Patent Publication Number: US-2021182921-A1

Title: Customized retail environments

Description:
BACKGROUND 
     Traditional physical stores maintain an inventory of items in customer-accessible areas such that customers can pick items from the inventory and take them to a cashier for purchase, rental, and so forth. In other instances, meanwhile, retail environments may include customizable and/or made-to-order items, such as food and drink items. Further, sensors may be used to generate information about events occurring in these facilities. While the information may be helpful to operators of the retail environments for non-customizable items selected by customers from inventory, use of the sensor-generated information may be limited and/or difficult in the case of customizable items. 
    
    
     
       BRIEF DESCRIPTION OF FIGURES 
       The detailed description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items or features. 
         FIG. 1A  illustrates an example facility associated with a system for enabling automated checkout (AC) techniques to allow users to enter the facility, order one or more customizable items, and exit the facility without performing a manual checkout of the customizable items. To do so, the system coupled to the environment may identify an entering user and charge an account associated with the user for a cost of the ordered, customizable items upon exit of the user. 
         FIG. 1B  illustrates the example facility of  FIG. 1A . In this example, the user orders a customizable item, but changes their mind before receiving the customizable item. 
       The order may be cancelled without the user being charged for the cost of the item, based at least on input from either the user or an associate of the facility. 
         FIG. 1C  illustrates the example facility of  FIG. 1A . In this example, the user orders a customizable item and also picks one or more non-customizable items. The system coupled to the environment may identify the user and charge the account associated with the user for a cost of the ordered, customizable item and the picked, non-customizable items upon exit of the user. 
         FIG. 1D  illustrates the example facility of  FIG. 1A . In this example, the user interacts with an associate of the facility to order a customizable item. The system coupled to the environment may determine a cost to charge the account of the user based at least on input from the user and/or the associate of the facility. 
         FIG. 1E  illustrates the example facility of  FIG. 1A . Here, the user orders a customizable item from within the facility. In this example the customizable item is available at a pickup location that is outside of the facility. The system coupled to the environment may charge the account associated with the user for a cost of the ordered, customizable item upon exit of the user, and a record may be provided for the user to pick up the customizable item at the pickup location. 
         FIG. 1F  illustrates the example facility of  FIG. 1A . Here, the user is ineligible for AC due to a variety of potential issues. The user orders a customizable item and also picks one or more non-customizable items. As the user exits the facility, the user provides payment for the customizable and non-customizable items via an alternative method than an account of the user being automatically charged. 
         FIG. 1G  illustrates the example facility of  FIG. 1A . Here, the example facility does not have AC entry and/or exit locations. In this example, the user may access an ordering location and/or an inventory location and leave the facility without passing through specific entry or exit gates. 
         FIG. 2  illustrates an example process for determining whether a user has ordered one or more items at a facility, in accordance with the present AC techniques. 
         FIG. 3  illustrates an example process for, in part, determining that a user has ordered one or more customizable items within a facility, determining that the user is eligible to exit through an exit location of the facility, and charging a payment instrument of the user based on the one or more items ordered by the user, in accordance with the present AC techniques. 
         FIG. 4  illustrates an example process for determining whether a user has ordered a customizable item at a facility, in accordance with the present AC techniques. 
         FIG. 5  is a block diagram of an example materials handling facility that includes sensors and an inventory management system configured to generate output regarding events occurring in the facility using the sensor data. 
         FIG. 6  illustrates a block diagram of one or more servers configured to support operation of the facility. As illustrated, the servers may include a checkout-eligibility component for determining whether a user is eligible to exit the facility with one or more picked items without performing a manual checkout of the items. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure describes, in part, systems for enabling physical retail stores and other facilities with customizable items to implement automated-checkout (AC) techniques for customers of the stores and/or facilities. For example, the described systems may enable a retail store to implement technology where users are able to order a customizable item and exit the store without performing manual checkout of the item, in addition to allowing users to pick non-customizable items from shelves and other inventory locations. The systems described herein thus enable customized retail facilities with AC, as opposed to an AC retail facility that is limited to pre-packaged, fixed price, or otherwise non-customizable items for purchase. 
     In some instances, customized retail facilities include inventory locations housing one or more items that may be ordered, received, picked, and/or returned by users. These inventory locations may be associated with one or more sensors configured to generate sensor data indicative of events that occur with respect to the items housed thereupon. For example, these sensors may generate sensor data indicative of a user picking an item from the inventory location, returning the item to the inventory location, and/or the like. These sensors may include overhead cameras, in-shelf cameras, weight sensors, and/or any other type of sensor configured to generate sensor data indicative of user interactions with the items. 
     In addition, the facility may include user input devices configured to allow a user and/or an associate of the facility to place and/or confirm an order for a customizable item. In some instances, the user input devices may be used to enter or select options for the customizable item, which may vary in price based on certain criteria, such as weight, volume, toppings, and the like. The customizable item may also be made-to-order, such as a food item that is not assembled or otherwise prepared until it is ordered. In some instances, the user input devices may be provided at an ordering location (e.g., a kiosk). Inventory locations may include a reception area (e.g., a food service counter) wherein a user may receive an ordered customizable item. For instance, within a grocery store, inventory locations may include shelves holding prepackaged food/beverage items, a butcher block for customizable meat selections, a deli section offering made-to-order sandwiches, a coffee counter, or the like. 
     In addition to including different inventory locations and potentially ordering locations, the facility may include, in some instances, one or more entry locations for entering the facility and one or more exit locations for exiting the facility. For example, the facility may include an AC entry location at which an entering user provides information for identifying an account of the user. For example, the AC entry location may include a scanner or other imaging device at which an entering user scans or otherwise provides a unique code associated with the account of the user, such as a code displayed on a mobile device of the user. Or, the entry location may include a microphone, camera, or other sensor that generates sensor information at the request of the user for use in identifying the account of the user. In still other instances, the AC entry location may include an input device for reading information from a payment card of a user, such as a credit card, debit card, prepaid card, etc. For example, the AC entry location may include a scanner or camera that scans or captures an image of a payment card, a card reader that receives information from a payment card via a swipe, dip, tap, or the like, or may include any other type of input device configured to receive payment or account information. 
     In some instances, the account of the user may be associated with a payment instrument of the user such that the payment instrument is able to be charged for items procured by the user, with the charge occurring automatically upon exit of the facility by the user and without the user needing to engage in a manual checkout process of the items. Accordingly, the facility may include an AC exit location where an exiting user provides information for identifying an account of the exiting user. The AC exit location may include, similar to the AC entry location, a scanner or other imaging device at which the exiting user scans or otherwise provides a unique code associated with the account of the user, such as the code displayed on the mobile device of the user. Or, the AC exit location may include a microphone, camera, or other sensor that generates sensor data at the request of the user for use in identifying the account of the exiting user. 
     Note that the facility may also include entry and exit locations at which users may enter and exit without providing identifying information. For instance, users may be allowed access to the facility in a manner similar to a traditional retail facility to allow users to shop or otherwise interact with items at the retail facility without needing to provide information for identifying user accounts. In some examples, the user may be allowed to enter the facility, then provide information for identifying a user account at an ordering location within the facility. Also, at least one exit location may resemble a traditional exit location at a retail facility, including an associate of the facility operating a point of sale (POS) device to manually check out the exiting user, such as an exiting user wishing to pay for items in cash. Of course, it is to be appreciated that the facility may include self-checkout kiosks or any other technology for enabling manual checkout of the items within the facility. 
     Within this example facility, if a user enters through an AC entry location and provides information identifying an account of the user, or the user enters the facility and provides information identifying the account of the user at an ordering location, then a system associated with the facility may generate a record indicating the presence of the user at the facility. The record may store an indication of the identity of the user, as well as an indication of whether the user is currently eligible to exit the facility (with items procured by the user) via the AC exit location. 
     In addition, sensor data generated from sensors at the facility may be used to determine current positioning of the user, which may also be stored in association with the record of the user. For example, overhead cameras, floor weight sensors, and/or the like may be used to maintain, in the record and at the knowledge and request/consent of the user, the current location of the user within the facility. Further, this sensor data (e.g., image data) may be used to locate the user as the user navigates through the store. Further, if the user interacts within one or more items housed at an inventory location, and/or orders a customizable item, the system may generate data indicative of the event. This data may comprise result data, indicating a result of the order of the customizable item or the interaction between the user and the customizable or non-customizable item. In some instances, this result data indicates an action taken (e.g., an order, a confirmation of an order, a pick of an item, a return of an item, etc.), a description or identity of the item acted upon (e.g., a latte, a bottle of ketchup, a pair of jeans, etc.), a quantity and/or size of the item involved, a location of the item (e.g., meat counter, aisle, shelf, lane, etc.), a cost of the item, and/or the like. 
