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 .
Response to Arguments
Regarding the specification objection in view of the abstract, the amendment to the abstract is sufficient to overcome the specification objection. Therefore, the specification objection is withdrawn.
Regarding the objection of claim 17, the claim has not been amended to cure the informality. Therefore, the claim objection of claim 17 is maintained.
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The claims are not directed to a mental process or method of organizing human activity. The claims recite active steps that cannot practically be performed in the human mind or through manual human activity. For example, the claims recite "automatically generating, based on the asset allocation parameters produced by the predictive model, an assets' deployment plan comprising suppression strategy instructions specifying allocation of particular fire suppression assets to particular deployment locations." This limitation requires the system to produce specific, executable tactical directives that specify which particular assets should be deployed to which particular locations. Unlike prior art systems that merely predict fire behavior and leave deployment decisions to human commanders, the claimed invention automates the generation of the deployment plan itself. The computational task of determining optimal asset-to-location allocations based on multiple data inputs (sensory data, asset availability, historical fire data) processed through a machine learning predictive model cannot practically be performed in the human mind.,” Examiner respectfully asserts that generating a deployment plan comprising instructions, as drafted, does not recite any particular additional elements but for saying it is performed “automatically.” The step of generating a plan is part of the abstract limitations for consideration under Step 2A, Prong 1. The claim merely recites generating a plan and does not recite the planned resources actually performing fire suppression using particular assets at locations. Examiner notes that this limitation, as drafted, does not recite the machine learning model performing the generation of the plan. Rather, the plan is generated based on parameters output from the machine learning model. Therefore, Examiner respectfully disagrees with Applicant’s assertion.
Regarding Applicant’s assertion of “Additionally, the claims recite "transmit[ting] the assets' deployment plan to the at least one command-and-control entity node over the wireless network." This active transmission of executable deployment instructions to physical command-and-control systems for execution is inherently technological and cannot be characterized as a mental process or method of organizing human activity.,” Examiner respectfully asserts that this limitation is not part of the abstract idea for consideration under Step 2A, Prong 1. Examiner respectfully asserts that this limitation is part of the additional elements for consideration under Step 2A, Prong 2 and Step 2B. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Regarding Applicant’s assertion of “Even if the claims were considered to recite an abstract idea, the claims are integrated into a practical application because they provide a specific technological improvement to wildfire suppression command-and-control systems. The specification explicitly identifies the deficiency in existing systems: they "incorporate data to predict fire movements and intensity" but "leave it to the commander to figure out how to act on that information," resulting in "inaccurate manual allocation of resources" that leads to "rapid fire spread, resulting in increased loss of property, lives, and/or environmental degradation." The claims solve this identified technological problem through at least (1) automatic generation of deployment plans comprising suppression strategy instructions that specify allocation of particular assets to particular locations-eliminating the human interpretive step that causes inaccurate resource allocation in prior systems; and (2) transmission of those executable deployment plans directly to command-and-control entities over the wireless network, enabling immediate implementation without requiring human translation of predictive data into tactical decisions This is not merely applying a generic computer to perform an abstract idea. The claims recite a specific technical solution that transforms the output of a fire analysis system from decision-support information (requiring human interpretation) into executable deployment instructions (ready for implementation). This represents an improvement to the functioning of wildfire command-and-control systems themselves.,” Examiner respectfully asserts that the use of a computer to “automatically” perform the abstract step of generating a plan comprising strategy instructions is not sufficient to prove integration into a practical application or anything significantly more because it is nothing more than mere use of a computer as a tool. Similarly, the mere use of a computer to transmit information, as drafted, is not sufficient to prove integration into a practical application or anything significantly more. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, Examiner notes that the use of a computer to generate strategic instructions would not improve the functioning of the computer itself, or any other technology or technical field. It is unclear how generating a plan would improve the functioning of “wildfire command-and-control” systems. The purported improvement of improving a plan, as drafted, would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “The claims are analogous to the patent-eligible claims in McRO, where the Federal Circuit found claims directed to an automated process that replaced a previously manual technique with a specific technological process were not directed to an abstract idea. Here, the claims replace the manual process of interpreting fire predictions and determining asset deployment with an automated system that directly generates specific deployment instructions.,” Examiner respectfully disagrees. The present claims do not provide a clear improvement to technology or computer functionality. The claims of McRo recite automatic lip synchronization and facial expression animation, which provided a clear improvement to a computer functionality (i.e. computer animation). In contrast, the instant claims do not recite an analogous improvement to a computer functionality. The claims merely apply a computer to generate a strategic plan, which does not improve a technical field. The claims employ generic computer functions to execute the abstract idea that, even while limiting the use of the idea to a particular technical environment, do not integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Accordingly, the present claims are rejected under 35 USC 101.
Regarding the 35 USC 102 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “Accordingly, for at least the above reasons, claim 1 is not anticipated by Farley. Claims 11 and 17 recite limitations similar to claim 1 and are, therefore, also not anticipated by Farley.,” Applicant’s arguments with respect to the previous prior art combination of the record have been considered but are moot because the new grounds of rejection does not rely on any reference applied in the prior art rejection for any teachings or matter specifically challenged in the argument. The claims are rejected under a new grounds of rejection, which was necessitated by amendment. Examiner has introduced the Killilea reference to cure the deficiencies of the prior art combination of the record. Therefore, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103.
Regarding Applicant’s assertion of “Furthermore, at least because Farley does not generate deployment plans comprising suppression strategy instructions specifying particular asset allocations, Farley necessarily does not disclose transmitting such plans to command-and-control entities.,” Examiner respectfully disagrees. As can be seen below with respect to Farley, the reference discloses transmitting the updated strategy to the client devices over the network and outputting strategy information including maps and resource locations for deploying suppression units in the real world. Therefore, Examiner respectfully disagrees with Applicant’s assertion in view of Farley.
Accordingly, the 35 USC 102 rejection has been withdrawn; however, the present claims are rejected under 35 USC 103.
Claim Objections
Claim 17 is objected to because of the following informalities: Examiner suggests amending the claim for the sake of antecedence by reciting “acquire sensory data from a plurality of sensors hosted on [[the]] at least one fire surveillance device.”
