DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following is a Non-Final Office Action in response to claims filed on Feb 6, 2024.
Claims 1-8 are currently pending and have been examined.
Information Disclosure Statement
The Information Disclosure Statement filed on Feb 6, 2024 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-6 are directed to a device (i.e., a machine), claim 7 is directed to a method (i.e., a process), and claim 8 is directed to a computer product (i.e. an article of manufacture). Thus, the eligibility 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)
While claims 1, 7, and 8, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
The abstract idea recited in claims 1, 7, and 8, is
receiving sign detection information in which a sign of malfunction of the device is detected;
receiving resource utilization plan information that indicates a plan for a usage and a utilization time of human and physical resources related to a predicted occurrence period of the malfunction of the device;
estimating and calculating transition data of a usage amount of the human and physical resources related to the predicted occurrence period of the malfunction of the device by inputting the usage and the utilization time of the human and physical resources included in the resource utilization plan information to a machine learning engine that generates transition data of a usage amount of human and physical resources based on a usage and a utilization period of human and physical resources and performing machine learning; and
determining a time and a method for taking countermeasures against the malfunction of the device based on the sign detection information and the transition data of the usage amount of the human and physical resources.
The claimed invention is directed to an abstract idea of maintenance scheduling under the following categories of abstract ideas:
Under the broadest reasonable interpretation, without the recitation of additional elements, the limitations above recite a process similar to collecting information (steps [A] and [B]) and analyzing the information (steps [C] and [D]). The limitations above involve estimating, calculating, and determining a time and a method which closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the claims describe receiving a resource utilization plan and managing human and physical resources. The process of scheduling maintenance and managing personnel availability are long-standing commercial and administrative practices, which is consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Finally, the claim limitation [C] recites estimating and calculating transition data of a usage amount… and dependent claim 2 further recites obtaining difference transition data by subtracting the transition data of the usage amount… from the transition data of risk level.” These are specific mathematical formula and calculations, which falls under “mathematical concepts” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(I).
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer.
The additional elements considered include:
Claim 1: “the maintenance response time suggestion device comprising one or more processors configured to perform operations comprising”; “machine learning engine”;
Claim 12: “causing a maintenance response time suggestion device to execute:”; “machine learning engine”;
Claim 15: “A non-transitory computer readable medium storing one or more instructions causing a computer to execute operations comprising”; “machine learning engine”;
In particular, the claim only recites the above-mentioned additional elements to receive, estimate, calculate and determine information. The computer in the steps is recited at a high-level of generality (i.e., as generic computer components performing a generic computer function; See Applicant’s Specification at least at pages 24-26 and Fig. 12 “the maintenance response time suggestion device 1 of the present embodiment described above can be implemented using a general-usage computer system including a CPU 901, a memory 902, a storage 903, a communication device 904, an input device 905, and an output device 906.”) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
That is, the function of limitations [A]-[D] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer. Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations [A]-[D] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for maintenance scheduling. Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claims 2-6, these claims recite limitations that further define the abstract idea noted in claim 1. Claim 2 further recites additional abstract step of estimating, calculating, and obtaining difference of transition data; Claim 3 further recites additional abstract step of updating variation parameter; Claim 4 further recites additional abstract information of the variation parameter; Claim 5 further recites additional abstract step of the machine learning engine generates a pattern; Claim 6 further recites additional abstract step of determining a time. These additional abstract steps and information do not change the abstract idea of the independent claim. These claims recite the additional element of computer components at a high level of generality (i.e. as a generic computer system performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component, as discussed in MPEP 2106.05(f). Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-8 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
Claims 1 and 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Reaume (US 20220317676 A1) in view of Kondo (JP 2020071558 A, translation provided).
Claims 1, 7, and 8, Reaume discloses (Claim 1) a maintenance response time suggestion device that suggests a response time for preventive maintenance against malfunction of a device (Abstract, para. [0001] and [0010], a system for automatically scheduling maintenance of equipment), the maintenance response time suggestion device comprising one or more processors configured to perform operations (para. [0010]-[0011] and [0056], processor) comprising:
(Claim 7) a maintenance response time suggestion method for suggesting a response time for preventive maintenance against malfunction of a device, the method causing a maintenance response time suggestion device to execute (Abstract, para. [0001] and [0010]):
(Claim 8) a non-transitory computer readable medium storing one or more instructions causing a computer to execute operations (para. [0056]) comprising:
receiving sign detection information in which a sign of malfunction of the device is detected (para. [0024] and [0027] discloses sensors 120 and computing devices 130 that provide advance warning of present or future component failures via mathematical modeling. The advance warning constitutes sign detection information);
receiving resource utilization plan information that indicates a plan for a usage and a utilization time of human and physical resources related to a predicted occurrence period of the malfunction of the device (Para. [0022], [0048] discloses collecting data regarding personnel availability and availability tools. A schedule of these resources constitutes a utilization plan); and
determining a time and a method for taking countermeasures against the malfunction of the device based on the sign detection information and the transition data of the usage amount of the human and physical resources (Claim 1 and para. [0029] discloses analyzing the pre-emptive maintenance data… and the failure time value to determine an optimal time to perform a maintenance event. Reaume determines the method of countermeasure, such as repair or replacement).
