DETAILED ACTION
Status of Claims
This communication is a first action on the merits. In the preliminary amendments filed 7/18/26, claim 3 and 4 were amended. Claim 6 was canceled. Claims 7-21 were added. Claims 1-5 and 7-21, as preliminarily amended, are pending and have been considered as follows.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 11-13 are objected to because of the following informalities: The claims recite “The display control method according to claim 10…”, but should read –The computer-readable non-transitory recording medium according to claim 10–. Appropriate correction is required.
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 1-5 and 7-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a patent-ineligible abstract idea without significantly more and are merely requiring generic computer implementation, which fails to transform that abstract idea into a patent-eligible invention. In view of the two-step test regarding determining subject matter eligibility, Examiner submits that the independent claim(s) 1, 5, and 10 recite(s) a device, a method, and a non-transitory recording medium for display control. Therefore, the claims as a whole are considered as being in a statutory category under Step 1 of the test.
Regarding Step 2A, prong 1, Examiner submits that the claims recite a judicial exception, specifically that of an abstract idea. The claimed invention is drawn to an abstract idea of display control, by specifically including “an estimation unit that estimates…an occurrence time at which a call occurs at the occurrence point for each of a plurality of the occurrence points”; “a simulation unit that executes a simulation of an emergency activity in which any one of a plurality of the emergency vehicles available for dispatch is dispatched to the occurrence point at the occurrence time for each of the plurality of occurrence points…”; “a calculation unit that…extracts…an occurrence point at which a distance between the emergency vehicle available for dispatch and the occurrence point is equal to or greater than a threshold value, and calculates a risk level such that the risk level of an area to which the extracted occurrence point belongs becomes high”; and “a display control unit that controls a display unit to display the risk level calculated by the calculation unit”.
First, the limitations of at least “an estimation unit that estimates…an occurrence time at which a call occurs at the occurrence point for each of a plurality of the occurrence points”; and “a simulation unit that executes a simulation of an emergency activity in which any one of a plurality of the emergency vehicles available for dispatch is dispatched to the occurrence point at the occurrence time for each of the plurality of occurrence points…”, as drafted, are drawn to a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a processor”, or “via the communications interface” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a unit” language, “estimating an occurrence time” in the context of this claim encompasses the user manually determining an expected occurrence time, e.g. mentally or by pen and paper. Similarly, the limitation of “a simulation unit that executes a simulation of an emergency activity in which any one of a plurality of the emergency vehicles available for dispatch is dispatched to the occurrence point at the occurrence time for each of the plurality of occurrence points…”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “by a unit” language, “executing a simulation of an emergency activity” in the context of this claim encompasses the user manually projecting an emergency activity, e.g. mentally or by pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Additionally, the limitation of at least “a calculation unit that…extracts…an occurrence point at which a distance between the emergency vehicle available for dispatch and the occurrence point is equal to or greater than a threshold value, and calculates a risk level such that the risk level of an area to which the extracted occurrence point belongs becomes high” is drawn to performing calculations relating to calculating which occurrence point that the distance between the available emergency vehicle and the occurrent point is equal or greater than a threshold value and calculating the associated risk level, which is drawn to the abstract idea grouping of Mathematical Concepts (i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations).
Furthermore, the limitations of at least “a calculation unit that…extracts…an occurrence point at which a distance between the emergency vehicle available for dispatch and the occurrence point is equal to or greater than a threshold value, and calculates a risk level such that the risk level of an area to which the extracted occurrence point belongs becomes high”, as drafted are drawn to a process that, under its broadest reasonable interpretation, falls within the abstract idea grouping of Certain Methods of Organizing Human Activity (i.e. fundamental economic principles or practices including hedging, insurance, and mitigating risk; commercial or legal interactions including agreements in the form of contracts; legal obligations; advertising, marketing or sales activity or behaviors; business relations; or managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions). That is, the claims are directed to the concept of display control. If a claim limitation/invention, under its broadest reasonable interpretation, can be construed as describing fundamental economic principles or practices including hedging, insurance, and mitigating risk, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. In particular, the steps together are accomplishing display control, specifically including calculating a risk level, which is related to the fundamental economic principles or practices including hedging, insurance, and mitigating risk. Accordingly, the claims recite an abstract idea.
