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 § 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.
Claim(s) 1 – 10 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, recites abstract idea but for the recitation of generic computer components:
Regarding claim 1,
identifying an event timepoint within the plurality of measured values
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves reviewing measured values to determine the occurrence of an event . See (MPEP 2106.04)).
arranging the plurality of labeled measured values in a time series based on the time stamps of the measured values;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves organizing information according to time stamps of the measured values. See (MPEP 2106.04)).
providing the measured values of the plurality of measured values with label values which are suitable for identifying whether a respective measured value is associated with the identified event timepoint and/or an event time range;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves reviewing measured values, determining whether each measured value corresponds to an identified event timepoint or event time range and assigning an appropriate label based on that determination. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
As evaluated below:
• The preamble is deemed insufficient to transform the judicial exception to a patentable
invention to a patentable invention because the preamble generally links the use of a
judicial exception to a particular technological environment or field of use, see MPEP
2106.05(h).
providing a plurality of measured values of an operating variable of a hand-held power tool,
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
wherein the measured values are each provided with time stamps;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
wherein the event timepoint defines a timepoint at which the hand-held power tool transitions from a first operating state to a second operating state;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
providing a training dataset comprising the time series of labeled measured values of the operating variable;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (II and III), additional elements are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
Regarding limitation (I and IV), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
ascertaining a rotation angle of a motor of the hand-held power tool for each measured value of the operating variable of the plurality of measured values; and
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves determining a rotation angle of a power tool based on measured values of the operating variable. See (MPEP 2106.04)).
calculating a rotation timepoint for each measured value of the operating variable based on (i) a time interval required to perform a complete revolution of the motor, and (ii) the time stamps,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mathematical concept: It involves performing mathematical calculation using numerical time values to derive rotation timepoints. See (MPEP 2106.04)).
wherein the rotation timepoint defines a timepoint at which a complete revolution of the motor is completed for a measured value based on the respective time stamp.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 3, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
receiving sensor data that depict the operating state of the hand-held power tool;
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
ascertaining the event timepoint within a time series of the sensor data;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves reviewing sensor data to determine the occurrence of an event timepoint within a timeseries data. See (MPEP 2106.04)).
synchronizing the time series of the operating variable with the time series of the sensor data; and
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
identifying the event timepoint in the time series of the operating variable based on the event timepoint of the time series of the sensor data.
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves correlating two sets of information to identify a corresponding event. See (MPEP 2106.04)).
Regarding claim 4, dependent upon claim 3, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the sensor data are data from an external sensor.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 5, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
providing a training dataset generated according to the method of claim 1;
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
executing training of the artificial intelligence system in order to ascertain the operating state and/or predict the event timepoint of the hand-held power tool based on the training dataset.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 6, dependent upon claim 5, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
An artificial intelligence system for ascertaining an operating state of a hand-held power tool and/or for predicting an event timepoint, wherein the artificial intelligence system is trained according to the method of claim 5.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 7, dependent upon claim 6, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein the artificial intelligence system is configured as an artificial neural network comprising at least one recurrent layer having an internal state memory.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 8, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
A computing unit configured to perform the method of claim 1.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 9, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
A computer program product comprising instructions which, when the program is executed by a data processing unit, prompt the data processing unit to perform the method of claim 1.
Deemed insufficient to transform the judicial exception to a patentable invention because the limitation is directed to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and are considered to adding the words “apply it” (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
Limitations directed to using the computer as a tool for implementing an abstract idea cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 10, In step 2A prong 1,
ascertain a first target value of the control parameter based on the sensor data and the input value,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating available information to determine a target value. See (MPEP 2106.04)).
ascertain a second target value of the control parameter based on the ascertained operating state of the hand-held power tool,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves determining a second target value based on an identified operating state. See (MPEP 2106.04)).
ascertain an output target value of the control parameter based on the first target value and the second target value,
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating multiple target values to determine an output target value. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
A hand-held power tool comprising: a computing unit; and at least one sensor configured to ascertain sensor data of at least one operating variable of the hand-held power tool, wherein the computing unit is configured to control the hand-held power tool, the computing unit configured to
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
receive the sensor data of the at least one operating variable of the hand-held power tool, receive an input value for a control parameter of the hand-held power tool based on a user input from a user of the hand-held power tool,
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
execute a state determination module and apply the state determination module to the sensor data and ascertain an operating state of the hand-held power tool,
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
output the output target value to an actuator of the hand-held power tool …
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g))).
