DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 05/04/2026.
Status of Claims
Claim 4 has been canceled.
Claims 1, 3, 5, 13, and 18 have been amended.
Claim 21 has been added.
Claims 1-3 and 5-21 are now pending.
Response to Arguments
Applicant's arguments filed on 05/04/2026 regarding the 35 U.S.C. 101, 102, and 103 rejections of the claims have been fully considered. The Applicant argues the following.
(1) As per the 101 rejection, the Applicant argues that the amended claims are not directed to any abstract idea, incorporate any alleged abstract idea into a practical application, and include additional features amounting to significantly more than any alleged abstract idea.
The Examiner respectfully disagrees. The Examiner maintains the position that the claims are still directed to the abstract group of Certain Methods of Organizing Human Activity because the claims describe a process of collecting, analyzing, and managing workflow information to assign work items to vehicles/operators. Assigning vehicle operators work items and providing routing, access, and equipment instructions for the work location reflect Certain Methods of Organizing Human Activity, as the work items and instructions directs the operators’ behavior. Per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The Examiner also maintains the position that the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more because the additional elements do not improve the functioning of a computer or improve another technology. The additional elements reflect the use of computer-based technology to perform an abstract idea (i.e. managing the assignment of vehicles and operators). Applying an abstract idea on a computer and/or generally linking the use of the abstract idea to a particular technological environment does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05 (f) and (h)). The computer system and components are not improved beyond their original functions and capabilities as a result of implementing the Applicant’s claims. Therefore, the 35 U.S.C. 101 is maintained.
(2) As per the 102 and 103 rejections, the Applicant argues that none of the cited references disclose, teach, or suggest at least the features of independent claim 1. Beth is silent with respect to detecting onboard tools via sensors or identifying a work item for a vehicle based on detected onboard tools and tools required for the work item.
The Examiner respectfully disagrees. The Examiner submits that based on the broadest reasonable interpretation of the claims and the updated claim mappings below, that Beth teaches all of the limitations in independent claim 1. Therefore, the 35 U.S.C. 102 and 103 rejections remain. See details below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 03/13/2026 and 03/16/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-3 and 5-21 are rejected under 35 U.S.C. 101 because the claimed invention, “Workflow Management Application For Vehicle Fleet”, is directed to an abstract idea, specifically Certain Methods of Organizing Human Activity, without significantly more. The claims as a whole do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the abstract idea because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer
Step 1: Claims 1-3 and 5-21 are directed to a statutory category, namely a machine.
Step 2A (1): Independent claims 1, 13, and 18 are directed to an abstract idea of Certain Methods of Organizing Human Activity, based on the following claim limitations: “acquire first workflow management information from the plurality of work vehicles, the first workflow management information including the one or more tools detected onboard a work vehicle of the plurality of work vehicles…; acquire second workflow management information associated with a plurality of work items, the second workflow management information including at least one tool required for a work item of the plurality of work items; identify, …, the work item of the plurality of work items for the work vehicle of the plurality of work vehicles based on at least the one or more tools detected onboard the work vehicle and the at least one tool required for the work item the first workflow management information and the second workflow management information; and provide the work item to the work vehicle (claim 1); acquire vehicle locations for a plurality of work vehicles; acquire work item locations of a plurality of work items; identify, …, a work item from the plurality of work items for a work vehicle of the plurality of work vehicles based on the vehicle locations and the work item locations,…; provide the work item to the work vehicle; receive an updated work item duration from the work vehicle; …. (claim 13); acquire first workflow management information from a plurality of work vehicles, the first workflow management information including an identity of an operator of a work vehicle of the plurality of work vehicles; determine, based on the identity of the operator, one or more credentials or skills of the operator; acquire second workflow management information associated with a plurality of work items, the second workflow management information including at least one credential or skill required for a work item of the plurality of work items; identify,…, the work item of the plurality of work items for the work vehicle of the plurality of work vehicles based on at least the one or more credentials or skills of the operator and the at least one credential or skill required for the work item the first workflow management information and the second workflow management information; generate,…, a map depicting a route from a vehicle location of the work vehicle to a work item location of the work item; and provide providing the map to a display of the work vehicle to be displayed to an operator of the work vehicle (claim 18) .”. These claims describe a process of collecting, analyzing, and managing workflow information to assign work items to vehicles/operators. Dependent claims 2-3, 5-10, 12, 14-16, and 19-20 further describe the collection, analysis, and management of workflow information and the process of assigning work items to vehicles and operators with the limitations of “wherein the first workflow management information includes vehicle location for the plurality of work vehicles, and the second workflow management information includes work item locations of the plurality of work items” (claim 2); “wherein the first workflow management information includes an identity of an operator of the work vehicle, and the second workflow management information includes at least one credential or skill required for the work item,… determine, based on the identity of the operator, one or more credentials or skills of the operator, and wherein the work item is identified for the work vehicle further based on the one or more credentials or skills of the operator and the at least one credential or skill required for the work item (claim 3); “detect current charge level for at least one tool of the one or more tools” (claim 5); “wherein the first workflow management information includes identities of operators of the plurality of work vehicles, and the second workflow management information includes work schedules of the operators of the plurality of work vehicles and expected durations of the plurality of work items” (claim 6); “acquire work item progress information from the plurality of work vehicles regarding the plurality of work items; and reassign the work item based on the work item progress information.” (claim 7); wherein providing the work item to the work vehicle includes: generating… a map depicting a route from a vehicle location of the work vehicle to a work item location of the work item; … to be displayed to an operator of the work vehicle.” (claims 8 and 16); “determine, …, that the route requires access credentials for entering an area of a building; and … to request the access credentials for the operator of the work vehicle.” (claim 9); “determine, …, that the route passes through an area requiring personal protection equipment; and provide a notification to the work vehicle indicating that the area requires the personal protection equipment.”(claim 10); “determine that the work vehicle has reached the work item location based on the work vehicle entering the geofence; start a tracking timer to track a duration of the work item; determine that the work item is complete based on the work vehicle exiting the geofence; and stopping the tracking timer.” (claim 12); “wherein the first workflow management information includes identities of operators of the plurality of work vehicles, and the second workflow management information includes credential information for the operators of the plurality of work vehicles and an indication of credentials required for the plurality of work items” (claim 14); “wherein the first workflow management information includes onboard tool information associated with onboard tools of the plurality of work vehicles, and the second workflow management information includes an indication of tools required for the plurality of work items” (claim 15);. Assigning vehicle operators work items and providing routing, access, and equipment instructions for the work location reflect certain methods of organizing human activity. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract grouping of Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1-3 and 5-21 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1-3 and 5-21 recite additional elements of “A vehicle work management system comprising: a plurality of work vehicles, each work vehicle of the plurality of work vehicles having one or more sensors configured to detect one or more tools onboard the work vehicle; and a workflow management application utilizing one or more machine learning models, the workflow management application configured to; …by the one or more sensors, …using the one or more machine learning models (claim 1); plurality of work vehicles (claims 1-2, 6-7, 13-15, and 18-19); workflow management application (claims 1, 3, 7, 9-12, and 21); one or more machine learning models (claims 1, 9-11, 13, 17 and 21); wherein the one or more sensors are configured to detect a current charge level for at least one tool of the one or more tools (claim 5);. the one or more machine learning models trained using historical work item durations associated with the plurality of work items, ... and train the one or more machine learning models on the updated work item duration (claims 13 and 21); onboard tools of the plurality of work vehicles (claim 15); generating a user interface; provide the user interface to a display of the work vehicle (claims 8 and 16); workflow management application is configured to interface, using one or more machine learning models, with an external system (claim 9); wherein the workflow management application is configured to create, using the one or more machine learning models, a geofence around the work item location (claims 11 and 17); A vehicle work management system comprising: at least one processing circuit having at least one processor and at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: (claim 13); A vehicle work management system comprising: at least one processing circuit having at least one processor and at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: (claim 18); and using a machine learning application (claims 18-20)”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing and display devices that are used to perform the abstract process identified in Step 2A (prong 1). The use of and the training of machine learning models are considered instructions to apply or implement a model on a computer.. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Collecting data using sensor is considered is deemed insignificant extra solution activity to the abstract idea (e.g. detection step). Per MPEP 2106.05(g), insignificant extra-solution activity does not integrate the abstract idea into a practical application or provide an inventive concept. Also limitations that amount to merely indicating a field of use or technological environment (e.g. work vehicle management) in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application (see MPEP 2106.05(h)). Therefore, claims 1-3 and 5-21 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1-3 and 5-21 recite additional elements as stated above in Step 2A(2). These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-3 and 5-21 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5-8, and 11-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Beth et al. (US 2022/0156665 A1).
As per claim 1 (Currently Amended), A vehicle work management system comprising (Beth e.g. Referring now to FIG. 1, an example system/platform 100 may integrate various software modules for representing and controlling manned and unmanned vehicles and provide them to a user such that they are available for mission planning, building, simulation or execution [0045].):
Beth teaches a plurality of work vehicles, each work vehicle of the plurality of work vehicles having one or more sensors configured to detect one or more tools onboard the work vehicle; and (Beth e.g. Embodiments herein are applicable to any application involving coordination of autonomous and/or manned vehicles. Example and non-limiting embodiments include one or more of: industrial equipment; robotic systems (including at least mobile robots, autonomous vehicle systems, and/or industrial robots); mobile applications (that may be considered “vehicles” and/or “agents”, as those terms are described herein), smart cities, and/or manufacturing systems [0006]. Illustrated in FIG. 28 is a schematic diagram of an agent, e.g., an autonomous vehicle 2800, in accordance with an embodiment of the current disclosure. Non-limiting exemplary components of the agent 2800 may include a robot 2810, various sensors 2812, a controller 2814, data storage 2816, a vehicle-to-vehicle (V2V) communication interface 2818, a communications interface 2820 for communicating with the platform 100, (FIG. 1), 200 (FIG. 2), 300 (FIG. 3) and other external entities via one or more of various types of networks [0113]. The sensors may include LIDAR 2822, IR sensors 2824, digital cameras 2826, RGB, and/or other video 2828 inputs/sensors, and other sensors such as thermal, stereo, hyper or multispectral sensors, or any other 2D, 3D, or other sensor, and the like, for data acquisition related to a mission and navigation of the vehicle [0115].)
Beth teaches a workflow management application utilizing one or more machine learning models, the workflow management application configured to (Beth e.g. Referring to FIGS. 7-25, the platform 100 may provide a user interface, e.g., as a web application graphical user interface (GUI) provided in an internet browser, to permit a user to build a workflow or streamline together multiple tasks for multiple agents for a mission into a template (“workflow”, “maneuver” and/or “mission”) [0064]. In embodiments, one or more machine learning techniques, e.g., back propagation, may be used to tune the parameters of a service and/or microservice for scheduling efficiency with respect to the scheduling of agents [0055]. In some embodiments, the platform 100 provides scheduling and routing to devices in one or more locations to deconflict traffic, reduce congestion, prioritize certain traffic, missions or tasks, or otherwise facilitate coordinated and regulated movement for the devices [0056].) :
Beth teaches acquire first workflow management information from the plurality of work vehicles, the first workflow management information including the one or more tools detected onboard a work vehicle of the plurality of work vehicles by the one or more sensors; (Beth e.g. Embodiments of the current disclosure provide for a system and/or platform that may be used to integrate different vehicle types such that they may be used together in a mission [0007]. The workflow designer tool may permit a user to plan a mission via selecting vehicles and mission parts or tasks. The platform may provide listings of vehicles that are compatible with one another and the platform, along with listing the vehicles' capabilities, to facilitate mission planning [0008]. Referring to FIGS. 1 and 4, collectively, mission data may be obtained by the platform 100. As shown in FIG. 4, in the non-limiting example of routing for a manned vehicle, mission data may be obtained at 402 by the platform in a variety of ways; for example by scanning of a code, such as a QR code at vehicle 102 via a mobile device of the operator, which may initiate a network communication to the platform 100 that routing is required by the operator for the vehicle 102 [0058]. Having identified the vehicle(s) and mission part(s) at 404 and 406, the platform 100 may perform a vehicle compatibility check at 408. For example, a user may indicate a particular unmanned vehicle is to provide transport for a second type of unmanned vehicle, which is to perform visual inspection of the towers, e.g., using a camera to capture images of the towers, or use other sensor data to collect inspection information, such as a laser point cloud [0068]. In embodiments, a vehicle compatibility check, as indicated at 410, may take various forms, for example including an initial compatibility determination with respect to the vehicle selected as compared with the user input for a mission task, such as capability to perform a given task, availability to do so, or the ability to adequately communicate with other vehicle(s) that may be involved. One non-limiting example of such a compatibility check is a determination as to whether the vehicle is available, e.g., open in terms of scheduling, has sufficient power or payload capacity, adequate sensors, etc. [0069]. n embodiments, having a large array of sensors and cameras may enable novel avenues of data fusion. In embodiments, each sensor may be operating in coordination with each camera onboard, which may facilitate aligning the sensor data with the camera data taking into account, for example, displacements, distortions, and timing discrepancies [0139].)
