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 .
DETAILED CORRESPONDENCE
Status of Claims
Claims 1, 8, 15 have been amended.
Claims 4, 11, 18 have been cancelled.
No claims have been added.
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, 5 – 10, 12 – 17, 19, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
receiving a fault message indicating a fault associated with an asset of the facility;
retrieving location data associated with the asset;
generating a model illustrating the location of the asset in the facility;
determining a preferred route for providing service to the asset, wherein the preferred route is determined historical data associated with a technician, the location data associated with the asset, and safety data, and wherein the historical data associated with the technician comprises data indicative of preferences of technician for navigating, sequencing, and/or performing tasks;
determining a preferred sequence for providing service to the asset;
generating a service plan based on the preferred route and the preferred sequence;
(Claim 1) executing, by the technician, the service plan to provide the service to the asset;
determining, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence, wherein the constraint corresponds to an unserviceable state of the asset;
redetermining, based on the constraint, at least one of a new preferred route or a new preferred sequence to provide the service to the asset;
storing the service plan based on at least one of the new preferred route or the new preferred sequence; and
updating the model to include graphical content indicative of the service plan based on at least one of the new preferred route or the new preferred sequence.
The invention is directed towards the abstract idea of resource management and scheduling, in this case, managing tasks for an employee by providing them with navigation instructions and task sequence/scheduling, which corresponds to both “Mental Processes” and “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed in the human mind and/or pen and paper, e.g., informing a user that a problem exists for an asset, determining navigational directions to reach/approach the asset, determining sequence instructions, using the navigational directions and sequence instructions to write down and provide a user with a plan to service the asset, writing down and providing a map (or the like) to aid the user with reaching the asset, and updating the map or rescheduling within information indicative of the service plan. More specifically, the invention is directed to providing a map to a technician with the necessary navigational and servicing information to allow the technician to reach one or more assets that require servicing, as well as the necessary information to allow the technician to service one or more assets (commercial interaction, business relations). Additionally, the invention relies on the abstract idea of collecting and comparing information and, based on a rule, identify options, in this case, collecting information concerning an asset, comparing the location of the asset with a technician, receiving and considering new information (i.e. information that can affect the schedule, e.g., traffic, technician progress, weather, and the like), and, based on a rule, e.g., preferred sequence, providing routing options. Finally, updating a schedule and/or navigation instructions based on available collected information and/or constraints and describing the information/constraints can also be performed by humans as this encompasses the human activities of a technician calling a dispatcher of an issue affecting completion of a task and the dispatcher updating the schedule/navigation instructions and informing the technician.
The limitations of:
receiving a fault message indicating a fault associated with an asset of the facility;
retrieving location data associated with the asset;
generating a model illustrating the location of the asset in the facility;
determining a preferred route for providing service to the asset, wherein the preferred route is determined historical data associated with a technician, the location data associated with the asset, and safety data, and wherein the historical data associated with the technician comprises data indicative of preferences of technician for navigating, sequencing, and/or performing tasks;
determining a preferred sequence for providing service to the asset;
generating a service plan based on the preferred route and the preferred sequence;
(Claim 1) executing, by the technician, the service plan to provide the service to the asset;
determining, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence, wherein the constraint corresponds to an unserviceable state of the asset;
redetermining, based on the constraint, at least one of a new preferred route or a new preferred sequence to provide the service to the asset;
storing the service plan based on at least one of the new preferred route or the new preferred sequence; and
updating the model to include graphical content indicative of the service plan based on at least one of the new preferred route or the new preferred sequence,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, a generic database, generic machine learning model, and generic sensors. That is, other than reciting a generic processor executing computer code stored on a computer medium, a generic database, generic machine learning model, and generic sensors nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor executing computer code stored on a computer medium, a generic database, generic machine learning model, and generic sensors in the context of this claim encompasses a user receive a request to service an asset and be provided with written instructions, e.g., a map, an asset location, and sequence information, to aid the user with reaching and servicing the asset, as well as making scheduling/rescheduling updates based on updated/new information, e.g., traffic, progress, weather, and the like. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, a generic database, generic machine learning model, and generic sensors, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic processor executing computer code stored on a computer medium, a generic database, and generic sensor to communicate and store information, as well as performing operations that a human can perform in their mind and/or pen and paper, i.e. providing (writing down) a map of the asset, the location the asset resides at, sequence information, and updating information. The generic processor executing computer code stored on a computer medium, a generic database, and generic sensor in the steps are recited at a high-level of generality (i.e., as a generic processor executing computer code stored on a computer medium, a generic database, and generic sensors can perform the insignificant extra solution steps of communicating and storing information (See MPEP 2106.05(g) while also reciting that the a generic processor executing computer code stored on a computer medium, a generic database, and generic sensors are merely being applied to perform the steps that can be performed in the human mind and/or pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic 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., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium, a generic database, and generic sensors.
