# US 2021/0240145 A1 · Automatic step bit detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240145 A1 - Document ID: US20210240145A1 - Status: Published application - Kind: A1 - Application number: 17/162,387 - Filed: 2021-01-29 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: Milwaukee Electric Tool Corporation - Applicants: Milwaukee Electric Tool Corporation - Inventors: Jonathan E. Abbott - Web page: https://patentgrep.com/patent/US20210240145A1 ## CPC - G05B 13/0265: {the criterion being a learning criterion} - G05B 13/027: {using neural networks only} - B23B 51/009: {Stepped drills} - B25F 5/00: Details or components of portable power-driven tools not particularly related to the operations performed and not otherwise provided for - B25F 5/001: {Gearings, speed selectors, clutches or the like specially adapted for rotary tools} - B25F 5/02: Construction of casings, bodies or handles {(B25F5/006, B25F5/008 take precedence)} - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N 3/092: Reinforcement learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - B23B: TURNING; BORING (arrangements for copying or controlling B23Q) - B25F: COMBINATION OR MULTI-PURPOSE TOOLS NOT OTHERWISE PROVIDED FOR; DETAILS OR COMPONENTS OF PORTABLE POWER-DRIVEN TOOLS NOT PARTICULARLY RELATED TO THE OPERATIONS PERFORMED AND NOT OTHERWISE PROVIDED FOR {(machines or arrangements of machines for performing specified combinations of different metal-working operations not covered by a single other subclass B23P23/00 and subgroups)} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Devices and methods for automatically controlling a step bit operation in a power tool. The method includes generating, by a sensor of the power tool, sensor data indicative of an operational parameter of the power tool wherein a step bit is coupled to the power tool. An electronic control assembly of the power tool receives the sensor data, where the electronic control assembly includes an electronic processor and a memory. The memory stores a machine learning control program for execution by the electronic processor. The electronic control assembly processes the sensor data using a machine learning control program of the electronic control assembly and generates, using the machine learning program, an output based on the sensor data. The output indicates step bit progress information. The electronic control assembly controls a motor supported by the housing of the power tool based on the output. ## Claims ### Claim 1 A method for automatically controlling a step bit operation in a power tool including a step bit, the method comprising: generating, by a sensor of the power tool, sensor data indicative of an operational parameter of the power tool; receiving, by an electronic control assembly of the power tool, the sensor data, the electronic control assembly including an electronic processor and a memory, the memory storing a machine learning control program for execution by the electronic processor; processing, by the electronic control assembly using the machine learning control program, the sensor data; generating, by the electronic control assembly using the machine learning control program, an output based on the sensor data, wherein the output indicates step bit progress information; and controlling, by the electronic control assembly, a motor of the power tool based on the output. ### Claim 2 (depends on 1) The method of claim 1, wherein the step bit progress information indicates at least one selected from the group consisting of: a level of advancement of the step bit into a workpiece relative to an intended step, an active advancement between steps, a detected step in the advancement of the step bit into the workpiece, a count of steps advanced into the workpiece, a diameter of a hole drilled into the workpiece, a depth of the hole drilled into the workpiece by the step bit, and a confidence value for detecting a step of the step bit in the advancement of the step bit into the workpiece. ### Claim 3 (depends on 2) The method of claim 2, wherein the intended step is a next step in the advancement of the step bit into the workpiece or a further step in the advancement of the step bit into the workpiece. ### Claim 4 (depends on 2) The method of claim 2, further comprising: detecting, by the electronic control assembly using the machine learning control program, that the intended step of the step bit has been reached during drilling by counting a number of steps of the step bit in the advancement of the step bit into the workpiece. ### Claim 5 (depends on 2) The method of claim 2, wherein the intended step is based on at least one selected from the group consisting of: a setting of a clutch ring of the power tool, configuration parameters received from an external device, a setting of a user input mechanism of the power tool, and a selected step bit mode of the power tool. ### Claim 6 (depends on 2) The method of claim 2, further comprising: sending a signal to an indicator of the power tool or an external device to alert a user based on at least one selected from the group consisting of: the step bit progress information, a quality of signal received from a power tool sensor, a confidence of an algorithm, a feedback value based on a forward feed rate, a feedback value based on a determined force, and a condition of the step bit. ### Claim 7 (depends on 1) The method of claim 1, wherein the sensor data indicative of the operational parameter of the power tool includes at least one selected from the group consisting of: a current, a voltage, a trigger input, a speed, an applied torque, a loading of the motor, an acceleration, an impact detection from a position sensor, a distance sensor, and a rotational motion from a gyroscope. ### Claim 8 (depends on 1) The method of claim 1, further comprising: configuring the power tool in a specified mode based on user input, wherein the specified mode is associated with one or more of a predetermined number of steps to advance the step bit into a workpiece, a number of steps included on the step bit, a specific step bit, a material of the workpiece to be drilled into, a step counting function of the power tool, a number of additional steps to take, a specific step value, and a feature on the step bit. ### Claim 9 (depends on 1) The method of claim 1 further comprising: recognizing, by the electronic control assembly using