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 .
Status of the Claims
In the communication filed on 04/23/2024 claims 1-13 and 22-27 are pending. Independent claims 1 and 7 were amended to incorporate limitations not previously presented. Claims 14-21 are cancelled.
Claim Objections
Claim 2 is objected to because of the following informalities: in line 6 replace “used” with --use-- for improved reading comprehension. For examination purposes below this limitation will be interpreted as “use”, however, appropriate correction is required.
Claim 9 is objected to because of the following informalities: in line 1 add --the-- in front of “one” to avoid a lack of antecedent basis issue. For examination purposes below this limitation will be interpreted as “the one or more maintenance procedures”, however, appropriate correction is required.
Claim 22 is objected to because of the following informalities: in line 6 replace “; and” with a period --.-- for improved reading comprehension. For examination purposes below this limitation will be interpreted as ending with a period, however, appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 7-10, and 22-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 is directed towards an apparatus and independent claim 7 is directed towards a method, as identified in Step 1 of the Subject Matter Eligibility Test outlined in MPEP 2106.
The claims recite receiving and transmitting information, performing calculations, storing information, and making decisions based on that information which are considered exceptions because the limitations fall under the methods of mental processes and mathematical concepts groupings of the abstract ideas, as identified in Step 2A Prong One.
The “maintenance procedures” include “at least one selected from a group of determining a maximum capacity of the power tool battery pack, cell balancing, cooling the power tool battery pack, and heating the power tool batty pack”. The applicant in ¶[113] of the disclosure describes using Coulomb counting for determining the maximum capacity of the power tool battery pack. It is well understood by one of ordinary skill that Coulomb counting amounts to a mathematical calculation. Thus, when the singular alternative of the “maintenance procedures” comprising performing “determining a maximum capacity of the power tool battery pack” a mathematical calculation is performed.
These judicial exceptions are not integrated into a practical application because managing information using an electronic controller and are processor amount to computational data functions, as identified in Step 2A Prong Two.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because “determining a maximum capacity of the power tool battery pack” is a mathematical calculation and thus it is part of the abstract idea which could be performed by an individual with the aid of pen and paper, as identified in Step 2B.
Dependent claims 2-3, 8-10, and 22-27 because they only further the abstract idea without additional practical use or significant structure. The dependent claims further add to the abstract idea by providing additional details as to how the data is obtained or calculated.
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.
(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, 3, 7-10, and 25 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Marty et al. (USPGPN 20110057603).
With respect to independent claims 1 and 7, Marty teaches a power tool battery charger (Fig. 1; smart battery charger 100).
Marty teaches a battery pack interface (Figs. 1 and 3; batteries 112-141 interface with smart battery charger 100 via the charger’s charging plug and the battery’s charging ports 302/304, see ¶[23]) configured to receive a power tool battery pack (¶[11]; power tool battery packs) and provide charging current to the battery pack (¶[13]; “The 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”).
Marty teaches an electronic controller including a processor (Claim 8; it is well understood the controller includes a processor to execute programs).
Marty teaches the electronic controller configured to receive a set of data associated with the power tool battery pack and use of the power tool battery pack (Fig. 4; ¶[28]; in step 420 the controller receives data containing usage history of the battery pack).
Marty teaches determine a time for performing a maintenance procedure based on the set of data wherein the maintenance procedure is at least one selected from a group of determining a maximum capacity of the power tool battery pack (Fig. 4; ¶[29-30]; in steps 430-440 the controller determines an optimal time to initiate a charge cycle on the battery wherein the charge cycle is based on an optimal charge level based on a determined maximum capacity).
Marty teaches determine one or more maintenance procedures to be performed on the power tool battery pack based on the set of data (Fig. 4; ¶[30]; in step 440 it is determined to charge to an optimal level based on the usage history of the battery pack).
Marty teaches perform the one or more maintenance procedures on the power tool battery pack at the determined time (Fig. 4; ¶[30]; in step 450 the charge cycle to charge the battery pack to an optimal charge level is started at the optimal time).
With respect to dependent claims 3 and 10, Marty teaches the invention as discussed above in claims 1 and 7, respectively. Further, Marty teaches wherein the maintenance procedure is to determine the maximum capacity of the power tool battery pack (¶[29-30]; the controller determines the maximum capacity of the battery).
With respect to claims 8 and 25, Marty teaches the invention as discussed above in claims 7 and 1, respectively. Further, Marty teaches the set of data includes one or more of a time of day, a day of week, a history of use of the power tool battery pack, or charge level of the power tool battery pack (Fig. 2; ¶[20]; the plot illustrates a usage history of the battery pack with the x-axis showing the time of day and the day of week for a 5-day week window and the y-axis showing the charge level of the battery pack).
With respect to claim 9, Marty teaches the invention as discussed above in claim 7. Further, Marty teaches wherein determining one or more maintenance procedures to be performed includes determining whether to perform the one or more maintenance procedures at the determined time (Fig. 4; in steps 430-450 it is determined whether to perform the optimal charge at the optimal time based on the usage history).
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.
