BACKGROUND The awareness of how emissions (e.g., carbon emissions) continue to negatively impact the environment has stimulated the search for ways to quantify accurately the carbon footprint of people in the conduct of their daily activities. So-call carbon calculators have emerged on the Internet for estimating carbon dioxide emissions. However, these existing carbon calculators rely heavily on user input, which may be inaccurate and provided on an irregular basis. Essentially, the application requires users, in part, to track the times and duration of their modes of travel; such record keeping can be cumbersome and manually intensive. Moreover, participation is largely confined to only a narrow group of people who are most concerned with the global environment. That is, the general populace has little incentive to diligently use these carbon calculator applications, given the inconvenience of accessing the Internet, and logging on the application, not to mention the tedious record keeping process. At the same time, service providers (e.g., wireless, cellular, etc.) and device manufacturers are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services.

SOME EXAMPLE EMBODIMENTS Therefore, there is a need for an approach for automatically estimating the emissions footprint of a user.

According to one embodiment, a method comprises acquiring movement data of a mobile device associated with a user. The method also comprises determining one or more modes of travel by the user based on the movement data. The method further comprises determining an emissions footprint of the user based on the determined one or more modes of travel.

According to another embodiment, an apparatus comprising at least one processor, and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause, at least in part, the apparatus to acquire movement data of a mobile device associated with a user. The apparatus is also caused, at least in part, to determine one or more modes of travel by the user based on the movement data. The apparatus is further caused, at least in part, to determine an emissions footprint of the user based on the determined one or more modes of travel.

According to another embodiment, a computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause, at least in part, an apparatus to acquire movement data of a mobile device associated with a user. The apparatus is also caused, at least in part, to determine one or more modes of travel by the user based on the movement data. The apparatus is further caused, at least in part, to determine an emissions footprint of the user based on the determined one or more modes of travel.

According to another embodiment, an apparatus comprises means for acquiring movement data of a mobile device associated with a user. The apparatus also comprises means for determining one or more modes of travel by the user based on the movement data. The apparatus further comprises means for determining an emissions footprint of the user based on the determined one or more modes of travel.

Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings:

FIG. IA is a diagram of a system capable of providing automatic determination of an emissions footprint, according to one embodiment; FIG. IB is a diagram of the components of an emissions service platform 107 configured to provide emissions footprint information, according to one embodiment;

FIG. 2 is a diagram of the components of a user equipment configured to provide automatic determination of emissions values, according to one embodiment;

FIG. 3 is a flowchart of a process for determining an emissions footprint of a user, according to one embodiment;

FIG. 4 is a flowchart of a process for determining a travel mode for computation of emissions footprint, according to one embodiment.

FIG. 5 is a diagram depicting collected movement data, according to one embodiment.

FIG. 6A is a diagram of histogram representations of collected movement data for determining modes of travel, according to one embodiment.

FIG. 6B is a diagram of travel segment information that can be determined from the histogram matching, according to one embodiment; FIG. 7 is a diagram of user interfaces utilized in the processes of FIGs. 3 and 4, according to various embodiments;

FIG. 8 is a diagram of hardware that can be used to implement an embodiment of the invention; FIG. 9 is a diagram of a chip set that can be used to implement an embodiment of the invention; and

FIG. 10 is a diagram of a mobile terminal (e.g., handset) that can be used to implement an embodiment of the invention.

DESCRIPTION OF SOME EMBODIMENTS Examples of a method, computer program, and apparatus for providing automatic determination of an emissions footprint are disclosed. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It is apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention.

FIG. IA is a diagram of a system capable of providing automatic determination of an emissions footprint, according to one embodiment. A user of a device may desire to determine a carbon emissions footprint of the user. As mentioned, traditional approaches, e.g., carbon calculators available over the Internet, are manually intensive and can result in inaccurate information.

To address this problem, a system 100 of FIG. IA introduces the capability to provide automatic determination of an emissions footprint, according to one embodiment. As used herein, "carbon dioxide", "CO2", or "carbon" may be used interchangeably to refer carbon dioxide. Although certain embodiments may be described with respect to carbon emissions or CO ₂ , other embodiments may be equally applicable to other types of emissions and/or the monitoring of other gaseous discharges, greenhouse emissions, ecological footprint, water footprint, Material Intensity Per Unit of Service (MIPS), and/or the like. As shown, one or more user equipment (UE) 101a- lOln can employ an emissions estimator application 103a-103n to invoke a data generation process that tracks the movement of the corresponding UEs 10Ia-IOIn for automatically determining the modes of travel associated with the users of these UEs lOla-lOln. The data can be locally processed to then determine the emissions values for the determined modes of travel. In addition to or in the alternative, the data is forwarded over a communication network 105 to an emissions service platform 107 for processing. According to certain embodiments, the emissions service platform 107 interoperates with a social network service 109 for coordinating the participation of users in the emissions service, and to enable users to share information about their emissions footprints.

According to one embodiment, an emissions estimator 103 of a UE 101 of a user may be utilized to collect information (e.g., cell identifier (CeIlID), GPS coordinates, acceleration data, audio data, etc.) about the user as the user travels. As further used herein, a CeIlID may refer to an identifier (e.g., number) associated with a geographic area covered by a base station in a mobile network. The UE 101 may then automatically detect one or more travel methods of the user based at least in part on the collected information (e.g., based on movement data). As used herein, "movement data" can be used to refer to data or sources thereof that provide context or other associated or explanatory information related to a user, a UE 101 or the like such as, for example, geographical location (e.g., based at least in part on CeIlID), values recorded by various sensor elements (e.g., accelerometer, global positioning system (GPS), microphone, etc.), user preferences, flight mode information, and/or the like. Moreover, the UE 101 may determine an estimate of the emissions due to the one or more methods of travel. Alternatively or additionally, the UE 101 may send the collected information to the emissions service platform 107 to determine the one or more travel methods and determine the emissions estimates. The emissions rates may be used to determine the emissions footprint of the user. The emissions footprint (e.g., a carbon footprint) can include a complete or partial estimate of the greenhouse gas emissions caused directly or indirectly by the user. Additionally, because the updates to the emissions footprint can be automatic and set with minimal interaction with the user (e.g., less effort for the user), the user may be more willing to determine an emissions footprint of the user. Moreover, because the emissions footprint may be determined automatically from collected information, the emissions footprint may be more accurate.

In one embodiment, the information used to determine the one or more travel methods or modes is collected using one or more user equipment (UE) lOla-lOln. The information can be collected using an emissions estimator application 103a-103n available on the UEs 101. Additionally, a UE 101 may be in communication, via a communication network 105, with the emissions service platform 107 and the social network service 109. The emissions service platform 107 can provide the UE 101 additional information about travel methods and/or emissions information, determine emissions of a user based on user movement data, or be used to output information of the user's carbon emissions to other people or entities. Moreover, some or all of the functionalities of the emissions service platform 107 may be performed by the emissions estimator application 103.

According to an exemplary embodiment, an emissions estimator application 103 (also referenced herein as an "emissions estimator") may be resident on or otherwise associated with a UE 101. In this regard, the emissions estimator 103 may be configured to automatically detect travel methods (e.g., car, motorcycle, bus, tram, metro, train, airplane, or human performed movements such as walking, running, biking, etc.) based at least in part on data received from one or more various sources and calculate carbon dioxide emission for the travel methods. In some embodiments, the emissions estimator 103 may be configured to automatically detect travel methods based at least in part on a screen saver or idle status indicator of a user device. For example, the emissions estimator 103 may detect that the screen saver is off which may indicate that the user may be most likely holding the device may be in the user's hand and, as such, sensor measurements (e.g., accelerometer) may be distorted. Additionally, readings within a certain time period before or after the screen saver mode may be distorted. Thus, the emissions estimator 103 can add a flag to data collected around this time period to indicate that the measurements may be distorted. These flags may be later used to filter the data to remove irregularities before calculating travel modes.

