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 .
Drawings
The drawings filed 10/30/2024 are considered acceptable for examination purposes.
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.
Claims 1-14 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a method. Claims 8-14 are directed to a machine or article of manufacture.
Regarding claim 1:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“based on the forecasting data, determining (i) a time within the future time period corresponding to an expected deployment of each of the set of mobile computing devices, and (ii) a target charge level corresponding to the time, the target charge level corresponding to an expected energy demand at each of the set of mobile computing devices following the expected deployment”
The limitation of determining a time within the future time period and a target charge level corresponding to the time can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“A method, comprising: obtaining forecasting data based on historical indications of energy consumed by each of a set of mobile computing devices during a plurality of time periods;”
“obtaining a future time period;”
“generating configuration data including the time and the target charge level; and”
“transmitting the configuration data to each of the set of mobile computing devices to control charging of respective batteries of each of the set of mobile computing devices.”
The limitations of obtaining forecasting data and a future time period falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of generating configuration data falls under mere instructions to apply an exception (MPEP 2106.05(f)). The limitation of transmitting the configuration data falls under post-solution activity and is thus also insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of obtaining forecasting data and a future time period, generating configuration data including the time and the target charge level, and transmitting the configuration data to each of the set of mobile computing devices represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “A method, comprising: obtaining forecasting data based on historical indications of energy consumed by each of a set of mobile computing devices during a plurality of time periods;”, US 20220399733 A1 to Kukla et al. (herein “Kukla”) describes “A basic idea is now in particular that the learning machine is supplied with training information from the past use phases and the past charging phases. This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases” (Paragraph 30). US 20140340051 A1 to Hargrave describes “The use event information is identified by its event type, battery usage and time usage and such information is stored in memory via the usage history information 540”.
With regards to “obtaining a future time period;”, Kukla describes “A 24-hour time frame is preferably used in the determination of the probable energy consumption and the probable duration of the upcoming charging phase” (Paragraph 29). Hargrave describes “The "Y" percent may be a particular amount of usage attributed to a historical use pattern, such as a one hour phone call 3-5 times a week, a particular smartphone application used every day at noon for 30 minutes or more, a presentation performed every Monday morning at 10 am for 1-2 hours, etc.” (Paragraph 19) and “Once a known use percentage "Y" has been tracked by a tracking application for one day, one week, one month, etc., the amount of battery consumption may become a known use variable that is applied to a charging operation for future battery charging efforts” (Paragraph 20).
With regards to “generating configuration data including the time and the target charge level”, Kukla describes “The charging schema for charging the energy storage unit is then set for the upcoming charging phase based on the required charge and the probable duration” (Paragraph 21). Hargrave describes “The amount of battery charge level 212 is then determined and compared to a target level of battery charge based on known usage history or other usage variables (e.g., current usage, estimated usage, time of day, battery capacity, user profile, etc.)” (Paragraph 23).
With regards to “transmitting the configuration data to each of the set of mobile computing devices to control charging of respective batteries of each of the set of mobile computing devices”, Kukla describes “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device. Finally, the energy storage unit is then charged using the charging device” (Paragraph 23). Hargrave describes “A signal may be transmitted to the charge interface in the device 228 or the charge unit 224 to disable/enable the battery charging 221 based on the logic operations described in detail above” (Paragraph 23).
