DETAILED ACTION
Notice of Pre-AIA or AIA Status
Applicant’s amendment, filed 01/23/2026, for application number 18/505,188 has been received and entered into record. Claims 1, 10, 13, 17 and 19 are amended. Thus, claims 1-20 are presented for examination.
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 § 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.
Claims 1, 3, 5-7, 11 are rejected under 35 U.S.C. 103 as being unpatentable over Slaby et al. (US 2017/0223634 A1) in view of Wang et al. (US 2017/0212169 A1).
Regarding claim 1, Slaby teaches An Information Handling System (IHS) (Figure 1, mobile device 100), that comprises:
an Embedded Controller (EC) (Figure 1, battery controller 110); and
a memory coupled to the EC (Figure 1, memory device 116), wherein the memory comprises program instructions that, upon execution by the EC, cause the IHS to perform operations that comprise:
determine whether the IHS suffered an accident, based at least in part on sensor information determined to indicate acceleration in excess of an acceleration threshold for a time period in excess of a duration threshold (“In practice, the accelerometer 126 can be implemented to detect a range of acceleration, such as an approximate range between 9.75 m/s2 and 9.85 m/s2, which can then be interpreted as a device falling.” Par 0020 and “At 408, a determination is made as to whether impact of the device has been detected within a set number of seconds. For example, the battery controller 110 of the mobile device 100 determines whether an impact of the device has occurred based on the accelerometer sensor inputs 122.” Par 0039 and Figure 4); and
determine a changed charge and discharge cycle based at least in part on a severity of the accident (“the battery controller 110 is implemented to receive a sensor input 122 of detected acceleration of the mobile device 100 from the accelerometer 126 (e.g., a sensor 124), and then initiate a switch from the secondary battery 104 to the primary battery 102 as the power source based on the detected acceleration of the mobile device.” Par 0021 and “When a fall and/or bounce of the mobile device 100 has completed, as determined based on a significant decrease in device acceleration (e.g., approximately zero acceleration), the battery controller 110 can initiate switching back to the secondary battery 104 from the primary battery 102” par 0023) [the charge/discharge cycle is changed (switching power sources) in response to detection of acceleration (severity of accident)].
However, Slaby does not explicitly teach in response to a determination that the IHS suffered an accident: perform a charge and discharge cycle of a battery; determine a measurement of the charge and discharge cycle; determine a current value of a metric of the battery, based, at least in part, on the measurement; retrieve a stored value of the metric determined before the accident; determine a post-accident metric delta, based at least in part, on a comparison between the current value and the stored value; and in response to a determination that the post-accident metric delta is in excess of a predetermined threshold: change a battery parameter.
In the analogous art, Wang teaches in response to a determination that the IHS suffered an accident:
perform a charge and discharge cycle of a battery (“In response to determining that the battery does not have the first capacity sufficient to support the conditioning cycle, micro-controller 212 charges the cells 290-294 of battery 150 (block 406),” Par 0040 [battery not having first capacity means its current charge level is too low to safely execute tasks, corresponding to an error state (accident)] and “In response to the real time battery voltage being equal to Vdch 242, micro-controller 212 stops or terminates the conditioning cycle (block 423) and estimates the battery capacity 238 (block 424)… micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and “performing periodic battery learning (or conditioning) cycles is required to determine whether the battery has adequate capacity to allow the backup functions to be completed.” Par 0005 and paragraphs 36-44 and Figures 2-4);
determine a measurement of the charge and discharge cycle (“During the discharge of battery 150, battery parameters 224 are measured including battery voltages 230, battery currents 232, and elapsed discharge times 234… Battery voltages 230 can be measured by voltage sensors 340, 342, 346 and 348. Battery currents 232 can be measured by current sensor 344. ” Par 0041);
determine a current value of a metric of the battery, based, at least in part, on the measurement (“A capacity of the battery 238 is estimated based on the battery parameters measured at the constant discharge rate.” Par 0036) [the capacity corresponds to the metric which is based on battery parameters (measurement)];
