Patent Publication Number: US-2018032972-A1

Title: Analyzing automotive inspections

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a continuation-in-part application of and claims priority to U.S. patent application Ser. No. 14/289,039 entitle “ANAYLYZING AUTOMOTIVE INSPECTIONS” filed on May 28, 2014 for Scott Osborn, which is incorporated herein by reference. 
    
    
     FIELD 
     The subject matter disclosed herein relates to automotive inspections and more particularly relates to analyzing automotive inspections. 
     BACKGROUND 
     Description of the Related Art 
     Automotive inspections are designed to discover service needs for an automobile. However, technician biases may result in some service needs not being discovered while other service needs are reported as required when there is no need for the service. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In order that the advantages of the embodiments of the invention will be readily understood, a more particular description of the embodiments briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only some embodiments and are not therefore to be considered to be limiting of scope, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: 
         FIG. 1  is a schematic block diagram illustrating one embodiment of an automotive inspection analysis system; 
         FIG. 2A  is a schematic block diagram illustrating one embodiment of an inspection results database; 
         FIG. 2B  is a schematic block diagram illustrating one embodiment of an inspection result; 
         FIG. 2C  is a schematic block diagram illustrating one embodiment of an inspection item recommendation; 
         FIG. 2D  is a schematic block diagram illustrating one embodiment of an inspection item database; 
         FIG. 2E  is a schematic block diagram illustrating one embodiment of an inspection item; 
         FIG. 2F  is a schematic block diagram illustrating one embodiment of an inspection vector; 
         FIG. 3A  is a schematic block diagram illustrating one embodiment of a computer; 
         FIG. 3B  is a schematic block diagram illustrating one embodiment of an analysis apparatus; 
         FIG. 4A  is a drawing illustrating one embodiment of inspection input; 
         FIG. 4B  is a drawing illustrating one embodiment of analysis selection; 
         FIG. 4C  is a drawing illustrating one embodiment of sales input; 
         FIG. 4D  is a drawing illustrating one embodiment of an inspection bias report; 
         FIG. 4E  is a text illustration showing one embodiment of an inspection bias report entry; 
         FIG. 5A  is a schematic flow chart diagram illustrating one embodiment of an automotive inspection analysis method; 
         FIG. 5B  is a schematic flow chart diagram illustrating one embodiment of an inspection bias identification method; 
         FIG. 5C  is a schematic flow chart diagram illustrating one embodiment of an assignment bias identification method; 
         FIG. 5D  is a schematic flow chart diagram illustrating one embodiment of a sales bias identification method; and 
         FIG. 5E  is a schematic flow chart diagram illustrating one embodiment of an inspection vector encoding method. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive and/or mutually inclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise. 
     Furthermore, the described features, advantages, and characteristics of the embodiments may be combined in any suitable manner. One skilled in the relevant art will recognize that the embodiments may be practiced without one or more of the specific features or advantages of a particular embodiment. In other instances, additional features and advantages may be recognized in certain embodiments that may not be present in all embodiments. 
     These features and advantages of the embodiments will become more fully apparent from the following description and appended claims, or may be learned by the practice of embodiments as set forth hereinafter. As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method, and/or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having program code embodied thereon. 
     Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. 
     Modules may also be implemented in software for execution by various types of processors. An identified module of computer readable program code may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. 
     Indeed, a module of program code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. Where a module or portions of a module are implemented in software, the computer readable program code may be stored and/or propagated on in one or more computer readable medium(s). 
     The computer readable medium may be a tangible, non-transitory computer readable storage medium storing the computer readable program code. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. 
     More specific examples of the computer readable storage medium may include but are not limited to a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, a holographic storage medium, a micromechanical storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, and/or store computer readable program code for use by and/or in connection with an instruction execution system, apparatus, or device. 
     Computer readable program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Python, Rudy, Java, Smalltalk, C++, PHP or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     The computer program product may be shared, simultaneously serving multiple customers in a flexible, automated fashion. The computer program product may be standardized, requiring little customization and scalable, providing capacity on demand in a pay-as-you-go model. 
     The computer program product may be stored on a shared file system accessible from one or more servers. The computer program product may be executed via transactions that contain data and server processing requests that use Central Processor Unit (CPU) units on the accessed server. CPU units may be units of time such as minutes, seconds, hours on the central processor of the server. Additionally the accessed server may make requests of other servers that require CPU units. CPU units are an example that represents but one measurement of use. Other measurements of use include but are not limited to network bandwidth, memory usage, storage usage, packet transfers, complete transactions etc. 
     When multiple customers use the same computer program product via shared execution, transactions are differentiated by the parameters included in the transactions that identify the unique customer and the type of service for that customer. All of the CPU units and other measurements of use that are used for the services for each customer are recorded. When the number of transactions to any one server reaches a number that begins to affect the performance of that server, other servers are accessed to increase the capacity and to share the workload. Likewise when other measurements of use such as network bandwidth, memory usage, storage usage, etc. approach a capacity so as to affect performance, additional network bandwidth, memory usage, storage etc. are added to share the workload. 
     The measurements of use used for each service and customer are sent to a collecting server that sums the measurements of use for each customer for each service that was processed anywhere in the network of servers that provide the shared execution of the computer program product. The summed measurements of use units are periodically multiplied by unit costs and the resulting total computer program product service costs are alternatively sent to the customer and or indicated on a web site accessed by the customer which then remits payment to the service provider. 
     In one embodiment, the service provider requests payment directly from a customer account at a banking or financial institution. In another embodiment, if the service provider is also a customer of the customer that uses the computer program product, the payment owed to the service provider is reconciled to the payment owed by the service provider to minimize the transfer of payments. 
     The computer program product may be integrated into a client, server and network environment by providing for the computer program product to coexist with applications, operating systems and network operating systems software and then installing the computer program product on the clients and servers in the environment where the computer program product will function. 
