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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims
particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8 & 10-14 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites the preamble “a method of a device” which is unclear as to the claimed statutory class. The class is interpreted as a method claim where a preamble of “a method performed by a device” is suggested for clarity.
Claims 1, 10 & 11 recite similar limitations of “associating at least one tightening class” then the claim recites “the tightening class identifying” where the first limitation can be plural but the second seems to confine the tightening class to a singular class. Clarity is required on the scope of the cited plurality.
Claims 1, 10 & 11 use the relative terms “associated with” and “associating” which is unclear. It seems there is a definitive determining of the tightening class(es) from the measured data (e.g. torque, angle or context data).
Claims 1, 10 & 11 recite a similar limitation “at least one set of context data associated with the tightening of the fastener by the tightening tool” where a “set of context data” is unclear. The set is a plural term with no context to what plurality of data from the fastening event is required or if the data is a set of multiple fastening events. The “set of context data” is not defined in the disclosure with any particular metes or bounds of the plurality of data.
Claims 1, 10 & 11 recite the “context data associated with the tighttening of the fastener” which is required to determine the claimed “tightening class”. However, there is no distinction on what is required to meet the essential “context data”. Examiner looks to the specification and finds [0012], [0014], [0075] & [0077] the context data is not defined to a particular set of data and instead is just any and all data for the present fastening or past fastening data to include data on computing limitations. The term “context” in “context data” does not further limit the claim from an unspecified term of data. If Applicant seeks a particular protection in the term “context data” the metes and bounds of “context” must be claimed.
Claims 1, 10 & 11 recite the similar limitation “supplying the trained machine-learning model with a further acquired set of observed torque and angle values for a fastener having been tightened by the tightening tool and at least one further set of context data associated with the tightening of the fastener by the tightening tool, wherein the trained machine-learning model outputs at least one estimated tightening class for the supplied further set of observed torque and angle values and the at least one further set of context data” where the term “further [acquired] set” context as to what is required to fulfill a further set of data that differs from the first processing and determination of tightening class(es). Is this step a pure repeat, a required set of different data for machine learning, based on an event the processing is looped to re-measure the data and h reference data and reconfirming the data. The specification list these and many more interpretations so it is unclear what protection is sought in the claims using the term “further [acquired] set”.
All dependent claims are rejected for their dependence on a rejected base claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1- 8 & 10-15 are rejected under 35 U.S.C. 102(a)(1 & 2) as being anticipated by Abbott (US 20220299946: “Abbott”).
Claim 1. Abbott discloses a method of a device (510 server) for determining a tightening class of a tightening operation [0005] performed by a tightening tool [Abstract], the method comprising: acquiring a set of observed torque and angle values for a fastener [0036] having been tightened by the tightening tool [0166]; acquiring at least one set of context data [0163: For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; sensed data generated by the tool sensors (e.g., current, voltage, acceleration, temperature, motor rotation velocity, motor rotation acceleration, torque)] associated with the tightening of the fastener [0036] by the tightening tool [0163][0166]; associating at least one tightening class (Fig. 12: 540) with the set of observed torque (1204) and angle values (1216) and the at least one set of context data (1202, 1204 previous torque data, 1206, 1208, 1210, 1212, 1214 & 1216 previous angle data) the tightening class identifying a type of tightening operation having been applied to the fastener [0111: 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], [0133: For example, the machine learning program executing on the machine learning controller 540 processes (e.g., classifies according to one of the aforementioned machine learning algorithms) the received sensor information and generates an output] & [0144: the machine learning controller 540 implements an artificial neural network to perform this classification. The artificial neural network includes, for example, six input nodes, and, for example, one hundred output nodes. Each output node, for example, corresponds to a different type of fastener identifiable by the machine learning controller 540]; training a machine-learning model with the acquired set of observed torque and angle values [0186-0187], the acquired at least one set of context data and the associated at least one tightening class [0111, 0133, 0144 & 0186-0187]; and supplying the trained machine-learning model (Fig. 12: 540) with a further acquired set of observed torque and angle values for a fastener [0036] having been tightened by the tightening tool and at least one further set of context data associated with the tightening of the fastener [0036] by the tightening tool (505), wherein the trained machine-learning model (504) outputs at least one estimated tightening class for the supplied further set of observed torque and angle values and the at least one further set of context data [0194-0195: In response to determining that the operation is not completed at process block 1810, the machine learning controller 540 determines what is required (e.g. operational parameters) to complete the operation at process block 1814. For example, the machine learning controller 540 may determine a new number of pulses and/or impacts required, an amount of energy required, rotation of the fastener, etc] & [0157: The tool logic may exhibit some degree of repeatable, and is modified only due to output randomness and/or change, even with outside input, communications, or sensing. the same repeated inputs].