Regarding claim 2, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein obtaining the forecasting data includes obtaining a first one of the time periods, an initial battery charge level, and a final battery charge level fall under data gathering and are thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of generating the forecasting data based on the records can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 3, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein generating the forecasting data includes: training a forecasting model based on the records falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 4, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations of generating, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period; and generating the forecasting data based on the amount of energy consumed can be reasonably performed with the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Regarding claim 5, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of obtaining carbon intensity data for each of a plurality of sub-periods in the future time period falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation wherein the configuration data includes the carbon intensity data falls under falls under post-solution activity and is thus also insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 6, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of obtaining respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 7, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation of generating respective times and target charge levels based on each set of forecasting data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 8:
Step 2A Prong One: The claim recites an abstract idea. Specifically:
“based on the forecasting data, determine (i) a time within the future time period corresponding to an expected deployment of each of the set of mobile computing devices, and (ii) a target charge level corresponding to the time, the target charge level corresponding to an expected energy demand at each of the set of mobile computing devices following the expected deployment”
The limitation of determining a time within the future time period and a target charge level corresponding to the time can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claim 1 includes the following limitations:
“A computing device, comprising: a communications interface; and a processor configured to:”
“obtain forecasting data based on historical indications of energy consumed by each of a set of mobile computing devices during a plurality of time periods;”
“obtain a future time period;”
“generate configuration data including the time and the target charge level; and”
“transmit the configuration data to each of the set of mobile computing devices to control charging of respective batteries of each of the set of mobile computing devices.”
The limitation of a computing device comprising a communications interface and a processor describes a generic system and thus falls under mere instructions to apply an exception (MPEP 2106.05(f)). The limitations of obtaining forecasting data and a future time period falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of generating configuration data falls under mere instructions to apply an exception (MPEP 2106.05(f)). The limitation of transmitting the configuration data falls under post-solution activity and is thus also insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of a computing device comprising a communications interface and a processor, obtaining forecasting data and a future time period, generating configuration data including the time and the target charge level, and transmitting the configuration data to each of the set of mobile computing devices represent functions that are well-understood, routine, and conventional when they are claimed in a merely generic manner within the industry.
With regards to “A computing device, comprising: a communications interface; and a processor configured to:”, Kukla describes “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server” (Paragraph 23). Hargrave describes “Another example embodiment of the present application may include an apparatus that includes a processor” (Paragraph 6) and “a battery charging interface” (Paragraph 6).
With regards to “obtain forecasting data based on historical indications of energy consumed by each of a set of mobile computing devices during a plurality of time periods;”, Kukla describes “A basic idea is now in particular that the learning machine is supplied with training information from the past use phases and the past charging phases. This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases” (Paragraph 30). Hargrave describes “The use event information is identified by its event type, battery usage and time usage and such information is stored in memory via the usage history information 540”.
With regards to “obtain a future time period;”, Kukla describes “A 24-hour time frame is preferably used in the determination of the probable energy consumption and the probable duration of the upcoming charging phase” (Paragraph 29). Hargrave describes “The "Y" percent may be a particular amount of usage attributed to a historical use pattern, such as a one hour phone call 3-5 times a week, a particular smartphone application used every day at noon for 30 minutes or more, a presentation performed every Monday morning at 10 am for 1-2 hours, etc.” (Paragraph 19) and “Once a known use percentage "Y" has been tracked by a tracking application for one day, one week, one month, etc., the amount of battery consumption may become a known use variable that is applied to a charging operation for future battery charging efforts” (Paragraph 20).
With regards to “generate configuration data including the time and the target charge level”, Kukla describes “The charging schema for charging the energy storage unit is then set for the upcoming charging phase based on the required charge and the probable duration” (Paragraph 21). Hargrave describes “The amount of battery charge level 212 is then determined and compared to a target level of battery charge based on known usage history or other usage variables (e.g., current usage, estimated usage, time of day, battery capacity, user profile, etc.)” (Paragraph 23).
With regards to “transmit the configuration data to each of the set of mobile computing devices to control charging of respective batteries of each of the set of mobile computing devices”, Kukla describes “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device. Finally, the energy storage unit is then charged using the charging device” (Paragraph 23). Hargrave describes “A signal may be transmitted to the charge interface in the device 228 or the charge unit 224 to disable/enable the battery charging 221 based on the logic operations described in detail above” (Paragraph 23).
Regarding claim 9, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein the processor is configured to obtain the forecasting data by obtaining a first one of the time periods, an initial battery charge level, and a final battery charge level fall under data gathering and are thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation of generating the forecasting data based on the records can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
Regarding claim 10, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein the processor is configured to generate the forecasting data by: training a forecasting model based on the records falls under mere instructions to apply an exception (MPEP 2106.05(f)).