     In one embodiment software is identified on the clients and servers including the network operating system where the computer program product will be deployed that are required by the computer program product or that work in conjunction with the computer program product. This includes the network operating system that is software that enhances a basic operating system by adding networking features. 
     In one embodiment, software applications and version numbers are identified and compared to the list of software applications and version numbers that have been tested to work with the computer program product. Those software applications that are missing or that do not match the correct version will be upgraded with the correct version numbers. Program instructions that pass parameters from the computer program product to the software applications will be checked to ensure the parameter lists match the parameter lists required by the computer program product. Conversely parameters passed by the software applications to the computer program product will be checked to ensure the parameters match the parameters required by the computer program product. The client and server operating systems including the network operating systems will be identified and compared to the list of operating systems, version numbers and network software that have been tested to work with the computer program product. Those operating systems, version numbers and network software that do not match the list of tested operating systems and version numbers will be upgraded on the clients and servers to the required level. 
     In response to determining that the software where the computer program product is to be deployed, is at the correct version level that has been tested to work with the computer program product, the integration is completed by installing the computer program product on the clients and servers. 
     Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. 
     Aspects of the embodiments are described below with reference to schematic flowchart diagrams and/or schematic block diagrams of methods, apparatuses, systems, and computer program products according to embodiments of the invention. It will be understood that each block of the schematic flowchart diagrams and/or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and/or schematic block diagrams, can be implemented by computer readable program code. The computer readable program code may be provided to a processor of a general purpose computer, special purpose computer, sequencer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The computer readable program code may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the schematic flowchart diagrams and/or schematic block diagrams block or blocks. 
     The computer readable program code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the program code which executed on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The schematic flowchart diagrams and/or schematic block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the schematic flowchart diagrams and/or schematic block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions of the program code for implementing the specified logical function(s). 
     It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated Figures. 
     Although various arrow types and line types may be employed in the flowchart and/or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and/or flowchart diagrams, and combinations of blocks in the block diagrams and/or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer readable program code. 
     The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements. 
       FIG. 1  is a schematic block diagram illustrating one embodiment of an automotive inspection analysis system  100 . The system  100  includes an analysis apparatus  105 , a network  110 , an inspection computer  115 , and a customer management system  120 . The analysis apparatus  105  may be embodied in a computer such as a server, server farm, a main frame computer, and the like. 
     The network  110  may be the Internet, a local area network, a wide-area network, a local area network, a mobile telephone network, a Wi-Fi network, and the like. The inspection computer  115  may be a portable computer such as a tablet computer and/or a laptop computer. Alternatively, the inspection computer  115  may be a mobile telephone, a computer workstation, a wearable computer, and the like. 
     The customer management system  120  may be embodied in a computer, a server, server farm, a mainframe computer, and the like. The customer management system  120  may store auto information such as a customer name, a customer address, a license plate number, a vehicle identification number, an auto year, an auto make, an auto model, an auto service record, reporting data, and the like. The reporting data may indicate a destination for inspection results such as a state motor vehicle authority. In one embodiment, the analysis apparatus  105  is also embodied in the server, server farm, and/or mainframe computer. 
     A technician may employ the inspection computer  115  while inspecting an automobile. When inspecting the automobile, the technician may retrieve customer information from the customer management system  120 . In one embodiment, the technician retrieves the auto information from the inspection computer  115  through the network  110 . Alternatively, the technician retrieves the auto information directly from the customer management system  120 . 
     The technician may further inspect the automobile and record the results of the inspection as inspection results as will be described hereafter. In one embodiment, the technician records the inspection results directly to the inspection computer  115  and the inspection results are communicated to the analysis apparatus  105 . In an alternative embodiment, the technician records the inspection results on a paper copy and enters the inspection results at the inspection computer  115 . 
     The technician may be prone to under identify some service needs. For example, if the technician is inexperienced, he may regularly overlook one type of service need. In addition, the technician may not identify service needs that he does not like to correct and/or is uncertain how to correct. As a result, service needs may go unidentified and unaddressed. Alternatively, a technician may be prone to identify service needs where there is none. For example, if the technician enjoys performing a service function and/or can complete the service function quickly, the technician may be prone to identify such a service need when there is no actual need. As a result, a customer may pay for unneeded service functions, resulting in ill will towards the technician and his employer. 
     The embodiments described herein analyze automotive inspections and identify a inspection bias as will be described hereafter. The inspection bias can be used to correct technician behavior, to identify training needs, identify misbehavior, and to generally improve the effectiveness of the automotive inspections. 
     A manager may distribute service tasks among one or more technicians. The manager may distribute the service tasks based on personal relationships rather than the skills of the technicians. As a result, some technicians may regularly perform service tasks for which they are unqualified while the talents of other technicians are underutilized. The embodiments described herein also identify assignment bias in assigning service tasks to technicians as will be described hereafter. As a result, the manager may be trained to better utilize the skills of the technicians. 
     The technician and/or manager may recommend one or more service tasks to the customer after making the inspection. The technician and/or manager may be inclined to oversell and/or undersell some service tasks because of personal preferences, personal opinions, or the like. The embodiments described herein also identify sales bias so that the manager and/or technician may be trained to emphasize the service tasks that are of most use to the customer. 
       FIG. 2A  is a schematic block diagram illustrating one embodiment of an inspection results database  200 . The inspection results database  200  may be stored in the analysis apparatus  105 . The inspection results database  200  maybe organized as one or more tables, one or more data structures, one or more flat files, or combinations thereof. The inspection results database  200  includes a plurality of inspection results  205 . Each inspection result  205  may be generated from the inspection of an automobile. In one embodiment, each inspection instance for a specified automobile generates a new inspection result  205 . 