     Continuing the example, envision that an identified user enters the facility and goes to an ordering location within the facility. For instance, the user may approach a user input device at an ordering location within the facility, such as an order kiosk. The user may place an order for a customizable item at the order kiosk. For instance, the user may order a cheeseburger with extra cheese from the order kiosk. The record of the user may now indicate an identity of the user, an account of the user, and a position of the user at or near the order kiosk. The system may also generate result data reflecting the order and associate the result data with the account of the user (e.g., a virtual cart of the user). In other words, the cheeseburger with extra cheese and its corresponding cost (including an “upcharge” cost for the extra cheese) may be added to the virtual cart of the user. 
     Further, envision that an identified user also goes to one or more inventory locations within the facility and picks up one or more non-customizable items. In some instances, the system may make this determination based on image data captured from overhead cameras or other sensors. For instance, the system may determine that the user has picked up a bag of chips from a rack inside the facility and a can of diet cola from a refrigerated display case. The bag of chips and can of diet cola may then also be added to the virtual cart of the user. 
     In some instances, the system may generate the result data reflecting the order along with a confidence level indicating a confidence associated with the result data. If the confidence level associated with the result data is greater than a threshold confidence level, then the result data may be associated with the virtual cart of the user. For example, if the system determines, with a confidence level that is greater than the threshold, that the user was the one standing at the order kiosk when the order for the cheeseburger with extra cheese was entered, then the cheeseburger with extra cheese may be added to the virtual cart of the user. Similarly, confidence levels may be generated for the picking of the bag of chips and can of diet cola. Further, the record associated with the user may indicate that the user is eligible to exit through the AC exit location, given that the user is identified and the event that the user performed was determined to be associated with high-confidence result data. 
     Upon finishing his or her shopping, the user may approach the AC exit location and, in some instances, scan or otherwise provide identifying information to enable the system to identify the exiting user. After scanning his or her unique code at the AC exit location, for instance, the user may exit the facility. The system, meanwhile, may thereafter charge an account of the identified exiting user for a cost of the items procured by the user within the facility. Of course, while the above example describes the user scanning a unique code (e.g., via a mobile device of the user), it is to be appreciated that the exiting user may be identified based on other sensor data, such as image data, voice data, or the like. 
     If, however, the confidence level associated with the result data is less than the threshold, then the system may perform one or more additional techniques for determining the result of the event. For example, the system may send user input device and/or sensor data (e.g., image data, weight-sensor data, etc.) to one or more human users for analysis. Further, given that the event is not associated with high-confidence result data, the record associated with the user may be updated to indicate that the user is not eligible to exit through the AC exit location. If, however, the result data is updated to indicate a high-confidence result (e.g., based on the human analysis, etc.), then the record may again be updated to indicate that the user is eligible to exit through the AC exit location, given that the user is identified and associated with high-confidence result data. 
     To summarize, high-touch use cases in retail facilities, such as made-to-order food and beverages, present unique challenges for the implementation of AC techniques. Traditionally, a customer has had to interface with a human (store associate) to complete a transaction. This disclosure presents a system capable of inserting high-confidence product ‘take’ events (i.e., purchases) into a virtual shopping cart of a user for processing. As such, AC techniques support simple or complex customizable product configurations (e.g., coffee customizations like extra shots, flavors, etc.). AC techniques may therefore be expanded to a broad variety of retail scenarios, including cafeterias, food halls, coffee bars, the purchase of age-restricted items (e.g., alcoholic beverages), print-on-demand type products (e.g., t-shirt with my name on it), and enjoying rides and/or food at an amusement park, zoo, or arcade, for example. 
     While some of the examples below are described with reference to a materials handling facility (e.g., a brick-and-mortar retail store, a fulfillment center, etc.), the systems and techniques may be implemented for detecting events in any type of facility, an airport, a classroom, an outdoor environment, an amusement park, or any other location. Certain implementations and embodiments of the disclosure will now be described more fully below with reference to the accompanying figures, in which various aspects are shown. However, the various aspects may be implemented in many different forms and should not be construed as limited to the implementations set forth herein. The disclosure encompasses variations of the embodiments, as described herein. Like numbers refer to like elements throughout. 
       FIGS. 1A-1G  collectively illustrate an example facility  100  associated with a system for enabling automated checkout (AC) techniques to allow users, such as example user  102 , to enter the facility  100 , order and/or pick one or more items, and exit the facility without performing a manual checkout of the items. To do so, the system coupled to the environment may identify a user and charge an account associated with the user for a cost of the ordered and/or picked items upon exit of the user. 
     As illustrated in  FIGS. 1A-1G , the example facility  100  includes an AC entry location  104  and an AC exit location  106 . The example facility  100  also includes an ordering location  108  and inventory locations  110 . In some examples, facility  100  may also include devices  112 , which may include various types of scanning devices and/or user input devices to help facilitate AC techniques, which will be described in more detail below. Facility  100  may also include sensors  114 . As suggested above, sensors may include cameras, microphones, floor weight sensors, and/or the like to help facilitate AC techniques. In general, the devices  112  and/or the sensors  114  may be associated with AC entry locations  104 , AC exit locations  106 , ordering locations  108 , and/or inventory locations  110 . Various example AC scenarios will now be described with reference to the example facility  100  shown in  FIGS. 1A-1G . 
     Referring to  FIG. 1A , the AC entry location  104  (e.g., entry gate) may request that entering users provide identifying information prior to entering the facility  100 . In the illustrated example, the user  102  enters through the AC entry location  104  by scanning a unique code presented on a mobile device  116  of the user  102 , such as at a scanning device  112 ( 1 ) at the AC entry location  104 . The scanning device  112 ( 1 ) may provide this information to a system, such as an inventory management system discussed in following figures, which may use this information for identifying the entering user  102 . Of course, while this example describes identifying the user  102  based on the user  102  scanning an unique code presented on the mobile device  116 , the system may additionally, or alternatively, identify the user  102  based on voice data (e.g., the user stating his or her name), image data (e.g., image data of a face of the user), password data (e.g., an alphanumeric string), credit card data, and/or any other type of data. For instance, the system may identify the user  102  based on data provided by sensor  114 ( 1 ), or based on credit card data provided by device  112 ( 1 ). In some examples, those users that have consented/requested to take part in the AC techniques may be identified, while the system may refrain from identifying other users entering the facility  100 . As suggested above, in some examples a facility may not have specified entry locations or gates. (A scenario without a specified entry location is described relative to  FIG. 1G , below.) 
     In the scenario illustrated in  FIG. 1A , upon the user  102  entering the facility  100  via the AC entry location  104  at a first time (T 1 ), the system generates a record indicating the presence of the identified user  102  within the facility  100 . This record may be continuously or periodically updated by a locating component of the system to generate current location data of the user  102  within the facility  100 , at the prior consent/request of the user  102 . In some instances, the sensors  114 , such as overhead cameras or the like, may be used to determine a current location of the user  102 . In addition, the record generated by the system at T 1  may indicate whether the user  102  is eligible to engage in the AC techniques provided by the facility  100 . For example, the record may indicate whether the user  102  is able to “just walk out” with any items he or she collects within the facility  100  without first performing a manual checkout for the items. In this example, at least in part because the system has identified the user  102 , the user  102  is eligible at T 1  to exit the facility  100  with item(s) without performing manual checkout of the items. 
     In some instances, the system may, additionally or alternatively to the user  102  being identified, store an indication that the user  102  is eligible to exit the facility without performing manual checkout of the items based on the user being associated with a payment instrument. For example, upon identifying the user  102  entering the facility  100 , the system may identify an account of the user  102  and may determine whether the account is associated with a valid payment instrument. If so, then the system may store an indication that the user  102  is eligible to exit the facility  100  with one or more items without performing a manual checkout of the items. In another example, the entering user  102  may swipe, scan, or otherwise provide identifying information associated with a payment instrument (e.g., credit card) of the user upon entering the facility  100 . The system may use this identifying information to determine whether the payment instrument is valid (potentially along with a limit of the payment instrument) and may store an indication that the user  102  is eligible to exit the facility  100  without performing manual checkout of the items (assuming the total of the items is less than the limit). In these instances, the system may or may not identify the user  102  but may instead simply associate the user in the facility  100  with the identified payment instrument. In yet another example, the AC exit location  106  may include a device  112  configured to accept cash, such that a user  102  may input a certain amount of cash and remain eligible for exiting the facility  100  without performing a manual checkout exit, so long as the user  102  does not obtain items having a cumulative value (e.g., with taxes, etc.) that exceeds the amount of inserted cash. 
