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 .
Response to Arguments
Applicant Amendments and Remarks filed on 06/23/2026 in response to the Non-Final office action mailed on 03/23/2026 have been fully considered and are addressed as follows:
Regarding the Claim Rejections under 35 USC § 101: The rejections are withdrawn, as the amendments to the claims properly addressed the rejections recited in the Non-Final office action.
Regarding the Claim Rejections under 35 USC §§ 102 and 103: With respect to the previous claim rejections under 35 U.S.C. §§ 102 and 103, Applicant has amended the independent claims and these amendments have changed the scope of the original application. Therefore, the Office has supplied new grounds of rejection attached below in the FINAL office action and therefore the prior arguments are considered moot.
FINAL OFFICE ACTION
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 1, 2, 4-8, 10-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over DeLuca et al. (US 2020/0217679 A1, hereinafter “DeLuca”) in view of Sharma et al. (US 2025/0296580 A1, hereinafter “Sharma”).
Regarding claim 1, DeLuca discloses an apparatus, comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to:
retrieve user profile data associated with a user of a vehicle, wherein the user profile data comprises historical usage information of the vehicle during one or more historical driving sessions by the user (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”; para. [0033]: “the receiving module 132 may be configured to receive historical information regarding charging stations. Historical information may include the past rate of use of a charging location, users' comments regarding charging location, or online complaints about a charging location from users, or common problems with a charging location”);
obtain first contextual information associated with a first driving session by the user (DeLuca at para. [0032]: “the receiving module 132 may be configured for receiving information directly from the electric vehicle system 110. For example, vehicle related data, such as charging data, speed data, location data, electric motor data, and the like, may be received by the receiving module 132”);
determine, (DeLuca at para. [0039]: “The analytics and prediction module 133 may find that the user should charge for 3 hours at the second charging stop because this matches the amount of time the user typically spends at a location of interest located near the second charging location and a full charge is not required going forward”; para. [0040]: “if a user runs into unexpected traffic congestions or inclement weather which delays travel time or adjusts the expected charge consumption, the analytics and prediction module 133 may alter the guidance in real time to provide a different route or different charge locations to fit the new charging needs and expected arrival time of the electric vehicle”; Changes in traffic situation, weather, or charge consumption (i.e., “deviation”) must be determined to provide an updated guidance or recommendation);
generate a recommendation associated with a modification in a driving range of the vehicle based on the determined deviation, (DeLuca at para. [0039]: “The analytics and prediction module 133 may also be configured for determining a charging schedule along with the guidance. The charging schedule may recommend how long users should charge an electric vehicle at certain charging locations along a route”; para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle”);
provide, via a user interface, the generated recommendation as an option for selection by the user (DeLuca at para. [0043]: “The recommendation module 134 may also be configured for displaying the recommended routes to users”); and
control one or more parameters of one or more electronic devices associated with the vehicle based on a selection of the generated recommendation (DeLuca at para. [0047]: “the electric vehicle system 110 may include a self-driving system such that the system for providing directional guidance for an electric vehicle 100 is enabled to control or drive the electric vehicle based on the directional guidance provided without any outside systems”).
However, DeLuca does not explicitly state:
determine, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session,
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation.
In the same field of endeavor, Sharma teaches:
determine, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session (Sharma at para. [0063]: “the evaluation module 212 may preprocessed the determined real-time vehicle properties, road properties, environmental factors, geographical factors and the driving pattern of the driver.” “The evaluation module 212 may extract relevant features from the preprocessed data to characterize the driving situation (or the drive mode).” “The machine learning based classification technique may include machine learning model, such as a decision tree, support vector machine (SVM), or neural network, trained on historical driving data to learn the patterns associated with different drive modes. The trained model may then classify the current drive mode based on the extracted features.”),
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation (Sharma at para. [0063]: “The evaluation module 212 may continuously monitors the real-time data and reclassifies the drive mode as required, thereby, allowing the system 118 to adapt to different driving situations and provide relevant feedback and recommendations to the driver”; para. [0085]: “the recommendation module 216 determine one or more actions to be performed for rectifying the determined one or more abnormality using the trained machine learning (ML) model. The one or more actions may include one or more recommendations on optimal operational parameters, battery charging stations, a travel route, and the drive mode of the electric vehicle 110”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of DeLuca by adding the comparison of Sharma with a reasonable expectation of success. The motivation to modify the apparatus of DeLuca in view of Sharma is to optimize energy consumption of electric vehicles.
