DETAILED ACTION
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.
Claims 21-40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Abbott et al. WO 2021/016437 A1 (“Abbott”).
Abbott discloses:
21. A power tool comprising:
a housing (e.g., Fig. 1 #105);
a motor supported by the housing (e.g., Fig. 5A #505);
a battery pack supported by the housing and configured to provide electrical power to the power tool (e.g., Fig. 4D #480, [0090]);
a user input configured to provide an input signal corresponding to a target speed of the motor (e.g., Fig. 5A #510, [0096]);
a plurality of sensors supported by the housing and configured to generate sensor data indicative of an operational parameter of the power tool (e.g., Fig. 5A #530, [0099]);
an electronic controller, the electronic controller including an electronic processor (e.g., Fig. 5B #575) and a memory (e.g., Fig. 5B #580), the memory including a machine learning control program (e.g., Fig. 5B #585) for execution by the electronic processor, the electronic controller configured to:
receive the target speed (e.g., Fig. 7 #705),
receive the sensor data (e.g., Fig. 7 #710),
generate, using the machine learning control program, an output including a field weakening parameter based on the sensor data (e.g., Fig. 7 #715,720, [0135], [0166]),
control the motor based on the generated output to achieve the target speed (e.g., Fig. 7 #725),
receive feedback data from the plurality of sensors indicative of control of the motor based on the generated output (e.g., Fig. 6 #625, [0123]),
adjust the machine learning control program to generate a second output including a second field weakening parameter based on the feedback data (e.g., Fig. 6 #630, [0135], [0166]), and
control the motor based on the generated second output to achieve the target speed (e.g., Fig. 6 #635).
22. The power tool of claim 21, wherein the electronic controller is further configured (e.g., [0113]) to:
access tool usage information indicative of previous control of the motor stored in the memory (e.g., Fig. 6 #605);
build the machine learning control program based on the tool usage information (e.g., Fig. 6 #610);
train the machine learning control program based on the tool usage information (e.g., Fig. 6 #610); and
store the machine learning control program in the memory (e.g., Fig. 6 #615).
23. The power tool of claim 21, wherein the machine learning control program is adjusted through training based on example sensor data and the feedback data (e.g., Fig. 6 #625,630, [0123]).
24. The power tool of claim 21, wherein the machine learning control program is a trainable machine learning control program (e.g., Fig. 6 #610).
25. The power tool of claim 1, wherein the sensor data includes one or more of a motor current, a battery pack impedance, a battery pack voltage, and a motion of the power tool (e.g., [0132], [0140], Fig. 9).
26. The power tool of claim 21, wherein the field weakening parameter and the second field weakening parameter each include one or more of an advance angle value, a conduction angle value that modifies a conduction angle of the motor, and a freewheel angle value, the freewheel angle value corresponding to when a motor winding of the motor is disconnected from an excitation voltage (e.g., [0135], [0166]).
27. The power tool of claim 21, wherein the electronic controller is further configured to filter the field weakening parameter and the second field weakening parameter using one or more filters (e.g., [0135], [0166], [0168]-[0169], [0171], [0187]).
28. The power tool of claim 21, wherein the electronic controller is further is further configured to:
receive one or more priority parameter values, wherein the machine learning control program generates the output based on the sensor data and the priority parameter values (e.g., [0135]: “In some embodiments, the machine learning controller 540 receives user characteristics of the current user of the power tool 500 in step 715, in addition to or instead of sensor data, and then generates an output in step 720 based on the user characteristics or based on the user characteristics and the sensor data received in step 715.”).
29. A method of operating a power tool to control field weakening, the method comprising:
receiving, by an electronic controller of the power tool, an input signal corresponding to a target speed of a motor of the power tool from a user input (e.g., Fig. 7 #705);
receiving, by the electronic controller, sensor data indicative of an operational parameter of the power tool from a plurality of sensors (e.g., Fig. 7 #710), the electronic controller including an electronic processor (e.g., Fig. 5B #575) and a memory (e.g., Fig. 5B #580), wherein the memory includes a machine learning control program (e.g., Fig. 5B #585) for execution by the electronic processor;
generating, using the machine learning control program, an output including a field weakening parameter based on the sensor data (e.g., Fig. 7 #715,720, [0135], [0166]);
controlling, by the electronic controller, the motor based on the generated output to achieve the target speed (e.g., Fig. 7 #725);
receiving, by the electronic controller, feedback data from the plurality of sensors indicative of control of the motor based on the generated output (e.g., Fig. 6 #625, [0123]);
adjusting, by the electronic controller, the machine learning control program to generate a second output including a second field weakening parameter based on the feedback data (e.g., Fig. 6 #630); and
controlling, by the electronic controller, the motor based on the generated second output to achieve the target speed (e.g., Fig. 6 #635).