Regarding Step 2A, prong 2, Examiner submits that the claim as a whole does not integrate the recited judicial exception into a practical application of the exception. Examiner submits that the claims at hand in fact do not include any recitation of additional elements in the claim beyond the judicial exception that would integrate the judicial exception into a practical application. To be considered statutory, the claims require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. In this regard, Examiner submits that there are no such additional elements that improve the functioning of a computer to any other technology or technical field, apply or use a judicial exception to effect a particular treatment, apply the judicial exception with or by use of a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. In fact, the claims include language drawn to merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and/or generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). Accordingly, the claims recite an abstract idea.
Regarding Step 2B drawn to determining if the claim recites additional elements amounting to significantly more than the judicial exception, Examiner submits that the claims in fact do not include any recitation of additional elements that would constitute anything significantly more. In particular, the claim only recites the units used to perform the steps of the invention (e.g., an estimation unit, a simulation unit, a calculation unit, and a display control unit). The units in the claimed steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of computing or processing) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and amount(s) to no more than: (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. In contrast to the Enfish decision where the claims focused on a specific improvement—a particular database technique—in how computers could carry out one of their basic functions of storage and retrieval of data, the present case is drawn to certain independently abstract ideas that use computers as tools. Enfish, 822 F.3d at 1335–36; see Bascom, 2016 WL 3514158, at *5; cf. Alice, 134 S. Ct. at 2360 (noting basic storage function of generic computer). Therefore, these claim limitations, either individually or as an ordered combination, do not amount to significantly more than the abstract idea itself and do not transform the nature of the claim from the judicial exception into a patent-eligible application. The claims are not patent eligible.
Regarding claims 2-4, 7-9, 11-21, the dependent claims do not include any additional elements that constitute statutory matter. The dependent claims are directed to the same abstract idea as recited in the independent claims and have been found to either recite additional details that are part of the abstract idea itself (when analyzed under Step 2A Prong One), or include additional details that, when analyzed under Step 2A Prong Two and Step 2B, recite additional elements that fail to integrate the abstract idea into a practical application (Step 2A Prong Two) and fail to add significantly more to the abstract idea (Step 2B). Specifically, the dependent claims further describe additional limitations drawn to at least calculating specific times, simulating specific emergency activities, and displaying certain information. The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claims) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself. Additionally, the dependent claims include limitations that as drafted, are drawn to performing calculations of specific times, which is drawn to the abstract idea grouping of Mathematical Concepts (i.e. mathematical relationships, mathematical formulas or equations, mathematical calculations). Therefore, the dependent claims are drawn to an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5 and 7-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Erdem et al. (US 2020/0143499 A1, herein Erdem) in view of Balaban (US 2010/0100510 A1, herein Balaban).