Regarding claim 2, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the vehicle is an electric vehicle (DeLuca at para. [0018]: “FIG. 1 depicts a block diagram of a system for providing directional guidance for an electric vehicle 100”), and wherein the user profile data further comprises charging information associated with one or more historical charging sessions of the electric vehicle (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”).
Regarding claim 4, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the historical usage information of the vehicle comprises: timestamp information associated with each driving session of the one or more historical driving sessions, route information associated with each driving session of the one or more historical driving sessions, weather information associated with a location of the vehicle or a route be traversed by the vehicle during each driving session of the one or more historical driving sessions, occupancy information associated with the vehicle during each driving session of the one or more historical driving sessions, speed information associated with the vehicle during each driving session of the one or more historical driving sessions, vehicle information associated with the vehicle, or a combination thereof (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”).
Regarding claim 5, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the first contextual information associated with the vehicle comprises vehicle information associated with the vehicle during the first driving session, charging information associated with the vehicle during the first driving session, route information associated with the first driving session, traffic information associated with a route to be traversed by the vehicle during the first driving session, weather information associated with a location of the vehicle or the route to be traversed by the vehicle during the first driving session, or a combination thereof (DeLuca at para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle. For example, if a user runs into unexpected traffic congestions or inclement weather which delays travel time or adjusts the expected charge consumption, the analytics and prediction module 133 may alter the guidance in real time to provide a different route or different charge locations to fit the new charging needs and expected arrival time of the electric vehicle”).
Regarding claim 6, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the deviation in the usage of the vehicle during the first driving session is determined based on one of: a modification in vehicle health information associated with the vehicle, a modification in charge information associated with the vehicle, a modification in speed information associated with the vehicle, a modification in route information associated with the first driving session, a modification in traffic information associated with a route to be traversed by the vehicle, a modification in occupancy information associated with the vehicle during the first driving session, environment information during the first driving session, or a combination thereof (DeLuca at para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle. For example, if a user runs into unexpected traffic congestions or inclement weather which delays travel time or adjusts the expected charge consumption, the analytics and prediction module 133 may alter the guidance in real time to provide a different route or different charge locations to fit the new charging needs and expected arrival time of the electric vehicle”).
Regarding claim 7, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the generated recommendation associated with the modification in the driving range of the vehicle corresponds to: a modification in a speed associated with the vehicle, a modification in charge information associated with the vehicle, a modification in vehicle health information associated with the vehicle, a modification in a route to be traversed by the vehicle, a modification in one or more parameters of one or more electronic devices associated with the vehicle, a modification in a start time associated with the first driving session, or a combination thereof (DeLuca at para. [0039]: “The analytics and prediction module 133 may also be configured for determining a charging schedule along with the guidance. The charging schedule may recommend how long users should charge an electric vehicle at certain charging locations along a route”; para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle”).
Regarding claim 8, DeLuca in view of Sharma teaches the apparatus of claim 7.
DeLuca further discloses wherein the one or more electronic devices associated with the vehicle comprises at least one of: a Heating, Ventilation, and Air Conditioning system, an infotainment system, an on-board diagnostics system, a Tire Pressure Monitoring System, a Battery Management System, a vehicle control unit, a navigation system, and an Advanced Driver Assistance System (DeLuca at para. [0047]: “the electric vehicle system 110 may include a self-driving system such that the system for providing directional guidance for an electric vehicle 100 is enabled to control or drive the electric vehicle based on the directional guidance provided without any outside systems”).
