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 . This Office Action is responsive to the communications filed on 20 March 2026. Claims 1-20 are pending.
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 1-7, 9-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire et al. (Hereinafter, Beaurepaire, US 2019/0308510 A1) in view of DiCarlo et al. (Hereinafter, DiCarlo, US 2016/0064961 A1), and further in view of Wingrove et al. (Hereinafter, Wingrove, US 2011/0167365 A1).
Per claim 1, Beaurepaire discloses an electronic device (e.g., computer system 900 as shown in Fig. 9) comprising:
an electronic display (e.g., display 914 as shown in Fig. 9; paragraph [0114]);
a processor (e.g., processor 902 as shown in Fig. 9; paragraph [0112]); and
a non-transitory computer-readable medium(e.g., memory 904 as shown in Fig. 9; paragraph [0117]) storing instructions that, when executed by the processor, causes the processor to cause for display on the electronic display a selected representation of information among a plurality of representations of the information (e.g., time-based representation 701 as shown in Fig. 7; paragraph [0065], “ … For example, if the user selects the highlighted day 705a (e.g. Monday), the output module 307 can present a message 709 to confirm the selected charging day (e.g., “You have selected Monday as your charging day”). In addition or alternatively, the interaction 707 can trigger a presentation of a map 711 of recommended charging stations 713a and 713b for the selected day. “; Examiner’s Note: Figure 7 illustrates a user interaction 707 where the user selects from a plurality of representations, i.e., different days) based at least in part on learned behavior of a user (paragraph [0055], “… In one embodiment, the time-based representation 521 can be an interactive user interface or user interface element, that enables a user to request more information or have access to additional options by selecting different elements of the time-based representation (e.g., selecting a day to see more information such as predicted remaining charge, predicted range, etc.).“; paragraph [0065]; Examiner’s Note: Baudelaire discloses displaying a selected representation of information among a plurality of representations of representations of the information based on the usage history, the usage pattern, the planned use of the vehicle, or a combination thereof based on identifying the user that is operating the vehicle from among a plurality of users. As paragraph [0065], Beaurepaire causes display of the remaining energy based on an analysis of historical usage data for the user.), but does not expressly disclose:
wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display;
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed: and
in response to determining that the goal is not satisfied, display a different representation of the information.
DiCarlo discloses wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display(e.g., operation 402 as shown in Fig. 4; Abstract; paragraph [0007] “The disclosed embodiments provide a system that manages use of a battery in a portable electronic device. During operation, the system monitors one or more indicators of user behavior associated with charging and discharging of the battery in the portable electronic device, and may modify one or more charging parameters based on the monitored indicators….”; paragraph [0033]; paragraphs [0046-0047]; paragraph[0068],” In some variations, a charging pattern associated with charging of the battery by a user is monitored (operation 402). Note that monitoring the charging pattern may be an indirect technique for monitoring user behavior associated with charging and discharging of the battery by the user. “ ).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use the user-behavior-driven battery charging of DiCarlo with Beaurepaire’s time-based representation of charge or fuel level for the purpose of mitigating battery charging problems as suggested by DiCarlo (See paragraph [0006]).
Wingrove discloses:
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed(Abstract; paragraphs [0006-0009]; paragraph [0017]; Wingrove discloses analyzing user preferences.): and
in response to determining that the goal is not satisfied, display a different representation of the information (paragraph [0019]; paragraph [0023]; Wingrove discloses reconfiguring the display based upon the preference of the user, i.e., goals).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use Wingrove’s system and method with Beaurepaire and DiCarlo’s time-based representation of charge or fuel level for the purpose of developing an adaptive interface system and a method for configuring a user interface as suggested by Wingrove(See paragraph [0005]).
Per claim 2, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein the plurality of representations comprise two or more of a percentage, a fraction, an icon, a graph, and a numeral (Beaurepaire, e.g., traditional representation 201 as shown in Fig. 2 illustrates that presenting representations of information as two or more of a percentage, a fraction, an icon, a graph, and a numeral is well-known; paragraph [0035]; paragraph [0057], “ …More specifically, the UI 581 depicts a graph of predicted distance 583 and actual distance 585 traveled by the vehicle 101 for each day over the predicted time frame along with the predicted energy capacity expected to be used each day. The UI 587 also depicts representations of the recommended charge 589 for each day with the size of the charge icon representing recommended recharging duration … “; Examiner’s Note: Fig. 5E illustrates displaying a graph and icon based at least in part on learned behavior of a user.).