Claims 2 and 22-27 are rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (USPGPN 20110057603), and further in view of Abbott et al. (USPGPN 20190227528; identified by the applicant in the Information Disclosure Statement (IDS)).
With respect to claim 2, Marty teaches the invention as discussed above in claim 1. Further, Marty teaches wherein the electronic controller further includes a charger controller including the processor (Claim 8; the controller executes charging control functions).
Marty teaches receive the set of data associated with the power tool battery pack and use of the power tool battery pack (Fig. 4; ¶[28]; in step 420 the controller receives data containing usage history of the battery pack).
Marty teaches determine the time for performing the maintenance procedure based on the set of data (Fig. 4; ¶[29-30]; in steps 430-440 the controller determines an optimal time to initiate a charge cycle on the battery wherein the charge cycle is based on an optimal charge level based on a determined maximum capacity).
Marty teaches determine the one or more maintenance procedures to be performed on the power tool battery pack based on the set of data (Fig. 4; ¶[30]; in step 440 it is determined to charge to an optimal level based on the usage history of the battery pack).
Marty teaches wherein the charger controller is configured to perform the one or more maintenance procedures on the power tool battery pack at the determined time (Fig. 4; ¶[30]; in step 450 the charge cycle to charge the battery pack to an optimal charge level is started at the optimal time).
However, Marty fails to explicitly teach a machine learning controller including a second processor and a machine learning control program; wherein the machine learning controller is configured to execute the instructions using the machine learning program.
Abbott teaches a machine learning controller including a second processor and a machine learning control program (Fig. 5B; a machine learning controller 540 including a second processor 575 and a machine learning control program 585).
Abbott teaches wherein the machine learning controller is configured to execute the instructions using the machine learning program (Fig. 5B; the machine learning controller 540 is configured to execute instructions using the machine learning control program 585).
Adding a machine learning (ML) controller alongside a normal controller helps handle complex, changing, or non-linear systems that standard controllers struggle to manage alone. As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Abbott’s electronic control assembly architecture to Marty’s smart battery charger. The advantage being the machine learning controller performs a machine learning program and outputs results to the second controller which then controls the system based on the output results (see Abbott’s abstract) thus improving system operation.
With respect to claim 22, Marty teaches the invention as discussed above in claim 7. Further, Marty teaches wherein the electronic controller includes a charger controller (Claim 8; the controller executes charging control functions).
Marty teaches receiving the set of data (Fig. 4; ¶[28]; in step 420 the controller receives data containing usage history of the battery pack).
Marty teaches determining the time for performing the maintenance procedure based on the set of data (Fig. 4; ¶[29-30]; in steps 430-440 the controller determines an optimal time to initiate a charge cycle on the battery wherein the charge cycle is based on an optimal charge level based on a determined maximum capacity).
However, Marty fails to explicitly teach a machine learning controller, wherein the machine learning controller is configured to execute the instructions.
Abbott teaches a machine learning controller including a second processor and a machine learning control program (Fig. 5B; a machine learning controller 540 including a second processor 575 and a machine learning control program 585).
Abbott teaches wherein the machine learning controller is configured to execute the instructions (Fig. 5B; the machine learning controller 540 is configured to execute instructions using the machine learning control program 585).
Adding a machine learning (ML) controller alongside a normal controller helps handle complex, changing, or non-linear systems that standard controllers struggle to manage alone. As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Abbott’s electronic control assembly architecture to Marty’s smart battery charger. The advantage being the machine learning controller performs a machine learning program and outputs results to the second controller which then controls the system based on the output results (see Abbott’s abstract) thus improving system operation.
With respect to claim 23, Marty teaches the invention as discussed above in claim 22. Further, Marty teaches determining the one or more maintenance procedures to be performed on the power tool battery pack based on the set of data (Fig. 4; ¶[30]; in step 440 it is determined to charge to an optimal level based on the usage history of the battery pack).
However, Marty fails to explicitly teach using the machine learning controller.
Abbott teaches using the machine learning controller (Fig. 5B; the machine learning controller 540 is configured to execute instructions using the machine learning control program 585).
Adding a machine learning (ML) controller alongside a normal controller helps handle complex, changing, or non-linear systems that standard controllers struggle to manage alone. As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Abbott’s electronic control assembly architecture to Marty’s smart battery charger. The advantage being the machine learning controller performs a machine learning program and outputs results to the second controller which then controls the system based on the output results (see Abbott’s abstract) thus improving system operation.
With respect to claim 24, Marty teaches the invention as discussed above in claim 23. Further, Marty teaches performing, by the charger controller, the one or more maintenance procedures on the power tool battery pack at the determined time (Fig. 4; ¶[30]; in step 450 the charge cycle to charge the battery pack to an optimal charge level is started at the optimal time).
With respect to claim 26, Marty teaches the invention as discussed above in claim 1. Further, Marty teaches wherein the electronic controller further includes a charger controller including the processor (Claim 8; the controller executes charging control functions).
Marty teaches determine the time for performing the maintenance procedure based on the set of data (Fig. 4; ¶[29-30]; in steps 430-440 the controller determines an optimal time to initiate a charge cycle on the battery wherein the charge cycle is based on an optimal charge level based on a determined maximum capacity).