Regarding claim 10, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with the modification in the driving range of the vehicle (DeLuca at para. [0035]: “Users may manually input the user information data to the receiving module 132”; para. [0041]: “the recommendation module 134 may consider the time to reach the destination, the actual and predicted availability of charging stations at charging locations along the route, and relevant locations of interest located near the charging locations (taking into account user information data, as described above)” “the recommendation module 134 may rank a route with multiple stops to charge higher than a route with a longer single charging stop if it knows a user prefers shorting charging periods, such preference being stored in the computer system 120 from the user information data” “the recommendation module 134 may prioritize charging locations which users have identified or frequently used in the past, with known locations of interest significantly relevant to that user, such information being stored in the computer system 120 as part of the user information data”); and
generate the recommendation associated with the modification in the driving range of the vehicle based on the received user input (DeLuca at para. [0035]: “The information received by the receiving module 132 can be used for analysis and resulting actions by the analytics and prediction module 133 and the recommendation module 134”).
Regarding claim 11, DeLuca in view of Sharma teaches the apparatus of claim 1.
DeLuca further discloses wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
receive a user input associated with a modification in one or more parameters of one or more electronic devices associated with the vehicle based on the generated recommendation (DeLuca at para. [0043]: “The recommendation module 134 may also be configured to receive input from the user from the user interface, such as users selecting a route, so that the computer system 120 may begin guidance for that route”); and
control the one or more parameters of the one or more electronic devices associated with the vehicle based on the received user input (DeLuca at para. [0047]: “the system for providing directional guidance for an electric vehicle 100 may be configured for controlling or driving the electric vehicle along the selected route”).
Regarding claim 12, DeLuca in view of Sharma teaches the apparatus of claim 1.
Sharma further teaches:
wherein the computer program code instructions are configured to, when executed, cause the apparatus to:
apply a machine learning model on the retrieved user profile data and the obtained first contextual information (Sharma at para. [0005]: “The system may, further, validate the predicted set of health parameters and the performance parameters associated with the electric vehicle by simulating the generated computer simulated instances of the electric vehicle in a virtual environment using the trained machine learning model. Following, the system may determine a behavior status, a performance status and a health status of the electric vehicle based on results of the validation”); and
generate the recommendation associated with the modification in the driving range of the vehicle based on an output of the machine learning model (Sharma at para. [0005]: “The system may determine one or more action to be performed for rectifying the determined one or more abnormality using the trained machine learning model. Herein, the one or more action may include recommendation on optimal operational parameters, battery charging stations, a travel route, and/or a drive mode of the electric vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of DeLuca in view of Sharma by applying the machine learning model of Sharma with a reasonable expectation of success. The motivation to modify the apparatus of DeLuca in view of Sharma is to optimize energy consumption of electric vehicles.