retrieve a stored value of the metric determined before the accident (“Non-volatile memory 222 also stores fully charged battery capacity 236, an estimated battery capacity 238, minimum battery capacity threshold 240, and discharge voltage Vdch 242.” par 0032) [the estimated battery capacity and minimum threshold 240 corresponds to a stored value determined before accident];
determine a post-accident metric delta, based at least in part, on a comparison between the current value and the stored value (“Micro-controller 212 determines if the capacity of the battery 238 exceeds a predetermined minimum threshold capacity 240 of the battery (decision block 430).” Par 0044 and claim 1) [the comparison of current value 238 against stored value 240 to determine if the battery is sufficient corresponds to the delta]; and
in response to a determination that the post-accident metric delta is in excess of a predetermined threshold (Figure 4, decision block 430):
change a battery parameter (“In response to determining that the capacity of the battery 238 exceeds the predetermined minimum threshold capacity 240 of the battery, micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and “the constant discharge rate and the controlled voltage during each particular conditioning cycle can be adjusted by micro-controller 212 to reduce wear and extend the battery life.” Par 0045 and claim 1).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby and Wang before him before the effective filing date of the claimed invention, to have modified Slaby to incorporate the teachings of Wang to compare the current and stored values to determine a post-accident metric delta to employ a partial discharge method to reduce battery wear and extend battery life as compared to the full discharge method. (Wang, paragraph 29)
Regarding claim 3, Slaby and Wang teach the IHS of claim 1. Slaby further teaches wherein the accident comprises at least one of: a drop, a fall, a throw, a hit, a crash, an impact, or a collision involving the IHS or a component thereof (“For example, the mobile device 100 may fall into a puddle of water,” par 0025 and “an enhancement to detecting an acceleration of the device (e.g., an indication that the device is falling)” par 0034).
Regarding claim 5, Slaby and Wang teach the IHS of claim 1, Slaby further teaches wherein the battery parameter comprises an IHS power mode (“maintain device power in any operation or standby mode.” Par 0011 and “ the battery controller 110 is also implemented to initiate the battery disconnect safeguards if the mobile device is in a standby mode, such as if the display is turned off, but the device is still otherwise powered and operational.” Par 0022) [the system monitors the IHS’s current operation/standby mode, corresponding to a power mode].
Regarding claim 6, Slaby and Wang teach the IHS of claim 1. Wang further teaches wherein the battery parameter comprises a threshold value in response to which the IHS is configured to execute a selected operation (“In response to the real time battery voltage being equal to Vdch 242, micro-controller 212 stops or terminates the conditioning cycle (block 423)” par 0044 and “In response to the elapsed time of timer 2 246 not being equal to Tcpc 248, micro-controller 212 determines if the real time battery voltage (as measured by Vin sensor 342) is equal to threshold battery voltage (i.e., end of discharge voltage Vdch 242) (block 422).” Par 0042).
Regarding claim 7, Slaby and Wang teach the IHS of claim 6. Slaby further teaches wherein the selected operation comprises entry into a low-power mode (“the battery controller 110 is also implemented to initiate the battery disconnect safeguards if the mobile device is in a standby mode, such as if the display is turned off, but the device is still otherwise powered and operational.” Par 0022) [the standby mode corresponds to the low-power mode (display is off)].
Regarding claim 11, Grobelny and Wang teach the IHS of claim 1, Wang further teaches wherein the program instructions, upon execution by the EC, further cause the IHS to notify a user of the IHS to disconnect an external device from the IHS (“I/O controllers 130 also support connection to and forwarding of output signals to one or more connected output device(s) 134, such as a monitor or display device or audio speaker(s) or light emitting diodes (LEDs).” Par 0025 and “If the battery does not have adequate capacity to allow the backup functions to be completed, the user or system should take appropriate actions to mitigate the risk of data loss.” Par 0005) [the signal on the display device/LEDs may prompt the user to take appropriate action, including disconnecting a device].