Marty teaches wherein the charger controller is configured to perform the one or more maintenance procedures on the power tool battery pack at the determined time (Fig. 4; ¶[30]; in step 450 the charge cycle to charge the battery pack to an optimal charge level is started at the optimal time).
However, Marty fails to explicitly teach a machine learning controller including a second processor and a machine learning control program; wherein the machine learning controller is configured to execute the instructions using the machine learning program.
Abbott teaches a machine learning controller including a second processor and a machine learning control program (Fig. 5B; a machine learning controller 540 including a second processor 575 and a machine learning control program 585).
Abbott teaches wherein the machine learning controller is configured to execute the instructions using the machine learning program (Fig. 5B; the machine learning controller 540 is configured to execute instructions using the machine learning control program 585).
Adding a machine learning (ML) controller alongside a normal controller helps handle complex, changing, or non-linear systems that standard controllers struggle to manage alone. As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Abbott’s electronic control assembly architecture to Marty’s smart battery charger. The advantage being the machine learning controller performs a machine learning program and outputs results to the second controller which then controls the system based on the output results (see Abbott’s abstract) thus improving system operation.
With respect to claim 27, Marty teaches the invention as discussed above in claim 1. Further, Marty teaches wherein the electronic controller further includes a charger controller including the processor (Claim 8; the controller executes charging control functions).
Marty teaches wherein the machine learning controller is configured to determine the one or more maintenance procedures to be performed on the power tool battery pack based on the set of data (Fig. 4; ¶[30]; in step 440 it is determined to charge to an optimal level based on the usage history of the battery pack).
However, Marty fails to explicitly teach a machine learning controller including a second processor and a machine learning control program; wherein the machine learning controller is configured to execute the instructions using the machine learning program.
Abbott teaches a machine learning controller including a second processor and a machine learning control program (Fig. 5B; a machine learning controller 540 including a second processor 575 and a machine learning control program 585).
Abbott teaches wherein the machine learning controller is configured to execute the instructions using the machine learning program (Fig. 5B; the machine learning controller 540 is configured to execute instructions using the machine learning control program 585).
Adding a machine learning (ML) controller alongside a normal controller helps handle complex, changing, or non-linear systems that standard controllers struggle to manage alone. As such, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Abbott’s electronic control assembly architecture to Marty’s smart battery charger. The advantage being the machine learning controller performs a machine learning program and outputs results to the second controller which then controls the system based on the output results (see Abbott’s abstract) thus improving system operation.
Claims 4-6 and 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Marty in view of Abbott, and further in view of Osada et al. (USPGPN 20250065776).
With respect to claims 4 and 11, Marty teaches the invention as discussed above in claims 1 and 7, respectively. However, Marty fails to explicitly teach wherein the maintenance procedure is cell balancing.
Osada teaches wherein the maintenance procedure is cell balancing (Fig. 2; ¶[85]; a balancing circuit 45 for balancing processing).
Cell balancing is performed to equalize the state of charge and voltage of all individual cells in a multi-cell battery pack, maximizing total capacity, extending lifespan, and preventing safety hazards. As such, it would have been obvious for one of ordinary skill in the art prior to the effective filing date to have adapted Osada’s cell balancing to Marty’s smart battery charging. The advantage being cell balancing may be performed based on the data obtained (see Osada’s abstract) thus improving system performance and efficiency.
With respect to claims 5 and 12, Marty teaches the invention as discussed above in claims 1 and 7, respectively. However, Marty fails to explicitly teach wherein the maintenance procedure includes cooling the power tool battery pack.
Osada teaches wherein the maintenance procedure includes cooling the battery pack (¶[359]; cooling of the battery pack may be performed).
Cooling or heating a battery pack keeps it within its ideal temperature range to maximize performance, safety, and lifespan. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Osada’s cooling and heating features to Marty’s smart battery charger. The advantage being heating and cooling of the battery pack may be performed thus minimizing the effects of the external temperatures (see ¶[359] of Osada) as such improving system performance.
With respect to claims 6 and 13, Marty teaches the invention as discussed above in claims 1 and 7, respectively. However, Marty fails to explicitly teach wherein the maintenance procedure includes heating the power tool battery pack.
Osada teaches wherein the maintenance procedure includes heating the battery pack (¶[359]; heating of the battery pack may be performed).
Cooling or heating a battery pack keeps it within its ideal temperature range to maximize performance, safety, and lifespan. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date to have adapted Osada’s cooling and heating features to Marty’s smart battery charger. The advantage being heating and cooling of the battery pack may be performed thus minimizing the effects of the external temperatures (see ¶[359] of Osada) as such improving system performance.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The additional prior art identified by the applicant in the Information Disclosure Statement (IDS) were considered by the examiner, however, for examination purposes were not relied upon for citation purposes.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Frank A Silva whose telephone number is (703)756-1698. The examiner can normally be reached Monday - Friday 09:30 am -06:30 pm 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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/FRANK ALEXIS SILVA/Examiner, Art Unit 2859
/DREW A DUNN/Supervisory Patent Examiner, Art Unit 2859
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