     Returning to the scenario in  FIG. 1A , in this example the user  102  enters the facility  100  and proceeds to an ordering location  108 . As described above, at the ordering location  108 , result data associated with events may be determined automatically via user input device  112  and/or sensor  114  data. For example, the ordering location  108  may be within the field-of-view of sensor  114 ( 2 ), which may represent one or more overhead cameras, in-shelf cameras, or the like. Image data from sensor  114 ( 2 ) may be used to determine the results of events that occur at the ordering location  108 . In addition, or in the alternative, the ordering location  108  may include one or more other sensors, such as a microphone or the like, which may generate sensor data for identifying the results of the events. 
     In the illustrated example, at a second time (T 2 ) the user  102  orders a customizable item  118  from the ordering location  108 . The user may order the customizable item  118  via device  112 ( 2 ) at ordering location  108 . A wide variety of device types and methods of interfacing with device  112 ( 2 ) are contemplated for ordering the customizable item  118 . For instance, the user may select the customizable item  118 , including customized options, from a touch-screen display of device  112 ( 2 ). In other cases, device  112 ( 2 ) may present a simple button for the user  102  to depress to indicate his or her order, or may be voice-activated for ordering purposes. For purposes of this scenario, envision that the user  102  interacts with a touch-screen display of device  112 ( 2 ) to order a latte, and further specifies a  16 -ounce size and an extra shot for the latte. 
     The system may use data generated by sensor  114 ( 2 ) and/or device  112 ( 2 ) to determine an event associated with the user  102 . In this example, the event is the order by the user  102  of the customizable item  118 . As illustrated, at T 2  the system detects this event (Event 1 ), a result of this event (Result 1 ), and a confidence level associated with the result (Confidence Level 1 ). The result may comprise an indication of the action (e.g., order), the identity of the item(s) involved (e.g., the 16-ounce latte with an extra shot, a breakfast sandwich with ham instead of sausage, etc.), a quantity and/or size of the item(s), an identity of the user  102 , and/or additional data. The confidence level, meanwhile, may indicate a degree to which the system is confident regarding the calculated result. In some instances, the result and the confidence level may be determined using one or more classifiers previously trained using training sensor data and manually labeled results, as described further below. Furthermore, the confidence level may be compared to a threshold and, if the confidence level is greater than a threshold, then the system may update a virtual cart of the identified user  102  based to reflect the calculated result. Stated another way, the system may add the 16-ounce latte with an extra shot to the virtual cart of the user  102  upon ensuring with a high-enough confidence level that it was user  102  that ordered that particular customized item  118 . If, however, the confidence level is less than the threshold, then the system may perform one or more further actions for determining and/or verifying the result and/or may refrain from updating the virtual cart of the user  102 . 
     In the illustrated example, at T 2  the system determines a result, and that a confidence level associated with the result is greater than a pre-determined threshold. Thus, the customizable item  118  that was determined to be ordered by the user  102 , in this example, is added to the virtual cart of the user. In addition, because this result was a high-confidence result, the system maintains, in the record associated with the user  102 , an indication that the user  102  is still eligible to exit the facility  100  without performing manual checkout of items. Stated otherwise, because the system is confident that the current virtual cart of the user  102  is accurate, the system refrains from updating the record to indicate that the user  102  is ineligible from exiting the facility  100  without performing a manual checkout. 
     Next, at a third time (T 3 ) the user  102  moves to an inventory location  110 ( 1 ) to receive the customizable item  118 . In some examples, receiving the customizable item  118  may not be sensed and/or tracked. For purposes of AC, the order of the customizable item  118  being added to the record associated with user  102  may be sufficient to allow the user  102  to exit the store. In other examples, various further actions by either the user  102  or a store associate  120  are contemplated. For instance, the user  102  or the store associate  120  may confirm the order, either at device  112 ( 2 ) or device  112 ( 3 ). The system may confirm the order based on sensor data from sensor  114 ( 3 ), indicating the user  102  receiving the customizable item  118 , or confirming receipt of the customizable item  118  via device  112 ( 3 ). In some cases, where the confidence level of the order event for customizable item  118  was not above a threshold, sensor  114  or device  112  data may be used by the system to increase the confidence level to meet the threshold value, thereby enabling AC. Further, in some cases, the receiving of the customizable item  118  by user  102  may be added by the system as an event (e.g., Event 2 ). 
     At a fourth time (T 4 ), in this example the user  102  exits the facility  100  at the AC exit location  106  by, for example, providing identifying information, or by simply exiting the facility  100  without scanning or providing identifying information. For example, similar to the AC entry location  104 , the AC exit location  106  (e.g., exit gate) may include a device  112 ( 4 ) that enables user  102  to scan a unique code from his or her mobile phone  116 , or provide any other type of identifying information. In still other instances, user  102  may walk out and the system may identify user  102  via facial-recognition techniques using data from sensor  114 ( 5 ), for example. In such instances user  102  may have requested and/or given permission for such automatic recognition techniques. As noted above, in some examples a facility may not have specified exit locations or gates. (A scenario without a specified exit location is described relative to  FIG. 1G , below.) 
     In response to the user  102  attempting to exit the facility  100 , the system may identify the record associated with the user  102 , determine that the user  102  is eligible to “just walk out”, and end a shopping session of user  102 . At a fifth time (T 5 ), the system may then process a corresponding transaction, such as charging an account (e.g., a payment instrument, an account previously associated at the system, etc.) of the user  102  for the customizable item  118  listed on the virtual cart of the user  102 . The transaction processing may also include supplying a notification and/or receipt or other record of the transaction to the user  102 , such as on their mobile device  116 . Note that the facility also may offer the user  102  a selection of methods to pay upon exit. For instance, the user may be able to select, via device  112 ( 4 ), mobile device  116 , or another device, to pay with cash or another form of payment instead of the payment instrument that was selected by the system. The user  102  may make this selection at any time, including a time prior to exit from the facility. The user  102  may also choose to complete their transaction with an associate  120  at the facility  100 , for instance. 
       FIG. 1B  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  again enters the facility  100  through the AC entry location  104  and scans his or her unique identifier displayed on the mobile device  116 . The system thus identifies the user at T 1  and generates a record indicating that the user  102  is eligible perform AC. The user  102  again orders a customizable item from the ordering location  108 . At T 2  the system determines a result, and that a confidence level associated with the result is greater than a pre-determined threshold. Thus, the customizable item is added to the virtual cart of the user  102 , and the record associated with the user  102  includes an indication that the user  102  is eligible to exit the facility  100  without performing manual checkout of the customizable item. However, in this example, the user  102  changes his or her mind about the customizable item sometime after ordering, but before receiving the customizable item. 
     A variety of methods are conceived for updating the record of the user  102  and/or the virtual cart to indicate that the user  102  has cancelled an order for an item. For instance, the user may cancel the item while still at the ordering location  108  via the device  112 ( 2 ), or the user  102  may return to the ordering location  108  to cancel the item. The user  102  may be able to access a representation of his or her virtual cart via a visual display of the device  112 ( 2 ). In some cases, the system may determine that user  102  is at the ordering location  108 , such as using sensor  114 ( 2 ), and automatically offer a representation of the virtual cart to the user  102 . In other instances, the user  102  or associate  120  may indicate a cancellation of the item via device  112 ( 3 ). For instance, user  102  may approach associate  120  and ask to have the item cancelled. 
     In the illustrated example in  FIG. 1B , at a third time (T 3 ) user  102  cancels the order for the customizable item by asking associate  120  with assistance in cancelling the order. The associate  120  interacts with device  112 ( 3 ) to cancel the order, and the record associated with user  102  is updated to indicate that the customizable item has been removed from the virtual cart. At a fourth time (T 4 ), user  102  exits the facility  100  at the AC exit location  106 , and an account of the user  102  is not charged, since the user  102  had no items in their virtual cart at the time of their exit. In some cases, no record is retained of this activity. In other cases, a record of the cancelled order may be retained for data analysis purposes. Also, a record of the cancelled order may or may not be provided to the user  102 . 
