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 .
DETAILED ACTION
Response to Arguments
Applicant’s arguments, with respect to claim(s) 1, 4-6, and 8-9 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.
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, 6, 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Yue, et al., hereinafter Yue (Chinese Patent App. Pub. No. 107908874) in view of Hodel, et al., hereinafter Hodel (U.S. Patent Application Pub. No. 2016/0098637).
Regarding Claim 1, Yue teaches: A method for determining a working condition of an excavator (Yue, Para. 0006 and 0039 – “a working condition identification method” for identifying “specific working conditions of engineering machinery equipment”; where the “equipment” may be an “excavator”), comprising:
acquiring real-time state parameter data of an excavator (Yue, Para. 0039 and 0045 – “receiving real-time data of the engineering machinery equipment 10 during real-time operation”; where the “equipment” may be an “excavator”) in where real-time data is obtained according to a “preset time period”),
respectively inputting the real-time state parameter data of the excavator where “prediction data” is sent to a “pre-established working condition identification model for working condition identification to obtain a predicted working condition result”; where the “prediction data” is generated by processing the “real-time data” received, and where real-time data is obtained according to a “preset time period”);
wherein, the excavator working condition determination model is obtained by performing training based on state parameter data samples carrying working condition type labels (Yue, Para. 0091-0093 – obtaining a “training set” and “test set” of data built off of “input samples and pre-established labels”, or parameter data samples and labels, for “training the training set to obtain a basic model, and testing the basic model to obtain a working condition recognition model”; where the training includes feedback training); and
determining a ratio of working condition type (Yue, Para. 0062, 0090 – “the action ratio over a period of time is calculated”, for example a “a piece of prediction data may include the proportion result of the action data”) c
While Yue teaches acquiring real-time state parameter data of an excavator in a preset time segment, and determining a ratio of working condition type, Yue does not teach acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments, obtaining a corresponding working condition type of the excavator in each preset time segment, and determining a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment.
However, Hodel teaches acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments (Hodel, Para. 0017 – an “operation classifier module” may receive “processed, calculated data”, where the “operation classified module” outputs “a series of machine operation predictions” expressed as “a series of operation-labeled time periods”; where the “series of operation-labeled time periods” is a plurality of time segments in series extracted from raw “time series data”), obtaining a corresponding working condition type of the excavator in each preset time segment (Hodel, Para. 0014, 0017 – the “operation classified module” outputting “machine operations” for each “time period” such as “dig, swing, dump, propel, idle, travel, load, carry, spread, …” etc.; where the machine may be “excavators”), and determining a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment (Hodel, Para. 0004, 0014, 0021, 0027, 0038 – determining a “percentage breakdown of what the machine 12 was doing for a particular selector” based on the “series of operation-labeled time periods”, i.e. a “percentage breakdown” , or ratio, “for a determined period of time” during a work cycle; where the machine may be “excavators”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Yue to further include acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments, obtaining a corresponding working condition type of the excavator in each preset time segment, and determining a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment, as taught by Hodel, in order to improve accuracy of working condition type prediction over an entire working period over multiple hours of an excavator.
In regards to Claim 6, Yue in view of Holdel teaches the method for determining the working condition of the excavator of Claim 1, and Yue further teaches wherein the real-time state parameter data (Yue, Para. 0045 – “receiving real-time data of the engineering machinery equipment”) comprises at least one of engine speed, pilot pressure, electric current, pump pressure (Yue, Para. 0047 – where the “real-time data” includes “data such as the engine speed, the main pressure of the main pump, the current of the solenoid valve, and the pilot pressure of the pilot pump”) and service time.
