DETAILED ACTION
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 . Claims 1-18 are pending.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a system (i.e., a machine), claim 7 to a method (i.e., a process), and claim 13 to a non-transitory, computer-readable medium (i.e., a manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 7, and 13 is (claim 1 being representative):
receiving information regarding dispatch of replacement parts for a plurality of devices;
determining a degradation rate for each device based on a weighted tree of telemetry data for each device including Self-Monitoring, Analysis and Reporting Technology (SMART) data for at least one storage device, wherein the device is a root node of the weighted tree, wherein components of the device are child nodes of the weighted tree, and wherein alerts associated with each component are leaf nodes of the weighted tree;
determining a device threshold for each device, wherein determining the device threshold comprises determining both a modeled device threshold and an actual device threshold, wherein the modeled device threshold is determined based on historical data, and wherein the actual device threshold is determined by a trained model based on a determination of a time required to dispatch the replacement parts, a time required for the dispatched replacement parts to reach a destination, a time required for a technician to reach the destination, and a time required to install the replacement parts; and
dispatching parts based on the weighted trees and the device thresholds.
The abstract idea steps italicized above relate to the dispatch of parts based on rules or instructions, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by page 5 of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above, especially – determining a degradation rate for each device based on a weighted tree of telemetry data for each device including Self-Monitoring, Analysis and Reporting Technology (SMART) data for at least one storage device, wherein the device is a root node of the weighted tree, wherein components of the device are child nodes of the weighted tree, and wherein alerts associated with each component are leaf nodes of the weighted tree; determining a device threshold for each device, wherein determining the device threshold comprises determining both a modeled device threshold and an actual device threshold, wherein the modeled device threshold is determined based on historical data, and wherein the actual device threshold is determined by a trained model based on a determination of a time required to dispatch the replacement parts, a time required for the dispatched replacement parts to reach a destination, a time required for a technician to reach the destination, and a time required to install the replacement parts –constitute a process that, under its broadest reasonable interpretation, covers mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, covers mathematical concepts, including mathematical relationships, mathematical formulas or equations, mathematical calculations, then it falls within the Mathematical Concepts grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
Claim 1 recites the following additional elements: at least one processor; a memory having instructions coded thereon; artificial intelligence (AI).
Claim 7 recites the following additional elements: an information handling system; artificial intelligence (AI).
Claim 13 recites the following additional elements: an article of manufacture comprising a non-transitory, computer-readable medium having computer-executable code thereon; processor; artificial intelligence (AI).
These elements are merely instructions to apply the abstract idea to a computer, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen on page 10 of applicant’s specification as filed. Further, any reference to “artificial intelligence (AI),” both in the specification and the claims, is done in a conclusory, results-oriented manner, which is equivalent to “apply it,” per MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. 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.
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f). Further, any reference to “artificial intelligence (AI),” both in the specification and the claims, is done in a conclusory, results-oriented manner, which is equivalent to “apply it,” per MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Claims 2-6, 8-12, and 14-18 merely narrow the abstract idea. This narrowing of the abstract idea does not integrate the abstract idea into practical application and does not add significantly more. The dependent claims are also ineligible.
Accordingly, claims 1-18 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed 1/27/26 have been fully considered. Examiner’s response follows, with applicant’s headings and page numbers used for consistency.
Rejections under 35 U.S.C. § 101
Applicant offers on pages 8-9, regarding the rejections under 35 U.S.C. § 101:
Claims 1-18 were rejected under 35 U.S.C. § 101 as allegedly being directed to a judicial exception. Office Action at 2. Applicant respectfully disagrees with these rejections for at least the reasons already discussed. In the interest of advancing prosecution, however, Applicant is making various amendments to the independent claims.
Claim 1, for example, now recites "determining a degradation rate for each device based on a weighted tree of telemetry data for each device including Self-Monitoring, Analysis and Reporting Technology (SMART) data for at least one storage device" as well as that "determining the device threshold comprises determining both a modeled device threshold and an actual device threshold, wherein the modeled device threshold is determined based on historical data, and wherein the actual device threshold is determined by a trained artificial intelligence (AI) model based on a determination of a time required to dispatch the replacement parts, a time required for the dispatched replacement parts to reach a destination, a time required for a technician to reach the destination, and a time required to install the replacement parts."
Applicant submits that these newly added limitations recite significant non-abstract functionality under step 2A of the § 101 analysis. In the alternative, Applicant submits that they recite "significantly more" under step 2B.