Beth teaches acquire second workflow management information associated with a plurality of work items, the second workflow management information including at least one tool required for a work item of the plurality of work items; (Beth e.g. Mission data may be obtained automatically as a subtask within a mission protocol or workflow, may be created by an intelligent process, such as an automated process that detects a vehicle is incompatible or conditionally incompatible, such as low on fuel, and automatically suggests a substitute vehicle, as further described herein [0058]. A user may indicate a particular unmanned vehicle is to provide transport for a second type of unmanned vehicle, which is to perform visual inspection of the towers, e.g., using a camera to capture images of the towers, or use other sensor data to collect inspection information, such as a laser point cloud [0068]. The platform 100 may determine at 410 if the vehicles selected are compatible, e.g., capable of performing a mission task, working together to accomplish the inspection, communicate (directly or indirectly through the platform 100), et. [0068]. This may be implemented, for example, during the workflow design, at the end of workflow design, after a previous workflow template is updated, e.g., to indicate a new vehicle, during the mission, and/or at another suitable point in time [0068].)
Beth teaches identify, using the one or more machine learning models, the work item of the plurality of work items for the work vehicle of the plurality of work vehicles based on at least the one or more tools detected onboard the work vehicle and the at least one tool required for the work item; and (Beth e.g. In embodiments, one or more machine learning techniques, e.g., back propagation, may be used to tune the parameters of a service and/or microservice for scheduling efficiency with respect to the scheduling of agents [0055]. As shown in FIG. 4, in the non-limiting example of routing for a manned vehicle, mission data may be obtained at 402 by the platform in a variety of ways; for example by scanning of a code, such as a QR code at vehicle 102 via a mobile device of the operator, which may initiate a network communication to the platform 100 that routing is required by the operator for the vehicle 102 [0058]. In certain aspects, a mobile application running on the mobile device of the operator may be authenticated once during sign on or authenticated via the location of the mobile device, e.g., within a geofence such as 124, in proximity to vehicle 102, etc. As will be appreciated, this may avoid the need for an operator to authenticate or manually input data prior to obtaining routing instructions, i.e., trust is established due to the combination of the physical location of the mobile device in context (e.g., in combination with the QR code scan event, proximity to a vehicle available for a mission, etc.) [0058]. In embodiments, following the obtaining at 402, a vehicle is identified at 404, e.g., vehicle 102 is identified by the platform due to the coded information scanned and transmitted via the mobile application running on the operator's mobile phone [0059]. This may provide for the platform 100 to identify mission part(s) associated with the vehicle 102, as indicated at 406 [0059]. For example, a predetermined mission may be planned for a manned vehicle, e.g., vehicle 102, on the basis of one or more factors present when the scanned code data is obtained by the platform 100. Non-limiting examples of the one or more factors include time, location, last mission, last mission status, mission imported from an external system, etc. [0059]. During the planning, the platform 100 may act to facilitate the formation of the business workflow or process, e.g., by identifying one or more vehicles, e.g., vehicles 102, 103, 104 or 106, that are available for use in the location 108, capable of performing the business workflow or process, etc. [0087].)
Beth teaches provide the work item to the work vehicle. (Beth e.g. The workflow designer tool may permit a user to plan a mission via selecting vehicles and mission parts or tasks. The platform may provide listings of vehicles that are compatible with one another and the platform, along with listing the vehicles' capabilities, to facilitate mission planning [0008]. The platform may have access to one or more agents, e.g., vehicles, that may be electrically powered, wherein the system automatically generates and coordinates a schedule for the agents to unload Ship A by time X with the agents performing electrical recharges [0008]. In some embodiments, the platform 100 provides scheduling and routing to devices in one or more locations to deconflict traffic, reduce congestion, prioritize certain traffic, missions or tasks, or otherwise facilitate coordinated and regulated movement for the devices [0056].)
As per claim 2 (Original), Beth teaches the vehicle work management system of Claim 1, Beth also teaches wherein the first workflow management information includes vehicle location for the plurality of work vehicles, and the second workflow management information includes work item locations of the plurality of work items. (Beth e.g. In some embodiments, missions are for manned and/or unmanned vehicles in private locations, such as ports, where the mission includes performance of tasks such as container or asset location, pickup, and relocation, with reporting to a back-end system such as a logistics tracking and reporting software [0008]. As shown in FIG. 1, the platform 100 may communicate with agents, e.g., vehicles, 102, 103, 104, 106 in a location (e.g., Location A 108, which may be a port (or other type of location described herein, and/or location N 110) as viewed from above as in the example of FIG. 1) [0045]. The location 108 may include various assets 112, 114, such as shipping containers [0045]. In an embodiment, the data of the location 108 and its content (vehicles, assets, environmental information, etc.) may be made available for display by the platform 100 to an end user device, such as a mobile device of an operator of a vehicle, e.g., vehicle 102, at the location 108 [0057]. Further, because platform 100 may continually, periodically, or intermittently update its mapping information or state for the location 108, the platform 100 may have access to additional data that is useful in scheduling and/or routing [0060]. In embodiments, the location of the mission may be a complex environment that requires multiple vehicles, manned and unmanned, to cooperate with one another [0063]. In a manned vehicle routing non-limiting example, a manned vehicle's routing instructions may be adjusted or modified based on real-time or near real-time data, such as obtained from other vehicles in the environment [0063]. The sentry host may then execute queries to determined assigned tasks to dispatch 2398 and subsequently schedule one or more tasks to agents 2940, which may be based at least in part on an agent's proximity to the task, e.g., closeness to an asset/object and/or a better position to execute the task than other agents [0124].)
