Electronic device and method for displaying information on an electronic display unit based on a user's learning behavior

Abstract

To provide electronic devices and methods for displaying information on electronic displays based on user learning behavior.SOLUTION: Electronic devices and methods for displaying information on electronic displays of electronic devices based on user learning behavior are disclosed. An electronic device includes an electronic display, a processor, and a non-transitory computer-readable medium storing instructions. When executed by the processor, the instruction causes the processor to cause, for display on the electronic display, a selected representation of information among a plurality of representations based at least in part on a user learning behavior.SELECTED DRAWING: Figure 7

Description

Technical Field

[0001] Embodiments described herein generally relate to an electronic display unit, and more specifically, to an electronic device and method for displaying information on an electronic display unit of an electronic device based on learned user behavior.

Background Art

[0002] Information may be displayed by an electronic display unit of an electronic device in a variety of different representations. For example, a numerical value may be represented as a number, a percentage, a graph, an icon, a color representation, etc. In a specific example, the charging state of a battery such as a battery of a mobile phone may be displayed as a percentage, a graph, an icon (e.g., an icon configured as a battery), a color (e.g., a red battery icon means low charge, a yellow battery icon means medium charge, and a green battery icon means mostly charged or fully charged), etc.

[0003] People react differently to the representation of information. Some people inherently prefer to see information as a percentage, while others may inherently prefer to see the same information as an icon. However, the electronic display unit may not display the best representation of information for a particular user.

[0004] Therefore, there may be a need for an alternative electronic device and method for displaying a representation of information on an electronic display unit of an electronic device.

Summary of the Invention

[0005] In one embodiment, an electronic device includes an electronic display, a processor, and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to generate a selected representation of information from among a plurality of representations for display on the electronic display based on at least a portion of a user's learning behavior.

[0006] In another embodiment, a method includes displaying, on an electronic display, a representation of information selected from among a plurality of representations based on a user's learning behavior.

[0007] In yet another embodiment, a vehicle includes an electronic display, a processor, and a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to generate a selected representation of vehicle information from among a plurality of representations for display on the electronic display based on at least a portion of a user's learning behavior.

[0008] These and additional features provided by the embodiments described herein will be more fully understood in consideration of the following detailed description in conjunction with the drawings.

Brief Description of the Drawings

[0009] The embodiments described in the drawings are provided to assist understanding and are in fact typical examples and are not intended to limit the subject matter defined by the claims. The following detailed description of the embodiments to assist understanding can be understood when read in conjunction with the following drawings, and like configurations are indicated by like reference numerals.

[0010]

Figure 1

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Figure 7

Mode for Carrying Out the Invention

[0011] Embodiments of the present disclosure are directed to an electronic device and a method for displaying information on an electronic display of the electronic device based on learned user behavior. The information may be displayed by the electronic display of the electronic device in various different representations. For example, a numerical value may be displayed as a number, a percentage, a graph, an icon, a color representation, etc. In many cases, the information has a purpose, and thus, for example, the information may be displayed to the user so that an action can be taken based on the information, and as a result, the user achieves a goal. In the example of the battery of a mobile phone, the goal may be for the user to charge the battery of the mobile phone before the charge level drops below a certain point (e.g., 10%).

[0012] Informative representations may cause the user to charge the battery of the mobile phone more quickly before the charge level reaches or drops below 10%. For example, a first person may charge the battery of her mobile phone more quickly to achieve the goal when the representation of the charge level of the mobile phone battery is in percentage. A second person may charge the battery of her mobile phone more quickly to achieve the same goal when the representation of the charge level of the mobile phone battery is a colored icon.

[0013] However, in many cases, some representations of information are fixed and cannot be changed by the user, the user does not know how to change the representation, or the user does not know what the optimal representation of information is for her.

[0014] In embodiments of the present disclosure, the optimal representation of information is automatically selected and displayed based on the learning behaviors of one or more users. As used herein, "select", "selected", and "selected representation" mean that the representation is automatically selected by the processor without user intervention, such that the user does not go through the steps of selecting a preferred representation of information for display, such as manually setting a preferred one from a set list. As used herein, a user's "learning behavior" means the behavior of a user of an electronic device that is learned by monitoring the user's interaction with the electronic device when different representations are displayed by the electronic device.