Although the claim recites “train a machine learning algorithm,” the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying a machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards resource management and scheduling. The Examiner asserts that the claimed invention is not improving machine learning or any other technology, resolving an issue that arose in machine learning or any other technology, or deeply rooted in machine learning or any other technology, but reciting generic technology at a high level of generality and applying it to the abstract idea and reciting information that the applicant believes, in their mind/opinion, is the best information to use to determine how they believe, in their mind/opinion, is the best way to manage resources and scheduling. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic processor executing computer code stored on a computer medium, a generic database, generic machine learning model, and generic sensors to perform the steps of:
receiving a fault message indicating a fault associated with an asset of the facility;
retrieving location data associated with the asset;
generating a model illustrating the location of the asset in the facility;
determining a preferred route for providing service to the asset, wherein the preferred route is determined historical data associated with a technician, the location data associated with the asset, and safety data, and wherein the historical data associated with the technician comprises data indicative of preferences of technician for navigating, sequencing, and/or performing tasks;
determining a preferred sequence for providing service to the asset;
generating a service plan based on the preferred route and the preferred sequence;
(Claim 1) executing, by the technician, the service plan to provide the service to the asset;
determining, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence, wherein the constraint corresponds to an unserviceable state of the asset;
redetermining, based on the constraint, at least one of a new preferred route or a new preferred sequence to provide the service to the asset;
storing the service plan based on at least one of the new preferred route or the new preferred sequence; and
updating the model to include graphical content indicative of the service plan based on at least one of the new preferred route or the new preferred sequence,
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claim 2 is directed towards the creation of a map due to a map not existing or location information not being available based on the abstract idea of collecting and comparing information.
Claim 3 is directed towards extra-solution activity and descriptive subject matter, in this case, describing displayed content.
Claim 5 is directed towards the extra-solution activity of storing information, as well as the human activity of writing down information.
Claims 6, 7 are directed towards the concept of collecting and comparing information and, based on a rule, determine the problem associated with an asset and provide/generate a recommendation (i.e. collecting information about an asset and comparing it against known information to determine the problem with the asset and a solution).
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for managing resources to aid in the servicing of an asset. Accordingly, the claims are not patent eligible.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 3, 5 – 10, 12 – 17, 19, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Minnier (US PGPub 2021/0374296 A1) in view of Brand et al. (US PGPub 2020/0210918 A1).