The emissions estimator 103 may be configured to use various algorithms such as, for example, statistical pattern recognition, to detect a current or previous travel method. These algorithms can filter the data to remove irregularities. Additionally, the emissions estimator 103 may store signals and other data collected during a data collection session (e.g., user travel). For example, during a data collection session, a microphone (or other audio data collecting device) may collect audio samples of a car engine, a train, and/or bus. As such, the emissions estimator 103 may analyze the data and determine that the travel method include a car, a train, and/or a bus, respectively, such as based upon a comparison of the collected data to predefined data indicative of each of the different modes of transportation. The emissions estimator 103 may also be configured to detect travel methods based at least in part on user preferences that may be stored on a storage device locally associated with the UE 101. In this regard and for example, the emissions estimator 103 may retrieve user preferences information from the storage device, identify the travel methods preferred by the user, analyze data (e.g., accelerometer data, GPS data, etc.) collected during travel, and determine the travel method(s) used during the travel, generally from amongst those preferred by the user.

Further, the emissions estimator 103 may be configured to detect a geographical location (e.g. a country) and/or information associated therewith based at least in part on a network to which the UE 101 is attached (e.g., via Mobile Country Code (MCC)). The emissions estimator 103 may be configured to send the geographical location to a network device, such as a server (e.g. a carbon dioxide emission server). In response, the emissions estimator 103 may receive from the network device at least one carbon dioxide emission value for that geographical location. For example, a carbon dioxide emission value could include an average emission rate of a typical automobile or a subway at the geographical location. As such, the emissions estimator 103 may be able to calculate carbon dioxide emissions for at least one travel method. In some embodiments, the emissions estimator 103 may receive carbon dioxide emission values for the various travel methods available for that geographical location. As such, the emissions estimator 103 may be able to identify multiple travel methods available in that geographical location, improve travel method detection, and/or calculate the carbon emissions for the travel methods. The emissions estimator 103 may also be configured to receive from the network device new travel methods associated with the geographical location (e.g., a geographical location may have a new type of transportation, such as a monorail).

FIG. IB is a diagram of the components of an emissions service platform 107, according to one embodiment. By way of example, the emissions service platform 107 includes one or more components for providing automatic determination of an emissions footprint. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. Moreover, it is contemplated that some or all of the functions of the emissions service platform 107 may be performed by the UE 101 via the emissions estimator 103. In this embodiment, the emissions service platform 107 includes a data collector 121 that can receive movement data from a UE 101, a travel mode determination module 123 that can compute a travel method of a user based on the movement data, an emissions footprint module 125 that can determine emissions of the user based on the movement data, and an emission compensation module 127 that can be used to balance the emissions activity with other activity good for the environment. Additionally, the emissions service platform 107 can include a location information module 129 that may be used to determine location coordinates from movement information (e.g., CeIlID), and a travel mode information module 131 that provide location and travel method information to a UE 101.

The emissions service platform 107 can include a data collector 121 and a travel mode determination module 123. Under some scenarios, the data collector 121 can be used to gather movement data from a UE 101. This can be accomplished by receiving a transmission of the movement data from the UE 101, which collects the movement data. Once movement data is collected or gathered from the UE 101, the emissions service platform 107 can use a travel mode determination module 123 to determine travel modes of the user by analyzing the movement data. Further, once the travel modes of the user are determined, the emissions footprint module 125 of the emissions service platform 107 can be configured to determine an emissions footprint of the user by analyzing the movement data. In one example, the emissions footprint of a travel mode of the movement data can be determined by multiplying an emissions rate (e.g., emissions output per time, emissions output per distance traveled, etc.) associated with a travel mode with a distance or time period of usage of the travel mode. An emissions footprint of the user can be determined by adding emissions footprints of multiple travel emissions footprints.

Additionally, in one embodiment, the emissions service platform 107 may include an emissions footprint module 125. The emissions footprint module 125 may include a database that may store carbon emission values (e.g., emissions rates) associated with various geographical locations (e.g., country, state, city, etc.). The database may also store target carbon emission values for at least one geographical location (e.g., target level for a year, month, etc.). Additionally, the database may store carbon emission values for at least one travel method available in the geographical location. Moreover, one or more travel methods may be further defined into sub-types. For example, a car may be defined as an old car and new car and as such, the database may store the emission values for each subtype. Additionally, the database can contain emission values for each car based on brand, model, and/or year. The database may also store additional information related to the geographical location, such as, for example, the coordinates of relevant transportation locations and hubs. In this regard, the emissions estimator 103 may send a request to the emissions footprint module 125 including a determined geographical location. In some embodiments, the emissions footprint module 125 may also include and/or be associated with another database that may store various information including, e.g., emissions of users (e.g., travelers), frequently used travel methods, user defined travel method availability, and/or the like. For example, a user can define the travel mode availability of the travel methods the user has access to, such as car, bus, etc. and eliminate travel modes that the user does not have access to, such as, eliminating a tram from available travel modes if there is no tram access around the user's home area. Moreover, the information may be used for various purposes including, for example, zone planning, improvement of transit possibilities in one or more geographical locations, and/or the like. In other embodiments, the emissions estimator 103 may send a request further including one or more travel methods and as such, request carbon emission values and other related information for one or more specific travel methods. The emissions footprint module 125 may in turn forward the correct emission values and other related information associated with the geographical location to the UE 101. The emissions estimator 103 may cause the received emission values and other related information to be stored on a storage device locally associated with the UE 101 such as, for example, memory device or on a storage device remotely associated with the UE 101. As such, the emissions estimator 103 may use the received carbon dioxide emission values for the geographical location and other related information in the calculation of carbon dioxide emission for user travel.

Additionally, the emissions service platform 107 may include an emission compensation module 127. The emission compensation module 127 may be configured to provide compensation (e.g., a carbon offset) based at least in part on the carbon emissions calculated by the emissions estimator 103 (e.g., purchasing 'credits' from emission reduction projects such as modernization of a power plant or the planting of trees). In this regard and in some embodiments, the emissions estimator 103 may be configured to remind the user to request compensation for the carbon emissions generated by the user. In other embodiments, the emissions estimator 103 may be configured, for example, by the user, to periodically initiate compensation of the emission calculated during a predetermined period of time (e.g., once a week) or after an event (e.g., when the calculated emissions reach a predetermined threshold value) from the emission compensation module 127. The predetermined period may coincide with the period for initiating compensation.

The emissions service platform 107 can include a location information module 129, which may include a database that may store information relating to location (e.g., geographical location) and/or the like. In some embodiments, the location information module 129 may store a list of Mobile Country Codes (MCC). In this regard, the location information module 129 may be configured to process requests for location information. For example, an emissions estimator 103 of a UE 101 may be configured to detect a CeIlID associated with the UE 101 and forward a request to determine a geographical location (e.g., country, state, city, etc.) based at least in part on the CeIlID. The location information module 129 may process the request from the emissions estimator 103 and forward the geographical location (e.g., country, state, city, etc.) and other related information associated with the CeIlID to the emissions estimator 103. The geographical location and other related data may be stored on a memory locally associated with the UE 101 or on a memory remotely associated with the UE 101. In some embodiments, the emissions estimator 103 may be configured to cause the geographical location and other related information to be stored. The geographical location and other related information may be later retrieved for processing as will be discussed below. The location information module 129 can also receive GPS coordinates associated with a UE 101 and a CeIlID. The GPS coordinates can be stored in a database and be used to correlate CeIlID with geographical location mappings.