Claims 2, 4, 8, 9, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Slaby et al. (US 2017/0223634 A1) and Wang et al. (US 2017/0212169 A1) in view of Grobelny et al. (US 2019/0196575 A1).
Regarding claim 2, Slaby and Wang teach the IHS of claim 1. However, Slaby and Wang do not explicitly teach wherein to determine that the IHS suffered the accident, the EC is configured to check an accident flag or register prior to a current reboot of the IHS.
In the analogous art, Grobelny teaches wherein to determine that the IHS suffered the accident, the EC is configured to check an accident flag or register prior to a current reboot of the IHS (“Additionally or alternatively, certain exception events may be pre-designated for diagnostic testing with a diagnostic flag (e.g., “1”), while other type of events are designated with a no-diagnostic flag (e.g., “0”) to allow normal booting.” Par 0048) [the exception events correspond to accidents].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to use a diagnostic flag to determine whether an accident occurred to allow for normal booting and maintaining battery health. (Grobelny, paragraph 48)
Regarding claim 4, Slaby and Wang teach the IHS of claim 1. However, Slaby and Wang do not explicitly teach wherein the battery parameter comprises at least one of: a battery charge rate, or a battery discharge rate.
In the analogous art, Grobelny teaches wherein the battery parameter comprises at least one of: a battery charge rate, or a battery discharge rate (“battery mode selection module 406 may allow BMU 266 to switch between a standard charging mode, an express charging mode, a primarily AC charging mode, an adaptive charging mode, and a custom charging mode; each of these charging modes having different settings or specifications (e.g., voltages, currents, temperatures, times, etc.) applied to the battery.” Par 0055) [the battery management policy’s different charging modes specify the physical parameters that define the charging/discharging rates].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to observe the battery charge/discharge rate to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Regarding claim 8, Slaby and Wang teach the IHS of claim 1. However, Slaby and Wang do not explicitly teach wherein the program instructions, upon execution by the EC, further cause the IHS to select the battery parameter to change based, at least in part, upon an accident policy.
In the analogous art, Grobelny teaches wherein the program instructions, upon execution by the EC, further cause the IHS to select the battery parameter to change based, at least in part, upon an accident policy (“The updated or adjusted battery management policy file is provided to module 404 and/or pass-through module 415 of SBIOS 306, and then to pass-through module 405 of EC 283, before battery configuration parameters are applied by battery mode selection module 406 and/or glide path module 407.” Par 0054 and “battery management policy file … may establish a conservative (low) maximum charging voltage or current.” Par 0071 and paragraph 51) [policy adjustment is driven by monitoring usage data and shock events].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to change a battery parameter in compliance with an accident policy to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Claim 20 corresponds to claim 8 and is rejected accordingly.
Regarding claim 9, Slaby and Wang teach the IHS of claim 1. However, Slaby and Wang do not explicitly teach wherein the program instructions, upon execution by the EC, further cause the IHS to select the battery parameter to change based, at least in part, upon execution of an Artificial Intelligence (AI) or Machine Learning (ML) model.
In the analogous art, Grobelny teaches wherein the program instructions, upon execution by the EC, further cause the IHS to select the battery parameter to change based, at least in part, upon execution of an Artificial Intelligence (AI) or Machine Learning (ML) model (“Analytics engine 410 compares the usage data for the particular IHS with usage data for other IHSs and/or other user(s), and adjusts a battery management policy file based upon the comparison.” Par 0053 and “For purposes of this disclosure, an IHS may include any instrumentality or aggregate of instrumentalities operable to compute…or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes.” Par 0019 and paragraphs 51 and 71).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to change a battery parameter in compliance with an AI/ML model to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Claim 19 corresponds to claim 9 and is rejected accordingly.
