DETAILED ACTION
This communication is responsive to the application, filed June 20, 2025. Claims 1-9 are pending in this application. The applicant has added new claims 8 and 9.
Examined under the first inventor to file provisions of the AIA
The present application was filed on September 29, 2023, which is on or after March 16, 2013, and thus is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 8 is objected to because of the following informalities: “the leaning data”. Claim 8 should recite “the learning data”. Appropriate correction is required.
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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Alshawabkeh et al. (US 2021/0383170 A1) in view of Matsuda (US 2018/0232269 A1) and further in view of Metzler et al. (US 2018/0356222 A1) and further in view of Venkatesan et al. (US 2019/0354420 A1).
As per claim 1: A device for maintenance management of surveying instruments comprising:
a processor and a memory, wherein
Alshawabkeh discloses [0017] each compute node includes processors and memory.
the processor is configured to:
perform machine learning by using the learning data, and
Alshawabkeh discloses [0053] each trained learning process is configured to implement a classification algorithm, such that once the classification model is built it can be used to predict the type of the errors in the future execution.
generate learning data by using a set of complaint information including a complaint content and a complaint receipt time of a complaint received from a user and operational status data of a surveying instrument relating to the complaint in a predetermined period before occurrence of the complaint, from collected data collected regarding a plurality of surveying instruments and including operational status information and complaint information on each surveying instrument, where the complaint content is a symptom of failure of the surveying instrument relating to the complaint and the operational status data includes an error log and an instrument log; and
Alshawabkeh discloses [0028] the test automation tool allows users to generate test cases that can test combinations of several system features (complaint content from users). Alshawabkeh further discloses [Figs. 1 and 2; 0031-0036] generating learning data using error log and device log. The log analysis system is provided that uses machine learning to determine the location and type of errors. Alshawabkeh discloses performing learning from collected data on a device, but fails to explicitly disclose complaint content is a symptom of failure of the surveying instrument relating to the complaint. Matsuda discloses a similar system, which further teaches [Figs. 3 and 4; 0041-0043] the user reports a complaint related to a surveying instrument based on user’s sensory evaluations, such as something wrong with measurements, angles, data, etc.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Alshawabkeh with that of Matsuda. One would have been motivated to generate complaint content of the surveying instrument relating to the complaint because the error detection unit of the surveying instrument might not detect an error without user’s evaluations [Matsuda; 0013].
Alshawabkeh and Matsuda disclose performing learning from collected data on a device, but fail to explicitly disclose generate learning data on a surveying instrument. Metzler discloses a similar system, which further teaches [0064] evaluating efficiency of surveying instruments. The output of the evaluation of the measured data may also be used for machine learning enhancements in order to provide higher quality measurements in the future or an improved choice of a surveying instrument.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Alshawabkeh and Matsuda with that of Metzler. One would have been motivated to generate learning data on a surveying instrument because it allows to provide higher quality measurements in the future [Metzler; 0064].
to generate a learning model for predicting a content and a time of a complaint that will occur in the future against a target surveying instrument when operational status data of the target surveying instrument is input.
Alshawabkeh, Matsuda, and Metzler disclose generating a learning model and predicting content that will occur based on surveying instrument, but fail to explicitly disclose predicting a failure that will occur in the future when operational status data is input. Venkatesan discloses a similar system, which further teaches [0019] predicting future faults in a component of a plurality of components using one or more machine learnings models. Venkatesan further discloses [0046] training an analytical machine learning model and perform root cause analysis of a fault status in a component and predict future fault statues for that component based on fault data input of the component.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Alshawabkeh, Matsuda, and Metzler with that of Venkatesan. One would have been motivated to predict fault that will occur in the future when operational status data is input because it allows to alert the system or manager of a future failure [Venkatesan; 0057].
As per claim 2: The maintenance management device according to claim 1, wherein for the complaint content, a text expressing the complaint content is classified and tagged according to the complaint content.
Alshawabkeh discloses [0028] the test automation tool allows users to generate test cases that can test combinations of several system features (text complaint content from users).
As per claim 3: The maintenance management device according to claim 1, wherein the processor checks the complaint receipt time, identifies the most recent error occurrence time before receipt of the complaint from the operational status information, and determines the predetermined period before occurrence of the error as an extraction period of the operational status data.
Alshawabkeh discloses [0028] the test automation tool allows users to generate test cases that can test combinations of several system features (complaint content from users). Alshawabkeh further discloses [Figs. 1 and 2; 0031-0036] generating learning data using error log and device log. The log analysis system is provided that uses machine learning to determine the location and type of errors. It is clear form the teachings that the logs and tests include timestamps and can be identified as most recent based on a timestamp.
As per claim 4: The maintenance management device according to claim 1, wherein the operational status data further includes measurement environment data.
Alshawabkeh discloses [Figs. 1 and 2; 0031-0036] generating learning data using error log and device log.
As per claim 5: The maintenance management device according to claim 1, further comprising a relearning unit configured to perform relearning by using the operational status data corresponding to a new complaint when new complaint information is input.
Alshawabkeh discloses [0060] continuing to train the learning process to enable the learning process to continue to improve over time by test execution logs (new complain information).
As per claim 6: Although claim 6 is directed towards a device claim, it is rejected under the same rationale as the device claims 1 and 3 above.
As per claim 7: Although claim 7 is directed towards a method claim, it is rejected under the same rationale as the device claims 1 and 3 above.
As per claim 8: The maintenance management device according to Claim 1, wherein the leaning data further includes position information on each surveying instrument, and the processor is configured to generate the learning model when the operational status data and position information of the target surveying instrument is input.
Metzler discloses [0064] the output of the evaluation of the measured data may also be sued for machine learning enhancement. The computer of the surveying instrument is configured to learn in order to provide quality measurements in the future, by an improved positioning of the surveying instrument based on the surveying conditions that are input.
Allowable Subject Matter
Claim 9 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments, see pages 1-3 of Remarks, filed June 20, 2025, with respect to 35 U.S.C. 101 rejection have been fully considered and are persuasive. The 35 U.S.C. 101 rejection of claims 1-7 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1-7 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
· US 2010/0315286 A1 – Cerniar discloses a user may input data points to provide increased precision and detection of errors for a survey device.
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.
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/JIGAR P PATEL/Primary Examiner, Art Unit 2114