The emissions service platform 107 may also include a travel mode information module 131. The travel mode information module 131 may store and/or provide information relating to various types of travel methods. In some embodiments, the travel type information may be used to provide advice to the user on how to decrease emissions. For example, the travel mode information module 131 may suggest alternative travel methods such as, for example, public transportation or other forms of ride sharing. In other embodiments, the emissions estimator 103 may be configured to send generated travel mode data for a specific geographical location to the travel mode information module 131, such as, for example, new travel methods identified by a user. In this regard, the generated travel mode data may include start and stop times of a travel mode, an identification of the travel mode, carbon emissions of the travel mode, a user defined flag (e.g., to determine whether a user has modified the data) of the travel mode, confidence value for the identification of the travel mode (e.g., a rating calculated by the emissions estimator 103 regarding the certainty in identifying the travel mode), an estimated traveled distance, a set of location coordinates for the travel, and/or additional information.

In one embodiment, the system 100 includes a social network service 109. An emissions estimator 103 or the emissions service platform 107 may send statistical information relating to calculated carbon emission data to the social network service 109. In the alternative, the emissions estimator 103 may send the information to a service, (e.g., the emissions service platform 107), which may then send the information to the social network service 109. The social network service 109 may authenticate the emissions estimator 103 user via an account management provider and a server associated with the social network service 109 that may parse the uploaded data upon authentication. The server may also have a social networking application program interface that may be used by third party applications to connect to and use the emission data. In this regard, users or subscribers to a social network may calculate their carbon emission and cause their carbon emissions to be displayed with their user profile. In some embodiments, the CO ₂ emission of users may be displayed anonymously. For example, a user may desire to keep track of personal emission rates as well as allowing the emission rates and statistics to be used for various purposes (e.g., zone planning) without revealing their identity. Moreover, users may compare their carbon emissions with the carbon emissions of other users (e.g., friends, relatives, colleagues, etc.), challenge other users to decrease their carbon emissions, or compete with other users, and/or the like.

Moreover, a user may modify or otherwise define one or more travel modes for one or more trips or travel periods. The information associated with the trip(s) modified by the user (e.g., statistical information relating to calculated carbon emissions for the trip(s)) may be marked with a flag (e.g., user modified flag). In this regard, the social network service 109 may monitor the user modified flags to prevent the use of the information associated with the modified trip(s) based at least in part on certain criteria. There may be a predetermined threshold relating to the credibility of the information associated with the modified trip(s). Therefore, if the percentage of trip(s) associated with user modified tag exceeds a certain threshold, the user's overall information associated with trip(s) may be classified as unreliable. Additionally, users may access historical emissions data (e.g., relating to carbon emissions) associated with other users. The historical emissions data may be used to provide suggestions regarding how to decrease carbon emissions. The historical emissions data may also be used to provide proposals, such as, for example, car pooling, public transportation or other types of ride sharing. The amount of carbon emissions that may be saved by the user can be derived from the use of the suggestion and/or proposed travel method(s) based on the historical data may be provided to the user.

As mentioned above, users, groups of users, and/or communities of various sizes may compare their carbon emissions with the carbon emissions of other users (e.g., friends, relatives, colleagues, etc.), group of users, and/or communities, challenge other users to decrease their carbon emissions, or compete with other users, and/or the like. In this regard, in some embodiments, there may be a functionality or service that may provide the name or other related information of a most green person and/or group or, in other words, the user with the least carbon emissions (e.g. within a certain community or company). In some cases, for example, there may be categories or classifications based at least in part on lifestyles (e.g., working professional, students, etc.), travel methods (e.g., car, train, bus, airplanes, etc.), or carbon emissions level, and/or the like. In other embodiments, in the context of games (e.g., online games) and/or other related activities, certain aspects of the game (e.g., bonuses, rewards, enhanced features, and/or the like) may be predicated or otherwise based at least in part on the carbon emissions of the user calculated by an emissions estimator 103.

As shown in FIG. IA, the system 100 comprises a user equipment 101 having connectivity to an emissions service platform 107 and a social network service 109 via a communication network 105. By way of example, the communication network 105 of system 100 includes one or more networks such as a data network (not shown), a wireless network (not shown), a telephony network (not shown), or any combination thereof. It is contemplated that the data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiberoptic network. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, mobile ad-hoc network (MANET), and the like.

The UE 101 is any type of mobile terminal, fixed terminal, or portable terminal including a mobile handset, station, unit, device, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, Personal Digital Assistants (PDAs), music player, media device, or any combination thereof. It is also contemplated that the UE 101 can support any type of interface to the user (such as "wearable" circuitry, etc.). Some UEs 101 may be equipped with an emissions estimator 103, while other UEs 101 may not be equipped with the emissions estimator 103. In other embodiments, a UE 101 (e.g., a music player) can be used to collect movement data about a user and then synchronize with another UE 101 (e.g., a desktop computer) to transmit the movement data to an emissions service platform 107.

By way of example, the UE 101, emissions service platform 107, and social network service 109 communicate with each other and other components of the communication network 105 using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network 105 interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model.

Communications between the network nodes are typically effected by exchanging discrete packets of data. Each packet typically comprises (1) header information associated with a particular protocol, and (2) payload information that follows the header information and contains information that may be processed independently of that particular protocol. In some protocols, the packet includes (3) trailer information following the payload and indicating the end of the payload information. The header includes information such as the source of the packet, its destination, the length of the payload, and other properties used by the protocol. Often, the data in the payload for the particular protocol includes a header and payload for a different protocol associated with a different, higher layer of the OSI Reference Model. The header for a particular protocol typically indicates a type for the next protocol contained in its payload. The higher layer protocol is said to be encapsulated in the lower layer protocol. The headers included in a packet traversing multiple heterogeneous networks, such as the Internet, typically include a physical (layer 1) header, a data- link (layer 2) header, an internetwork (layer 3) header and a transport (layer 4) header, and various application headers (layer 5, layer 6 and layer 7) as defined by the OSI Reference Model.

FIG. 2 is a diagram of the components of a user equipment 101 configured to provide automatic determination of emissions values, according to one embodiment. By way of example, the UE 101 includes one or more components for providing automatic determination of emissions values (e.g., a carbon footprint). It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In this embodiment, the UE 101 includes an accelerometer module 201, a communication interface module 203, a runtime module 205, a power management module 207, a memory 209, a location module 211, and a user interface 213.

In one embodiment, a UE 101 includes an accelerometer module 201. An accelerometer is an instrument that can measure acceleration. Using a three-axis accelerometer, with axes X, Y, and Z, provides the acceleration in three directions with known angles. A portion of the UE 101 can be marked as a reference point in determining direction. Because the acceleration due to gravity is known, when a UE 101 is stationary, the accelerometer module 201 can determine the angle the UE 101 is pointed as compared to Earth's gravity. In one embodiment, the accelerometer data can be segmented into portions (e.g., a 5 second portion). The accelerometer data can be compared with other information using various algorithms to determine a classification of a movement associated with the accelerometer data (e.g., walking, bicycling, idle, automotive, rail- based movement, etc.).

Additionally, the UE 101 may include a communication interface module 203. The communication interface module 203 may be used by the runtime module 205 to communicate with service platforms (e.g., an emissions service platform 107 and/or a social network service 109) performing various functions. Additionally, the communications interface module 203 can be used to communicate with other UEs 101 or other devices.

