DETAILED ACTION
Claims 1-11 and 13-15 are pending in the present application.
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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-11 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Cao et al. ('Anomaly Detection for Screw Tightening Timing Data With LSTM Recurrent Neural Network', In: 2019 15th International Conference on Mobile Ad-hoc and Sensor Networks (MSN), pages 348-352, publ. 2019-12-11; all reference to copy of record in the file wrapper on 4/26/2024; hereinafter Cao) in view of Beacham et al. (US 20220111496; hereinafter Beacham).
Regarding claim 1, Cao teaches a method of a device for enabling determination of a tightening class of a tightening operation performed (at least abstract discloses regarding the analysis of tightening curves for screws including difference classes of preformed torque-angle curves – see also table 1 and figs. 3-4), the method comprising:
acquiring a set of observed torque and angle values for a fastener having been tightened by the tightening tool (see p.349, heading A. regarding the dataset having torque and angle values which are observed; see figs. 3 and 4);
identifying, from the acquired set of observed torque and angle values, a rundown phase and an end-tightening phase of the tightening of the fastener (p. 349, final ¶’s on both columns teach regarding the torque increasing as the rotational angle increases-i.e. the run down phase- and a final torque returning to zero-i.e. the end-tightening phase; see at least fig. 3 showing the rundown phase and end-tightening phases; please note that the rundown phase is the portion where the screw is being rundown into the attachment substrate/material shown in the line portions in the middle of the shown curves/graphs and the end-tightening phase is the portion of the curve with the large drop off at the end of the graphs of the angle-torque curves);
normalizing the torque values of the end-tightening phase with a determined torque value range of the end-tightening phase and the angle values of the end-tightening phase with a determined angle value range of the end-tightening phase (see p.349-350, heading B. regarding preprocessing the data such that the data set has the same dimension; see p.350, Algorithm 1 showing an example of preprocess normalization; see fig. 4 showing examples of curves normalized via sampling); and
training a machine-learning model with the normalized torque and angle values of the end-tightening phase and at least one tightening class associated with the normalized torque and angle values of the end-tightening phase, the tightening class identifying a type of tightening operation having been applied to the fastener (abstract; p. 349, heading A. teaches regarding training a neural network with the dataset including at least four categories: smooth, medium sawtooth, large sawtooth and irregular curves; see figs. 3-4 and table 1).
Cao does not directly and specifically state regarding a tightening tool (though such tool is implied).
However, Beacham directly and specifically teaches a tightening tool (“driving tool” abstract) in a method (abstract) for using machine learning regarding angle-torque traces of the tightening of fasteners (abstract discloses that the machine learning device is “provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the teaching regarding angle-torque trace AI analysis of Cao with the specific knowledge of the disclosed driving tool for providing the tightening in angle-torque trace AI analysis of Beacham. This is because such a tool allows for tightening/driving the fastener/screw. This is important in order to provide data to the AI/machine learning method and analyze the torque-angle curve (see abstracts of both Cao and Beacham).
Regarding claim 2, Cao teaches supplying the trained machine-learning model with a further acquired and normalized set of observed end-tightening phase torque and angle values for a fastener having been tightened by the tightening tool, wherein the trained machine-learning model outputs at least one estimated tightening class for the supplied further normalized set of observed end-tightening phase torque and angle values (abstract; see fig. 4, see p.350, heading C.).
Regarding claim 3, Cao teaches determining whether or not the acquired torque values exceed a predetermined torque threshold value; and if so: the acquired torque values and corresponding angle values are determined to pertain to an end-tightening phase; and if not: the acquired torque values and corresponding angle values are determined to pertain to a rundown phase, upon identifying, from the acquired set of observed torque and angle values, a rundown phase and an end-tightening phase of the tightening of the fastener (p. 349, final ¶’s on both columns teach regarding the torque increasing as the rotational angle increases-i.e. the run down phase- and a final torque returning to zero-i.e. the end-tightening phase with respect to a cut-off value which is a “threshold”; see at least fig. 3 showing the rundown phase and end-tightening phases; see fig. 4).
Regarding claim 4, Cao teaches that the normalizing further comprises:
normalizing the torque values of the rundown phase with a determined torque value range of the rundown phase and the angle values of the rundown phase with a determined angle value range of the rundown phase (normalizing via sampling in the run down phase; see fig. 4 showing examples of this normalization; ; please note that the rundown phase is the portion where the screw is being rundown into the attachment substrate/material shown in the line portions in the middle of the shown curves/graphs); and the training of the machine-learning model further comprises: training the machine-learning model with the normalized torque and angle values of the rundown phase and at least one tightening class associated with the normalized torque and angle values of the rundown phase (p.350, heading C. teaches regarding normalized/sampled date being further normalized/regularized and used for training the model).