[0015] As described in more detail below, the behavior of the user of the electronic device is learned over time, and the representation of information that is most likely to align with one or more goals is automatically selected and displayed on the electronic device.

[0016] Various embodiments of the electronic device and method for displaying information on an electronic display unit of the electronic device based on the learned user behavior are described below.

[0017] Referring to FIG. 1, an example electronic device configured as a mobile phone 100 is shown. It should be understood that the electronic device described in this specification is not limited to a mobile phone, and the electronic device may be any electronic device having an electronic display unit that presents information.

[0018] The mobile phone 100 includes an electronic display unit 102 that presents information to the user. The electronic display unit 102 may present any type of information. In the example shown in the figure, the electronic display unit 102 displays information in the form of the charging state of the battery of the mobile phone and the current time. The representation 104 of the charging state is set as an icon configured as a battery in a non-limiting example. The colored portion within the icon indicates how "full" the battery is, i.e., the current percentage of the charging state. As described in more detail below, there are more possible representations of the charging state that can be presented to the user, and some representations may be more desirable than others when achieving a goal.

[0019] In the example shown in the figure, the representation 106 of the current time is provided as a 12-hour digital clock representation (9:34 PM). However, there are other possible representations for the current time. For example, the representation may be an analog clock face, a 24-hour digital clock representation, and others.

[0020] It should be understood that there are an infinite number of types of information that may have diverse representations (e.g., calendar dates, vehicle data, economic information, measurement information, etc.).

[0021] Referring to FIG. 2, non-limiting examples of different representations of the charging state of the battery of the mobile phone 100 are shown. It should be understood that the embodiments are not limited to the representations shown in FIG. 2, and other representations are possible.

[0022] Representation 104A is an icon configured as a battery. The colored portion inside the icon indicates the charge state of the battery of the mobile phone 100. The larger the colored portion, the greater the charge state of the battery. In some embodiments, the icon may change color, for example, red when the charge state is below a low threshold, yellow when the charge state is between the low and high thresholds, and green when the charge state is above the high threshold. The icon may be animated. For example, the icon may blink or move when the charge state falls below a certain threshold so as to draw the user's attention to the icon.

[0023] Representation 104B is a percentage value displayed by the user and numerically represents the charge state of the battery of the mobile phone 100. Some users may respond more favorably to the percentage value than to the icon of Representation 104A. Representation 104B may also change color and be animated as described above with respect to Representation 104A.

[0024] Representation 104C is a fraction representing the current charge state of the battery of the mobile phone 100. For example, instead of displaying "75%", Representation 104C provides the fraction 3 / 4. Representation 104C may also change color and be animated as described above with respect to Representation 104A.

[0025] Representation 104D is a numerical value representing the charge state of the battery from the perspective of the remaining expected usage time. For example, the mobile phone 100 knows the current charge state and accesses the usage history of the user of the mobile phone 100, which provides an average energy usage (e.g., based on one day's worth of time, use of representative applications, frequency of phone calls, etc.), to calculate the estimated remaining time. In this way, the user can have an idea of how much time she can use the mobile phone 100 based on the current charge state and a representative usage pattern. Representation 104D may also change color and be animated as described above with respect to Representation 104A.

[0026] Representation 104E is a pie chart depicting the current charge state of the battery of mobile phone 100. As the charge state decreases, the percentage of the colored portion of the pie chart also decreases. Representation 104E may also be changed in color and animated as described above with respect to Representation 104A.

[0027] Representation 104F is similar to Representation 104B, except that it shows the value of the percentage of the charge used rather than the remaining charge. Representation 104F may also be changed in color and animated as described above with respect to Representation 104A.

[0028] Thus, information about the charge state of the battery of mobile phone 100 may be represented by multiple representations. One or more of the multiple representations may best achieve the user's goal, as described in more detail below. The multiple representations may be provided in a database accessible by a processor of the electronic device. The multiple representations may include, for example, instructions on how to convert numerical information into a selected representation.

