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 .
Detailed Action
Response to Arguments
Applicant's arguments/amendments have been considered but are moot in view of the new ground(s) of rejection, albeit using references previously cited on the record.
Applicant’s amendments to the Specification and Claim 15 have been considered and successfully overcome the previous objection to each.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 10-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Lippman, U.S. Patent No. 10,368,313 in view of Chan et al. U.S. PGPUB No. 2018/0081417.
Per Claim 10, Lippman discloses a system comprising:
a device (104) including:
a smart battery including a battery memory and one or more battery processors storing first computer-readable instructions that cause the one or more battery processors to store battery usage and state of charge (SOC) data on the battery memory (Col. 2 line 62 – Col. 3 line 31, Col. 4 line 54 – Col. 5 line 15, Figure 2A; Battery 116 comprises controller 228, which also comprises a memory, for reporting battery status data and current charge levels of the battery.);
a clock (Col. 2 lines 50-61; Devices 104 are listed as being one of a plurality of electronic devices that are well-known to comprise clocking devices (i.e. oscillators) for creating precise, stable, and repetitive electronic signals.),
a device communication system (Col. 4 lines 26-32, Figure 2A; communications interface 216),
one or more device processors (Column 4 lines 1-10, Figure 2A; processor 200), and
a device memory storing second computer-readable instructions (Col. 4 lines 1-10, Fig. 2A; memory 208); and
a server (120) including a server communication system, one or more server processors, and a server memory storing third computer-readable instructions (Col. 5 line 46 – 27; Communications interface 266, processor 250, and memory 258);
wherein the second computer-readable instructions, when executed by the one or more device processors, cause the one or more device processors to:
detect events associated with the battery (Fig. 4; device startup, Fig. 7; completion of an operational period);
compile event data based on the detected events, the event data including, for each of one or more detected events, one or more of: battery usage data stored on the battery memory associated with the event, SOC data stored on the memory associated with the event, an indication of an event type associated with the event, a time associated with the event, an indication of a backup voltage level during the event, a battery temperature associated with the event, a battery cumulative charge at the time of the event, or a battery charge source associated with the event (Col. 12 lines 1-28; Each device 104 reports status data to the server 120. The status data includes a data set corresponding to at least one operational period. Each data set includes a battery identifier, device identifier, a measured battery capacity and measured energy consumption); and
transmit the compiled event data to the server, via the device communication system (Col. 4 lines 26-32);
wherein the third computer-readable instructions, when executed by the one or more server processors, cause the one or more server processors to:
receive the compiled event data from the device, via the server communication system (Fig. 2B, Col. 6 lines 7-14; server communications interface 266; Col. 7 lines 35-53, collector 304; Col. 12 lines 52-63, Fig. 7, numeral 710);
determine device activity data associated with the device based on the compiled event data; and analyze the device activity data using an electrical consumption model to estimate electrical consumption for the device based on battery consumption associated with the device (Col. 3 lines 46-58, Col. 7 lines 35-53, Col. 9 lines 13-26; The data received by collector 304 is used by predictor 308 to generate predicted performance metrics associated with the batteries and user devices from which the data is collected. Col. 6 lines 15-36; Asset management application 286, stored in memory 258 of the server, uses collected data stored within repository 290 to derive performance metrics from said data.),
and execute a diagnostic application to determine at least one metric associated with the estimated electrical consumption (Col. 6 lines 15-36; Asset management application 286, stored in memory 258 of the server, uses collected data stored within repository 290 to derive performance metrics from said data.)
Lippman does not specifically teach the predictor utilizing at least one of a simulation or a machine learning model, or identifying at least one attribute impacting power consumption associated with the device.
However, Chan similarly teaches a portable electronic device (Paragraph 20; device 120) providing data to a server (Paragraph 22) and further teaches the use of machine learning and simulations for generating usage implementation details for the device (Paragraphs 21, 24, 32, 43, 61, and 62).