Regarding claim 5, Cao lacks direct and specific teaching that the supplying of the trained machine-learning model with a further acquired and normalized set of observed end-tightening phase torque and angle values further comprises: supplying the trained machine-learning model with a further acquired and normalized set of observed rundown phase torque and angle values for a fastener having been tightened by the tightening tool, wherein the trained machine-learning model outputs at least one estimated tightening class for the supplied further normalized set of observed rundown phase torque and angle values.
However, Cao does disclose an iteration of such acquired and normalized data and a further sampling iteration (headings B. and C. respectively).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the acquired and normalized set of observed end-tightening phase torque and angle values of Cao with any number of further data sets. This is because it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges (here further/plural acquired and normalized data) involves only routine skill in the art. MPEP 2144.05 (II-A)
Regarding claim 8, Cao teaches the determined torque value range and/or angle value range being divided into smaller sub-ranges utilized for the normalization (sampling; see heading B. on pp.349-350).
Regarding claim 9, Cao teaches the normalization being performed comprising min-max normalization (p.350, 1st ¶ of col. 1 teaches regarding using floor and ceiling functions to round the samples up and down; further see p.350, heading C. teaches regarding state 0 representing “completely get rid of this” and state 1 representing “completely keep this” normalization of data).
Regarding claim 10, Cao does not directly and specifically state regarding providing an alert indicating the at least one estimated tightening class.
However, Beacham directly and specifically teaches a tightening tool (“driving tool” abstract) in a method (abstract) for using machine learning regarding angle-torque traces of the tightening of fasteners (abstract) with “the results being provided to an operator of the tool 1000 directly after driving the fastener/bolt” ([0051]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the concepts regarding angle-torque trace AI analysis of Cao with the specific knowledge of the disclosed operator notification/alert based on the analysis of Beacham. This is because an alert/notification allows for “immediate removal or adjustment of the bolt” (see [0051] Beacham). This is important to provide data to the operator to adjust or reset the fastener/bolt.
Regarding claim 11, Cao does not directly and specifically state that the alert is provided to an operator of the tightening tool, to the tightening tool itself, to a supervision control room or to a remote cloud function.
However, Beacham directly and specifically teaches a tightening tool (“driving tool” abstract) in a method (abstract) for using machine learning regarding angle-torque traces of the tightening of fasteners (abstract) with “the results being provided to an operator of the tool 1000 directly after driving the fastener/bolt” ([0051]).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the concepts regarding angle-torque trace AI analysis of Cao with the specific knowledge of the disclosed operator notification/alert based on the analysis of Beacham. This is because an alert/notification allows for “immediate removal or adjustment of the bolt” (see [0051] Beacham). This is important to provide data to the operator to adjust or reset the fastener/bolt.
Regarding claim 13, Cao teaches a computer program product stored on a non-transitory a computer readable medium (see at least Algorithm 1, p.350; see also [0030] and fig. 1 of Beacham), said computer program product for enabling determination of a tightening class of a tightening operation performed (at least abstract discloses regarding the analysis of tightening curves for screws including difference classes of preformed torque-angle curves – see also table 1 and figs. 3-4), wherein said computer program product comprising computer instructions to cause one or more processing units to perform the following operations (see at least Algorithm 1, p.350; see also [0030] and fig. 1 of Beacham):
acquiring a set of observed torque and angle values for a fastener having been tightened by the tightening tool (see p.349, heading A. regarding the dataset having torque and angle values which are observed; see figs. 3 and 4);
identifying, from the acquired set of observed torque and angle values, a rundown phase and an end-tightening phase of the tightening of the fastener (p. 349, final ¶’s on both columns teach regarding the torque increasing as the rotational angle increases-i.e. the run down phase- and a final torque returning to zero-i.e. the end-tightening phase; see at least fig. 3 showing the rundown phase and end-tightening phases; please note that the rundown phase is the portion where the screw is being rundown into the attachment substrate/material shown in the line portions in the middle of the shown curves/graphs and the end-tightening phase is the portion of the curve with the large drop off at the end of the graphs of the angle-torque curves);
normalizing the torque values of the end-tightening phase with a determined torque value range of the end-tightening phase and the angle values of the end-tightening phase with a determined angle value range of the end-tightening phase (see p.349-350, heading B. regarding preprocessing the data such that the data set has the same dimension; see p.350, Algorithm 1 showing an example of preprocess normalization; see fig. 4 showing examples of curves normalized via sampling); and
training a machine-learning model with the normalized torque and angle values of the end-tightening phase and at least one tightening class associated with the normalized torque and angle values of the end-tightening phase, the tightening class identifying a type of tightening operation having been applied to the fastener (abstract; p. 349, heading A. teaches regarding training a neural network with the dataset including at least four categories: smooth, medium sawtooth, large sawtooth and irregular curves; see figs. 3-4 and table 1).
Cao does not directly and specifically state regarding a tightening tool (though such tool is implied).