Regarding Claim 8, Yue teaches: An excavator (Yue, Para. 0039 – “engineering machinery equipment”; where the “equipment” may be an “excavator”), comprising:
an apparatus for determining a working condition of an excavator which is configured to determine the working condition type of the excavator (Yue, Para. 0007 and 0039 – “a working condition identification device that can identify the specific working conditions of engineering machinery and equipment”; where the “equipment” may be an “excavator”), the apparatus for determining a working condition of an excavator comprises:
a parameter data acquisition module, configured to acquire real-time state parameter data of an excavator (Yue, Para. 0039 and 0045 – “receiving real-time data of the engineering machinery equipment 10 during real-time operation”; where the “equipment” may be an “excavator”), in where real-time data is obtained according to a “preset time period”),
an excavator working condition determining module, configured to respectively input the real-time state parameter data of the excavatorwhere “prediction data” is sent to a “pre-established working condition identification model for working condition identification to obtain a predicted working condition result”; where the “prediction data” is generated by processing the “real-time data” received);
wherein, the excavator working condition determination model is obtained by performing training based on state parameter data samples carrying working condition type labels (Yue, Para. 0091-0093 – obtaining a “training set” and “test set” of data built off of “input samples and pre-established labels”, or parameter data samples and labels, for “training the training set to obtain a basic model, and testing the basic model to obtain a working condition recognition model”; where the training includes feedback training); and
a ratio calculation module, configured to determine a ratio of working condition type (Yue, Para. 0062, 0090 – “the action ratio over a period of time is calculated”, for example a “a piece of prediction data may include the proportion result of the action data”) c
While Yue teaches acquiring real-time state parameter data of an excavator in a preset time segment, and determining a ratio of working condition type, Yue does not teach acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments, obtaining a corresponding working condition type of the excavator in each preset time segment, and a ratio calculation module, configured to determine a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment.
However, Hodel teaches acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments (Hodel, Para. 0017 – an “operation classifier module” may receive “processed, calculated data”, where the “operation classified module” outputs “a series of machine operation predictions” expressed as “a series of operation-labeled time periods”; where the “series of operation-labeled time periods” is a plurality of time segments in series extracted from raw “time series data”), obtaining a corresponding working condition type of the excavator in each preset time segment (Hodel, Para. 0014, 0017 – the “operation classified module” outputting “machine operations” for each “time period” such as “dig, swing, dump, propel, idle, travel, load, carry, spread, …” etc.; where the machine may be “excavators”), and a ratio calculation module, configured to determine a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment (Hodel, Para. 0004, 0014, 0021, 0027, 0038 – an “application profile module” for determining a “percentage breakdown of what the machine 12 was doing for a particular selector” based on the “series of operation-labeled time periods”, i.e. a “percentage breakdown”, or ratio, “for a determined period of time” during a work cycle; where the machine may be “excavators”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the excavator of Yue to further include acquiring real-time state parameter data of an excavator in each preset time segment in a target time period, wherein the target time period is divided into a plurality of preset time segments, obtaining a corresponding working condition type of the excavator in each preset time segment, and a ratio calculation module, configured to determine a ratio of working condition type corresponding to each preset time segment based on the corresponding working condition type of the excavator in each preset time segment, as taught by Hodel, in order to improve accuracy of working condition type prediction over an entire working period over multiple hours of an excavator.
Regarding Claim 9, Yue in view of Hodel teaches: An electronic device (Yue, Para. 0008 – “an engineering machinery device that can identify the specific working conditions it is performing”), comprising a memory (Yue, Para. 0018 – “a memory”), a processor (Yue, Para. 0018 – “a processor”), and a computer program stored in the memory and executable by the processor (Yue, Para. 0018 – “a working condition identification device, wherein the working condition identification device is installed in the memory and comprises one or more software function modules executed by the processor”), wherein the computer program, when executed by the processor, causes the processor to perform the steps of the method for determining the working condition of the excavator according to claim 1 (See Yue in view of Hodel in Claim 1 Above).
Claim(s) 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Yue in view of Hodel, and further in view of Yodawara, et al., hereinafter Yodawara (U.S. Patent Application Pub. No. 2021/0233230).
In regards to Claim 4, Yue in view of Hodel teaches the method for determining the working condition of the excavator of Claim 2, and Yue in view of Hodel further teaches wherein after respectively inputting the real-time state parameter data of the excavator in each preset time segment into the excavator working condition determination model to obtain the corresponding working condition type of the excavator in each preset time segment output by the excavator working condition determination model (Yue, Para. 0060-0069 and 0110 – “binarized real-time action data and normalized real-time operation data obtained according to the preset time period are obtained by binarizing and normalizing the real-time action data and real-time operation data within the preset time period, respectively”; where the “binarized real-time action data and normalized real-time operation data” are generated by processing the “real-time data” and are calculated to obtain “prediction data” which is “sent to a pre-established working condition identification model for working condition identification”; Hodel, Para. 0017 – the “operation classified module” outputting “machine operations” for each “time period”, of a series of time periods, such as “dig, swing, dump, propel, idle, travel, load, carry, spread, …” etc.), the method further comprises:
where the “binarized real-time action data and normalized real-time operation data obtained according to the preset time period are obtained”, by processing the “real-time data” and “sent to a pre-established working condition identification model for working condition identification” during the “preset time period”) “combining the real-time operation data and/or the real-time action data to obtain real-time operation combination feature data and real-time action combination feature data”, such that the data is aggregated, and obtaining “binarized real-time action data and normalized real-time operation data obtained according to the preset time period”; Hodel, Para. 0017 – “a series of operation-labeled time periods”), and storing the corresponding working condition type of the excavator in each preset time segment and the target state parameter dataset of the excavator in each preset time segment (Yue, Para. 0070-0075 – where obtained “operating condition result[s]” are “used for subsequent application scenarios” such as being “fed back” in a model for training, such that they would be stored for usage; Hodel, Para. 0017 – “a series of operation-labeled time periods”)
While Yue in view of Hodel teaches the corresponding working condition type of the excavator in each preset time segment and the real-time state parameter data of the excavator in each preset time segment, and performing aggregate calculation on the real-time state parameter data of the excavator in each preset time segment to obtain a target state parameter dataset of the excavator in each preset time segment, and storing the corresponding working condition type of the excavator in each preset time segment and the target state parameter dataset of the excavator in each preset time segment, Yue in view of Hodel does not teach uploading the corresponding working condition type onto a cloud data platform, nor does it teach performing aggregate calculation by the cloud data platform and storing the corresponding working condition type into a cloud data warehouse.