As per claim 1, Erdem teaches of a display control device comprising:
an estimation unit that estimates, on the basis of a predictive distribution of an occurrence point representing a point where a call for an emergency vehicle occurs, an occurrence time at which a call occurs at the occurrence point for each of a plurality of the occurrence points (abstract which describes building one or more event-predictive models based on historical data and gathering current data, as well as processing the gathered current data with the build event-predictive models to predict respective timeframes and locations for one or more future emergency events; and pg. 2, [0016] which describes how the system may analyze the current and predicted environmental data to predict timeframes and locations for at least one future emergency event; and pg. 2, [0019] which describes building one or more event-predictive models based on historical data, gathering current data, and processing the gathered current data with the build one or more event-predictive models to predict respective timeframes and locations for one or more future emergency events);
a calculation unit that extracts, from the plurality of occurrence points, an occurrence point at which a distance between the emergency vehicle available for dispatch and the occurrence point is equal to or greater than a threshold value, and calculates a risk level such that the risk level of an area to which the extracted occurrence point belongs becomes high (abstract and pg. 2, [0019] which describes identifying, based on the timeframes and locations for one or more future emergency events and a current and schedule resource locations and statuses for a plurality of resources, a resource deficient area; and pg. 5, [0057] which describes how the resource allocation system identifies resource deficient areas based on current and schedule resource positioning and predicted emergency events, based on at least times and locations of predicted emergency events, available resources, current resource location, and estimated travel time to predicted future emergency events locations); and
a display control unit that controls a display unit to display the risk level calculated by the calculation unit (pg. 2, [0020] which describes mapping the one or more future emergency events and current emergency events and overlaying current and scheduled resource locations and statuses for the plurality of resources, the identifying being based on the mapping; and pg. 5, [0056] which describes how the resource allocation system maps real-time and predicted emergency events and overlays current and scheduled resource positioning, where the mapping may and overlaying may be visual (e.g., on a digital map); and pg. 6, [0060] and Fig. 4 which describes the visual mapping where predicted events are assigned to respective regions, and regions with deficient resources are identified by color; and pg. 9, [0009] which describes how the system may map the one or more future emergency events and current emergency events and overlay current and scheduled resource locations and statuses for the plurality of resources, identifying the resource deficient area being based on the map and overlay).
Erdem teaches of systems and methods for geographic resource distribution and assignment. However, Erdem fails to explicitly teach of executing a simulation of an emergency activity in which any one of a plurality of the emergency vehicles available for dispatch is dispatched to the occurrence point at the occurrence time. Balaban teaches of a dynamic discrete decision simulation system, including a simulation unit that executes a simulation of an emergency activity in which any one of a plurality of the emergency vehicles available for dispatch is dispatched to the occurrence point at the occurrence time for each of the plurality of occurrence points on the basis of the occurrence time of each of the plurality of occurrence points as a result of estimation by the estimation unit and an operational status of each of the plurality of emergency vehicles (pg. 1, [0012] which describes how the innovation can assist crisis response organizations to simulate and/or improve the management of regional (and national) emergency assets and operations; and pg. 2, [0013] which describes how the integrated system can be designed to accurately mimic real-world disaster response scenarios and can be used to assess how various configurations of emergency resources and operating policies might impact the effectiveness of responses to various large-scale incidents; and pg. 3, [0042] which describes how the system can include a dynamic discrete decision processing system that employs information from agents and stores to establish an intelligent output, and also establish a dynamic simulation of the situation which sets forth parameters such as response services, personnel, casualties, property damage, etc.; and pg. 3, [0045] which describes how the system is a flexible, dynamic and realistic simulation-based system which can be employed in crises or situational management planning, response, and training; and pg. 4, [0055-0060] which describes how the invention monitors and simulates an event or crisis, where a user can manually enter or describe a disaster scenario, geographic criteria can be delineated, decision models are built and the simulation operation is effected, and the results of the simulation/decision process can be dynamically displayed via a user interface; and pg. 7, [0104] which describes how the simulator will be dynamically informed of the updated data and operational rules which are generated as the system runs, for example, the rule-based system may create only general rules such as sending emergency medical services ambulances to the scene, but may not specify the optimal rule parameters such as number of ambulances that should be dispatched, where optimization can add specificity to the general rules, making the rules more operational; and pg. 8, [0115] which describes how the innovation incorporates the agent-based model into the discrete event simulator to simulate the behavior of the responders to the various disaster (or crisis) scenarios).
Erdem teaches of systems and methods for geographic resource distribution and assignment. Balaban teaches of a dynamic discrete decision simulation system, specifically including a simulation unit, as claimed. Both references are drawn to managing and allocating resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Erdem with the simulation unit as taught by Balaban for the purpose of using simulation models to compensate for disadvantages of analytical models and to work around many of the unrealistic assumptions required for analytical models, where simulation is useful in modeling complex systems with many interactions because it carries the stochastic, dynamic nature of real systems (Balaban, pg. 9, [0128]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiently modeling the relevant and necessary data to optimize resource allocation.