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-10 are directed to a system, claims 11-16 are directed to a method, and claims 17-20 are directed to a non-transitory computer readable medium. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 11, and 17 recite generating a deployment plan, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including following rules or instructions. Claim 1 recites limitations, similarly recited in claims 11 and 17, including “parse the sensory data to derive a plurality of key features; acquire available fire suppression assets'-related data…; retrieve local historical fires'-related data related to previous fires' suppression engagements based on the plurality of the key features and the available fire suppression assets'-related data; generate at least one feature vector based on the plurality of key features, the available fire suppression assets'-related data and the local historical fires'-related data; generate a predictive model for producing asset allocation parameters for generation of the assets' deployment plan for the at least one least one command-and-control entity node; generate, based on the asset allocation parameters produced by the predictive model, an assets' deployment plan comprising suppression strategy instructions specifying allocation of particular fire suppression assets to particular deployment location,” that, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by the fire analysis server,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “by the fire analysis server,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by the fire analysis server” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Claims 1, 11, and 17 recite generating a deployment plan, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Claim 1 recites limitations, similarly recited in claims 11 and 17, including “parse the sensory data to derive a plurality of key features; acquire available fire suppression assets'-related data…; retrieve local historical fires'-related data related to previous fires' suppression engagements based on the plurality of the key features and the available fire suppression assets'-related data; generate at least one feature vector based on the plurality of key features, the available fire suppression assets'-related data and the local historical fires'-related data; generate a predictive model for producing asset allocation parameters for generation of the assets' deployment plan for the at least one least one command-and-control entity node; generate, based on the asset allocation parameters produced by the predictive model, an assets' deployment plan comprising suppression strategy instructions specifying allocation of particular fire suppression assets to particular deployment location,” that, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by the fire analysis server,” covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by the fire analysis server,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “by the fire analysis server” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 3, 5, and 13 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 4, 6, 7-10, 12, 14-16, and 18-20 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 11, and 17 do not integrate the judicial exception into a practical application. Independent claim 1 is directed to a system comprising “a processor of a fire analysis server (FAS) node configured to host a machine learning (ML) module and connected to at least one fire surveillance device and to at least one command-and-control entity node over a wireless network; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to.” Independent claim 11 is directed to a method performed “by a fire analysis server (FAS) configured to host a machine learning module (ML).” Claim 17 is directed to a “non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform,” which is recited in the preamble of the claim. Claim introduces the additional elements, similarly recited in claims 11 and 17, including performing steps “automatically,” as well as “acquire sensory data from a plurality of sensors hosted on the at least one fire surveillance device,” “acquire available fire suppression assets'-related data from a local storage,” “query a local fires'-related database to retrieve local historical fires'-related data…,” “ and provide the at least one feature vector to the ML module configured to generate a predictive model for producing asset allocation parameters for generation of the assets' deployment plan for the at least one least one command-and-control entity node,” and “transmit the assets' deployment plan to the at least one command-and-control entity node over the wireless network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claims 1, 11, and 17 further recite the additional element of “wherein the plurality of sensors comprises at least one gas analyzer configured to detect one or more chemical compounds associated with wildfire combustion.” The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 3, 5, and 13 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claims 2 and 12 recite the additional element of “wherein the instructions further cause the processor to retrieve remote historical fires'-related data from at least one remote database based on the local historical fires'-related data.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 4 recite the additional element of “wherein the instructions further cause the processor to parse surveillance data comprising audio interactions between at least one surveyor on site and a bot associated with the at least one command-and-control entity node.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claims 6, 14, and 18 recite the additional element of “wherein the instructions further cause the processor to continuously monitor incoming sensory data to determine if at least one value of the incoming sensory data deviates from a value of previous sensory data by a margin exceeding a pre-set threshold value.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claims 7, 15, and 19 recite the additional element of “transmit the updated assets' deployment plan to the at least one command-and-control entity node over the wireless network to replace a previously transmitted assets' deployment plan.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claims 8, 16, and 20 recite the additional element of “wherein the instructions further cause the processor to record the asset allocation parameters on a blockchain ledger along with the features retrieved from the sensory data and corresponding available fire suppression assets'-related data.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 9 recite the additional element of “wherein the instructions further cause the processor to retrieve at least one asset allocation parameter from the blockchain responsive to a consensus among the FAS node and the at least one command-and-control entity node.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Dependent claim 10 recite the additional element of “wherein the instructions further cause the processor to execute a smart contract to record data reflecting execution of the assets' deployment plan associated with the asset allocation parameters and the at least one command-and-control entity node on the blockchain for future audits.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the base claims above, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 11, and 17 do not comprise anything significantly more than the judicial exception. Independent claim 1 is directed to a system comprising “a processor of a fire analysis server (FAS) node configured to host a machine learning (ML) module and connected to at least one fire surveillance device and to at least one command-and-control entity node over a wireless network; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to.” Independent claim 11 is directed to a method performed “by a fire analysis server (FAS) configured to host a machine learning module (ML).” Claim 17 is directed to a “non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform,” which is recited in the preamble of the claim. Claim introduces the additional elements, similarly recited in claims 11 and 17, including performing steps “automatically,” as well as “acquire sensory data from a plurality of sensors hosted on the at least one fire surveillance device,” “acquire available fire suppression assets'-related data from a local storage,” “query a local fires'-related database to retrieve local historical fires'-related data…,” “ and provide the at least one feature vector to the ML module configured to generate a predictive model for producing asset allocation parameters for generation of the assets' deployment plan for the at least one least one command-and-control entity node,” and “transmit the assets' deployment plan to the at least one command-and-control entity node over the wireless network.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 3, 5, and 13 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claims 2 and 12 recite the additional element of “wherein the instructions further cause the processor to retrieve remote historical fires'-related data from at least one remote database based on the local historical fires'-related data.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 4 recite the additional element of “wherein the instructions further cause the processor to parse surveillance data comprising audio interactions between at least one surveyor on site and a bot associated with the at least one command-and-control entity node.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claims 6, 14, and 18 recite the additional element of “wherein the instructions further cause the processor to continuously monitor incoming sensory data to determine if at least one value of the incoming sensory data deviates from a value of previous sensory data by a margin exceeding a pre-set threshold value.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claims 7, 15, and 19 recite the additional element of “transmit the updated assets' deployment plan to the at least one command-and-control entity node over the wireless network to replace a previously transmitted assets' deployment plan.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes or certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claims 8, 16, and 20 recite the additional element of “wherein the instructions further cause the processor to record the asset allocation parameters on a blockchain ledger along with the features retrieved from the sensory data and corresponding available fire suppression assets'-related data.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception See MPEP 2106.05(h).