Chan further teaches analyzing aggregated activities, characteristics of devices, and/or power consumptions, for the purpose of generating power conservation suggestions for plans of activities and corresponding sets of implementation details (Paragraph 43; Each of the above highlighted features read on the broadly claimed “attribute”. Additionally, Paragraphs 74, 76, and 86 detail (steps 304 and 306) that a power management program 300 determines power consumption of the device based on real-time monitoring of a plurality of executing apps (i.e. activities). In the event that the power level of the device is insufficient to support the consumption of power (step 309) and dynamic power management is enabled (step 311), the the power management program 300 activates a set of implementation details (i.e. suggestions) that manage a consumption of power within the device.).
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for the predictor 308 of Lippman to utilize simulations and/or machine learning models, as taught by Chan, to generate the battery/energy related predictions because simulations can utilize large amounts of data, such as historical usage, degradation factors, utility costs, etc, to create extremely accurate predictive models useful for implementing battery/device usage and distribution. Additionally, the identification of specific attributes helps generate more accurate suggestions that the user can implement to achieve a plan of activities for the mobile device that allows for the mobile device to support the actions without running out of power (Chan, Paragraphs 80-86).
Per Claim 11, Lippman discloses the system of claim 10, wherein the device is one of: a mobile computing device (Col. 2 lines 50-61; “mobile devices such as mobile computers, smartphones, handheld RFID readers”), a mobile printer, a scanner device, and a robot device.
Per Claim 12, Lippman discloses the system of claim 10, wherein the second computer-readable instructions, when executed by the one or more device processors, further cause the one or more device processors to: capture additional device data including one or more of: power usage data associated with the device (Col. 12 lines 1-27; measured battery capacity, measured energy consumption), a screen on time associated with the device, screen brightness data associated with the device, a scan rate associated with the device, usage data associated with one or more business applications installed on the device, a wireless signal strength associated with the device, a device location, an indication of device physical memory utilization, an indication of physical memory utilization per application, an indication of battery utilization per application, an indication of data transmission associated with the device, an indication of data transmission per application (Col. 12 lines 1-27; “The data set can also include, in some examples, a measured count of tasks performed by the device 104 during the operation period (e.g. a count of labels printed, RFID tags written, or the like.”), an indication of reception usage associated with the device, or an indication of reception usage per application; and transmit, via the device communication system, the additional device data to the server (Col. 12 lines 1-27, Fig. 7 numeral 705[Wingdings font/0xE0]710).
Per Claim 13, Lippman discloses the system of claim 12, wherein the third computer-readable instructions, when executed by the one or more server processors, further cause the one or more server processors to: receive the additional device data, via the server communication system; and determine the device activity data based further on the additional device data (Col. 12 line 52 Col. 13 line 50, Fig. 7 numerals 710-725).
Per Claim 15, Lippman discloses the system of claim 10, wherein the detected events associated with the battery include one or more of: a low battery event, a battery swap mode event, a battery swap entry event, a battery swap exit event, a battery charge on event, a battery charge off event, a battery status event (Col. 7 line 54 – Col. 8 line 14, Fig. 4; “start-up of the device” represents a battery status event), a battery temperature event, a battery cumulative charge event, a device suspend event, a device suspend recovery event, or a device shutdown event (Col. 12 lines 1-27, Fig. 7 numeral 705; Each device is configured to report status data to the server at the end of each operational period. The operational periods have been defined to represent a work shift for a worker. Therefore, upon the completion of a work shift, the use of the device will be “suspended” or “shutdown”.).
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Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Lippman, U.S. Patent No. 10,368,313 in view of Chan et al. U.S. PGPUB No. 2018/0081417 in further view of Jindal et al. U.S. PGPUB No. 2023/0333166.
Per Claim 14, Lippman discloses determining at least one metric associated with the estimated electrical consumption (Col. 3 lines 46-58; “predicted performance metrics”; Col. 6 lines 28-36).
Lippman does not specifically teach the at least one metric including one or more of: an emission rate, a precious metal mining rate, or a landfill rate.