As per claim 5, it refers to a display control method for performing the above steps. It recites limitations already addressed by claim 1 above, and is therefore rejected under the same art and rationale. Furthermore, Erdem et al. (US 2020/0143499 A1, herein Erdem) discloses invention including a method (pg. 1-2, [0001, 0019]).
As per claim 10, it refers to a computer-readable non-transitory recording medium for performing the above steps. It recites limitations already addressed by claim 1 above, and is therefore rejected under the same art and rationale. Furthermore, Erdem et al. (US 2020/0143499 A1, herein Erdem) discloses the invention includes a storage medium (pg. 7, [0070]).
As per claim 2, Erdem in view of Balaban discloses all the elements of claim 1, and Erdem further teaches wherein:
the estimation unit calculates, for each of the plurality of occurrence points, a required time indicating a time required for an emergency vehicle dispatched to the occurrence point at the occurrence time to respond, for each combination of the occurrence point and the occurrence time;
calculating, for each of the plurality of occurrence points, a round-trip time according to a movement distance from the emergency vehicle available for dispatch to the occurrence point, and calculates a response completion time representing a time at which the emergency vehicle available for dispatch completes a response by adding the required time and the round-trip time to the occurrence time (pg. 2, [0019] which describes building one or more event-predictive models based on historical data, gathering current data, and processing the gathered current data with the build one or more event-predictive models to predict respective timeframes and locations for one or more future emergency events; and pg. 3, [0034] which describes the resource allocation system including the resource allocation server to execute one or more machine-learning libraries to estimate a current of future optimized resource allocation, as well as improving the model by analyzing model responsiveness, including response time estimates; and pg. 3, [0038] which describes how the database may be associated with the resource allocations system that stores a variety of information relating to current and historical environmental data, impact events, response times, active resources, and/or machine learning libraries; and pg. 4, [0048] which describes the resource allocation system that identifies current resource location and status, which may receive location data and/or resource status data, e.g. responding to emergency event, in transit offline, waiting, from roadside resources; and pg. 5, [0057] which describes how the resource allocation system identifies resource deficient areas based on current and schedule resource positioning and predicted emergency events, based on at least times and locations of predicted emergency events, available resources, current resource location, and estimated travel time to predicted future emergency events locations); and
the calculation unit sets, for each of a plurality of the emergency vehicles, that the emergency vehicle is not available for dispatch in a time zone earlier than the response completion time, extracts an occurrence point at which a distance between the emergency vehicle having a shortest distance to the occurrence point and available for dispatch and the occurrence point is equal to or greater than a threshold value, and calculates a risk level such that the risk level of an area to which the extracted occurrence point belongs becomes high (abstract and pg. 2, [0019] which describes identifying, based on the timeframes and locations for one or more future emergency events and a current and schedule resource locations and statuses for a plurality of resources, a resource deficient area; and pg. 4, [0048] which describes the resource allocation system that identifies current resource location and status, which may receive location data and/or resource status data, e.g. responding to emergency event, in transit offline, waiting, from roadside resources; and pg. 5, [0057] which describes how the resource allocation system identifies resource deficient areas based on current and schedule resource positioning and predicted emergency events, based on at least times and locations of predicted emergency events, available resources, current resource location, and estimated travel time to predicted future emergency events locations).