Per claim 3, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein: the plurality of representations are stored in a database (Beaurepaire, e.g., geographic database 109 as shown in Fig. 8; paragraph [0039], “… geographic database 109 (e.g., storing digital map data) and a user database 111 (e.g., storing user vehicle usage data, vehicle usage patterns, and related data) ... “); and the instructions further cause the processor to transform the information into the selected representation for display on the electronic display (Beaurepaire, e.g., time-based representation 501 as shown in Fig. 5A; Abstract; paragraph [0003], “ …The method further comprises presenting a user interface (e.g., an interactive user interface) depicting a representation of the predicted time as an indicator of an energy status of the vehicle or device. “; paragraph [0042], “…The usage data can be stored in the user database 111 …. “; paragraph [0050]).
Per claim 4, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein the instructions, when executed by the processor, cause the processor to determine the learned behavior by: changing a displayed representation of the information that is displayed on the electronic display over time(Beaurepaire, paragraph [0050], “ FIG. 5A illustrates an example of generating a time-based representation 501 of a remaining energy level 503 of a vehicle 101 which accounts for a reserve level 505 and a buffer level 507, according to one embodiment …. “; Examiner’s Note: The display is changed based on the user’s predicted fuel usage over time.), wherein the displayed representation is an individual representation of the plurality of representations (Beaurepaire, paragraph [0051], “ In one embodiment, the prediction module 303 can also account for multiple users of the same vehicle 101 or UE 113. For example, when a vehicle 101 is shared across multiple users (e.g., family members, friends, fleet vehicles, etc.), the prediction module 305 can determine and evaluate factors including, but not limited to: (1) who will be the next person using the vehicle 101 or UE 113; (2) when and how long the next person will be using the vehicle 101 or UE 113; (3) what distance and energy level is needed for the next person; etc. …. “); and monitoring interactions with the electronic device by a user for each displayed representation of the information to determine the learned behavior (Beaurepaire, paragraph [0055], “ … In one embodiment, the time-based representation 521 can be an interactive user interface or user interface element, that enables a user to request more information or have access to additional options by selecting different elements of the time-based representation (e.g., selecting a day to see more information such as predicted remaining charge, predicted range, etc.). “).
Per claim 5, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 4, wherein the instructions, when executed by the processor, further cause the processor to: determine whether one or more interactions of the learned behavior satisfy a goal when each displayed representation of the information is displayed (Beaurepaire, paragraph [0037]; Examiner’s Note: Beaurepaire determines whether a the user’s vehicle has the required charge or fuel level to satisfy a goal such as vacation trip based on past and current usage estimates.); and select an individual displayed representation as the selected representation when the individual displayed representation satisfies the goal (Beaurepaire, e.g., representation 203 as shown in Fig. 2A and Fig. 5A; Examiner’s Note: Beaurepaire teaches displaying a graphic of the energy usage and an estimate of a user meeting a usage goal based in part on the user’s behavior.) .
Per claim 6, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 5, wherein the selected representation causes satisfaction of the goal at a highest frequency among remaining displayed representations of the plurality of representations (Beaurepaire, paragraph [0065], “…In one embodiment, the time-based representation 701 is interactive and allows a user to perform an interaction 707, for instance, to select from among the recommended charging days or any other presented days. For example, if the user selects the highlighted day 705a (e.g. Monday), the output module 307 can present a message 709 to confirm the selected charging day (e.g., "You have selected Monday as your charging day"). In addition or alternatively, the interaction 707 can trigger a presentation of a map 711 of recommended charging stations 713a and 713b for the selected day. “).
Per claim 7, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein the instructions, when executed by the processor, cause the processor to determine the learned behavior by determining attributes of the user by monitoring interactions of the user with the electronic device (Beaurepaire, e.g., Step 401 as shown in Fig. 4; paragraph [0042]; paragraph [0043], “In one embodiment, the data module 301 processes usage data to build data models of the usage history and/or usage patterns for the user. The models can be used for temporal (e.g., daily, weekly, monthly, etc.) or "activity-based" (e.g., commuting, shopping trip, vacation, etc.) breakdowns of the usage data ... “).
Per claim 9, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein the electronic device is a mobile phone (Beaurepaire, e.g., UE 113 as shown in Fig. 1; paragraph [0079]).
