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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al (US 20180005132) in view of Trinh et al (US 20200379454) in view of Guo et al (US 20230400847).
As to claim 1, Singh discloses a malfunction prediction system that predicts a malfunction of a work machine (FIGS. 1-2), the malfunction prediction system comprising:
an operation information acquisition section that acquires operation information of the work machine (FIG. 2, apparatus 200 acquires alert data 228; see [0027], the alert data is associated with operating parameters of the work vehicle);
an inspection information acquisition section that acquires inspection information of the work machine (FIG. 2, apparatus 200; see [0025], the warranty database 202 includes maintenance logs and failure information associated with a work vehicle (e.g., an identifier of a work vehicle) and the parts and associated maintenance database 203 includes Technician Assistance Center (DTAC) data including part lists and/or technical documentation used when performing maintenance and/or repairs on the respective work vehicles);
a part replacement/repair information acquisition section that acquires part replacement/repair information of the work machine (FIG. 2, apparatus 200; see [0025], the parts and associated maintenance database 203 includes Technician Assistance Center (DTAC) data including part lists and/or technical documentation used when performing maintenance and/or repairs on the respective work vehicles); and
a malfunction prediction section that predicts a malfunction probability of each part of the work machine, based on the operation information acquired by the operation information acquisition section, the inspection information acquired by the inspection information acquisition section, the part replacement/repair information acquired by the part replacement/repair information acquisition section (FIG. 2, apparatus 200; see FIG. 3, [0036] and [0044], the probability of the machine failure occurring in the work machine is determined based on the model and the identified alert sequence (block 316) by, for example, the identifier 225 identifying the machine failure and alert sequence mapped and/or linked with the corresponding probability; see FIG. 7).
Singh fails to explicitly disclose that the malfunction prediction section that predicts the malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section, wherein the malfunction prediction section calculates, as the malfunction probability, a malfunction probability related to aging indicating a possibility of a temporal malfunction, a malfunction probability related to sudden occurrence indicating a possibility of appearance of a malfunction sign, and an overall malfunction probability obtained by taking into consideration the malfunction probability related to aging and the malfunction probability related to sudden occurrence.
However, Trinh teaches predicting a malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section (see Abstract, The server may assign an anomaly score based on the differences between the predicted values and the measured values; see [0051], The failure classification and prediction model store 260 stores machine learning models that are used to identify specific components or aspects of a piece of equipment 150 that may need inspection and/or repair … The models that are trained to classify failures may estimate failure probabilities of the equipment 150 or of a particular component of the equipment 150; see [0057], [0073], [0077]; FIG. 2, data store 220).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Singh using Trinh’s teachings to include predicting a malfunction probability of each part of the work machine, based on the deviation information between the malfunction probability of each part of the work machine and inspection performance of the work machine stored in a storage section in order to reduce maintenance cost and prevent permanently damage components of the equipment by detecting anomalies of the equipment often before the equipment show signs of failure (Trinh; [0032]-[0033]).
The combination of Singh and Trinh fails to explicitly disclose wherein the malfunction prediction section calculates, as the malfunction probability, a malfunction probability related to aging indicating a possibility of a temporal malfunction, a malfunction probability related to sudden occurrence indicating a possibility of appearance of a malfunction sign, and an overall malfunction probability obtained by taking into consideration the malfunction probability related to aging and the malfunction probability related to sudden occurrence.
