DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a final Office action in response to Applicant’s submission filed on 9/15/2025. Currently claims 12-13 and 15-25 are pending and claims 12, 17, 18, 20 are independent. Claims 12, 16, 17, 18, 19, 20 have been amended from the original claim set dated 1/15/2024. Claim 14 has been newly cancelled and claim 23-25 are new.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2021 207 959.8, filed on 7/23/2021.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action. In light of the amendments, the 35 USC § 101 rejection (non-statutory subject matter) of claim 19 is withdrawn.
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 12-13 and 15-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more.
With respect to claims 12-13 and 15-25, following USPTO guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 12 and its dependent claims (claims 13, 15-16, 19, 21, 23-25) are directed to a statutory category, namely a method system/machine. Claim 17 is directed to a statutory category, namely a method. Claim 18 and its dependent claims (claims 22) are directed to a statutory category, namely an article of manufacture. Claim 20 is directed to a statutory category, namely a method. an article of manufacture.
Step 2A (Prong 1): Claims 12, 17, 18, 20, which are substantially similar claims to one another, are directed to the abstract idea of “Mental processes”, or more particularly, “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system to manage and analyze a person’s schedule to determine a charging strategy for an EV. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function a person performs when deciding when to charge their car or phone. The abstract elements of claims 12, 17, 18, 20 recite in part “Train model…Provide departure time specifications…Analyze departure time…Determine strategy…Charge device…”. Dependent claims 12-16, 19, 21, 22-25 add to the abstract idea the following limitations which recite in part “Consumption curves comprise information…Departure time is used to determine strategy…Curves are provided…Train model further…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 12, 17, 18, 20.
Step 2A (Prong 2): Independent claims 12, 17, 18, 20, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Model…Device…Storage medium…Computer program product…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Dependent claims 15 adds the additional element which recites in part “energy management system…” which again limits the claims to a networked/computer based environment, but this is again insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)). Additionally, claims 21-25 include the additional element of “appliance,” however this also fails to integrate the abstract idea into a practical application because it is simply being used to transmit data (See MPEP 2106.05(f)).
Additionally, dependent claims 13, 16, 19 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 12, 17, 18, 20, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Model…Device…Storage medium…Computer program product…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (manage and analyze a person’s schedule to determine a charging strategy for an EV) on a general purpose computer (See MPEP 2106.05(f)).
Dependent claim 15 includes additional elements, when considered both individually and as an ordered combination and in view of their respective independent claims, which are insufficient to amount to significantly more than the judicial exception. Specifically, dependent claim 15 includes the additional element which recites in part “energy management system…” This is a similar additional elements that are addressed above in claims 12, 17, 18, 20, and is not significantly more because this is again merely the software and/or hardware components used to implement the abstract idea (manage and analyze a person’s schedule to determine a charging strategy for an EV) on a general purpose computer (See MPEP 2106.05(f)).
Additionally, claims 21-25 include the additional element of “appliance,” however this also fails to be significantly more than the abstract idea because it is simply being used to transmit data (See MPEP 2106.05(f)).
Additionally, dependent claims 13, 16, 19 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 12-13 and 15-25 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 12-13 and 15-25 are rejected under 35 U.S.C. 103 as being unpatentable over Sinha et al. (US 20190217739 A1) in view of Desai et al. (US 20120215369 A1) further in view of Sofue et al. (JP 2011188731 A)
Regarding claims 12, 17, 18, 19, 20, Sinha discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Sinha lacks providing a data-based departure time model which is trained to provide a departure time specification on the basis of a calendrical time specification and on the basis of one or more temporal consumption variable curves of vehicle-external consumption variables within a specified period of time, wherein the one or more consumption variable curves characterize a usage of one or more energy consumers, wherein the departure time model is further trained in order to provide the departure time specification on the basis of one or more outlier signals, each of which specifies a deviation by one of the consumption variable curves from a regular pattern, wherein the data-based departure time model provides the departure time specification on the basis of the one or more outlier signals, wherein the one or more outlier signals are determined by a respective trained outlier detection model on the basis of a corresponding consumption variable curve; and analyzing the data-based departure time model by prespecifying the calendrical time specification and the one or more consumption variable curves within the specified period of time in order to determine the departure time specification.