       FIG. 1C  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  again enters the facility  100  through the AC entry location  104  and scans his or her unique identifier displayed on the mobile device  116 . The system thus identifies the user at T 1  and generates a record indicating that the user  102  is eligible perform AC. The user  102  again orders a customizable item  118  from the ordering location  108 . At T 2  the system determines a result, and that a confidence level associated with the result is greater than a pre-determined threshold. Thus, the customizable item  118  is added to the virtual cart of the user, and the record associated with the user  102  includes an indication that the user  102  is eligible to exit the facility  100  without performing manual checkout of the customizable item  118 . In this example, at T 3  user  102  receives the customizable item  118  and continues browsing the facility  100 . 
     In the example shown in  FIG. 1C , at a fourth time (T 4 ) the user  102  removes (or “picks”) a non-customizable item  122  from inventory location  110 ( 2 ). The system may use data generated by sensor  114 ( 4 ) to determine another event associated with the user  102 . In this example, the event is the pick by the user  102  of the non-customizable item  122 . In response, the system may use sensor data indicative of this event to determine result data associated with this event. As illustrated, at T 4  the system detects this event (Event 2 ), a result of this event (Result 2 ), and a confidence level associated with the result (Confidence Level 2 ). The result may comprise an indication of the action (e.g., pick, return, etc.), the identity of the item(s) involved (e.g., can of diet cola, protein bar, etc.), a quantity of the item, an identity of the user, a price of the item, and/or additional data. The confidence level and the threshold associated with the event may be similar to those described above with respect to Event 1 . In the illustrated example, the confidence level is greater than the threshold, and the system may update the virtual cart of the user  102  to include the non-customizable item  122 . 
     In this case, at a fifth time (T 5 ), user  102  exits the facility  100  at the AC exit location  106 . In response to the user  102  attempting to exit the facility  100 , the system may identify the record associated with the user  102 , determine that the user  102  is eligible to “just walk out”, and end a shopping session of user  102 . At a sixth time (T 6 ), the system may then process a corresponding transaction, such as charging an account of the user  102  for the customizable item  118  and the non-customizable item  122  listed on the virtual cart of the user  102 . The transaction processing may also include supplying a notification and/or receipt or other record of the transaction to the user  102 , such as on their mobile device  116 . 
       FIG. 1D  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  again enters the facility  100  through the AC entry location  104  and scans his or her unique identifier displayed on the mobile device  116 . The system thus identifies the user at T 1  and generates a record indicating that the user  102  is eligible perform AC. In this example, user  102  proceeds to inventory location  110 ( 1 ) to order customizable item  124 . For purposes of illustration, envision that user  102  orders customizable item  124  from associate  120 , specifying a ham sandwich with lettuce, tomato, and mustard, no mayonnaise, and wrapped “to-go.” In this example, associate  120  interacts with device  112 ( 3 ) to enter the order into the system. As illustrated, at T 2  the system detects this event (Event 1 ). The system may also use sensor  114 ( 3 ) to determine that user  102  requested and/or received the customizable item  124 , therefore determining a result of this event (Result 1 ), and assigning a confidence level associated with the result (Confidence Level 1 ). In this case, once again the confidence level meets a predetermined threshold. Thus, the customizable item  124  is added to the virtual cart of the user  102 , and the record associated with the user  102  includes an indication that the user  102  is eligible to exit the facility  100  without performing manual checkout of the customizable item  124 . At a third time (T 3 ), user  102  exits the facility  100  at the AC exit location  106 . Once again, in response to the user  102  attempting to exit the facility  100 , the system may identify the record associated with the user  102 , determine that the user  102  is eligible to “just walk out”, and end a shopping session of user  102 . At a fourth time (T 4 ), the system may then process a corresponding transaction, such as charging an account of the user  102  for the customizable item  124  and supplying a receipt of the transaction to the user  102 . 
     Referring again to  FIG. 1D , a variety of techniques are envisioned for the user  102  ordering a customizable item  124  from an associate  120  at an inventory location  110  in a manner that enables AC, both by identifying the user as being associated with the order and by recording customizable options with the order. Envision a butcher counter, in which there may be a consultation process with the associate  120  (e.g., butcher) to help user  102  select the customizable item  124 . For instance, the butcher may need to ask the customer about a type of meat, amount (e.g., weight), seasoning, other preparation options (e.g., tied, trimmed), etc. This scenario may include additional sensor input, such as weighing the meat or identifying the user  102  talking to the associate  120 . A variety of these options may be recognized by the system and added to a record associated with the user  102 . For example, the system may match weight data from a scale with a record of a corresponding customizable item, and add this information to a virtual cart of user  102 . In some examples, the system may register a confirmation of the user receiving and/or accepting the customizable item  124  to match the order to the user  102 , such as using sensor  114 ( 3 ), or by receiving confirmation of the order from the user  102  or the associate  120 , such as through device  112 ( 3 ). For instance, the system may cause a representation of the customizable item and/or an associated price to be presented to the user  102  on a visual display of device  112 ( 3 ) so that the user may provide confirmation. In other cases, the user  102  may simply push a button to confirm the order and/or be identified. 
       FIG. 1E  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  again enters the facility  100  through the AC entry location  104  and scans his or her unique identifier displayed on the mobile device  116 . The system thus identifies the user at T 1  and generates a record indicating that the user  102  is eligible perform AC. The user  102  again orders a customizable item from the ordering location  108 . However, in this case, the customizable item is relatively large, and there is a pickup location outside of the facility  100 . For purposes of illustration, consider that user  102  has ordered an air conditioning unit to take home and be installed in their residence. The facility  102  may have a pickup location (e.g., warehouse, loading dock, etc.) for such items. In some examples, the “customizable item” may include an item that is not able to be “picked” out of an inventory location  110  within a facility  100 , rather than an item that requires selecting options or made-to-order items. For instance, some items may be too large, too heavy, or otherwise too unwieldy for a person to reasonably be expected to pick the item off a shelf 
     As illustrated in  FIG. 1E , at T 2  the system determines a result, and that a confidence level associated with the result is greater than a pre-determined threshold. Thus, the customizable item is added to the virtual cart of the user  102 , and the record associated with the user  102  includes an indication that the user  102  is eligible to exit the facility  100  without performing manual checkout of the customizable item. At T 3  user  102  exits the facility  100 . In this example, at T 4  the system processes the transaction. In some instance, T 4  may be while the user  102  is exiting the facility  100  or soon after, or T 4  may not occur until the user is at the pickup location and has received the ordered customizable item. Note that the pickup facility may also have sensors or devices to assist with identifying the user  102 , matching the person picking up the item to the user  102  that ordered the item, confirming receipt of the item by the user  102 , etc. Such sensors and/or devices at the pickup facility may also be communicatively coupled to the system at the facility  100 . 
       FIG. 1F  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  again enters the facility  100  through the AC entry location  104  and scans his or her unique identifier displayed on the mobile device  116 . The system thus identifies the user at T 1  and generates a record indicating that the user  102  is eligible perform AC. The user  102  again orders a customizable item  126  from the ordering location  108 . At T 2  the system determines a result. However, in this instance a confidence level associated with the result does not meet a pre-determined threshold. For instance, perhaps the area around the ordering location  108  was crowded, and the system was unable to differentiate the user  102  from another person that may have placed the order with a sufficient confidence level. Thus, the customizable item  126  is added to the virtual cart of the user  102 , but the record associated with the user  102  includes an indication that the user  102  is not eligible to exit the facility  100  with AC for the customizable item  126 . At T 3  user  102  receives the customizable item  126  and attempts to exit the facility  100  at T 4 . 
     In this example, because the record associated with the user  102  indicates that the user  102  is ineligible to exit through the AC exit location  106  (at least until the user performs a manual checkout of the items), the system may generate and output, to the user  102 , notification data indicating that the user is to provide payment by a method other than AC. This notification data may be output via the mobile device  116  of the user  102 , via a device  112 ( 4 ) at the AC exit location  106 , or in any other audible, visual, and/or tactile manner. It is to be appreciated that this notification data may be presented at any other time. For example, the notification data may be sent to and presented upon the mobile device  116  of the user  102  upon the system determining the occurrence of the low-confidence event and/or at any other time prior to exit of the user  102  from the facility  100 . At T 5  the transaction may be processed by the user  102  paying for the customizable item  126  by a variety of alternative methods before exiting the facility  100 . For instance, the user  102  may interact with an associate at a point of sale (POS) device at an exit location or another location within the facility  100 , the user  102  may perform a self-checkout via a self-checkout process, such as inserting or scanning a credit card or cash at device  112 ( 4 ), or the checkout may be performed in any other manner. 