Regarding claim 11, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitations wherein the processor is configured to: generate, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period; and generate the forecasting data based on the amount of energy consumed can be reasonably performed with the human mind/with pen and paper and thus fall under the “Mental Processes” grouping of abstract ideas.
Regarding claim 12, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein the processor is configured to: obtain carbon intensity data for each of a plurality of sub-periods in the future time period falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)). The limitation wherein the configuration data includes the carbon intensity data falls under falls under post-solution activity and is thus also insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 13, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein the processor is configured to: obtain respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices falls under data gathering and is thus insignificant extra-solution activity (MPEP 2106.05(g)).
Regarding claim 14, the additional limitations do not integrate the judicial exception into practical application or add significantly more to the judicial exception. The limitation wherein the processor is configured to: generate respective times and target charge levels based on each set of forecasting data can be reasonably performed with the human mind/with pen and paper and thus falls under the “Mental Processes” grouping of abstract ideas.
The examiner notes that amending the independent claims (claim 1 and 8) to recite a limitation analogous to “control charging of respective batteries of each of the set of mobile computing devices according to the received configuration data” would serve to overcome the 101 rejection.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 6-8, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220399733 A1 to Kukla et al. (herein "Kukla") in light of US 20200313434 A1 to Khanna et al. (herein "Khanna").
Regarding claim 1, Kukla teaches a method (abstract), comprising:
obtaining forecasting data based on historical indications of energy consumed by a mobile computing device during a plurality of time periods (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”, “This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases”);
obtaining a future time period (Paragraph 29 “A 24-hour time frame is preferably used in the determination of the probable energy consumption and the probable duration of the upcoming charging phase”);
based on the forecasting data, determining (i) a time within the future time period corresponding to an expected deployment of the mobile computing device (Paragraph 72 “In this figure, the alternating sequence of use and charging phases N, N*, L, L* can be seen as well as an upcoming charging phase L* and a subsequent use phase N*” specifically the subsequent use phase, Fig. 4, Paragraph 73 “The learning machine 18 is supplied with training information 20 from the past use phases N and the past charging phases L. The learning machine 18 then derives information about the upcoming charging phase L* and the subsequent use phase N* from the training information 20, for example directly the duration D* and the probable energy consumption E*”)), and (ii) a target charge level corresponding to the time, the target charge level corresponding to an expected energy demand at the mobile computing device following the expected deployment (Paragraph 19 “A required charge for the energy storage unit is then calculated based on the probable energy consumption”);
generating configuration data including the time and the target charge level (Paragraph 21 “The charging schema for charging the energy storage unit is then set for the upcoming charging phase based on the required charge and the probable duration”); and
transmitting the configuration data to the mobile computing device to control charging of the battery of the mobile computing device (Paragraph 23 “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device. Finally, the energy storage unit is then charged using the charging device”).
Kukla does not teach obtaining forecasting data, generating configuration data, and transmitting the configuration data for a set of mobile computing devices.
However, Khanna teaches predicting power consumption for a plurality of devices (abstract “generate predicted values of power consumption for a plurality of devices”).
Both Kukla and Khanna are analogous to the claimed invention because both are in the field of predicting electrical consumption. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Khanna. Such a combination would merely be combining prior art elements (a system for predicting power consumption and controlling charging to reach a needed level, a system that predicts power consumption for multiple devices) according to known methods to yield predictable results, where the predictable result would be a system for predicting power consumption and controlling charging to reach a needed level that can be used for multiple devices at once.
Regarding claim 6, the combination of Kukla and Khanna teaches the method of claim 1.
Kukla does not teach obtaining respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices.
Khanna also teaches obtaining respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices (Paragraph 22 “The training data 120 can include power usage data of each of a plurality of device classes”, “Each device class can correspond to a particular type of device”).
While the forecasting data of Khanna may not specifically be the forecasting data of claim 1, Kukla already teaches the forecasting data of claim 1 (see claim 1 analysis). It is the examiner’s interpretation that the principle of obtaining respective sets of power forecasting data for a plurality of mobile device types can be applied to any kind of power forecasting data in general.