… for controlling the hand-held power tool.
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (I, III and V), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (II and IV), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
As analyzed above, the additional elements, analyzed above, do not integrate the noted judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
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 and 5 – 9 rejected under 35 U.S.C. 103 as being unpatentable over Abbott, Pub.
No.: US20210240145A1, in view of Erbele et al., Pub. No.: US20220410360A1 and Lee, Pub. No.: US20200249651A1.
Regarding claim 1, Abbott teaches: A method for generating a training dataset for training an artificial intelligence system in order to
(Abbott, “[0055] In one example, the machine learning controller 120 implements an artificial neural network [a training dataset for training an artificial intelligence system]. The artificial neural network typically includes an input layer, a plurality of hidden layers or nodes, and an output layer.”)
ascertain an operating state and/or to predict an event timepoint of a hand-held power tool, the method comprising:
(Abbott, “[0090] … The indicators 535 may include, for example, LEDs, a speaker, or a display screen and may generate various signals indicative of, for example, an operational state or mode of the power tool 500, an abnormal condition or event detected during the operation of the power tool 500 [ascertain an operating state and/or to predict an event timepoint of a hand-held power tool], and the like. For example, the indicators 530 may indicate measured electrical characteristics of the power tool 500, the state or status of the power tool 500, an operating mode of the power tool 500 (discussed in further detail below), and the like. In some embodiments, the indicators 535 include elements to convey information to a user through audible or tactile outputs, …”)
providing the measured values of the plurality of measured values with label values which are suitable for identifying whether a respective measured value
(Abbott, “[0058] … In other words, the activation switch 545 selectively enables and disables the machine learning controller 540. As described above with respect to FIGS. 1-4D, the machine learning controller 540 includes a trained machine learning controller that utilizes previously collected power tool usage data to analyze and classify new usage data from the power tool 500 [providing the measured values of the plurality of measured values with label values which are suitable for identifying whether a respective measured value]. As explained in more detail below, the machine learning controller 540 can identify conditions, applications, and states of the power tool...”)
Abbott does not teach:
providing a plurality of measured values of an operating variable of a hand-held power tool, wherein the measured values are each provided with time stamps;
identifying an event timepoint within the plurality of measured values, wherein the event timepoint defines a timepoint at which the hand-held power tool transitions from a first operating state to a second operating state;
… is associated with the identified event timepoint and/or an event time range;
arranging the plurality of labeled measured values in a time series based on the time stamps of the measured values; and providing a training dataset comprising the time series of labeled measured values of the operating variable.
Erbele teaches:
providing a plurality of measured values of an operating variable of a hand-held power tool, wherein the measured values are each provided with time stamps;
(Erbele, “[0062] In some embodiments, the signal of the operating variable is captured in method step S1 as a time series of measured values of the operating variable [providing a plurality of measured values of an operating variable of a hand-held power tool], or as measured values of the operating variable as a variable of the electric motor that correlates with the time series [wherein the measured values are each provided with time stamps], …”)
identifying an event timepoint within the plurality of measured values, wherein the event timepoint defines a timepoint at which the hand-held power tool transitions from a first operating state to a second operating state;
(Erbele, “[0187] A further embodiment of the invention is described in the following text with reference to FIG. 8 . In this case, after the transition from the region 320 (“impact” operating state) to the region 310 (“no impact” operating state) [transitions from a first operating state to a second operating state], a reduction in the motor speed takes place. The amplitude or amount of the reduction is specified in the figure with ΔD as a measure between an average f″ of the motor speed in the region 320 and the reduced motor speed f′ [identifying an event timepoint within the plurality of measured values]. This reduction can be set by the user in certain embodiments, in particular by specifying a target value of the speed of the handheld power tool 100 [wherein the event timepoint defines a timepoint at which the hand-held power tool], which lies at the level of the branch f′ in FIG. 8 .”)
is associated with the identified event timepoint and/or an event time range;
(Erbele, “[0062] In some embodiments, the signal of the operating variable is captured in method step S1 as a time series of measured values of the operating variable [providing a plurality of measured values of an operating variable of a hand-held power tool], or as measured values of the operating variable as a variable of the electric motor that correlates with the time series [wherein the measured values are each provided with time stamps], …”)
Erbele and Abbott are related to the same field of endeavor (i.e.: optimization of power tools). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Erbele with teachings of Abbott to add comparing an operating parameter signal of power tool to a model signal form associated with a known stage of the work process and evaluating the degree of correspondence using a threshold value to enable more accurate determination of the tool’s progress or operating state (Erbele, Abstract).