       FIG. 2B  is a schematic block diagram illustrating one embodiment of an inspection result  205  of the inspection results database  200  of  FIG. 2 . The inspection result  205  may be organized as one or more tables, one or more data structures, one or more flat files, or combinations thereof. In the depicted embodiment, each inspection result  205  includes an inspection identifier  230 , a region  220 , an auto make  202 , an auto model  204 , a license number  206 , a service location  208 , an auto year  210 , an auto mileage  212   a  technician identifier  214 , one or more inspection item recommendations  216 , a manager identifier  218 , a customer identifier  222 , audio/visual attachments  226 , a completion time  228 , and recent service  229 . 
     The inspection identifier  230  may specify a one or more inspections that were performed on the automobile. Each inspection may be associated with one or more inspection items as will be described hereafter. For example, the inspection identifier  230  may specify a multi-point inspection, a comprehensive inspection, a diagnostic flowsheet inspection, and air-conditioning inspection, a break inspection, a battery inspection, a shop inspection, or the like. 
     The region  220  may describe a geographic region. Alternatively, the region  220  describes a climactic region. The auto make  202  may describe the make of the automobile being inspected. The auto model  204  may describe the model of the automobile. The license number  206  may be the license number of the automobile. In addition, the license number  206  may include a vehicle identification number (VIN) or the like. The service location  208  may identify the facility where the inspection is performed, or the facility where the technician is based. The service location  208  may also identify an operator of the service location. 
     The auto year  210  may be the model year of the automobile. The auto make  202 , auto model  204 , license number  206 , and auto year  210  may be retrieved from the customer management system  120 . The auto mileage  212  may be recorded by the technician during the inspection. 
     The technician identifier  214  may uniquely identify the technician. The technician identifier  214  may be an employee number, a biometric, or combinations thereof. The technician identifier  214  may include the technician&#39;s name, an image of the technician, contact information for the technician, or combinations thereof. 
     Each inspection item recommendation  216  is linked to a corresponding an inspection item  232  for an inspection  230  as will be described hereafter. For example, an inspection item  232  may be “inspect brake pad wear.” The inspection item recommendation  216  is described in greater detail in  FIG. 2C . 
     The manager identifier  218  may identify the manager supervising the technician that is inspecting the automobile. The customer identifier  222  may uniquely identify the customer of the automobile inspection. The customer identifier  222  may be a customer name and contact information. In one embodiment, the customer identifier  222  references the customer information from the customer management system  120 . 
     The audio/visual attachments  226  may include image files, audio files, and/or video files recorded during the inspection and/or related to the inspection. For example, the technician may record images, audio commentary, and/or video commentary showing elements of the inspection. 
     The completion time  228  may record the time interval required for the technician to complete the inspection of the automobile. In one embodiment, the completion time  228  includes a start time and an end time. The recent service  229  may record service of the automobile has recently received. For example, the recent service  229  may record the changing of wiper blades along with the date of the service. 
       FIG. 2C  is a schematic block diagram illustrating one embodiment of an inspection item recommendation  216 . The inspection item recommendation  216  may be organized as a table entry, a data structure, a flat file, or combinations thereof. The inspection item recommendation  216  is the inspection item recommendation  216  of  FIG. 2B . In the depicted embodiment, the inspection item recommendation  216  includes an inspection item  232 , a recommendation  254 , a recommendation sale  224 , a sales personnel  256 , and a service technician. 
     The inspection item  232  identifies inspection item from an inspection item database. The inspection item  232  may provide parameters, instructions, and the like for the inspection item recommendation  216 . The recommendation  254  may comprise one of a no service required recommendation and a service recommendation. The no service required recommendation indicates that no service is needed now. The service recommendation indicates that service is needed now and/or soon. In one embodiment, the recommendation  254  includes a warning recommendation. The warning recommendation may indicate that service is needed in the near future. For example, the warning recommendation may indicate that service will likely be needed in the next 2 months. 
     For example, if the technician determines that there is no service need with regards to the brake pad wear, the technician records a no service required recommendation for the recommendation  254 . However if the technician determines that there is a service need, the technician records a service recommendation for the inspection in the recommendation  254 . 
     In one embodiment, the service recommendation and/or warning recommendation may specify a service task. For example, the service recommendation may include the service task “replace brake pads.” 
     The recommendation sale  224  indicates if each service recommendation was sold and performed. In one embodiment, the recommendation sale  224  includes a binary value indicating whether or not the service recommendation was sold and performed. Alternatively, the recommendation sale  224  comprises a price for services performed in response to the service recommendation. 
     The sales personnel  256  may record the person selling the service recommendation to the customer. The sales personnel  256  may be the technician that performed the inspection, another technician, and/or a manager. The service technician  260  records the technician performing the service. 
       FIG. 2D  is a schematic block diagram illustrating one embodiment of an inspection item database  230 . The inspection item database  230  may reside in the analysis apparatus  105 . The inspection item database  230  may be organized as one or more tables, one or more data structures, one or more flat files, or combinations thereof. The inspection item database  230  includes a plurality of inspection items  232 . The inspection items  232  may be entered by an administrator. 
       FIG. 2E  is a schematic block diagram illustrating one embodiment of the inspection item  232  of the inspection item database  230  of  FIG. 2D . Each inspection item  232  may include an inspection task  234 , a target recommendation  236 . In addition, each inspection item  232  may include a locale  238 , a target assignment  240 , instructions  258 , a category  410 , a region modifier  270 , a make modifier  272 , a model modifier  274 , a year modifier  276 , a mileage modifier  278 , a recent service modifier  280 , a sales target  282 , and a technician assignment  284 . 
     The inspection task  234  may identify the inspection action to be performed. In a certain embodiment, the inspection task  234  further specifies an inspection action for an auto year  210  and/or auto mileage  212 . For example, the auto year  210  may specify that the inspection item  232  is to be performed for automobiles with an auto year  210  of  2013 . Thus the inspection item  232  may be specific to the auto year  210  and/or to the auto mileage  212 . In an alternate embodiment, the inspection task  234  may specify an auto make  202 , an auto model  204 , a region  220 , a service location  208 , a technician, an operator, and the like. The instructions  258  may describe the procedure for performing the inspection. In addition, the instructions  258  may include sales instructions. 