Regarding claim 7, the combination of Kukla and Khanna teaches the method of claim 6.
Kukla also teaches generating a plurality of times and charging levels (Paragraph 72 “In this figure, the alternating sequence of use and charging phases N, N*, L, L* can be seen as well as an upcoming charging phase L* and a subsequent use phase N*”, Fig. 4).
Khanna already teaches predicting energy consumption for a plurality of devices.
It is the examiner’s interpretation that the principle of generating a time and a target charge level can be applied to any number of devices, not just one.
Regarding claim 8, Kukla teaches a computing device, comprising:
obtain forecasting data based on historical indications of energy consumed by a mobile computing device during a plurality of time periods (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”, “This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases”);
obtain a future time period (Paragraph 29 “A 24-hour time frame is preferably used in the determination of the probable energy consumption and the probable duration of the upcoming charging phase”);
based on the forecasting data, determine (i) a time within the future time period corresponding to an expected deployment of the mobile computing device (Paragraph 72 “In this figure, the alternating sequence of use and charging phases N, N*, L, L* can be seen as well as an upcoming charging phase L* and a subsequent use phase N*” specifically the subsequent use phase, Fig. 4, Paragraph 73 “The learning machine 18 is supplied with training information 20 from the past use phases N and the past charging phases L. The learning machine 18 then derives information about the upcoming charging phase L* and the subsequent use phase N* from the training information 20, for example directly the duration D* and the probable energy consumption E*” ), and (ii) a target charge level corresponding to the time, the target charge level corresponding to an expected energy demand at the mobile computing device following the expected deployment (Paragraph 19 “A required charge for the energy storage unit is then calculated based on the probable energy consumption”);
generate configuration data including the time and the target charge level (Paragraph 21 “The charging schema for charging the energy storage unit is then set for the upcoming charging phase based on the required charge and the probable duration”); and
transmit the configuration data to the mobile computing device to control charging of respective batteries of each of the set of mobile computing devices (Paragraph 23 “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device. Finally, the energy storage unit is then charged using the charging device”).
Kukla does not teach a communications interface, a processor, obtaining forecasting data, generating configuration data, and transmitting the configuration data for a set of mobile computing devices.
However, Khanna teaches a communications interface (Paragraph 51 “network interface 904”), a processor (Paragraph 51 “processor(s) 902”), predicting power consumption for a plurality of devices (abstract “generate predicted values of power consumption for a plurality of devices”).
Both Kukla and Khanna are analogous to the claimed invention because both are in the field of predicting electrical consumption. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Khanna. Such a combination would merely be combining prior art elements (a system for predicting power consumption and controlling charging to reach a needed level, a system that predicts power consumption for multiple devices) according to known methods to yield predictable results, where the predictable result would be a system for predicting power consumption and controlling charging to reach a needed level that can be used for multiple devices at once.
Regarding claim 13, the combination of Kukla and Khanna teaches the computing device of claim 8.
Kukla does not teach obtaining respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices.
Khanna also teaches obtaining respective sets of forecasting data corresponding to each of a plurality of types of the mobile computing devices (Paragraph 22 “The training data 120 can include power usage data of each of a plurality of device classes”, “Each device class can correspond to a particular type of device”).
While the forecasting data of Khanna may not specifically be the forecasting data of claim 1, Kukla already teaches the forecasting data of claim 1 (see claim 8 analysis). It is the examiner’s interpretation that the principle of obtaining respective sets of power forecasting data for a plurality of mobile device types can be applied to any kind of power forecasting data in general.
Regarding claim 14, the combination of Kukla and Khanna teaches the computing device of claim 13.
Kukla also teaches generating a plurality of times and charging levels (Paragraph 72 “In this figure, the alternating sequence of use and charging phases N, N*, L, L* can be seen as well as an upcoming charging phase L* and a subsequent use phase N*”, Fig. 4).
Khanna already teaches predicting energy consumption for a plurality of devices (see claim 8 analysis).
It is the examiner’s interpretation that the principle of generating a time and a target charge level can be applied to any number of devices, not just one.