In regards to claims 1, 8, 15, Minnier discloses (Claim 1) a method for inspection or maintenance planning at a facility, the method comprising; (Claim 8) a computer system for inspection or maintenance planning at a facility, the computer system comprising; (Claim 15) a non-transitory computer-readable medium containing instructions for inspection or maintenance planning at a facility, the non-transitory computer-readable medium storing instructions that, when executed by at least one processor, configure the at least one processor to perform:
In regards to:
(Claim 8) at least one memory having processor-readable instructions stored therein; and
(Claim 8) at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the processor configure the processor to perform a plurality of functions, including functions for
(Fig. 1, 4, 5)
receiving, by at least one processor, a fault message indicating a fault associated with an asset of the facility, […] (¶ 29, 49 wherein the system receives a fault message associated with an asset);
retrieving, by the at least one processor from a plant model database, location data associated with the asset (¶ 38, 39, 48 wherein the system retrieves the location of the asset);
generating, by the at least one processor, a digital model illustrating a location of the asset in the facility (¶ 29, 38, 39, wherein the system generates a digital map illustrating the location of the asset);
determining, by at least one processor, a preferred route for providing service to the asset, […] (¶ 39 wherein the system determines a preferred route for providing service to the asset);
determining, by the at least one processor […], a preferred sequence for providing service to the asset (¶ 38, 44, 48, 49 wherein the system prioritizes alarms, provides manuals, prior technician notes, and any information to guide the user to provide service to one or more assets);
generating, by the at least one processor, a service plan based on the preferred route and the preferred sequence (¶ 29, 39, 48, 49 wherein the system generates a service plan for a technician to service an asset);
[…];
[…];
[…];
[…]; and
updating, by the at least one processor, the digital model to include graphical content indicative of the service plan […] (¶ 29, 39, 48, 49, 52, 54 wherein the system updates the digital model to include graphical content indicative of the service plan, e.g., information associated with the asset, markers, location information, and etc.).
Minnier discloses a system and method for resolving problems detected with an asset and providing a technician with information to not only address the problem, but with navigation instructions to guide the technician to the asset, as well as a monitoring system that detects, receives, and displays alarms in order to schedule servicing. Despite this, Minnier fails to explicitly disclose whether the determination of a route can be affected by various types of constraints that are used to train a machine learning model to determine a preferred route for providing service to an asset, respond and modify a schedule in response to receiving updated information, and using sensors to identify and report issues.
To be more specific, Minnier fails to explicitly disclose:
receiving, by at least one processor, a fault message indicating a fault associated with an asset of the facility, wherein the fault is automatically detected by one or more sensors associated with the asset;
determining, by at least one processor, a preferred route for providing service to the asset, wherein the preferred route is determined using a machine learning model trained on historical data associated with a technician, the location data associated with the asset, and safety data, and wherein the historical data associated with the technician comprises data indicative of preferences of technician for navigating, sequencing, and/or performing tasks;
determining, by the at least one processor using the machine learning model, a preferred sequence for providing service to the asset
(Claim 1) executing, by the technician, the service plan to provide the service to the asset;
(Claim 1) determining, by the at least one processor, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence, wherein the constraint corresponds to an unserviceable state of the asset;
(Claim 1) redetermining, by the at least one processor and based on the constraint, at least of a new preferred route or a new preferred sequence to provide the service to the asset;
(Claim 8) determining, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence;
(Claim 8) redetermining, based on the constraint, at least one of a new preferred route or a new preferred sequence to provide the service to the asset
(Claim 15) determining, during execution of the service plan by the technician, that a constraint affects at least one of the preferred route or the preferred sequence;
(Claim 15) redetermining, based on the constraint, at least one of a new preferred route or a new preferred sequence to provide the service to the asset
storing, in a database, the service plan based on at least one of the new preferred route or the new preferred sequence; and
updating, by the at least one processor, the digital model to include graphical content indicative of the service plan based one at least one of the new preferred route or the new preferred sequence
However, Brand, which is also directed towards resolving issues of an asset by routing a technician to the asset, further teaches that the determination of a route can be affected by one or more constraints. Brand further teaches that the determination of a route can be performed by a machine learning algorithm that has been trained on various types of data to best determine routes for technicians (¶ 262, 321, 443, 458, 472, 473). Brand teaches that such constraints can include, but are not limited to, historical data associated with a technician, e.g., preferences of a technician for navigating, sequencing, and/or performing tasks, location associated with the asset, and safety data. Brand further teaches that other constraint types can include constraints associated with the asset to be serviced, but are in a state that make them unserviceable by the technician, e.g., underground line is broken, a dangerous fault in the asset that could result in a dangerous situation, natural or man-made disaster (e.g., flood, hurricane, thunderstorm, earthquake, and etc.) that place the asset in an unserviceable state, or the technician requiring an additional part that they currently do not possess, thereby place the asset in an unserviceable state (¶ 382, 438, 449, 508).