Regarding claim 12, Slaby and Wang teach the IHS of claim 1. However, Slaby and Wang do not explicitly teach wherein the program instructions, upon execution by the EC, further cause the IHS to store an indication of the battery parameter changed in an Advanced Configuration and Power Interface (ACPI) table, and wherein an Operating System (OS) of the IHS is configured to run a diagnostics routine based, at least in part, upon the indication.
In the analogous art, Grobelny teaches wherein the program instructions, upon execution by the EC, further cause the IHS to store an indication of the battery parameter changed in an Advanced Configuration and Power Interface (ACPI) table, and wherein an Operating System (OS) of the IHS is configured to run a diagnostics routine based, at least in part, upon the indication (“Battery data 501 is received by battery data collector 504 and stored in a corresponding table by table processor 505… User activity 503 is detected by OS 308 and sent to triggering callback module 507. ” Par 0064 and “Additionally or alternatively, certain exception events may be pre-designated for diagnostic testing with a diagnostic flag (e.g., “1”), while other type of events are designated with a no-diagnostic flag (e.g., “0”) to allow normal booting.” Par 0048).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to store changed battery parameters in an ACPI table and have the OS run a diagnostics routine based on the indication to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Regarding claim 13, Slaby teaches a memory device (Figure 1, mobile device 100, Figure 5, device 500) that comprises program instructions stored thereon that, upon execution by a processor of an Information Handling System (IHS), cause the IHS to instantiate an Operating System (OS) (Figure 5, OS 516) configured to perform operations that comprise:
receive an indication that the IHS suffered an accident, based at least in part on sensor information determined to indicate acceleration in excess of an acceleration threshold for a time period in excess of a duration threshold (“In practice, the accelerometer 126 can be implemented to detect a range of acceleration, such as an approximate range between 9.75 m/s2 and 9.85 m/s2, which can then be interpreted as a device falling.” Par 0020 and “At 408, a determination is made as to whether impact of the device has been detected within a set number of seconds. For example, the battery controller 110 of the mobile device 100 determines whether an impact of the device has occurred based on the accelerometer sensor inputs 122.” Par 0039 and Figure 4); and
determine a changed charge and discharge cycle based at least in part on a severity of the accident (“the battery controller 110 is implemented to receive a sensor input 122 of detected acceleration of the mobile device 100 from the accelerometer 126 (e.g., a sensor 124), and then initiate a switch from the secondary battery 104 to the primary battery 102 as the power source based on the detected acceleration of the mobile device.” Par 0021 and “When a fall and/or bounce of the mobile device 100 has completed, as determined based on a significant decrease in device acceleration (e.g., approximately zero acceleration), the battery controller 110 can initiate switching back to the secondary battery 104 from the primary battery 102” par 0023) [the charge/discharge cycle is changed (switching power sources) in response to detection of acceleration (severity of accident)].
However, Slaby does not explicitly teach in response to a determination that the IHS suffered an accident: perform a charge and discharge cycle of a battery; determine a measurement of the charge and discharge cycle; determine, by execution of an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement; retrieve a stored value of the metric determined before the accident; determine a post-accident metric delta, based at least in part, on a comparison between the current value and the stored value in response to a determination that the post-accident metric delta is in excess of a predetermined threshold: modify a battery parameter, wherein the changed charge and discharge cycle comprises a deep cycle with a full discharge followed by a full charge when the accident is severe.