Dependent claim 9 recite the additional element of “wherein the instructions further cause the processor to retrieve at least one asset allocation parameter from the blockchain responsive to a consensus among the FAS node and the at least one command-and-control entity node.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Dependent claim 10 recite the additional element of “wherein the instructions further cause the processor to execute a smart contract to record data reflecting execution of the assets' deployment plan associated with the asset allocation parameters and the at least one command-and-control entity node on the blockchain for future audits.” This limitation, as drafted, is nothing more than merely generally linking the use of a judicial exception to a particular technical field. The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the base claims above, are not anything significantly more than the judicial exception.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 11-13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Farley (US 20230342526 A1) in view of Killilea et al. (US 20240225023 A1).
Regarding claim 1, Farley teaches a system for generation of wildfire suppression asset allocation based on wildfire- related data (Fig. 18), comprising:
a processor of a fire analysis server (FAS) node configured to host a machine learning (ML) module and connected to at least one fire surveillance device and to at least one command-and- control entity node over a wireless network (Fig. 18 and [0145] teaches the data processor, as well as in [0056] teaches an information technology system comprising a data processor and a wildfire risk evaluation module executable by the data processor, wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field; see also: [0052, 0067-0069, 0086, 0367]); and
a memory on which are stored machine-readable instructions that when executed by the processor ([0056] teaches an information technology system comprising a data processor and a wildfire risk evaluation module executable by the data processor, wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field, wherein [0052] teaches the software platform aids decision makers in simulating possible outcomes, wherein the simulator is made available over a data network and integrated into dispatching software via an application programming interface, as well as in [0367] teaches the method is implemented via a software application of a computer; see also: Fig. 18, [0067-0069]), cause the processor to:
acquire sensory data from a plurality of sensors hosted on at least one fire surveillance device ([0072] teaches data is collected from remote sensing platforms including air and space home instruments including cameras, spectrometers, and lidar sensors, as well as in Fig. 8 and [0068] teach the data processor module ingests data from a variety of air and space borne sensors and from standardized forms indicating the type and location of suppression resources, wherein the satellite based sensors are used to provide near real time data based on environmental conditions such as temperature, wind speed, wind direction, fuel type, vegetation moisture, and vegetation height, as well as in [0065] teaches various remotely-sensed data sources and suppression resource locations are ingested by the data processing module; see also: [0061, 0066-0067]),
parse the sensory data to derive a plurality of key features ([0072] teaches data is collected from remote sensing platforms including air and space home instruments including cameras, spectrometers, and lidar sensors, as well as in Fig. 8 and [0068] teach the data processor module ingests data from a variety of air and space borne sensors and from standardized forms indicating the type and location of suppression resources, wherein the satellite based sensors are used to provide near real time data based on environmental conditions such as temperature, wind speed, wind direction, fuel type, vegetation moisture, and vegetation height, as well as in [0065] teaches various remotely-sensed data sources and suppression resource locations are ingested by the data processing module, wherein [0082-0083] teach extracting temporal information and extracting features in the input data related to fire growth and insanity that vary both in time and space; see also: [0061, 0066-0067, 0070, 0077]);
acquire available fire suppression assets'-related data from a local storage ([0055] teaches using available information regarding the current and forecast environment in which the fire is burning including the fire’s current perimeter and the suppression resources, as well as in [0086] teaches training and validation is performed by analyzing forecast performance on past wildfires using shape similarity metrics, wherein [0070] teaches incident management status updates describing suppression resource position are joined and incident managers file updates every operational period using a standard reporting procedure, which can be publicly available for download and includes details on the fire’s activity, resource commitments and needs, and values at risk, wherein the report quantifies resources committed to the incident, wherein [0077] teaches current and past ICS-209 forms are downloaded from publicly available databases and satellite derived wildfire occurrence data is gathered in order to identify resource information and the location of each resource type; see also: [0067, 0095]);
query a local fires'-related database to retrieve local historical fires'-related data related to previous fires' suppression engagements based on the plurality of the key features and the available fire suppression assets'-related data ([0061] teaches the salient features required for large scale planning includes training the probabilistic fire behavior model based on observations of past fire behavior, remotely sensed environment data, and records of suppression resource locations, wherein the neural network can accept inputs including recent, current, and forecasted winds, as well as in [0058] teaches the planning agent operates over an environment represented as a spatial grid, wherein the planning agent receives observations of the state of the environment in the form of a pixel matrix that represents time invariant environmental conditions, time varying environmental conditions, values at risk, vegetation, and a vector describing the location and type of each suppression unit, as well as in [0078] teaches the wildfire mechanics module accepts data from the data processor that is in the form of spatiotemporal data matrices describing the wind, weather, topography, and suppression resource type and location, wherein this data can be utilized as an input into the wildfire mechanics module that is built with a neural architecture, wherein [0055] teaches utilizing historical experience and available information to achieve control in fire suppression, wherein [0070] teaches incident management status updates describing suppression resource position are joined and incident managers file updates every operational period using a standard reporting procedure, which can be publicly available for download and includes details on the fire’s activity, resource commitments and needs, and values at risk, wherein the report quantifies resources committed to the incident; see also: [0059, 0066-0067, 0070, 0095]);
generate at least one feature vector based on the plurality of key features, the available fire suppression assets'-related data and the local historical fires'-related data ([0058] teaches the planning agent operates over an environment represented as a spatial grid, wherein the planning agent receives observations of the state of the environment in the form of a pixel matrix that represents time invariant environmental conditions, time varying environmental conditions, values at risk, vegetation, and a vector describing the location and type of each suppression unit, as well as in [0078] teaches the wildfire mechanics module accepts data from the data processor that is in the form of spatiotemporal data matrices describing the wind, weather, topography, and suppression resource type and location, wherein this data can be utilized as an input into the wildfire mechanics module that is built with a neural architecture, wherein [0066] teaches the planning agent incorporates data from its environment, such as sensor data, and the pool of available suppression resources to produce a value maximizing set of actions using the model architecture, wherein [0061] teaches the salient features required for large scale planning includes training the probabilistic fire behavior model based on observations of past fire behavior, remotely sensed environment data, and records of suppression resource locations, wherein the neural network can accept inputs including recent, current, and forecasted winds; see also: [0062-0064, 0067, 0070]);