Per claim 10, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 1, wherein the electronic device is a vehicle (Beaurepaire, e.g., vehicle 101 as shown in Fig. 1; paragraph [0078]).
Per claim 11, Beaurepaire discloses a method comprising displaying on an electronic display a representation of information selected among a plurality of representations of the information (e.g., time-based representation 701 as shown in Fig. 7; paragraph [0065], “ … For example, if the user selects the highlighted day 705a (e.g. Monday), the output module 307 can present a message 709 to confirm the selected charging day (e.g., “You have selected Monday as your charging day”). In addition or alternatively, the interaction 707 can trigger a presentation of a map 711 of recommended charging stations 713a and 713b for the selected day. “; Examiner’s Note: Figure 7 illustrates a user interaction 707 where the user selects from a plurality of representations, i.e., different days) based at least in part on learned behavior of a user (paragraph [0055], “… In one embodiment, the time-based representation 521 can be an interactive user interface or user interface element, that enables a user to request more information or have access to additional options by selecting different elements of the time-based representation (e.g., selecting a day to see more information such as predicted remaining charge, predicted range, etc.).“; paragraph [0065]; Examiner’s Note: Baudelaire discloses displaying a selected representation of information among a plurality of representations of representations of the information based on the usage history, the usage pattern, the planned use of the vehicle, or a combination thereof based on identifying the user that is operating the vehicle from among a plurality of users. As paragraph [0065], Beaurepaire causes display of the remaining energy based on an analysis of historical usage data for the user. ), but does not expressly disclose:
wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display;
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed: and
in response to determining that the goal is not satisfied, display a different representation of the information.
DiCarlo discloses wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display(e.g., operation 402 as shown in Fig. 4; Abstract; paragraph [0007] “The disclosed embodiments provide a system that manages use of a battery in a portable electronic device. During operation, the system monitors one or more indicators of user behavior associated with charging and discharging of the battery in the portable electronic device, and may modify one or more charging parameters based on the monitored indicators….”; paragraph [0033]; paragraphs [0046-0047]; paragraph[0068],” In some variations, a charging pattern associated with charging of the battery by a user is monitored (operation 402). Note that monitoring the charging pattern may be an indirect technique for monitoring user behavior associated with charging and discharging of the battery by the user. “ ).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use the user-behavior-driven battery charging of DiCarlo with Beaurepaire’s time-based representation of charge or fuel level for the purpose of mitigating battery charging problems as suggested by DiCarlo (See paragraph [0006]).
Wingrove discloses:
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed(Abstract; paragraphs [0006-0009]; paragraph [0017]; Wingrove discloses analyzing user preferences.): and
in response to determining that the goal is not satisfied, display a different representation of the information (paragraph [0019]; paragraph [0023]; Wingrove discloses reconfiguring the display based upon the preference of the user, i.e., goals).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use Wingrove’s system and method with Beaurepaire and DiCarlo’s time-based representation of charge or fuel level for the purpose of developing an adaptive interface system and a method for configuring a user interface as suggested by Wingrove(See paragraph [0005]).
Per claim 12, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 11, wherein the plurality of representations comprise two or more of a percentage, a fraction, an icon, a graph, and a numeral ( Beaurepaire, e.g., traditional representation 201 as shown in Fig. 2 illustrates that presenting representations of information as two or more of a percentage, a fraction, an icon, a graph, and a numeral is well-known; paragraph [0035]; paragraph [0057], “ …More specifically, the UI 581 depicts a graph of predicted distance 583 and actual distance 585 traveled by the vehicle 101 for each day over the predicted time frame along with the predicted energy capacity expected to be used each day. The UI 587 also depicts representations of the recommended charge 589 for each day with the size of the charge icon representing recommended recharging duration … “; Examiner’s Note: Fig. 5E illustrates displaying a graph and icon based at least in part on learned behavior of a user.).
Per claim 13, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 11, wherein: the plurality of representations are stored in a database; and the method further includes transforming the information into the selected representation (Beaurepaire, e.g., time-based representation 501 as shown in Fig. 5A; Abstract; paragraph [0003], “ …The method further comprises presenting a user interface (e.g., an interactive user interface) depicting a representation of the predicted time as an indicator of an energy status of the vehicle or device. “; paragraph [0042], “…The usage data can be stored in the user database 111 …. “; paragraph [0050]).