Erdem teaches of systems and methods for geographic resource distribution and assignment. However, Erdem fails to explicitly teach of calculating a round-trip time and a respond completion time using the simulation unit. Balaban teaches of a dynamic discrete decision simulation system, including wherein:
the simulation unit calculates, for each of the plurality of occurrence points, a round-trip time according to a movement distance from the emergency vehicle available for dispatch to the occurrence point by executing the simulation of an emergency activity, and calculates a response completion time representing a time at which the emergency vehicle available for dispatch completes a response by adding the required time and the round-trip time to the occurrence time (pg. 1, [0012] which describes how the innovation can assist crisis response organizations to simulate and/or improve the management of regional (and national) emergency assets and operations; and pg. 2, [0013] which describes how the integrated system can be designed to accurately mimic real-world disaster response scenarios and can be used to assess how various configurations of emergency resources and operating policies might impact the effectiveness of responses to various large-scale incidents; and pg. 3, [0042] which describes how the system can include a dynamic discrete decision processing system that employs information from agents and stores to establish an intelligent output, and also establish a dynamic simulation of the situation which sets forth parameters such as response services, personnel, casualties, property damage, etc.; and pg. 3, [0045] which describes how the system is a flexible, dynamic and realistic simulation-based system which can be employed in crises or situational management planning, response, and training; and pg. 4, [0055-0060] which describes how the invention monitors and simulates an event or crisis, where a user can manually enter or describe a disaster scenario, geographic criteria can be delineated, where the GIS can be used provide network solutions (e.g. shortest route, spanning tree), decision models are built and the simulation operation is effected, and the results of the simulation/decision process can be dynamically displayed via a user interface; and pg. 7, [0104] which describes how the simulator will be dynamically informed of the updated data and operational rules which are generated as the system runs, for example, the rule-based system may create only general rules such as sending emergency medical services ambulances to the scene, but may not specify the optimal rule parameters such as number of ambulances that should be dispatched, where optimization can add specificity to the general rules, making the rules more operational; and pg. 8, [0115] which describes how the innovation incorporates the agent-based model into the discrete event simulator to simulate the behavior of the responders to the various disaster (or crisis) scenarios).
Erdem teaches of systems and methods for geographic resource distribution and assignment. Balaban teaches of a dynamic discrete decision simulation system, specifically including a simulation unit, as claimed. Both references are drawn to managing and allocating resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Erdem with the simulation unit as taught by Balaban for the purpose of using simulation models to compensate for disadvantages of analytical models and to work around many of the unrealistic assumptions required for analytical models, where simulation is useful in modeling complex systems with many interactions because it carries the stochastic, dynamic nature of real systems (Balaban, pg. 9, [0128]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiently modeling the relevant and necessary data to optimize resource allocation.
As per claim 7, it refers to the method of claim 5 used for performing the above steps. It recites limitations already addressed by claim 2 above, and is therefore rejected under the same art and rationale.
As per claim 11, it refers to the non-transitory recording medium of claim 10 used for performing the above steps. It recites limitations already addressed by claim 2 above, and is therefore rejected under the same art and rationale.
As per claim 3, Erdem in view of Balaban discloses all the elements of claim 1, and Balaban further teaches wherein the simulation unit executes a simulation of an emergency activity in which an emergency vehicle available for dispatch requiring a shortest time to arrive at the occurrence point or an emergency vehicle available for dispatch having a shortest distance to the occurrence point is dispatched to the occurrence point at the occurrence time (pg. 3, [0042] which describes how the system can include a dynamic discrete decision processing system that employs information from agents and stores to establish an intelligent output, and also establish a dynamic simulation of the situation which sets forth parameters such as response services, personnel, casualties, property damage, etc.; and pg. 4, [0055-0060] which describes how the invention monitors and simulates an event or crisis, where a user can manually enter or describe a disaster scenario, geographic criteria can be delineated, where the GIS can be used provide network solutions (e.g. shortest route, spanning tree), decision models are built and the simulation operation is effected, and the results of the simulation/decision process can be dynamically displayed via a user interface; and pg. 7, [0104] which describes how the simulator will be dynamically informed of the updated data and operational rules which are generated as the system runs, for example, the rule-based system may create only general rules such as sending emergency medical services ambulances to the scene, but may not specify the optimal rule parameters such as number of ambulances that should be dispatched, where optimization can add specificity to the general rules, making the rules more operational).