However, Guo teaches wherein the malfunction prediction section calculates, as the malfunction probability, a malfunction probability related to aging indicating a possibility of a temporal malfunction (see [0069]: the predictive maintenance system can generate a predicted equipment health status information that indicates a health status of the equipment based on previously measured characteristics of the equipment (referred to herein as offline information) assuming a typical rate of deterioration of the equipment (e.g., due to wear and tear); see [0083]), a malfunction probability related to sudden occurrence indicating a possibility of appearance of a malfunction sign (see [0069]: the predictive maintenance system can generate an estimated equipment health status information that indicates estimates of a current health status of the equipment based on real-time data (e.g., real-time data collected from sensors associated with the equipment, real-time spectroscopy information, real-time manufacturing conditions of the equipment, and/or any other suitable real-time data)), and an overall malfunction probability obtained by taking into consideration the malfunction probability related to aging and the malfunction probability related to sudden occurrence (see [0069] and [0099]: the predictive maintenance system can generate an adjusted equipment health status information that combines the predicted health status information based on offline data and the estimated health status; see [0056]: “Bayesian analysis” refers to a statistical paradigm that evaluates a prior probability using available evidence to determine a posterior probability; see [0072]-[0075]: the predictive maintenance system described herein can identify anomalies, or imminent failures of manufacturing equipment; see [0189]-[0190]: by generating prescriptive maintenance recommendations, the predictive maintenance system can identify temporary solutions to an identified upcoming likely failure of a component … by identifying probable failures associated with a detected anomaly during an imminent failure, and by identifying likely causes of a failure, the predictive maintenance system can reduce the number of manual troubleshooting hours required to identify root causes of failures).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify the combination of Singh and Trinh using Guo’s teachings to include wherein the malfunction prediction section calculates, as the malfunction probability, a malfunction probability related to aging indicating a possibility of a temporal malfunction, a malfunction probability related to sudden occurrence indicating a possibility of appearance of a malfunction sign, and an overall malfunction probability obtained by taking into consideration the malfunction probability related to aging and the malfunction probability related to sudden occurrence in order to generate a more accurate real-time status of the manufacturing equipment and to improve efficiency of manufacturing equipment by reducing downtime of equipment due to unforeseen anomalies in equipment (e.g., broken components) and by reducing the need for manual inspection and troubleshooting (Guo; [0075], [0187]).
As to claim 2, modified Singh fails to explicitly disclose further comprising: a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section.
However, Trinh teaches a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section (see [0044], The predictive maintenance server 110 may train one or more machine learning models that assign anomaly scores to a piece of equipment 150; see [0050]-[0051], The anomaly detection model store 250 may store a plurality of trained machine learning models that are used to determine the anomaly scores of one or more pieces of equipment 150 … The failure classification and prediction model store 260 stores machine learning models that are used to identify specific components or aspects of a piece of equipment 150 that may need inspection and/or repair … The models that are trained to classify failures may estimate failure probabilities of the equipment 150 or of a particular component of the equipment 150; see [0077], the trained machine learning model's predicted values show a large deviation from the actual values).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Singh using Trinh’s teachings to include a learning section that learns the deviation information between the malfunction probability predicted by the malfunction prediction section and the inspection performance of the work machine, wherein the malfunction prediction section predicts the malfunction probability of each part of the work machine, based on the deviation information learned by the learning section in order to reduce maintenance cost and prevent permanently damage components of the equipment by detecting anomalies of the equipment often before the equipment show signs of failure (Trinh; [0032]-[0033]).
As to claim 3, modified Singh further discloses further comprising: a server in communication with the work machine, wherein the operation information acquisition section, the inspection information acquisition section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the server (FIG. 8 and [0053]).
As to claim 4, modified Singh further discloses further comprising: a portable terminal in communication with the server, wherein the portable terminal is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section (see [0058]).
As to claim 5, modified Singh further discloses further comprising: a portable terminal in communication with the work machine, wherein the operation information acquisition section, the inspection information section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the portable terminal (FIGS. 1-2, central data processing center 102/200; FIG. 8 and [0053]).
As to claim 6, modified Singh further discloses wherein the portable terminal is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section (see FIG. 7, [0052]-[0053] and [0058]).
As to claim 7, modified Singh further discloses wherein the operation information acquisition section, the inspection information acquisition section, the part replacement/repair information acquisition section, the malfunction prediction section, and the learning section are provided in the work machine (FIG. 8 and [0053], platform 800 which can be provided in the work vehicles).
As to claim 8, modified Singh further discloses wherein the work machine is provided with a display section that displays the malfunction probability predicted by the malfunction prediction section (see FIG. 7, [0052]-[0053] and [0058]).
Response to Arguments
Applicant’s amendment and arguments, filed on 06/17/2026, with respect to the rejection(s) of claim(s) 1 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Guo et al (US 20230400847).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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