Abbott in view of Erbele do not teach:
arranging the plurality of labeled measured values in a time series based on the time stamps of the measured values; and providing a training dataset comprising the time series of labeled measured values of the operating variable
Lee teaches:
arranging the plurality of labeled measured values in a time series based on the time stamps of the measured values; and providing a training dataset comprising the time series of labeled measured values of the operating variable.
(Lee, “[0008] According to another embodiment, an apparatus includes: a processor; and a memory for storing processor-executable instruction, wherein the processor is configured to run a data analytic model, and wherein the data analytic model includes a data preprocessing module and a classifier, wherein the data preprocessing module is configured to: receive a plurality of time-series sensor data obtained in one or more manufacturing processes of an electronic device; [providing a training dataset comprising the time series of labeled measured values of the operating variable] arrange the plurality of time-series sensor data in a two-dimensional (2D) data array [arranging the plurality of labeled measured values in a time series based on the time stamps of the measured values], and wherein the classifier is configured to: identify a pattern in the 2D data array that correlates to a fault condition; and provide a fault indicator of the fault condition in the one or more manufacturing processes of the electronic device; and determine that the electronic device includes a fault based on the fault indicator.”)
Lee, Abbott and Erbele are related to the same field of endeavor (i.e.: optimization of hand-held power tool). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Lee with teachings of Abbott and Erbele to arrange time series sensor data and processing the date with a convolution neural network to identify patterns associated with a particular condition to effectively extract complex data and improve accuracy (Lee, Abstract).
Regarding claim 5, Abbott in view of Erbele and Lee teach the method of claim 1.
Abbott further teaches: A method for training an artificial intelligence system in order to ascertain an operating state of a hand-held power tool, the method comprising: providing a training dataset generated according to the method of claim 1; and executing training of the artificial intelligence system in order to ascertain the operating state and/or predict the event timepoint of the hand-held power tool based on the training dataset.
(Abbott, “[0126] … The machine learning controller 540 receives various types of information from the power tool 500 [providing a training dataset generated according to the method of claim 1] and the electronic processor 550 based on the particular task for which the machine learning controller 540 is configured. For example, FIG. 9 illustrates a schematic diagram 900 of the various types of information that may be utilized by the machine learning controller 540 to generate outputs, make determinations and predictions, and the like [predict the event timepoint of the hand-held power tool based on the training dataset] . In the illustrated diagram, the machine learning controller 540 may receive, for example, an indication of the operation time of the power tool 500 (e.g., how long the power tool 500 is used in each session, …”)
Regarding claim 6, Abbott in view of Erbele and Lee teach the method of claim 5.
Abbott further teaches: An artificial intelligence system for ascertaining an operating state of a hand-held power tool and/or for predicting an event timepoint, wherein the artificial intelligence system is trained according to the method of claim 5.
(Abbott, “[0126] … The machine learning controller 540 receives various types of information from the power tool 500 and the electronic processor 550 based on the particular task for which the machine learning controller 540 is configured [wherein the artificial intelligence system is trained according to the method of claim 5]. For example, FIG. 9 illustrates a schematic diagram 900 of the various types of information that may be utilized by the machine learning controller 540 to generate outputs, make determinations and predictions, and the like, …”)
Regarding claim 7, Abbott in view of Erbele and Lee teach the method of claim 6.
Abbott further teaches: wherein the artificial intelligence system is configured as an artificial neural network comprising at least one recurrent layer having an internal state memory.
(Abbott, “[0105] In some embodiments, building and training the machine learning control 585 includes building and training a recurrent neural network [wherein the artificial intelligence system is configured as an artificial neural network comprising at least one recurrent layer having an internal state memory]. Recurrent neural networks allow analysis of sequences of inputs instead of treating every input individually.”)
Regarding claim 8, Abbott in view of Erbele and Lee teach the method of claim 1.
Erbele further teaches: A computing unit configured to perform the method of claim 1.
(Erbele, “[0051] The method according to the invention can also comprise the execution of the method steps SMa, SMb and SMc in a control unit of the handheld power tool and/or on a central computer [A computing unit configured to perform the method of claim 1], in particular by transmitting the signals, determined in step S1 and associated with the exemplary applications, of the operating variable via an Internet connection.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Erbele with teachings of Abbott and Lee for the same reasons disclosed for claim 1.