More specifically, Brand teaches that the professional’s preferences pertaining to when tasks should be assigned or how they should be assigned (sequencing) are taken into account when determining when a service should be scheduled or as additional information that a customer is provided with to determine when they would like to schedule for servicing. For example, the system allows for professionals to offer times of service of when they can perform a service and an associated cost that corresponds to the service window or based on historical data pertaining to the technician performing tasks. Brand further allows for professionals to enter preferences of what tasks should be performed prior to scheduling additional tasks. As a result, Brand teaches that the system stores information regarding historical data and preferences associated with professionals to determine service assignment and routing for a technician.
(For support see: ¶ 254, 321, 336, 338, 390)
Next, Brand teaches that location data associated with the asset is also utilized by the machine learning algorithm as this allows the system to determine how professionals should be routed or who to assign to a particular task. As a non-limiting example, professionals may be routed to locations in proximity to a predicted service request or may simply route the professional to a requested service request.
(For support see: ¶ 242, 243, 262, 485, 510)
Additionally, Brand teaches that safety data is also taken into consideration to determine scheduling. Specifically, Brand teaches that if there is inclement weather then a service request will be scheduled or rescheduled for a different time. For example, if a technician is required to work outside or perform utility work and there is a storm, then the system will not schedule the service for that time period as this can pose a danger to the technician, e.g., performing electrical work in the rain or lightning strike, or performing work in a flood, hurricane, earthquake, and more. As an additional example, a service may request a particular task to be performed prior to performing other tasks, e.g., requiring vaccination before a request or requiring certain tools to be available in order to safely perform a procedure.
(For support see: ¶ 193, 309, 338, 382, 449, 515)
Furthermore, as discussed above, Brand teaches that the system collects and considers information from various sources to determine how to schedule and route a professional. Specifically, Brand teaches that the system collects and analyzes real-time information that can affect a professional’s schedule, e.g., inclement weather, traffic, professional’s progress, cancellations, and etc., and utilizes the results of the analysis to determine how to manage the schedule of jobs, the professional, and other professionals. Brand teaches that this allows the system to update already assigned jobs and routes in response to real-time event data in order to optimize scheduling of professionals and jobs and ensure that a job will be addressed while managing the schedules of professional to ensure that they will be able to fulfill an assigned job or a job received by the system (¶ 116, 141, 143, 144, 310, 348, 382, 449, 458). In other words, Brand teaches that even after a technician has selected a job and is performing a job, the system can identify events that can affect the technician’s route, and will update routes for the professional and other professionals. As a result, Brand teaches a dynamic, real-time scheduling and task status monitoring system and method that stores scheduled tasks for technicians, monitors the state of the task, monitors real-time constraints that can affect the completion and/or scheduling of the task, and update and store changes made to schedules to allow the system to continue monitoring a task and determine whether additional updates are needed (¶ 28, 104, 116, 141, 143, 144, 163, 376, 455).
(See also: ¶ 191, 227, 242, 243, 283, 321, 390, 508, 510, 512, 515 wherein Brand, which is also directed towards resolving issues of an asset by routing a technician to the asset, further teaches that the determination of a route can be affected by one or more constraints and, accordingly, would require the determination of a new route. Brand teaches that such constraints can include, but are not limited to, part availability, scheduling constraints, resolution duration, technician availability, and so forth. As a result, Brand teaches that schedules can be reorganized, technicians can be reorganized, and so forth, thereby resulting in the system having to determine a new route for a technician, e.g., changing the schedule of a plurality of tasks assigned to technician, determining a new route for another technician by way of reassigning a task from a first technician to a second technician, rescheduling of a task (which, in turn, results in removal/addition of a route for a technician), and so forth. Brand teaches that this multi-route model allows for a more efficient and effective way of managing resources, ensuring that sufficient time is being allotted to the completion of the task, ensuring that the task can be completed, and etc.)