     The target recommendation  236  may specify a percentage of automobiles that are statistically likely to have a service need for the inspection item  232 . In one embodiment, the target recommendation  236  specifies a percentage of automobiles that are likely to have the service need based on the auto make  202 , the auto model  204 , the region  220 , the auto year  210 , the auto mileage  212  and/or recent service  229  that the automobile has received. Alternatively, the target recommendation  236  may be modified based on the auto make  202 , the auto model  204 , the region  220 , the auto year  210 , the auto mileage  212 , and/or recent service  229  using the region modifier  270 , make modifier  272 , model modifier  274 , year modifier  276 , mileage modifier  278 , and recent service modifier  280 . In one embodiment, the target recommendation includes a target recommendation upper bound and a target recommendation lower bound. 
     A function of the inspection item recommendations  216  for a first inspection item  232  that exceed the target recommendation  236  by either being recommended more frequently or less frequently than the target recommendation  236 , or the target recommendation upper bound and target recommendation lower bound may identify a inspection bias as will be described hereafter. In one embodiment, the target recommendation  236  includes a guard band. The guard band may specify an acceptable percentage for the function of the inspection item recommendations  216  above the target recommendation  236  or the target recommendation upper bound and an acceptable percentage for the function of the inspection item recommendations  216  below the target recommendation  236  of the target recommendation lower bound. The guard band may be adjusted for each technician, manager, service location, and/or region based on a number of similar inspections performed by the technician, manager, service location, and/or region. For example, if the technician performs a small number of similar inspections, the guard band may be large. However, if the technician performs a large number of similar inspections, the guard band may be small. 
     For example, the target recommendation  236  may be 10 percent. The guard band may further specify that an additional 5 percent above the target recommendation  236  and/or an additional 3 percent below the target recommendation  236 . The function of the inspection item recommendations  216  that exceed the guard band of the target recommendation  236  may identify the inspection bias. 
     The locale  238  may indicate where the inspection item  232  is to be used. For example, the locale  238  may indicate one or more states, one or more cities, one or more shop locations, and the like. The locale  238  may distinguish inspection items that only apply in selected jurisdictions. 
     In one embodiment, the target assignment  240  indicates a target for assignments of service recommendations to technicians. The target assignment  240  may specify required levels of training and experience for a technician to be assigned to service task resulting from a service recommendation for the inspection item  232  In one embodiment, the target assignment  240  may indicate that each technician with the required levels of training and experience within a service location be equally likely to be assigned a service task. In an alternative embodiment, the target assignment  240  may specify that technicians with more experience be more likely to be assigned to the service task. 
     Alternatively, the target assignment  240  may be set by the administrator. The target assignment  240  may be used to determine assignment bias as will be described hereafter. The category  410  may assign the inspection item  232  to a specified category of related inspection items  232 . 
     The sales target  282  may be a percentage of recommendations  254  that are service recommendations that typically should be converted into recommendation sales  224 . In one embodiment, the sales target  282  specifies a percentage of service recommendations that typically should be converted into recommendation sales  224  based on the auto make  202 , the auto model  204 , the region  220 , the auto year  210 , the auto mileage  212  and/or recent service  229  that the automobile has received. 
     The region modifier  270 , make modifier  272 , model modifier  274 , year modifier  276 , mileage modifier  278 , and recent service modifier  280  may store values that are used to modify the target recommendation  236  and/or target sales  282  in response to the region  220 , the auto make  202 , the auto model  204 , the auto year  210 , the auto mileage  212 , and recent service  229  respectively. The region modifier  270 , make modifier  272 , model modifier  274 , year modifier  276 , mileage modifier  278 , and recent service modifier  280  may be set by the administrator or calculated from inspection data. For example, if the target recommendation  236  does not specify a percentage of automobiles that are statistically likely to have a service need for the inspection item  232  based on the auto make  202 , the auto model  204 , the region  220 , the auto year  210 , the auto mileage  212  and/or recent service of the automobile being inspected, the region modifier  270 , make modifier  272 , model modifier  274 , year modifier  276 , mileage modifier  278 , and recent service modifier  280  may be used to modify the target recommendation  236  to more closely reflect the service needs of the automobile being inspected. 
     The technician assignment  284  may indicate the technician that was assigned to perform the service task in response to the service recommendation. The technician assignment  284  may be different from the technician that performed the inspection recorded by the technician identifier  214 . 
       FIG. 2F  is a schematic block diagram illustrating one embodiment of an inspection vector  215 . The inspection vector  215  may be organized as a data structure in a memory. A unique inspection vector  215  may be encoded for each inspection result  205  in the inspection results database  200 . In the depicted embodiment, the inspection vector  215  includes the inspection identifier  230 , an encoded region  320 , an encoded auto make  322 , an encoded auto model  324 , an encoded auto year  326 , an encoded auto mileage  328 , one or more encoded inspection item recommendations  330 , and an encoded recent service  332 . 
     In one embodiment, the encoded region  320 , encoded auto make  322 , encoded auto model  324 , encoded auto year  326 , encoded auto mileage  328 , one or more encoded inspection item recommendations  330 , and encoded recent service  332  encode the region  220 , auto make  202 , auto model  204 , auto year  210 , auto mileage  212 , inspection item recommendations  216 , and recent service  229  respectively of an inspection results  205 . One or more of the encoded region  320 , encoded auto make  322 , encoded auto model  324 , encoded auto year  326 , encoded auto mileage  328 , one or more encoded inspection item recommendations  330 , and encoded recent service  332  selected as inspection vector elements  334 . The inspection vector elements  334  may be encoded as an integer value. Alternatively, inspection vector elements  334  may be encoded as a real number value. In a certain embodiment, inspection vector elements  334  are each encoded as one or more bits in a bitmap. A bitmap may be encoded as a one hot bitmap, wherein only one bit of each bitmap is asserted. 