Desai, from the same field of endeavor, teaches providing a data-based departure time model which is trained (Desai ¶84 - With appropriate "training" of the system, ie when the profile 304 has been suitably adapted to typical usage patterns of the premises) to provide a departure time specification on the basis of a calendrical time specification and on the basis of one or more temporal consumption variable curves of vehicle-external consumption variables within a specified period of time (Desai Fig 5 – Desai ¶123 - Home and away times may be estimated based upon times of increase/decrease in total energy consumption. A number of parameters, which may be stored in the profile, may be used to control the operation and accuracy of this mechanism. For example, the profile may include a threshold indicating the amount of a "step" increase or decrease in consumption that will be taken to indicate departures and arrivals of residents. Furthermore, a parameter specifying a minimum period of increased/decreased consumption may be included, which determines how long an increase or decrease must persist before it is taken to correspond with arrival or departure of residents), wherein the one or more consumption variable curves characterize a usage of one or more energy consumers, wherein the departure time model is further trained in order to provide the departure time specification on the basis of one or more outlier signals, each of which specifies a deviation by one of the consumption variable curves from a regular pattern, wherein the data-based departure time model provides the departure time specification on the basis of the one or more outlier signals, wherein the one or more outlier signals are determined by a respective trained outlier detection model on the basis of a corresponding consumption variable curve (Desai ¶132 - It has been found through analysis/review of historical consumption patterns that the occupancy departure time (in the evening) is irregular. This is because the time at which people leave for the day is irregular in comparison to their arrival time in the morning. In accordance with the preferred profile parameters, an estimated departure time is calculated as the time at which consumption for the building is within 25% to 35% of the average standby consumption within the expected time frame (4:30 pm to 6:30 pm)); and analyzing the data-based departure time model by prespecifying the calendrical time specification (Desai ¶16 - For example, the profile may include information relating to daily and/or weekly occupancy patterns, such as the time of day during which the premises is occupied, and the number of occupants) and the one or more consumption variable curves within the specified period of time in order to determine the departure time specification (Desai ¶108 - typical weekday and weekend occupancy patterns may be preset in an initial "generic" profile, assuming away times between 8:00 am and 6:00 pm weekdays, and permanent occupancy over the weekend. The profile may also distinguish between "awake" and "sleep" times, assuming that highest energy consumption will occur during periods when the residence is occupied, and the residents are awake. Actual patterns of heating/cooling energy consumption may differ from those predicted, and the corresponding times in the profile may be adapted accordingly).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the energy management techniques of Desai because Desai discloses “The system may also be configured to provide the user with suggestions for improving energy efficiency, and/or introduce automated controls to implement such strategies (Desai ¶110)”. Additionally, Sinha further details that “The present invention relates more particularly to systems and methods for optimizing an energy system within a building (Sinha ¶2)” so it would be obvious to consider including the additional energy management techniques that Desai discloses because it would improve the optimization methods of Sinha by adding efficiency suggestions.
Sinha further lacks determining the charging strategy for the electrical energy storage device based on the trained data-based departure time model and the respective trained outlier detection models; and charging the electrical energy storage device of the electric vehicle according to the determined charging strategy so that the electrical energy storage device is fully charged in advance of the departure time specification that accounts for the one or more outlier signals based on the usage of the one or more vehicle-external energy consumers.
Sofue, from the same field of endeavor, teaches determining the charging strategy for the electrical energy storage device based on the trained data-based departure time model and the respective trained outlier detection models; and charging the electrical energy storage device of the electric vehicle according to the determined charging strategy so that the electrical energy storage device is fully charged in advance of the departure time specification that accounts for the one or more outlier signals based on the usage of the one or more vehicle-external energy consumers (Sofue ¶28 - Charging schedule adjustment using user's wake-up information The behavior pattern of users on weekdays should be almost the same. However, there is a possibility that there is a day when the wake-up time and thus the departure time are different for some event. Therefore, when the charging control device 2 accesses the home security server and detects the wake-up information (wake-up time) of the user (D4), that is, the home security server monitors the amount of electricity of the electric meter and lighting of the light. Therefore, if the user's wake-up time is detected by the amount of electricity used (electric meter fluctuation) or a light sensor (lights on), the user's scheduled departure time is predicted to be earlier or later than usual. (D5) In this case, the number of minutes after the wake-up time may be determined by using the statistical information of the past preparation time in which the normal user's behavior pattern is recorded. In accordance with the scheduled departure time, it is moved forward or later to reset (D6). In this case, when the scheduled departure time becomes early, rapid charging is performed, and when the scheduled departure time becomes late, the utilization rate is increased by extending the charging time by the solar cell. Then, the charging schedule is reset according to the predicted scheduled departure time (D19)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the charge controller techniques of Sofue because Sofue discloses “charging to the battery 4 is controlled according to the charging schedule, so that the charging cost can be reduced (Sofue)”. Additionally, Sinha further details that “In some embodiments, the processing circuit is configured to determine whether to charge or discharge the battery of the battery powered vehicle by performing an optimization with the charging constraints by generating a cost function accounting for cost and revenue generated from purchasing energy from an energy grid to charge the battery of the battery powered vehicle (Sinha ¶10)” so it would be obvious to consider including the additional charge controller techniques that Sofue discloses because it would further enable optimizing charging to better account for charging costs.
