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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it exceeds 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 2, 6, and 32 are objected to because of the following informalities:
In claim 2, line 12, there is no antecedent basis for “the electronic processor of the electronic controller”.
In claim 6, line 1, because this claim depends on canceled claim 5, its scope is indefinite. For examination purposes, the claim is treated as depending directly on claim 1.
In claim 6, line 2, “a battery pack” should be changed to --the battery pack--.
In claim 32, “a power tool” is recited in lines 1 and 8-9, and “a power tool device” is recited in line 16. It is unclear whether these terms refer to the same element or different elements. For examination purposes they are treated as referring to the same element.
Appropriate correction is required.
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, 6, 11-12, 14, 17, 21, and 26 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by MARTY (Pub. No.: US 2011/0057603 A1; cited on IDS with date 12/1/2024).
Regarding claim 1, MARTY discloses a power tool battery charger (100, Fig. 1; ¶ 0011: the charge cycles are managed by a device utilizing the battery, such as a laptop computer, a cell phone, a PDA, a power tool, a hand-held scanner, or other device; ¶ 0013: Turning now to FIG. 1, a smart battery charger 100 suitable for managing charge cycles on a plurality of batteries in order to prolong the operation life of the batteries is shown) comprising:
a housing (¶ 0013: smart battery charger 100 includes a body 105 that houses the components of the battery charger 100. The housing may be plastic, metal, aluminum, or other suitable material);
at least one charging circuit (a charging circuit is implied in order to provide the charging current to a battery) coupled to the housing and configured to charge a battery pack (e.g., 112, Fig. 1) coupled thereto (¶ 0013-0014: charging port 110 is shown with battery 112, battery 114, battery 116, battery 118, battery 120, and battery 122. The charging port 130 is shown with battery 132, battery 134, battery 136, battery 138, battery 140, and battery 141…charging plug and the charging port couple to enable an electrical current to pass between the battery charger 100 and an individual battery placed in the charger…charging port 110 and charging port 130 are sized to fit the battery plus the box in which a battery is packaged…a communications plug on the battery charger to couple with a communications port on the battery. The communications port allows a computer-storage media in the battery to be accessed by the battery charger when the battery and charger are coupled together);
an electronic controller (not shown in the figures) coupled to the housing, and in communication with the at least one charging circuit (claim 8: A smart battery charger comprising…a controller coupled to the communications port and the charging port, the controller executing a program that initiates a charge cycle on the battery at an optimal time to minimize a total charge on the battery over a life of the battery), the electronic controller configured to:
receive power tool device data from a power tool device (e.g., one of the batteries disclosed in ¶ 0013, or a power tool as disclosed in ¶ 0011), wherein the power tool device data comprise usage data of the power tool device (¶ 0028: At step 420, a usage history for the battery is retrieved. In one embodiment, the usage history is retrieved from computer-readable memory or storage located in the battery. In another embodiment, the usage history for the battery is stored on the battery charger or device utilizing the battery);
generate, based on the power tool device data, charger operation data indicating at least one of a charging rate of the at least one charging circuit (recited in the alternative, and MARTY discloses the “charging target” alternative), a charging target of the at least one charging circuit (¶ 0030: At step 440, the optimal charge on the battery is determined from the usage history for the battery. The optimal charge is a minimum charge plus an amount of energy historically used during a single use cycle. For example, if 50% of the battery's capacity is typically used during a single-use cycle and the minimum charge is 20%, then the optimal charge on the battery would be 70%. Charging the battery to the optimal charge reduces the total charge on the battery over the life of the battery. In one embodiment, an additional buffer is added to the minimum charge and the amount of energy historically used during a single cycle to calculate the optimal charge. At step 450, a charge cycle is initiated at the optimal time, and the battery is charged during the charge cycle to the optimal charge), or a time indication for when to adjust at least one of the charging rate or charging target of the at least one charging circuit (recited in the alternative, and MARTY discloses the “charging target” alternative); and
operate the at least one charging circuit based on the charger operation data (¶ 0030: see above).
Regarding claim 6, MARTY discloses the power tool device is a battery pack and the usage data include at least one of retake time data of the battery pack, working hours data of the battery pack, a warranty of the battery pack, and an age of the battery pack (¶ 0019-0022, 0028-0030, 0035-0036).
Regarding claim 11, MARTY discloses the power tool device is a battery pack and the electronic controller is configured to generate the charger operation data by: determining a charging state for the battery pack; and generating the charger operation data based on the charging state and the power tool device data (¶ 0030).
Regarding claim 12, MARTY discloses the charging state for the battery pack includes independent variables including a charging rate and a charging threshold (¶ 0020, 0030; “charging rate” is implied when determining “enough time to complete the charge cycle”).