In one embodiment, the UE 101 may include a power management module 207. The power management module 207 may comprise various power saving/sleep state functionalities, such as, for example, power saving/sleep state based at least in part on sensory inactivity and user defined power saving/sleep state periods, power save modes, and/or the like. In this regard and according to one embodiment, the emissions estimator 103 (e.g., via using a runtime module 205) may be activated and take a sample of data from a sensor (e.g., accelerometer). For example, the emissions estimator 103 may determine that the UE 101 is stationary and thus, the emissions estimator 103 may return to a power saving/sleep state period. In some embodiments, each time the emissions estimator returns to a power saving/sleep state if the UE 101 is stationary, the sleep state time may be increased but not beyond a predetermined amount of time. In another example, the emissions estimator 103 may determine that the UE 101 is moving and thus proceed to detect the travel method. The emissions estimator 103 may determine that the travel method does not emit carbon emissions (e.g., walking, riding bicycle, etc.) and may thus return to a power saving/sleep state. In this regard and as mentioned above, the power saving/sleep state time period may increase but not beyond a preset time. Alternatively, the emissions estimator may determine that the travel method emits carbon emissions, in which case the emissions estimator 103 may start recording data from the acceleration. In some embodiments, several activities may activate the emissions estimator 103 such as, for example, a change in CeIlID, a lost WLAN connection, activation of GPS, accelerometer data patterns, etc.

Moreover, the UE 101 may include a location module 211. The location module 211 can determine a user's location. The user's location can be determined by a triangulation system such as GPS, A-GPS, Cell of Origin, or other location extrapolation technologies. Standard GPS and A-GPS systems can use satellites to pinpoint the location of a UE 101. A Cell of Origin system can be used to determine the cellular tower that a cellular UE 101 is synchronized with. This information provides a coarse location of the UE 101 because the cellular tower can have a unique CeIlID that can be geographically mapped. The location module 211 may also utilize multiple technologies to detect the location of the UE 101. Information regarding the location of the UE 101 can be stored in the memory 209 of the UE 101. In certain embodiments, the location module 211 determines a CeIlID associated with the location of the UE 101. The UE 101 can then request, via the communication interface module 203, that a location information module 129 determine a geographical mapping associated with the CeIlID. The location information module 129 can determine coordinates to send the UE 101 by associating the CeIlID with a geographic location. Moreover, the runtime module 205 can store received coordinates in a memory 209 for use to determine a mode of travel.

In one embodiment, a UE 101 includes a user interface 213. The user interface 213 can include various methods of communication. For example, the user interface 213 can have outputs including a visual component (e.g., a screen), an audio component, a physical component (e.g., vibrations), and other methods of communication. User inputs can include a touch-screen interface, a scroll-and-click interface, a button interface, a microphone, etc. Moreover, the user interface may be used to sense a mode of travel (e.g., sensing an automobile engine sound by the microphone).

FIG. 3 is a flowchart of a process for determining an emissions footprint of a user, according to one embodiment. In one embodiment, the runtime module 205 performs the process 300 and is implemented in, for instance, a chip set including a processor and a memory as shown FIG. 9. In certain embodiments, portions of the activities accomplished by the runtime module 205 may be accomplished by a travel mode determination module 123 and/or emissions footprint module 125 of an emissions service platform 107. In one embodiment, the user can activate an emissions estimator 103 application to execute on the runtime module 205 to determine an emissions footprint of the user. The runtime module 205 can then acquire movement data of a UE 101 associated with the user and utilize the movement data to determine one or more modes of travel by the user based on the movement data. Then, the runtime module 205 can determine an emissions footprint of the user based on the one or more modes of travel.

At step 301, the runtime module 205 acquires movement data of the UE 101 (e.g., a mobile device) associated with the user. As previously mentioned, the movement data can include various sensor data such as, e.g., accelerometer data, GPS data, CeIlID data, flight mode information, auditory data, etc. Additionally, the UE 101 can collect other types of data (e.g., user preferences, etc.) that may be useful in determining one or more modes of travel by the user. For example, user preferences can include information about the type of automobile the user owns or a preferred mode of travel such as via metro. In certain embodiments, the data can be collected in the form of measurement data (e.g., accelerometer values, GPS coordinates, CeIlID, etc.) and a time stamp. Different types of measurement data can be collected at different times either periodically or due to an event and can be collected based on user preferences or availability of the sensor data. The data can be formatted in a manner that the collected data can be processed by the runtime module 205, another UE 101, or a service (e.g., via a server). For example, the data can be collected in a tabular format with information in each row having a time stamp, a latitude, a longitude, an accuracy value of the latitude, an accuracy value of the longitude, and an altitude.

Then, at step 303, the movement data can be analyzed by the runtime module 205 to determine user traveling periods. In one example, collected accelerometer data can be partitioned into segments, which can be determined based on user preferences, and which can be categorized (e.g., a car, a rail-based movement, walking, bicycling, idle, boating, etc.) using a minimum distance classification algorithm. These partitions can be denoted as short segments, relative to the entire data segment. In one embodiment, the runtime module 205 filters the classifiers into groups of walking and non-walking portions. The non-walking portions can be considered traveling periods of the user because users will generally walk to and from modes of transportation. Thus, a walking mode of travel can be selected to filter the movement data and determine the traveling periods. Additional processes for determining traveling periods are described in the processes of FIG. 4. Moreover, additional methods can be used to determine traveling periods of the user. For example, a traveling period may be initiated if the user activates a flight mode of the UE 101. Additionally, this flight mode status information provides information that the user is on a flight while the UE 101 is in flight mode. When the UE 101 returns from flight mode to a normal mode, the UE 101 may attempt to retrieve information about the flight (e.g., emission data via text message from an airline or Bluetooth®). When a traveling period (e.g., a flight mode, a non-walking period, etc.) begins, runtime module 205 can detect and store one or more of location information (e.g., CeIlID, GPS data, etc.), time information, accelerometer information, and/or the like. Then, when the traveling period ends, additional location information associated with the end of the travel can be collected. In some examples, when a non-walking portion begins or ends, the runtime module 205 can associate the closest in time location information to the traveling period.

At step 305, the runtime module 205 can request and receive travel mode information from a travel mode information module 131 of the emissions service platform 107. The runtime module 205 can send movement data to the service and request the service to return available transportation modes (e.g., a bus, a train, a boat, etc.) at a location (e.g., a bus stop, a train stop, a marina, etc.) associated with the beginning of a traveling period or at locations associated with the middle of a traveling period. The runtime module 205 can then be caused to receive the available travel modes based on the movement data. Alternatively or additionally, the UE 101 can store the available travel mode data in a memory 209 of the UE 101 and the UE 101 need not request the travel mode information from the emissions service platform 107. Thus, the UE 101 can have access to the travel mode information and perform many of the functions of the emissions service platform 107. The runtime module 205 can then determine the available travel modes from the information in the memory 209. In one example, GPS coordinates around a certain area can be associated with a bus stop. Additionally, if GPS coordinates of the traveling period are over water, it can be determined that the transportation modes available should be able to travel over water (e.g., a plane, a boat, etc.).