     Note that other scenarios are envisioned in which a user may be ineligible to exit the facility  100  with AC. For example, a user  102  may not provide identifying information upon entering the facility, in turn, the system is unable to identify the user  102 . In this case the system may generate a record associated with the unidentified user  102  and may continue to locate the user within the facility  100 . However, because the user  102  is not identified, and thus the system is unable to locate an account of the unidentified user  102 , the record may indicate that the user  102  is not eligible to exit with items without performing a manual checkout of the items. In another example, a user  102  may pick a non-customizable item from an inventory location, but the system may be unable to determine which item the user  102  selected. 
     In some examples, an event may be upgraded from relatively low-confidence to relatively high-confidence prior to the user  102  exiting the facility  100 , and the system may update the record associated with the user  102  to indicate that the user  102  is now eligible to exit through the AC exit location  106 . For instance, a previously unidentified item picked by the user  102  may in fact be identified prior to the user  102  exiting the facility  100 . For example, the user  102  may use the mobile device  116  or another device in the facility to scan an identifier of the item and send this information to the system. In another example, the system may have processed additional sensor data to determine a high-confidence result of the event, may have received an indication of the result of the event (e.g., the identity of the item) from one or more human users, or may have determined a high-confidence result of the event in any other manner. In one example, when the system determines that a low-confidence result associated with an event, the system may request that the user  102 , an associate, or other user verify the result of the event in order to update the result to high-confidence. For example, the system may cause a display, on a device in the facility, to show a screen requesting that the user  102  or an associate verify the identity of the item determined by the system, a quantity of the item determined by the system, and/or the like. This screen may be presented on the mobile device  116  of the user  102 , a device  112  of the facility  100 , and/or on any other device. In response to receiving this information from the user  102  or associate, the system may update the result from low- to high-confidence such that the user  102  is now eligible to exit through the AC exit location  106 . Similarly, it is to be appreciated that the system may be able to identify a previously unidentified user  102  prior to the user  102  exiting the facility  100  (e.g., using facial-recognition techniques, the user scanning his or her unique code, etc.), and the system may update a record to indicate that the user  102  has become eligible to exit the facility  100  with one or more items via the AC exit location  106 . 
       FIG. 1G  illustrates another example AC scenario within the facility  100  of  FIG. 1A . In this example, the user  102  enters the facility. In this example, there are no particular AC entry or exit locations and/or entry or exit gates. For instance, the user  102  may simply walk up to an ordering location  108 . Envision, for example, an ordering kiosk for a coffee stand within an airport. At the ordering location  108 , the user  102  orders a customizable item  126 . Therefore, at T 1 the system may identify the user  102  and generate a record indicating that the user  102  is eligible perform AC. In this example, the user  102  may have been identified as part of entering the order via the input device  112 ( 2 ). Additionally, or alternatively, the user  102  may have been identified, and/or additional identifying data (e.g., biometric recognition data, etc.) may have been provided by sensors  114  at the facility  100 , including sensor  114 ( 2 ) at the ordering location  108  and/or other sensors  114  before and/or after the user  102  placed the order (e.g., sensor  114 ( 1 )). Also note that at T 1  the system may simply begin tracking user  102  without having necessarily identified user  102 . For instance, the system may recognize user  102  as a person that made the order, but may not link user  102  to an account until user  102  provides identifying information upon checkout, for instance. 
     Continuing with the scenario in  FIG. 1G , after T 1  the customizable item  126  is added to the virtual cart of the user  102 , and at T 2  user  102  receives the customizable item  126  and exits the facility  100  at T 3 . Since the facility does not include particular exit location in this example, at T 4  the transaction is processed upon the system determining that the user is exiting and/or has exited the facility, such as via data from sensors  114 . In some cases, the transaction may be processed simply based on the order event. For instance, the system may receive the order at ordering location  108 , and upon determining that user  102  placed the order at a confidence level that meets a predetermined threshold, the system may proceed to charge a payment instrument of the user without necessarily waiting for the user to receive the item and/or exit the facility. In some examples, the system may not receive and/or begin processing the order until the system is able to confirm a valid payment instrument of the user. For instance, the user may order a custom coffee from the ordering location  108 , but the system may not send the order to associate  120  (e.g., a barista) until the system confirms that an account of the user is associated with a valid payment instrument, or until the user  102  enters a valid payment instrument, or actually pays for the coffee, etc. 
       FIG. 2  illustrates an example process  200  for determining whether a user has ordered one or more items at a facility. This process, and other processes discussed herein, may be implemented in hardware, software, or a combination thereof. In the context of software, the described operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more hardware processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. Those having ordinary skill in the art will readily recognize that certain steps or operations illustrated in the figures above may be eliminated, combined, or performed in an alternate order. Any steps or operations may be performed serially or in parallel. Furthermore, the order in which the operations are described is not intended to be construed as a limitation. 
     At  202 , a system coupled to one or more sensors in a facility may receive an indication that a user has entered the facility. For example, the system may receive an indication that a user has scanned identifying information, such as a unique code presented on a mobile device of the user, at a scanning device at an entry location of the facility. In another example, the received indication may be based on image data captured by one or more cameras located at the facility, with the image data showing the user entering the facility. In still another example, the indication may be based on weight sensors on a floor of the facility identifying an entering user, voice data of the entering user, or the like. Further, the indication may be received at a time that the user enters an order at a user input device. 
     At  204 , the system may receive an indication that an item has been ordered. For example, the system may receive user input data from a user input device suggesting that a customizable item has been ordered. The user input device may be located within an ordering location or an inventory location of the facility, or at another location. The user input data may include item identification data and/or customizable options associated with the item. The user input device may communicate the user input data to the system along with a timestamp associated with the order. 
     At  206 , the system may determine whether the user ordered the item. The system may use sensor data generated by at least one sensor at the facility to determine whether the user was at the user input device at the same time or approximately the same time as the timestamp associated with the order, for instance. For example, the system may keep a list of users and times that the users were positioned at and/or entered an order into the user input device. In the case of a crowded facility, or a fast-moving order line, the system may adjust a granularity of the timestamp matching, timestamp recording, and/or list keeping, to assist in providing a higher confidence level that any given user is associated with an order. For instance, the system may provide the sensor data and/or additional data into one or more trained classifiers, which may output result data indicating the order as being associated with a user interacting with a user input device, or a user positioned at a user input device at a time of the order, or the like. In addition, the one or more classifiers may output a confidence level indicating a degree of accuracy of the result data. Additionally, or alternatively, the system may receive the indication of the order, then consult the sensor data to attempt to determine which user placed the order. In other words, the system may receive the order, then “go looking” for the user that placed the order by accessing sensor data collected at a time the order was placed. 
     In an instance where the system determines that the user is “known” to have placed the order, process  200  may proceed to  208 , where the system may store an indication that the user ordered the item. For example, the system may store the indication with a record associated with the user and/or in a virtual cart of the user. The system may determine that the user placed the order when a result associated with the event of the user placing the order is determined to have a confidence level greater than a threshold value, for instance. In another instance where the system determines that it is “unknown” whether the user placed the order, at  210  the system may collect more sensor data until a determination of a person that placed the order can be made. At  212 , the system may be able to confirm that the user placed the order for the item. For example, the system may be able to increase a confidence level associated with the event of the order beyond a threshold value. Additionally or alternatively, the system may receive confirmation of the order from the user or from an associate of the facility. Thus, process  200  may proceed from  212  to  208 , described above. 
     At  214 , the system may detect that the user is exiting the facility. Then at  216  the system may process a transaction with the user using automatic-checkout (AC) techniques. That is, the system may identify a payment instrument or other account previously associated with an account of the user in the system and may use this payment instrument or account information to charge the user for a cost of the acquired items. Thus, the user has been able to purchase (or otherwise acquire) items from the facility by simply walking into the facility, procuring one or more items, and walking out of the facility (potentially along with providing identifying information upon entry and/or exit of the facility). 
       FIG. 3  illustrates an example process  300  for, in part, determining that a user has ordered one or more customizable items within a facility, determining that the user is eligible to exit through an exit location of the facility, and charging a payment instrument of the user based on the one or more items ordered by the user. At  302 , the system described above may receive account identification data scanned at an entry location of the facility. For example, the system may receive an indication that a user used a mobile device of the user to scan a code presented on the device at the entry location. At  304 , the system may, in response, determine an account associated with the user based at least in part on the account identification data. That is, the system may map the account identification data to a unique account at the system associated with the user that entered the facility. 