Claim(s) 2-4 and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kukla in light of Khanna, further in light of US 9520732 B1 to Noble, Jr. et al. (herein "Noble").
Regarding claim 2, the combination of Kukla and Khanna teaches the method of claim 1.
The combination of Kukla and Khanna does not specifically teach wherein obtaining the forecasting data includes:
obtaining, for each of the set of mobile computing devices, a record including:
a first one of the time periods,
an initial battery charge level of the mobile computing device corresponding to a start of the first time period, and
a final battery charge level of the mobile computing device corresponding to an end of the first time period.
However, Noble teaches obtaining, for a mobile computing device, a record including:
a first one of the time periods (Claim 1 “the triggering event comprising an activity beginning that requires power to be provided from a battery of the mobile device to function”, “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”),
an initial battery charge level of the mobile computing device corresponding to a start of the first time period (Claim 1 “taking a first reading of a current battery charge of the battery by the computer-program application upon detecting the triggering event”), and
a final battery charge level of the mobile computing device corresponding to an end of the first time period (Claim 1 “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”).
While Noble does not teach generating the forecasting data based on the records for a plurality of mobile devices, Kukla teaches obtaining forecasting data based on records of power consumption and Khanna teaches a system for multiple devices (see claim 1 analysis). More specifically with regards to “generating the forecasting data” Kukla teaches “prediction being derived from historic data” (Paragraph 21). It is the examiner’s interpretation that the principle of generating forecasting data based on records can be applied to any kind of historical data representing power consumption, such as the initial and final battery charge level of Noble. Similarly, it is the examiner’s interpretation that the principle of generating forecasting data based on records can be applied to any number of devices, not just one.
Both Kukla and Noble are analogous to the claimed invention because both are in the field of managing battery charge. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Noble. Such a combination would merely be combining prior art elements (a system that predicts power consumption based on past power consumption data, and a system that specifically calculates power consumption by measuring the difference between a first and second measurement of battery charge), where the predictable result would be a system that predicts power consumption based on past power consumption data which can be created by measuring the difference between a first and second measurement of battery charge.
Regarding claim 3, the combination of Kukla, Khanna, and Noble teaches the method of claim 2.
Kukla also teaches wherein generating the forecasting data includes: training a forecasting model based on the records (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”).
While Kukla does not specifically teach training a forecasting model based on the records of Noble, it is the examiner’s interpretation that the principle of training a forecasting model based on power consumption data can be applied to any kind of data representing power consumption such as the initial and final battery charge level of Noble.
Regarding claim 4, the combination of Kukla, Khanna, and Noble teaches the method of claim 2.
The combination of Kukla and Khanna does not teach generating, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period.
Noble also teaches generating, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period (Claim 1 “determining the current battery usage based on a difference between the first reading and the second reading of the current battery charge”).
Kukla also teaches generating the forecasting data based on the amount of energy consumed (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”, “This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases”).
While Kukla does not teach generating the forecasting data based on an amount of energy consumed generated from an initial and final battery charge level, it is the examiner’s interpretation that the principle of training a forecasting model based on power consumption data can be applied to any kind of data representing power consumption such as the power consumption generated from an initial and final battery charge level of Noble.
Regarding claim 9, the combination of Kukla and Khanna teaches the computing device of claim 8.
The combination of Kukla and Khanna does not specifically teach wherein the processor is configured to obtain the forecasting data by:
obtaining, for each of the set of mobile computing devices, a record including:
a first one of the time periods,
an initial battery charge level of the mobile computing device corresponding to a start of the first time period, and
a final battery charge level of the mobile computing device corresponding to an end of the first time period.
However, Noble teaches:
obtaining, for each of the set of mobile computing devices, a record including:
a first one of the time periods (Claim 1 “the triggering event comprising an activity beginning that requires power to be provided from a battery of the mobile device to function”, “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”),
an initial battery charge level of the mobile computing device corresponding to a start of the first time period (Claim 1 “taking a first reading of a current battery charge of the battery by the computer-program application upon detecting the triggering event”), and
a final battery charge level of the mobile computing device corresponding to an end of the first time period (Claim 1 “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”).