In the analogous art, Wang teaches in response to a determination that the IHS suffered an accident:
perform a charge and discharge cycle of a battery (“In response to determining that the battery does not have the first capacity sufficient to support the conditioning cycle, micro-controller 212 charges the cells 290-294 of battery 150 (block 406),” Par 0040 [battery not having first capacity means its current charge level is too low to safety execute tasks, corresponding to an error state (accident)] and “In response to the real time battery voltage being equal to Vdch 242, micro-controller 212 stops or terminates the conditioning cycle (block 423) and estimates the battery capacity 238 (block 424)… micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and “performing periodic battery learning (or conditioning) cycles is required to determine whether the battery has adequate capacity to allow the backup functions to be completed.” Par 0005 and paragraphs 36-44 and Figures 2-4);
determine a measurement of the charge and discharge cycle (“During the discharge of battery 150, battery parameters 224 are measured including battery voltages 230, battery currents 232, and elapsed discharge times 234… Battery voltages 230 can be measured by voltage sensors 340, 342, 346 and 348. Battery currents 232 can be measured by current sensor 344. ” Par 0041);
retrieve a stored value of the metric determined before the accident (“Non-volatile memory 222 also stores fully charged battery capacity 236, an estimated battery capacity 238, minimum battery capacity threshold 240, and discharge voltage Vdch 242.” par 0032) [the estimated battery capacity and minimum threshold 240 corresponds to a stored value determined before accident];
determine a post-accident metric delta, based at least in part, on a comparison between the current value and the stored value (“Micro-controller 212 determines if the capacity of the battery 238 exceeds a predetermined minimum threshold capacity 240 of the battery (decision block 430).” Par 0044 and claim 1) [the comparison of current value 238 against stored value 240 to determine if the battery is sufficient corresponds to the delta]; and
in response to a determination that the post-accident metric delta is in excess of a predetermined threshold (Figure 4, decision block 430):
modify a battery parameter (“In response to determining that the capacity of the battery 238 exceeds the predetermined minimum threshold capacity 240 of the battery, micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and “the constant discharge rate and the controlled voltage during each particular conditioning cycle can be adjusted by micro-controller 212 to reduce wear and extend the battery life.” Par 0045 and claim 1).
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby and Wang before him before the effective filing date of the claimed invention, to have modified Slaby to incorporate the teachings of Wang to compare the current and stored values to determine a post-accident metric delta to employ a partial discharge method to reduce battery wear and extend battery life as compared to the full discharge method. (Wang, paragraph 29)
However, Slaby and Wang do not explicitly teach determine, by execution of an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement; wherein the changed charge and discharge cycle comprises a deep cycle with a full discharge followed by a full charge when the accident is severe.
In the analogous art, Grobelny teaches determine, by execution of an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement (“Analytics engine 410 compares the usage data for the particular IHS with usage data for other IHSs and/or other user(s), and adjusts a battery management policy file based upon the comparison.” Par 0053 and “For purposes of this disclosure, an IHS may include any instrumentality or aggregate of instrumentalities operable to compute…or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes.” Par 0019 and “Monitoring module 302 may be configured to perform operations such as, for example: using a battery gas gauge to monitor battery charge cycles 351, determining the state of charge (SoC) and/or battery cell state of health (SoH) 353,” par 0044) [the analytics engine (corresponding to an AI/ML model) processes usage data (measurements) to determine battery metrics (health and charge policies)]; and
wherein the changed charge and discharge cycle comprises a deep cycle with a full discharge followed by a full charge when the accident is severe (“The discharge at a constant rate is to continue until either T=Tcpc or V=Vdch, whichever is reached first. Tcpc is the duration at constant discharge rate corresponding to the minimum battery capacity threshold 240. If T=Tcpc is reached first, this means that V>Vdch and battery 150 is good. If V=Vdch is reached first, battery 150 is bad.” Par 0043 and “In response to determining that the capacity of the battery 238 exceeds the predetermined minimum threshold capacity 240 of the battery, micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and Figure 4) [the Vdch cycle represents full discharge cycle and block 436 corresponds to following full charge cycle].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to use analytics to determine battery metrics and complete a full discharge and full charge cycle in case of a severe accident to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Regarding claim 14, Slaby, Wang and Grobelny teach the memory device of claim 13. Grobelny further teaches wherein the OS is configured to receive the indication from an Embedded Controller (EC) coupled to the processor (“BMU 266 may send usage data 401 to analytics engine 410 of cloud 314 via pass-through module 402 of EC 283, pass-through module 408 of SBIOS 306, and data agent 409 of OS 308.” Par 0053 and Figures 2-4).
