Patent Publication Number: US-9892376-B2

Title: Operator performance report generation

Description:
FIELD OF THE DISCLOSURE 
     The present disclosure relates to agricultural equipment. More specifically, the present disclosure relates to generating operator performance reports for operators of agricultural equipment. 
     BACKGROUND 
     There is a wide variety of different types of agricultural equipment that are operated by an operator. Many of these pieces of agricultural equipment have mechanisms that are controlled by the operator in performing agricultural operations. For instance, a combine can have multiple different mechanical, electrical, hydraulic, pneumatic and electromechanical subsystems, all of which need to be operated by the operator. The systems may require the operator to set a wide variety of different settings and provide various control inputs in order to control the combine. Some inputs not only include controlling the combine direction and speed, but also concave and sieve settings, rotor speed settings, and a wide variety of other settings and control inputs. 
     There are currently some existing methods which allow operators or farm equipment managers to obtain dashboard information indicative of the operation of a piece of agricultural equipment. This information is usually informative in nature. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A set of data indicative of sensed parameters on an agricultural machine is evaluated against a reference data set to obtain an evaluation value indicative of how the set of data compares to the reference data. A performance score is generated based on the evaluation value and indicates a performance of a given operator in operating the agricultural machine. An operator performance report is generated, with recommendations, based on the performance score. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of one exemplary operator performance computation architecture. 
         FIGS. 2A and 2B  (collectively  FIG. 2 ) is a more detailed block diagram of the architecture shown in  FIG. 1 . 
         FIG. 3  is a flow diagram illustrating one embodiment of the operation of the architecture shown in  FIGS. 1 and 2 , in computing performance data indicative of an operator&#39;s performance. 
         FIG. 4  shows one embodiment of a reference data store in greater detail. 
         FIG. 4A  is a flow diagram illustrating one exemplary embodiment of the operation of a recommendation engine. 
         FIGS. 5A-5G  are still more detailed block diagrams of different channels for generating different performance pillar scores. 
         FIG. 6  is one exemplary user interface display that illustrates one exemplary operator performance report format. 
         FIG. 7  is a block diagram showing one embodiment of the architecture shown in  FIGS. 1 and 2 , deployed in a cloud computing architecture. 
         FIGS. 8-13  show various embodiments of mobile devices that can be used in the architecture shown in  FIGS. 1 and 2 . 
         FIG. 14  is a block diagram of one illustrative computing environment which can be used in the architecture shown in  FIGS. 1, 2 and 7 . 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram of one embodiment of a performance report generation architecture  100 . Architecture  100  illustratively includes an agricultural machine  102 , a data evaluation layer  104 , a pillar score generation layer  106 , and a pillar score aggregation layer  108 . Layer  108  generates operator performance reports  110 , and can also generate closed loop, real time (or asynchronous) control data  112  which can be provided back to agricultural machine  102 . Architecture  100  is also shown having access to a reference data store  114 . 
     In the embodiment shown in  FIG. 1 , agricultural machine  102  illustratively includes raw data sensing layer  116  and derived data computation layer  118 . It will be noted that layer  118  can be provided on machine  102 , or elsewhere in architecture  100 . It is shown on machine  102  for the sake of example only. 
     Raw data sensing layer  116  illustratively includes a plurality of different sensors (some of which are described in greater detail below) that sense machine operating parameters as well as environmental data, such as product quality and the type and quality of material being expelled from the agricultural machine  102 . The raw data sensor signals are provided from raw data sensing layer  116  to derived data computation layer  118  where some computation is performed on those sensor signals, in order to obtain derived data  120 . In one embodiment, derived data computation layer  118  performs computations that do not require a great deal of computational overhead or storage requirements. 
     Derived data  120  is provided to data evaluation layer  104 . In one embodiment, data evaluation layer  104  compares the derived data  120  against reference data stored in reference data store  114 . The reference data can be historical data from operator  101 , or from a variety of other sources, such as data collected for operators in the fleet for a single farm that employs operator  101 , or from relevant data obtained from other operators as well. Data evaluation layer  104  generates evaluation values  122  based upon an evaluation of how the derived data  120  for operator  101  compares to the reference data in data store  114 . 
     Evaluation values  122  are provided to pillar score generation layer  106 . Layer  106  illustratively includes a set of score calculators that calculate a performance score  124  for each of a plurality of different performance pillars (or performance categories) that can be used to characterize the performance of operator  101  in operating agricultural machine  102 . The particular performance pillars, and associated scores  124 , are described in greater detail below. 
     Each of the pillar scores  124  are provided to pillar score aggregation layer  108 . Layer  108  illustratively generates a composite score and operator performance reports  110 , based upon the various pillar scores  124  that are received for operator  101 . The performance reports can take a wide variety of different forms, and can include a wide variety of different information, some of which is described below in greater detail with respect to  FIG. 6 . In one embodiment, reports  110  illustratively include the composite score (which is an overall score for operator  101 ) indicative of the performance of operator  101 , and is based upon the individual pillar scores  124  for the individual performance pillars (or performance categories). It can also illustratively include recommendations which are actionable items that can be performed by operator  101 , in order to improve his or her performance in operating agricultural machine  102 . While considering the included contextual information. 
     In one embodiment, layer  108  also generates closed loop, real time (or asynchronous) control data  112  which can be fed back to agricultural machine  102 . Where the data is fed back in real time, it can be used to adjust the operation, settings, or other control parameters for machine  102 , on-the-fly, in order to improve the overall performance. It can also be used to display information to operator  101 , indicating the operator&#39;s performance scores, along with recommendations of how operator  101  should change the settings, control parameters, or other operator inputs, in order to improve his or her performance. The data can also illustratively be provided asynchronously, in which case it can be downloaded to the agricultural machine  102  intermittently, or at preset times, in order to modify the operation of machine  102 . 
     Before describing the overall operation of architecture  100 , a more detailed block diagram of one embodiment of the architecture will be described.  FIGS. 2A and 2B  are collectively referred to as  FIG. 2 .  FIG. 2  shows one embodiment of a more detailed block diagram of architecture  100 . Some of the items shown in  FIG. 2  are similar to those shown in  FIG. 1 , and are similarly numbered. 
       FIG. 2  specifically shows that raw data sensing layer  116  in machine  102  illustratively includes a plurality of machine sensors  130 - 132 , along with a plurality of environment sensors  134 - 136 . Raw data sensing layer  116  can also obtain raw data from other machine data sources  138 . By way of example, machine sensors  130 - 132  can include a wide variety of different sensors that sense operating parameters and machine conditions on machine  102 . For instance, they can include speed sensors, mass flow sensors that measure the mass flow of product through the machine, various pressure sensors, pump displacement sensors, engine sensors that sense various engine parameters, fuel consumption sensors, among a wide variety of other sensors, some of which are described in greater detail below. 