     In one embodiment, the inspection vector elements  334  comprise both an original unencoded value that may be an integer value or a real number value and an encoded one hot bitmap. The one hot bitmap portion of each inspector vector element  334  may be used to sort and identify original values that are relevant to the current automotive inspection or automotive inspections that are being evaluated for inspection bias, assignment bias, and/or sales bias. 
       FIG. 3A  is a schematic block diagram illustrating one embodiment of a computer  300 . The computer  300  may be representative of the inspection computer  115 . In addition, the analysis apparatus  105  and/or the customer management system  120  may be embodied in one or more computers  300 . The computer  300  includes a processor  305 , a memory  310 , and communication hardware  315 . The memory  310  may comprise a semiconductor storage device, hard disk drive, an optical storage device, a micromechanical storage device, or combinations thereof. The memory  310  may store program code. The processor  305  may execute the program code. The communication hardware  315  may communicate with other devices. 
       FIG. 3B  is a schematic block diagram illustrating one embodiment of an analysis apparatus  105 . The apparatus  105  may be embodied in the computer  300 . In a certain embodiment, the apparatus  105  is embodied in the inspection computer  115 , the customer management system  120 , or combinations thereof. The apparatus  105  includes a recording module  355  and an identification module  360 . The recording module  355  and the identification module  360  may be embodied in a computer readable storage medium such as the memory  310 . The computer readable storage media may store program code that is executed by the processor  305  to perform the functions of the recording module  355  and the identification module  360 . 
     In one embodiment, the processor  305  records a plurality of inspection results  205 . The processor  305  may identify a inspection bias in response to a function of first inspection item recommendations  216  for a first inspection item  232  of the plurality of inspection results  205  exceeding a first target recommendation  236  for the first inspection item  232  as will be described hereafter. 
       FIG. 4A  is a drawing illustrating one embodiment of inspection input  405 . In the depicted embodiment, the inspection input  405  is received on a tablet computer inspection computer  115 . The technician may input information to the inspection computer  115 . In addition, information may be retrieved from the customer management system  120 . 
     In the depicted embodiment, the inspection input  405  includes the customer identifier  222 , the manager identifier  218 , the technician identifier  214 , the auto make  202 , the auto model  204 , the license number  206 , the auto year  210 , the auto mileage  212 , and the service location  208 . In addition, the inspection input  405  may include one or more categories  410 . In the depicted embodiment, miscellaneous, under hood, tires and brakes, under car, steering, front suspension, and rear suspension categories  410  are shown. The technician may select a category  410  to display inspection items  232  associated with the category  410 . In the depicted embodiment, the miscellaneous category  410  is selected. 
     During an inspection, the technician may perform the inspection for each inspection item  232  and select one of a no service required recommendation  415 , a warning recommendation  420 , or a service recommendation  425  that will be recorded as the recommendation  254 . The warning recommendation  420  may not be available for all inspection items  232 . 
       FIG. 4B  is a drawing illustrating one embodiment of analysis selection  450 . The analysis selection  450  may be an interface on a computer  300  that is used to analyze the results of inspections for inspection bias, assignment bias, and/or sales bias. In the depicted embodiment, the user is presented with a region list  430 , a service location list  436 , and a technician list  440 . The user may employ selection controls  432  to choose selected regions  434 , selected service locations  438 , and/or selected technicians  442 . The inspection results will be analyzed for the selected regions  434 , selected service locations  438 , and/or selected technicians  442 . 
     In addition, the user may select one or more inspection identifiers  230  from an inspection list  456 . In one embodiment, only inspection results for the selected inspection identifiers  230  may be analyzed. In addition, the user may select a mileage range  448 , a year range  452 , and/or a make  454 . The mileage range  448 , year range  452 , and make  454  may be used to select specified auto years  210 , auto mileages  212 , and auto makes  202  for analysis. 
     In one embodiment, the user selects a target recommendation upper bound  444 . In addition, the user may select a target recommendation lower bound  446 . The target recommendation upper bound  444  and target recommendation lower bound  446  may be used to identify inspection bias as will be described hereafter. 
       FIG. 4C  is a drawing illustrating one embodiment of sales input  480 . The sales input  480  may be an interface on the computer  300  and/or inspection computer  115 . A user such as the technician and/or manager may use the sales input  480  to indicate whether service recommendations  415  were purchased by the customer. In the depicted embodiment, the sales input  480  lists the category  410 , the inspection task  234 , a finding  468 , a recommended action  470 , and a price  472  for each sales recommendation. The user may further indicate if a recommendation sale  224  occurred, such as by checking a box. 
       FIG. 4D  is a drawing illustrating one embodiment of an inspection bias report  485 . The inspection bias report  485  may be generated by the analysis apparatus  105  to show inspection bias. In the depicted embodiment, the report  485  includes sample information  458  for one or more technicians. The sample information  458  includes a number of inspections per period, an average auto year for the automobiles inspected, an average auto mileage for the automobiles inspected, and an average number of service recommendations  415  by the technician. 
     Inspection bias report  485  may further include a plurality of inspection items  232  with inspection bias report entries  460  for each inspection item  232  as will be described hereafter in  FIG. 4E . 
       FIG. 4E  is a text illustration showing one embodiment of an inspection bias report entry  460 . In the depicted embodiment, the inspection bias report entry  460  includes a number of service recommendations  462  by a technician, a service recommendation percentage  464  for the technician, and a bias indicator  466 . 
     The number of service recommendations  462  may indicate a number of times the technician made a service recommendation  415  for the inspection item  432  within the sample of inspections. The service recommendation percentage  464  is a percentage of service recommendations  415  for the inspection item  432  within the sample of inspections. 