However, Beacham directly and specifically teaches a tightening tool (“driving tool” abstract) in a method (abstract) for using machine learning regarding angle-torque traces of the tightening of fasteners (abstract discloses that the machine learning device is “provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the teaching regarding angle-torque trace AI analysis of Cao with the specific knowledge of the disclosed driving tool for providing the tightening in angle-torque trace AI analysis of Beacham. This is because such a tool allows for tightening/driving the fastener/screw. This is important to provide data to the AI/machine learning method and analyze the torque-angle curve (see abstracts of both Cao and Beacham).
Regarding claim 14, Cao teaches regarding a model configured to enable determination of a tightening class of a tightening operation performed (at least abstract discloses regarding the analysis of tightening curves for screws including difference classes of preformed torque-angle curves – see also table 1 and figs. 3-4 with at least Algorithm 1, p.350; see also [0030] and fig. 1 of Beacham):
acquire a set of observed torque and angle values for a fastener having been tightened by the tightening tool (see p.349, heading A. regarding the dataset having torque and angle values which are observed; see figs. 3 and 4);
identify, from the acquired set of observed torque and angle values, a rundown phase and an end-tightening phase of the tightening of the fastener (p. 349, final ¶’s on both columns teach regarding the torque increasing as the rotational angle increases-i.e. the run down phase- and a final torque returning to zero-i.e. the end-tightening phase; see at least fig. 3 showing the rundown phase and end-tightening phases; please note that the rundown phase is the portion where the screw is being rundown into the attachment substrate/material shown in the line portions in the middle of the shown curves/graphs and the end-tightening phase is the portion of the curve with the large drop off at the end of the graphs of the angle-torque curves);
normalize the torque values of the end-tightening phase with a determined torque value range of the end-tightening phase and the angle values of the end-tightening phase with a determined angle value range of the end-tightening phase (see p.349-350, heading B. regarding preprocessing the data such that the data set has the same dimension; see p.350, Algorithm 1 showing an example of preprocess normalization; see fig. 4 showing examples of curves normalized via sampling); and to
train a machine-learning model with the normalized torque and angle values of the end-tightening phase and at least one tightening class associated with the normalized torque and angle values of the end-tightening phase, the tightening class identifying a type of tightening operation having been applied to the fastener (abstract; p. 349, heading A. teaches regarding training a neural network with the dataset including at least four categories: smooth, medium sawtooth, large sawtooth and irregular curves; see figs. 3-4 and table 1).
Cao does not directly and specifically state regarding a device comprising a processing unit (though such a device processing unit is implied) and a tightening tool (though such tool is implied).
However, Beacham directly and specifically teaches a tightening tool (“driving tool” abstract) in an apparatus (abstract) with a processing unit (see fig. 1; [0030]) for using machine learning regarding angle-torque traces of the tightening of fasteners (abstract discloses that the machine learning device is “provided with a number of sample angle-torque traces from sample fasteners and can self-determine a stored trace including tolerances for acceptable angle-torque trace data from the samples in an unsupervised learning process”).
Therefore, before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to modify the teaching regarding angle-torque trace AI analysis of Cao with the specific knowledge of the disclosed driving tool and processing unit for providing the tightening in angle-torque trace AI analysis of Beacham. This is because such a tool and processing unit allows for tightening/driving the fastener/screw and analyzing the data via processor. This is important to provide data to the AI/machine learning method and analyze the torque-angle curve (see abstracts of both Cao and Beacham).
Regarding claim 15, Cao teaches the device of claim 14, further being operative to:
supply the trained machine-learning model with a further acquired and normalized set of observed end-tightening phase torque and angle values for a fastener having been tightened by the tightening tool, wherein the trained machine-learning model outputs at least one estimated tightening class for the supplied further normalized set of observed end-tightening phase torque and angle values (abstract; see fig. 4, see p.350, heading C.).
Allowable Subject Matter
Claims 6-7 are 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.
The following is a statement of reasons for the indication of allowable subject matter:
The best prior art of record Cao et al. ('Anomaly Detection for Screw Tightening Timing Data With LSTM Recurrent Neural Network', In: 2019 15th International Conference on Mobile Ad-hoc and Sensor Networks (MSN), pages 348-352, publ. 2019-12-11; of record in the file wrapper on 4/26/2024) and Beacham et al. (US 20220111496), fail to specifically teach the invention as claimed. The limitations in independent claim 1 when combined with the specific limitations regarding the different models being trained with different phases of the data in dependent claims 6 and 7 distinguish the present invention from the combined prior art.
Hence the prior art of record fails to teach the invention as set forth in claims 6 and 7. The examiner cannot find specific teaching of the invention, nor reasons within the cited art to combine the elements of these references other than applicant’s own reasoning to fully encompass the current pending claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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/PHILIP L COTEY/ Examiner, Art Unit 2855
/LAURA MARTIN SWEENEY/ Supervisory Patent Examiner, Art Unit 2855
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