     Environment sensors  134 - 136  can also include a wide variety of different sensors that sense different things regarding the environment of machine  102 . For instance, when machine  102  is a type of harvesting machine (such as a combine), sensors  134 - 136  can include crop loss sensors that sense an amount of crop that is being lost, as opposed to harvested. In addition, they can include crop quality sensors that sense the quality of the harvested crop. They can also include, for instance, various characteristics of the material that is discarded from machine  102 , such as the length and volume of straw discarded from a combine. They can include sensors from mobile devices in the operator&#39;s compartment, irrigation sensors or sensor networks, sensors on unmanned aerial vehicles or other sensors. Environment sensors  134 - 136  can sense a wide variety of other environmental parameters as well, such as terrain (e.g., pitch and roll sensors), weather conditions (such as temperature, humidity, etc.), among others. 
     Other machine data sources  138  can include a wide variety of other sources. For instance, they can include systems that provide and record alerts or warning messages regarding machine  102 . They can include the count and category for each warning, diagnostic code or alert message, and they can include a wide variety of other information as well. 
     Machine  102  also illustratively includes processor  140  and a user interface display device  141 . Display device  141  illustratively generates user interface displays (under control of processor  140  or another component) that allows user  101  to perform certain operations with respect to machine  102 . For instance, the user interface displays on the device  141  can include user input mechanisms that allow the user to enter authentication information, start the machine, set certain operating parameters for the machine, or otherwise control machine  102 . 
     In many agricultural machines, data from sensors (such as from raw data sensing layer  116 ) are illustratively communicated to other computational components within machine  102 , such as computer processor  140 . Processor  140  is illustratively a computer processor with associated memory and timing circuitry (not separately shown). It is illustratively a functional part of machine  102  and is activated by, and facilitates the functionality of, other layers, sensors or components or other items on machine  102 . In one embodiment, the signals and messages from the various sensors in layer  116  are communicated using a controller area network (CAN) bus. Thus, the data from sensing layer  116  is illustratively referred to as CAN data  142 . 
     The CAN data  142  is illustratively provided to derived data computation layer  118  where a number of computations are performed on that data to obtain derived data  120 , that is derived from the sensor signals included in CAN data  142 . Derived data computation layer  118  illustratively includes derivation computation components  144 , estimation components  146  and can include other computation components  148 . Derivation computation components  144  illustratively calculate some of the derived data  120  based upon CAN data  142 . Derivation computation components  144  can illustratively perform fairly straight forward computations, such as averaging, computing certain values as they occur over time, plotting those values on various plots, calculating percentages, among others. 
     In addition, derivation computation components  144  illustratively include windowing components that break the incoming data sensor signals into discrete time windows or time frames that are processed both discretely, and relative to data in other or adjacent time windows. Estimation components  146  illustratively include components that estimate derived data. In one embodiment components  146  illustratively perform estimation on plotted points to obtain a function that has a metric of interest. The metric of interest, along with the underlying data, can be provided as derived data  120 . This is but one exemplary embodiment of a computation component  144 , and a wide variety of others can be used as well. Other computation components  148  can include a wide variety of components to perform other operations. For instance, in one embodiment, components  148  include filtering and other signal conditioning components that filter and otherwise condition the sensor signals received from raw data sensing layer  116 . Components  148  can of course include other components as well. 
     Regardless of the type of components  144 ,  146  and  148  in layer  118 , it will be appreciated that layer  118  illustratively performs computations that require relatively light processing and memory overhead. Thus, in one embodiment, layer  118  is disposed on machine  102  (such as on a device located in the cab or other operator compartment of machine  102 ) or on a hand held or other mobile device that can be accessed on machine  102  by user  101 . In another embodiment, derived data computation layer  118  is located elsewhere, other than on machine  102 , and processor  140  communicates CAN data  142  to layer  118  using a communication link (such as a wireless or wired communication link, a near field communication link, or another communication link). 
     In any case, derived data  120  is obtained from layer  118  and provided to data evaluation layer  104 . Again, this can be done by processor  140  (or another processor) using a wireless link (such as a near field communication link, a cellular telephone link, a Wi-Fi link, or another wireless link), or using a variety of hard wired links. Data evaluation layer  104  illustratively includes comparison components  150 , one or more classifier components  152 , and it can include other components  154  as well. It will be appreciated that, in one embodiment, derived data  120  is illustratively associated with a specific user  101  either by processor  140 , or in another way. For instance, when user  101  begins operating machine  102 , it may be that processor  140  requests user  101  to enter authentication information (such as a username and password or other authentication information) when user  101  attempts to start up machine  102 . In that way, processor  140  can identify the particular user  101  corresponding to CAN data  142  and derived data  120 . 
     Layer  104  includes comparison components  150 , classifier components  152 , other components  154  and processor  155 . Comparison components  150  illustratively compare the derived data  120  for this operator  101  against reference data stored in reference data store  114 . The reference data can include a plurality of different reference data sets  156  and it can also include user preferences  158 , which are described in greater detail below. The reference data sets can be used to compare the derived data  120  of user  101  against the user&#39;s historical derived data, against data for other operators in the same fleet as user (or operator)  101 , or against another set of relevant reference data. In any case, comparison components  150  illustratively perform a comparison of derived data  120  against reference data sets  156 . They provide an output indicative of that comparison, and classifier components  152  illustratively classify that output into one of a plurality of different performance ranges (such as good, medium or poor, although these are exemplary and more, fewer, or different ranges can be used). In one embodiment, for instance, comparison component  150  and classifier components  152  comprise fuzzy logic components that employ fuzzy logic to classify the received values into a good category, a medium category or a poor category, based on how they compare to the reference data. In another embodiment, classifier components  152  provide an output value in a continuous rating system. The output value lies on a continuum between good and poor, and indicates operator performance. In the present description, categories are described, but this is for the sake of example only. These categories indicate whether the performance of user  101 , characterized by the received derived data values, indicate that the performance of user  101  in operating machine  102  is good, medium or poor, relative to the reference data set to which it was compared. 
