place in advance the proper number of personnel, supplies, provisions, and other resources, while continuously optimizing care around the trajectory throughout real time Patient Condition Profile monitoring and changes with the aim to of shorten the patient trajectory, meaning improve patient outcomes through increases time to treatment, and shorten hospital stays while improving recovery.
The present action states that to combine Martin's teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown's explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited for the same reasons given in claim 6. However, present claim 7 builds upon Browns ability to use machine learning techniques to predict task work flows, while claim 7 additional is combining the predictive capability of surges in demand, future incidents and emergencies, other patient trajectories and mapping them through the entire healthcare field to provide already in place in advance the proper number of personnel, supplies, provisions, on-site manufacturing, and other resources, while continuously optimizing care around the trajectory throughout real time Patient Condition Profile monitoring and changes with the aim to shorten the patient trajectory, meaning improve patient outcomes through increases time to treatment, and shorten hospital stays while improving recovery.
Examiner does not find applicant’s argument persuasive. No specific recitation of claim 7 that is recited is argued but however examiner notes claim 7 actually recites “The method of claim 6, wherein the system analyzes a receiving entities profile for characteristics including at least one of bed availability, staff availability, supply availability and predicted patient load by patient condition for that facility; wherein the system analyzes the encoded profiles to determine optimal outcome and coordinates EMS unit response and patient trajectory based on current and forecasted conditions. wherein the profiles are encoded to enable storage and processing using a digital computing device;” and Brown in combination with Martin teaches in [0054] discloses, “Although the embodiments , described above are directed to evaluating and determining availability of health care workers ( e.g. , humans ) , the resource assessment mod ule 114 can also evaluate relevant dynamic operating data 104 to determine information regarding the availability of other system resources . For example , the other system resources can include supplies , instruments , equipment , machines , technology , and the like that are needed to per form and / or facilitate performance of the healthcare tasks . Thus , in some embodiments , the resources availability data 116 can also include information regarding availability of other resources , such as current availability status of the resources ( e.g. , whether they are in - use , clean , dirty , in repair , offline , overloaded , power levels , etc. ) , expected availability status of the resources ( e.g. , and the like .”) ([0063] discloses, “In the embodiment shown , the healthcare information systems / sources 102 include one or more databases that provide static / semi - static system data 106 for an operating entity or group of operating entities , including task definitions / requirement data 202 , worker information 204 , system geospatial data 206 , finance data 210 and patient information 252.” And see [ 0073 ] The patient information 252 can include patient information regarding current patients of one or more healthcare systems ( e.g. , patients that have entered the healthcare system via at least one entry point ) and their medical needs . For example , the patient information 252 can include care plan information 240 that describes or defines care plans for the patients ( if available ) . For example , the care plan can include information a list or timeline of the various prescribed clinical treatment to for the patient in association with a course of patient care . In some implementations , the care plan information can also be associated with information identifying patient rest and recovery periods / times , such as amounts of time and / or periods of time during which the patient is required or preferred to rest ( e.g. , between procedures or appointments and the like ) . In some implementations , the care plan information can be automatically generated and provided by an artificial intelligence ( AI ) system configured to evaluate a patient's condition , diagnosis , needs and medical history and generate a care plan accordingly . In some implementations , the AI system can also evaluate information regarding the patient's insurance plan / carrier and / or form of payment in association with determining the type of services that are available to the patient for including in the patient's care plan ( e.g. , only those services that are approved or anticipated for approval by the patient's insurance provider ) . The patient information 252 can also include clinical order data 242 providing clinical order prescribed for a patient . The patient information can also include medical history information for current and past patients , such as that provided in the EHR data 244.”) ([0087] discloses, “The task identification component 304 can further process the extracted / received task information ( e.g. , the newly reported tasks data 214 , the task scheduling data 218 , and / or the forecasted task data 222 ) to identify all ( or defined subsets ) of the discrete tasks reflected in the task information for performance over the defined , upcoming timeframe” and see [0040] discloses, “The healthcare delivery optimization server device 108 can provide various features and functionalities that facilitate optimizing utilization of healthcare resources and delivery of healthcare services . In one or more embodiments , the healthcare delivery optimization server device 108 can facilitate optimizing scheduling of different health care tasks and assigning resources to the different healthcare tasks in real - time in a manner that synchronizes and harmonizes patient needs and provider capabilities under the dynamic operating conditions associated with the healthcare environment . The healthcare environment can include individual operating entities , as well as a macro ecosystem that combines the individual operating entities into a unified integrated healthcare system . For example , the operating entities can include various types of healthcare facilities that provide healthcare services , including ( but not limited to ) , hospitals , clinics , ambulatory surgical centers , birth centers , blood banks , specialty clinics or medical offices , dialysis centers , hospice homes , imaging and radiology centers , therapy centers , mental health treatment centers , nursing homes , orthopedic and other rehabilitation centers , urgent care facilities , and the like . The operating entities can also include healthcare entities that provide mobile or in - home care services patients ( e.g. , traveling nurses , emergency medical services ).” And see [0090] discloses, “For instance , the task identification component 304 can determine , based on monitored physiological data for a patient , that emergency services are needed for dispatch to the patient and generate task information that identifies the one or more discrete tasks involved with provision of the emergency services to the patient.”) It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Martin’s teachings of scheduling and predicting service calls in an area utilizing various machine deep learning approaches as previously cited with Brown’s explicit teachings of predicting using ensemble methods and decision trees and utilizing patient data to determine patient trajectory as previously cited for the same reasons given in claim 6.
Examiner maintains the 103 rejection.
In sum, Applicant submits that the cited Newton and Brown references do not teach or suggest all the limitations of any of the pending claims, either alone or in combination with Martin to sustain a rejection under 35 U.S.C. §103 and should be removed as a basis to reject the claims under 35 USC 102 and 35 USC 103. Accordingly, Applicant submits that claims 1 and 13, as currently amended, are in condition for allowance and respectfully requests such allowance. Similarly, claims which depend from claim 1, directly or indirectly, should be allowed for at least these same reasons.
Examiner maintains the 103 rejection due to the aforementioned arguments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687