provide the at least one feature vector to the ML module configured to generate a predictive model for producing asset allocation parameters for generation of the assets' deployment plan for the at least one least one command-and-control entity node ([0055] teaches leveraging an algorithm to quantitatively solve the resource allocation problem by formulating it as a discrete time partially observable Markov decision process and using a policy gradient algorithm to approximate its optimal solutions, wherein the planning agent constructs a probabilistic model of how its actions map to future environmental states in order to minimize assets burned over a finite time horizon, wherein [0058] teaches the agent emits an action indicating the new location assignment for each available resource in the region and the number of resources of each type required at the following timestep, wherein the agent receives rewards inversely proportional to the value of resources burned during the previous period and the cumulative resource travel, wherein the agent receives observations of the state of this environment in the form of a pixel matrix that represents time invariant environmental conditions, time varying environmental conditions, values at risk, vegetation, and a vector describing the location and type of each suppression unit, wherein [0066] teaches the planning agent incorporates data from its environment, such as sensor data, and the pool of available suppression resources to produce a value maximizing set of actions using the model architecture, wherein [0078] teaches the wildfire mechanics module accepts data from the data processor that is in the form of spatiotemporal data matrices describing the wind, weather, topography, and suppression resource type and location, wherein this data can be utilized to produce spatiotemporally-explicit predictions of expected fire trajectory over the following 24 hours, and wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field; see also: [0061, 0066, 0070]);
automatically generate, based on the asset allocation parameters produced by the predictive model, an assets' deployment plan comprising suppression strategy instructions specifying allocation of particular fire suppression assets to particular deployment locations ([0059] teaches the agent’s goal is to identify strategies that maximize an objective function related to the loss of values at risk, wherein the autonomous planning agent can provide resource allocation policies in the presence of multiple concurrent wildfires and accounts for uncertainty in fire growth and containment, wherein the planning agent is trained to minimize asset loss for sets of heterogenous suppression resources, wherein the planning agent produces a policy for a given set of suppression resources in response to current and forecast environmental conditions, wherein the policy and supporting data, e.g. predicted fire growth trajectory, are made available to client devices, e.g. phones, through an API service and data network, wherein the client device displays maps and graphics illustrating the policy and expected results to fire manager and other personnel responsible for dispatching resources in the field, as well as in [0058] teaches the planning agent operates in order to receive observations and emit an action indicating the new location assignment for each available resource in the region and the number of resources of each type required at the following timestep, as well as in [0109] teaches the agent emits structured action directives that indicate the spatial location at which to move each available suppression resource and the number and type of resources required in the environment at the following timestep, wherein after each timestep, the fire simulate is used to update the environment with new fire ignitions, resources are moved to their new locations, and the agent receives a reward corresponding to the outcome of the previous timestep, wherein [0053-0054] teach performing resource distribution and suggesting potential assignment strategies for handling resource demands at a large spatial scale; see also: [0055, 0061, 0160]): and
transmit the assets' deployment plan to the at least one command-and-control entity node over the wireless network ([0058] teaches the planning agent operates in order to receive observations and emit an action indicating the new location assignment for each available resource in the region and the number of resources of each type required at the following timestep, wherein [0059] teaches the agent’s goal is to identify strategies that maximize an objective function related to the loss of values at risk, wherein the planning agent produces a policy for a given set of suppression resources in response to current and forecast environmental conditions, wherein the policy and supporting data, e.g. predicted fire growth trajectory, are made available to client devices, e.g. phones, through an API service and data network, wherein the client device displays maps and graphics illustrating the policy and expected results to fire manager and other personnel responsible for dispatching resources in the field, as well as in [0145] teaches an application programming interface allows clients to access planning agent actions and fire mechanics predictions over the data network, wherein the users may interface with the planning agent using the client device over the network, wherein the users can request action strategies, near-term predictions, and post-processed projections of strategy rewards that can be used by fire managers for deploying suppression units in the real world, as well as in Fig. 37 and [0349] teach a user interacts with the decision support system through a client device, wherein data is transferred over a network, wherein the resolution strategy module is invoked and the results are returned and displayed on the device’s visual outputs as maps and graphs, as well as in [0109] teaches the agent emits structured action directives that indicate the spatial location at which to move each available suppression resource and the number and type of resources required in the environment at the following timestep, wherein after each timestep, the fire simulate is used to update the environment with new fire ignitions, resources are moved to their new locations, and the agent receives a reward corresponding to the outcome of the previous timestep; see also: [0053-0055, 0061, 0066, 0159-0160]).
However, Farley does not explicitly teach wherein the plurality of sensors comprises at least one gas analyzer configured to detect one or more chemical compounds associated with wildfire combustion.
From the same or similar field of endeavor, Killilea teaches wherein the plurality of sensors comprises at least one gas analyzer configured to detect one or more chemical compounds associated with wildfire combustion ([0194] teaches one or more smoke detection sensors that are capable of detecting one or more volatile phenols such as any markers of wildfire smoke, such as particulates or any other marker of wildfire smoke, wherein the smoke detections sensors may be located in proximity to a given growing location, such as a vineyard, wherein the plurality of sensors are provided that are geographically spread out in order to enable early detection and provide better smoke modeling and prediction, as well as in [0195] teaches smoke sensors and the smoke sensor system include sensors capable of measuring smoke parameters including fine particular matter (e.g. PM1.0, PM2.5, PM10, etc., with PM2.5 referring to particles having aerodynamic diameters less than or equal to 2.5 micrometers, carbon dioxide, total volatile organic compounds, and black carbon, wherein the sensor is capable of measuring CO, tVOC, and PM2.5; see also: [0166, 0191, 0196, 0199]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Farley to incorporate the teachings of Killilea to include wherein the plurality of sensors comprises at least one gas analyzer configured to detect one or more chemical compounds associated with wildfire combustion. One would have been motivated to do so in order to enable early detection and provide better smoke modeling by providing a plurality of sensors that are geographically spread out (Killilea, [0194]).