     At  306 , the system may determine, using first sensor data, that the user obtained a first item. For example, the sensor data may comprise image data, weight-sensor data, or any other type of data that may indicate that the user picked an item from an inventory location, received an item from another user in the facility, or otherwise obtained an item while in the facility. At  308 , the system determines, using the first sensor data, first item identification data identifying the first item obtained by the user. For example, the system may input the sensor data and/or additional data into one or more trained classifiers for determining the first item identification of the first item. At  310 , the system stores, in association with the account of the user, the first item identification data. For example, the system may store, in the account, an indication that the user has picked or otherwise received the first item. In some instances, this indication is stored based at least in part on the result of the event being associated with a high confidence. Further, the system may maintain, for the user, the indication that the user is eligible to exit the facility, with items, without first performing a manual checkout of the items. 
     At  312 , the system receives an order via an input device indicating that a second item has been ordered. Further, the order may be associated with a first timestamps, and/or the order may include second item identification data. At  314 , the system may determine, using second sensor data, that the user was positioned at the input device at a second timestamp. For example, the second sensor data may comprise image data or any other type of data that may indicate that the user was standing in front of, in arm&#39;s reach of, or otherwise proximate to the input device, and/or was at an ordering location of the facility, at a time indicated by the second timestamp. At  316 , the system may confirm, at least in part by comparing the first timestamp with the second timestamp, that the user placed the order for the second item via the input device. For instance, the system may determine that a difference in times between the first timestamp and the second timestamp is small enough and/or is otherwise within an acceptable range or margin of error for the system to be confident in confirming that the user placed the order. Also, at  318 , the system may store, in association with the account of the user, the second item identification data. 
     The user is now eligible to exit the facility, with items, through the AC exit location. The system may also update the record associated with the user to indicate that the user is eligible to “just walk out”. At  320  the system may determine that the user is exiting or has exited the facility. In addition, at  322 , the system may charge a payment instrument or other account of the user for at least a cost of the first and second items. 
       FIG. 4  illustrates an example process  400  for determining whether a user has ordered one or more customizable items at a facility. At  402 , the system again receives an indication that a user is located in a facility, using any one or more of the techniques described above. At  404 , the system determines whether an order has been received. For example, the system may receive user input data indicating the order. The user input data may be received via a user input device and/or via a sensor at the facility. The user input data may be entered or otherwise provided by the user, or the user input data may have been entered or otherwise provided by an associate at the facility. For instance, the associate make verbally receive the order from the user, or may enter the order based on consulting with the user. If an order is not received, then the system continues to monitor for such an order. 
     At  406 , the system determines, using sensor data generated by sensors at the facility, that the user has ordered a customizable item at the facility. The system may update a record of the user to reflect the order of the customizable item. Further, at  408 , the system may store, in the record associated with the user, item data associated with the customizable item. The item data may include, for instance, an identity of the item(s) involved, a quantity and/or size of the item(s), information regarding customizable options of the item(s), and/or additional data. The record of the user may include any of a variety of types of additional information, such as location data of the user at the facility, whether the user is eligible to remove items from the facility without performing a manual checkout of the items, and an identity of the user  102 , for example. At  410 , the system may charge a payment instrument of the user for at least a cost of the customizable item based at least on the item data. For example, the system may determine that the user is intending to exit, is exiting, and/or has exited the facility, and charge the user for the cost of one or more items in a virtual cart of the user. 
     Several additional scenarios are contemplated in which a user may be determined to be ineligible to exit the facility using AC. For example, the system may lose track of a user or the identity of the user while the user is at the facility. In other examples, an event may require additional authorization and/or identification of the user. For example, if the user has picked one or more items having a combined cost that is greater than a threshold, then the system may determine that the user is to provide verification of his or her identity (e.g., by showing an official identification to an associate at the facility) and, thus, is currently ineligible to exit using AC. In another example, if the user picks a number of items that is greater than a threshold, or has picked a particular item (e.g., alcohol, etc.), then then the system may determine that the user is to provide verification of his or her identity and, thus, is currently ineligible to exit using AC. In another example, if the system determines that the user has visited the facility a number of times (e.g., five) within a threshold amount of time (e.g., a single business day), then the system may again determine that the user is ineligible to exit using AC until the user provides additional verification. 
     In still other scenarios, a user that has entered the facility may be associated with a payment instrument (e.g., credit card, debit card, prepaid card) associated with a predefined limit (e.g., $20, $1,000, etc.). For instance, the user may have scanned or otherwise provided identifying information associated with the payment instrument upon entering the facility, or the system may have determined the payment instrument associated with the user after identifying the user in another way. In these instances, upon a virtual cart reaching a total cost that nears, reaches, or exceeds the limit of the payment instrument, the system may determine that the user is ineligible to exit the facility without performing a manual checkout of his or her items. Of course, while a few examples are provided, it is to be appreciated that other events may occur that cause the system to update the record associated with the user in various ways. 
       FIGS. 5 and 6  represent an illustrative materials handing environment, such as the materials handling facility  502 , in which the techniques described herein may be applied to cameras monitoring the environments as described below. However, the following description is merely one illustrative example of an industry and environment in which the techniques described herein may be utilized. The materials handling facility  502  (or “facility”) comprises one or more physical structures or areas within which one or more items  504 ( 1 ),  504 ( 2 ), . . . ,  504 (Q) (generally denoted as  504 ) may be held. As used in this disclosure, letters in parentheses such as “(Q)” indicate an integer result. The items  504  comprise physical goods, such as books, pharmaceuticals, repair parts, electronic gear, groceries, and so forth. 
     The facility  502  may include one or more areas designated for different functions with regard to inventory handling. In this illustration, the facility  502  includes a receiving area  506 , a storage area  508 , and a transition area  510 . The receiving area  506  may be configured to accept items  504 , such as from suppliers, for intake into the facility  502 . For example, the receiving area  506  may include a loading dock at which trucks or other freight conveyances unload the items  504 . 
     The storage area  508  is configured to store the items  504 . The storage area  508  may be arranged in various physical configurations. In one implementation, the storage area  508  may include one or more aisles  512 . The aisle  512  may be configured with, or defined by, inventory locations  514  on one or both sides of the aisle  512 . The inventory locations  514  may include one or more of shelves, racks, cases, cabinets, bins, floor locations, or other suitable storage mechanisms for holding or storing the items  504 . The inventory locations  514  may be affixed to the floor or another portion of the facility&#39;s structure, or may be movable such that the arrangements of aisles  512  may be reconfigurable. In some implementations, the inventory locations  514  may be configured to move independently of an outside operator. For example, the inventory locations  514  may comprise a rack with a power source and a motor, operable by a computing device to allow the rack to move from one location within the facility  502  to another. 
     One or more users  516 ( 1 ),  516 ( 2 ), . . . ,  516 (U), totes  518 ( 1 ),  518 ( 2 ), . . . ,  518 (T) (generally denoted as  518 ) or other material handling apparatus may move within the facility  502 . For example, the users  516  may move about within the facility  502  to pick or place the items  504  in various inventory locations  514 , placing them on the totes  518  for ease of transport. An individual tote  518  is configured to carry or otherwise transport one or more items  504 . For example, a tote  518  may include a basket, a cart, a bag, and so forth. In other implementations, other agencies such as robots, forklifts, cranes, aerial drones, and so forth, may move about the facility  502  picking, placing, or otherwise moving the items  504 . 
     One or more sensors  520  may be configured to acquire information in the facility  502 . The sensors  520  in the facility  502  may include sensors fixed in the environment (e.g., ceiling-mounted cameras) or otherwise, such as sensors in the possession of users (e.g., mobile phones, tablets, etc.). The sensors  520  may include, but are not limited to, cameras  520 ( 1 ), weight sensors, radio frequency (RF) receivers, temperature sensors, humidity sensors, vibration sensors, and so forth. The sensors  520  may be stationary or mobile, relative to the facility  502 . For example, the inventory locations  514  may contain cameras  520 ( 1 ) configured to acquire images of pick or placement of items  504  on shelves, of the users  516 ( 1 ) and  516 ( 2 ) in the facility  502 , and so forth. In another example, the floor of the facility  502  may include weight sensors configured to determine a weight of the users  516  or another object thereupon. 