As per claim 3 (Currently Amended), Beth teaches the vehicle work management system of Claim 1, wherein the first workflow management information includes an identity of an operator of the work vehicle (Beth e.g. As shown in FIG. 4, in the non-limiting example of routing for a manned vehicle, mission data may be obtained at 402 by the platform in a variety of ways; for example by scanning of a code, such as a QR code at vehicle 102 via a mobile device of the operator, which may initiate a network communication to the platform 100 that routing is required by the operator for the vehicle 102 [0058]. In certain aspects, a mobile application running on the mobile device of the operator may be authenticated once during sign on or authenticated via the location of the mobile device, e.g., within a geofence such as 124, in proximity to vehicle 102, etc. As will be appreciated, this may avoid the need for an operator to authenticate or manually input data prior to obtaining routing instructions, i.e., trust is established due to the combination of the physical location of the mobile device in context (e.g., in combination with the QR code scan event, proximity to a vehicle available for a mission, etc.) [0058].), the second workflow management information includes at least one credential or skill required for the work item, and the workflow management application is configured to: determine, based on the identity of the operator, one or more credentials or skills of the operator, and wherein the work item is identified for the work vehicle further based on the one or more credentials or skills of the operator and the at least one credential or skill required for the work item. (Beth e.g. In embodiments, following the obtaining at 402, a vehicle is identified at 404, e.g., vehicle 102 is identified by the platform due to the coded information scanned and transmitted via the mobile application running on the operator's mobile phone [0059]. For example, a predetermined mission may be planned for a manned vehicle, e.g., vehicle 102, on the basis of one or more factors present when the scanned code data is obtained by the platform 100. Non-limiting examples of the one or more factors include time, location, last mission, last mission status, mission imported from an external system, etc. [0059]. In embodiments, the routing data may be provided to the operator's mobile phone for display of routing guidance in the mobile application, as indicated at 424. In one non-limiting example, the platform 100 may provide or output at 424 displayable data or coordinate data that is combined with displayable data resident at the mobile phone application of the operator in the form of a map to provide turn-by-turn directions for guiding the operator of vehicle 102 to container 114 and any other part of the mission, e.g., to the delivery location [0062]. In certain examples, missions or parts thereof may be adjusted based on vehicle or operator capabilities or expected actions, e.g., manned and unmanned vehicles may be configured to adjust mission tasks or parameters thereof such as speed to accommodate a manned vehicle's acceptable or desirable operating parameters, an unmanned vehicle may be configured to update its map state to accommodate expected travel time, location and reaction for manned vehicles, etc. [0080]. In embodiments, the routing or scheduling data may be semi-automatically adjusted, e.g., highlighting or indicated routes, missions or mission parts that need operator attention [0081]. In embodiments, if vehicle( s) have not been assigned to mission part(s), vehicle(s) or agent(s) may bid on the mission parts, e.g., based on availability, capability, location, etc. [0096]. Much like the MCS, an Agent Registry Structure (ARS) may provide a flexible approach to defining the specifications and capability of a wide variety of agents into an agent registry. These registry entries are utilized by Intersect to select agents to be tasked on missions appropriate to their capabilities [0146].)
As per claim 5 (Currently Amended) Beth teaches the vehicle work management system of Claim 1, wherein the one or more sensors are configured to detect a current charge level for at least one tool of the one or more tools (Beth e.g. The platform may have access to one or more agents, e.g., vehicles, that may be electrically powered, wherein the system automatically generates and coordinates a schedule for the agents to unload Ship A by time X with the agents performing electrical recharges [0008]. In embodiments, one or more machine learning techniques, e.g., back propagation, may be used to tune the parameters of a service and/or microservice for scheduling efficiency with respect to the scheduling of agents [0055]. In embodiments, scheduling efficiency may include, but is not limited to: a shortest time to perform a particular action; monetary cost-efficiency, e.g., a least expensive way to perform a particular action; energy cost-efficiency, e.g., fuel and/or battery life; a prioritization based efficiency; etc. [0055]. In certain aspects, coordinated agent command values 2628 of the plurality generated by different microservices may be of different types, e.g., a first microservice may be tasked with coordinating recharging of electrical vehicles that perform aspects of the shared maneuver and a second microservice may be tasked with deconflicting the electrical vehicles (among themselves and/or with other vehicles) along one or more routes utilized by the electrical vehicles for performing the shared maneuver [0107]. In embodiments, one or more of the microservices 2630, 2632, 2634 may perform one or more of the following: monitor fuel consumption for an agent, perform rerouting of an agent to account for planned and/or unplanned circumstances, e.g., bathroom breaks, supply chain delays, equipment malfunctions, weather events, etc. [0107].)
As per claim 6 (Original), Beth teaches the vehicle work management system of Claim 1, Beth also teaches wherein the first workflow management information includes identities of operators of the plurality of work vehicles, and the second workflow management information includes work schedules of the operators of the plurality of work vehicles and expected durations of the plurality of work items (Beth e.g. The platform may have access to one or more agents, e.g., vehicles, that may be electrically powered, wherein the system automatically generates and coordinates a schedule for the agents to unload Ship A by time X with the agents performing electrical recharges [0008]. In some embodiments, the platform 100 provides scheduling and routing to devices in one or more locations to deconflict traffic, reduce congestion, prioritize certain traffic, missions or tasks, or otherwise facilitate coordinated and regulated movement for the devices [0056]. In embodiments, the platform 100 may deconflict agents, e.g., vehicles, based on one or more goals/intents of each agent being deconflicted. For example, a first vehicle delivering a time sensitive cargo, e.g., bananas, may be prioritized over a second vehicle transporting non-critical backup components to a warehouse. As another example, a first vehicle transporting cargo that is deemed to be late may be prioritized over a second vehicle transporting cargo that is deemed to be ahead of schedule [0056]. As shown in FIG. 4, in the non-limiting example of routing for a manned vehicle, mission data may be obtained at 402 by the platform in a variety of ways; for example by scanning of a code, such as a QR code at vehicle 102 via a mobile device of the operator, which may initiate a network communication to the platform 100 that routing is required by the operator for the vehicle 102 [0058]. In certain aspects, a mobile application running on the mobile device of the operator may be authenticated once during sign on or authenticated via the location of the mobile device, e.g., within a geofence such as 124, in proximity to vehicle 102, etc. As will be appreciated, this may avoid the need for an operator to authenticate or manually input data prior to obtaining routing instructions, i.e., trust is established due to the combination of the physical location of the mobile device in context (e.g., in combination with the QR code scan event, proximity to a vehicle available for a mission, etc.) [0058]. In certain examples, missions or parts thereof may be adjusted based on vehicle or operator capabilities or expected actions, e g , manned and unmanned vehicles may be configured to adjust mission tasks or parameters thereof such as speed to accommodate a manned vehicle's acceptable or desirable operating parameters, an unmanned vehicle may be configured to update its map state to accommodate expected travel time, location and reaction for manned vehicles, etc. [0080]. In embodiments, the routing or scheduling data may be semi-automatically adjusted, e.g., highlighting or indicated routes, missions or mission parts that need operator attention [0081]. In some embodiments, workflows or missions may be scheduled, e.g., to take place at a specific time, to recur, to begin after completion of a related mission or detection of the presence of an object such as cargo being situated in a given location, such as detected using computer vision and object detection [0088].)