Regarding claim 14, MARTY discloses the charging state comprises a multidimensional charging state, wherein the multidimensional charging state is parameterized by a charging rate function comprising independent variables including a charging target and a charging rate (¶ 0020, 0030; “charging rate” is implied when determining “enough time to complete the charge cycle”).
Regarding claim 17, MARTY discloses the power tool device is a battery pack, the usage data comprise retake time data, and the electronic controller is configured to generate the charger operation data based on the retake time data, wherein the retake time data indicate at least one of: a time between when the battery pack reaches a charging target and when the battery pack is taken off the power tool battery charger; a time between when the battery pack is put on the power tool battery charger and when the battery pack is taken off from the power tool battery charger; a time between when the battery pack reaches a charging target and when the battery pack is used next on another power tool device; a time between subsequent times when the battery pack is taken off the power tool battery charger or another power tool battery charger; or a time between subsequent times when the battery pack is put on the power tool battery charger or another power tool battery charger (¶ 0020-0022, 0028-0029, 0035-0036).
Regarding claim 21, MARTY discloses the usage data indicates working hours for the power tool device (¶ 0019-0022, 0028-0030).
Regarding claim 26, MARTY discloses the charger operation data include a charging target, and wherein the charging target is determined from the power tool device data by the electronic controller (¶ 0030).
Claim Rejections - 35 USC § 103
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 2-3 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over MARTY as applied to claims 1, 6, 11-12, 14, 17, 21, and 26 above, and further in view of ABBOTT (Pub. No.: US 2019/0227528 A1; cited on IDS with date 12/27/2024).
Regarding claim 2, MARTY discloses the power tool battery charger as applied to claim 1, and further discloses [processing] the power tool device data, using the machine learning control program; [generating], using the machine learning control program, an output based on the power tool device data; and the electronic processor of the electronic controller receives the output from the [machine learning control program] and generates the charger operation data using the output from the [machine learning control program] (¶ 0029).
MARTY fails to disclose a machine learning controller including a second electronic processor, the machine learning controller supported by the housing, coupled to the electronic controller, and including a machine learning control program, the machine learning controller being configured to:
receive the power tool device data from the electronic controller;
process the power tool device data, using the machine learning control program, wherein the machine learning control program is a trained machine learning control program;
generate, using the machine learning control program, an output based on the power tool device data; and
send the output to the electronic controller;
wherein the electronic processor of the electronic controller receives the output from the machine learning controller and generates the charger operation data using the output from the machine learning controller.
ABBOTT discloses a machine learning controller including a second electronic processor, the machine learning controller supported by the housing, coupled to the electronic controller, and including a machine learning control program (¶ 0080, 0083, 0101), the machine learning controller being configured to:
receive the power tool device data from the electronic controller (¶ 0100);
process the power tool device data, using the machine learning control program, wherein the machine learning control program is a trained machine learning control program (¶ 0100);
generate, using the machine learning control program, an output based on the power tool device data (¶ 0190); and
send the output to the electronic controller (¶ 0100);
wherein the electronic processor of the electronic controller receives the output from the machine learning controller and generates the charger operation data using the output from the machine learning controller (¶ 0080, 0100, 0190).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the machine learning controller in order to improve real-time performance.
Regarding claim 3, MARTY as modified by ABBOTT teaches the machine learning control program implements at least one selected from a group of an artificial neural network that takes power tool device data as an input and a support vector machine that takes power tool device data as an input (ABBOTT, ¶ 0052, 0055-0058).
Regarding claim 7, MARTY as modified by ABBOTT teaches the machine learning controller is configured to receive feedback data and to adjust the machine learning control program based on the feedback data (ABBOTT, ¶ 0004).
Claim(s) 32-33, 36-38, 40, and 42-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over FUNK (Pub. No.: US 2021/0376630 A1; cited on IDS with date 12/1/2024) in view of ABBOTT (Pub. No.: US 2019/0227528 A1; cited on IDS with date 12/27/2024).