     During operation of the facility  502 , the sensors  520  may be configured to provide information suitable for tracking how objects move or other occurrences within the facility  502 . For example, a series of images acquired by a camera  520 ( 1 ) may indicate removal of an item  504  from a particular inventory location  514  by one of the users  516  and placement of the item  504  on or at least partially within one of the totes  518 . 
     While the storage area  508  is depicted as having one or more aisles  512 , inventory locations  514  storing the items  504 , sensors  520 , and so forth, it is understood that the receiving area  506 , the transition area  510 , or other areas of the facility  502  may be similarly equipped. Furthermore, the arrangement of the various areas within the facility  502  is depicted functionally rather than schematically. For example, multiple different receiving areas  506 , storage areas  508 , and transition areas  510  may be interspersed rather than segregated in the facility  502 . 
     The facility  502  may include, or be coupled to, an inventory management system  522 , which may perform some or all of the techniques described above with reference to  FIGS. 1-4 . For example, the inventory management system may maintain a virtual cart of each user within the facility. The inventory management system may also store a record associated with each user indicating the identity of the user, the location of the user, and whether the user is eligible to exit the facility with one or more items without performing a manual checkout of the items. The inventory management system may also generate and output notification data to the users, indicating whether or not they are so eligible. 
     As illustrated, the inventory management system  522  may reside at the facility  502  (e.g., as part of on-premises servers), on the servers  532  that are remote from the facility  502 , a combination thereof. In each instance, the inventory management system  522  is configured to identify interactions and events with and between users  516 , devices such as sensors  520 , robots, material handling equipment, computing devices, and so forth, in one or more of the receiving area  506 , the storage area  508 , or the transition area  510 . As described above, some interactions may further indicate the existence of one or more events  524 , or predefined activities of interest. For example, events  524  may include the entry of the user  516  to the facility  502 , stocking of items  504  at an inventory location  514 , picking of an item  504  from an inventory location  514 , returning of an item  504  to an inventory location  514 , placement of an item  504  within a tote  518 , movement of users  516  relative to one another, gestures by the users  516 , and so forth. Other events  524  involving users  516  may include the user  516  providing authentication information in the facility  502 , using a computing device at the facility  502  to authenticate identity to the inventory management system  522 , and so forth. Some events  524  may involve one or more other objects within the facility  502 . For example, the event  524  may comprise movement within the facility  502  of an inventory location  514 , such as a counter mounted on wheels. Events  524  may involve one or more of the sensors  520 . For example, a change in operation of a sensor  520 , such as a sensor failure, change in alignment, and so forth, may be designated as an event  524 . Continuing the example, movement of a camera  520 ( 1 ) resulting in a change in the orientation of the field of view  528  (such as resulting from someone or something bumping the camera  520 ( 1 )) may be designated as an event  524 . 
     By determining the occurrence of one or more of the events  524 , the inventory management system  522  may generate output data  526 . The output data  526  comprises information about the event  524 . For example, where the event  524  comprises an item  504  being removed from an inventory location  514 , the output data  526  may comprise an item identifier indicative of the particular item  504  that was removed from the inventory location  514  and a user identifier of a user that removed the item. 
     The inventory management system  522  may use one or more automated systems to generate the output data  526 . For example, an artificial neural network, one or more classifiers, or other automated machine learning techniques may be used to process the sensor data from the one or more sensors  520  to generate output data  526 . For example, the inventory management system may perform some or all of the techniques for generating and utilizing a classifier for identifying user activity in image data, as described in detail above. The automated systems may operate using probabilistic or non-probabilistic techniques. For example, the automated systems may use a Bayesian network. In another example, the automated systems may use support vector machines to generate the output data  526  or the tentative results. The automated systems may generate confidence level data that provides information indicative of the accuracy or confidence that the output data  526  or the tentative data corresponds to the physical world. 
     The confidence level data may be generated using a variety of techniques, based at least in part on the type of automated system in use. For example, a probabilistic system using a Bayesian network may use a probability assigned to the output as the confidence level. Continuing the example, the Bayesian network may indicate that the probability that the item depicted in the image data corresponds to an item previously stored in memory is 95%. This probability may be used as the confidence level for that item as depicted in the image data. 
     In another example, output from non-probabilistic techniques such as support vector machines may have confidence levels based on a distance in a mathematical space within which the image data of the item and the images of previously stored items have been classified. The greater the distance in this space from a reference point such as the previously stored image to the image data acquired during the occurrence, the lower the confidence level. 
     In yet another example, the image data of an object such as an item  504 , user  516 , and so forth, may be compared with a set of previously stored images. Differences between the image data and the previously stored images may be assessed. For example, differences in shape, color, relative proportions between features in the images, and so forth. The differences may be expressed in terms of distance with a mathematical space. For example, the color of the object as depicted in the image data and the color of the object as depicted in the previously stored images may be represented as coordinates within a color space. 
     The confidence level may be determined based at least in part on these differences. For example, the user  516  may pick an item  504 ( 1 ) such as a perfume bottle that is generally cubical in shape from the inventory location  514 . Other items  504  at nearby inventory locations  514  may be predominantly spherical. Based on the difference in shape (cube vs. sphere) from the adjacent items, and the correspondence in shape with the previously stored image of the perfume bottle item  504 ( 1 ) (cubical and cubical), the confidence level that the user  106  has picked up the perfume bottle item  504 ( 1 ) is high. 
     In some situations, the automated techniques may be unable to generate output data  526  with a confidence level above a threshold result. For example, the automated techniques may be unable to distinguish which user  516  in a crowd of users  516  has picked up the item  504  from the inventory location  514 . In other situations, it may be desirable to provide human confirmation of the event  524  or of the accuracy of the output data  526 . For example, some items  504  may be deemed age restricted such that they are to be handled only by users  516  above a minimum age threshold. 
     In instances where human confirmation is desired, sensor data associated with an event  524  may be processed to generate inquiry data. The inquiry data may include a subset of the sensor data associated with the event  524 . The inquiry data may also include one or more of one or more tentative results as determined by the automated techniques, or supplemental data. The subset of the sensor data may be determined using information about the one or more sensors  520 . For example, camera data such as the location of the camera  520 ( 1 ) within the facility  502 , the orientation of the camera  520 ( 1 ), and a field of view  528  of the camera  520 ( 1 ) may be used to determine if a particular location within the facility  502  is within the field of view  528 . The subset of the sensor data may include images that may show the inventory location  514  or that the item  504  was stowed. The subset of the sensor data may also omit images from other cameras  520 ( 1 ) that did not have that inventory location  514  in the field of view  528 . The field of view  528  may comprise a portion of the scene in the facility  502  that the sensor  520  is able to generate sensor data about. 
     Continuing the example, the subset of the sensor data may comprise a video clip acquired by one or more cameras  520 ( 1 ) having a field of view  528  that includes the item  504 . The tentative results may comprise the “best guess” as to which items  504  may have been involved in the event  524 . For example, the tentative results may comprise results determined by the automated system that have a confidence level above a minimum threshold. 
     The facility  502  may be configured to receive different kinds of items  504  from various suppliers and to store them until a customer orders or retrieves one or more of the items  504 . A general flow of items  504  through the facility  502  is indicated by the arrows of  FIG. 5 . Specifically, as illustrated in this example, items  504  may be received from one or more suppliers, such as manufacturers, distributors, wholesalers, and so forth, at the receiving area  506 . In various implementations, the items  504  may include merchandise, commodities, perishables, or any suitable type of item  504 , depending on the nature of the enterprise that operates the facility  502 . The receiving of the items  504  may comprise one or more events  524  for which the inventory management system  522  may generate output data  526 . 
     Upon being received from a supplier at receiving area  506 , the items  504  may be prepared for storage. For example, items  504  may be unpacked or otherwise rearranged. The inventory management system  522  may include one or more software applications executing on a computer system to provide inventory management functions based on the events  524  associated with the unpacking or rearrangement. These inventory management functions may include maintaining information indicative of the type, quantity, condition, cost, location, weight, or any other suitable parameters with respect to the items  504 . The items  504  may be stocked, managed, or dispensed in terms of countable, individual units or multiples, such as packages, cartons, crates, pallets, or other suitable aggregations. Alternatively, some items  504 , such as bulk products, commodities, and so forth, may be stored in continuous or arbitrarily divisible amounts that may not be inherently organized into countable units. Such items  504  may be managed in terms of measurable quantity such as units of length, area, volume, weight, time, duration, or other dimensional properties characterized by units of measurement. Generally speaking, a quantity of an item  504  may refer to either a countable number of individual or aggregate units of an item  504  or a measurable amount of an item  504 , as appropriate. 