Erdem teaches of systems and methods for geographic resource distribution and assignment. Balaban teaches of a dynamic discrete decision simulation system, specifically including a simulation unit, as claimed. Both references are drawn to managing and allocating resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Erdem with the simulation unit as taught by Balaban for the purpose of using simulation models to compensate for disadvantages of analytical models and to work around many of the unrealistic assumptions required for analytical models, where simulation is useful in modeling complex systems with many interactions because it carries the stochastic, dynamic nature of real systems (Balaban, pg. 9, [0128]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiently modeling the relevant and necessary data to optimize resource allocation.
As per claim 8, it refers to the method of claim 5 used for performing the above steps. It recites limitations already addressed by claim 3 above, and is therefore rejected under the same art and rationale.
As per claim 12, it refers to the non-transitory recording medium of claim 10 used for performing the above steps. It recites limitations already addressed by claim 3 above, and is therefore rejected under the same art and rationale.
As per claim 4, Erdem in view of Balaban discloses all the elements of claim 1, and Erdem further teaches wherein the display control unit controls the display unit to display location information of a plurality of emergency vehicles, the predictive distribution, and the risk level calculated by the calculation unit (pg. 2, [0020] which describes mapping the one or more future emergency events and current emergency events and overlaying current and scheduled resource locations and statuses for the plurality of resources, the identifying being based on the mapping; and pg. 5, [0056] which describes how the resource allocation system maps real-time and predicted emergency events and overlays current and scheduled resource positioning, where the mapping may and overlaying may be visual (e.g., on a digital map); and pg. 6, [0060] and Fig. 4 which describes the visual mapping where predicted events are assigned to respective regions, and regions with deficient resources are identified by color; and pg. 9, [0009] which describes how the system may map the one or more future emergency events and current emergency events and overlay current and scheduled resource locations and statuses for the plurality of resources, identifying the resource deficient area being based on the map and overlay).
As per claim 9, it refers to the method of claim 5 used for performing the above steps. It recites limitations already addressed by claim 4 above, and is therefore rejected under the same art and rationale.
As per claim 13, it refers to the non-transitory recording medium of claim 10 used for performing the above steps. It recites limitations already addressed by claim 4 above, and is therefore rejected under the same art and rationale.
As per claim 14, Erdem in view of Balaban discloses all the elements of claim 1, and Erdem further teaches wherein a data storage unit includes, for each of a plurality of emergency vehicles, status of dispatch, location information of the plurality of emergency vehicles, location information of fire station to which the plurality of emergency vehicles belongs, identification information of the fire station, and combination of positions and times at which the plurality of emergency vehicles were called in the past (pg. 3, [0034] which describes how the resource allocation server may execute one or more machine-learning libraries to estimate a current or future optimized resource allocation, and control or instruct the distribution of specific resources, where the machine-learning libraries may be trained to specific geographic locations based on historic environmental and event data; and pg. 3, [0038] which describes the database associated with the resource allocations system that stores a variety of information relating to current and historical environmental data, impact events, response times, active resource and/or machine learning libraries).
Balaban also further teaches wherein a data storage unit includes, for each of a plurality of emergency vehicles, status of dispatch, location information of the plurality of emergency vehicles, location information of fire station to which the plurality of emergency vehicles belongs, identification information of the fire station, and combination of positions and times at which the plurality of emergency vehicles were called in the past (pg. 3, [0049] which describes the model’s geo-database that includes layers of geographic, asset, and other geo-referenced information; and pg. 7, [0108] which describes the workspace that is a collection of databases that store the temporary fact data about the system, where the data comes from the simulation, rules bases and other integrated applications such as the geographic information system; and pg. 9, [0130] and Table 3 which describes database tables that describe the simulation including at least description of emergency resources, destination points of emergency resources, emergency resource locations, etc.).