Regarding claim 13, DeLuca discloses a method for providing a user with a recommendation associated with a modification in a driving range of a vehicle, comprising the steps of:
retrieving user profile data associated with the user of the vehicle, wherein the user profile data comprises historical usage information of the vehicle during one or more historical driving sessions by the user (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”; para. [0033]: “the receiving module 132 may be configured to receive historical information regarding charging stations. Historical information may include the past rate of use of a charging location, users' comments regarding charging location, or online complaints about a charging location from users, or common problems with a charging location”);
obtaining first contextual information associated with a first driving session by the user (DeLuca at para. [0032]: “the receiving module 132 may be configured for receiving information directly from the electric vehicle system 110. For example, vehicle related data, such as charging data, speed data, location data, electric motor data, and the like, may be received by the receiving module 132”);
determining, (DeLuca at para. [0039]: “The analytics and prediction module 133 may find that the user should charge for 3 hours at the second charging stop because this matches the amount of time the user typically spends at a location of interest located near the second charging location and a full charge is not required going forward”; para. [0040]: “if a user runs into unexpected traffic congestions or inclement weather which delays travel time or adjusts the expected charge consumption, the analytics and prediction module 133 may alter the guidance in real time to provide a different route or different charge locations to fit the new charging needs and expected arrival time of the electric vehicle”; Changes in traffic situation, weather, or charge consumption (i.e., “deviation”) must be determined to provide an updated guidance or recommendation);
generating the recommendation associated with the modification in the driving range of the vehicle based on the determined deviation, (DeLuca at para. [0039]: “The analytics and prediction module 133 may also be configured for determining a charging schedule along with the guidance. The charging schedule may recommend how long users should charge an electric vehicle at certain charging locations along a route”; para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle”); and
rendering the generated recommendation on a user interface as an option for selection by the user (DeLuca at para. [0043]: “The recommendation module 134 may also be configured for displaying the recommended routes to users”); and
receiving a user input associated with a modification in one or more parameters of one or more electronic devices associated with the vehicle based on the generated recommendation (DeLuca at para. [0043]: “The recommendation module 134 may also be configured to receive input from the user from the user interface, such as users selecting a route, so that the computer system 120 may begin guidance for that route”); and
controlling the one or more parameters of the one or more electronic devices associated with the vehicle based on the received user input (DeLuca at para. [0047]: “the system for providing directional guidance for an electric vehicle 100 may be configured for controlling or driving the electric vehicle along the selected route”).
However, DeLuca does not explicitly state:
determining, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session,
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation.
In the same field of endeavor, Sharma teaches:
determining, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session (Sharma at para. [0063]: “the evaluation module 212 may preprocessed the determined real-time vehicle properties, road properties, environmental factors, geographical factors and the driving pattern of the driver.” “The evaluation module 212 may extract relevant features from the preprocessed data to characterize the driving situation (or the drive mode).” “The machine learning based classification technique may include machine learning model, such as a decision tree, support vector machine (SVM), or neural network, trained on historical driving data to learn the patterns associated with different drive modes. The trained model may then classify the current drive mode based on the extracted features.”),
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation (Sharma at para. [0063]: “The evaluation module 212 may continuously monitors the real-time data and reclassifies the drive mode as required, thereby, allowing the system 118 to adapt to different driving situations and provide relevant feedback and recommendations to the driver”; para. [0085]: “the recommendation module 216 determine one or more actions to be performed for rectifying the determined one or more abnormality using the trained machine learning (ML) model. The one or more actions may include one or more recommendations on optimal operational parameters, battery charging stations, a travel route, and the drive mode of the electric vehicle 110”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of DeLuca by adding the comparison of Sharma with a reasonable expectation of success. The motivation to modify the method of DeLuca in view of Sharma is to optimize energy consumption of electric vehicles.
Regarding claim 14, DeLuca in view of Sharma teaches the method of claim 13.
DeLuca further discloses wherein the vehicle is an electric vehicle (DeLuca at para. [0018]: “FIG. 1 depicts a block diagram of a system for providing directional guidance for an electric vehicle 100”), and wherein the user profile data further comprises charging information associated with one or more historical charging sessions of the electric vehicle (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”).
Regarding claim 15, DeLuca in view of Sharma teaches the method of claim 13.
DeLuca further discloses further comprising the steps of:
receiving a user input associated with the modification in the driving range of the vehicle (DeLuca at para. [0035]: “Users may manually input the user information data to the receiving module 132”; para. [0041]: “the recommendation module 134 may consider the time to reach the destination, the actual and predicted availability of charging stations at charging locations along the route, and relevant locations of interest located near the charging locations (taking into account user information data, as described above)” “the recommendation module 134 may rank a route with multiple stops to charge higher than a route with a longer single charging stop if it knows a user prefers shorting charging periods, such preference being stored in the computer system 120 from the user information data” “the recommendation module 134 may prioritize charging locations which users have identified or frequently used in the past, with known locations of interest significantly relevant to that user, such information being stored in the computer system 120 as part of the user information data”); and
generating the recommendation associated with the modification in the driving range of the vehicle based on the received user input (DeLuca at para. [0035]: “The information received by the receiving module 132 can be used for analysis and resulting actions by the analytics and prediction module 133 and the recommendation module 134”).