As per claim 7 (Original), Beth teaches the vehicle work management system of Claim 1, Beth also teaches wherein the workflow management application is configured to: acquire work item progress information from the plurality of work vehicles regarding the plurality of work items; and reassign the work item based on the work item progress information. (Beth e.g. Referring to FIGS. 1 and 4, collectively, mission data may be obtained by the platform 100. As shown in FIG. 4, in the non-limiting example of routing for a manned vehicle, mission data may be obtained at 402 by the platform in a variety of ways [0058]. In embodiments, following the obtaining at 402, a vehicle is identified at 404, e.g., vehicle 102 is identified by the platform due to the coded information scanned and transmitted via the mobile application running on the operator's mobile phone [0059]. This may provide for the platform 100 to identify mission part(s) associated with the vehicle 102, as indicated at 406 [0059]. In a non-limiting example of a multi-vehicle mission, a vehicle may be determined to be incompatible or conditionally incompatible, such as low on fuel. In such a circumstance, the platform 100 may automatically suggest a substitute vehicle as indicated at 404, respond to a vehicle's request for assistance, etc. [0059]. During performance, the platform 100 may act to track and update the progress of the business workflow or process, e.g., by providing updated map state information that corresponds to performance or completion of a mission part, a workflow, and/or a stage thereof [0087]. As such, users and/or subscribers of the ERP or logistics application provided by remote device 128 may be notified or kept up to date with the mission progress, any difficulties encountered, etc. [0087]. In a manned vehicle routing non-limiting example, a manned vehicle's routing instructions may be adjusted or modified based on real-time or near real-time data, such as obtained from other vehicles in the environment [0063]. As will be appreciated, this may provide for adjustment or modification to the mission protocol or part thereof, such as updated routing guidance based on human operator inputs (e.g., human operator deviating from a location of the route or timing thereof), based on unmanned vehicle locations or behaviors (e.g., movement to avoid one another or the manned vehicle, vehicle requests or offers assistance, etc.) [0063]. As further described herein, adjustments or modifications to routing or other mission data may be accomplished using a variety of inputs from vehicles, human operators, or a combination thereof, which are provided as input to intelligent processes that are configured for dynamic mission updates, e.g., for handling complex traffic and congestion management tasks [0063]. In certain aspects, identifying the manned vehicle may be performed via scanning a manned vehicle identification number in a bar-code attached to or associated with the manned vehicle [0159]. In certain aspects, identifying the manned vehicle may further include obtaining GPS position data of the manned vehicle and/or geographic data surrounding the manned vehicle. In certain aspects, the manned vehicle is a car, truck, or drone [0159].)
As per claim 8 (Original), Beth teaches the vehicle work management system of Claim 1, wherein providing the work item to the work vehicle includes: generating a user interface including a map depicting a route from a vehicle location of the work vehicle to a work item location of the work item; and provide the user interface to a display of the work vehicle to be displayed to an operator of the work vehicle (Beth e.g. In an embodiment, the data of the location 108 and its content (vehicles, assets, environmental information, etc.) may be made available for display by the platform 100 to an end user device, such as a mobile device of an operator of a vehicle, e.g., vehicle 102, at the location 108. In certain aspects, a routing for manned vehicle, e.g., vehicle 102, is provided by the platform 100 [0057]. In the non-limiting example of providing routing for a manned vehicle, the platform 100 obtains routing data at 412. Here, the platform 100 may have access to data indicating a route 116 leading from vehicle 102 to container 114. This routing data may be associated with a mission part, e.g., the picking part of the mission, as indicated at 414 [0060]. Further, because platform 100 may continually, periodically, or intermittently update its mapping information or state for the location 108, the platform 100 may have access to additional data that is useful in scheduling and/or routing [0060]. For example, in generating routing data and/or scheduling data, e.g., for vehicle 102, the platform 100 may be able to perform a check to determine that the route 116 is currently occupied by another vehicle, e.g., vehicle 106, according to the platform's current map state. Therefore, platform may choose a different or alternative route 126 for the vehicle 102 to complete its mission so as to avoid other vehicles, e.g., vehicle 106, and zones that are prohibited, e.g., 118, 120. The platform 100 may then generate the routing data at 416 for the mission [0060]. In embodiments, the routing data may be provided to the operator's mobile phone for display of routing guidance in the mobile application, as indicated at 424. In one non-limiting example, the platform 100 may provide or output at 424 displayable data or coordinate data that is combined with displayable data resident at the mobile phone application of the operator in the form of a map to provide turn-by-turn directions for guiding the operator of vehicle 102 to container 114 and any other part of the mission, e.g., to the delivery location [0062]. The routing device may be further configured to display the specific mission and the recommended routing data for the identified manned vehicle on a screen of the routing device. In certain aspects, the routing device may display this data along with moving statuses of other manned vehicle(s) and/or the unmanned vehicle(s) on the map of the private or the closed location [0159].).
As per claim 11 (Original), Beth teaches the vehicle work management system of Claim 8, Beth also teaches wherein the workflow management application is configured to create, using the one or more machine learning models, a geofence around the work item location. (Beth e.g. Additional environmental information may be available to the platform 100, e.g., preferred or required routes, paths or roads 116, designated areas where vehicles are not permitted 118, 120, physical boundaries 122, e.g., between land and water, geofence location 124, etc. [0045]. In certain aspects, a mobile application running on the mobile device of the operator may be authenticated once during sign on or authenticated via the location of the mobile device, e.g., within a geofence such as 124, in proximity to vehicle 102, etc. [0058].)