Erdem teaches of systems and methods for geographic resource distribution and assignment. Balaban teaches of a dynamic discrete decision simulation system, specifically including a simulation unit, as claimed. Both references are drawn to managing and allocating resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Erdem with the simulation unit as taught by Balaban for the purpose of using simulation models to compensate for disadvantages of analytical models and to work around many of the unrealistic assumptions required for analytical models, where simulation is useful in modeling complex systems with many interactions because it carries the stochastic, dynamic nature of real systems (Balaban, pg. 9, [0128]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiently modeling the relevant and necessary data to optimize resource allocation.
As per claim 15, Erdem in view of Balaban discloses all the elements of claim 1, and Erdem further teaches wherein a demand forecasting unit generates a forecast distribution for occurrence points indicating locations where each of the plurality of emergency vehicles was called (abstract which describes building one or more event-predictive models based on historical data and gathering current data, as well as processing the gathered current data with the build event-predictive models to predict respective timeframes and locations for one or more future emergency events; and pg. 2, [0016] which describes how the system may analyze the current and predicted environmental data to predict timeframes and locations for at least one future emergency event; and pg. 2, [0019] which describes building one or more event-predictive models based on historical data, gathering current data, and processing the gathered current data with the build one or more event-predictive models to predict respective timeframes and locations for one or more future emergency events; and pg. 5, [0057] which describes how the resource allocation system identifies resource deficient areas based on current and schedule resource positioning and predicted emergency events, based on at least times and locations of predicted emergency events, available resources, current resource location, and estimated travel time to predicted future emergency events locations).
As per claim 20, it refers to the method of claim 7 used for performing the above steps. It recites limitations already addressed by claim 15 above, and is therefore rejected under the same art and rationale.
As per claim 16, Erdem in view of Balaban discloses all the elements of claim 15, and Erdem further teaches wherein the demand forecasting unit uses a learned model learned in advance by using a machine learning model using emergency transport information, past population information of each location, and past weather information of each location to generate a prediction distribution (abstract which describes building one or more event-predictive models based on historical data and gathering current data, as well as processing the gathered current data with the build event-predictive models to predict respective timeframes and locations for one or more future emergency events; and pg. 2, [0016] which describes how the system may analyze the current and predicted environmental data to predict timeframes and locations for at least one future emergency event; and pg. 2, [0019] which describes building one or more event-predictive models based on historical data, gathering current data, and processing the gathered current data with the build one or more event-predictive models to predict respective timeframes and locations for one or more future emergency events).
As per claim 21, it refers to the method of claim 20 used for performing the above steps. It recites limitations already addressed by claim 16 above, and is therefore rejected under the same art and rationale.
As per claim 17, Erdem in view of Balaban discloses all the elements of claim 1, and Erdem further teaches wherein the calculation unit plots extracted occurrence points in map data partitioned by a plurality of meshes and calculates degree of risk level for each of the plurality of meshes (pg. 2, [0020] which describes mapping the one or more future emergency events and current emergency events and overlaying current and scheduled resource locations and statuses for the plurality of resources, the identifying being based on the mapping; and pg. 5, [0056] which describes how the resource allocation system maps real-time and predicted emergency events and overlays current and scheduled resource positioning, where the mapping may and overlaying may be visual (e.g., on a digital map); and pg. 6, [0060] and Fig. 4 which describes the visual mapping where predicted events are assigned to respective regions, and regions with deficient resources are identified by color; and pg. 9, [0009] which describes how the system may map the one or more future emergency events and current emergency events and overlay current and scheduled resource locations and statuses for the plurality of resources, identifying the resource deficient area being based on the map and overlay).
As per claim 18, Erdem in view of Balaban discloses all the elements of claim 16, and Erdem further teaches wherein the calculation unit assigns the occurrence point to a center of a cluster corresponding to a target emergency vehicle (pg. 4, [0051] which describes how the resource allocation system may recompute improved resource positioning and may monitor current and predictive environmental data for changes and recalculated improved resource positioning based thereon, where resource location “hot spots” may be predetermined for each geographic region, where utilizing K-means clustering on historical data, predetermined locations may be identified within each geographic region for pre-positioning vehicles and the resource allocation system may analyze an area surrounding the hotspot to identify resource positioning locations).