     The classified evaluation values  122  are then provided to pillar score generation layer  106 . In the embodiment shown in  FIG. 2 , pillar score generation layer  106  includes performance pillar score generators  160 , supporting pillar score generators  162  and processor  163 . Performance pillar score generators  160  illustratively include generators that generate pillar scores corresponding to performance pillars that better characterize the overall performance of operator  101  in various performance categories. In one embodiment, the pillar scores are generated for productivity, power utilization, fuel economy, material loss and material quality. Supporting pillar score generators  162  illustratively generate scores for supporting pillars that, to some degree, characterize the performance of user  101 , but perhaps less so than the pillar scores generated by generators  160 . Thus, supporting pillar scores include scores for logistics and uptime. 
     It can thus be seen that, in the present embodiment, performance pillar score generators  160  include productivity score generator  164 , power utilization score generator  166 , fuel consumption score generator  168 , material (e.g., grain) loss score generator  170 , and material (e.g., grain) quality score generator  172 . Supporting pillar score generators  162  illustratively include logistics score generator  174  and uptime information generator  176 . 
     As one example, productivity score generator  164  can include logic for generating a score based on an evaluation of a productivity versus yield slope in evaluation values  122 . 
     Power utilization score generator  166  illustratively considers information output by the fuzzy logic classifiers  152  in layer  104  that are indicative of an evaluation of the engine power used by machine  102 , under the control of user (or operator)  101 . It thus generates a supporting pillar score indicative of that evaluation. 
     Fuel economy score generator  168  can be a logic component that considers various aspects related to fuel economy, and outputs a score based on those considerations. By way of example, where machine  102  is a combine, fuel economy score generator  168  can consider the separator efficiency, the harvest fuel efficiency, and non-productive fuel efficiency that are output by the fuzzy logic components in data evaluation layer  104 . Material loss score generator  170  can include items such as the crop type, the measured loss on machine  102  using various loss sensors, an evaluation of the loss using fuzzy logic components, and an evaluation of the tailings, also using fuzzy logic components  152  in data evaluation layer  104 . Based upon these considerations, material loss score generator  170  generates a material loss score indicative of the performance of machine  102  (under the operation of user  101 ) with respect to material loss. 
     Material quality score generator  172  illustratively includes evaluation values  122  provided by the fuzzy logic components  152  in layer  104  that are indicative of an evaluation of material other than grain that has been harvested, whether the harvested product (such as the corn or wheat) is broken or cracked, and whether the harvested product includes foreign matter (such as cob or chaff), and it can also include evaluation values  122  that relate to the size and quality of the residue expelled from machine  102 . 
     Logistics score generator  174  can include logic that evaluates the performance of the machine  102  during different operations. For instance, it can evaluate the performance of the machine (under the operation of user  101 ) during field setup, during unloading, during harvesting, and during idling. It can also include measures such as the distance that the machine traveled in the field and on the road, an individual percentage breakdown in terms of total time, and other information. This is but one example. 
     Uptime information generator  176  illustratively generates uptime information (such as a summary) either based on evaluation values  122  provided by layer  104 , or based on derived data  120  that has passed through layer  104  to layer  106 . The uptime supporting information can be indicative of the performance of the machine based on how much time it is in each machine state, and it can also illustratively consider whether any alert codes or diagnostic trouble codes were generated, and how often they were generated, during the machine operation. The uptime information is illustratively provided to (or available to) other items in architecture  100 , as context information. 
     All of the pillar scores and supporting pillar scores (indicated by  124  in  FIG. 2 ) are illustratively provided to pillar score aggregation layer  108 . Layer  108  illustratively includes an aggregator component  180 , composite score generator  182 , recommendation engine  184  (that accesses recommendation rules  185 ), processor  186  and report generator  188 . Aggregator component  180  illustratively aggregates all of the pillar scores and supporting pillar scores  124  using a weighting applied to each score. The weighting can be based on user preferences (such as if the user indicates that fuel economy is more important than productivity), they can be default weights, or they can be a combination of default weights and user preferences or other weights. Similarly, the weighting can vary based upon a wide variety of other factors, such as crop type, crop conditions, geography, machine configuration, or other things. 
     Once aggregator component  180  aggregates and weights the pillar scores  124 , composite score generator  182  illustratively generates a composite, overall score, for operator  101 , based upon the most recent data received from the operation of machine  102 . Recommendation engine  184  generates actionable recommendations which can be performed in order to improve the performance of operator  101 . Engine  184  uses the relevant information, pillar score  124 , evaluation values  124  and other information as well as, for instance, expert system logic, to generate the recommendations. This is described in greater detail below with respect to  FIG. 4A . The recommendations can take a wide variety of different forms. 
     Once the composite score and the recommendations are generated, report generator component  188  illustratively generates an operator performance report  110  indicative of the performance of operator  101 . Operator performance report  110  can be generated periodically, at the request of a manager, at the request of operator  101 , or another user, it can be generated daily, weekly, or in other ways. It can also be generated on-demand, while operation is ongoing. In one embodiment, operator performance report  110  illustratively includes a composite score  190  generated by composite score generator  182  and the recommendations  192  generated by recommendation engine  194 . Layer  108  can also illustratively generate control data  112  that is passed back to machine  102  to adjust the control of machine  102  in order to improve the overall performance. 
     Report  110  can, in one embodiment, be loaded onto a device so it can be viewed in real time by operator  101 , in the operating compartment of vehicle  102 , or it can be viewed in real time by a farm manger or others, it can be stored for later access and viewing by operator  101  or other persons, or it can be transmitted (such as through electronic mail or other messaging transmission mechanisms) to a main office, to a farm manager, to the user&#39;s home computer, or it can be stored in cloud storage. In one embodiment, it can also be transmitted back to a manufacturer or other training center so that the training for operator  101  can be modified based on the performance reports, or it can be used in other ways as well. 
       FIG. 3  is a flow diagram illustrating one embodiment of the overall operation of the architecture shown in  FIG. 2  in generating an operator performance report  110 .  FIG. 3  will now be described in conjunction with  FIGS. 2 and 4 . Then,  FIGS. 5A-5G  will be described to show a more detailed embodiment of portions of architecture  100  used to generate performance pillar scores. 
     In one embodiment, processor  140  first generates a startup display on user interface display device  141  to allow user  101  to start machine  102 . Displaying the startup display is indicated by block  200  in  FIG. 3 . The user  101  then enters identifying information (such as authentication information or other information). This is indicated by block  202 . User  101  then begins to operate machine  102 . This is indicated by block  204 . 