More specifically with regards to “generating the forecasting data” Kukla teaches “prediction being derived from historic data” (Paragraph 21). It is the examiner’s interpretation that the principle of generating forecasting data based on records can be applied to any kind of historical data representing power consumption, such as the initial and final battery charge level of Noble. Similarly, it is the examiner’s interpretation that the principle of generating forecasting data based on records can be applied to any number of devices, not just one.
Both Kukla and Noble are analogous to the claimed invention because both are in the field of managing battery charge. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Noble. Such a combination would merely be combining prior art elements (a system that predicts power consumption based on past power consumption data, and a system that specifically calculates power consumption by measuring the difference between a first and second measurement of battery charge), where the predictable result would be a system that predicts power consumption based on past power consumption data which can be created by measuring the difference between a first and second measurement of battery charge.
Regarding claim 10, the combination of Kukla, Khanna, and Noble teaches the computing device of claim 9.
Kukla also teaches wherein the processor is configured to generate the forecasting data by: training a forecasting model based on the records (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”).
While Kukla does not specifically teach training a forecasting model based on the records of Noble, it is the examiner’s interpretation that the principle of training a forecasting model based on power consumption data can be applied to any kind of data representing power consumption such as the initial and final battery charge level of Noble.
Regarding claim 11, the combination of Kukla, Khanna, and Noble teaches the computing device of claim 9.
The combination of Kukla and Khanna do not teach wherein the processor is configured to:
generate, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period.
Noble also teaches wherein the processor is configured to:
generate, from the initial battery charge level and the final battery charge level, an amount of energy consumed by the mobile computing device during the first time period (Claim 1 “determining the current battery usage based on a difference between the first reading and the second reading of the current battery charge”).
Kukla also teaches generating the forecasting data based on the amount of energy consumed (Paragraph 30 “The probable energy consumption and the probable duration of the upcoming charging phase are preferably determined by means of a learning machine which is trained using the past use phases and/or using the past charging phases”, “This training information is suitably the respective duration of the past charging phases and the respective energy consumption of the past use phases”).
While Kukla does not teach generating the forecasting data based on an amount of energy consumed generated from an initial and final battery charge level, it is the examiner’s interpretation that the principle of training a forecasting model based on power consumption data can be applied to any kind of data representing power consumption such as the power consumption generated from an initial and final battery charge level of Noble.
Claim(s) 5 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kukla in light of Khanna, further in light of US 20240034180 A1 to Bhimani et al. (herein "Bhimani").
Regarding claim 5, the combination of Kukla and Khanna teaches the method of claim 1.
The combination of Kukla and Khanna does not teach obtaining carbon intensity data for each of a plurality of sub-periods in the future time period; wherein the configuration data includes the carbon intensity data.
However, Bhimani teaches obtaining carbon intensity data for each of a plurality of sub-periods in the future time period (Paragraph 83 “The process flow or algorithm can access the demand and footprint prediction models (112) to determine, or generate an estimation of, emissions or emission savings associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”); wherein the configuration data includes the carbon intensity data (Paragraph 83 “and select a specific candidate or potential time block with the lowest emissions—as the specific optimized time or interval to charge, recharge, or transfer electricity from, the electric vehicle—from among the plurality of candidate time blocks”).
While Bhimani relates to charging electric vehicles, it is the examiner’s interpretation that the principle of obtaining carbon intensity data and accounting for the carbon intensity data when charging can be applied to recharging any kind of device.
Both Kukla and Bhimani are analogous to the claimed invention because both are in the field of managing charging. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Bhimani to minimize carbon emissions (Paragraph 36 “Hence, the carbon footprint relating to the fleet of electric vehicles (e.g., hybrid electric vehicles or REV, battery electric vehicles or BEVs, etc.) and/or the homes can be significantly reduced or improved”).
Regarding claim 12, the combination of Kukla and Khanna teaches the computing device of claim 8.
