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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/05/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding claim 1, the claim is directed to a method for generating a machine learning model comprising step(s) of:
“generating synthetic data descriptive of an operation of the elevator by simulating the operation of the elevator with a simulation model of the elevator”. The claim does not put any limit on how the simulation model is generated. According to the disclosure, the simulation model is algorithm that receives input data to “enable a computation of predefined physical parameters, such as, stiffness, damping, friction, current and so on” (see p. 9 of specification of instant application). And, determining parameters such as friction and current involves simple mathematical calculations. Therefore, the plain meaning of the step is generating synthetic data based on mathematical calculations;
“accessing history data generated by operating the elevator corresponding to the elevator of the simulation model”, “comparing the synthetic data with the history data”, “generating data descriptive of differences between the synthetic data and the history data”, “calibrating the simulation model of the elevator based on the data descriptive of the differences”, and “generating calibrated synthetic data descriptive of at least one malfunction of the elevator with a calibrated simulation model of the elevator”. These steps may be practically performed in the human mind and simple human activity using observation, evaluation, judgment, and opinion. For example, accessing history data could be performed by observation, comparing data with history data is merely evaluation of data, generating data could be simply result of the comparing written down by pen and paper, and calibrating is further evaluating data; and
“training the machine learning model with a training dataset based on the calibrated synthetic data to generate the machine learning model for evaluating a condition of the elevator”. As recited in the claim, the machine learning model is generated by a computer-implemented method; thus, by a computer. The computer is used as a tool to perform generic computer function and to perform abstract idea as identified above, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Accordingly, the claim does not integrate the abstract idea into a practical application. See Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327 (Fed. Cir. 2016) (improvement to computer architecture); Diehr, 450 U.S. 175 (1981) (transformation); MPEP 2106.05.
Therefore, claim 1 is rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
In addition, claims 16 and 28-29 recite similar limitations of claim 1; thus, for the same reason as explain in claim 1 above, these claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
In addition, claims 2-15 and 17-27 further elaborate the recited limitations in independent claims 1 and 16, without adding any additional elements to integrate the identified abstract idea into practical application. Thus, for the same reasons as explained in regard to claim 1 above, these claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-14 and 16-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chai (NPL from IDS dated 03/05/2024: A Non-Intrusive Deep Learning Based Diagnosis System for Elevators) in view of Gonzalez (NPL from IDS dated 03/05/2024: A Digital Twin for Operational Evaluation of Vertical Transportation Systems).
Regarding claims 1 and 16, Chai discloses a computing system for generating a machine learning model for evaluating a condition of an elevator and a computer-implemented method (e.g. Abstract: AI diagnosis system for elevator; system inherently comprises method for operating the system) for generating a machine learning model (e.g. Abstract: self-learning implies generating the machine learning model based on input data) for evaluating a condition of an elevator (e.g. Abstract: give warning to prevent equipment breakdown), the system/method comprising:
generating synthetic data descriptive of an operation of the elevator by simulating the operation of the elevator (e.g. p. 21001 section V: collect anomaly samples based on simulations; p. 20998: manually create some abnormal current signals to facilitate training process of the deep learning model) with a simulation model of the elevator;
Chai fails to disclose, but Gonzalez teaches:
accessing history data generated by operating the elevator corresponding to the elevator of the simulation model (e.g. p. 114398, Fig. 11 & table 3: measured variables);
comparing the synthetic data with the history data (e.g. p. 114398 & Fig. 11 & table 3: compare simulated variables with measured variables);
generating data descriptive of differences between the synthetic data and the history data (p. 114395: equation 26);
calibrating the simulation model of the elevator based on the data descriptive of the differences (e.g. p. 114396: equation 31: update model based on determined differences in equation 26);
generating calibrated synthetic data descriptive of at least one malfunction of the elevator with a calibrated simulation model of the elevator (e.g. Abstract & Figs. 11-12 & tables 3-4: suggest corrective action to improve the system based on the updated reference model simulation); and
training the machine learning model with a training dataset based on the calibrated synthetic data to generate the machine learning model for evaluating a condition of the elevator (e.g. Abstract & p. 114389: generating synthetic data to feed machine learning algorithms).
Thus, it would have been obvious to one skilled in the art to modify the teachings of Chai with the teachings of Gonzalez to utilize history data (i.e. prior measured data) to update machine learning algorithms to improve and simulate the effect of corrective actions to simplify the algorithms and/or model.
Regarding claims 2 and 17, Chai discloses the simulation model of the elevator is established by using a number of elevator specific parameters of the elevator for which the machine learning model is generated (e.g. Tables 2-3 and 5-6: brake and door closing conditions associated with the elevator).
Regarding claims 3 and 18, Gonzalez teaches the simulation model is an object-oriented dynamic model (e.g. Abstract).
Regarding claims 4 and 19, Gonzalez teaches the history data is accessed by at least one of: obtaining a number of parameters of the elevator with a number of sensors (e.g. Abstract & p. 11389: sensors); obtaining data of a control signal of an entity of the elevator retrieving stored history data from data storage.