Then, at step 307, the runtime module 205 can determine one or modes of travel by the user based on the movement data. In one embodiment, a flight mode of travel can be detected by the UE 101 being placed in a flight mode. In another embodiment, the runtime module 205 can process the movement data to determine if the movement is human performed movement (e.g., walking, biking, running, etc.) by comparing the movement data to predefined parameters indicative of each of the different types of human performed movements. More specific algorithms and methods of comparison are described in the processes of FIG. 4. If the movement is human performed, the runtime module 205 can also determine the periodicity of the movement. If the movement is not human performed (e.g., motor driven), the runtime module 205 may determine the type of travel method (e.g., a travel mode such as a car, train, bus, motor bike, etc.). In some embodiments, the runtime module 205 may also determine the periodicity of non- human performed movement. Additionally or alternatively, the runtime module 205 can determine an average speed of travel. This can be accomplished by determining a travel path based on the location information within the traveling period and associating each of those locations with a time. The average speed can be based on total change in distance between two points over the total amount of time it took to travel the distance. Average speed during portions of the path can be aggregated to calculate an average speed over the whole path. Moreover, auditory data that can be collected from a microphone can be used to determine a mode of travel. For example, the microphone can detect sounds from an engine to determine that the mode of travel uses an engine or a particular type of engine based on the auditory data. Additionally or alternatively, the mode of travel (e.g., a car, a bus, etc.) may transmit information to the UE 101 describing the mode of travel and associated emissions values by wireless means.

The runtime module 205 may also process the received travel mode information from step 305 and identify the available travel methods for a geographical location based at least in part on the received travel mode information. As such, travel detection algorithms used to determine the travel modes may be simplified, based at least in part on the identified travel methods available (and hence reduced travel methods alternatives and calculation required). Additionally, the accuracy of travel detection may be increased. In this regard, possible travel modes for geographical locations may be limited by the received information and the runtime module 205 may detect a travel mode based at least in part on the received information. For example, the runtime module 205 may be configured to determine at least one travel mode or rule out at least one travel mode based at least in part on at least one traveled path. For example, if the traveled path of a traveling period includes movement over a geographical location that is associated with water (e.g., a lake, sea, river, ocean etc.), non-water related traveling methods (e.g., a train) can be eliminated from available travel modes.

Moreover, the runtime module 205 may detect a travel mode based at least in part on user preferences. In this regard, the user preferences may be updated based at least in part on the received information. In some embodiments, the runtime module 205 may monitor the geographical location of a user and the user preferences may be ignored if the user is not a location within a predetermined distance from the geographical location associated with the user preferences (e.g., user home geographical location). Additionally, the runtime module 205 may receive an update of new travel methods with their associated carbon emission values and other related information for the geographical location based at least in part on the received information. These updates may be automatic (e.g., periodic, or triggered by some action, e.g. change of serving network) or initiated by a user (e.g., user initiated or manual travel method update from the server). In some embodiments, the received information may comprise the coordinates of relevant transportation locations and hubs. As such, the runtime module 205 may detect a travel method based at least in part on the coordinates of relevant transportation hubs and locations. For example, the runtime module 205 may detect a travel method based at least in part on a starting location (or coordinate) of a travel and an ending location (or coordinate) of the travel.

At step 309, the runtime module 205 can optionally request and receive emissions values for the determined mode of travel from a service (e.g., an emissions service platform 107). Alternatively or additionally, these emissions values can be stored in a memory 209 of the UE 101; and the UE 101 need not request and receive the emissions values. These emissions values may be updated on a periodic basis (e.g., once a month, once a week, etc.), or the update can be triggered by an event (e.g., leaving one country and entering a new country). The update can be also caused by the user of the UE 101 requesting the updated emissions values. Thus, the emissions service platform 107 can provide updated emissions values associated with the user. In one example, the emissions service platform 107 can provide the updated values based on the location of the UE 101. For example, car emissions standards and therefore average car emissions in one state or country may be different than the car emissions of another country. In some examples, a method of transportation can be correlated to certain emissions values based on the mode of travel and the locations of travel. If it is determined that the user is taking a bus associated with a particular city, the emissions values can be based on average bus values in the city. For example, some cities may use a natural gas engine bus, while other cities may use buses having unleaded gasoline engines or diesel gasoline engines.

Then, at step 311, the runtime module 205 determines an emissions footprint of the user for at least a particular time period based on the determined one or more modes of travel. An emissions footprint can include an estimated set of greenhouse emissions caused directly or indirectly by the user over the particular time period. Additionally, an emissions footprint can include a total set of greenhouse gas emissions caused by the user over one or more particular time periods. The runtime module 205 may also be configured to calculate carbon emissions based at least in part on traveled distance, travel mode, travel time, and/or the like. The runtime module 205 may also be configured to calculate carbon emissions based at least in part on traveled distance, travel mode, travel time, and/or the like. Carbon emissions information can be correlated to a distance and speed traveled by user using a particular travel mode. Emissions values can be stored in a database as an emissions rate (e.g., the amount of carbon dioxide produced per second or the amount of carbon dioxide produced for distance traveled). Additionally, the emissions rates for modes of travel can depend on the speed traveled (e.g., traveling at 70 miles per hour has a greater carbon production than traveling at 55 miles per hour). Moreover, distance or the travel time may be multiplied to the emissions rate to determine a quantity of emissions produced. This quantity may be used to determine the emissions footprint of the user based on one or more modes of travel over one or more time periods. In some embodiments, the runtime module 205 may configured to detect another UE 101 with an emissions estimator 103 within a predetermined distance, that is, within a predefined proximity. In this regard, the runtime module and the other UE 101 may exchange information, such as, for example, update carbon emission values for a geographical location or a mode of travel. In other embodiments, the runtime module 205 may also be configured to detect a short-range communications device or service (e.g., Bluetooth®) within a predetermined distance, which may be, for example, associated with a vehicle (e.g., car, bus, airplane etc.) or location (e.g., airport, bus terminal) that may output information (e.g., information regarding the vehicle's emissions, the vehicle's fuel consumption, vehicle/bus telemetry, vehicle travel mode type, vehicle speed, driving habits, etc.). As such, the runtime module 205 may determine a mode of travel and/or calculate carbon emissions based at least in part on the information received from the other UE 101, a short-range communication device or service, and/or the like. Additionally or alternatively, if the users are in the same emission category or classification, the emissions estimator applications 103 from each UE 101 may share or exchange information with one another for the purpose of, for example, improving or otherwise minimizing carbon emission levels. In some embodiments, the emission category or classification may be based at least in part on lifestyles (e.g., working professional, students, etc.), travel methods (e.g., car, train, bus, airplanes, etc.), or carbon emissions level, and/or the like. In other embodiments, proximity sensing may be used to improve the calculation of emission. In this regard, actual average emission for a vehicle (e.g., car, bus, etc.) may be calculated dynamically or in real time via short range communication aboard the vehicle. As such, updates of vehicle data (e.g., updates of emission values or emissions rates) may be performed dynamically or in real time. In this regard, it may be beneficial to determine the number of users traveling aboard the vehicle. As such, if multiple users in the vehicle have an emissions estimator application 103 on their UEs 101, information relating to calculated carbon emission for each user may be exchanged among the users aboard the vehicle and the carbon emission for each user may be adjusted based at least in part on the information exchanged. In an example embodiment, the total amount of carbon emission for all the users may be divided amongst the users traveling on the vehicle and each user may be attributed an equal amount of emission.

The above approach allows a user of a UE 101 to track the user's carbon emissions. Moreover, the user is able to automatically track the times that the user travels and the modes of travel that the user uses. These modes of travel and the times of travel can be used in conjunction with emissions rates of the travel mode to determine an emissions footprint of the user. This approach can advantageously enable the user to conserve energy by providing the user information of the user's emissions footprint. FIG. 4 is a flowchart of a process for determining a travel mode, according to one embodiment. In various embodiments, the runtime module 205 of a UE 101 or a travel mode determination module 123 and emissions footprint module 125 performs the process 400 and is implemented in, for instance, a chip set including a processor and a memory as shown FIG. 9. In this scenario, is the process 400 is explained in regards to execution by the runtime module 205 of the UE 101, however, it is contemplated that other modules of service platforms may be used to perform one or more steps of the process 400. A user can initiate an emissions estimator 103 application on the UE 101 to determine the mode of travel of the user. In step 401, accelerometer and location data associated with the user are retrieved from a memory (e.g., a database). The accelerometer and location data can be collected by a UE 101 associated with the user in the same manner as the described in step 301.