Regarding claim 9, Abbott in view of Erbele and Lee teach the method of claim 1.
Abbott further teaches: A computer program product comprising instructions which, when the program is executed by a data processing unit, prompt the data processing unit to perform the method of claim 1.
(Abbott, “[0051] The server 110 includes a server electronic control assembly having a server electronic processor 425, a server memory 430, a transceiver 427, and a machine learning controller 120. The transceiver 427 allows the server 110 to communicate with the power tool 105, the external device 107, or both. The server electronic processor 425 [when the program is executed by a data processing unit, prompt the data processing unit to perform the method of claim 1] receives tool usage data from the power tool 105 (e.g., via the external device 107), stores the received tool usage data in the server memory 430, and, in some embodiments, uses the received tool usage data for building or adjusting a machine learning controller 120.”)
Claim(s) 3 – 4 rejected under 35 U.S.C. 103 as being unpatentable over Abbott in view of Erbele, Lee and in further view of MURPHY et al., Pub. No.: US20230184839A1.
Regarding claim 3, Abbott in view of Erbele and Lee teach the method of claim 1.
Erbele further teaches: wherein identifying the event timepoint comprises: receiving sensor data that depict the operating state of the hand-held power tool;
(Erbele, “[0094] The electric motor of the handheld power tool sets [of the hand-held power tool] an input spindle in rotation, and an output spindle is connected to the tool receptacle. An anvil is connected to the output spindle for conjoint rotation and a hammer is connected to the input spindle such that, as a result of the rotary movement of the input spindle, it executes an intermittent movement in the axial direction of the input spindle and an intermittent rotational movement about the input spindle, wherein the hammer in this way intermittently strikes the anvil and thus emits an impact pulse and angular momentum to the anvil and thus to the output spindle. A first sensor transmits a first signal, for example for determining a motor rotational angle [receiving sensor data that depict the operating state], to the control unit…”)
ascertaining the event timepoint within a time series of the sensor data;
(Erbele, “[0197] In one embodiment, the signal of the operating variable 200 is captured in method step S1 as a time series of measured values of the operating variable [ascertaining the event timepoint within a time series of the sensor data], and in a method step S1a following the method step S1, the time series of the measured values of the operating variable is transformed into a series of the measured values of the operating variable as a variable of the electric motor 180 that correlates with the time series, for example a rotational angle of the tool receptacle 140, the motor rotational angle, an acceleration, a jerk, in particular a higher order jerk, an output, or an energy.”)
It would have been obvious to one of ordinary skill in the art before the effective filling date of the present application to combine the teachings of Erbele with teachings of Abbott and Lee for the same reasons disclosed for claim 1.
Abbott in view of Erbele and Lee do not teach:
synchronizing the time series of the operating variable with the time series of the sensor data; and
identifying the event timepoint in the time series of the operating variable based on the event timepoint of the time series of the sensor data.
MURPHY teaches:
synchronizing the time series of the operating variable with the time series of the sensor data; and
(MURPHY, “[0074] A time series of sensor measurements can include measurements from one or more sensors. For example, a time series of sensor measurements can include one or more of a current measurement, a voltage measurement, and/or a temperature measurement. Sensor measurements from different sensors can be temporally aligned (e.g., to start at a common reference time), synchronized, asynchronous, associated with a time stamp, and/or can otherwise be related [synchronizing the time series of the operating variable with the time series of the sensor data].”)
identifying the event timepoint in the time series of the operating variable based on the event timepoint of the time series of the sensor data.
(MURPHY, “[0086] The battery property(s) are preferably determined for each datum of the sensor measurements, but can be determined for a subset of datum and/or for any suitable information. When a state is used for determining the battery property, the same state value can be used for each datum of the sensor measurements, the current state value can be used (e.g., the most recently available state value can be used for a given time point such as using different states when the state is updated during the time series of sensor measurements) [identifying the event timepoint in the time series of the operating variable based on the event timepoint of the time series of the sensor data], battery properties can be updated based on a current state (e.g., previously computed battery properties can be updated when an updated battery state is available), and/or the battery properties can be determined in any manner based on the battery state.”)
MURPHY, Abbott, Erbele and Lee are related to the same field of endeavor (i.e.: optimization of hand-held power tool). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of MURPHY with teachings of Abbott, Erbele and Lee to detect abnormal operating conditions from measured operational data during normal system operation to enable earlier detection of potential undesirable operation conditions (MURPHY, ¶[0023] – [0026]).