Regarding claim 17, DeLuca in view of Sharma teaches the method of claim 13.
DeLuca further discloses wherein the user interface is displayed on at least one of an infotainment unit associated with the vehicle, or a user device associated with the user of the vehicle (DeLuca at para. [0043]: “The user interface may be embedded as part of the computer system 120, it may be part of the electric vehicle system 110 (such as an on board display screen or GPS), or it may be part of the user device 113”).
Regarding claim 18, DeLuca discloses a non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to:
retrieve user profile data associated with a user of a vehicle, wherein the user profile data comprises historical usage information of the vehicle during one or more historical driving sessions by the user (DeLuca at para. [0022]: “the network repository 114 may be a data center saving and cataloging user data sent by the electric vehicle system 110 and/or user device 113 to generate both historical and predictive reports regarding a users' and vehicle's movement or navigational habits, historical charging tendencies, different available charging locations, and driving condition predications”; para. [0033]: “the receiving module 132 may be configured to receive historical information regarding charging stations. Historical information may include the past rate of use of a charging location, users' comments regarding charging location, or online complaints about a charging location from users, or common problems with a charging location”);
obtain first contextual information associated with a first driving session by the user (DeLuca at para. [0032]: “the receiving module 132 may be configured for receiving information directly from the electric vehicle system 110. For example, vehicle related data, such as charging data, speed data, location data, electric motor data, and the like, may be received by the receiving module 132”);
determine, information (DeLuca at para. [0039]: “The analytics and prediction module 133 may find that the user should charge for 3 hours at the second charging stop because this matches the amount of time the user typically spends at a location of interest located near the second charging location and a full charge is not required going forward”; para. [0040]: “if a user runs into unexpected traffic congestions or inclement weather which delays travel time or adjusts the expected charge consumption, the analytics and prediction module 133 may alter the guidance in real time to provide a different route or different charge locations to fit the new charging needs and expected arrival time of the electric vehicle”; Changes in traffic situation, weather, or charge consumption (i.e., “deviation”) must be determined to provide an updated guidance or recommendation);
generate a recommendation associated with a modification in a driving range of the vehicle based on the determined deviation, (DeLuca at para. [0039]: “The analytics and prediction module 133 may also be configured for determining a charging schedule along with the guidance. The charging schedule may recommend how long users should charge an electric vehicle at certain charging locations along a route”; para. [0040]: “the analytics and prediction module 133 may track driving conditions and alter or modify routes in real time if driving conditions change to increase or decrease charging needs of an electric vehicle”); and
provide, via a user interface, the generated recommendation as an option for selection by the user (DeLuca at para. [0043]: “The recommendation module 134 may also be configured for displaying the recommended routes to users”); and
receive a user input associated with a modification in one or more parameters of one or more electronic devices associated with the vehicle based on the generated recommendation (DeLuca at para. [0043]: “The recommendation module 134 may also be configured to receive input from the user from the user interface, such as users selecting a route, so that the computer system 120 may begin guidance for that route”); and
control the one or more parameters of the one or more electronic devices associated with the vehicle based on the received user input (DeLuca at para. [0047]: “the system for providing directional guidance for an electric vehicle 100 may be configured for controlling or driving the electric vehicle along the selected route”).
However, DeLuca does not explicitly state:
determine, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session,
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation.