[0029] Figure 3 shows another electronic device configured as an electric vehicle 100' having an electronic display unit 102' configured as a cluster display. The electronic display unit 102' may display any type of information, such as vehicle information, in the form of a representation 104' indicating the charge state of the vehicle battery and a representation 106' indicating the speed of the electric vehicle 100'. It should be understood that the embodiments are not limited to electric vehicles and may be used in internal combustion engine vehicles. Similar to the example of Figure 2, the representation 106' indicating speed may appear in many different forms. The speed may be a digital display of numbers as indicated by the representation 106', but the representation may also be displayed as an analog speedometer gauge, a bar graph, an icon, etc. For example, the representation 106' may change color and be animated. One or more possible representations of the vehicle speed may best achieve the goal (i.e., perform better than other methods). In the context of a speedometer, the goal may relate to driving below the speed limit. The goal may be objectively defined, for example, such that the driver drives for a certain percentage of the time below the speed limit. For example, the representation of the speed that achieves the goal may be the representation with the highest percentage of driving time that the driver drives below the speed limit among all the representations of the vehicle speed. Additional information regarding the determination of whether the goal is achieved is provided below with respect to Figure 5.

[0030] Figure 4 shows non-limiting examples of different representations of the charge state of the battery of the electric vehicle 100'. It should be understood that the embodiments are not limited to the representations shown in Figure 4 and that other representations are possible. The representations in Figure 4 are similar in some respects to the representations shown in Figure 2.

[0031] The representation 104A′ is an icon configured as a battery. The colored part inside the icon indicates the charge state of the battery of the electric vehicle 100′. The larger the colored part, the greater the charge state of the battery. In some embodiments, the icon may change color, for example, red when the charge state is below a low threshold, yellow when the charge state is between the low threshold and the high threshold, and green when the charge state exceeds the high threshold. The icon may be animated. For example, the icon may blink or move when the charge state falls below a certain threshold so as to attract the user's attention to the icon.

[0032] The representation 104B′ is a percentage value displayed by the user and numerically represents the charge state of the battery of the electric vehicle 100′. Some users may respond more favorably to the percentage value than to the icon of the representation 104′. The representation 104B′ may also change color and be animated as described above with respect to the representation 104A′.

[0033] The representation 104C′ is an analog gauge display representing the current charge state of the battery of the electric vehicle 100′. In this regard, the representation 104C′ is similar to the fuel gauge of an internal combustion engine vehicle. The representation 104C′ may also change color and be animated as described above with respect to the representation 104A′.

[0034] Expression 104D is a numerical value representing the state of charge of the battery in terms of the remaining miles expected until the battery is depleted. For example, the electric vehicle 100' knows the current state of charge, accesses the usage history of the user of the vehicle 100', determines the change in elevation within the driver's route, records the current temperature, etc., and calculates the average energy usage such as watt-hours per mile for calculating the remaining estimated miles. In this way, the user can estimate how much further she can operate the electric vehicle 100' based on the current state of charge and the expected energy usage. Expression 104D' may also be changed in color and animated as described above with respect to Expression 104A'.

[0035] Expression 104E' is a value of time in hours and minutes depicting the remaining amount of time that the driver can operate the electric vehicle 100' based on the current state of charge and the expected energy usage based on the above factors. Using the current state of charge and the expected watt-hours per mile, the estimated remaining time before the battery is depleted is calculated and displayed. Expression 104E' may also be changed in color and animated as described above with respect to Expression 104A'.

[0036] Expression 104F' is similar to Expression 104B' except that it shows a percentage value of the amount of charge used rather than the remaining amount of charge. Expression 104F' may also be changed in color and animated as described above with respect to Expression 104A'.

[0037] Regardless of the type of information or type of representation, embodiments of the present disclosure monitor user behavior and determine learning behaviors that are used to select the best representation of information for an individual user. As used herein, "best representation" means a representation among multiple representations that objectively better achieves a goal than the remaining representations. In the example of FIG. 4, if representation 104C' more frequently achieves the goal of a driver to charge the electric vehicle 100' before the battery charge state reaches 20% than representations 104A', 104B', 104D', 104E', and 104F', then representation 104C' is selected as the selected representation and is displayed by the electronic display unit 102'.

[0038] Referring to FIG. 5, an example method 120 is shown for determining a user's learning behavior and using that learning behavior to select a representation for display by an electronic display unit. In block 121, individual representations of information (i.e., displayed representations) are displayed to the user by the electronic display unit. The information can be any type of information such as, for example, the battery charge state, speed, and time as described above. The individual representations are continuously presented by the electronic display unit for a certain period of time (e.g., several hours, several days, one month, etc.).