While well taken, applicant’s arguments are not persuasive. Examiner maintains that the claims recite the following abstract idea:
receiving information regarding dispatch of replacement parts for a plurality of devices;
determining a degradation rate for each device based on a weighted tree of telemetry data for each device including Self-Monitoring, Analysis and Reporting Technology (SMART) data for at least one storage device, wherein the device is a root node of the weighted tree, wherein components of the device are child nodes of the weighted tree, and wherein alerts associated with each component are leaf nodes of the weighted tree;
determining a device threshold for each device, wherein determining the device threshold comprises determining both a modeled device threshold and an actual device threshold, wherein the modeled device threshold is determined based on historical data, and wherein the actual device threshold is determined by a trained model based on a determination of a time required to dispatch the replacement parts, a time required for the dispatched replacement parts to reach a destination, a time required for a technician to reach the destination, and a time required to install the replacement parts; and
dispatching parts based on the weighted trees and the device thresholds.
There is nothing inherently technical about these limitations. They recite: 1) receiving information; 2) determining a device degradation rate based on a weighted tree; 3) determining a device threshold; 4) dispatching parts. These are steps an administrator and technician could accomplish when working in tandem, where the weighted tree may be as simple as probability or decision tree used to facilitate decision-making.
Applicant’s suggestion that the claimed invention represents an improvement to technology at Step 2A Prong Two and/or Step 2B is also not persuasive. The additional elements that inform this decision are generic computing elements recited at a high level of generality. Based on MPEP 2106.05(f), this is not enough to integrate the abstract idea into practical application and/or add significantly more.
Accordingly, examiner maintains the rejections under 35 U.S.C. § 101.
Rejections under 35 U.S.C. § 103
Applicant’s clarifying remarks and amendments concerning the rejections under 35 U.S.C. § 103 are persuasive. These rejections are withdrawn.
Examiner has not been able to identify the following features in the prior art, whether in a singular reference or multiple references viewed in combination:
determining a degradation rate for each device based on a weighted tree of telemetry data for each device including Self-Monitoring, Analysis and Reporting Technology (SMART) data for at least one storage device, wherein the device is a root node of the weighted tree, wherein components of the device are child nodes of the weighted tree, and wherein alerts associated with each component are leaf nodes of the weighted tree;
determining a device threshold for each device, wherein determining the device threshold comprises determining both a modeled device threshold and an actual device threshold, wherein the modeled device threshold is determined based on historical data, and wherein the actual device threshold is determined by a trained artificial intelligence (AI) model based on a determination of a time required to dispatch the replacement parts, a time required for the dispatched replacement parts to reach a destination, a time required for a technician to reach the destination, and a time required to install the replacement parts.
In an updated search, examiner identified the following references, which, while generally relevant to the field of endeavor, stop short of the specificity required by the claim:
US 5210704, which teaches in the Abstract: A wearout monitor for failure prognostics is a prognosis tool to predict incipient failure in rotating mechanical equipment. The wearout monitor provides maintenance management of a plant or process with information essential to planning preventive maintenance strategies. The monitor also assists in constructing a data base for development and implementation of policies for plant life extension, refurbishment, and modernization. The apparatus identifies systems of operation degradation of the whole system, as well as diagnosis of signs of commencing aging cycles of specific equipment, components or parts of equipment during operation. Data from the system is stored and also supplied to a central processing unit which includes an expert system, rule-based failure data bank, a predictor, a performance evaluator and a system identifier. The results of the predictions are supplied to management terminals or other indicators for subsequent use. Combination of prognostics and diagnostics of the symptoms of existing fault in mechanical equipment allows continuous on-line monitoring of systems to predict failures at early stages before leading to catastrophic breakdown and to assure safe and economic operation. By providing correlations between defect sizes and life expectancy of a rotating mechanical component, the monitor can provide the operator of the equipment with a warning time that indicates the time before loss of operation, thereby being critical to operation of transport systems wherein gearboxes can lead to loss of transmission power and subsequent loss of life particularly in helicopters.
US 20090313508, which teaches in the Abstract: Architecture for aggregating health alerts from a number of related components into a single aggregated health state that can be analyzed to isolate the component responsible for the fault condition. In a hierarchy of related components within various component groups in a computer system, a number of health indicators can indicate alerts occurring in one or more of the related components whereas the fault condition occurs in only one component upon which the other components depend. The health indicators of related components are aggregated into an aggregated health state for each component group. These aggregated health states are analyzed to identify the related component associated with a root cause of the alert condition for an affected component group.
US 20100017241, which teaches in the Abstract: A method of developing maintenance optimization model includes finding relevant criteria to drive the design towards operational performances at minimal cost for the end user, selecting inputs necessary to assess criteria selected, defining mathematical models to jointly drive the equipment/sub-system/system design and its support towards better supportability, and presenting the maintenance optimization model results to enable the exploration of the cause and effect relationships between design decisions and their operational and support impacts. The selected inputs may cover all the potential factors influencing the criteria values. The mathematical modeling can facilitate leveraging the intuitive "cause and effect" relationship between design and support, and affordability. The maintenance optimization model method, system, and computer program product provides a solid basis for the supportability evaluation of equipment/system/sub-system choices, particularly when integrated in a systematic way into the design process and used from the beginning of the development cycle.