Per claim 14, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 11, wherein the learned behavior is determined by: changing a displayed representation of the information that is displayed by the electronic display over time (Beaurepaire, paragraph [0050], “; FIG. 5A illustrates an example of generating a time-based representation 501 of a remaining energy level 503 of a vehicle 101 which accounts for a reserve level 505 and a buffer level 507, according to one embodiment …. “; Examiner’s Note: The display is changed based on the user’s predicted fuel usage over time.), wherein the displayed representation is an individual representation of the plurality of representations (paragraph [0051], “In one embodiment, the prediction module 303 can also account for multiple users of the same vehicle 101 or UE 113. For example, when a vehicle 101 is shared across multiple users (e.g., family members, friends, fleet vehicles, etc.), the prediction module 305 can determine and evaluate factors including, but not limited to: (1) who will be the next person using the vehicle 101 or UE 113; (2) when and how long the next person will be using the vehicle 101 or UE 113; (3) what distance and energy level is needed for the next person; etc. …. “), wherein the displayed representation is an individual representation of the plurality of representations (Beaurepaire, paragraph [0051], “In one embodiment, the prediction module 303 can also account for multiple users of the same vehicle 101 or UE 113. For example, when a vehicle 101 is shared across multiple users (e.g., family members, friends, fleet vehicles, etc.), the prediction module 305 can determine and evaluate factors including, but not limited to: (1) who will be the next person using the vehicle 101 or UE 113; (2) when and how long the next person will be using the vehicle 101 or UE 113; (3) what distance and energy level is needed for the next person; etc. …. “); and monitoring interactions with an electronic device including the electronic display by a user for each displayed representation of the information to determine the learned behavior (Beaurepaire, paragraph [0055], “ … In one embodiment, the time-based representation 521 can be an interactive user interface or user interface element, that enables a user to request more information or have access to additional options by selecting different elements of the time-based representation (e.g., selecting a day to see more information such as predicted remaining charge, predicted range, etc.). “).
Per claim 15, Beaurepaire, DiCarlo, and Wingrove the method of claim 11,further comprising: determining whether one or more interactions of the learned behavior satisfy a goal when each displayed representation of the information is displayed; and selecting an individual displayed representation as the selected representation when the individual displayed representation satisfies the goal (Beaurepaire, paragraph [0037]; Examiner’s Note: Beaurepaire determines whether a the user’s vehicle has the required charge or fuel level to satisfy a goal such as vacation trip based on past and current usage estimates.); and select an individual displayed representation as the selected representation when the individual displayed representation satisfies the goal (e.g., representation 203 as shown in Fig. 2A and Fig. 5A; Examiner’s Note: Beaurepaire teaches displaying a graphic of the energy usage and an estimate of a user meeting a usage goal based in part on the user’s behavior.) .
Per claim 16, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 15, wherein the selected representation causes satisfaction of the goal at a highest frequency among remaining displayed representations of the plurality of representations (Beaurepaire, paragraph [0065], “…In one embodiment, the time-based representation 701 is interactive and allows a user to perform an interaction 707, for instance, to select from among the recommended charging days or any other presented days. For example, if the user selects the highlighted day 705a (e.g. Monday), the output module 307 can present a message 709 to confirm the selected charging day (e.g., "You have selected Monday as your charging day"). In addition or alternatively, the interaction 707 can trigger a presentation of a map 711 of recommended charging stations 713a and 713b for the selected day. “).
Per claim 17, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 11, wherein the learned behavior is determined by determining attributes of the user by monitoring interactions of the user with an electronic device including the electronic display (Beaurepaire, e.g., Step 401 as shown in Fig. 4; paragraph [0042]; paragraph [0043], “In one embodiment, the data module 301 processes usage data to build data models of the usage history and/or usage patterns for the user. The models can be used for temporal (e.g., daily, weekly, monthly, etc.) or "activity-based" (e.g., commuting, shopping trip, vacation, etc.) breakdowns of the usage data ... “).