Regarding claim 15, Slaby, Wang and Grobelny teach the memory device of claim 13. Grobelny further teaches wherein the program instructions, upon execution by the processor, further cause the OS to select the battery parameter to modify based, at least in part, upon execution of an Artificial Intelligence (AI) or Machine Learning (ML) model (“Analytics engine 410 compares the usage data for the particular IHS with usage data for other IHSs and/or other user(s), and adjusts a battery management policy file based upon the comparison.” Par 0053 and “For purposes of this disclosure, an IHS may include any instrumentality or aggregate of instrumentalities operable to compute…or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes.” Par 0019).
Regarding claim 16, Slaby, Wang and Grobelny teach the memory device of claim 13. Grobelny further teaches wherein the program instructions, upon execution by the processor, further cause the OS to select the battery parameter to modify modification based, at least in part, upon an accident policy (“The updated or adjusted battery management policy file is provided to module 404 and/or pass-through module 415 of SBIOS 306, and then to pass-through module 405 of EC 283, before battery configuration parameters are applied by battery mode selection module 406 and/or glide path module 407.” Par 0054 and Figure 4) [policy adjustment is driven by monitoring usage data and shock events].
Regarding claim 17, Slaby teaches a method, comprising:
determining, by an Embedded Controller (EC), whether an Information Handling System (IHS) has suffered an accident based at least in part on sensor information determined to indicate acceleration in excess of an acceleration threshold for a time period in excess of a duration threshold (“In practice, the accelerometer 126 can be implemented to detect a range of acceleration, such as an approximate range between 9.75 m/s2 and 9.85 m/s2, which can then be interpreted as a device falling.” Par 0020 and “At 408, a determination is made as to whether impact of the device has been detected within a set number of seconds. For example, the battery controller 110 of the mobile device 100 determines whether an impact of the device has occurred based on the accelerometer sensor inputs 122.” Par 0039 and Figure 4); and
determining a changed charge and discharge cycle based at least in part on a severity of the accident (“the battery controller 110 is implemented to receive a sensor input 122 of detected acceleration of the mobile device 100 from the accelerometer 126 (e.g., a sensor 124), and then initiate a switch from the secondary battery 104 to the primary battery 102 as the power source based on the detected acceleration of the mobile device.” Par 0021 and “When a fall and/or bounce of the mobile device 100 has completed, as determined based on a significant decrease in device acceleration (e.g., approximately zero acceleration), the battery controller 110 can initiate switching back to the secondary battery 104 from the primary battery 102” par 0023) [the charge/discharge cycle is changed (switching power sources) in response to detection of acceleration (severity of accident)].
However, Slaby does not explicitly teach in response to determining the IHS suffered an accident: performing a charging and discharging cycle of a battery; determining a measurement of the charging and discharging cycle; determining, by executing an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement; retrieving a stored value of the metric determined before the accident; determining a post-accident metric delta, based at least in part, on comparing the current value to the stored value; and in response to determining that the post-accident metric delta exceeds a predetermined threshold: sending an indication, from the EC to a Battery Management Unit (BMU), to cause a reduction in a charging rate of a battery, and wherein the changed battery charge and discharge cycle comprises a shallow cycle with a partial discharge followed by a partial charge when the accident is less than severe.