Regarding claims 11 and 17, the claims recite limitations already addressed by the rejection of claim 1. Regarding claim 11, Farley teaches a method for generation of wildfire suppression asset allocation based on wildfire- related data ([0056] teaches an information technology system comprising a data processor and a wildfire risk evaluation module executable by the data processor, wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field, wherein [0052] teaches the software platform aids decision makers in simulating possible outcomes, wherein the simulator is made available over a data network and integrated into dispatching software via an application programming interface, as well as in [0367] teaches the method is implemented via a software application of a computer; see also: Fig. 18, [0067-0069]), comprising: by a fire analysis server (FAS) configured to host a machine- learning module (ML) (Fig. 18 and [0145] teaches the data processor, as well as in [0056] teaches an information technology system comprising a data processor and a wildfire risk evaluation module executable by the data processor, wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field; see also: [0052, 0067-0069, 0086, 0367]). Regarding claim 17, Farley teaches a non-transitory computer readable medium comprising instructions, that when read by a processor, cause the processor to perform (Fig. 18 and [0145] teaches the data processor, as well as in [0056] teaches an information technology system comprising a data processor and a wildfire risk evaluation module executable by the data processor, wherein [0059] teaches the policy and supporting data can be made available to client devices through an API and data network that illustrates the policy and expected results to fire managers and other personnel responsible for dispatching resources in the field; see also: [0052, 0067-0069, 0086, 0126, 0367]). Therefore, claims 11 and 17 are rejected as being unpatentable over Farley in view of Killilea.
Regarding claims 2 and 12, the combination of Farley and Killilea teaches all the limitations of claims 1 and 11 above.
Farley further teaches wherein the instructions further cause the processor to retrieve remote historical fires'-related data from at least one remote database based on the local historical fires'-related data, wherein the remote historical fires'-related data is collected at locations associated with a plurality of previous fire suppression procedures ([0055] teaches making complex decisions about the quantity and type of resources required to achieve this objective using their historical experience and available information about the current and forecast environment in which the fire is burning, wherein suppression resources are drawn from a fixed-size pool of resources charged with the protection of a given spatial region, as well as in [0061] teaches the probabilistic fire behavior model is trained directly on observations of past fire behavior, remotely sensed environment data, and records of suppression resource location, wherein [0080] teaches the wildfire mechanics module uses previous satellite based thermal detections of wildfire occurrence and the current environment to predict fire occurrence at each location on a landscape at a future time; see also: [0053, 0063, 0090, 0205-0206]).
Regarding claims 3 and 13, the combination of Farley and Killilea teaches all the limitations of claims 2 and 12 above.
Farley further teaches wherein the instructions further cause the processor to generate the at least one feature vector based on the plurality of key features, the available fire suppression assets'-related data and the local historical fires'-related data combined with the remote historical fires'-related data ([0055] teaches making complex decisions about the quantity and type of resources required to achieve this objective using their historical experience and available information about the current and forecast environment in which the fire is burning, wherein suppression resources are drawn from a fixed-size pool of resources charged with the protection of a given spatial region, wherein[0058] teaches the planning agent operates over an environment represented as a spatial grid, wherein the planning agent receives observations of the state of the environment in the form of a pixel matrix that represents time invariant environmental conditions, time varying environmental conditions, values at risk, vegetation, and a vector describing the location and type of each suppression unit, as well as in [0078] teaches the wildfire mechanics module accepts data from the data processor that is in the form of spatiotemporal data matrices describing the wind, weather, topography, and suppression resource type and location, wherein this data can be utilized as an input into the wildfire mechanics module that is built with a neural architecture, wherein [0066] teaches the planning agent incorporates data from its environment, such as sensor data, and the pool of available suppression resources to produce a value maximizing set of actions using the model architecture, wherein [0061] teaches the salient features required for large scale planning includes training the probabilistic fire behavior model based on observations of past fire behavior, remotely sensed environment data, and records of suppression resource locations, wherein the neural network can accept inputs including recent, current, and forecasted winds, wherein [0080] teaches the wildfire mechanics module uses previous satellite based thermal detections of wildfire occurrence and the current environment to predict fire occurrence at each location on a landscape at a future time; see also: [0053, 0063, 0090, 0205-0206]).
Claim(s) 4-7, 14-15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Farley (US 20230342526 A1) in view of Killilea et al. (US 20240225023 A1) in view of Wang et al. (US 20240080363 A1) (hereafter referred to as Wang ‘363).
Regarding claim 4, the combination of Farley and Killilea teaches all the limitations of claim 1 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to parse surveillance data comprising audio interactions between at least one surveyor on site and a bot associated with the at least one command-and-control entity node.
From the same or similar field of endeavor, Wang ‘363 teaches wherein the instructions further cause the processor to parse surveillance data comprising audio interactions between at least one surveyor on site and a bot associated with the at least one command-and-control entity node ([0032] teaches achieving providing different video pictures and more by allowing voice chat to support the sending and receiving of different types of data and files, while event reporting supports the use of text and voice, video, and other attachments, wherein [0059] teaches obtaining geographic data, vegetation data, forest fire factor data, fire extinguishing resources and other data from the data lake, and calculates the fire through deep learning algorithms, wherein the current fire data can be combined with location data of the command and dispatch terminal to deduce the optimal rescue path for on-site rescuers, wherein the actual communication performance on site, the interaction between terminals and the command and dispatch platform use audio and voice messages in the communication environment, wherein when using audio/video communication, different encodings can be used based on the network availability, wherein [0060] teaches the integrated communication middle station provides the functions of establishing audio calls and text communications for various conditions, wherein the algorithm center can support converting the platform’s voice into text using a language processing unit and sending it into the terminal, wherein the algorithm can predict and simulate the spread of fire based on the current and historical data and can directly output dispatching instructions, wherein these dispatching instructions can be issues in the form of voice or text, wherein through the platform text to speech synthesis algorithm and natural language recognition algorithm, the scheduling instructions can be converted between voice and text, wherein the algorithm middle station uses location information to generate scheduling decisions for on-site personnel, wherein [0069] teaches the terminal can utilize sensing data to make decisions, wherein the sensing terminal can automatically adjust the sensing strategy in response to a detected condition; see also: [0149]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley and Killilea to incorporate the teachings of Wang ‘363 to include wherein the instructions further cause the processor to parse surveillance data comprising audio interactions between at least one surveyor on site and a bot associated with the at least one command-and-control entity node. One would have been motivated to do so in order to reduce unnecessary interventions by the rescuers by allowing the system to automatically receive rescuer audio and output dispatching instructions (Wang, ‘363, [0060]). By incorporating the teachings of Wang ‘363, one would have been able to automatically issue scheduling instructions in voice format based on an artificial intelligence algorithm that simulates the spread of fire (Wang, ‘363, [0149]).