However, Jindal similarly teaches obtaining battery data from a client device, analyzing said data, and further teaches using machine learning models to predict failure and need for replacement of the batteries (Paragraphs 52-55, Fig. 3 numerals 318-330; Paragraph 114; The performance prediction model may predict when a battery can show up a fail-status or degraded-status and therefore should be replaced. A battery can be marked as “replace now”.). The prediction of replacement anticipates the “landfill rate” as broadly claimed.
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine Jindal’s battery replacement (landfill rate) metric with the teachings of Lippman/Chan because if a battery will be inadequate for the needs of the client devices and their respective roles, then it is best to know prior to a device failure so it can be timely replaced to reduce downtime.
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Claims 16-19 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Lippman, U.S. Patent No. 10,368,313 in view of Jindal et al. U.S. PGPUB No. 2023/0333166.
Per Claim 16, Lippman discloses a system comprising:
a mobile device (Col. 2 lines 50-61; “mobile devices such as mobile computers, smartphones, handheld RFID readers”) including a battery (Col. 2 line 62 – Col. 3 line 31, Col. 4 line 54 – Col. 5 line 15, Figure 2A; Battery 116 comprises controller 228, which also comprises a memory, for reporting battery status data and current charge levels of the battery.), a clock (Col. 2 lines 50-61; Devices 104 are listed as being one of a plurality of electronic devices that are well-known to comprise clocking devices (i.e. oscillators) for creating precise, stable, and repetitive electronic signals.), a mobile device communication system (Col. 4 lines 26-32, Figure 2A; communications interface 216), one or more mobile device processors (Column 4 lines 1-10, Figure 2A; processor 200), and a mobile device memory storing first computer-readable instructions (Col. 4 lines 1-10, Fig. 2A; memory 208) that,
when executed by the one or more mobile device processors, cause the one or more mobile device processors to collect and transmit (Col. 4 lines 26-32) mobile device data, including battery and usage status data, via the mobile device communication system (Col. 12 lines 1-28; Each device 104 reports status data to the server 120. The status data includes a data set corresponding to at least one operational period. Each data set includes a battery identifier, device identifier, a measured battery capacity and measured energy consumption.);
a server (120) including a server communication system, one or more server processors, and a server memory storing second computer-readable instructions (Col. 5 line 46 – 27; Communications interface 266, processor 250, and memory 258) that, when executed by the one or more server processors, cause the one or more server processors to:
receive mobile device data, via the server communication system (Fig. 2B, Col. 6 lines 7-14; server communications interface 266; Col. 7 lines 35-53, collector 304; Col. 12 lines 52-63, Fig. 7, numeral 710);
analyze the mobile device data to determine one or more of: durations of one or more shifts associated with the mobile device, a required power consumption associated with the mobile device, a measured power consumption associated with the mobile device, a predicted power consumption over time associated with the mobile device, a predicted rate of battery purchases associated with the mobile device, a predicted rate of battery disposals associated with the mobile device, a predicted impact of a power consumption of the mobile device on emission rates, or a predicted impact of a power consumption of the mobile device on landfill rates (Col. 3 lines 46-58, Col. 7 lines 35-53, Col. 9 lines 13-26, ; The data received by collector 304 is used by predictor 308 to generate predicted performance metrics associated with the batteries and user devices from which the data is collected. Col. 6 lines 15-36; Asset management application 286, stored in memory 258 of the server, uses collected data stored within repository 290 to derive performance metrics from said data. The server generates at least a “predicted power consumption associated with the mobile device”, see Fig. 7 numeral 725).
Lippman does not specifically teach the server processors training a machine learning model based on historical mobile device data and using the trained machine learning model to analyze the mobile device data for performing the step of “to determine one or more of:..”.
However, Jindal teaches a server 100 utilizing machine learning models 110/114. The machine learning models can be trained using historical battery attributes (Paragraphs 27, 28, 38, 44, and 69; “In an example, first machine learning model(s) 110 may be trained on input data using machine learning and data mining methods to predict the battery swelling, battery memory effect, and/or battery performance degradation. The input data may be selected from a set of time-series historical battery attributes associated with a plurality of batteries.”). The machine learning models are used to analyze and make energy and battery related predictions (Paragraphs 19-21, 26-28; “Furthermore, server 100 may include recommendation unit 112 to apply a second machine learning model 114 to the predicted battery condition to predict a remaining life of battery 104.”; The remaining life battery 104 reads on the limitation).