Reaume para. [0026], [0028], discloses the use of “machine learning (including artificial intelligence or ‘AI’) models” to analyze data for predicting time value associated with expected future failure. However, Reaume does not explicitly teach the detail for an machine learning engine that generates transition data for the resources.
Specifically, Reaume fails to explicitly teach, estimating and calculating transition data of a usage amount of the human and physical resources related to the predicted occurrence period of the malfunction of the device by inputting the usage and the utilization time of the human and physical resources included in the resource utilization plan information to a machine learning engine that generates transition data of a usage amount of human and physical resources based on a usage and a utilization period of human and physical resources and performing machine learning.
Nonetheless, Kondo is in the similar field of a resource management support device and method, which specifically teaches, estimating and calculating transition data of a usage amount of the human and physical resources related to the predicted occurrence period of the malfunction of the device by inputting the usage and the utilization time of the human and physical resources included in the resource utilization plan information to a machine learning engine that generates transition data of a usage amount of human and physical resources based on a usage and a utilization period of human and physical resources and performing machine learning (Kondo, abstract, para. [0011] and [0022], Kondo teaches a resource management support device that uses a machine learning algorithm 1021 to store resource status and number of users to estimate a predicted required allocation at a future time. In Abstract and para. [0057] teaches the machine learning engine calculates how resource amounts will increase or decrease by a predetermined rate over a period. The increase/decrease of resource levels over time constitutes the transition data of a usage amount).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the scheduling maintenance system and method of Reaume to incorporate the machine learning based resource transition modeling of Kondo for the motivation of providing a more technically accurate maintenance window in efficiently managing resources (para. [0010], [0013], 0060]).
Claim 6, the combination of Reaume and Kondo make obvious of the maintenance response time suggestion device according to claim 1. Reaume further discloses,
determining a time to take countermeasures against the malfunction of the device, which is included in the predicted occurrence period of the malfunction of the device (Reaume, para. [0003], [0022], discloses scheduling the maintenance event to occur before the predicted failure time to avoid the cost of a breakdown).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Reaume (US 20220317676 A1) in view of Kondo (JP 2020071558 A, translation provided), and further in view of Suzuki et al (US 20150206104 A1).
Claim 2, the combination of Reaume and Kondo make obvious of the maintenance response time suggestion device according to claim 1.
However, the combination fails to expressly teach:
estimating and calculating transition data of a risk level due to the malfunction of the device based on the sign detection information, and
obtaining difference transition data by subtracting the transition data of the usage amount of the human and physical resources from the transition data of the risk level and determines a time at which a value of the difference transition data matches a threshold of a predetermined countermeasure method for an index of the difference as a countermeasure time of the predetermined countermeasure method.
Nonetheless, Suzuki is in the analogous field of maintenance management, which specifically teaches, estimating and calculating transition data of a risk level due to the malfunction of the device based on the sign detection information (Suzuki: abstract, teaches a maintenance management device that diagnoses anomalies and estimates a first grace period leading up to the occurrence of a failure. Para. [0009], [0014], Suzuki teaches the grace period represents the time-series progression of risk. As the machine operates, the grace period decreases, which is a calculation of transition data of a risk level based on the initial anomaly detection), and
obtaining difference transition data by subtracting the transition data of the usage amount of the human and physical resources from the transition data of the risk level (para. [0009] teaches a method of determining maintenance timing by comparing the grace period (risk) against the maintenance cost (resource). Para. [0070]-[0072] Suziki utilizes a risk map where the two variables are plotted against one another in a coordinate system. Risk zones are based on relationship between the two values (e.g., high risk zone where grace period is low and cost is high)) and determines a time at which a value of the difference transition data matches a threshold of a predetermined countermeasure method for an index of the difference as a countermeasure time of the predetermined countermeasure method (para. [0072] teaches using thresholds to define the boundaries of maintenance zones. In para. [0077] teaches the administrator determines the timing of maintenance based on when the combined data point crosses these thresholds into a specific zone).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the scheduling maintenance system and method of Reaume to include the automated system of Suziki for a risk map observation by setting a numerical threshold for the difference transition data. When the risk minus resource value reaches a specific threshold, the system triggers the countermeasure time for a specific method (e.g., repair or replacement) as taught in Reaume (para. [0029]) for the motivation of providing a concert decision-making trigger to the automated system.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Reaume (US 20220317676 A1) in view of Kondo (JP 2020071558 A, translation provided), and further in view of Akutsu et al (US 20220237549 A1).