Finally, Brand teaches that it would have been obvious and beneficial to determine that servicing is required and needs to be scheduled by utilizing sensors. Specifically, Brand teaches that sensors provide the benefit of alerting users when a malfunction occurs or is predicted to occur and enables to autonomously schedule servicing without any user intervention, which one of ordinary skill in the art would have found obvious and beneficial to incorporate into the system and method of Minnier as this would only improve upon the efficiency and effectiveness of identifying and resolving issues.
(¶ 459, 486, 488)
As a result, one of ordinary skill in the art looking upon the teachings of Brand would have found it beneficial to train a machine learning model to mine and utilize data relevant to scheduling, assigning, and routing a technician to a particular task as this would result in a more optimized routing system that ensures that a task will be completed within a period of time while factoring in the safety of the technician and their preferences.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the resource and asset management system and method of Minnier with the ability to train a machine learning algorithm that has been trained on historical data associated with a technician, the location data associated with the asset, real-time data (inclement weather, traffic, professional’s progress, cancellations, and etc.), safety data, various constraints, and using sensors to identify and report issues, wherein the historical data associated with the technician comprises data indicative of preferences of technician for navigating, sequencing, and/or performing tasks, as taught by Brand, as this results in the better utilization of resources while being more efficient and effective at resolving problems affecting an asset, ensuring the safety of technicians, and maintaining and providing updated servicing schedules.
In regards to claims 2, 9, 16, the combination of Minnier and Brand discloses the method of claim 1 (the system of claim 8; non-transitory computer-readable medium of claim 15), further comprising:
determining, by the at least one processor after receiving the fault message, that the plant model database does not include location data associated with the asset;
receiving, by the at least one processor from a user, location data associated with the asset; and
updating, by at least one processor, the plant model database to include the location data associated with the asset
(¶ 29, 52, 54 wherein the system determines that a map or location data does not exist, e.g., due to a new building, new equipment, or just identified event, and, in response, receives location information of the asset, and updates the system to include the location of the asset for display on a map for use by a technician).
In regards to claims 3, 10, 17, the combination of Minnier and Brand discloses the method of claim 1 (the system of claim 8; non-transitory computer-readable medium of claim 15), wherein the graphical content includes at least one of:
at least one path for accessing the asset; and
a present location of the technician
(¶ 39 wherein the system provides navigational guidance from the technician’s current location to the location of the asset).
In regards to claims 5, 12, 19, the combination of Minnier and Brand discloses the method of claim 1 (the system of claim 8; non-transitory computer-readable medium of claim 15), further comprising: logging, by the at least one processor, fault data associated with the asset into a historical fault database (¶ 44, 48 wherein the system logs fault information as historical information for future use).
In regards to claims 6, 13, 20, the combination of Minnier and Brand discloses the method of claim 1 (the system of claim 8; non-transitory computer-readable medium of claim 15), further comprising:
determining, by the at least one processor, at least one of:
a potential failure mode of the asset associated with the fault message;
a root cause of the fault associated with the fault message; and
one or more probable failure effects; and
(¶ 29, 44, 48, 49 wherein the system determines the cause of the fault)
generating, by the at least one processor, a maintenance decision based on at least one of the potential failure mode, the root cause, and the one or more probable failure effects
(¶ 29, 44, 48, 49 wherein the system generates a maintenance decision based on at least one of the potential failure modes, the root cause, and the one or more probable failure effects, i.e. a technician is notified that service is required for the asset).