Then, at step 403, the runtime module 205 can determine classifications (e.g., idle, walking, bicycling, automotive, locomotive, boating etc.) of short segments based on time periods of the accelerometer data. Longer segments (less than the entire duration) can comprise one or more classified or unclassified short segments. Acceleration information can be determined by monitoring a voltage of a signal from an accelerometer. In one embodiment, a signal energy E of an acceleration signal x(t) is calculated using Parseval's theorem. In other embodiments, the signal can be converted using an analog to digital converter to receive a raw acceleration value. The raw acceleration value can then be processed (e.g., by dividing a raw value integer by a constant or a function) to convert the raw acceleration value to a processed acceleration value. If the accelerometer data is collected by a 3-axis accelerometer, a total acceleration of the UE 101 can be calculated by taking the square root of the sum of the squares of the acceleration information obtained from each axis. Additionally, a relative total acceleration can also be calculated based on three axes by accounting for the angles of acceleration of the three axes. In some embodiments, a total relative acceleration is used to determine the classifications, in other embodiments; the total acceleration can be utilized. Ranges of values of the acceleration can be mapped onto the classifications. For example, walking can be associated with a range of acceleration values. Additionally, patterns of acceleration values can be used to map to classifications. For example, these can be patterns based on short segments. One such pattern algorithm that may be used is a minimum distance classifier. A minimum distance classifier can be used to calculate a distance from a sample set of data collected to the ideal elements (e.g., a mean acceleration value) that represents each of the classifications. The runtime module 205 can then select the class that is the shortest distance from the sample set of data. This classification is used for the short segments. At step 405, longer walking time period segments are determined based on the classified short walking segments. The longer segments of the short segments can be filtered using histogram matching or other filtering means (e.g., a median filter) to remove irregularities and determine a start and an end walking time. Then, at step 407, the runtime module 205 determines candidates for travel time periods based on the walking time periods. Travel time periods can be considered non-walking traveling events (e.g., bicycling, motor travel, etc.). Thus a travel segment can be determined based on walking classifications and walking time periods. The travel time period of the travel segment can be determined based on an assumption that walking time periods are utilized between each travel time period. For example, during a trip, a user can walk from a house to a car, then travel in the car, then again start walking from the car to a train, travel in the train, then walk from the train to a bus, travel in the bus, and then walk from the bus to a building. As such, a travel time period can be a time period that can be determined as not a walking time period or an idle time period. For the determined travel time period, information can be gathered about the travel. For example, location information can be gathered near the start time and the end time of the travel (step 409). This can be used to determine the total distance from the starting point to the ending point. Moreover, location information during the determined travel time period can be used to determine the total travel distance (step 411). For example, the location points can be used as markers to determine a total traveled distance by calculating the sum of the distance from each location data point to the next location data point. The location points can also be used to determine the linearity of travel. The linearity of travel is how close the travel is to a straight line. Additionally, at step 413, total distance over total time can be used to determine an average speed of travel over the travel time period. Additionally, distance over time for each of the location points can be used to determine the speed of travel during segments of travel. For example, the average speed for each segment can be used to determine emissions to provide more accurate emissions values because emissions rates may vary based on the speed of travel.

Then, in an optional embodiment, at step 415, types of travel available at the start point and end point of the travel time period can be determined. In certain embodiments, this can be determined by querying a service or a database. The database may be part of the UE 101. The types of travel available may be determined by the terrain of the travel (e.g., a car will not be an available method of travel when traveling over the sea). Additionally, other methods of travel associated with a location (e.g., within a threshold range of a location) may be available. For example, the database can include information about the locations of bus stops or other public transportation in a geographic area. If the UE 101 location information shows that the user is within a predetermined range of the bus stop, bus travel will be an available method. Next, at step 417, the runtime module 205 determines the mode of travel for one or more travel time periods. In certain embodiments, the mode of travel of a time period can be one of the types of travel available. This simplifies algorithms used to determine modes of travel by eliminating unavailable methods of transportation. In one embodiment, the mode of travel can be based, at least in part, on the classifications of step 403. The greatest amount of classifications over a travel time period can represent the mode of travel for that travel time period. For example, if ninety percent of the travel time period has a classification of a locomotive or other rail mode of travel, the runtime module 205 determines that the mode of travel is over a rail. Additionally, the distribution of idle segments can be used to determine the type of travel. Idle segments occurring before or at the beginning of a travel time period can be indicative of vehicle waiting or waiting at a bus stop. Idle segments occurring during a travel period can be indicative of traffic. Moreover, the geographical distance of the start, middle and end points of a travel time period can be used to determine if any travel actually occurred. For example, if a certain threshold of traveling distance does not occur within the travel time period, the travel mode may be considered non-motor- based. If a travel mode is considered non-motor-based, the runtime module 205 need not ascertain emissions values for the travel mode. Further, the average speed of travel may be used to determine the mode of travel. For example, an average speed of 120 miles per hour can be indicative of a high-speed train, while an average speed of 25 miles per hour can be indicative of a car in city traffic. Additionally or alternatively, the linearity of the travel of a travel time period can be used to determine the mode of travel. For example, a tram is typically more linear than other rail-based modes of travel and automotive -based modes of travel.

According to the above approach, a UE 101 can track a user's greenhouse emissions by determining a mode of travel of the user. The above approach advantageously allows for the saving of power consumption of the UE 101 while tracking the user by combining acceleration sensor information with location data to determine the mode of travel. This additionally lengthens the battery life of a UE 101. Utilizing an acceleration sensor is less energy demanding than collecting GPS coordinates or determining a CeIlID location by querying a server. Moreover, using the acceleration information for determining periods of travel allows for a lower sampling rate of location data to determine mode of travel.

FIG. 5 is a diagram 500 depicting collected movement data, according to one embodiment. In this embodiment, the movement data includes accelerometer data 501 and location data 503. Additionally, time data can be collected and stored. The accelerometer data 501 is segmented into short time period segments. These segments can be classified, as discussed in the processes of FIG. 4 into types of movement. In this embodiment, the types of movement can be used to determine walking segments or phases 505, 507 and a travel segment or phase 509. The travel segment 509 can be determined based on being between two walking segments 505, 507. The travel segment 509 can then be associated with the location data, for example a start point 511, and an end point 513 or 515. The starting and ending point location data need not exactly correspond with the starting and ending points of the travel segment 509. This allows for less frequent collection of location data points, which can save power consumption of a UE 101 and thus increase battery life of the UE 101 while using the emissions estimator application 103. Moreover, the accelerometer data may also be used to determine walking classifications and walking segments 505, 507 while the user is walking and the UE 101 is moving. Additionally, when a walking segment 505 transitions to a travel segment, the emissions estimator application 103 can be triggered to collect location data 511 (e.g., GPS coordinates or a CeIlID). Further, in some embodiments, during walking segments, energy can be conserved by disabling location data collection.

FIG. 6A is a diagram of histogram representations of collected movement data for determining modes of travel, according to one embodiment. Histogram 600, displays a normalized frequency of relative total acceleration of accelerometer data associated with a travel mode in three categories 601, 603, 605. The categories 601, 603, 605 can be ranges of relative total acceleration values. More or less than three categories of relative total acceleration values may be used. Moreover, additional information can be used as a basis for the categories other than relative total acceleration (e.g., total power). The standard deviation diagram 610 displays the standard deviations 611, 613, 615 corresponding to the three categories 601, 603, 605. The histogram 600 and standard deviation diagram 610 can be used to create a template associated with a travel mode type. Collected movement data can be compared with the template to determine if the collected movement data corresponds to that particular travel mode. During the comparison, collected movement data can be parsed into segments. Histograms can be prepared for each of the segments. Then, the segmented histograms can be compared to the histogram templates to determine the travel mode. The comparison can be based on whether the histograms of the collected data match the histograms of the template with a standard deviation tolerance limit of the frequency distribution template.