Balaban further teaches wherein the calculation unit calculates, for each of generation points in the prediction distribution, an object indicating the emergency vehicle having the shortest distance to the generation point (pg. 3, [0042] which describes how the system can include a dynamic discrete decision processing system that employs information from agents and stores to establish an intelligent output, and also establish a dynamic simulation of the situation which sets forth parameters such as response services, personnel, casualties, property damage, etc.; and pg. 4, [0055-0060] which describes how the invention monitors and simulates an event or crisis, where a user can manually enter or describe a disaster scenario, geographic criteria can be delineated, where the GIS can be used provide network solutions (e.g. shortest route, spanning tree), decision models are built and the simulation operation is effected, and the results of the simulation/decision process can be dynamically displayed via a user interface; and pg. 7, [0104] which describes how the simulator will be dynamically informed of the updated data and operational rules which are generated as the system runs, for example, the rule-based system may create only general rules such as sending emergency medical services ambulances to the scene, but may not specify the optimal rule parameters such as number of ambulances that should be dispatched, where optimization can add specificity to the general rules, making the rules more operational).
Erdem teaches of systems and methods for geographic resource distribution and assignment. Balaban teaches of a dynamic discrete decision simulation system, specifically including a simulation unit, as claimed. Both references are drawn to managing and allocating resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Erdem with the simulation unit as taught by Balaban for the purpose of using simulation models to compensate for disadvantages of analytical models and to work around many of the unrealistic assumptions required for analytical models, where simulation is useful in modeling complex systems with many interactions because it carries the stochastic, dynamic nature of real systems (Balaban, pg. 9, [0128]). By doing so, one would reasonably expect the overall appeal of the invention to improve in efficiently modeling the relevant and necessary data to optimize resource allocation.
As per claim 19, Erdem in view of Balaban discloses all the elements of claim 18, and Erdem further teaches wherein the calculation unit extracts each occurrence point where distance between the target emergency vehicle and the occurrence point is equal to or greater than a threshold and extracts one or more occurrence points belonging to the cluster in which a number of assigned occurrence points is greater than a threshold (abstract and pg. 2, [0019] which describes identifying, based on the timeframes and locations for one or more future emergency events and a current and schedule resource locations and statuses for a plurality of resources, a resource deficient area; pg. 4, [0051] which describes how the resource allocation system may recompute improved resource positioning and may monitor current and predictive environmental data for changes and recalculated improved resource positioning based thereon, where resource location “hot spots” may be predetermined for each geographic region, where utilizing K-means clustering on historical data, predetermined locations may be identified within each geographic region for pre-positioning vehicles and the resource allocation system may analyze an area surrounding the hotspot to identify resource positioning locations; and pg. 5, [0057] which describes how the resource allocation system identifies resource deficient areas based on current and schedule resource positioning and predicted emergency events, based on at least times and locations of predicted emergency events, available resources, current resource location, and estimated travel time to predicted future emergency events locations; and pg. 6, [0060] and Fig. 4 which describes the visual mapping where predicted events are assigned to respective regions, and regions with deficient resources are identified by color; and pg. 9, [0009] which describes how the system may map the one or more future emergency events and current emergency events and overlay current and scheduled resource locations and statuses for the plurality of resources, identifying the resource deficient area being based on the map and overlay).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Toohey (US 200/0034030 A1) teaches of providing predictive results based upon real time data feeds from multiple parties.
Pauws et al. (US 2019/0122532 A1) teaches of a personal emergency response system with predictive emergency dispatch risk assessment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEY Y YOUNG whose telephone number is (571)270-5294. The examiner can normally be reached Mondays, Tuesdays, and Thursdays, 9:00a-3:00p, EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ASHLEY Y YOUNG/Examiner, Art Unit 3625
/RENAE FEACHER/Primary Examiner, Art Unit 3625
Prosecution Insights