[0039] While displaying individual representations, the user's actions are monitored by the electronic device (block 122). In some embodiments, the user's actions are continuously monitored as indicated by the dashed arrows. The user's actions are based on the interaction between the user's electronic display and / or the electronic device associated with the electronic display. The user's actions being monitored may relate to the goal of presenting information. Embodiments are in no way limited by actions, types of interactions, or goals. In the non-limiting example shown by FIG. 2, the goal may be for the user to charge her mobile phone 100 before the charge level reaches 20%. Thus, the interactions related to the goal may be the charge level of the battery of the mobile phone 100 when the user plugs the mobile phone 100 in for charging, the frequency of plugging the mobile phone 100 in for charging, the duration of the charging, etc. For example, these types of interactions may be monitored, logged, and stored in a database.

[0040] To determine how well a display representation achieves a particular goal, measurement criteria related to the goal can be used. Any measurement criteria may be used. For example, the measurement criteria may be the percentage of the number of times an event occurs or does not occur. In the example of the mobile phone, the measurement criteria may be the percentage of the number of times the user charges the battery when the battery is above 20%.

[0041] After a certain period, the process moves to block 123 which determines whether the goal has been achieved by the previous display representation. As a non-limiting example, the goal may be achieved when the user exceeds a predetermined measurement criterion by a threshold amount. As a non-limiting example, the measurement criterion may be the percentage by which the user charges the battery before the battery reaches a 20% charge state, and the threshold may be 80% of that number. If the percentage is less than 80%, the goal is not achieved and the process moves to block 124. If the percentage is 80% or more, then the process next moves to block 125. In block 125, the electronic device continuously displays the representation that achieved the goal in block 123. For example, if the representation is the value of the percentage of the charge state and the user charges the battery 95% of the times before the battery reaches a 20% charge state, the percentage representation will be used in the future to represent the charge state information.

[0042] In some embodiments, the user's actions continue to be monitored and learned so that if the goal is not achieved in the future, the process proceeds from block 125 to block 124 so that a new representation is evaluated.

[0043] In block 124, the representation is updated to a new representation that has not yet been evaluated. As a non-limiting example, if the representation that did not achieve the goal in block 123 is an icon, the representation updated in block 124 may be a percentage value. The process then returns to block 121 and repeats the evaluation of the updated representation. If the updated representation achieves the goal in block 123, it is continuously displayed in block 125. If the updated representation does not achieve the goal in block 123, the representation is updated again to a new representation in block 124.

[0044] In some embodiments, if none of the representations achieve the goal at block 123, the most performant representation of the information is selected. In the example of a mobile phone battery, even if the percentage is lower than a threshold metric, e.g., 80%, the most performant representation of the information may be the representation with the highest percentage of charge when the battery charge state is greater than 20%.

[0045] Embodiments of the present disclosure include variations of the process depicted by FIG. 5, and thus the embodiments are not limited by the steps and the order of steps depicted by FIG. 5. As one variation, each of the plurality of representations is evaluated individually over time units. For example, the first representation is evaluated to determine if it is a learning behavior over the entire first time period, then the second representation is evaluated to determine if it is a learning behavior over the entire second time period, and so on until all of the plurality of representations are evaluated. In a non-limiting example, the representation that achieves the goal is the representation with the best performance with respect to the metric. In the example of a mobile phone battery, when the charge state is greater than 20%, the representation with the highest percentage of the time the user charges the phone may be selected as achieving the goal and thus may be selected for display by the electronic display unit.

[0046] FIG. 6 shows another example method 130 of selecting a representation from among a plurality of representations of information for display on an electronic display based on learned user behavior. The method 130 of FIG. 6 operates by evaluating many different users with a crowdsourcing method. Generally, the characteristics of many users are learned to develop individual user profiles based on the characteristics. Each user profile has an associated preferred representation. Each individual user is monitored to determine the user's learning behavior. The user is assigned a user profile based on her learning behavior. The representation of the information associated with the assigned user profile is selected for display by the electronic display unit.

[0047] In block 131, multiple representations of information are presented to multiple users. For example, the multiple users may include thousands of users. As a non-limiting example, for each user, each representation of the information of the multiple representations is displayed for a certain period of time. For example, each user of the mobile phone 100 may view the representations 104A - 104F shown in FIG. 2 throughout the period.

