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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kanack et al (US 2020/0238487).
In reference to claim 1, Kanack et al discloses a power tool (10) comprising
a crimper head (72) comprising a pair of jaws (32) configured to crimp a workpiece [see paragraph 0038],
a piston cylinder (22) configured to actuate at least one of the pair of jaws [see paragraph 0038],
a motor (12) configured to drive the piston cylinder to perform a crimping application [see paragraph 0038],
a sensor (68, 114, 112) configured to provide a sensor signal indicative of the crimping application [see paragraphs 0043-0045], and
an electric processor (100) connected to the sensor and the motor, the electric processor configured to
receive the sensor signal
determine a first operating characteristic based on the sensor signal,
determine a second operating characteristic based on the sensor signal,
determine based on the first operating characteristic and the second operating characteristic, the crimping application of the power tool, and
output an indication of the crimping application [see paragraph 0051, 0053, 0090, 0104].
In reference to claim 2, the sensor is a position sensor, and wherein the sensor signal is a position signal received from the position sensor indicating a position of the crimper head. [see paragraph 0047; states that the different sensed parameters, i.e. pressure. speed of motor, current flow, vary based on the position of the jaws; therefore the position of the jaws can be derived for these parameters and sensors].
In reference to claim 3, the sensor is a pressure sensor (68), and wherein the sensor signal is a pressure signal received from the pressure sensor indicating a pressure in the piston cylinder [see paragraph 0043].
In reference to claim 4, the sensor is a speed sensor (114), and wherein the sensor signal is a pressure signal received from the speed sensor indicating a speed of the motor [see paragraph 0044].
In reference to claim 5, the sensor is a current sensor (112), and wherein the sensor signal is a current signal received from the current sensor indicating a current flow through the motor [see paragraph 0045].
In reference to claim 6, Kanack et al further discloses wherein, to determine the crimping application of the power tool, the electronic processor is configured to
provide the first operating characteristic and the second operating characteristic to a machine learning model, and receive, from the machine learning model, the crimping application [see paragraph 0104].
In reference to claim 7, Kanack et al further discloses the electronic processor (100) is further configured to provide the first operating characteristic and the second operating characteristic to a machine learning model,
receive, from the machine learning model, a plurality of probabilities, each probability associated with a contemplated crimping application, and
select the contemplated crimping application associated with the highest probability of the plurality of probabilities [see paragraph 0104].
In reference to claim 8, Kanack et al discloses a method for evaluating a crimping application using an electronic processor, the method comprising
receiving a sensor signal from a sensor (68, 112, 114), wherein the sensor signal is indicative of the crimping application,
determining a first operating characteristic based on the sensor signal
determining a second operating characteristic based on the sensor signal,
determining, based on the first operating characteristic and the second operating characteristic, the crimping application, and
outputting an indication of the crimping application [see paragraph 0051, 0053, 0090, 0104].
In reference to claim 9, the sensor is a position sensor, and the sensor signal is a position signal received from the position sensor indicating a position of the crimper head. [see paragraph 0047; states that the different sensed parameters, i.e. pressure. speed of motor, current flow, vary based on the position of the jaws; therefore the position of the jaws can be derived for these parameters and sensors].
In reference to claim 10, the sensor is a pressure sensor (68), and the sensor signal is a pressure signal received from the pressure sensor indicating a pressure in the piston cylinder [see paragraph 0043].
In reference to claim 11, the sensor is a speed sensor (114), and the sensor signal is a pressure signal received from the speed sensor indicating a speed of the motor [see paragraph 0044].
In reference to claim 12, the sensor is a current sensor (112), and the sensor signal is a current signal received from the current sensor indicating a current flow through the motor [see paragraph 0045].
In reference to claim 13, Kanack et al further discloses the determining the crimping application includes: providing the first operating characteristic and the second operating characteristic to a machine learning model, and receiving from the machine learning model, the crimping application [see paragraph 0104].
In reference to claim 14, Kanack et al further discloses providing the first operating characteristic and the second operating characteristic to a machine learning model,
receiving, from the machine learning model, a plurality of probabilities, each probability associated with a contemplated crimping application, and
selecting the contemplated crimping application associated with the highest probability of the plurality of probabilities [see paragraph 0104].
In reference to claim 15, Kanack et al discloses a power tool (10) comprising
a crimper head (72) comprising a pair of jaws (32) configured to crimp a workpiece [see paragraph 0038],
a piston cylinder (22) configured to actuate at least one of the pair of jaws [see paragraph 0038],
a first sensor (68) configured to provide a first sensor signal associated with a crimping application [see paragraphs 0043],
a second sensor (114) configured to provide a second sensor signal associated with the crimping application [see paragraph 0044], and
an electric processor (100) connected to the first sensor and the second sensor, the electric processor configured to
receive the first sensor signal,
receive the second sensor signal,
determine, based on the first sensor signal, a first operating characteristic
determine, based on the second sensor signal, a second operating characteristic,
determine, based on the first operating characteristic and the second operating characteristic, the crimping application of the power tool, and
output an indication of the crimping application [see paragraph 0051, 0053, 0090, 0104].
In reference to claim 16, the first sensor is at least a pressure sensor (68) [see paragraph 0043], and the second sensor is a speed sensor (114) [see paragraph 0044].
In reference to claim 17, the first operating characteristic is further determined based on the second sensor signal.
In reference to claim 18, wherein to output the indication of the crimping application, the electronic processor (100) is configured to generate a report including the indication of the crimping application [see paragraph 0104].
In reference to claim 19, the report includes a usage graph (data curves) [see paragraph 0096].
In reference to claim 20, the electronic processor (100) is configured to output the report to a display of the power tool [see paragraph 0053].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Debra Sullivan whose telephone number is (571)272-1904. The examiner can normally be reached Monday-Friday 8am-4:30pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Templeton can be reached on (571) 270-1477. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Debra M Sullivan/
Primary Examiner, Art Unit 3725
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