Regarding claim 13, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Desai further teaches the one or more consumption variable curves comprise information on energy consumption in a building system or usage-based variables of a hot water temperature of a heating system (Desai ¶55 - FIG. 5 shows a display of a daily energy consumption report according to an embodiment of the invention).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the energy management techniques of Desai because Desai discloses “The system may also be configured to provide the user with suggestions for improving energy efficiency, and/or introduce automated controls to implement such strategies (Desai ¶110)”. Additionally, Sinha further details that “The present invention relates more particularly to systems and methods for optimizing an energy system within a building (Sinha ¶2)” so it would be obvious to consider including the additional energy management techniques that Desai discloses because it would improve the optimization methods of Sinha by adding efficiency suggestions.
Regarding claim 15, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Desai further teaches the consumption variable curves are provided by an energy management system for a building system (Desai ¶51 - FIG. 1 is a schematic diagram of a system for managing energy consumption associated with a premises).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the energy management techniques of Desai because Desai discloses “The system may also be configured to provide the user with suggestions for improving energy efficiency, and/or introduce automated controls to implement such strategies (Desai ¶110)”. Additionally, Sinha further details that “The present invention relates more particularly to systems and methods for optimizing an energy system within a building (Sinha ¶2)” so it would be obvious to consider including the additional energy management techniques that Desai discloses because it would improve the optimization methods of Sinha by adding efficiency suggestions.
Regarding claim 16, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Desai further teaches the departure time model is further trained in order to provide the departure time specification on the basis of one or more smart home event signals (Desai ¶37 - Where available, the system may interface to "smart" appliances installed at the premises, which are operable by the controller via one or more appliance communications systems. For example, it may be possible to communicate with some smart appliances via a suitable network connection), wherein the data-based departure time model provides the departure time specification on the basis of the one or more smart home event signals (Desai ¶81 - For example, the controller 102 may be programmed and configured to detect that a particular appliance is switched on at a time when, according to the profile 304, the premises is not usually occupied and the appliance is not usually in use).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the energy management techniques of Desai because Desai discloses “The system may also be configured to provide the user with suggestions for improving energy efficiency, and/or introduce automated controls to implement such strategies (Desai ¶110)”. Additionally, Sinha further details that “The present invention relates more particularly to systems and methods for optimizing an energy system within a building (Sinha ¶2)” so it would be obvious to consider including the additional energy management techniques that Desai discloses because it would improve the optimization methods of Sinha by adding efficiency suggestions.
Regarding claims 21, 22, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Desai further teaches the one or more consumption variable curves characterize a usage of a domestic appliance and/or a heating and hot water system of the building (Desai ¶81 - For example, the controller 102 may be programmed and configured to detect that a particular appliance is switched on at a time when, according to the profile 304, the premises is not usually occupied and the appliance is not usually in use).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the energy management techniques of Desai because Desai discloses “The system may also be configured to provide the user with suggestions for improving energy efficiency, and/or introduce automated controls to implement such strategies (Desai ¶110)”. Additionally, Sinha further details that “The present invention relates more particularly to systems and methods for optimizing an energy system within a building (Sinha ¶2)” so it would be obvious to consider including the additional energy management techniques that Desai discloses because it would improve the optimization methods of Sinha by adding efficiency suggestions.
Regarding claim 23, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Sofue further teaches the one or more vehicle-external energy consumers includes a domestic appliance in a same household as the electric vehicle, the one or more outlier signals include an appliance outlier signal indicating that the domestic appliance is consuming electrical energy earlier than is expected based on the regular pattern, the data-based departure time model changes the departure time specification to an earlier time based on the appliance outlier signal, the charging strategy is determined based on the earlier time, and the electrical energy storage device of the electrical vehicle is fully charged in advance of the earlier time according to the charging strategy (Sofue ¶28 - Charging schedule adjustment using user's wake-up information The behavior pattern of users on weekdays should be almost the same. However, there is a possibility that there is a day when the wake-up time and thus the departure time are different for some event. Therefore, when the charging control device 2 accesses the home security server and detects the wake-up information (wake-up time) of the user (D4), that is, the home security server monitors the amount of electricity of the electric meter and lighting of the light. Therefore, if the user's wake-up time is detected by the amount of electricity used (electric meter fluctuation) or a light sensor (lights on), the user's scheduled departure time is predicted to be earlier or later than usual. (D5) In this case, the number of minutes after the wake-up time may be determined by using the statistical information of the past preparation time in which the normal user's behavior pattern is recorded. In accordance with the scheduled departure time, it is moved forward or later to reset (D6). In this case, when the scheduled departure time becomes early, rapid charging is performed, and when the scheduled departure time becomes late, the utilization rate is increased by extending the charging time by the solar cell. Then, the charging schedule is reset according to the predicted scheduled departure time (D19)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the charge controller techniques of Sofue because Sofue discloses “charging to the battery 4 is controlled according to the charging schedule, so that the charging cost can be reduced (Sofue)”. Additionally, Sinha further details that “In some embodiments, the processing circuit is configured to determine whether to charge or discharge the battery of the battery powered vehicle by performing an optimization with the charging constraints by generating a cost function accounting for cost and revenue generated from purchasing energy from an energy grid to charge the battery of the battery powered vehicle (Sinha ¶10)” so it would be obvious to consider including the additional charge controller techniques that Sofue discloses because it would further enable optimizing charging to better account for charging costs.