Per claim 19, Beaurepaire discloses a vehicle comprising:
an electronic display (e.g., display device 914 as shown in Fig. 9);
a processor (e.g., processor 902 as shown in Fig. 9);
a non-transitory computer-readable medium (e.g., memory 904 as shown in Fig. 9; paragraph [0117]) storing instructions that, when executed by the processor, causes the processor to cause for display on the electronic display a selected representation of information among a plurality of representations of the information (e.g., time-based representation 701 as shown in Fig. 7; paragraph [0065], “ … For example, if the user selects the highlighted day 705a (e.g. Monday), the output module 307 can present a message 709 to confirm the selected charging day (e.g., “You have selected Monday as your charging day”). In addition or alternatively, the interaction 707 can trigger a presentation of a map 711 of recommended charging stations 713a and 713b for the selected day. “; Examiner’s Note: Figure 7 illustrates a user interaction 707 where the user selects from a plurality of representations, i.e., different days) based at least in part on learned behavior of a user (paragraph [0055], “… In one embodiment, the time-based representation 521 can be an interactive user interface or user interface element, that enables a user to request more information or have access to additional options by selecting different elements of the time-based representation (e.g., selecting a day to see more information such as predicted remaining charge, predicted range, etc.).“; paragraph [0065]; Examiner’s Note: Baudelaire discloses displaying a selected representation of information among a plurality of representations of representations of the information based on the usage history, the usage pattern, the planned use of the vehicle, or a combination thereof based on identifying the user that is operating the vehicle from among a plurality of users. As paragraph [0065], Beaurepaire causes display of the remaining energy based on an analysis of historical usage data for the user.) , but does not expressly disclose:
wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display;
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed: and
in response to determining that the goal is not satisfied, display a different representation of the information.
DiCarlo discloses wherein the learned user behavior is learned based on user interaction with the electronic device and the plurality of representations displayed on the electronic display(e.g., operation 402 as shown in Fig. 4; Abstract; paragraph [0007] “The disclosed embodiments provide a system that manages use of a battery in a portable electronic device. During operation, the system monitors one or more indicators of user behavior associated with charging and discharging of the battery in the portable electronic device, and may modify one or more charging parameters based on the monitored indicators….”; paragraph [0033]; paragraphs [0046-0047]; paragraph[0068],” In some variations, a charging pattern associated with charging of the battery by a user is monitored (operation 402). Note that monitoring the charging pattern may be an indirect technique for monitoring user behavior associated with charging and discharging of the battery by the user. “ ).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use the user-behavior-driven battery charging of DiCarlo with Beaurepaire’s time-based representation of charge or fuel level for the purpose of mitigating battery charging problems as suggested by DiCarlo (See paragraph [0006]).
Wingrove discloses:
determine whether a goal of charging the electronic device is not satisfied based on a user behavior when the selected representation of information is displayed(Abstract; paragraphs [0006-0009]; paragraph [0017]; Wingrove discloses analyzing user preferences.): and
in response to determining that the goal is not satisfied, display a different representation of the information (paragraph [0019]; paragraph [0023]; Wingrove discloses reconfiguring the display based upon the preference of the user, i.e., goals).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use Wingrove’s system and method with Beaurepaire and DiCarlo’s time-based representation of charge or fuel level for the purpose of developing an adaptive interface system and a method for configuring a user interface as suggested by Wingrove(See paragraph [0005]).
Per claim 20, Beaurepaire, DiCarlo, and Wingrove disclose the vehicle of claim 19, wherein: the vehicle comprises a battery (Beaurepaire, paragraph [0040]); the vehicle information is a state of charge of the battery; and the plurality of representations comprises two or more of: a battery icon, a percentage, a graph (Beaurepaire, e.g., traditional representation 201 as shown in Fig. 2 illustrates that presenting representations of information as two or more of a percentage, a fraction, an icon, a graph, and a numeral is well-known; paragraph [0035]; paragraph [0057], “ …More specifically, the UI 581 depicts a graph of predicted distance 583 and actual distance 585 traveled by the vehicle 101 for each day over the predicted time frame along with the predicted energy capacity expected to be used each day. The UI 587 also depicts representations of the recommended charge 589 for each day with the size of the charge icon representing recommended recharging duration … “; Examiner’s Note: Fig. 5E illustrates displaying a graph and icon based at least in part on learned behavior of a user.).
Claims 8 and 18 rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire et al. (Hereinafter, Beaurepaire, US 2019/0308510 A1) in view of DiCarlo et al. (Hereinafter, DiCarlo, US 2016/0064961 A1), and further in view of Wingrove et al. (Hereinafter, Wingrove, US 2011/0167365 A1).