[0048] In block 132, while multiple users interact with their electronic devices, multiple user profiles are created based on the characteristics of the users. The characteristics are thus based on the interaction with the electronic devices. The interactions monitored are not limited to those directly related to the goal. Using the example of charging the battery of a mobile phone, when and how frequently the user charges her phone is an interaction that is evaluated to determine the characteristics of the user. However, other interactions not directly related to the battery charging are also logged. For example, interactions such as the frequency of use of the mobile phone, the number of times the mobile phone is used in a day, the types of applications utilized, what is purchased by using the mobile phone, the brand and model of the mobile phone, etc. may be used as characteristics. The interactions can indicate the user's personality and can further indicate the preferred representation of information among the multiple representations. It should be noted that the logging of the user's interactions and the monitoring of the user's behavior may be done anonymously, excluding information that can be personally recognized. In some embodiments, the learning of the user's behavior is done completely locally on the electronic device and the user's behavior is not shared with third parties.

[0049] For each user, the interactions with the electronic device are logged, and the current display representation is also recorded. The interactions and display representations for multiple users are then utilized to form user profiles. In some examples, machine learning algorithms are used to develop user profiles. The interactions and display representations for multiple users are provided as input to a machine learning algorithm, such as a clustering algorithm, which then creates clusters or groups. Each cluster may become a user profile. Any known or undeveloped clustering algorithm or other machine learning algorithm capable of receiving the interactions and representations and creating individual user profiles (i.e., clusters) may be utilized.

[0050] Ideally, each user would have similar user characteristics. Using the example of a mobile phone, a user who uses the mobile phone late at night may use the mobile phone for a long time and frequently run out of battery. Each user profile may have a representation of information that performs best according to the stated goals. The best performing representation is assigned as the selected representation for that user profile.

[0051] In block 133, the characteristics of individual users are monitored over time. These characteristics may be the same interactions used to develop multiple user profiles. The characteristics of individual users are compared to the characteristics of multiple user profiles. The user profile with the characteristics that best match the characteristics of the individual user is assigned to the individual user. As a non-limiting example, the interactions and display representations of an individual user are provided as input into a machine learning algorithm or model used to develop multiple user profiles. The machine learning algorithm or model assigns the user to the closest user profile (i.e., the closest cluster).

[0052] In block 134, the electronic device displays a selected representation of information related to the user's assigned user profile. Thus, the system displays the best representation of information for the user based on the evaluations of many other users. Users with similar characteristics are shown the same representation of information. For example, users having characteristics A, B, and C are similar and are assigned a first user profile shown in representation 104A of FIG. 2. On the other hand, users having characteristics X, Y, and Z are similar and are assigned a second user profile shown in representation 104D of FIG. 2. In this way, users are shown an ideal representation based on their personality.

[0053] Embodiments of the present disclosure may be implemented by any type of electronic device and may be embodied as computer-readable instructions stored on a non-transitory memory device. FIG. 7 depicts an example electronic device 100 (e.g., a computer) configured to perform the functions described herein. The example electronic device 100 provides, in accordance with the embodiments shown and described herein, a system for displaying information on an electronic display based on learned user behavior and / or a non-transitory computer-usable medium having computer-readable program code embodied as hardware, software, and / or firmware for displaying information on an electronic display based on learned user behavior. In some embodiments, the electronic device 100 is configured as a general-purpose computer having essential hardware, software, and / or firmware, but in some embodiments, the electronic device 100 may be configured as a mobile phone, a vehicle, an electrical appliance, etc. It should be understood that the software, hardware, and / or firmware components depicted in FIG. 7 may also be provided within other computer devices external to the electronic device 100 (e.g., a data storage device, a remote server computer device, etc.).

[0054] As also shown in FIG. 7, the electronic device 100 (or other additional computer device) may include a battery 150 for supplying power to the electronic device 100, a processor 145, input / output hardware 146, network interface hardware 147, a data storage component 148 (which may include user data 149A, user profile data 149B, representation data 149C, and any other data 149D for performing the functions described herein), and a non-transitory memory component 140. The memory component 140 may be configured as a volatile and / or non-volatile computer-readable medium and, as such, may include random access memory (including SRAM, DRAM, and / or other types of random access memory), flash memory, registers, a CD (compact disc), a DVD (digital versatile disc), and / or other types of storage components. Additionally, the memory component 140 may be configured to store an operation logic 141, a representation logic 142 for selecting a representation of information for display, and a display logic 143 for displaying a representation of information on an electronic display (each of these may be embodied, for example, as computer-readable program code, firmware, or hardware). A local interface 144 is also included in FIG. 7 and may be implemented as a bus or other interface for facilitating communication among the components of the electronic device 100.