In the analogous art, Wang teaches in response to determining the IHS suffered an accident:
performing a charge and discharge cycle of a battery (“In response to determining that the battery does not have the first capacity sufficient to support the conditioning cycle, micro-controller 212 charges the cells 290-294 of battery 150 (block 406),” Par 0040 [battery not having first capacity means its current charge level is too low to safety execute tasks, corresponding to an error state (accident)] and “In response to the real time battery voltage being equal to Vdch 242, micro-controller 212 stops or terminates the conditioning cycle (block 423) and estimates the battery capacity 238 (block 424)… micro-controller 212 charges the cells 290-294 of battery 150 back to a full charge (capacity) (block 436).” Par 0044 and “performing periodic battery learning (or conditioning) cycles is required to determine whether the battery has adequate capacity to allow the backup functions to be completed.” Par 0005 and paragraphs 36-44 and Figures 2-4);
determining a measurement of the charge and discharge cycle (“During the discharge of battery 150, battery parameters 224 are measured including battery voltages 230, battery currents 232, and elapsed discharge times 234… Battery voltages 230 can be measured by voltage sensors 340, 342, 346 and 348. Battery currents 232 can be measured by current sensor 344. ” Par 0041);
retrieving a stored value of the metric determined before the accident (“Non-volatile memory 222 also stores fully charged battery capacity 236, an estimated battery capacity 238, minimum battery capacity threshold 240, and discharge voltage Vdch 242.” par 0032) [the estimated battery capacity and minimum threshold 240 corresponds to a stored value determined before accident];
determining a post-accident metric delta, based at least in part, on comparing between the current value to the stored value (“Micro-controller 212 determines if the capacity of the battery 238 exceeds a predetermined minimum threshold capacity 240 of the battery (decision block 430).” Par 0044 and claim 1) [the comparison of current value 238 against stored value 240 to determine if the battery is sufficient corresponds to the delta].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Grobelny and Wang before him before the effective filing date of the claimed invention, to have modified Grobelny to incorporate the teachings of Wang to compare the current and stored values to determine a post-accident metric delta to employ a partial discharge method to reduce battery wear and extend battery life as compared to the full discharge method. (Wang, paragraph 29)
However, Slaby and Wang do not explicitly teach determining, by executing an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement; in response to determining that the post-accident metric delta exceeds a predetermined threshold: sending an indication, from the EC to a Battery Management Unit (BMU), to cause a reduction in a charging rate of a battery, wherein the changed battery charge and discharge cycle comprises a shallow cycle with a partial discharge followed by a partial charge when the accident is less than severe.
In the analogous art, Grobelny teaches determining, by executing an Artificial Intelligence (AI) or Machine Learning (ML) model, a current value of a metric of the battery, based, at least in part, on the measurement (“Analytics engine 410 compares the usage data for the particular IHS with usage data for other IHSs and/or other user(s), and adjusts a battery management policy file based upon the comparison.” Par 0053 and “For purposes of this disclosure, an IHS may include any instrumentality or aggregate of instrumentalities operable to compute…or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes.” Par 0019 and “Monitoring module 302 may be configured to perform operations such as, for example: using a battery gas gauge to monitor battery charge cycles 351, determining the state of charge (SoC) and/or battery cell state of health (SoH) 353,” par 0044) [the analytics engine (corresponding to an AI/ML model) processes usage data (measurements) to determine battery metrics (health and charge policies)];
in response to determining that the post-accident metric delta exceeds a predetermined threshold: sending an indication, from the EC to a Battery Management Unit (BMU), to cause a reduction in a charging rate of a battery (“if the user charges or discharges the battery too aggressively (e.g., more than n times a day, or a threshold number of incomplete charges per day, etc.), these techniques may purposefully restrict battery performance in order to increase the useful life of the battery,” par 0068 and “Commands issued by module 404 may reach BMU 266 via pass-through module 405 of EC 283.” Par 0051),
wherein the changed battery charge and discharge cycle comprises a shallow cycle with a partial discharge followed by a partial charge when the accident is less than severe (“When the user employs the battery, they do shallow discharges and plug back the IHS with battery still charge to 30% or more,” par 0063 “battery mode selection module 406 may allow BMU 266 to switch between a standard charging mode, an express charging mode, a primarily AC charging mode, an adaptive charging mode, and a custom charging mode;” par 0055 and “Primarily AC: Extends battery life by lowering the charge threshold,” par 0059) [primary AC mode may correspond to a partial charge].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang and Grobelny before him before the effective filing date of the claimed invention, to have modified Slaby and Wang to incorporate the teachings of Grobelny to use analytics to determine battery metrics and complete a partial discharge and partial charge cycle in case of a less severe accident to maintain battery health and allow users to take advantage of the full battery capacity. (Grobelny, paragraph 18)
Regarding claim 18, Slaby, Wang and Grobelny teach the method of claim 17, Grobelny further teaches further comprising selecting a magnitude of the reduction in the charging rate of the battery based, at least in part, upon a severity of the accident (“The maximum charging voltage or current may have a first value in response to the local user showing a more aggressive behavior than the other user, the maximum charging voltage or current may have a second value in response to the local user showing a less aggressive behavior than the other user, and the first value may be smaller than the second value.” Par 0008) [the severity of user behavior influences charging rate].