The combination of Kukla and Khanna does not teach wherein the processor is configured to:
obtain carbon intensity data for each of a plurality of sub-periods in the future time period; wherein the configuration data includes the carbon intensity data.
However, Bhimani teaches obtaining carbon intensity data for each of a plurality of sub-periods in the future time period (Paragraph 83 “The process flow or algorithm can access the demand and footprint prediction models (112) to determine, or generate an estimation of, emissions or emission savings associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”); wherein the configuration data includes the carbon intensity data (Paragraph 83 “and select a specific candidate or potential time block with the lowest emissions—as the specific optimized time or interval to charge, recharge, or transfer electricity from, the electric vehicle—from among the plurality of candidate time blocks”).
While Bhimani relates to charging electric vehicles, it is the examiner’s interpretation that the principle of obtaining carbon intensity data and accounting for the carbon intensity data when charging can be applied to recharging any kind of device.
Both Kukla and Bhimani are analogous to the claimed invention because both are in the field of managing charging. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Bhimani to minimize carbon emissions (Paragraph 36 “Hence, the carbon footprint relating to the fleet of electric vehicles (e.g., hybrid electric vehicles or REV, battery electric vehicles or BEVs, etc.) and/or the homes can be significantly reduced or improved”).
Claim(s) 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kukla in light of Khanna, further in light of US 20140340051 A1 to Hargrave.
Kukla teaches a computing device (Paragraph 5 “Other examples for mobile devices are headphones, headsets, wearables, smart phones and similar devices”), comprising:
a battery (Paragraph 3 “energy storage unit”);
receive, via the communications interface (Paragraph 23 “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device”), configuration data including a time corresponding to an expected deployment of the computing device (Paragraph 72 “In this figure, the alternating sequence of use and charging phases N, N*, L, L* can be seen as well as an upcoming charging phase L* and a subsequent use phase N*” specifically the subsequent use phase, Fig. 4, Paragraph 73 “The learning machine 18 is supplied with training information 20 from the past use phases N and the past charging phases L. The learning machine 18 then derives information about the upcoming charging phase L* and the subsequent use phase N* from the training information 20, for example directly the duration D* and the probable energy consumption E*”), and a target charge level corresponding to an expected energy demand at the computing device following the expected deployment (Paragraph 19 “A required charge for the energy storage unit is then calculated based on the probable energy consumption”);
control charging of the battery to reach the target charge level before the time (Paragraph 23 “Finally, the energy storage unit is then charged using the charging device during the charging phase (that is to say the formerly upcoming charging phase) according to the charging schema”).
Kukla does not teach a communications interface and a processor. Kukla also does not specifically teach: determine that the computing device has been connected with a charger.
However, Khanna teaches a communications interface (Paragraph 51 “network interface 904”) and a processor (Paragraph 51 “processor(s) 902”).Both Kukla and Khanna are analogous to the claimed invention because both are in the field of predicting electrical consumption. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Khanna. Such a combination would merely be combining prior art elements (a system for predicting power consumption and controlling charging to reach a needed level, a system that predicts power consumption for multiple devices) according to known methods to yield predictable results, where the predictable result would be a system for predicting power consumption and controlling charging to reach a needed level that can be used for multiple devices at once.
The combination of Kukla and Khanna does not teach determining that the computing device has been connected with a charger.
However, Hargrave teaches determining that the computing device has been connected with a charger (Paragraph 24 “The battery information is identified at operation 306 to determine if external power is being provided at operation 308, and if so, the charging will be performed at operation 310”).
Both Kukla and Hargrave are analogous to the claimed invention because both are in the field of managing recharging. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Hargrave in order to determine if there is some sort of fault with the battery (Paragraph 25 “Continuing with FIG. 3, if the external power is not detected 308, then a determination is made as to whether there is a voltage fault 314, a temperature fault 316 and/or whether the battery is low 318”).
Regarding claim 17, the combination of Kukla and Hargrave teaches the computing device of claim 15.