30. The method of claim 29, further comprising:
accessing, by the electronic controller, tool usage information indicative of previous control of the motor stored in the memory (e.g., Fig. 6 #605);
building, by the electronic controller, the machine learning control program based on the tool usage information (e.g., Fig. 6 #610);
training, by the electronic controller, the machine learning control program based on the tool usage information (e.g., Fig. 6 #610); and
storing, by the electronic controller, the machine learning control program in the memory (e.g., Fig. 6 #615).
31. The method of claim 29, wherein generating the machine learning control program includes training, by the electronic controller, the machine learning control program based on example sensor data and the feedback data (e.g., Fig. 6 #625,630, [0123]).
32. The method of claim 29, wherein the machine learning control program is a trainable machine learning control program (e.g., Fig. 6 #610).
33. The method of claim 29, wherein the sensor data includes one or more of a motor current, a battery pack impedance, a battery pack voltage, and a motion of the power tool (e.g., [0132], [0140], Fig. 9).
34. The method of claim 29, wherein the field weakening parameter and the second field weakening parameter each include one or more of an advance angle value, a conduction angle value that modifies a conduction angle of the motor, and a freewheel angle value, the freewheel angle value corresponding to when a motor winding of the motor is disconnected from an excitation voltage (e.g., [0135], [0166]).
35. The method of claim 29, further comprising:
filtering, by the electronic controller, the field weakening parameter and the second field weakening parameter using one or more filters (e.g., [0168]-[0169], [0171], [0187]).
36. The method of claim 29, further comprising:
receiving, by the electronic controller, one or more priority parameter values, wherein the machine learning control program generates the output based on the sensor data and the priority parameter values (e.g., [0135]: “In some embodiments, the machine learning controller 540 receives user characteristics of the current user of the power tool 500 in step 715, in addition to or instead of sensor data, and then generates an output in step 720 based on the user characteristics or based on the user characteristics and the sensor data received in step 715.”).
37. A power tool system comprising:
a server including a server electronic processor and a server memory, the server electronic processor configured (e.g., [0113]) to:
access tool usage information indicative of previous control of a plurality of power tools of the power tool system stored in the server memory (e.g., Fig. 6 #605),
build a machine learning control program based on the tool usage information (e.g., Fig. 6 #610),
train the machine learning control program based on the tool usage information (e.g., Fig. 6 #610), and
transmit the machine learning control program to the plurality of power tools (e.g., Fig. 6 #620, [0113]); and
a power tool of the plurality of power tools (e.g., Fig. 1 #105), the power tool including:
a housing (e.g., Fig. 1 #105),
a motor supported by the housing (e.g., Fig. 5A #505),
a battery pack supported by the housing and configured to provide electrical power to the power tool (e.g., Fig. 4D #480, [0090]),
a user input configured to provide an input signal corresponding to a target speed of the motor (e.g., Fig. 5A #510, [0096]),
a plurality of sensors supported by the housing and configured to generate sensor data indicative of an operational parameter of the power tool (e.g., Fig. 5A #530, [0099]),
a tool electronic controller, the tool electronic controller including a tool electronic processor (e.g., Fig. 5B #575) and a tool memory (e.g., Fig. 5B #580), the tool electronic controller configured to:
receive the machine learning control program from the server electronic processor (e.g., [0113]),
store the machine learning control program in the tool memory for execution by the tool electronic processor (e.g., Fig. 5B #585),
receive the target speed (e.g., Fig. 7 #705),
receive the sensor data (e.g., Fig. 7 #710, [0132]),
generate, using the machine learning control program, an output including a field weakening parameter based on the sensor data (e.g., Fig. 7 #715,720, [0135], [0166]),
control the motor based on the generated output to achieve the target speed (e.g., Fig. 7 #725), and
receive feedback data from the plurality of sensors indicative of the control of the motor based on the generated output (e.g., Fig. 6 #625, [0123]).
38. The power tool system of claim 37, wherein the server electronic processor is further configured to:
receive, via the tool electronic controller, the feedback data from the plurality of sensors (e.g., Fig. 6 #625, [0123]);
adjust the machine learning control program to generate a second output including a second field weakening parameter based on the feedback data (e.g., Fig. 6 #630, [0135], [0166]); and
transmit the adjusted machine learning control program to the power tool (e.g., Fig. 6 #635).
39. The power tool system of claim 37, wherein the machine learning control program is adjusted through training based on example sensor data and the feedback data (e.g., Fig. 6 #625,630, [0123]).
40. The power tool system of claim 37, wherein the field weakening parameter and the second field weakening parameter each include one or more of an advance angle value, a conduction angle value that modifies a conduction angle of the motor, and a freewheel angle value, the freewheel angle value corresponding to when a motor winding of the motor is disconnected from an excitation voltage (e.g., [0135], [0166]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN A JARRETT whose telephone number is (571)272-3742. The examiner can normally be reached M-F 9:00-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at 571-272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN A JARRETT/ Primary Examiner, Art Unit 2116
03/07/26
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