In regards to claims 7, 14, the combination of Minnier and Brand discloses the method of claim 6 (the system of claim 13), further comprising:
generating, by the at least one processor, a recommendation based on at least one of the potential failure mode of the asset, the root cause of the fault, and the one or more probable failure effects (¶ 29, 44, 48, 49 wherein the system generates a maintenance decision based on at least one of the potential failure modes, the root cause, and the one or more probable failure effects, i.e. a technician is notified that service is required for the asset),
wherein the recommendation is at least one of:
a recommendation for a future maintenance operation; and
a recommendation for a future facility design
(¶ 44 wherein the recommendation is scheduling service for the asset, e.g., upcoming maintenance and future work).
Response to Arguments
Applicant's arguments filed 3/5/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The Examiner asserts that the claimed invention is not directed towards improving technology, resolving an issue that arose in technology, or deeply rooted in technology, but directed towards the abstract idea of resource management and scheduling. The applicant’s argument that the invention cannot be performed or executed in the human mind because they are tied to a machine/device is unpersuasive. The claimed invention has only recited generic technology at a high level of generality and applying it to the abstract idea.
As explained in the rejection, the Examiner asserts that the claimed invention can, indeed, be performed by a human, in their mind, and/or pen and paper, informing a user that a problem exists for an asset, determining navigational directions to reach/approach the asset, determining sequence instructions, using the navigational directions and sequence instructions to write down and provide a user with a plan to service the asset, writing down and providing a map (or the like) to aid the user with reaching the asset, receiving and considering new information (i.e. information that can affect the schedule, e.g., traffic, technician progress, weather, and the like), and updating the map within information indicative of the service plan. More specifically, the invention is directed to providing a map to a technician with the necessary navigational and servicing information to allow the technician to reach one or more assets that require servicing, as well as the necessary information to allow the technician to service one or more assets. Additionally, the invention relies on the abstract idea of collecting and comparing information and, based on a rule, identify options, in this case, collecting information concerning an asset, comparing the location of the asset with a technician, and, based on a rule, e.g., preferred sequence, providing routing options. Finally, updating a schedule and/or navigation instructions based on available collected information and/or constraints and describing the information/constraints can also be performed by humans as this encompasses the human activities of a technician calling a dispatcher of an issue affecting completion of a task and the dispatcher updating the schedule/navigation instructions and informing the technician.
The Examiner asserts that the use of the generic technology is being applied to the abstract idea for the benefits that the technology provides, i.e. faster, more efficient, and the like, as admitted by the applicant on Page 9, last 4 lines, in the Remarks received on 3/5/2026. Moreover, the claimed invention is not improving upon machine learning/artificial intelligence (ML/AI) nor the techniques that are relied upon to train the technology, but using the technology as a tool to perform existing processes that can be performed by humans (MPEP § 2106.05(f)(2)). Again, the claimed invention has recited this technology at a high level of generality and applying it to the abstract idea. The claimed invention generically recites that the ML is trained and then describes the training data. This is not an improvement to ML technology nor its techniques, but, again, reciting the technology at a high level of generality and applying it to the abstract idea and describing the training data. Further still, reciting and applying updated data is not an improvement to the technology, but merely describing the data, as well as generically reciting “retraining” at a high level of generality.
The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training is merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Finally, the use of sensors to detect a fault is nothing more than the recitation of generic technology and applying it to the abstract idea to perform the extra-solution activity of collecting data that the generic sensor is configured to detect and transmitting data. The claimed invention is not improving upon senor technology nor resolving an issue that arose in sensor technology, but simply utilizing generic sensors to collect and report information in as much the same way that a human can observer and identify an issue and reporting the issue to have it resolved.
Rejection under 35 USC 102/103
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Lerick et al. (WO 2016/069746 A1); Stebbins et al. (US PGPub 2021/0097454 A1); Monovich et al. (US PGPub 2020/0210919 A1); Monovich et al. (WO 2020/140038 A1) – which disclose the use of machine learning/artificial intelligence to assist with scheduling of service requests
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 3/16/2026