FIG. 6B is a diagram of travel segment information that can be determined from the histogram matching, according to one embodiment. In certain embodiments, the collected accelerometer data shown in FIG. 5 can be represented in diagram 621. The information can be distributed into a walking phase 1, a histogram matching section, and a walking phase 2. The histogram matching section can be determined based on the walking phases. During the histogram matching section, the collected accelerometer data can be matched with a template as described in FIG. 6A representing a histogram of known travel modes (e.g., a car travel mode). Diagram 623 displays that the histogram matching has determined that the traveling phase utilizes a car travel mode. Once again, the periods of travel can demarcated as being between two walking phases or segments. Additional filters may be used to determine different types of travel categorized in similar ways. For example, diagram 625 includes a histogram matching that has matched an idle phase before a phase that matches a car phase template. This can be indicative of a bus travel type template. The bus template can include an idle phase of waiting for the bus at a bus stop before traveling in a bus travel phase, which when analyzing accelerometer data can seem similar to car accelerometer data. Thus, the collected accelerometer data of diagram 625 depicts a bus travel mode as displayed in diagram 627.

FIG. 7 is a diagram of user interfaces 700 utilized in the processes of FIGs. 3 and 4, according to various embodiments. In this regard, the emissions estimator application 103 may start from a main view 701. The user may then browse various different tabs which may open up additional views such as, for example, a statistics view 703, a setup view 705, an about view 707. The main view 701 may be available in one tab. The statistics view 703 may include three sub views which may be accessed from a menu that display a carbon emissions overview, a type of traffic that the user was in, and trips of the user. The setup view 705 may also have a similar menu with four different views. In this regard, a travel types view 709 may present a list of available transport methods for recognition that the user can set active on inactive. The travel types view 709 may also allow the user to add new transport methods or change the properties of the existing ones. In certain examples, the settings can have a car properties view 711 that includes carbon emission settings for a type of car that the user utilizes regularly. Additionally, the user is able to activate or deactivate data collection types, such as acceleration, audio, GPS, CeIlID, UE 101 state information (e.g., active, idle, flight mode), etc., in a data collection settings view 713. The data collection types may be used to determine segments of travel as well as modes of travel. Under one scenario, the GPS data collection can be disabled to conserve energy on the UE 101. Moreover, additional general settings and home location settings can be set. Home location settings can be used to select preferred means of travel to and from the home location. Other location settings (e.g., an office location setting) may also be used.

The processes described herein for providing automatic determination of an emissions footprint may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below. FIG. 8 illustrates a computer system 800 upon which an embodiment of the invention may be implemented. Although computer system 800 is depicted with respect to a particular device or equipment, it is contemplated that other devices or equipment (e.g., network elements, servers, etc.) within FIG. 8 can deploy the illustrated hardware and components of system 800. Computer system 800 is programmed (e.g., via computer program code or instructions) to provide automatic determination of an emissions footprint as described herein and includes a communication mechanism such as a bus 810 for passing information between other internal and external components of the computer system 800. Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (0, 1) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range. Computer system 800, or a portion thereof, constitutes a means for performing one or more steps of providing automatic determination of an emissions footprint.

A bus 810 includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus 810. One or more processors 802 for processing information are coupled with the bus 810.

A processor 802 performs a set of operations on information as specified by computer program code related to providing automatic determination of an emissions footprint. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus 810 and placing information on the bus 810. The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive

OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor 802, such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical or quantum components, among others, alone or in combination.

Computer system 800 also includes a memory 804 coupled to bus 810. The memory 804, such as a random access memory (RAM) or other dynamic storage device, stores information including processor instructions for providing automatic determination of an emissions footprint. Dynamic memory allows information stored therein to be changed by the computer system 800. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory 804 is also used by the processor 802 to store temporary values during execution of processor instructions. The computer system 800 also includes a read only memory (ROM) 806 or other static storage device coupled to the bus 810 for storing static information, including instructions, that is not changed by the computer system 800. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus 810 is a non- volatile (persistent) storage device 808, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system 800 is turned off or otherwise loses power.

Information, including instructions for providing automatic determination of an emissions footprint, is provided to the bus 810 for use by the processor from an external input device 812, such as a keyboard containing alphanumeric keys operated by a human user, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system 800. Other external devices coupled to bus 810, used primarily for interacting with humans, include a display device 814, such as a cathode ray tube (CRT) or a liquid crystal display (LCD), or plasma screen or printer for presenting text or images, and a pointing device 816, such as a mouse or a trackball or cursor direction keys, or motion sensor, for controlling a position of a small cursor image presented on the display 814 and issuing commands associated with graphical elements presented on the display 814. In some embodiments, for example, in embodiments in which the computer system 800 performs all functions automatically without human input, one or more of external input device 812, display device 814 and pointing device 816 is omitted.

In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC) 820, is coupled to bus 810. The special purpose hardware is configured to perform operations not performed by processor 802 quickly enough for special purposes.

Examples of application specific ICs include graphics accelerator cards for generating images for display 814, cryptographic boards for encrypting and decrypting messages sent over a network, speech recognition, and interfaces to special external devices, such as robotic arms and medical scanning equipment that repeatedly perform some complex sequence of operations that are more efficiently implemented in hardware.

Computer system 800 also includes one or more instances of a communications interface 870 coupled to bus 810. Communication interface 870 provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general the coupling is with a network link 878 that is connected to a local network 880 to which a variety of external devices with their own processors are connected. For example, communication interface 870 may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface 870 is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface 870 is a cable modem that converts signals on bus 810 into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface 870 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. Wireless links may also be implemented. For wireless links, the communications interface 870 sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface 870 includes a radio band electromagnetic transmitter and receiver called a radio transceiver. In certain embodiments, the communications interface 870 enables connection to the communication network 105 for provide automatic determination of an emissions footprint to the UE 101.

The term computer-readable medium is used herein to refer to any medium that participates in providing information to processor 802, including instructions for execution. Such a medium may take many forms, including, but not limited to, non- volatile media, volatile media and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as storage device

808. Volatile media include, for example, dynamic memory 804. Transmission media include, for example, coaxial cables, copper wire, fiber optic cables, and carrier waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio, optical and infrared waves. Signals include man-made transient variations in amplitude, frequency, phase, polarization or other physical properties transmitted through the transmission media. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. The term computer-readable storage medium is used herein to refer to any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both of processor instructions on a computer-readable storage media and special purpose hardware, such as ASIC 820.

Network link 878 typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link 878 may provide a connection through local network 880 to a host computer 882 or to equipment 884 operated by an Internet Service Provider (ISP). ISP equipment 884 in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet 890.

A computer called a server host 892 connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host 892 hosts a process that provides information representing video data for presentation at display 814. It is contemplated that the components of system 800 can be deployed in various configurations within other computer systems, e.g., host 882 and server 892.