However, Yodawara teaches uploading the corresponding working condition type onto a cloud data platform (Yodawara, Para. 0080 – transmitting, or uploading, “a request including an attitude condition, a performance information request for specifying performance information of the work machine, and model information of the work machine to the specified server”), performing aggregate calculation by the cloud data platform (Yodawara, Para. 0082-0083 – where the servers include “the calculation expression used for calculation of the performance information and the specification data of the work machine”, and the servers perform “calculation of the performance information specified by the performance information request on the basis of the attitude condition, the performance information request, and the model information that are included in the request”) and storing the corresponding working condition type into a cloud data warehouse (Yodawara, Para. 0211 – where the server includes a “storage unit” having “a performance table” for storing “performance data” such as “attitude condition, work state information, operating radius, and the like”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method for determining the working condition of the excavator including the above limitations of Yue in view of Hodel to include uploading the corresponding working condition type onto a cloud data platform, performing aggregate calculation by the cloud data platform and storing the corresponding working condition type into a cloud data warehouse, as taught by Yodawara, in order to improve calculations and provide support for determining working condition types.
In regards to Claim 5, Yue in view of Hodel and Yodawara teaches the method for determining the working condition of the excavator of Claim 4, and Yue in view of Hodel and Yodawara further teaches wherein after uploading the corresponding working condition type of the excavator in each preset time segment and the real-time state parameter data of the excavator in each preset time segment onto the cloud data platform (Yue, Para. 0060-0069 and 0110 – where the “binarized real-time action data and normalized real-time operation data obtained according to the preset time period are obtained”, by processing the “real-time data” and “sent to a pre-established working condition identification model for working condition identification” during the “preset time period”; Hodel, Para. 0017 – “a series of operation-labeled time periods”; Yodawara, Para. 0080 – transmitting, or uploading, “a request including an attitude condition, a performance information request for specifying performance information of the work machine, and model information of the work machine to the specified server” – See Claim 4), the method further comprises:
performing a secondary training on the excavator working condition determination model based on the corresponding working condition type of the excavator in each preset time segment and the target state parameter dataset of the excavator in each preset time segment to obtain a secondarily retrained excavator working condition determination model (Yue, Para. 0060-0069 and 0073-0076 – where the “confirmed comparison result is fed back to the training process of the operating condition identification model to continue the training, thereby updating the operating condition identification model to make it more accurate”; where the confirmed comparison result is obtained from determining a “working condition” from real-time data obtained during a “preset time period”, as previously cited; Hodel, Para. 0017 – “a series of operation-labeled time periods”);
updating the excavator working condition determination model based on the secondarily retrained excavator working condition determination model (Yue, Para. 0073-0076 – where the “confirmed comparison result is fed back to the training process of the operating condition identification model to continue the training, thereby updating the operating condition identification model to make it more accurate”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hodel, et al. (U.S. Patent Application Pub. No. 20160078363) teaches a method for developing machine operation classifiers for a machine including determining one or more training labels associated with the one or more training features and building a predictive model for determining machine operation classifiers using a computer, where the predictive model is used for receiving new data associated with the machine and determining a predicted label based on the new data.
Hiemer, et al. (U.S. Patent Application Pub. No. 2018/0114381) teaches method for determining operating conditions of a working machine by a classifier generated by a machine learning process determine information which best describes the current driving condition of a working machine.
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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/H.L./Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665