Claim 2. Dependent on the method of claim 1. Abbott further discloses the context data comprises at least one of torque and angle values having been applied by the tightening tool during a previously performed tightening operation [0163] [0076] & [0079-0080].
Claim 3. Dependent on the method of claim 1. Abbott further discloses the context data comprises properties of the fastener [0036] and/or joint being tightened by the fastener [0163: to collect the tool data, approximately 2000 fasteners are seated… For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; 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); indications of fastener seating level, and indication of seating completion success].
Claim 4. Dependent on the method of claim 1. Abbott further discloses the context data comprises environmental properties [0163: to collect the tool data, approximately 2000 fasteners are seated… For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; 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); indications of fastener seating level, and indication of seating completion success].
Claim 5. Dependent on the method of claim 1. Abbott further discloses providing an alert (535) indicating the at least one estimated tightening class [0100: the power tool 500 alerts the user regarding a power tool condition. For example, a fast deceleration of the motor 505 may indicate that an abnormal condition is present. In some embodiments, the power tool 500 communicates with the external device 107, and the external device 107 generates a graphical user interface that conveys information to the user].
Claim 6. Dependent on the method of claim 1. Abbott further discloses the alert (535) is provided to an operator of the tightening tool (505), to the tightening tool (505) itself, to a supervision control room or to a remote cloud function (107) [0100: the power tool 500 alerts the user regarding a power tool condition. For example, a fast deceleration of the motor 505 may indicate that an abnormal condition is present. In some embodiments, the power tool 500 communicates with the external device 107, and the external device 107 generates a graphical user interface that conveys information to the user].
Claim 7. Dependent on the method according to claim 6. Abbott further discloses the tightening tool provides an audible and/or visual alert to the operator of the tool [0100: the power tool 500 alerts the user regarding a power tool condition. For example, a fast deceleration of the motor 505 may indicate that an abnormal condition is present. In some embodiments, the power tool 500 communicates with the external device 107, and the external device 107 generates a graphical user interface that conveys information to the user].
Claim 8. Dependent on the method of claim 1. Abbott further discloses the machine learning [Table 1] is based on one or more of neural networks [0067], random forest-based classification [Table 1] and regression analysis [0187].
Claim 10. Abbott further discloses a computer program product [0062] stored on a non-transitory computer readable medium (580), said computer program product [0112] for determining a tightening class of a tightening operation (540) performed by a tightening tool (505), wherein said computer program product comprising computer instructions to cause one or more processing units (550) [0094] to perform the following operations: acquiring a set of observed torque and angle values for a fastener having been tightened by the tightening tool (505); acquiring at least one set of context data (1202, 1204 previous torque data, 1206, 1208, 1210, 1212, 1214 & 1216 previous angle data) associated with the tightening of the fastener by the tightening tool; associating at least one tightening class (Fig. 12: 540) with the set of observed torque (1204) and angle values (1216) and the at least one set of context data (1202, 1204 previous torque data, 1206, 1208, 1210, 1212, 1214 & 1216 previous angle data) the tightening class identifying a type of tightening operation having been applied to the fastener [0111: 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], [0133: For example, the machine learning program executing on the machine learning controller 540 processes (e.g., classifies according to one of the aforementioned machine learning algorithms) the received sensor information and generates an output] & [0144: the machine learning controller 540 implements an artificial neural network to perform this classification. The artificial neural network includes, for example, six input nodes, and, for example, one hundred output nodes. Each output node, for example, corresponds to a different type of fastener identifiable by the machine learning controller 540]; training a machine-learning model with the acquired set of observed torque and angle values [0186-0187], the acquired at least one set of context data and the associated at least one tightening class [0111, 0133, 0144 & 0186-0187]; and supplying the trained machine-learning model (Fig. 12: 540) with a further acquired set of observed torque and angle values for a fastener having been tightened by the tightening tool and at least one further set of context data associated with the tightening of the fastener by the tightening tool, wherein the trained machine-learning model outputs at least one estimated tightening class for the supplied further set of observed torque and angle values and the at least one further set of context data [0194-0195: In response to determining that the operation is not completed at process block 1810, the machine learning controller 540 determines what is required (e.g. operational parameters) to complete the operation at process block 1814. For example, the machine learning controller 540 may determine a new number of pulses and/or impacts required, an amount of energy required, rotation of the fastener, etc] & [0157: The tool logic may exhibit some degree of repeatable, and is modified only due to output randomness and/or change, even with outside input, communications, or sensing. the same repeated inputs].