     As user  101  is operating the machine, the sensors in raw data sensing layer  116  sense the raw data and provide signals indicative of that data to derived data computation layer  118 . This is indicated by block  206  in the flow diagram of  FIG. 3 . As briefly discussed above, the data can include machine data  208  sensed by machine sensors  130 - 132 . It can also include environmental data  210  sensed by environment sensors  134 - 136 , and it can include other data  212  provided by other machine data sources  138 . Providing the raw data to derived data computation layer  118  is indicated by block  214  in  FIG. 3 . As discussed above, this can be over a CAN bus as indicated by block  216 , or in other ways as indicated by block  218 . 
     Derived data  120  is then generated by the components  144 ,  146  and  148  in layer  118 . The derived data is illustratively derived so that data evaluation layer  104  can provide evaluation data used in generating the pillar scores. Deriving the data for each pillar is indicated by block  220  in  FIG. 3 . This can include a wide variety of computations, such as filtering  222 , plotting  224 , windowing  226 , estimating  228  and other computations  230 . 
     The derived data  120  is then provided to data evaluation layer  104  which employs comparison components  150  and the fuzzy logic classifier components  152 . Providing the data to layer  104  is indicated by block  232  in  FIG. 3 . It can be provided using a wireless network  234 , a wired network  236 , it can be provided in real time as indicated by block  238 , it can be saved and provided later (such as asynchronously)  240 , or it can be provided in other ways  242  as well. 
     Data evaluation layer  104  then evaluates the derived data against reference data, to provide information for each pillar. This is indicated by block  244  in  FIG. 3 . The data can be evaluated using comparison  246 , using classification  248 , or using other mechanisms  250 . 
     In one embodiment, the comparison components  150  compare the derived data  120  for operator  101  against reference data.  FIG. 4  shows a more detailed embodiment of reference data store  114 .  FIG. 4  shows that, in one embodiment, reference data sets  156  illustratively include individual operator reference data  252 . Reference data  252  illustratively includes historical reference data for this specific operator  101 . It can also include fleet reference data  254  which comprises reference data corresponding to all of the operators in the fleet to which operator  101  belongs. It can include high performing geographically relevant reference data  256  as well. This illustratively comprises reference data from other operators in a geographically relevant region (such as where the weather, soil type, field sizes, farming practices, etc. are similar to that where operator  101  resides). It can include performance data across various fleets, and the operators that generated the performance data can be identified or anonymous. To generate references for the fuzzy logic components, reference of data for medium and poor performing operations is used. However, comparisons can be made against only high performance data or other subsets of data as well. Also, the data can be for individual operators, or it can be aggregated into a single set of reference data (e.g., for all of the high performing operators in the geographically relevant region, etc.). Of course, it can include other reference data  258  as well. 
     Also, in the embodiment shown in  FIG. 4 , the reference data sets  156  illustratively include context data  260 . The context data can define the context within which the reference data was gathered, such as the particular machine, the machine configuration, the crop type, the geographic location, the weather, machine states, other information generated by uptime information generator  176  or other information. 
     It will be noted that the reference data in store  114  can be captured and indexed in a wide variety of different ways. In one embodiment, the raw CAN data  142  can be stored along with the derived data  120 , the evaluation values  122 , user preferences  158 , the pillar scores  124 , context data and the recommendations. The data can be indexed by operator, by machine and machine head identifier, by farm, by field, by crop type, by machine state (that is, the state of the machine when the information was gathered, e.g., idle, idle while unloading, waiting to unload, harvesting, harvesting while unloading, field transport, road transport, headland turn, etc.), by settings state (that is, the adjustment settings in the machine including chop setting, drop settings, etc.), and by configuration state (that is, the hardware configuration of the machine). It can be indexed in other ways as well. 
     Once evaluation layer  104  performs the comparison against the reference data and classifies a measure of that comparison using fuzzy logic heuristics, the evaluation values  122  represent the results of the classification and are provided to pillar score generation layer  106 . This is indicated by block  270  in  FIG. 3 . Pillar score generation layer  106  then generates a pillar score for each performance pillar (and the logistics supporting pillar), based on the plurality of evaluation values  122 . This is indicated by block  272  in  FIG. 3 . 
     The pillar scores can be generated by combining the evaluation values for each individual pillar, and weighting and scaling them. This is indicated by block  274 . A pillar score generator then calculates a pillar score for each performance pillar (e.g., each performance category) and supporting pillar (e.g., supporting performance category). This is indicated by block  276  in  FIG. 3 . In doing so, as discussed above, the pillar score generators can illustratively consider user preferences, machine configuration data, context data (e.g., the information generated by logistics information generator  176 ), or a wide variety of other context data or other data. This is indicated by block  278 . The pillar scores can be generated in other ways  280  as well. 
     Pillar scores  124  are then provided to pillar score aggregation layer  108 . This is indicated by block  282  in  FIG. 3 . Report generator component  188  then generates the operator performance reports  110  based upon the pillar scores, the composite scores, the underlying data, user preferences, context data and the recommendations. Generating the report  110  and control data  112  is indicated by block  284 . Doing this by aggregating the pillar scores is indicated by block  286 , generating the composite score is indicated by block  288 , generating actionable recommendations is indicated by block  290 , and generating and feeding back the control data  112  is indicated by block  292 . 
     Before discussing a more detailed implementation, the operation of recommendation engine  184  in generating recommendations will be described.  FIG. 4A  is a flow diagram showing one embodiment of this. 
       FIG. 4A  shows a flow diagram illustrating one embodiment of the operation of recommendation engine  184  in  FIG. 2 . Recommendation engine  184  first receives the performance pillar scores  124 , along with the evaluation values  122  and any other desired supporting information from the other parts of the system. This is indicated by block  251  in  FIG. 4A . The other data can include reference information  253 , context data  255 , or a wide variety of other information  257 . 
     Engine  184  then identifies triggered symptoms that are triggered in expert system logic, based on all of the received information. This is indicated by block  259  shown in  FIG. 4A . 
     The expert system logic then diagnoses various opportunities to improve performance based on the triggered symptoms. The diagnosis will illustratively identify areas where recommendations might be helpful in improving performance. This is indicated by block  261  in  FIG. 4A . 
     Engine  184  then accesses expert system, logic-based rules  185  to generate recommendations. This is indicated by block  263 . The rules  185  illustratively operate to generate the recommendations based on the diagnosis, the context information and any other desired information. 
     Engine  184  then outputs the recommendations as indicated by block  265 . The recommendations can be output to farm managers or other persons, as indicated by block  267 . They can be output on-demand, as indicated by block  269 . They can be output intermittently or on a periodic basis (e.g., daily, weekly, etc.) as indicated by block  271 , or they can be output in other ways as well, as indicated by block  273 . 