As per claim 12 (Original), Beth teaches the vehicle work management system of Claim 11, Beth also teaches wherein the workflow management application is configured to: determine that the work vehicle has reached the work item location based on the work vehicle entering the geofence (Beth e.g. Additional environmental information may be available to the platform 100, e.g., preferred or required routes, paths or roads 116, designated areas where vehicles are not permitted 118, 120, physical boundaries 122, e.g., between land and water, geofence location 124, etc. [0045]. This environmental information may be provided to the platform from the owner or operator of the location 108, from a site visit to the location 108, from an external source (e.g., satellite imagery or mapping service data), produced synthetically, e.g., for a simulated location, or a combination of the foregoing [0045]. In an embodiment, the data of the location 108 and its content (vehicles, assets, environmental information, etc.) may be made available for display by the platform 100 to an end user device, such as a mobile device of an operator of a vehicle, e.g., vehicle 102, at the location 108 [0057]B. In certain aspects, a mobile application running on the mobile device of the operator may be authenticated once during sign on or authenticated via the location of the mobile device, e.g., within a geofence such as 124, in proximity to vehicle 102, etc. [0058].); start a tracking timer to track a duration of the work item; determine that the work item is complete based on the work vehicle exiting the geofence; and stopping the tracking timer. (Beth e.g. During the planning, the platform 100 may act to facilitate the formation of the business workflow or process, e.g., by identifying one or more vehicles, e.g., vehicles 102, 103, 104 or 106, that are available for use in the location 108, capable of performing the business workflow or process, etc. [0087]. During performance, the platform 100 may act to track and update the progress of the business workflow or process, e.g., by providing updated map state information that corresponds to performance or completion of a mission part, a workflow, and/or a stage thereof [0087]. For example, upon completion of a mission part, e.g., sending a vehicle to pick a container such as container 114, the platform 100 may update its map state, as outlined for example in FIG. 5, and thereafter trigger a data output, such as in indication or an alert, to an external system, e.g., remote device 128, which may be an ERP or logistics application server hosting associated software [0087]. In some embodiments, workflows or missions may be scheduled, e.g., to take place at a specific time, to recur, to begin after completion of a related mission or detection of the presence of an object such as cargo being situated in a given location, such as detected using computer vision and object detection [0088].)
As per claim 13 (Currently Amended), Beth teaches a vehicle work management system comprising: at least one processing circuit having at least one processor and at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to (Beth e.g. Referring now to FIG. 1, an example system/platform 100 may integrate various software modules for representing and controlling manned and unmanned vehicles and provide them to a user such that they are available for mission planning, building, simulation or execution [0045]. Illustrated in FIG. 26 is an apparatus 2600 for orchestrating a plurality of agents, e.g., vehicles, microservices, and/or other devices as described herein. The apparatus 2600 may form part of the platform 100, 200, 300 and/or any other computing device described herein, e.g., the apparatus 2600 may be one or more processors of one or more servers (or other computing devices) of the platform 100, 200, 300 [0103].): Beth teaches acquire vehicle locations for a plurality of work vehicles; acquire work item locations of a plurality of work items; identify, using one or more machine learning models, a work item from the plurality of work items for a work vehicle of the plurality of work vehicles based on the vehicle locations and the work item locations,… provide the work item to the work vehicle; (Beth e.g. In some embodiments, the platform 100 provides scheduling and routing to devices in one or more locations to deconflict traffic, reduce congestion, prioritize certain traffic, missions or tasks, or otherwise facilitate coordinated and regulated movement for the devices [0056]. In one non-limiting example, the platform 100 associates the vehicles with locations at 500 to have an inventory of vehicles at or planned to enter the location during a given time frame. At 502, the platform 100 may identify the current positions of the vehicles in the space, for example using GPS coordinates, beacon systems, round trip communication times between vehicles, computer vision from one or more vehicles in the location already, etc. [0078]. Thereafter, the locations of the vehicles may be associated with map data at 504, e.g., vehicle positions are plotted against a map of the location using the coordinates [0078]. This may provide a map state 506, which may be outputted and/or otherwise made available to interested subscribing or consuming devices, e.g., the aforementioned mobile application of an operator of a manned vehicle [0078]. In embodiments, the recommended routing data may be determined at the cloud server so that a specific project may be effectively orchestrated in a manned and an unmanned/autonomous vehicles mixed situation by a routing algorithm (potentially using AI/ML) [0159]. In certain aspects, non-limiting examples of data that may be referenced by the routing algorithm (with AI/ML system) include: live hazard or vehicle congestion in the location; statistic congestion data, e.g., by time; typical route(s) between position A and B; feedback data regarding the previous specific project; a customer's ERP (Enterprise Resource Plaining) or management data; and/or any other specific/unique feature(s) of the processing or the API about the routing algorithm [0159]. The routing device may be further configured to display the specific mission and the recommended routing data for the identified manned vehicle on a screen of the routing device. In certain aspects, the routing device may display this data along with moving statuses of other manned vehicle(s) and/or the unmanned vehicle(s) on the map of the private or the closed location [0159].), Beth teaches the one or more machine learning models trained using historical work item durations associated with the plurality of work items; receive an updated work item duration from the work vehicle; and train the one or more machine learning models on the updated work item duration. (Beth e.g. In embodiments, services and/or microservices provided by the platform may be on a same architecture ("arch") level as agents, e.g., vehicles and/or other assets as described herein. A mission and/or operating environment may be configured by one or more blockchains, and/or associated applications, based on a workflow. As such, in embodiments, autonomous agents, physical and/or virtual, human agents, and/or real-time services may submit and/or participate in tasks and/or workflows [0054]. In embodiments, services and/or microservices may participate in publish and/or subscription actions, as described herein, which may be in a real-time environment. In such embodiments, the actions of a service and/or microservice may be recorded and/or verified via a blockchain. For example, transactions within a blockchain may record when a service and/or microservice subscribes to another service, is subscribed to and/or publishes data. In embodiments, services and/or microservices may have twin store values for optimization of inputs, training parameters, and/or other features described herein. In embodiments, one or more machine learning techniques, e.g., back propagation, may be used to tune the parameters of a service and/or microservice for scheduling efficiency with respect to the scheduling of agents. In embodiments, scheduling efficiency may include, but is not limited to: a shortest time to perform a particular action; monetary cost efficiency, e.g., a least expensive way to perform a particular action; energy cost-efficiency, e.g., fuel and/or battery life; a prioritization based efficiency; etc. [0055].)