Regarding claims 5 and 20, Chai discloses the number of parameters of the elevator is obtained from at least one of:
at least one accelerometer associated to an elevator car (e.g. p. 20994: vibration parameters measured from accelerometers);
a motor encoder of an elevator door (e.g. p. 20994: encoder to measure door displacement).
Regarding claims 6 and 21. Chai discloses the control signal is an input current of a door motor (e.g. p. 20997: section B).
Regarding claims 7 and 22, Gonzalez teaches a calibration of the simulation model of the elevator is performed by adjusting at least one definition of the simulation model of the elevator with information derivable from the history data (e.g. Abstract & p. 114389: generating synthetic data to feed machine learning algorithms; p. 114396: equation 31: update model based on determined differences in equation 26).
Regarding claims 8 and 23, Chai and Gonzalez in combination discloses the calibrated synthetic data descriptive of at least one malfunction of the elevator is generated by:
determining a number of malfunctions typical to the elevator (e.g. Chai: tables 3 & 6); and
simulating the determined number of malfunctions with the simulation model of the elevator (e.g. Chai in tables 3 & 6: different typical malfunctions of elevator, e.g. brake and door closing issue; Gonzalez in p. 114396: equation 31: update model based on determined differences in equation 26. Thus, the combination of Chai and Gonzalez discloses a system that would update model for evaluating elevator condition based on difference/error signals between simulated and measured variables).
Regarding claims 9 and 24, Chai and Gonzalez in combination discloses the number of malfunctions typical to the elevator is determined based on at least one of the following:
maintenance requests of the elevator,
maintenance operations performed to the elevator,
troubleshooting reports of the elevator,
error signals received from the elevator (e.g. Chai in tables 3 & 6: different typical malfunctions of elevator, e.g. brake and door closing issue; Gonzalez in p. 114396: equation 31: update model based on determined differences in equation 26. Thus, the combination of Chai and Gonzalez discloses a system that would update model for evaluating elevator condition based on difference/error signals between simulated and measured variables).
Regarding claims10 and 25, Gonzalez teaches the training dataset is generated from the calibrated synthetic data descriptive of at least one malfunction of the elevator by generating a number of representations of a predefined type (e.g. Figs. 5-6: time domain and frequency domain).
Regarding claims 11 and 26, Gonzalez teaches the predefined type of representations is expressed in at least one of: frequency domain (e.g. Fig. 6), time domain (e.g. Fig. 5).
Regarding claims 12 and 27, Chai discloses the machine learning model under generation for evaluating the condition of the elevator is a convolutional neural network, CNN (e.g. p. 20995, 1st paragraph of section C).
Regarding claim 13, Chai discloses method for evaluating a condition of an elevator (e.g. Abstract: AI diagnosis system for elevator; system inherently comprises method for operating the system), the method comprises comprising:
receiving input data (p. 20995: section C: using sensors to detect responses of the lift system; p. 20998: manually create some abnormal current signals to facilitate training process of the deep learning model) from the elevator under evaluation (e.g. p. 21001 section V: collect anomaly samples based on simulations or real-life cases);
inputting the input data to a machine learning model generated according to claim 1 (e.g. p. 20997: section B: using collected samples, e.g. current signals, to build multivariate samples for model learning; p. 21000: multivariate variables and sequences as input for the model); and
setting, in accordance with an output of the machine learning model, a detection result to indicate one of the following: (i) the elevator operates properly, (ii) the elevator malfunctions (e.g. p. 21002: section C: results).
Regarding claim 14: Chai discloses the input data is obtained by obtaining at least one parameter indicative of an operation of the elevator (e.g. p. 20997: current signal).
Regarding claim 28, Chai discloses an elevator comprising a computing system for executing a machine learning model generated according to claim 1 for evaluating a condition of the elevator (e.g. p. 21002: section C: results).
Regarding claim 29, Chai discloses a computer program (e.g. Abstract: AI model is a program) embodied on a non-transitory computer readable medium (e.g. p. 21001: memory) and comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to claim 1 (e.g. Abstract).
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chai (NPL from IDS dated 03/05/2024: A Non-Intrusive Deep Learning Based Diagnosis System for Elevators) in view of Gonzalez (NPL from IDS dated 03/05/2024: A Digital Twin for Operational Evaluation of Vertical Transportation Systems) as applied to claim 13 above, and further in view of Tyni et al. (US 2007/0016332 A1).
Regarding claim 15, Chai and Gonzalez in combination fails to disclose, but Tyni teaches the detection result indicating that the elevator operates properly further comprises data indicative of an expected lifetime of the elevator (e.g. Abstract & [0038]: using algorithm to determine or predict failure of an elevator door, and determine average useful life of a door based on detected failure data).
Thus, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention to modify the teachings of Chai and Gonzalez with the teachings of Tyni to provide prediction of condition of an elevator door so as to plan maintenance date/time in advance (e.g. [0016]).
Conclusion
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/KAWING CHAN/ Primary Examiner, Art Unit 2837
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