[0055] Processor 145 may include any processing component configured to receive and execute instructions of computer-readable code (e.g., from data storage component 148 and / or memory component 140). Input / output hardware 146 may include an electronic display, keyboard, mouse, printer, camera, microphone, speaker, touch screen, and / or other devices for receiving, transmitting, and / or presenting data. For example, network interface hardware 147 may include any wired or wireless network hardware, such as a modem, LAN port, Wi-Fi (wireless fidelity) card, WiMax card, mobile communication hardware, and / or other hardware, for communicating with other networks and / or devices, such as receiving data from various information sources.

[0056] The data storage component 148 may be locally connected to the electronic device 100 and / or may exist remotely from the electronic device 100 and may be configured to store one or more data for access by the electronic device 100 and / or other components. As shown in FIG. 7, in at least one embodiment including data related to the user, such as, for example, user characteristics, user behavior, etc., the data storage component 148 may include user data 149A. The user data 149A may be stored in one or more data storage devices. Similarly, the user profile data 149B may be stored by the data storage component 148 and may include information regarding the user profile as described above if such a user profile is utilized to select a representation of information. The presentation data 149C includes data regarding various types of graphic representations used to represent information that may be presented to the user. For example, the presentation data 149C may include information regarding how to display the representation (e.g., the appearance of an icon or the type of font). Other data 149D for performing the functions described herein may also be stored in the data storage component 148. In some embodiments, the electronic device 100 may be coupled to a remote server or other data storage device that stores the relevant data.

[0057] The memory component 140 may include an operation logic 141, a presentation logic 142, and a display logic 143. The operation logic 141 may include an operating system and / or other software for managing components of the electronic device 100. The operation logic 141 may also include computer-readable program code for displaying a graphical user interface used by a user to input parameters and review simulation results. The presentation logic 142 may be present within the memory component 140 and may be configured to facilitate functions described herein, such as learning a user's behavior, determining whether a goal is achieved, and selecting a presentation for display among multiple presentations. The presentation logic 142 may also be configured to compare characteristics of individual users to assign user profiles and presentations to users.

[0058] The components shown in FIG. 7 are merely exemplary and are not intended to limit the scope of this disclosure. More specifically, the components in FIG. 7 are shown as being within the electronic device 100, but this is a non-limiting example. In some embodiments, one or more components may be external to the electronic device 100.

[0059] Embodiments of the present disclosure should be immediately understood to be directed to an electronic device and method for displaying information on an electronic display portion of the electronic device based on learned user behavior. The most appropriate presentation of information for the user is selected and displayed based on the learned user behavior. The most appropriate presentation is a presentation among multiple presentations that achieves a goal or objectively has the best performance. The embodiments enable the user interface to be automatically customized for individual users such that the user can achieve certain goals without requiring the user to manually program settings.

[0060] In contrast to the description of the intended use, note that the description in this specification of the components of the present disclosure that are "configured" or "programmed" in a particular way to embody a particular characteristic or to function in a particular way is a structural description. More specifically, the reference in the specification to a component being "configured" or "programmed" in a particular way denotes the current physical state of the component and, therefore, must be taken as an explicit description of the structural features of the component.

[0061] The order of execution or performance of the operations in the examples of the disclosure shown and described herein is not essential unless otherwise specified. That is, the operations may be performed in any order unless otherwise specified, and the examples of the disclosure may include additional or fewer operations than those disclosed herein. For example, performing or accomplishing a particular operation before, simultaneously with, or after another operation is considered to be within the scope of the aspects of the disclosure.

[0062] Note that the terms "substantially", "about", and "approximately" may be used in this specification to represent the degree of inherent uncertainty that may be attributable to any quantitative comparison, value, dimension, or other representation. These terms may also be used in this specification to represent the degree to which a quantitative representation may vary from the stated reference without causing a change in the basic function of the subject matter of the discussion.