As per claim 14 (Original), Beth teaches the vehicle work management system of Claim 13, Beth also teaches wherein: the instructions cause the at least one processor to: acquire identities of operators of the plurality of work vehicles; acquire credential information for the operators of the plurality of work vehicles; and acquire an indication of credentials required for the plurality of work items from (See claim 3 response); and identifying the work item is performed based on the credential information for the operators and the indication of the credentials required for the plurality of work items (Beth e.g. During the planning, the platform 100 may act to facilitate the formation of the business workflow or process, e.g., by identifying one or more vehicles, e.g., vehicles 102, 103, 104 or 106, that are available for use in the location 108, capable of performing the business workflow or process, etc. [0087]. During performance, the platform 100 may act to track and update the progress of the business workflow or process, e.g., by providing updated map state information that corresponds to performance or completion of a mission part, a workflow, and/or a stage thereof [0087]. In certain examples, missions or parts thereof may be adjusted based on vehicle or operator capabilities or expected actions, e g , manned and unmanned vehicles may be configured to adjust mission tasks or parameters thereof such as speed to accommodate a manned vehicle's acceptable or desirable operating parameters, an unmanned vehicle may be configured to update its map state to accommodate expected travel time, location and reaction for manned vehicles, etc. [0080]. In embodiments, the routing or scheduling data may be semi-automatically adjusted, e.g., highlighting or indicated routes, missions or mission parts that need operator attention [0081].)
As per claim 15 (Original), Beth teaches the vehicle work management system of Claim 13, Beth also teaches wherein: the instructions cause the at least one processor to: acquire onboard tool information associated with onboard tools of the plurality of work vehicles from the plurality of work vehicles; and acquire an indication of tools required for the plurality of work items (See claim 1 response.); and identifying the work item is performed based on the onboard tool information and the indication of the tools required for the plurality of work items (Beth e.g. In embodiments, a vehicle compatibility check, as indicated at 410, may take various forms, for example including an initial compatibility determination with respect to the vehicle selected as compared with the user input for a mission task, such as capability to perform a given task, availability to do so, or the ability to adequately communicate with other vehicle(s) that may be involved. One non-limiting example of such a compatibility check is a determination as to whether the vehicle is available, e.g., open in terms of scheduling, has sufficient power or payload capacity, adequate sensors, etc. [0069]. During performance, the platform 100 may act to track and update the progress of the business workflow or process, e.g., by providing updated map state information that corresponds to performance or completion of a mission part, a workflow, and/or a stage thereof [0087]. In some embodiments, workflows or missions may be scheduled, e.g., to take place at a specific time, to recur, to begin after completion of a related mission or detection of the presence of an object such as cargo being situated in a given location, such as detected using computer vision and object detection [0088]. In embodiments, the controller 2814 may include an incident detailed examination neural network (IDENN) 2830, which may be used to detect relevant events and identify areas of interest relevant to a mission plan of the vehicle. The IDENN 2830 may be enabled to quickly (e.g., in near real-time) and efficiently (e.g., using fewer processing cycles than existing technology) process the data generated from the vehicle sensors (e.g., digital cameras 2826, LIDAR 2822, IR sensors 2924, and the like) and optionally from external sensors and/or data sources to detect issues during the vehicle's mission path [0118].).
As per claim 16 (Original), Beth teaches the vehicle work management system of Claim 13, Beth also teaches wherein providing the work item to the work vehicle includes: generating a user interface including a map depicting a route from a vehicle location of the work vehicle to a work item location of the work item; and providing the user interface to a display of the work vehicle to be displayed to an operator of the work vehicle (See claim 8 response.).
As per claim 17 (Original), Beth teaches the vehicle work management system of Claim 16, Beth also teaches wherein the instructions cause the at least one processor to create, using the one or more machine learning models, a geofence around the work item location (See claim 11 response.).
As per claim 18 (Currently Amended), Beth teaches a vehicle work management system comprising: at least one processing circuit having at least one processor and at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to (Beth e.g. Referring now to FIG. 1, an example system/platform 100 may integrate various software modules for representing and controlling manned and unmanned vehicles and provide them to a user such that they are available for mission planning, building, simulation or execution [0045]. Illustrated in FIG. 26 is an apparatus 2600 for orchestrating a plurality of agents, e.g., vehicles, microservices, and/or other devices as described herein. The apparatus 2600 may form part of the platform 100, 200, 300 and/or any other computing device described herein, e.g., the apparatus 2600 may be one or more processors of one or more servers (or other computing devices) of the platform 100, 200, 300 [0103].): Beth teaches acquire first workflow management information from a plurality of vehicles, the first workflow management information includes an identity of an operator of the work vehicle of the plurality of work vehicles; determine, based on the identity of the operator, one or more credentials or skills of the operator; (See claim 3 response.)
Beth teaches acquire second workflow management information associated with a plurality of work items, the second workflow management information including at least one credential or skill required for a work item of the plurality of work items; (See claim 3 response.)
Beth teaches identify, using a machine learning application, the work item of the plurality of work items for the work vehicle of the plurality of work vehicles based on at least the one or more credentials or skills of the operator and the at least one credential or skill required for the work item; (See claim 3 response.)
Beth teaches generate, using the machine learning application, a map depicting a route from a vehicle location of the work vehicle to a work item location of the work item; and provide the map to a display of the work vehicle to be displayed to an operator of the work vehicle (See claim 8 response.)
As per claim 19 (Original), Beth teach the vehicle work management system of Claim 18, Beth also teaches wherein the instructions cause the at least one processor to: receive work item progress information from the plurality of work vehicles regarding the plurality of work items; and reassign, using the machine learning application, the work item to a different work vehicle based on the work item progress information (See claim 7 response).
As per claim 20, Beth teaches the vehicle work management system of Claim 18, Beth also teaches wherein the instructions cause the at least one processor to create, using the machine learning application, a geofence around the work item location (See claim 11 response.).
As per claim 21 (New) Beth teaches the vehicle work management system of claim 1, wherein the one or more machine learning models are trained using historical work item durations associated with the plurality of work items, and wherein the workflow management application is configured to: receive an updated work item duration from the work vehicle; and train the one or more machine learning models on the updated work item duration (See claim 13 response.)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Beth et al. (US 2022/0156665 A1) in view of Beaurepaire (US 2017/0146350 A1).
As per claim 9 (Original), Beth teaches the vehicle work management system of Claim 8, Beth in view of Beaurepaire teach wherein the workflow management application is configured to: determine, using the one or more machine learning models, that the route requires access credentials for entering an area of a building; and interface, using the one or more machine learning models, with an external system to request the access credentials for the operator of the work vehicle.