Claim 11. Abbott discloses a device (510) configured to determine a tightening class [0068] of a tightening operation performed by a tightening tool (505) [Abstract], the device (510) comprising a processing unit [0061] (540 machine language controller doing brunt of processing) operative to cause the device (510) to: acquire a set of observed torque and angle values for a fastener [0036] having been tightened by the tightening tool (505)[0166][0172]; acquire at least one set of context data [0163: For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; sensed data generated by the tool sensors (e.g., current, voltage, acceleration, temperature, motor rotation velocity, motor rotation acceleration, torque)] associated with the tightening of the fastener by the tightening tool [0163] [0166]; associate at least one tightening class (Fig. 12: 540) with the set of observed torque (1204) and angle values (1216) and the at least one set of context data (1202, 1204 previous torque data, 1206, 1208, 1210, 1212, 1214 & 1216 previous angle data) the tightening class identifying a type of tightening operation having been applied to the fastener [0111: 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], [0133: For example, the machine learning program executing on the machine learning controller 540 processes (e.g., classifies according to one of the aforementioned machine learning algorithms) the received sensor information and generates an output] & [0144: the machine learning controller 540 implements an artificial neural network to perform this classification. The artificial neural network includes, for example, six input nodes, and, for example, one hundred output nodes. Each output node, for example, corresponds to a different type of fastener identifiable by the machine learning controller 540]; train a machine-learning model with the acquired set of observed torque and angle values [0186-0187], the acquired at least one set of context data and the associated at least one tightening class [0111, 0133, 0144 & 0186-0187]; and supply the trained machine-learning model (Fig. 12: 540) with a further acquired set of observed torque and angle values for a fastener having been tightened by the tightening tool and at least one further set of context data associated with the tightening of the fastener by the tightening tool (505), wherein the trained machine-learning model (504) outputs at least one estimated tightening class for the supplied further set of observed torque and angle values and the at least one further set of context data [0194-0195: In response to determining that the operation is not completed at process block 1810, the machine learning controller 540 determines what is required (e.g. operational parameters) to complete the operation at process block 1814. For example, the machine learning controller 540 may determine a new number of pulses and/or impacts required, an amount of energy required, rotation of the fastener, etc] & [0157: The tool logic may exhibit some degree of repeatable, and is modified only due to output randomness and/or change, even with outside input, communications, or sensing. the same repeated inputs].
Claim 12. Dependent on the device of claim 11. Abbott further discloses the context data comprises at least one of torque and angle values having been applied by the tightening tool (505) during a previously performed tightening operation [0163] [0076] & [0079-0080].
Claim 13. Dependent on the device of claim 11. Abbott further discloses the context data comprises properties of the fastener and/or joint being tightened by the fastener [0163: to collect the tool data, approximately 2000 fasteners are seated… For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; 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); indications of fastener seating level, and indication of seating completion success].
Claim 14. Dependent on the device of claim 11. Abbott further discloses the context data comprises environmental properties [0163: to collect the tool data, approximately 2000 fasteners are seated… For each seating, various data is collected such as, for example, fastener type, length, diameter; material type and thickness; number and order of layers of materials; user experience, size, and age; battery type, charge level and age; 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); indications of fastener seating level, and indication of seating completion success].
Claim 15. Dependent on the device of claim 11. Abbott further discloses providing an alert (535) indicating the at least one estimated tightening class [0100: the power tool 500 alerts the user regarding a power tool condition. For example, a fast deceleration of the motor 505 may indicate that an abnormal condition is present. In some embodiments, the power tool 500 communicates with the external device 107, and the external device 107 generates a graphical user interface that conveys information to the user].
Prior Art Considered but not Utilized
The prior art made of record and not relied upon and is considered pertinent to applicant's disclosure is provided in the following table:
Prior Art Document Identifier
Inventor
Comment
KR 20220141180
LEE YOON SOO et al.
A machine learning-based fastening quality analysis system and a method thereof are disclosed. According to an embodiment of the present invention, the machine learning-based fastening quality analysis system
CN 109238546
MA, YUE et al.
The invention belongs to assembling and fastening technology field, claims a bolt pre-tightening force predicting method based on machine learning,
CN 112393934
QIN B et al.
method involves determining the number of input nodes, hidden layer nodes, and the number of output nodes of the fault diagnosis model of the extreme learning machine
DE 102020215167
ROMER ACHIM
determining a final tightening torque and/or a final tightening angle for a screw in a screw connection
WO 2021100267
ITO HIROSHI et al.
executing machine learning, first, the information processing device 1 first receives sensor data (joint angle, torque value, current value, etc.) of the measuring unit 11 when actually grasping the target object,
CN 113869502
NANGONG EN et al.
a bolt tightening failure reason analysis method based on a deep neural network, and the method comprises the steps: creating a tightening scene, obtaining a feature index,
Conclusion
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/MONICA S YOUNG/Examiner, Art Unit 2855
/PETER J MACCHIAROLO/Supervisory Patent Examiner, Art Unit 2855
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