Regarding claim 5, the combination of Farley, Killilea, and Wang ‘363 teaches all the limitations of claim 4 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to generate the plurality of features based on the surveillance data collected and recorded by the bot.
From the same or similar field of endeavor, Wang ‘363 further teaches wherein the instructions further cause the processor to generate the plurality of features based on the surveillance data collected and recorded by the bot ([0033] teaches the artificial intelligence middle platform is used to provide management services including algorithm deployment, wherein the input parameters or video sources come from the multi-mode heterogenous network aggregation and uploading dynamically allocated according to the physical locations including various sensors, alarms, video data, and more, wherein the data generated by the algorithm center is dynamically downloaded to the corresponding terminal, wherein the feature values generated by the AI algorithm will be fed back in for data fusion and stored in the corresponding library, wherein the prediction algorithms, such as fire spread prediction, can output a predicted diffusion range after a period of time, wherein [0022] teaches the information gathered by the communicating sensing terminals includes image or video feature values, wherein [0059] teaches obtaining geographic data, vegetation data, forest fire factor data, fire extinguishing resources and other data from the data lake, and calculates the fire through deep learning algorithms, wherein the current fire data can be combined with location data of the command and dispatch terminal to deduce the optimal rescue path for on-site rescuers, wherein the actual communication performance on site, the interaction between terminals and the command and dispatch platform use audio and voice messages in the communication environment, wherein when using audio/video communication, different encodings can be used based on the network availability, wherein [0060] teaches the integrated communication middle station provides the functions of establishing audio calls and text communications for various conditions, wherein the algorithm center can support converting the platform’s voice into text using a language processing unit and sending it into the terminal, wherein the algorithm can predict and simulate the spread of fire based on the current and historical data and can directly output dispatching instructions, wherein these dispatching instructions can be issues in the form of voice or text, wherein through the platform text to speech synthesis algorithm and natural language recognition algorithm, the scheduling instructions can be converted between voice and text, wherein the algorithm middle station uses location information to generate scheduling decisions for on-site personnel; see also: [0032, 0069-0070]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley, Killilea, and Wang ‘363 to incorporate the further teachings of Wang ‘363 to include wherein the instructions further cause the processor to generate the plurality of features based on the surveillance data collected and recorded by the bot. One would have been motivated to do so in order to reduce unnecessary interventions by the rescuers by allowing the system to automatically receive rescuer audio and output dispatching instructions (Wang, ‘363, [0060]). By incorporating the teachings of Wang ‘363, one would have been able to automatically issue scheduling instructions in voice format based on an artificial intelligence algorithm that simulates the spread of fire (Wang, ‘363, [0149]).
Regarding claims 6, 14, and 18, the combination of Farley and Killilea teaches all the limitations of claims 1, 11, and 17 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to continuously monitor incoming sensory data to determine if at least one value of the incoming sensory data deviates from a value of previous sensory data by a margin exceeding a pre-set threshold value.
From the same or similar field of endeavor, Wang ‘363 teaches wherein the instructions further cause the processor to continuously monitor incoming sensory data to determine if at least one value of the incoming sensory data deviates from a value of previous sensory data by a margin exceeding a pre-set threshold value ([0022] teaches receiving data from various sensors with a sampling interval and accuracy that are dynamically changed according to the rate of change of perceptual data, preset thresholds, network conditions, and sending frequency, so it can take into account response time and network bandwidth occupation, wherein data can be collected from several sensing terminals and judged based on whether the data collected by the sensing terminals is abnormal, wherein when abnormal, the first device connected to the sensing terminal generates first alarm information and a first alarm, wherein the data difference in the sensing terminal data collected by the sensing terminal may exceed the threshold range, which may be utilized to determine that the collected data is abnormal, wherein [0058-0059] teach the terminal may send fire alarm information to the server, which then starts the signal sampling and identification algorithm, wherein signals obtained by sensors are sent to the server and used to assess the spread of the fire; see also: [0018, 0073, 0084]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley and Killilea in order to incorporate the teachings of Wang ‘363 to include wherein the instructions further cause the processor to continuously monitor incoming sensory data to determine if at least one value of the incoming sensory data deviates from a value of previous sensory data by a margin exceeding a pre-set threshold value. One would have been motivated to do so in order to reduce unnecessary interventions by the rescuers by allowing the system to automatically receive rescuer audio and output dispatching instructions (Wang, ‘363, [0060]). By incorporating the teachings of Wang ‘363, one would have been able to automatically issue scheduling instructions in voice format based on an artificial intelligence algorithm that simulates the spread of fire (Wang, ‘363, [0149]).
Regarding claims 7, 15, and 19, the combination of Farley, Killilea, and Wang ‘363 teaches all the limitations of claims 6, 14, and 18 above.