       FIGS. 5A-5G  show a more detailed implementation of architecture  100 , in which machine  102  is a combine.  FIGS. 5A-5G  each show a processing channel in architecture  100  for generating a pillar score or a supporting pillar score.  FIGS. 5A-5G  will now be described as but one example of how architecture  100  can be implemented with a specific type of agricultural machine  102 . 
       FIG. 5A  shows a processing channel in architecture  100  that can be used to generate the productivity pillar score. Some of the items shown in  FIG. 5A  are similar to those shown in  FIG. 2 , and they are similarly numbered. In the embodiment shown in  FIG. 5A , machine sensors  130 - 132  in raw data sensing layer  116  illustratively include a vehicle speed sensor  300 , a machine configuration sensor  302  and a crop sensor, such as a mass flow sensor  306  that measures mass flow of product through machine  102 . The components in derived data computation layer  118  illustratively include components for generating derived data such as a productivity computation component  308  that calculates productivity that indicates the overall grain productivity of machine  102 . This can be in tons per hour, tons per hectare or other units or a combination of such metrics. They also include a windowing component  314  that divides the data into temporal windows or time frames and provides it to layer  104 . 
     Evaluation layer  104  illustratively includes a grain productivity fuzzy logic evaluation mechanism  317  that not only compares the output from layer  118  to the various reference data sets  156  in reference data store  114 , but also classifies a measure of that comparison. In one embodiment, the output of layer  104  is illustratively a unitless number in a predefined range that indicates whether the operator performed in a good, average or poor range, relative to the reference data to which it was compared. Again, as mentioned above, the good, average or poor categories are exemplary only. Other outputs such as a continuous metric can be used or more, fewer, or different categories could be used as well. 
       FIG. 5A  also shows that pillar score generation layer  106  illustratively includes a grain productivity metric generator that comprises the productivity score generator  164 . Generator  164  receives the unitless output of layer  104  and generates a productivity pillar score  124  based on the input. The productivity score is indicative of the productivity performance of operator  101 , based upon the current data. This information is provided to layer  108 . 
       FIG. 5B  shows one embodiment of a processing channel in architecture  100  that can be used to generate the logistics supporting pillar score. Some of the items shown in  FIG. 5B  are similar to those shown in  FIG. 2 , and they are similarly numbered.  FIG. 5B  shows that layer  116  includes a time sensor  318  that simply measures the time that machine  102  is running. It also includes a machine state data sensor  320  that indicates when machine  102  is in each of a plurality of different states. A vehicle speed sensor  300  is also shown, although it is already described with respect to  FIG. 5A . It can also be a separate vehicle speed sensor as well. Derived data computation layer  118  illustratively includes machine state determination component  322 . Based on the machine state data received by sensor  320 , component  322  identifies the particular machine state that machine  102  resides in, at any given time. The machine state can include idle, harvesting, harvesting while unloading, among a wide variety of others. 
     Components in layer  118  also illustratively include a plurality of additional components. Component  324  measures the distance machine  102  travels in each traveling state. Component  340  computes the time machine  102  is in each state. The times can illustratively computed in relative percentages or in units of time. 
     The output of components  324  and  340 , are provided to fuzzy logic components  344  and  350  that compares the data provided by components  324  and  340  against reference data for productive time and idle time and evaluates it against that reference data. Again, in one embodiment, the output of the fuzzy logic components is a unitless value in a predetermined range that indicates whether the performance of operator  101  was good, average or poor relative to the reference data. Layer  104  can include other components for generating other outputs, and it can consider other information from layers  116  and  118  or from other sources. 
     Logistics metric generator  166  illustratively computes a logistics metric, in the embodiment shown in  FIG. 5B , based upon all of the inputs illustrated. The logistics metric is a measure of the operator&#39;s logistics performance based on the various comparisons against the reference data sets, and it can be based on other things as well. 
       FIG. 5C  shows a block diagram of one implementation of a computing channel in architecture  100  for calculating the fuel economy performance pillar score. In the embodiment shown in  FIG. 5C , layer  116  illustratively includes a grain productivity sensor (or calculator)  352  that senses (or calculates) grain productivity for the combine (e.g., machine  102 ). It can be the same as component  308  in  FIG. 5A  or different. It can provide an output indicative of grain productivity in a variety of different measures or units. It also includes a fuel consumption sensor  354  that measures fuel consumption in units of volume per unit of time. It includes a machine state sensor  356  that identifies machine state (this can be the same as component  322  in  FIG. 5B  or different), a vehicle speed sensor  358  that measures vehicle speed (which can be the same as sensor  300  in  FIG. 5A  or different). 
     Layer  118  includes component  360  that calculates a harvest fuel efficiency ratio for harvesting states and component  362  calculates a non-productive fuel efficiency ratio for nonproductive states. 
     Windowing components  382  and  384  break the data from components  360  and  362  into discrete timeframes. Layer  104  includes average distance components  386  and  388  which receive inputs from reference functions  390  and  392  and output an indication of the distance of the lines fit to the data output by components  382  and  384  from reference functions  390  and  392 . 
     Layer  104  illustratively includes a harvest fuel efficiency evaluator  420 , and a non productive fuel efficiency evaluator  422 . Component  420   d  receives the output from component  386  (and possibly other information) and compares it against reference data, evaluates the measure of that comparison and outputs a value that is indicative of the performance of operator  101  in terms of harvest fuel efficiency. Component  422  does the same thing for nonproductive fuel efficiency. 
     Layer  106  in  FIG. 5C  illustratively includes a fuel economy metric generator as fuel economy score generator  168  (shown in  FIG. 2 ). It receives the inputs from components  420  and  422  and can also receive other inputs and generates a fuel economy pillar score for operator  101 . The fuel economy pillar score is indicative of the fuel economy performance of operator  101 , based on the current data collected from machine  102 , as evaluated against the reference data. 
       FIG. 5D  shows one embodiment of a computing channel in architecture  100  shown in  FIG. 2  for calculating the material loss performance pillar score. It can be seen that material loss score generator  170  (from  FIG. 2 ) comprises grain loss metric generator  170  shown in  FIG. 5D . In the embodiment shown in  FIG. 5D , layer  116  includes a left hand shoe loss sensor component  426  that senses show loss and calculates a total percentage of shoe loss. It also includes separator loss sensor  436  that senses separator loss and computes a total percentage of separator loss, a tailings volume sensor  446  that senses a volume of tailings, and mass flow sensor  448 . Sensor  448  can be the same as server  306  in  FIG. 5A  or different. 