     The bias indicator  466  may indicate that the service recommendation percentage  464  exceeds either the target recommendation upper bound  444  and/or the target recommendation lower bound  446 . In one embodiment, the bias indicator  466  indicates that the service recommendation percentage  464  exceeds the target recommendation upper bound  444  plus a guard band or the target recommendation lower bound  446  plus the guard band. In the depicted embodiment, the bias indicator  466  is an arrow that may point down if the service recommendation percentage  464  exceeds the target recommendation lower bound  446  and point up if the service recommendation percentage  464  exceeds the target recommendation upper bound  444 . 
     Alternatively, the bias indicator  466  may be a color. For example, the bias indicator  466  may be a green color if the service recommendation percentage  464  exceeds the target recommendation upper bound  444  and a red color if the service recommendation percentage  464  exceeds the target recommendation lower bound  446 . 
       FIG. 5A  is a schematic flow chart diagram illustrating one embodiment of an automotive inspection analysis method  500 . The method  500  may identify inspection bias. In addition, the method  500  may identify assignment bias and/or sales bias. The method  500  may be performed by a processor  305 . In one embodiment, the method  500  is performed by program code stored on a computer readable storage medium such as the memory  310  and executed by a processor  305  to perform the functions of the method  500 . 
     The method  500  starts, and in one embodiment, the processor  305  retrieves  502  the auto information from the customer management system  120 . For example, technician may enter the license number at the inspection computer  115  and retrieve  500  to the auto information. 
     The processor  305  may further record  504  one or more inspection results  205  from an auto inspection. In one embodiment, a technician records  504  the inspection result  205  directly to the inspection input  405  on the inspection computer  115  and the inspection computer  115  communicates the inspection result  205  to the analysis apparatus  105 . Alternatively, the technician may copy the inspection results from a paper copy to the inspection computer  115  and the inspection computer  115  communicates the inspection result  205  to the analysis apparatus  105 . 
     The processor  305  may further record  505  sales input  480 . The sales input  480  may be entered to a computer  300  such as the inspection computer  115 . Alternatively, the sales input  480  may be transferred to the computer  300 . 
     The processor  305  may calculate  506  a first target recommendation  236  for a first inspection item recommendation  216  of a first inspection result  205  from inspection vectors  215  encoded from the plurality of prior inspection results  205 . The first inspection item recommendation  216  may be a current inspection item recommendation  216  for a current auto inspection. The first target recommendation  236  may be calculated  506  as a function of one or more of the inspection vector elements  334 . The inspection vector elements  334  may be encoded from the region  220 , the auto year  210 , the auto mileage  212 , and the recent service  229 . In one embodiment, the first target recommendation is calculated from the inspection vectors  215  as a function of one or more inspection vector elements  334  and the recent service modifier  280 . The calculation  506  of the target recommendation  236  is described in more detail in  FIG. 5B . 
     The processor  305  may identify  507  an inspection bias. The processor  305  may identify  507  the inspection bias in response to the first inspection item recommendation  216  exceeding the first target recommendation  236 . Alternatively, the processor  305  may identify  507  the inspection bias in response to a function of first inspection item recommendation  216  for a first inspection item  232  of the plurality of inspection results  205  exceeding the first target recommendation  236  for the first inspection item  232 . In one embodiment, the processor  305  may identify  507  the inspection bias in response to a function of a plurality of inspection item recommendations  216  of the plurality of inspection results  205  exceeding the first target recommendation  236 . 
     In one embodiment, the inspection bias indicates one of a technician misbehavior and a technician training need for a technician performing an automotive inspection. 
     The function of the inspection item recommendations  216  may be an average of inspection item recommendations  216 . In one embodiment, the function of the inspection item recommendations  216  is selected from the group consisting of an arithmetic mean, a geometric mean, a harmonic mean, a quadratic mean, a generalized mean, a weighted mean, a truncated mean, an interquartile mean, a midrange, a Winsorized mean, a mode, and a median of the inspection item recommendations  216 . For example, the function of the inspection item recommendations  216  may be the median of all inspection item recommendations  216  for an inspection item  232 . 
     The function of the inspection item recommendations  216  may be calculated for one or more technicians, one or more service locations, one or more regions, and/or one or more operators. The inspection bias may be identified  507  for a set selected from the group consisting of technicians, service locations, regions, and operators. For example, the function of the inspection item recommendations  216  may be calculated as an arithmetic mean of the service recommendations of each inspection item recommendation  216  for a specified inspection item  232  in a region  220 . 
     In one embodiment, the inspection bias is identified  507  if the function of the inspection item recommendations  216  exceeds at least one of the target recommendation upper bound  444  and the target recommendation lower bound  446 . For example, if the target recommendation upper bound  444  is 40 percent and the mean of the inspection item recommendations  216  is 44 percent, the inspection bias is identified  507 . 
     The processor  305  may further identify  508  assignment bias. The analysis apparatus  105  may identify  508  the assignment bias if a function of technician assignments  284  for the first inspection item  232  of the plurality of inspection results  205  exceeds a first target assignment  240  for the first inspection item  232 . Identifying  508  the assignment bias is described in more detail in  FIG. 5C . 
     The processor  305  may identify  510  a sales bias. In one embodiment, the sales bias is identified  510  in response to a ratio of the recommendation sales  224  to the service recommendations  415  being less than the sales target  282 . Identifying  510  the sales bias is described in more detail in  FIG. 5D . 
     The analysis apparatus  105  may generate  512  a report and the method  500  ends. The report may include identified inspection biases, identified assignment biases, identified sales biases, and the comparison of service recommendations and recommendation sales  224 . In one embodiment, the report includes the inspection bias report  485 . The report may be used to correct inspection and assignment practices, as well as improve the sale of services. 