Regarding claim 4, Abbott in view of Erbele, Lee and MURPHY teach the method of claim 3.
Abbott further teaches: wherein the sensor data are data from an external sensor.
(Abbott, “[0120] … sensed data generated by the tool sensors (e.g., current, voltage, acceleration, temperature, motor rotation velocity, motor rotation acceleration, torque); sensed data generated by external sensors (e.g., torque, tool acceleration) [wherein the sensor data are data from an external sensor]; indications of step bit advancement, and indication of step counting success ,...”)
Claim 10 rejected under 35 U.S.C. 103 as being unpatentable over Abbott in view of Erbele.
Regarding claim 10, Abbott teaches: A hand-held power tool comprising:
(Abbott, “[0049] FIG. 1 illustrates a first power tool system 100. The first power tool system 100 includes a power tool 105 [A hand-held power tool], an external device 107, a server 110, and a network 115.”)
a computing unit; and at least one sensor configured to ascertain sensor data of at least one operating variable of the hand-held power tool,
(Abbott, “[0012] A power tool is provided for automatically controlling a step bit operation. The power tool includes a housing, a motor supported by the housing, and a sensor supported by the housing [a computing unit]. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool [and at least one sensor configured to ascertain sensor data of at least one operating variable of the hand-held power tool]. The power tool also includes a memory and an electronic control assembly including an electronic processor that is coupled to the memory...”)
wherein the computing unit is configured to control the hand-held power tool, the computing unit configured to:
(Abbott, “[0012] A power tool is provided for automatically controlling a step bit operation [wherein the computing unit is configured to control the hand-held power tool, the computing unit]. The power tool includes a housing, a motor supported by the housing, and a sensor supported by the housing. The sensor is configured to generate sensor data indicative of an operational parameter of the power tool. The power tool also includes a memory and an electronic control assembly including an electronic processor that is coupled to the memory…”)
receive the sensor data of the at least one operating variable of the hand-held power tool,
(Abbott, “[0120] … During operation of the power tool 500, the electronic processor 550 receives output sensor data (step 710) from the sensors 530 [receive the sensor data of the at least one operating variable of the hand-held power tool,]. As discussed above, the output sensor data provide varying information regarding the operation of the power tool 500 (referred to as operational parameters) including,...”)
receive an input value for a control parameter of the hand-held power tool based on a user input from a user of the hand-held power tool,
(Abbott, “[0020] In some embodiments, the electronic control assembly is further configured to configure the power tool in a specified mode based on user input [receive an input value for a control parameter of the hand-held power tool based on a user input from a user of the hand-held power tool], where the specified mode is associated with at least one selected from the group of: a predetermined number of steps to advance a step bit into a workpiece, a number of steps included on a step bit, a specific step bit, a material of a workpiece to be drilled into, a set of most likely desired steps, and a step counting function of the power tool.”)
ascertain a second target value of the control parameter based on the ascertained operating state of the hand-held power tool,
(Abbott, “[0058] … For example, when the machine learning controller 120 classifies whether a step bit has reached a target step, a first support vector machine may determine whether the target step has been reached based on the motor speed and the operation time, while a second support vector machine may determine whether the target step has been reached based on the motor speed and the output torque [ascertain a second target value of the control parameter based on the ascertained operating state of the hand-held power tool]. The machine learning controller 120 may then determine whether the target step is reached when both support vector machines classify the drill operation as the target step has been reached. In other embodiments, a single support vector machine can use more than two input variables and define a hyperplane that separates those operations that have reached the target step from those that have not reached the target step...”)
output the output target value to an actuator of the hand-held power tool for controlling the hand-held power tool.