the machine learning control program, a characteristic profile of the step bit based on the sensor data; and learning, by the electronic control assembly, over multiple uses of the power tool, at least one selected from the group consisting of: common sizes of holes drilled, common number of steps drilled, and types of step bits utilized in the multiple uses of the power tool, wherein the controlling of the motor is based on the at least one selected from the group consisting of: common sizes of holes drilled, common number of steps drilled, and types of step bits utilized in the multiple uses of the power tool. ### Claim 10 A power tool for automatically controlling a step bit operation, the power tool comprising: a housing; a motor supported by the housing; a sensor configured to generate sensor data indicative of an operational parameter of the power tool; a memory; and an electronic control assembly including an electronic processor connected to the memory, wherein the memory stores a machine learning control program that when executed by the electronic processor configures the electronic control assembly to: receive the sensor data, process, using the machine learning control program, the sensor data, generate, using the machine learning control program, an output based on the sensor data that indicates step bit progress information of a step bit of the power tool, and control the motor based on the output. ### Claim 11 (depends on 10) The power tool of claim 10, wherein control the motor based on the output includes one of stopping the motor or varying a speed of the motor. ### Claim 12 (depends on 10) The power tool of claim 10, wherein the step bit progress information indicates at least one selected from the group consisting of: a level of advancement of the step bit into a workpiece relative to an intended step, a detected step in the advancement of the step bit into the workpiece, a count of steps advanced into the workpiece, a diameter of a hole drilled into the workpiece, a depth of the hole drilled into the workpiece by the step bit, and a confidence value for detecting a step of the step bit in the advancement of the step bit into the workpiece. ### Claim 13 (depends on 12) The power tool of claim 12, wherein the intended step is a next step in the advancement of the step bit into the workpiece or a further step in the advancement of the step bit into the workpiece. ### Claim 14 (depends on 12) The power tool of claim 12, wherein the electronic control assembly is further configured to: detect, using the machine learning control program, that the intended step of the step bit has been reached by counting a number of steps of the step bit in the advancement of the step bit into the workpiece. ### Claim 15 (depends on 12) The power tool of claim 12, wherein the intended step is based on at least one selected from the group consisting of: a setting of a clutch ring of the power tool, configuration parameters received from an external device, a setting of a user input mechanism of the power tool, and a selected step bit mode of the power tool. ### Claim 16 (depends on 12) The power tool of claim 12, wherein the electronic control assembly is further configured to send a signal to an indicator of the power tool or an external device to alert a user based on at least one selected from the group consisting of: the step bit progress information, an active advancement between steps, a quality of signal received from a power tool sensor, a confidence of an algorithm, a feedback value based on a determined feed rate, a feedback value based on a determined force, and a condition of the step bit. ### Claim 17 (depends on 10) The power tool of claim 10, wherein the sensor data indicative of the operational parameter of the power tool includes at least one selected from the group consisting of: a current, a voltage, a trigger input, a speed, an applied torque, a loading of the motor, an acceleration, a rotational motion from a gyroscope, a distance from a distance sensor, and an impact motion from one or more inductive impact sensors. ### Claim 18 (depends on 10) The power tool of claim 10, wherein the electronic control assembly is further configured to: configure the power tool in a specified mode based on user input, wherein the specified mode is associated with at least one selected from the group consisting of: a predetermined number of steps to advance the step bit into a workpiece, a number of steps included on the step bit, a specific step bit, a material of the workpiece to be drilled into, a set of most likely desired steps, and a step counting function of the power tool. ### Claim 19 (depends on 10) The power tool of claim 10, wherein the electronic control assembly is further configured to: recognize, using the machine learning control program, one or more of a characteristic profile of the step bit based on the sensor data or a user use of the step bit; and learning, using the machine learning control program, over multiple uses of the power tool, at least one selected from the group consisting of: common sizes of holes drilled, common number of steps drilled, and types of step bits utilized in the multiple uses of the power tool, wherein the controlling of the motor is based on the at least one selected from the group consisting of: common sizes of holes drilled, the common number of steps drilled, and the types of step bits utilized in the multiple uses. ### Claim 20 A power tool for automatically controlling a step bit operation, the power tool comprising: a housing; a motor supported by the housing; a sensor configured to generate sensor data indicative of an operational parameter of the power tool; a memory; and an electronic control assembly including an electronic processor connected to the memory, wherein the memory stores a machine learning control program that when executed by the electronic processor configures the electronic control assembly to: receive the sensor data, process, using the machine learning control program, the sensor data, generate, using the machine learning control program, an output based on the sensor data that indicates step bit progress information of a step bit of the power tool, recognize, using the machine learning control program, one or more of a characteristic profile of the step bit based on the sensor data or a user use of the step bit, and control the motor based on the output.