       FIG. 5B  is a schematic flow chart diagram illustrating one embodiment of an inspection bias identification method  550 . The method  550  may be performed by a processor  305 . In one embodiment, the method  550  is performed by program code stored on a computer readable storage medium such as the memory  310  and executed by a processor  305  to perform the functions of the method  550 . 
     The method  550  starts, and in one embodiment, the processor  305  determines  552  target recommendations  236 . The processor  305  may determine  552  the target recommendations  236  for each inspection item  232 . In one embodiment, an administrator enters original target recommendations  236  using the analysis selection  450 . For example, the administrator may set a target recommendation upper bound  444  and a target recommendation lower bound  446 . The original target recommendations  236 , target recommendation upper bound  444 , and target recommendation lower bound  446  may further be modified as will be described hereafter. 
     In one embodiment, the processor  305  determines  552  the target recommendations  236  from stored data. For example, the target recommendations  236  may be calculated from all past recommendations  254  for each inspection item  232 . In one embodiment, the target recommendations  236  are calculated based on the region  220 , auto make  202 , auto model  204 , auto year  210 , auto mileage  212 , and recent service  229 . For example, the target recommendation  236  for automatic transmission fluid may be a function of the auto mileage  212  and recent service  229 . 
     In one embodiment, the processor  305  determines  552  the target recommendations  236  from the inspection vectors  215 . The processor  305  may employ the one hot bitmaps of the inspection vector elements  334  to identify similar inspection results  205 . The processor  305  may further calculate the target recommendations  236  from the unencoded values for the inspection vector elements  334 . By using the one hot bitmaps to identify the similar inspection results  205 , the calculation of the target recommendations  236  are greatly accelerated. In one embodiment, the target recommendations  236  are calculated in real time based on the similar inspection results  205 . As a result, a technician may be given immediate feedback as to whether an inspection item recommendation  216  exhibits inspection bias. 
     In one embodiment, the target recommendation  236  TR is calculated using Equation 1, where B is a base target recommendation that is entered by the administrator, K and E are non-zero constants, J and F are non-zero constants, AY is years since the auto year  210  and AM is the auto mileage  212 . 
       TR= B +( K *AŶ J )+( E *AM̂ F )  Equation 1
 
     In an alternative embodiment, the target recommendation  236  is calculated as a function of the auto year  210  and the auto mileage  212 . For example, the target recommendation  236  TR may be calculated using Equation 2. 
       TR=( K *AŶ J )+( E *AM̂ F )  Equation 2
 
     In one embodiment, the target recommendation  236  TR is calculated using Equation 3, where C and D are non-zero constants and MS is months since recent service  229 . 
       TR=( K *AŶ J )+( E *AM̂ F )+( C *MŜ D )  Equation 3
 
     In one embodiment, the target recommendation  236  TR is calculated using Equation 4, where G is a non-zero constant and MS is an earliest service date for the recent service modifier  280 . 
       TR=( K *AŶ J )+( E *AM̂ F )+( C *(min(MS,ES)̂ D   Equation 4
 
     The inspection vector elements  334  and corresponding values from the inspection results  205  may be for similar inspection results  205  determined from the one hot bitmaps. In one embodiment, the target recommendation  236  is a function of the inspection item recommendations  216  for one or more technicians, one or more service locations, and/or one or more regions. The function of the inspection item recommendations  216  may be selected from the group consisting of an arithmetic mean, a geometric mean, a harmonic mean, a quadratic mean, a generalized mean, a weighted mean, a truncated mean, an interquartile mean, a midrange, a Winsorized mean, a mode, and a median. For example, the function of the inspection item recommendations  216  may be the median of all inspection item recommendations  216  for an inspection item  232 . 
     Alternatively, the target recommendation  236  for inspection item  232  may be calculated as an arithmetic mean of all inspection item recommendations  216  for the inspection item  232  for all technicians in a specified region. In one embodiment, the target recommendation  236  may be calculated as a midrange of the inspection item recommendations  216  for a specified region  220 . 
     In one embodiment, the target recommendations  236  are based on the recent service  229  and/or the recent service modifier  280 . For example, the target recommendation  236  for wiper blades may be a function of a changed wiper blades recent service  229  and the recent service modifier  280 . The recent service modifier  280  may indicate that wiper blades should be changed as early as 6 months and no later than 12 months after the wiper blades were last changed during recent service  229 . 
     The processor  305  may further determine  554  the target recommendation lower bound  446  and determine  556  the target recommendation upper bound  444 . In one embodiment, both the target recommendation lower bound  446  and the target recommendation upper bound  440  for are input by the administrator. Alternatively, the target recommendation lower bound  446  and the target recommendation upper bound  444  may be calculated from the target recommendation  236 . 
     In one embodiment, a Gaussian distribution is calculated for the target recommendations  236 . The target recommendation upper bound  444  and the target recommendation lower bound  446  may each be set at a specified number of standard deviations from the mean of the Gaussian distribution. The specified number of standard deviations may be set by the administrator. 
     In an alternative embodiment, the target recommendation upper bound  444  and the target recommendation lower bound  446  may be determined by the manufacture of the automobile. In addition, the target recommendation upper bound  444  and the target recommendation lower bound  446  may be modified using past recommendations  254  for each inspection item  232 . 
     In one embodiment, the target recommendation upper bound  444  and the target recommendation lower bound  446  include the guard band. In one embodiment, the guard band GB is calculated using Equation 5, where NA is a number of automobiles inspected such as by a technician at a service location, and L is a nonzero constant. The target recommendation upper bound  444  may be increased by the guard band and the target recommendation lower bound  446  may be decreased by the guard band. 
       GB=( L /√NA)  Equation 5
 
     The processor  305  may further adjust  558  the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  in response to the auto make  202  and/or the auto model  204 . For example, the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  for an auto make  202  and/or an auto model  204  that typically require service more frequently or less frequently than the manufacturer&#39;s recommendations may be adjusted to reflect observed service needs. 