US 20160292579, which teaches in the Abstract: A decision tree analysis system and method for navigating a multi-dimensional decision tree uses acceptable alternative child nodes of a target child node to select an end child node for a parent node, where the parent node and the final child node define a single step of a navigation path for the multi-dimensional decision tree. The acceptable alternative child nodes are based on an acceptance delta parameter for a particular attribute, which defines a value range about an attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node.
US 20160371163, which teaches in the Abstract: Systems and methods for smart diagnoses and triage of failures with identity continuity. In some embodiments, an Information Handling System (IHS) includes a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: execute a Power-On Self Test (POST) routine; in response to a determination that the POST routine has failed, execute a firmware-based diagnostics routine; in response to a determination that the firmware-based diagnostics routine has failed, execute, via a service Operating System (OS), a service OS-based diagnostics routine configured to identify whether the firmware-based diagnostics failure is due to a hardware or software fault; and in response to the service OS-based diagnostics routine identifying a hardware fault or failing to remediate a software fault, obtain a user's account information and report the hardware fault or the software remediation failure.
Accordingly, examiner withdraws the rejections under 35 U.S.C. § 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 5210704, which teaches in the Abstract: A wearout monitor for failure prognostics is a prognosis tool to predict incipient failure in rotating mechanical equipment. The wearout monitor provides maintenance management of a plant or process with information essential to planning preventive maintenance strategies. The monitor also assists in constructing a data base for development and implementation of policies for plant life extension, refurbishment, and modernization. The apparatus identifies systems of operation degradation of the whole system, as well as diagnosis of signs of commencing aging cycles of specific equipment, components or parts of equipment during operation. Data from the system is stored and also supplied to a central processing unit which includes an expert system, rule-based failure data bank, a predictor, a performance evaluator and a system identifier. The results of the predictions are supplied to management terminals or other indicators for subsequent use. Combination of prognostics and diagnostics of the symptoms of existing fault in mechanical equipment allows continuous on-line monitoring of systems to predict failures at early stages before leading to catastrophic breakdown and to assure safe and economic operation. By providing correlations between defect sizes and life expectancy of a rotating mechanical component, the monitor can provide the operator of the equipment with a warning time that indicates the time before loss of operation, thereby being critical to operation of transport systems wherein gearboxes can lead to loss of transmission power and subsequent loss of life particularly in helicopters.
US 20090313508, which teaches in the Abstract: Architecture for aggregating health alerts from a number of related components into a single aggregated health state that can be analyzed to isolate the component responsible for the fault condition. In a hierarchy of related components within various component groups in a computer system, a number of health indicators can indicate alerts occurring in one or more of the related components whereas the fault condition occurs in only one component upon which the other components depend. The health indicators of related components are aggregated into an aggregated health state for each component group. These aggregated health states are analyzed to identify the related component associated with a root cause of the alert condition for an affected component group.
US 20100017241, which teaches in the Abstract: A method of developing maintenance optimization model includes finding relevant criteria to drive the design towards operational performances at minimal cost for the end user, selecting inputs necessary to assess criteria selected, defining mathematical models to jointly drive the equipment/sub-system/system design and its support towards better supportability, and presenting the maintenance optimization model results to enable the exploration of the cause and effect relationships between design decisions and their operational and support impacts. The selected inputs may cover all the potential factors influencing the criteria values. The mathematical modeling can facilitate leveraging the intuitive "cause and effect" relationship between design and support, and affordability. The maintenance optimization model method, system, and computer program product provides a solid basis for the supportability evaluation of equipment/system/sub-system choices, particularly when integrated in a systematic way into the design process and used from the beginning of the development cycle.
US 20160292579, which teaches in the Abstract: A decision tree analysis system and method for navigating a multi-dimensional decision tree uses acceptable alternative child nodes of a target child node to select an end child node for a parent node, where the parent node and the final child node define a single step of a navigation path for the multi-dimensional decision tree. The acceptable alternative child nodes are based on an acceptance delta parameter for a particular attribute, which defines a value range about an attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node.
US 20160371163, which teaches in the Abstract: Systems and methods for smart diagnoses and triage of failures with identity continuity. In some embodiments, an Information Handling System (IHS) includes a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution by the processor, cause the IHS to: execute a Power-On Self Test (POST) routine; in response to a determination that the POST routine has failed, execute a firmware-based diagnostics routine; in response to a determination that the firmware-based diagnostics routine has failed, execute, via a service Operating System (OS), a service OS-based diagnostics routine configured to identify whether the firmware-based diagnostics failure is due to a hardware or software fault; and in response to the service OS-based diagnostics routine identifying a hardware fault or failing to remediate a software fault, obtain a user's account information and report the hardware fault or the software remediation failure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SARAH MONFELDT can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/ Primary Examiner, Art Unit 3629