     Windowing components  451 ,  453  and  455  receive inputs from components  426 ,  436  and  448  and break them into discrete time windows. These signals can be filtered and are provided to layer  104 . Data evaluation layer  104  illustratively includes shoe total loss evaluator  452 , separator total loss evaluator  456 , and a tailings evaluator  460 . 
     Total shoe loss evaluator  452  illustratively comprises a fuzzy logic component that receives the total shoe loss from component  451  in layer  118  and compares that against total shoe loss reference data from data store  114 . It then evaluates the measure of that comparison to provide a unitless value indicative of whether the performance of operator  101 , in terms of total shoe loss, is classified as good, average or poor. 
     Similarly, separator total loss evaluator  456  each comprises a fuzzy logic component that receives the total separator loss from component  453  and compares it against reference data for total separator loss, and then evaluates the measure of that comparison to determine whether the performance of operator  101 , in terms of total separator loss, is classified as good, average or poor. 
     Tailings evaluator  460  is illustratively a fuzzy logic component that receives an input from component  455 , that is indicative of tailings volume and perhaps productivity. It then compares those items against tailings reference data in data store  114  and classifies the measure of that comparison into a good, average or poor classification. Thus, component  460  outputs a unitless value indicative of whether the performance of operator  101 , in terms of tailings evaluation, is good, average or poor. 
     It can also be seen in  FIG. 5D  that, in one embodiment, all of the evaluator components  452 ,  456  and  460  receive an input from crop type component  450 . Component  450  illustratively informs components  452 ,  456  and  460  of the crop type currently being harvested. Thus, the evaluator components  452 ,  456  and  460  can consider this in making the comparisons and classifications, relative to reference data. 
     Grain loss metric generator  170  receives inputs from the various evaluator components in layer  104  and aggregates those values and computes a performance pillar score for material loss. In doing so, generator  170  illustratively considers user preferences  468  that are provided, relative to material loss. These can be provided in terms of a total percentage, or otherwise. They illustratively indicate the importance that the user places on the various aspects of this particular performance pillar. The output of generator  170  is thus an overall material loss performance score that indicates how operator  101  performed in terms of material loss. 
       FIG. 5E  is a more detailed block diagram showing one embodiment of a computing channel in architecture  100  to obtain a performance pillar score for material quality. Thus, it can be seen that material quality score generator  172  shown in  FIG. 2  comprises grain/residue quality metric generator  172  shown in  FIG. 5E .  FIG. 5E  shows that, in one embodiment, raw data sensing layer  116  includes sensor  470  that senses the types of material in the grain elevator. Sensor  470  illustratively senses the volume of material, other than grain, (such as chaff and cobs). Damaged crop sensor  480  illustratively senses the percent of material that is damaged (such as broken, crushed or cracked). 
     Residue properties sensor  486  can sense various properties of residue. The properties can be the same or different depending on whether the combine is set to chop or windrow. 
       FIG. 5E  shows that derived data computation layer  118  illustratively includes components  472 ,  482  and  488  that filters the signals from sensors  470 ,  480  and  486 . This can be breaking signals into temporal windows and calculating a representative value for each window or otherwise. 
     In the embodiment shown in  FIG. 5E , data evaluation layer  104  illustratively includes a material other than grain evaluator  500 , a crop damage evaluator  502 , and a residue quality evaluator  506 . It can be seen that components  500 ,  502  and  508  can all illustratively be informed by user preferences with respect to grain quality thresholds or by reference data  450  for the specific crop type. 
     In any case, evaluator  500  illustratively receives the input from component  472  in layer  118  and compares the filtered material other than grain value, for light material, against corresponding reference data in data store  114 . It then classifies the result of that comparison into a good, average or poor class. The class is thus indicative of whether the performance of operator  101 , in terms of material other than grain in the grain elevator, is good, average or poor. 
     Crop damage evaluator  502  receives the input from component  482  in layer  118  that is indicative of a percent of product in the grain elevator that is damaged. It compares that information against corresponding reference data from reference data store  114  and classifies the result of that comparison into a good, average or poor class. It thus provides a value indicative of whether the performance of operator  101 , in terms of the product in the grain elevator being damaged, is good, average or poor. 
     Residue quality evaluator  506  receives inputs from component  488  in layer  116  and  118  and compares those inputs against corresponding reference data in reference data store  114 . It then classifies the result of that comparison into a good, average or poor class. Thus, it provides an output indicative of whether the performance of operator  101 , in terms of residue quality, is good, average or poor. 
     Grain/residue quality metric generator  172  receives inputs from the various components in layer  104  and uses them to calculate a grain/residue quality score for the material quality performance pillar. This score is indicative of the overall performance of operator  101 , in operating machine  102 , in terms of grain/residue quality. The score is illustratively provided to layer  108 . 
       FIG. 5F  shows one embodiment of a processing channel in architecture  100  shown in  FIG. 2 , to calculate the engine power utilization score for the power utilization pillar, on a combine. Thus, power utilization score generator  166  is shown in  FIG. 5F . In the embodiment shown in  FIG. 5F , raw data sensing layer  116  illustratively includes engine speed sensor  510 , and an engine load sensor  514 . Layer  118  illustratively includes an engine usage component  516  that receives the inputs from sensors  510  and  514  and calculates engine usage (such as power in kilowatts). Filtering component  518  filters the value from component  518 . Windowing component  520  breaks the output from component  518  into discrete temporal windows. 
     The output from component  520  is provided to layer  104  which includes engine power utilization evaluator  522 . Engine power utilization evaluator  522  is illustratively a fuzzy logic component that receives the output from component  520  in layer  118  and compares it against engine power utilization reference data  523  in reference data store  114 . It then classifies the result of that comparison into a good, average or poor class. Thus, the output of component  522  is a unitless value that indicates whether the performance of operator  101 , in terms of engine power utilization is good, average or poor. 
     Score generator  174  receives the output from evaluator  522  and calculates a performance pillar score for engine power utilization. The output from component  174  is thus a performance pillar score indicative of whether the overall performance of operator  101 , in operating machine  102 , is good, average or poor in terms of engine power utilization. The score is illustratively provided to layer  108 . 
       FIG. 5G  is a more detailed block diagram showing one embodiment of the architecture  100  shown in  FIG. 2  in generating the uptime summary. In the embodiment shown in  FIG. 5G , layer  116  includes machine data sensor  116 . Machine data sensor  116  illustratively senses a particular machine state that machine  102  is in, and the amount of time it is in a given state. It can also sense other things. 