     The processor  305  may also adjust  560  the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  in response to the auto mileage  212 . For example, the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  may be increased in response to high auto mileage  212  and decreased in response to low auto mileage  212 . The adjustment  560  of the target recommendation  236  may be based on similar inspection results  205  as determined by the one hot bitmaps of the inspection vectors  215 . 
     In one embodiment, the processor  305  adjusts  562  the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  in response to the auto year  210 . For example, the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  may be increased in response to an early model year  210  and decreased in response to a late model year  210 . The adjustment  562  of the target recommendation  236  may be based on similar inspection results  205  as determined by the one hot bitmaps of the inspection vectors  215 . 
     The processor  305  may adjust  564  the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  in response to recent service  229 . For example, the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  may be increased in response to earlier recent service  229  and decreased in response to later recent service  229 . The adjustment  564  of the target recommendation  236  may be based on similar inspection results  205  is determined by the one hot bitmaps of the inspection vectors  215 . 
     The processor  305  may adjust  566  the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  in response to the region  220  and the method  550  ends. For example, the target recommendation  236 , the target recommendation lower bound  446 , and/or the target recommendation upper bound  444  may be increased in response to a region modifier  270  indicating mild weather and decreased in response to the region modifier  270  indicating severe weather. 
       FIG. 5C  is a schematic flow chart diagram illustrating one embodiment of an assignment bias identification method  600 . In addition, the method  600  may identify assignment bias and/or sales bias. The method  600  may be performed by a processor  305 . In one embodiment, the method  600  is performed by program code stored on a computer readable storage medium such as the memory  310  and executed by a processor  305  to perform the functions of the method  600 . 
     The method  600  starts, and in one embodiment, the processor  305  determines  602  a target assignment distribution from the target assignment  240  of an inspection item  232 . The target assignment distribution may be assigned by the administrator. Alternatively, the target assignment distribution may be calculated from the target assignment  240  based on the experience and training of each technician at a service location. In one embodiment, each technician with the necessary training and experience may be assigned an equal percentage of the target assignment distribution. 
     In one embodiment, the target assignment distribution includes an assignment guard band. The assignment guard band may be a specified real number of standard deviations from the target assignment distribution. 
     The processor  305  may further identify  604  a function of technician assignments  284  exceeding the target assignment distribution as assignment bias and the method  600  ends. Assignment bias may be identified if the function of the technician assignments  284  exceeds the target assignment distribution. In one embodiment, the function of the technician assignments  284  that exceeds the assignment guard band of the target assignment distribution is identified as assignment bias. 
     The function of technician assignments may be selected from the group consisting of an arithmetic mean, a geometric mean, a harmonic mean, a quadratic mean, a generalized mean, a weighted mean, a truncated mean, an interquartile mean, a midrange, a Winsorized mean, a mode, and a median. The technician assignments may be retrieved from the recommendation sale  224  of the inspection results  205 . 
       FIG. 5D  is a schematic flow chart diagram illustrating one embodiment of a sales bias identification method  650 . The method  650  may identify inspection bias. In addition, the method  650  may identify assignment bias and/or sales bias. The method  600  may be performed by a processor  305 . In one embodiment, the method  650  is performed by program code stored on a computer readable storage medium such as the memory  310  and executed by a processor  305  to perform the functions of the method  650 . 
     The method  650  starts, and in one embodiment, the processor  305  determines  652  a sales target  282  for an inspection item  232 . In one embodiment, the processor  305  determines  652  the sales target  282  from stored data. For example, the sales target  282  may be calculated from all past recommendations  254  and recommendations sales  224  for each inspection item  232  of the plurality of inspection results  205 . For example, recommendation sales  224  may be divided by service recommendations  415  to generate the sales target  282 . In one embodiment, the sales targets  282  are calculated based on the region  220 , service location  208 , auto make  202 , auto model  204 , auto year  210 , auto mileage  212 , and/or recent service  229 . For example, the sales target  282  for an air filter replacement may be based on the auto mileage  212  and the recent service  229 . 
     The processor  305  may compare  654  a ratio of service recommendations  415  and recommendation sales  224  to the sales target  282 . The processor  305  may identify  656  the ratio of service recommendations  415  to recommendation sales  224  that is less than a sales target  282  as sales bias and the method  650  ends. 
       FIG. 5E  is a schematic flow chart diagram illustrating one embodiment of an inspection vector encoding method  700 . The method  700  may encode the inspection vectors  215  from the inspection results  205  for calculating the target recommendation  236 . The method  700  may be performed by the processor  305 . 
     The method  700  starts, and in one embodiment, the processor  305  determines  702  inspection vector elements  334  for the inspection vector  215 . The inspection vector elements  334  may be one or more of the region  220 , auto make  202 , auto model  204 , auto year  210 , auto mileage  212 , inspection item recommendations  216 , and recent service  229  respectively of an inspection results  205 . 
     The processor  305  further encodes  704  the inspection results  205  as inspection vectors  215  and the method  700  ends. In one embodiment, the processor  305  in code  700  for one hot bitmaps for each of the selected inspection vector elements  334 . The processor  305  may further include original values such as integer and/or real number values for the inspection vector elements  334  in the inspection vectors  215 . The inspection vectors  215  allow target recommendations  236  to be rapidly and/or efficiently calculated from the inspection results  205 . As a result, target recommendations  236  may be calculated in real time and/or calculated for each inspection item recommendation  216  of each auto inspection. 
     The embodiments record inspection results  205  and identify an inspection bias using inspection results  205 . In addition, the embodiments may identify assignment bias and sales bias. By identifying biases resulting from automobile inspections, the embodiments support the management of service locations in improving the performance of technicians through training and supervision. 
     The embodiments may be practiced in other specific forms. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.