Per claim 8, Beaurepaire, DiCarlo, and Wingrove disclose the electronic device of claim 7, but do not expressly disclose wherein the selected representation is determined by: comparing the attributes of the user with a plurality of user profiles, wherein the plurality of user profiles is based on attributes determined from interactions of a plurality of users with a plurality of electronic devices, and each user profile is associated with a preferred representation of the information; selecting a selected user profile among the plurality of user profiles, wherein the selected user profile has attributes closest to the attributes of the user; and the preferred representation of the information of the selected user profile is selected as the selected representation of the information.
However, Seth discloses wherein the selected representation is determined by: comparing the attributes of the user with a plurality of user profiles (e.g., Block 306 as shown in Fig. 3; paragraph [0041], “At block 306, the electronic processor 202 generates a probable user profile based on the first usage indicator and the first user identifier. For example, the electronic processor 202 creates a database record that includes the usage indicators and user identifiers (received at blocks 302 and 304). As noted above, user profiles are unique to and associated with individual users of the computing device 102….” ), wherein the plurality of user profiles is based on attributes determined from interactions of a plurality of users with a plurality of electronic devices, and each user profile is associated with a preferred representation of the information (paragraph [0041], “ …As described in more detail below, the probable user profile may be compared to previously-generated user profiles, each of which are associate with a unique user, to determine which user is currently using the computing device 102. ” “); selecting a selected user profile among the plurality of user profiles, wherein the selected user profile has attributes closest to the attributes of the user (e.g., Block 308 as shown in Fig. 3; paragraph [0042]); and the preferred representation of the information of the selected user profile is selected as the selected representation of the information (e.g., Block 310 as shown in Fig. 3; paragraph [0043]; paragraph [0046]).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use the automated user profile generation of Seth with the time-based representation of Beaurepaire, DiCarlo, and Wingrove for the purpose of automatically generating and detecting user profiles as suggested by Seth (See paragraph [0001]).
Per claim 18, Beaurepaire, DiCarlo, and Wingrove disclose the method of claim 17, but do not expressly disclose wherein the selected representation is determined by: comparing the attributes of the user with a plurality of user profiles, wherein the plurality of user profiles is based on attributes determined from interactions of a plurality of users with a plurality of electronic devices, and each user profile is associated with a preferred representation of the information; selecting a selected user profile among the plurality of user profiles, wherein the selected user profile has attributes closest to the attributes of the user; and the preferred representation of the information of the selected user profile is selected as the selected representation of the information.
However, Seth discloses wherein the selected representation is determined by: comparing the attributes of the user with a plurality of user profiles (e.g., Block 306 as shown in Fig. 3; paragraph [0041], “At block 306, the electronic processor 202 generates a probable user profile based on the first usage indicator and the first user identifier. For example, the electronic processor 202 creates a database record that includes the usage indicators and user identifiers (received at blocks 302 and 304). As noted above, user profiles are unique to and associated with individual users of the computing device 102….” ), wherein the plurality of user profiles is based on attributes determined from interactions of a plurality of users with a plurality of electronic devices, and each user profile is associated with a preferred representation of the information (paragraph [0041], “ …As described in more detail below, the probable user profile may be compared to previously-generated user profiles, each of which are associate with a unique user, to determine which user is currently using the computing device 102. ” “); selecting a selected user profile among the plurality of user profiles, wherein the selected user profile has attributes closest to the attributes of the user (e.g., Block 308 as shown in Fig. 3; paragraph [0042]); and the preferred representation of the information of the selected user profile is selected as the selected representation of the information (e.g., Block 310 as shown in Fig. 3; paragraph [0043]; paragraph [0046]).
It would have been obvious for a person of ordinary skill in the art before the effective fling date of the claimed invention to use the automated user profile generation of Seth with the time-based representation of Beaurepaire, DiCarlo, and Wingrove for the purpose of automatically generating and detecting user profiles as suggested by Seth (See paragraph [0001]).
Response to Arguments
Applicant’s arguments, see Notice of Appeal, filed March 20, 2026 with respect to the rejection(s) of claim(s) 20 March 2026 under 35 U.S.C. § 103 as being unpatentable over Beaurepaire (US 2019/0308510 A1) in view of Chaudhri (US 2007/0101279 A1), and further in view of Pathak (US 2019/0369699 A1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made under 35 U.S.C. § 103 as being unpatentable over Beaurepaire (US 2019/0308510 A1) in view of DiCarlo et al. (US 2016/0064961 A1), and further in view of Wingrove et al. (US 2011/0167365 A1)
Conclusion
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DARRIN HOPE
Examiner
Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
Prosecution Insights