     Layer  118  illustratively includes a diagnostic trouble code (DTC) component  524  that generates various diagnostic trouble codes, based upon different sensed occurrences in machine  102 . They are buffered in buffer  525 . DTC count component  526  calculates the number of DTC occurrences per category, and the number and frequency of occurrence of various alarms and warnings indicated by machine data  116 . By way of example, component  526  may calculate the number of times the feeder house gets plugged or the number of other alarms or warnings that indicate that machine  102  is undergoing an abnormally high amount of wear. The alarms and warnings can be event based, time based (such as how many separator hours the machine has used), or based on other things. 
     Layer  104  includes alert/warning evaluator  528  that compares the various information from machine  102  against reference data to generate information indicative of the operator&#39;s performance. The information is provided to summary generator  176 . 
     Uptime summary generator  176  in layer  106  receives the outputs from component  528  and uses them to generate uptime summary information indicative of the performance of operator  101 , in operating machine  102 , in terms of uptime. The uptime summary information can be provided to layer  108 , or used by other parts of the system, or both. 
       FIG. 6  shows one embodiment of an exemplary report format for an operator performance report  110 . The report format shown  FIG. 6  is exemplary only, and is indicated by number  530 . Also, it will be appreciated that each of the sections in  FIG. 6  can be modified either by the user, by an administrator or by other personnel, in order to show different information, as desired by the user. 
     The report format  530  can illustratively include a user-branded, or manufacture branded section  532 . It may include operator and machine identifier section  534  that identifies the particular operator  101  and the particular machine  102  that the operator is operating. It can include a date range section  536  that shows the date range for the report, and a report frequency indicator  538  that indicates how frequently the report is generated. In the embodiment shown in  FIG. 6 , report format  530  is only reporting information for three of the five performance pillar score categories described above. It is reporting information for the productivity performance pillar, the material quality performance pillar and the fuel consumption (or fuel economy) performance pillar. It will be appreciated, of course, that additional or fewer performance pillars can be included in the report format as well, and those shown are shown for exemplary purposes only. 
       FIG. 6  shows that the report format  530  includes an overview section  540 . Overview section  540  illustratively includes a set of performance pillar score indicators  542 ,  544  and  546 . The score indicators shown in  FIG. 6  are shown as gauges  548 ,  550  and  552  with a corresponding numerical performance score indicator  554 ,  556  and  558 . It will be appreciated that the particular displays shown in  FIG. 6  are exemplary only and others could be used. 
     In the embodiment shown in  FIG. 6 , overview section  540  also includes a set of hour indicators  560  and  562  that indicate the time of operation of components that are deemed of interest by the user. In one embodiment, for example, hour indicator  560  indicates the number of engine hours that operator  101  used, for the information in the current report. Other hour indicators can be used as well. 
       FIG. 6  also shows that, in one embodiment, for each pillar score shown in the overview section  540 , a more detailed section is provided as well. For instance,  FIG. 6  includes a productivity detail section  564 , a quality detail section  566  and a fuel economy detail section  568 . 
     Productivity detail section  564  includes detailed information about the various items sensed or computed in generating the overall productivity performance pillar score shown in the overview section  540 . It thus includes information indicative of the time spent harvesting, the average speed, the percent of time that the operator was unloading on the go (such as while harvesting) and the average area per hour covered by the user. It can also provide indicators indicative of the crop condition, the relative amount of time spent chopping or dropping, and the overall environmental conditions. Further, it can include a machine settings section indicating what the machine settings were (such as for the sieves, the concaves, the pre-cleaner and chaffer, etc.) along with a sensing section indicative of various sensed parameters (such as fan speed, cylinder speed, vane adjustment and feeder house drive RPMs, etc.). 
     In the embodiment shown in  FIG. 6 , quality detail section  566  illustratively includes more detailed information that was used in generating the quality performance pillar score. For instance, it can include detailed information regarding overall separator loss, shoe loss, grain quality, straw quality and tailings volume. It can also illustratively include images sections that show photographic images taken by the operator or otherwise. For instance, images section  570  shows images that were taken and that relate to separator and shoe loss. Images section  572  includes images that were taken and are relevant to grain quality. 
     In the embodiment shown in  FIG. 6 , fuel economy detail section  568  includes detailed information that was used in generating the fuel economy performance pillar score shown in overview section  540 . Therefore, it can include such things as overall fuel consumption while harvesting, while transporting within the field, while traveling on the road, and non-productive fuel consumption. Of course, it can include other information as well. It will be appreciated that this is only one embodiment of a report format. A wide variety of others can be used as well. 
     In another embodiment, the performance results can also be provided plotted over a field map generated from, for example, a satellite image of the field. The plotted results can include each metric (the five pillar scores) and the composite score. The plot can show (at the same time or selectively) other information as well. This will thus show how the operator performed at different locations in the field. 
     The present discussion has mentioned processors and servers. In one embodiment, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. They can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. They can also be actuated in a wide variety of different ways. For instance, they can be actuated using a point and click device (such as a track ball or mouse). They can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. They can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, they can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, they can be actuated using speech commands. 
     A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used so the functionality is performed by fewer components. Also, more blocks can be used with the functionality distributed among more components. 
       FIG. 7  is a block diagram of architecture  100 , shown in  FIG. 1 , except that its elements are disposed in a cloud computing architecture  500 . Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, cloud computing delivers the services over a wide area network, such as the internet, using appropriate protocols. For instance, cloud computing providers deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components of architecture  100  as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a cloud computing environment can be consolidated at a remote data center location or they can be dispersed. Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a service provider at a remote location using a cloud computing architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways. 
     The description is intended to include both public cloud computing and private cloud computing. Cloud computing (both public and private) provides substantially seamless pooling of resources, as well as a reduced need to manage and configure underlying hardware infrastructure. 
     A public cloud is managed by a vendor and typically supports multiple consumers using the same infrastructure. Also, a public cloud, as opposed to a private cloud, can free up the end users from managing the hardware. A private cloud may be managed by the organization itself and the infrastructure is typically not shared with other organizations. The organization still maintains the hardware to some extent, such as installations and repairs, etc. 
     In the embodiment shown in  FIG. 7 , some items are similar to those shown in  FIGS. 1 and 2  and they are similarly numbered.  FIG. 7  specifically shows that layers  104 ,  106  and  108  can be located in cloud  502  (which can be public, private, or a combination where portions are public while others are private). Therefore, user  101  can operate machine  102  using a user device  504  that includes layer  118 . Machine  102  can access layers  104 ,  106  and  108  through cloud  502 . 