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Slaby et al. (US 2017/0223634 A1) and Wang et al. (US 2017/0212169 A1) in view of Grobelny et al. (US 2019/0196575 A1) and in further view of Vichare et al. (US 2021/0271303 A1).
Regarding claim 10, Slaby, Wang and Grobelny teach the IHS of claim 9. Grobelny further teaches wherein the program instructions, upon execution by the EC, further cause the IHS to execute the AI or ML model based, at least in part, upon: a type of the accident, or a severity of the accident (“the severity of each identified event may also be optionally categorized by event reduction and event identification logic 304 and recorded in a BMU exception log on storage 263.” Par 0047) [this generates data used by analytics engine (functioning as an AI/ML model) to adjust the battery management policy].
However, Slaby, Wang and Grobelny do not explicitly teach wherein execution of the AI or ML model comprises calculation of the current value of the metric of the battery based, at least in part, on the measurement of the charge and discharge cycle.
In the analogous art, Vichare teaches wherein execution of the AI or ML model comprises calculation of the current value of the metric of the battery based, at least in part, on the measurement of the charge and discharge cycle (“the battery capacity correction factor is generated by a first machine learning model trained in part using aggregated parameters of use of rechargeable battery… the monitored parameters of the use of the rechargeable battery comprise a number of discharge cycles and a depth of discharge cycles of the rechargeable battery. ” par 0005 and “Based on such monitored battery operations, the particularized battery capacity machine learning model may be used to generate a battery capacity correction factor … used to adjust the battery capacity measurement” par 0063 and “in addition to the depth and number of recharge cycles, various additional factors may significantly impact the charge capacity of a rechargeable battery.” Par 0018) [the machine learning model generates the current value of the metric of the battery (battery capacity correction factor) by measuring the depth and number of recharge and discharge cycles].
It would have been obvious to a person having ordinary skill in the art, having the teachings of Slaby, Wang, Grobelny and Vichare before him before the effective filing date of the claimed invention, to have modified Slaby, Wang and Grobelny to incorporate the teachings of Vichare to have the AI/ML model use measurements of the charge and discharge cycle to determine the current value of the metric of the battery to provide more accurate reporting of the remaining power, preventing premature shutdowns and allowing the IHS to remain available to the user for longer durations. (Vichare, paragraph 19)
Response to Arguments
Applicant’s arguments, see pages 2-4, filed 1/21/2026, with respect to the rejection(s) of claim(s) 1, 10, 13 and 17 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Slaby and Wang for claim 1; Slaby, Wang and Grobelny for claims 13 and 17; and Slaby, Wang, Grobelny and Vichare for claim 10.
Slaby teaches determining whether the system has suffered an accident based on sensor information indicating an acceleration threshold being exceeded (range of acceleration) for a time period that is exceeded (during a few seconds). Examiner respectfully points to the updated mappings of claims 1, 13 and 17.
Vichare teaches a machine learning model that uses the number and depth (measurements) of the recharge and discharge cycles of the battery to determine correction factors that adjust the battery capacity measurement, corresponding to the current value of the metric of the battery. Examiner respectfully points to the updated mapping of claim 10.
No additional arguments were presented as to the remaining claims. As such, the rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AYMAN FATIMA/Examiner, Art Unit 2176
/JAWEED A ABBASZADEH/Supervisory Patent Examiner, Art Unit 2176