Regarding claim 32, FUNK discloses a battery pack (150, Figs. 1-4) for use with a power tool (110, Figs. 1-4; ¶ 0019-0020: system 100 is an example embodiment in which a rechargeable battery pack 150 and corresponding battery charger 180 may operate. As shown in FIG. 1, the system 100 may include a battery-powered tool 110. The tool 110 may be configured to be powered by and communicate with the rechargeable battery pack 150. The battery pack 150 may be configured to be recharged by and communicate with the battery charger 180. In this regard, the battery pack 150 may communicate with the battery charger 180 while powering the tool 110, and in some cases, while being charged on or transferred to the battery charger 180…the tool 110 may be an outdoor power equipment tool. The outdoor power equipment tool may be a mower, a blower, a chainsaw, a trimmer, an edger, a snow removal tool, a tiller, or the like…the battery pack 150 may be configured to power a mower, trimmer, and blower or the like owned by the same user), comprising:
a housing (¶ 0024: It should be understood that the battery pack 150 discussed herein may include housing (not shown) which may encase one or more battery cells (e.g., of type 21700) therein; ¶ 0027: the processing circuitry 210 may be embodied as a portion of an on-board computer housed in the battery pack 150 with a battery management system (BMS) 234 or communications manager 232 to control operation of the battery pack 150 relative to its interaction with other tools);
a plurality of battery cells arranged within the housing (¶ 0024: see above) and configured to provide electrical power to a power tool (110, Figs. 1-4) when coupled thereto (¶ 0019-0020: see above);
at least one charging circuit coupled to the housing (¶ 0021: the battery pack 150 may include circuitry to enable the battery pack 150 to be charged by the battery charger 180 and to enable the power from the battery pack 150 to be delivered to the tool 110) and configured to:
provide electrical power to the plurality of battery cells to charge the plurality of battery cells when coupled to a power tool battery charger (180, Figs. 1, 3-4; ¶ 0021: see above); and
discharge the plurality of battery cells to provide the electrical power to a power tool (110, Figs. 1-4) when coupled to the power tool (¶ 0021: see above);
an electronic controller coupled to the housing, including an electronic processor (210/212, Fig. 2), and in communication with the at least one charging circuit (¶ 0027: the processing circuitry 210 may be embodied as a portion of an on-board computer housed in the battery pack 150 with a battery management system (BMS) 234 or communications manager 232 to control operation of the battery pack 150 relative to its interaction with other tools); and
[a machine learning control program] (¶ 0067-0068: in order to determine the next run time of the tool 110, the battery pack 150 may, either through machine learning or input from the user, create a battery usage profile that can be sent to the battery charger 180 that includes when and how much the battery pack 150 should be charged. When the battery usage profile is derived from machine learning, the battery pack 150, through analyzing any combination of engine RPM, working assembly RPM, torque, run time run hours, position, orientation, temperature data, speed data, mode of operation, lubricating oil pressure or level, water pressure, volume delivered, instances of protective actions, motion, or the like, may determine a schedule of the battery pack 150 in combination with the charge level and use of the battery pack 150 and the particular tool 110 used),
wherein the electronic controller configured to:
receive power tool device data from a power tool device (110, Figs. 1-4), wherein the power tool device data comprise data indicative of use of the power tool device (¶ 0061: tool data, may be extracted from the tool 110 by the battery pack 150 at operation 402. Battery data may be received from the BMS 234 of the battery pack at operation 410. The battery data may indicate a power off condition at operation 420 (e.g., current draw below a predetermined threshold). The battery pack 150 may initiate a power off to remove power provision to the tool 110 at operation 422. At operation 430, the battery pack 150 may detect removal from the tool 110. The battery pack 150 may report the operational parameters, e.g., tool data and battery data, and the occurrence of the power off condition to the battery charger 180 (via the network 170) at operation 432. At operation 440, the battery charger 180 may detect insertion of the battery pack 150 into the battery charger 180 for charging in accordance with the parameters received from the battery pack 150. The battery pack 150 may then be charged by the battery charger 180 in accordance with the operational parameters at operation 442);
generate, based on the power tool device data, charger operation data indicating at least one of a charging rate of the at least one charging circuit (recited in the alternative, and FUNK discloses the “charging target” alternative), a charging target of the at least one charging circuit (¶ 0068-0069: When the battery usage profile is derived from machine learning, the battery pack 150, through analyzing any combination of engine RPM, working assembly RPM, torque, run time run hours, position, orientation, temperature data, speed data, mode of operation, lubricating oil pressure or level, water pressure, volume delivered, instances of protective actions, motion, or the like, may determine a schedule of the battery pack 150 in combination with the charge level and use of the battery pack 150 and the particular tool 110 used. In this regard, the battery pack 150 may learn that the user typically mows 2 hours on Friday and then completes leaf blowing and trimming operations for 2 hours on Saturday. Accordingly, the battery usage profile, which contains the usage and times associated with the usage, may be communicated to the battery charger 180 for charging the battery pack 150 appropriately. In some cases, however, the user, through the user application 322, may instruct the battery pack 150 of the schedule of the user. In this case, the battery usage profile may be based on the schedule inputted by the user, and then in accordance with further example embodiments, may be updated by the battery pack 150 based on an analysis of the use of the tool 110. The battery usage profile may also distinguish which particular user is using the device. For example, the battery pack 150 may learn that a first user typically mows 2 hours on Friday and then completes leaf blowing and trimming operations for 2 hours on Saturday and that a second user mows 3 hours on Thursday and completes a trimming operation for 1 hour on Sunday and create a battery usage profile accordingly. At operation 840, the method may also include communicating the next run time amount to the battery charger 180 in order for the battery pack 150 to be charged in accordance with the predicted amount, and then causing the battery charger 180 to charge the battery pack 150 in accordance with the instructions received from the battery pack 150 at operation 850), or a time indication for when to adjust at least one of the charging rate or charging target of the at least one charging circuit (recited in the alternative, and FUNK discloses the “charging target” alternative), wherein the electronic controller generates the charger operation data based on an output from the [machine learning control program] (¶ 0067-0069: see above); and
operate the at least one charging circuit based on the charger operation data (¶ 0068-0069: see above).