Farley further teaches a feature vector ([0058] teaches the planning agent operates over an environment represented as a spatial grid, wherein the planning agent receives observations of the state of the environment in the form of a pixel matrix that represents time invariant environmental conditions, time varying environmental conditions, values at risk, vegetation, and a vector describing the location and type of each suppression unit, as well as in [0078] teaches the wildfire mechanics module accepts data from the data processor that is in the form of spatiotemporal data matrices describing the wind, weather, topography, and suppression resource type and location, wherein this data can be utilized as an input into the wildfire mechanics module that is built with a neural architecture, wherein [0066] teaches the planning agent incorporates data from its environment, such as sensor data, and the pool of available suppression resources to produce a value maximizing set of actions using the model architecture, wherein [0061] teaches the salient features required for large scale planning includes training the probabilistic fire behavior model based on observations of past fire behavior, remotely sensed environment data, and records of suppression resource locations, wherein the neural network can accept inputs including recent, current, and forecasted winds; see also: [0062-0064, 0067, 0070]);
and transmit the updated assets' deployment plan to the at least one command-and-control entity node over the wireless network to replace a previously transmitted assets' deployment plan ([0109] teaches the agent emits structured action directives that indicate the spatial location at which to move each available suppression resource and the number and type of resources required in the environment at the following timestep, wherein after each timestep, the fire simulator is used to update the environment with new fire ignitions, resources are moved to their new locations, and the agent receives a reward corresponding to the outcome of the previous timestep, wherein [0053-0054] teach performing resource distribution and suggesting potential alternate assignment strategies for handling resource demands at a large spatial scale, as well as in [0058] teaches the planning agent operates in order to receive observations and emit an action indicating the new location assignment for each available resource in the region and the number of resources of each type required at the following timestep, wherein [0059] teaches the agent’s goal is to identify strategies that maximize an objective function related to the loss of values at risk, wherein the planning agent produces a policy for a given set of suppression resources in response to current and forecast environmental conditions, wherein the policy and supporting data, e.g. predicted fire growth trajectory, are made available to client devices, e.g. phones, through an API service and data network, wherein the client device displays maps and graphics illustrating the policy and expected results to fire manager and other personnel responsible for dispatching resources in the field, as well as in [0145] teaches an application programming interface allows clients to access planning agent actions and fire mechanics predictions over the data network, wherein the users may interface with the planning agent using the client device over the network, wherein the users can request action strategies, near-term predictions, and post-processed projections of strategy rewards that can be used by fire managers for deploying suppression units in the real world, as well as in Fig. 37 and [0349] teach a user interacts with the decision support system through a client device, wherein data is transferred over a network, wherein the resolution strategy module is invoked and the results are returned and displayed on the device’s visual outputs as maps and graphs; see also: [0053-0055, 0061, 0066, 0159-0160]).
However, Farley does not explicitly teach wherein the instructions further cause the processor to, responsive to the at least one value of the incoming sensory data deviating from the value of the previous sensory data by the margin exceeding the pre-set threshold value, generate an updated feature vector based on the incoming sensory data and generate an updated the assets' deployment plan based on at least one asset allocation parameter produced by the predictive model in response to the updated feature vector.
From the same or similar field of endeavor, Wang ‘363 further teaches wherein the instructions further cause the processor to, responsive to the at least one value of the incoming sensory data deviating from the value of the previous sensory data by the margin exceeding the pre-set threshold value, generate an updated feature vector based on the incoming sensory data and generate an updated the assets' deployment plan based on at least one asset allocation parameter produced by the predictive model in response to the updated feature vector ([0022] teaches receiving data from various sensors with a sampling interval and accuracy that are dynamically changed according to the rate of change of perceptual data, preset thresholds, network conditions, and sending frequency, so it can take into account response time and network bandwidth occupation, wherein data can be collected from several sensing terminals and judged based on whether the data collected by the sensing terminals is abnormal, wherein when abnormal, the first device connected to the sensing terminal generates first alarm information and a first alarm, wherein the data difference in the sensing terminal data collected by the sensing terminal may exceed the threshold range, which may be utilized to determine that the collected data is abnormal, wherein [0057] teaches sending an alarm signal to the background and sending the data to the base station, wherein the base station dynamically adjusts the communication resources to the flame detection terminal and the camera, wherein the camera can send real-time, continuous videos and pictures to the server, wherein [0058-0059] teach the terminal may send fire alarm information to the server, which then starts the signal sampling and identification algorithm, wherein signals obtained by sensors are sent to the server and used to assess the spread of the fire, wherein the sensed data can be combined with location data of the command and dispatch terminal in order to deduce the optimal rescue path for on-site rescuers, wherein [0060-0061] teach the real-time dispatch of artificial intelligence business platform and other information can be used to calculate and dynamically adjust the communication strategy in order to ensure real-time, dynamic, and coherent communication connection of the command and dispatch terminal and the on-site sensor equipment, as well as in [0126-0127] teach dynamically adjusting the base station based on updated data; see also: [0018, 0073, 0084]; Examiner’s Note: See the 35 USC 103 combination below for teachings pertaining to the unbolded claim language.).
While Wang ‘363 does not explicitly teach a feature vector, the primary reference Farley teaches a feature vector. Therefore, the combination of the feature processing of Wang ‘363, when combined into the feature vector structuring of Farley, teaches all the limitations of the claims. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley, Killilea, and Wang ‘363 to incorporate the further teachings of Wang ‘363 to include wherein the instructions further cause the processor to, responsive to the at least one value of the incoming sensory data deviating from the value of the previous sensory data by the margin exceeding the pre-set threshold value, generate an updated feature vector based on the incoming sensory data and generate an updated the assets' deployment plan based on at least one asset allocation parameter produced by the predictive model in response to the updated feature vector. One would have been motivated to do so in order to reduce unnecessary interventions by the rescuers by allowing the system to automatically receive rescuer audio and output dispatching instructions (Wang, ‘363, [0060]). By incorporating the teachings of Wang ‘363, one would have been able to automatically issue scheduling instructions in voice format based on an artificial intelligence algorithm that simulates the spread of fire (Wang, ‘363, [0149]).
Claim(s) 8-10, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Farley (US 20230342526 A1) in view of Killilea et al. (US 20240225023 A1) in view of Wang et al. (US 20250071040 A1) (hereafter referred to as Wang ‘040).
Regarding claims 8, 16, and 20, the combination of Farley and Killilea teaches all the limitations of claims 1, 11, and 17 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to record the asset allocation parameters on a blockchain ledger along with the features retrieved from the sensory data and corresponding available fire suppression assets'-related data.
From the same or similar field of endeavor, Wang ‘040 teaches wherein the instructions further cause the processor to record the asset allocation parameters on a blockchain ledger along with the features retrieved from the sensory data and corresponding available fire suppression assets'-related data ([1990-1991] teach establishing a forest fire prevention gridded information databases including geographic location information, grid IoT equipment management, fire risk prediction, grid IoT equipment information base, and more, wherein when a fire occurs, the fire-fighting response strategy is displayed according to the store database information, wherein [2005] teaches when establishing the IoT device information library, it includes the monitoring and control of sensing devices and gateway devices of the grid, wherein the sensing device can collect parameters related to temperature, humidity, wind speed, and more corresponding to the area of the grid, wherein [2312] teaches the blockchain security management platform implements a method in which IoT device data is connected to the cloud and the data can be uploaded to the blockchain to ensure data non-tamper ability and traceability, and wherein [2356] teaches the blockchain service interface is used to ensure the security of the data generated by IoT devices, wherein the blockchain security system established without tampering can be customized for smart contracts and consensus mechanisms; see also: [0324, 1958, 2009, 2011-2015]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley and Killilea to incorporate the teachings of Wang ‘040 to include wherein the instructions further cause the processor to record the asset allocation parameters on a blockchain ledger along with the features retrieved from the sensory data and corresponding available fire suppression assets'-related data. One would have been motivated to do so in order to store IoT device data on the blockchain to ensure data non-tamper ability and traceability (Wang ‘040, [2312]). By incorporating the teachings of Wang ‘040, one would have been able to optimally allocate resources in the grid according to the established information database (Wang ‘040, [1991-1996]).