Regarding claim 24, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Sofue further teaches the domestic appliance is a coffee maker (Sofue ¶28 - Therefore, when the charging control device 2 accesses the home security server and detects the wake-up information (wake-up time) of the user (D4), that is, the home security server monitors the amount of electricity of the electric meter and lighting of the light. Therefore, if the user's wake-up time is detected by the amount of electricity used (electric meter fluctuation) or a light sensor (lights on) {i.e. coffee maker})
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the charge controller techniques of Sofue because Sofue discloses “charging to the battery 4 is controlled according to the charging schedule, so that the charging cost can be reduced (Sofue)”. Additionally, Sinha further details that “In some embodiments, the processing circuit is configured to determine whether to charge or discharge the battery of the battery powered vehicle by performing an optimization with the charging constraints by generating a cost function accounting for cost and revenue generated from purchasing energy from an energy grid to charge the battery of the battery powered vehicle (Sinha ¶10)” so it would be obvious to consider including the additional charge controller techniques that Sofue discloses because it would further enable optimizing charging to better account for charging costs.
Regarding claim 25, Sinha in view of Desai further in view of Sofue discloses a method for ascertaining a departure time specification, which specifies a most probable departure time specification of an electric vehicle from a building, in order to determine a charging strategy for an electrical energy storage device of the electric vehicle (Sinha ¶9 - the processing circuit configured to generate a predicted departure time causes the optimization to determine to charge or discharge the battery of the battery powered vehicle so that a charge level of the battery is a predefined amount at the predicted departure time).
Sofue further teaches the domestic appliance is a hot water tank (Sofue ¶28 - Therefore, when the charging control device 2 accesses the home security server and detects the wake-up information (wake-up time) of the user (D4), that is, the home security server monitors the amount of electricity of the electric meter and lighting of the light. Therefore, if the user's wake-up time is detected by the amount of electricity used (electric meter fluctuation) or a light sensor (lights on) {i.e. hot water tank})
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the energy optimization methodology/system of Sinha by including the charge controller techniques of Sofue because Sofue discloses “charging to the battery 4 is controlled according to the charging schedule, so that the charging cost can be reduced (Sofue)”. Additionally, Sinha further details that “In some embodiments, the processing circuit is configured to determine whether to charge or discharge the battery of the battery powered vehicle by performing an optimization with the charging constraints by generating a cost function accounting for cost and revenue generated from purchasing energy from an energy grid to charge the battery of the battery powered vehicle (Sinha ¶10)” so it would be obvious to consider including the additional charge controller techniques that Sofue discloses because it would further enable optimizing charging to better account for charging costs.
Response to Arguments
Applicant's arguments filed 9/15/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
As identified above, and in light of the amendments, the 35 USC § 101 rejection (non-statutory subject matter) of claim 19 is withdrawn.
Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to USPTO guidance for 35 USC § 101 rejections contained within MPEP 2106, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application.
The Applicant argues that the claimed invention is further integrated into a practical application by addressing charging strategy aspect of the claimed invention. While this charging strategy aspect might be an improvement to the judgement process of charging a vehicle, and as such, have practical applicability, this practical applicability is not synonymous with USPTO guidance. Specifically, the claimed invention needs have significant additional elements as to where the claimed invention is effectively integrated into those additional elements. As identified above, the additional elements limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Examiner will note, however, that the inclusion of the limitation “charging the electrical energy storage device of the electric vehicle according to the determined charging strategy” brings the claims closer to overcoming the 101 rejection, but it is not clear to the examiner that the system itself is performing that functionality (as opposed to a person simply charging the vehicle according to a plan). Including language such as “automatically controlling the charging rate according to the plan…” or something similar which clearly shows the system is performing this functionality independently is a potential path to overcoming the rejection.
Regarding the 35 USC § 103 rejections on the original Office Action, Applicant amended the independent claims to further limit the claims with respect to outlier signals and charging the vehicle based on them. In light of this amendment, Examiner agrees that the original reference did not clearly teach this, however the amendment necessitated further search and consideration. As a result of this further search and consideration, prior art was found that does teach these limitations (Sofue as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624