       FIG. 7  also depicts another embodiment of a cloud architecture.  FIG. 7  shows that it is also contemplated that some elements of architecture  100  are disposed in cloud  502  while others are not. By way of example, data store  114  can be disposed outside of cloud  502 , and accessed through cloud  502 . In another embodiment, layer  108  (or other layers) can be outside of cloud  502 . Regardless of where they are located, they can be accessed directly by device  504 , through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service through a cloud or accessed by a connection service that resides in the cloud. All of these architectures are contemplated herein. 
     Further,  FIG. 7  shows that a remote view component  507  (which can be another user device, or another component) can be used by one or more other viewers  509 , who are remote from machine  102 . Viewers  509  can view the reports or other information if properly authenticated. 
     It will also be noted that architecture  100 , or portions of it, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as handheld computers, cell phones, smart phones, multimedia players, personal digital assistants, etc. 
       FIG. 8  is a simplified block diagram of one illustrative embodiment of a handheld or mobile computing device that can be used as a user&#39;s or client&#39;s hand held device  16 , in which the present system (or parts of it) can be deployed.  FIGS. 9-13  are examples of handheld or mobile devices. 
       FIG. 8  provides a general block diagram of the components of a client device  16  that can run components of architecture  100  or that interacts with architecture  100 , or both. In the device  16 , a communications link  13  is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning Examples of communications link  13  include an infrared port, a serial/USB port, a cable network port such as an Ethernet port, and a wireless network port allowing communication though one or more communication protocols including wireless connections to networks. 
     Under other embodiments, applications or systems are received on a removable Secure Digital (SD) card that is connected to a SD card interface  15 . SD card interface  15  and communication links  13  communicate with a processor  17  (which can also embody processors  140 ,  155 ,  163  or  186  from  FIG. 2 ) along a bus  19  that is also connected to memory  21  and input/output (I/O) components  23 , as well as clock  25  and location system  27 . 
     I/O components  23 , in one embodiment, are provided to facilitate input and output operations. I/O components  23  for various embodiments of the device  16  can include input components such as buttons, touch sensors, multi-touch sensors, optical or video sensors, voice sensors, touch screens, proximity sensors, microphones, tilt sensors, and gravity switches and output components such as a display device, a speaker, and or a printer port. Other I/O components  23  can be used as well. 
     Clock  25  illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor  17 . 
     Location system  27  illustratively includes a component that outputs a current geographical location of device  16 . This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. 
     Memory  21  stores operating system  29 , network settings  31 , applications  33 , application configuration settings  35 , data store  37 , communication drivers  39 , and communication configuration settings  41 . Memory  21  can include all types of tangible volatile and nonvolatile computer-readable memory devices. It can also include computer storage media (described below). Memory  21  stores computer readable instructions that, when executed by processor  17 , cause the processor to perform computer-implemented steps or functions according to the instructions. Processor  17  can be activated by other components to facilitate their functionality as well. 
     Examples of the network settings  31  include things such as proxy information, Internet connection information, and mappings. Application configuration settings  35  include settings that tailor the application for a specific enterprise or user. Communication configuration settings  41  provide parameters for communicating with other computers and include items such as GPRS parameters, SMS parameters, connection user names and passwords. 
     Applications  33  can be applications that have previously been stored on the device  16  or applications that are installed during use, although these can be part of operating system  29 , or hosted external to device  16 , as well. 
       FIG. 9  shows one embodiment in which device  16  is a tablet computer  600 . In  FIG. 9 , computer  600  is shown with user interface display  530  (From  FIG. 6 ) displayed on the display screen  602 . Screen  602  can be a touch screen (so touch gestures from a user&#39;s finger  604  can be used to interact with the application) or a pen-enabled interface that receives inputs from a pen or stylus. It can also use an on-screen virtual keyboard. Of course, it might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer  600  can also illustratively receive voice inputs as well. 
       FIGS. 10 and 11  provide additional examples of devices  16  that can be used, although others can be used as well. In  FIG. 10 , a feature phone, smart phone or mobile phone  45  is provided as the device  16 . Phone  45  includes a set of keypads  47  for dialing phone numbers, a display  49  capable of displaying images including application images, icons, web pages, photographs, and video, and control buttons  51  for selecting items shown on the display. The phone includes an antenna  53  for receiving cellular phone signals such as General Packet Radio Service (GPRS) and 1×rtt, and Short Message Service (SMS) signals. In some embodiments, phone  45  also includes a Secure Digital (SD) card slot  55  that accepts a SD card  57 . 
     The mobile device of  FIG. 11  is a personal digital assistant (PDA)  59  or a multimedia player or a tablet computing device, etc. (hereinafter referred to as PDA  59 ). PDA  59  includes an inductive screen  61  that senses the position of a stylus  63  (or other pointers, such as a user&#39;s finger) when the stylus is positioned over the screen. This allows the user to select, highlight, and move items on the screen as well as draw and write. PDA  59  also includes a number of user input keys or buttons (such as button  65 ) which allow the user to scroll through menu options or other display options which are displayed on display  61 , and allow the user to change applications or select user input functions, without contacting display  61 . Although not shown, PDA  59  can include an internal antenna and an infrared transmitter/receiver that allow for wireless communication with other computers as well as connection ports that allow for hardware connections to other computing devices. Such hardware connections are typically made through a cradle that connects to the other computer through a serial or USB port. As such, these connections are non-network connections. In one embodiment, mobile device  59  also includes a SD card slot  67  that accepts a SD card  69 . 
       FIG. 12  is similar to  FIG. 10  except that the phone is a smart phone  71 . Smart phone  71  has a touch sensitive display  73  that displays icons or tiles or other user input mechanisms  75 . Mechanisms  75  can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone  71  is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone.  FIG. 13  shows phone  71  with the display of  FIG. 6  displayed thereon. 
     Note that other forms of the devices  16  are possible. 
       FIG. 14  is one embodiment of a computing environment in which architecture  100 , or parts of it, (for example) can be deployed. With reference to  FIG. 14 , an exemplary system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processor  140 ,  155 ,  163  or  186 ), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. Memory and programs described with respect to  FIGS. 1 and 2  can be deployed in corresponding portions of  FIG. 14 . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 14  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 14  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  851  that reads from or writes to a removable, nonvolatile magnetic disk  852 , and an optical disk drive  855  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and magnetic disk drive  851  and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 14 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 10 , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A visual display  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a handheld device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG. 14  include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 14  illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     It should also be noted that the different embodiments described herein can be combined in different ways. That is, parts of one or more embodiments can be combined with parts of one or more other embodiments. All of this is contemplated herein. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.