FUNK fails to disclose a machine learning controller including a second electronic processor, the machine learning controller supported by the housing, coupled to the electronic controller, and including a machine learning control program; and the electronic controller generates the charger operation data based on an output from the machine learning controller.
ABBOTT discloses a machine learning controller (540, Fig. 5B) including a second electronic processor (575, Fig. 5B), the machine learning controller supported by the housing, coupled to the electronic controller (as shown in Figures 5A & 5B), and including a machine learning control program (¶ 0008: machine learning controller includes a first processor and a first memory, is coupled to the sensor, and includes a machine learning control program); and the electronic controller generates the charger operation data based on an output from the machine learning controller (¶ 0079: battery pack 480 includes a machine learning controller 485. Although not illustrated, the battery pack 480 may, in some embodiments, communicate with the external device 107, a server, or a combination thereof through, for example, a network. Alternatively, or in addition, the battery pack may communicate with a power tool 455, such as a power tool 455 attached to the battery pack 480. The external device 107 and the server may be similar to the external device 107 and server 110, 210, 310, 410 described above with respect to FIGS. 1-4A. The machine learning controller 485 of the battery pack 480 may be similar to any of the machine learning controllers 220, 320, 420 described above. In one embodiment, the machine learning controller 220, 320, 420 controls operation of the battery pack 480. For example, the machine learning controller 485 may help identify different battery conditions that may be detrimental to the battery pack 480 and may automatically change (e.g., increase or decrease) the amount of current provided by the battery pack 480, and/or may change some of the thresholds that regulate the operation of the battery pack 480. For example, the battery pack 480 may, from instructions of the machine learning controller 485, reduce power to inhibit overheating of the battery cells; ¶ 0190: TABLE 6…Machine Learning Application [Ideal charging rates]…Potential Inputs to Machine Learning Controller [Past tool/battery use, time of day, stage of construction, battery charge states, presence of batteries]…Potential Output(s) from Machine Learning Controller [A charger may reduce speed of charging if the charger does not think a rapid charge will be necessary for a user (may extend overall battery life)]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the machine learning controller in order to improve real-time performance.
Regarding claim 33, FUNK as modified by ABBOTT discloses the machine learning controller is configured to: receive the power tool device data from the electronic controller; process the power tool device data, using the machine learning control program, wherein the machine learning control program is a trained machine learning control program; generate, using the machine learning control program, the output based on the power tool device data; and send the output to the electronic controller (FUNK, ¶ 0067-0068; ABBOTT, ¶ 0080-0083, 0100-0101, 0190).
Regarding claim 36, FUNK discloses the power tool device data include usage data of the power tool device (¶ 0061, 0067-0068).
Regarding claim 37, FUNK as modified by ABBOTT teaches the machine learning controller is configured to receive feedback data and to adjust the machine learning control program based on the feedback data (ABBOTT, ¶ 0004).
Regarding claim 38, FUNK as modified by ABBOTT teaches the feedback data comprise user feedback data indicative of user feedback related to operation of the battery back based on the charger operation data (ABBOTT, ¶ 0077, 0116, 0129, 0190).
Regarding claim 40, FUNK as modified by ABBOTT teaches the machine learning controller is configured to adjust the machine learning control program based on the feedback data by retraining the machine learning control program using the feedback data (ABBOTT, ¶ 0004, 0077, 0111, 0162).
Regarding claim 42, FUNK as modified by ABBOTT teaches the feedback data indicate a performance of the battery pack and the machine learning controller is configured to adjust the machine learning control program based on the feedback data using reinforcement learning (ABBOTT, ¶ 0077, 0112, 0134).
Regarding claim 43, FUNK discloses the electronic controller is configured to generate the charger operation data by: determining a charging state for the battery pack; and generating the charger operation data based on the charging state and the power tool device data (¶ 0035, 0043).
Regarding claim 44, FUNK discloses the charging state for the battery pack includes independent variables including a charging rate and a charging threshold (¶ 0043, 0046, 0067, 0069).
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL HERNANDEZ whose telephone number is (571)270-7916. The examiner can normally be reached Monday-Friday 9a-5p ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Drew Dunn can be reached at (571) 272-2312. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manuel Hernandez/Examiner, Art Unit 2859 10/7/2026
/DREW A DUNN/Supervisory Patent Examiner, Art Unit 2859
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