In the same field of endeavor, Sharma teaches:
determine, based on a comparison between historical driving behavior associated with a given activity and current driving behavior associated with the first driving session (Sharma at para. [0063]: “the evaluation module 212 may preprocessed the determined real-time vehicle properties, road properties, environmental factors, geographical factors and the driving pattern of the driver” “The evaluation module 212 may extract relevant features from the preprocessed data to characterize the driving situation (or the drive mode)” “The machine learning based classification technique may include machine learning model, such as a decision tree, support vector machine (SVM), or neural network, trained on historical driving data to learn the patterns associated with different drive modes. The trained model may then classify the current drive mode based on the extracted features”),
wherein the recommendation includes information for maintaining a mobility pattern or a charging pattern associated with the user despite the determined deviation (Sharma at para. [0063]: “The evaluation module 212 may continuously monitors the real-time data and reclassifies the drive mode as required, thereby, allowing the system 118 to adapt to different driving situations and provide relevant feedback and recommendations to the driver”; para. [0085]: “the recommendation module 216 determine one or more actions to be performed for rectifying the determined one or more abnormality using the trained machine learning (ML) model. The one or more actions may include one or more recommendations on optimal operational parameters, battery charging stations, a travel route, and the drive mode of the electric vehicle 110”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the medium of DeLuca by adding the comparison of Sharma with a reasonable expectation of success. The motivation to modify the medium of DeLuca in view of Sharma is to optimize energy consumption of electric vehicles.
Regarding claim 19, DeLuca in view of Sharma teaches the non-transitory computer-readable storage medium of claim 18.
DeLuca further discloses wherein the computer program code instructions are configured to, when executed, cause the at least one processor to:
receive a user input associated with the modification in the driving range of the vehicle (DeLuca at para. [0035]: “Users may manually input the user information data to the receiving module 132”; para. [0041]: “the recommendation module 134 may consider the time to reach the destination, the actual and predicted availability of charging stations at charging locations along the route, and relevant locations of interest located near the charging locations (taking into account user information data, as described above)” “the recommendation module 134 may rank a route with multiple stops to charge higher than a route with a longer single charging stop if it knows a user prefers shorting charging periods, such preference being stored in the computer system 120 from the user information data” “the recommendation module 134 may prioritize charging locations which users have identified or frequently used in the past, with known locations of interest significantly relevant to that user, such information being stored in the computer system 120 as part of the user information data”); and
generate the recommendation associated with the modification in the driving range of the vehicle based on the received user input (DeLuca at para. [0035]: “The information received by the receiving module 132 can be used for analysis and resulting actions by the analytics and prediction module 133 and the recommendation module 134”).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over DeLuca in view of Sharma further in view of Hinderer et al. (US 11,897,357 B1, hereinafter “Hinderer”).
Regarding claim 3, DeLuca in view of Sharma teaches the apparatus of claim 2.
However, DeLuca in view of Sharma does not explicitly state:
wherein the charging information associated with the one or more historical charging sessions of the electric vehicle comprises: timestamp information associated with each charging session of the one or more historical charging sessions, location information associated with each charging session of the one or more historical charging sessions, cost information associated with each charging session of the one or more historical charging sessions, or a combination thereof.
In the same field of endeavor, Hinderer teaches:
wherein the charging information associated with the one or more historical charging sessions of the electric vehicle comprises: timestamp information associated with each charging session of the one or more historical charging sessions, location information associated with each charging session of the one or more historical charging sessions, cost information associated with each charging session of the one or more historical charging sessions, or a combination thereof (Hinderer at col. 19, ln. 53-57: “User specific data can include data that may be relevant for learning usage patterns of the first host system 350. This can include, for example, information such as the user's specific location history, charging locations, charging dates, charging times, or other information”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of DeLuca in view of Sharma by adding the charging information of Hinderer with a reasonable expectation of success. The motivation to modify the apparatus of DeLuca in view of Sharma further in view of Hinderer is to optimize energy consumption of electric vehicles.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JISUN CHOI/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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