Beth teaches determine, using the one or mor machine learning models, a route that includes both public and private locations (Beth e.g. Further, certain embodiments of the system provide an interface for accessing mapping, routing and scheduling data, including for use indoors, at private or campus locations, and/or other areas/locations that typically are not mapped [0007]. The platform may provide listings of vehicles that are compatible with one another and the platform, along with listing the vehicles' capabilities, to facilitate mission planning In some embodiments, missions are for manned and/or unmanned vehicles in private locations, such as ports, where the mission includes performance of tasks such as container or asset location, pickup, and relocation, with reporting to a back-end system such as a logistics tracking and reporting software [0008]. Still yet other embodiments may provide for a routing device for a manned vehicle in a private or a closed location, e.g., a campus [0159]. In certain aspects, the specific mission may include: a mission to deliver an asset, cargo or luggage from a first position to a second position in the private or closed location; recommended routing data from the first to the second position, wherein, in certain aspects, the moving of the manned vehicle can be traced/updated live/real-time; map data of the location, which may be stored in an application installed and/or executing at the routing device; and/or moving statuses of other manned vehicle(s) and/or unmanned/autonomous vehicle(s) in the location [0159]. In embodiments, the recommended routing data may be determined at the cloud server so that a specific project may be effectively orchestrated in a manned and an unmanned/autonomous vehicles mixed situation by a routing algorithm (potentially using AI/ML) [0159].).
Beth does not explicitly teach, however, Beaurepaire teaches determine that the route requires access credentials for entering an area of a building; and interface with an external system to request the access credentials for the operator of the work vehicle (Beaurepaire e.g. In one embodiment, the access management platform 109 may identify one or more restricted access area(s) along the route. Then, the access management platform 109 may query whether the at least one user has the required permission to access the at least one access restricted location. The access management platform 109 may determine that the at least one user does not have the required permission, whereupon the access management platform 109 requests for access rights from an authorized user before the user embarks towards a particular destination and/or when the navigation starts [0041]. In one example embodiment, when a visitor (User A) enters the destination information (e.g., home location of user B) in a navigation application, the routing algorithm checks whether there is any area with restricted access on the suggested route. If restricted areas exist, and user A does not have access rights, the application automatically sends a request to the authorizing party (User B) for access rights to enter the restricted area. Subsequently, after receiving the access rights from authorizing party, the visitor can view indoor map data of the building. In another embodiment, the access management platform 109 may determine whether the at least one user is authorized to share the at least one route within a private venue and/or grant access right to a private venue [0041]. In another example embodiment, at least one building may be public during office hours (e.g., 9 a.m.-5 p.m.) but requires access rights beyond the office hours. The access management platform 109 may take into consideration the contextual parameters of the building, and may request for access rights for any users trying to access the building beyond regular office hours [0085].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Beth’s routing system to include determining that the route requires access credentials for entering an area of a building and interface with an external system to request the access credentials for the operator as taught by Beaurepaire in order to improve service providers timeliness (Beaurepaire e.g. [0001]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Beth et al. (US 2022/0156665 A1) in view of Breaux, III et al. (US 2023/0156569 A1).
As per claim 10 (Original), Beth teaches the vehicle work management system of Claim 8, Beth in view of Breaux teach wherein the workflow management application is configured to: determine, using the one or more machine learning models, that the route passes through an area requiring personal protection equipment; and provide a notification to the work vehicle indicating that the area requires the personal protection equipment.
Beth teaches determine, using the one or more machine learning models, that the route passes through a hazard area (Beth e.g. In embodiments, the recommended routing data may be determined at the cloud server so that a specific project may be effectively orchestrated in a manned and an unmanned/autonomous vehicles mixed situation by a routing algorithm (potentially using AI/ML) [0159]. In certain aspects, non-limiting examples of data that may be referenced by the routing algorithm (with AI/ML system) include: live hazard or vehicle congestion in the location; statistic congestion data, e.g., by time; typical route(s) between position A and B;…[0159].)
Beth does not explicitly teach, however, Breaux teaches determine, using the one or more machine learning models, that the route passes through an area requiring personal protection equipment; and provide a notification to the work vehicle indicating that the area requires the personal protection equipment (Breaux, III e.g. Yet another embodiment discloses a system having a processor and a memory storing program code, which, when executed on the processor, performs an operation for managing mobile device usage based on context [0007]. The operation itself includes detecting, by an application executing in the mobile device, an event to trigger context-based management of the mobile device [0007]. As used herein, the term “context” may include an environment, setting, or specific circumstances that surround an event, a sequence of events, or a collection of events. Context may include a location or relative location using technology such as GPS, GNSS, cell tower triangulation, BLUETOOTH beacons, WIFI, dead reckoning, image recognition, audio signatures, atmospheric pressure values, and other sensors known by those skilled in the art for understanding location or relative location [0031]. Context may also include attributes about an individual using a mobile device, such as age, job function, safety history, risk assessment, certification, security clearance, activity level, gait, heart rate, breathing rate, position (e.g. crouched, sitting, standing), exposure to hazardous chemicals, presence of personal protective equipment (PPE), presence of high sound levels, state of personal lighting devices [0031]. Context may also include attributes about the environment, such as time of day, lighting, current weather conditions, presence of hazardous chemicals or substances, high sound levels, presence of water, nearby active equipment, attributes of people, places, or things nearby, and sudden hazards or emergency situations [0031]. The management server 114 may be embodied as any physical computer (e.g., a desktop computer, workstation, laptop computer, and the like), a virtual server instance executing on the cloud, and the like, that is capable of performing the functions described herein, such as defining and managing network policies, generating one or more machine learning models for determining usage context, generating one or more machine learning models for determining user behavior and usage patterns, etc. [0033]. The management service 116 may send notifications such as reminders and alerts to the messaging component 514 based on a current usage context, such as reminders for required personal protective equipment (PPE), known hazard or risk alerts, relevant safety reminders, emergency and/or evacuation alert information, and the like [0059]. Further, the control application 104 may generate a prompt to ensure that a user acknowledges the determined policies. For example, the control application 104 may prompt the user to ensure that a personal protective equipment (PPE) is being worn, to ensure that job safety requirements are acknowledged, and the like [0084]. Yet another context may be triggered when the user and mobile device arrives at a given work site and enters a specified context domain [0085].)
The Examiner submits that before the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Beth’s routing system to determine that the route passes through an area requiring personal protection equipment and providing a notification to the work vehicle as taught by Breaux, III in order to ensure operator/driver safety.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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