(Abbott, “[0087] In response to the electronic processor 550 receiving the activation signal from the trigger switch 555, the electronic processor 550 activates the switching network 517 to provide power to the motor 505. The switching network 517 controls the amount of current available to the motor 505 and thereby controls the speed and torque output of the motor 505 [output the output target value to an actuator of the hand-held power tool]. The mode pad 527 allows a user to select a mode of the power tool 500 and indicates to the user the currently selected mode of the power tool 500. In some embodiments, the mode pad 527 includes a single actuator [for controlling the hand-held power tool]…”)
Abbott does not teach:
ascertain a first target value of the control parameter based on the sensor data and the input value, execute a state determination module and apply the state determination module to the sensor data and ascertain an operating state of the hand-held power tool,
ascertain an output target value of the control parameter based on the first target value and the second target value, and
Erbele teaches:
ascertain a first target value of the control parameter based on the sensor data and the input value, execute a state determination module and apply the state determination module to the sensor data and ascertain an operating state of the hand-held power tool,
(Erbele, “[0167] Advantageously, the establishment of the work status learned according to the above statements is supplemented by a further method step S6, in which a first routine of the handheld power tool 100 is executed at least partially on the basis of the work status ascertained in method step S5, as set out below. In this case, it is assumed in each case that the work status to be ascertained, as a result of which the handheld power tool executes the abovementioned first routine in method step S6 [execute a state determination module and apply the state determination module to the sensor data and ascertain an operating state of the hand-held power tool], was defined by a machine learning phase as described above by way of the parameters of the model signal shape 240 and/or threshold value of the match [ascertain a first target value of the control parameter based on the sensor data and the input value]. However, in alternative embodiments, it is likewise provided that the first routine is estimated, in unknown applications, with the aid of known applications with similar characteristics.”)
ascertain an output target value of the control parameter based on the first target value and the second target value, and
(Erbele, “[0065] Preferably, a work status of the first routine is output to a user of the handheld power tool using an output device of the handheld power tool. Output by means of the output device can be understood as meaning in particular the display or documentation of the work status…”)
Erbele and Abbott are related to the same field of endeavor (i.e.: optimization of hand-held power tool). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Erbele with teachings of Abbott to add comparing an operating parameter signal of power tool to a model signal form associated with a known stage of the work process and evaluating the degree of correspondence using a threshold value to enable more accurate determination of the tool’s progress or operating state (Erbele, Abstract).
Allowable Subject Matter
Claim 2 is objected to as being dependent upon a rejected base claim and would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action. The prior art made of record does not teach, make obvious, or suggest the claim limitations as disclosed in applicant's claims.
ascertaining a rotation angle of a motor of the hand-held power tool for each measured value of the operating variable of the plurality of measured values; and calculating a rotation timepoint for each measured value of the operating variable based on (i) a time interval required to perform a complete revolution of the motor, and (ii) the time stamps, wherein the rotation timepoint defines a timepoint at which a complete revolution of the motor is completed for a measured value based on the respective time stamp
Closest prior art(s) teach:
Erbele et al., Pub. No.: US20220410360A1, (2020).
Erbele teaches monitoring an operating parameter of a handheld power tool’s electric motor, determining an application class based on the parameter signal, and providing reference information including a model signal pattern and a corresponding threshold. The operating parameter signal is compared with the model signal pattern and a correspondence evaluation is generated based on how closely the signal matches the model pattern relative to the threshold. However, Erbele does not teach determining the motor’s rational position for each operating variable measurement and using the measurement timestamps together with the time required for a complete motor revolution to calculate a corresponding rotation timepoint. The calculated rotation timepoint represents the time at which the motor completes a full revolution associated with each measured value.
Abbott et al., Pub. No.: US20210240145A1, (2021).
Abbott teaches using sensor data the reflects an operational parameter of a power tool equipped with a step bit, processing the sensor data with a machine learning model to determine step bit progress information and automatically controlling the tool’s motor based on the determined progress information. However, Abbott does not teach determining the motor’s rational position for each operating variable measurement and using the measurement timestamps together with the time required for a complete motor revolution to calculate a corresponding rotation timepoint. The calculated rotation timepoint represents the time at which the motor completes a full revolution associated with each measured value.
Lee et al., Pub. No.: US20200249651A1, (2019).
Lee teaches collecting time series sensor data from a manufacturing process, organizing it in to 2D data structure, and inputting it into a convolutional neural network. The neural network identifies patterns associated with fault conditions and generates a fault indicator, which is then used to determine whether the electronic device is defective. However, Lee does not teach determining the motor’s rational position for each operating variable measurement and using the measurement timestamps together with the time required for a complete motor revolution to calculate a corresponding rotation timepoint. The calculated rotation timepoint represents the time at which the motor completes a full revolution associated with each measured value.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lammel et al., Pub. No.: US9383234B2.
Lammel teaches a sensor for acquiring measured values and for outputting data samples having at least one first register for storing a sensor time, which includes time information on phase position and/or period of the data samples, the first register being able to be read out externally.
MURUI et al., Pub. No.: US12053864B2.
MURUI teaches an electric tool system includes a motor, a control unit, and an output shaft. The motor includes a stator and a rotor. The rotor rotates with respect to the stator. The control unit performs vector control on the motor. The output shaft is to be coupled to a tip tool.
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/M.T.M./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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