[0063] It should be understood that while particular embodiments are shown and described herein, various other changes and modifications may be made without departing from the spirit and scope of the subject matter described in the claims. Moreover, although various aspects of the subject matter described in the claims are described herein, such aspects need not be used in combination. The appended claims are therefore intended to cover all changes and modifications within the scope of the subject matter described in the claims.

Claims

1. An electronic display unit, a processor, a non-transitory computer-readable medium storing instructions which, when executed by the processor, cause the processor to generate a selected representation of information from among a plurality of representations for display on the electronic display unit based on at least a portion of a user's learning behavior, wherein the plurality of representations includes two or more of percentages, fractions, icons, graphs, and numbers, an electronic device.

2. The plurality of representations are stored in a database, wherein the instructions further cause the processor to convert the information into the selected representation for display on the electronic display unit, the electronic device according to claim 1.

3. When executed by the processor, the instructions are to change a display representation of the information displayed on the electronic display unit over time, the display representation being an individual representation of the plurality of representations, to monitor a user's interaction with the electronic device with respect to each display representation of the information to determine the learning behavior, thereby causing the processor to determine the learning behavior, the electronic device according to claim 1.

4. When executed by the processor, the instructions further cause the processor to determine whether one or more interactions of the learning behavior achieve a goal when each display representation of the information is displayed, select the individual display representation as the selected representation when the individual display representation achieves the goal, the electronic device according to claim 3.

5. The selected representation achieves the goal with the highest frequency among the remaining display representations of the plurality of representations, the electronic device according to claim 4.

6. When executed by the processor, the instructions cause the processor to determine the learning behavior by determining the user's characteristics by monitoring the interaction between the user and the electronic device, the electronic device according to claim 1.

7. The selected representation is to compare the user's characteristics with a plurality of user profiles, wherein the plurality of user profiles are based on characteristics determined from interactions between a plurality of users and a plurality of electronic devices, and each user profile is related to a preferred representation of the information, Selecting a selected user profile from among the plurality of user profiles, the selected user profile having characteristics closest to the characteristics of the user, The preferred representation of the information of the selected user profile is selected as the selected representation of the information, The electronic device according to claim 6, determined by

8. The electronic device according to claim 1, wherein the electronic device is a mobile phone.

9. The electronic device according to claim 1, wherein the electronic device is a vehicle.

10. Including displaying, on an electronic display unit, a representation of information selected from among a plurality of representations based on a user's learning behavior, The plurality of representations includes two or more of a percentage, a fraction, an icon, a graph, and a number, the method.

11. The plurality of representations are stored in a database, The method according to claim 10, further including converting the information into the selected representation.

12. The learning behavior is Changing the display representation of the information displayed on the electronic display unit over time, the display representation being an individual representation of the plurality of representations, Monitoring the interaction of the user with the electronic device including the electronic display unit for each display representation of the information to determine the learning behavior, The method according to claim 10, determined by

13. Determining whether one or more interactions of the learning behavior achieve a goal when each display representation of the information is displayed, When an individual display representation achieves the goal, selecting the individual display representation as the selected representation, further including the method according to claim 12.

14. The selected representation achieves the goal with the highest frequency among the remaining display representations of the plurality of representations, the method according to claim 13.

15. The learning behavior is determined by determining the characteristics of the user by monitoring the interaction between the user and the electronic device including the electronic display unit, the method according to claim 10.

16. The selected representation is Comparing the characteristics of the user with a plurality of user profiles, The plurality of user profiles are based on characteristics determined from the interactions between a plurality of users and a plurality of electronic devices, Each user profile is related to a preferred representation of the information, and selecting a selected user profile from among the plurality of user profiles, the selected user profile having characteristics closest to the characteristics of the user, and the preferred representation of the information of the selected user profile being selected by the selected representation of the information, and The method according to claim 15, determined by. **Claim 17** An electronic display unit, A processor, A non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to generate a selected representation of vehicle information from among a plurality of representations for display on the electronic display unit based on at least a portion of the user's learning behavior, and The plurality of representations include two or more of a percentage, a fraction, an icon, a graph, and a number, vehicle. **Claim 18** The vehicle includes a battery, The vehicle information is the state of charge of the battery, The vehicle according to claim 17, wherein the plurality of representations include two or more of a battery icon, a percentage, and a graph.

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