Regarding claim 9, the combination of Farley, Killilea and Wang ‘040 teaches all the limitations of claim 8 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to retrieve at least one asset allocation parameter from the blockchain responsive to a consensus among the FAS node and the at least one command-and-control entity node.
From the same or similar field of endeavor, Wang ‘040 further teaches wherein the instructions further cause the processor to retrieve at least one asset allocation parameter from the blockchain responsive to a consensus among the FAS node and the at least one command-and-control entity node ([1990-1991] teach establishing a forest fire prevention gridded information databases including geographic location information, grid IoT equipment management, fire risk prediction, grid IoT equipment information base, and more, wherein when a fire occurs, the fire-fighting response strategy is displayed according to the store database information, wherein [2005] teaches when establishing the IoT device information library, it includes the monitoring and control of sensing devices and gateway devices of the grid, wherein the sensing device can collect parameters related to temperature, humidity, wind speed, and more corresponding to the area of the grid, wherein [2309] teaches the blockchain service interface is used to ensure the security of data generated by IoT devices and users, which cannot be tampered with, including smart contracts and consensus mechanisms, wherein [2333-2338] teach the blockchain security management platform can be used as an organization/unit to process data through smart contracts to realize data uploading and reach a consensus with other organizations/units to realize data on-chain and have tamper-proof features, wherein [1986-1987] teach the forest fire prevention grid information database includes grid division where each grid is the next-level management unit of the administrative village, wherein [2000] teaches the administrative village/township is taken as the superior unit to further divide the forest area, wherein [2010] teaches the use of grid management can be used for the work assignment of urban staff management, wherein [2312] teaches the blockchain security management platform implements a method in which IoT device data is connected to the cloud and the data can be uploaded to the blockchain to ensure data non-tamper ability and traceability, and wherein [2356] teaches the blockchain service interface is used to ensure the security of the data generated by IoT devices, wherein the blockchain security system established without tampering can be customized for smart contracts and consensus mechanisms; see also: [0324, 1912, 1958, 2009, 2011-2015]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley, Killilea, and Wang ‘040 to incorporate the further teachings of Wang ‘040 to include wherein the instructions further cause the processor to retrieve at least one asset allocation parameter from the blockchain responsive to a consensus among the FAS node and the at least one command-and-control entity node. One would have been motivated to do so in order to store IoT device data on the blockchain to ensure data non-tamper ability and traceability (Wang ‘040, [2312]). By incorporating the teachings of Wang ‘040, one would have been able to optimally allocate resources in the grid according to the established information database (Wang ‘040, [1991-1996]).
Regarding claim 10, the combination of Farley, Killilea, and Wang ‘040 teaches all the limitations of claim 8 above.
However, Farley fails to explicitly teach wherein the instructions further cause the processor to execute a smart contract to record data reflecting execution of the assets' deployment plan associated with the asset allocation parameters and the at least one command-and-control entity node on the blockchain for future audits.
From the same or similar field of endeavor, Wang ‘040 further teaches wherein the instructions further cause the processor to execute a smart contract to record data reflecting execution of the assets' deployment plan associated with the asset allocation parameters and the at least one command-and-control entity node on the blockchain for future audits ([1990-1991] teach establishing a forest fire prevention gridded information databases including geographic location information, grid IoT equipment management, fire risk prediction, grid IoT equipment information base, and more, wherein when a fire occurs, the fire-fighting response strategy is displayed according to the store database information, wherein [2005] teaches when establishing the IoT device information library, it includes the monitoring and control of sensing devices and gateway devices of the grid, wherein the sensing device can collect parameters related to temperature, humidity, wind speed, and more corresponding to the area of the grid, wherein [2309] teaches the blockchain service interface is used to ensure the security of data generated by IoT devices and users, which cannot be tampered with, including smart contracts and consensus mechanisms, wherein [2333-2338] teach the blockchain security management platform can be used as an organization/unit to process data through smart contracts to realize data uploading and reach a consensus with other organizations/units to realize data on-chain and have tamper-proof features, wherein [1986-1987] teach the forest fire prevention grid information database includes grid division where each grid is the next-level management unit of the administrative village, wherein [2000] teaches the administrative village/township is taken as the superior unit to further divide the forest area, wherein [2010] teaches the use of grid management can be used for the work assignment of urban staff management, wherein [2312] teaches the blockchain security management platform implements a method in which IoT device data is connected to the cloud and the data can be uploaded to the blockchain to ensure data non-tamper ability and traceability, and wherein [2356] teaches the blockchain service interface is used to ensure the security of the data generated by IoT devices, wherein the blockchain security system established without tampering can be customized for smart contracts and consensus mechanisms; see also: [0324, 1912, 1958, 2009, 2011-2015]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Farley, Killilea, and Wang ‘040 to incorporate the further teachings of Wang to include wherein the instructions further cause the processor to execute a smart contract to record data reflecting execution of the assets' deployment plan associated with the asset allocation parameters and the at least one command-and-control entity node on the blockchain for future audits. One would have been motivated to do so in order to store IoT device data on the blockchain to ensure data non-tamper ability and traceability (Wang ‘040, [2312]). By incorporating the teachings of Wang ‘040, one would have been able to optimally allocate resources in the grid according to the established information database (Wang ‘040, [1991-1996]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lareau (US 20230375716 A1) discloses a tracking wildfires and plume composition using weather radar data
Raucher (US 20210283439 A1) discloses enabling early detection of wildfires by identifying a chemical signature of wildfires through spectroscopy
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.
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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