Claim 3, the combination of Reaume and Kondo make obvious of the maintenance response time suggestion device according to claim 1.
However, the combination fails to expressly teach:
updating a variation parameter of a pattern shape of the transition data of the usage amount of the human and physical resources by inputting the usage and the utilization time of the human and physical resources included in each of a plurality of pieces of resource utilization plan information to the machine learning engine and performing machine learning so that a difference between the determined time to take countermeasures against the malfunction of the device and a time to take countermeasures determined by a person decreases.
Nonetheless, Akutsu is in the field of human resource allocation supporting system and method, specifically teaches,
updating a variation parameter of a pattern shape of the transition data of the usage amount of the human and physical resources by inputting the usage and the utilization time of the human and physical resources included in each of a plurality of pieces of resource utilization plan information to the machine learning engine and performing machine learning so that a difference between the determined time to take countermeasures against the malfunction of the device and a time to take countermeasures determined by a person decreases (Akutsu: Abstract and para. [0009] teaches a machine learning model for human resource allocation that calculates an optimum parameter group based on correct data that obeys manual correction of the allocation plan. In para. [0150]-[1051], further states the system uses the manual correction by a user as the ground to retrain the machine learning model. This teaches updates the variation parameters of the method to minimize the difference between the AI generated plan and the human’s corrected plan).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the scheduling maintenance system and method of Reaume to include the supervised feedback loop of updating a variation parameter of a pattern shape of the transition data of the usage amount of the human and physical resources by inputting the usage and the utilization time of the human and physical resources included in each of a plurality of pieces of resource utilization plan information to the machine learning engine and performing machine learning so that a difference between the determined time to take countermeasures against the malfunction of the device and a time to take countermeasures determined by a person decreases, as taught by Akutsu. Reaume acknowledges that maintenance scheduling is subject to complex real-world constraints (para. [0022]) and use of machine learning model. By using human manual corrections over training and testing data to tune the machine learning parameters as taught by Akutsu. One ordinary skilled in the art would be motivated for the suggestion device to become more accurate ad better aligned with the practical expertise of human maintenance supervisors.
Claim 4, the combination of Reaume, Kondo, and Akutsu make obvious of the maintenance response time suggestion device according to claim 3. The combination further teaches,
wherein the variation parameter is a rising period, a convergence period, and a maximum value of the usage amount of the human and physical resources (Kondo abstract, teaches modeling the transition of resource usages as increases or decreases by a predetermined rate. Akutsu para. [0173] teaches modeling resource transition over time, calculating transfer ratios and the number of target persons.
It would have been obvious for one of ordinary skill in the art, before the effective filling of the invention, to modify the scheduling maintenance system and method of Reaume to include the modeling with transition curve or pattern shape for resource usage as taught by Kondo would find it mathematically routine to define the curve using standard statistical parameters of rising period, a convergence period, and a maximum value as taught by Akutsu with the motivation to tune the variation parameters so that the system adapts to the specific operation style of the maintenance team to achieve a maintenance suggestion device that is more realistic and aligned with expert human constraints.
Claim 5, the combination of Reaume, Kondo, and Akutsu make obvious of the maintenance response time suggestion device according to claim 3. The combination further teaches,
wherein the machine learning engine generates a pattern of the transition data of a resource usage amount of the human and physical resources for each usage of the human and physical resources (Kondo: Abstract and para. [0011] teaches estimating resource usage based on past information of the number of users and calendar characteristics which are categorization of human and physical resource statuses. Akutsu para. [0163] teaches the machine learning model is generated for each of a plurality of condition item groups).
It would have been obvious for one of ordinary skill in the art, before the effective filling of the invention, to include the standard practice in machine learning model to categorize input data as suggested by Akutsu’s condition items to generate distinct transition patterns for different types of usages (e.g., planned event usage vs. daily operation usage) because the rising period and maximum value would differ between a scheduled publicity event and a routine maintenance task for the motivation of providing a more accurate model with better application of well-known statistical modeling principles to achieve the predictable result of a more refined and expert-aligned maintenance schedule.
Relevant Prior Art Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure. The additional cited art, including but not limited to the excerpts below, further establishes the state of the art at the time of Applicant’s invention and shows the following was known:
Koga et al. (US 20180368297 A1)
Kurisawa et al. (US 20220367924 A1)
Agarwal et al. (US 11844134 B1)
A. Mouzoune and S. Taibi, "Towards an intelligence based conceptual framework for e-maintenance," 2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA), Rabat, Morocco, 2013, pp. 1-8, doi: 10.1109/SITA.2013.6560789.
Conclusion
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/WENREN CHEN/Examiner, Art Unit 3626