Kukla also teaches wherein the processor is configured to receive the configuration data from a server (Paragraph 23 “The charging schema is set either by the charging device itself or by another device, for example the mobile device or an additional device or a server, for example of the manufacturer of the device, and then transmitted therefrom to the charging device”).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kukla in light of Khanna, further in light of Hargrave, further in light of Bhimani.
The combination of Kukla, Khanna and Hargrave teaches the computing device of claim 15.
The combination of Kukla, Khanna and Hargrave does not teach wherein the configuration data includes carbon intensity levels for a plurality of sub-periods; and wherein the processor is configured to control charging of the battery by:
selecting the one of the sub-periods with the lowest carbon intensity level; and
charging the battery during the selected sub-period.
However, Bhimani teaches wherein the configuration data includes carbon intensity levels for a plurality of sub-periods (Paragraph 83 “The process flow or algorithm can access the demand and footprint prediction models (112) to determine, or generate an estimation of, emissions or emission savings associated with charging, recharging, or transferring electricity from, the electric vehicle in each of the candidate or potential time blocks”); and wherein the processor is configured to control charging of the battery by:
selecting the one of the sub-periods with the lowest carbon intensity level (Paragraph 83 “and select a specific candidate or potential time block with the lowest emissions—as the specific optimized time or interval to charge, recharge, or transfer electricity from, the electric vehicle—from among the plurality of candidate time blocks”); and
charging the battery during the selected sub-period (Paragraph 83 “and select a specific candidate or potential time block with the lowest emissions—as the specific optimized time or interval to charge, recharge, or transfer electricity from, the electric vehicle—from among the plurality of candidate time blocks”).
While Bhimani relates to charging electric vehicles, it is the examiner’s interpretation that the principle of obtaining carbon intensity data and accounting for the carbon intensity data when charging can be applied to recharging any kind of device.
Kukla, Khanna and Bhimani are analogous to the claimed invention because all are in the field of managing charging. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Bhimani to minimize carbon emissions (Paragraph 36 “Hence, the carbon footprint relating to the fleet of electric vehicles (e.g., hybrid electric vehicles or REV, battery electric vehicles or BEVs, etc.) and/or the homes can be significantly reduced or improved.”)
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kukla in light of Khanna, further in light of Hargrave, further in light of Noble.
The combination of Kukla, Khanna and Hargrave teaches the computing device of claim 15.
Kukla also teaches transmitting the deployment data to a server (Paragraph 52 “A configuration in which the method is executed on a server, in particular as a cloud service, is also advantageous. The server is connected to the charging device and/or the mobile device via a data connection” where it is clear that any gathered deployment data may be transmitted to a server).
The combination of Kukla and Hargrave does not teach wherein the processor is further configured to:
collect deployment data including:
a time period,
an initial battery charge level of the computing device corresponding to a start of the time period, and
a final battery charge level of the computing device corresponding to an end of the time period; and
However, Noble teaches collecting deployment data including:
a time period (Claim 1 “the triggering event comprising an activity beginning that requires power to be provided from a battery of the mobile device to function”, “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”),
an initial battery charge level of the computing device corresponding to a start of the time period (Claim 1 “taking a first reading of a current battery charge of the battery by the computer-program application upon detecting the triggering event”),
and a final battery charge level of the computing device corresponding to an end of the time period (Claim 1 “taking a second reading of the current battery charge of the battery by the computer-program application after a period of time has passed from when the triggering event was detected”).
Both Kukla and Noble are analogous to the claimed invention because both are in the field of managing battery charge. It would be obvious to one of ordinary skill in the art to incorporate the system of Kukla with the system of Noble. Such a combination would merely be combining prior art elements (a system that predicts power consumption based on past power consumption data, and a system that specifically calculates power consumption by measuring the difference between a first and second measurement of battery charge), where the predictable result would be a system that predicts power consumption based on past power consumption data which can be created by measuring the difference between a first and second measurement of battery charge.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM XIANG ZHANG whose telephone number is (571)272-1276. The examiner can normally be reached M-F (8:30 AM - 5 PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 5712722748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.X.Z./ Examiner, Art Unit 2117
/Christopher E. Everett/ Primary Examiner, Art Unit 2117
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