At least some embodiments of the invention are related to the use of computer system 800 for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system 800 in response to processor 802 executing one or more sequences of one or more processor instructions contained in memory 804. Such instructions, also called computer instructions, software and program code, may be read into memory 804 from another computer-readable medium such as storage device 808 or network link 878. Execution of the sequences of instructions contained in memory 804 causes processor 802 to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC 820, may be used in place of or in combination with software to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware and software, unless otherwise explicitly stated herein. The signals transmitted over network link 878 and other networks through communications interface 870, carry information to and from computer system 800. Computer system 800 can send and receive information, including program code, through the networks 880, 890 among others, through network link 878 and communications interface 870. In an example using the Internet 890, a server host 892 transmits program code for a particular application, requested by a message sent from computer 800, through Internet 890, ISP equipment 884, local network 880 and communications interface 870. The received code may be executed by processor 802 as it is received, or may be stored in memory 804 or in storage device 808 or other non-volatile storage for later execution, or both. In this manner, computer system 800 may obtain application program code in the form of signals on a carrier wave.

Various forms of computer readable media may be involved in carrying one or more sequence of instructions or data or both to processor 802 for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host 882. The remote computer loads the instructions and data into its dynamic memory and sends the instructions and data over a telephone line using a modem. A modem local to the computer system 800 receives the instructions and data on a telephone line and uses an infra-red transmitter to convert the instructions and data to a signal on an infra-red carrier wave serving as the network link 878. An infrared detector serving as communications interface 870 receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus 810. Bus 810 carries the information to memory 804 from which processor 802 retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory 804 may optionally be stored on storage device 808, either before or after execution by the processor 802.

FIG. 9 illustrates a chip set 900 upon which an embodiment of the invention may be implemented. Chip set 900 is programmed to provide automatic determination of an emissions footprint as described herein and includes, for instance, the processor and memory components described with respect to FIG. 8 incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set can be implemented in a single chip. Chip set 900, or a portion thereof, constitutes a means for performing one or more steps of providing automatic determination of an emissions footprint. In one embodiment, the chip set 900 includes a communication mechanism such as a bus 901 for passing information among the components of the chip set 900. A processor 903 has connectivity to the bus 901 to execute instructions and process information stored in, for example, a memory 905. The processor 903 may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively or in addition, the processor 903 may include one or more microprocessors configured in tandem via the bus 901 to enable independent execution of instructions, pipelining, and multithreading. The processor 903 may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP) 907, or one or more application-specific integrated circuits (ASIC) 909. A DSP 907 typically is configured to process real-world signals (e.g., sound) in real time independently of the processor 903. Similarly, an ASIC 909 can be configured to performed specialized functions not easily performed by a general purposed processor. Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips.

The processor 903 and accompanying components have connectivity to the memory 905 via the bus 901. The memory 905 includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to provide automatic determination of an emissions footprint The memory 905 also stores the data associated with or generated by the execution of the inventive steps.

FIG. 10 is a diagram of exemplary components of a mobile terminal (e.g., handset) for communications, which is capable of operating in the system of FIG. IA, according to one embodiment. In some embodiments, mobile terminal 1000, or a portion thereof, constitutes a means for performing one or more steps of providing automatic determination of an emissions footprint. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. As used in this application, the term "circuitry" refers to both: (1) hardware-only implementations (such as implementations in only analog and/or digital circuitry), and (2) to combinations of circuitry and software (and/or firmware) (such as, if applicable to the particular context, to a combination of processor(s), including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions). This definition of "circuitry" applies to all uses of this term in this application, including in any claims. As a further example and if applicable to the particular context, as used in this application, the term "circuitry" would also cover an implementation of merely a processor (or multiple processors) and its (or their) accompanying software/or firmware. The term "circuitry" would also cover if applicable to the particular context, for example, a baseband integrated circuit or applications processor integrated circuit in a mobile phone or a similar integrated circuit in a cellular network device or other network devices.

Pertinent internal components of the telephone include a Main Control Unit (MCU) 1003, a Digital Signal Processor (DSP) 1005, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit 1007 provides a display to the user in support of various applications and mobile terminal functions that perform or support the steps of providing automatic determination of an emissions footprint. The display 10 includes display circuitry configured to display at least a portion of a user interface of the mobile terminal (e.g., mobile telephone). Additionally, the display 1007 and display circuitry are configured to facilitate user control of at least some functions of the mobile terminal. An audio function circuitry 1009 includes a microphone 1011 and microphone amplifier that amplifies the speech signal output from the microphone 1011. The amplified speech signal output from the microphone 1011 is fed to a coder/decoder (CODEC) 1013.

A radio section 1015 amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna 1017. The power amplifier (PA) 1019 and the transmitter/modulation circuitry are operationally responsive to the MCU 1003, with an output from the PA 1019 coupled to the duplexer 1021 or circulator or antenna switch, as known in the art. The PA 1019 also couples to a battery interface and power control unit 1020.

In use, a user of mobile terminal 1001 speaks into the microphone 1011 and his or her voice along with any detected background noise is converted into an analog voltage. The analog voltage is then converted into a digital signal through the Analog to Digital Converter (ADC) 1023. The control unit 1003 routes the digital signal into the DSP 1005 for processing therein, such as speech encoding, channel encoding, encrypting, and interleaving. In one embodiment, the processed voice signals are encoded, by units not separately shown, using a cellular transmission protocol such as global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, and the like.

The encoded signals are then routed to an equalizer 1025 for compensation of any frequency- dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator 1027 combines the signal with a RF signal generated in the RF interface 1029. The modulator 1027 generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter 1031 combines the sine wave output from the modulator 1027 with another sine wave generated by a synthesizer 1033 to achieve the desired frequency of transmission. The signal is then sent through a PA 1019 to increase the signal to an appropriate power level. In practical systems, the PA 1019 acts as a variable gain amplifier whose gain is controlled by the DSP 1005 from information received from a network base station. The signal is then filtered within the duplexer 1021 and optionally sent to an antenna coupler 1035 to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna 1017 to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 1001 are received via antenna 1017 and immediately amplified by a low noise amplifier (LNA) 1037. A down-converter 1039 lowers the carrier frequency while the demodulator 1041 strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer 1025 and is processed by the DSP 1005. A Digital to Analog Converter (DAC) 1043 converts the signal and the resulting output is transmitted to the user through the speaker 1045, all under control of a Main Control Unit (MCU) 1003-which can be implemented as a Central Processing Unit (CPU) (not shown).

The MCU 1003 receives various signals including input signals from the keyboard 1047. The keyboard 1047 and/or the MCU 1003 in combination with other user input components (e.g., the microphone 1011) comprise a user interface circuitry for managing user input. The MCU 1003 runs a user interface software to facilitate user control of at least some functions of the mobile terminal 1001 to provide automatic determination of an emissions footprint. The MCU 1003 also delivers a display command and a switch command to the display 1007 and to the speech output switching controller, respectively. Further, the MCU 1003 exchanges information with the DSP

1005 and can access an optionally incorporated SIM card 1049 and a memory 1051. In addition, the MCU 1003 executes various control functions required of the terminal. The DSP 1005 may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP 1005 determines the background noise level of the local environment from the signals detected by microphone 1011 and sets the gain of microphone 1011 to a level selected to compensate for the natural tendency of the user of the mobile terminal 1001.

The CODEC 1013 includes the ADC 1023 and DAC 1043. The memory 1051 stores various data including call incoming tone data and is capable of storing other data including music data received via, e.g., the global Internet. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. The memory device 1051 may be, but not limited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage, or any other non- volatile storage medium capable of storing digital data.

An optionally incorporated SIM card 1049 carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card 1049 serves primarily to identify the mobile terminal 1001 on a radio network. The card 1049 also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile terminal settings.

While the invention has been described in connection with a number of embodiments and implementations, the invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims. Although features of the invention are expressed in certain combinations among the claims, it is contemplated that these features can be arranged in any combination and order.