Jindal additionally uses a machine learning model 326 to make any of a plurality of battery life predictions and recommendations, such as an expected time to upgrade the battery and a battery degradation rate (Paragraph 54, Figure 3).
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for the predictor 308 of Lippman to utilize trained machine learning algorithms/models, as taught by Jindal, to generate the battery/energy related predictions because machine learning models are widely used in predictive analytics to accurately forecast future usage/trends.
Per Claim 17, Lippman discloses the system of claim 16, wherein the mobile device is one of: a mobile computing device, a mobile printer, a scanner device, or a robot device (Col. 2 lines 50-61; “mobile devices such as mobile computers, smartphones, handheld RFID readers”).
Per Claim 18, Lippman does not specify that the batteries used by the mobile devices are that of a lithium-ion battery.
However, Jindal teaches mobile devices such as laptops, cellular phones, tablets and the like utilizing rechargeable lithium-ion batteries (Paragraphs 1 and 24).
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for Lippman to utilize lithium-ion rechargeable batteries, as taught by Jindal, because lithium-ion batteries a well-known and used in mobile devices due to their smaller form factor.
Per Claim 19, similar to independent claim 16, Lippman does not specifically teach the use of trained machine learning models.
However, Jindal was introduced to teach trained machine learning models (see above rejection of claim 16 for details). Additionally, Jindal teaches and the training data is at least one of the historical mobile device data (Paragraph 27; “In an example, first machine learning model(s) 110 may be trained on input data using machine learning and data mining methods to predict the battery swelling, battery memory effect, and/or battery performance degradation. The input data may be selected from a set of time-series historical battery attributes associated with a plurality of batteries.”) or historical additional device data corresponding to historical devices indicative of durations of one or more historical shifts, a historical required power consumption, a historical measured power consumption, a historical predicted power consumption over time, a historical predicted rate of battery purchases, a historical predicted rate of battery disposals, a historical predicted impact of a power consumption on emission rates, or a historical predicted impact of a power consumption on landfill rates.
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for the predictor 308 of Lippman to utilize trained machine learning algorithms/models, as taught by Jindal, to generate the battery/energy related predictions because machine learning models are widely used in predictive analytics to accurately forecast future usage/trends.
Per Claim 22, Lippman discloses the system of claim 16, wherein the second computer-readable instructions, when executed by the one or more server processors, further cause the one or more server processors to: identify one or more mobile device setting changes, improvements in wireless coverage, application updates, application memory usage changes, application battery usage changes, or behavioral changes impacting the power consumption associated with the mobile device (Col. 10 line 44 – Col. 11 line 26, Fig. 4 numerals 420-435; An alternative role for the device represents a “behavioral change”.).
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Claims 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Lippman, U.S. Patent No. 10,368,313 in view of Jindal et al. U.S. PGPUB No. 2023/0333166 in further view of Chan et al. U.S. PGPUB No. 2018/0081417.
Per Claims 20 and 21, Lippman does not specifically teach the predictor utilizing (Monte-Carlo) simulations to analyze the mobile device data.
However, Chan similarly teaches a portable electronic device (Paragraph 20; device 120) providing data to a server (Paragraph 22) and further teaches the use of machine learning and simulations for generating usage implementation details for the device (Paragraphs 21, 24, and 62).
- It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention for the predictor 308 Lippman to utilize simulations, as taught by Chan, to generate the battery/energy related predictions because simulations can utilize large amounts of data, such as historical usage, degradation factors, utility costs, etc, to create extremely accurate predictive models useful for implementing battery/device usage and distribution.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN T MISIURA whose telephone number is (571)272-0889. The examiner can normally be reached on M-F: 8-4:30PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner' s supervisor, Andrew Jung can be reached on (571) 272-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Brian T Misiura/
Primary Examiner, Art Unit 2175
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