     After arriving through the receiving area  506 , items  504  may be stored within the storage area  508 . In some implementations, like items  504  may be stored or displayed together in the inventory locations  514  such as in bins, on shelves, hanging from pegboards, and so forth. In this implementation, all items  504  of a given kind are stored in one inventory location  514 . In other implementations, like items  504  may be stored in different inventory locations  514 . For example, to optimize retrieval of certain items  504  having frequent turnover within a large physical facility  502 , those items  504  may be stored in several different inventory locations  514  to reduce congestion that might occur at a single inventory location  514 . Storage of the items  504  and their respective inventory locations  514  may comprise one or more events  524 . 
     When a customer order specifying one or more items  504  is received, or as a user  516  progresses through the facility  502 , the corresponding items  504  may be selected or “picked” from the inventory locations  514  containing those items  504 . In various implementations, item picking may range from manual to completely automated picking. For example, in one implementation, a user  516  may have a list of items  504  they desire and may progress through the facility  502  picking items  504  from inventory locations  514  within the storage area  508 , and placing those items  504  into a tote  518 . In other implementations, employees of the facility  502  may pick items  504  using written or electronic pick lists derived from customer orders. These picked items  504  may be placed into the tote  518  as the employee progresses through the facility  502 . Picking may comprise one or more events  524 , such as the user  516  in moving to the inventory location  514 , retrieval of the item  504  from the inventory location  514 , and so forth. 
     After items  504  have been picked, they may be processed at a transition area  510 . The transition area  510  may be any designated area within the facility  502  where items  504  are transitioned from one location to another or from one entity to another. For example, the transition area  510  may be a packing station within the facility  502 . When the item  504  arrives at the transition area  510 , the items  504  may be transitioned from the storage area  508  to the packing station. The transitioning may comprise one or more events  524 . Information about the transition may be maintained by the inventory management system  522  using the output data  526  associated with those events  524 . 
     In another example, if the items  504  are departing the facility  502  a list of the items  504  may be obtained and used by the inventory management system  522  to transition responsibility for, or custody of, the items  504  from the facility  502  to another entity. For example, a carrier may accept the items  504  for transport with that carrier accepting responsibility for the items  504  indicated in the list. In another example, a customer may purchase or rent the items  504  and remove the items  504  from the facility  502 . The purchase or rental may comprise one or more events  524 . 
     The inventory management system  522  may access or generate sensor data about the facility  502  and the contents therein including the items  504 , the users  516 , the totes  518 , and so forth. The sensor data may be acquired by one or more of the sensors  520 , data provided by other systems, and so forth. For example, the sensors  520  may include cameras  520 ( 1 ) configured to acquire image data of scenes in the facility  502 . The image data may comprise still images, video, or a combination thereof. The image data may be processed by the inventory management system  522  to determine a location of the user  516 , the tote  518 , the identity of the user  516 , and so forth. As used herein, the identity of the user may represent a unique identifier of the user (e.g., name, number associated with user, username, etc.), an identifier that distinguishes the user amongst other users being located with the environment, or the like. 
     The inventory management system  522 , or systems coupled thereto, may be configured to identify the user  516 , as well as to determine other candidate users. In one implementation, this determination may comprise comparing sensor data with previously stored identity data. For example, the user  516  may be identified by showing their face to a facial recognition system, by presenting a token carrying authentication credentials, providing a fingerprint, scanning a barcode or other type of unique identifier upon entering the facility, and so forth. Identity of the user  516  may be determined before, during, or after entry to the facility  502 . Determination of the user&#39;s  516  identity may comprise comparing sensor data associated with the user  516  in the facility  502  to previously stored user data. 
     In some instances, the inventory management system group users within the facility into respective sessions. That is, the inventory management system  522  may utilize the sensor data to determine groups of users that are effectively “together” (e.g., shopping together). In some instances, a particular session may include multiple users that entered the facility  502  together and, potentially, that navigate the facility together. For example, when a family of two adults and two children enter the facility together, the inventory management system may associate each user with a particular session. Locating groups in addition to individual users may help in determining the outcome of individual events, given that users within a session may not only individually order, pick, return, or otherwise interact with items, but may also pass the items back and forth amongst each other. For instance, a child in the above example may pick the box of cereal before handing the box to her mother, who may place it in her tote  518 . Noting the child and the mother as belonging to the same session may increase the chances of successfully adding the box of cereal to the virtual shopping cart of the mother. 
     By determining the occurrence of one or more events  524  and the output data  526  associated therewith, the inventory management system  522  is able to provide one or more services to the users  516  of the facility  502 . By utilizing one or more human associates to process inquiry data and generate response data that may then be used to produce output data  526 , overall accuracy of the system may be enhanced. The enhanced accuracy may improve the user experience of the one or more users  516  of the facility  502 . In some examples, the output data  526  may be transmitted over a network  530  to one or more servers  532 . 
       FIG. 6  illustrates a block diagram of the one or more servers  532 . The servers  532  may be physically present at the facility  502 , may be accessible by the network  530 , or a combination of both. The servers  532  do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Common expressions associated with the servers  532  may include “on-demand computing,” “software as a service (SaaS),” “cloud services,” “data centers,” and so forth. Services provided by the servers  532  may be distributed across one or more physical or virtual devices. 
     The servers  532  may include one or more hardware processors  602  (processors) configured to execute one or more stored instructions. The processors  602  may comprise one or more cores. The servers  532  may include one or more input/output (I/O) interface(s)  604  to allow the processor  602  or other portions of the servers  532  to communicate with other devices. The I/O interfaces  604  may comprise Inter-Integrated Circuit (I2C), Serial Peripheral Interface bus (SPI), Universal Serial Bus (USB) as promulgated by the USB Implementers Forum, and so forth. 
     The servers  532  may also include one or more communication interfaces  606 . The communication interfaces  606  are configured to provide communications between the servers  532  and other devices, such as the sensors  520 , the interface devices, routers, and so forth. The communication interfaces  606  may include devices configured to couple to personal area networks (PANs), wired and wireless local area networks (LANs), wired and wireless wide area networks (WANs), and so forth. For example, the communication interfaces  606  may include devices compatible with Ethernet, Wi-Fi™, and so forth. The servers  532  may also include one or more busses or other internal communications hardware or software that allow for the transfer of data between the various modules and components of the servers  532 . 
     The servers  532  may also include a power supply  608 . The power supply  608  is configured to provide electrical power suitable for operating the components in the servers  532 . 
     The servers  532  may further include one or more memories  610 . The memory  610  comprises one or more computer-readable storage media (CRSM). The CRSM may be any one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, a mechanical computer storage medium, and so forth. The memory  610  provides storage of computer-readable instructions, data structures, program modules, and other data for the operation of the servers  532 . A few example functional modules are shown stored in the memory  610 , although the same functionality may alternatively be implemented in hardware, firmware, or as a system on a chip (SOC). 
     The memory  610  may include at least one operating system (OS) component  612 . The OS component  612  is configured to manage hardware resource devices such as the I/O interfaces  604 , the communication interfaces  606 , and provide various services to applications or components executing on the processors  602 . The OS component  612  may implement a variant of the FreeBSD™ operating system as promulgated by the FreeBSD Project; other UNIX™ or UNIX-like variants; a variation of the Linux™ operating system as promulgated by Linus Torvalds; the Windows® Server operating system from Microsoft Corporation of Redmond, Wash. USA; and so forth. 
     One or more of the following components may also be stored in the memory  610 . These components may be executed as foreground applications, background tasks, daemons, and so forth. A communication component  614  may be configured to establish communications with one or more of the sensors  520 , one or more of the devices used by associates, other servers  532 , or other devices. The communications may be authenticated, encrypted, and so forth. 
     The memory  610  may store an inventory management system  616 . The inventory management system  616  is configured to provide the inventory functions as described herein with regard to the inventory management system  522 . For example, the inventory management system  616  may track movement of items  504  in the facility  502 , generate user interface data, and so forth. 
     The inventory management system  616  may access information stored in one or more data stores  618  in the memory  610 . The data store  618  may use a flat file, database, linked list, tree, executable code, script, or other data structure to store the information. In some implementations, the data store  618  or a portion of the data store  618  may be distributed across one or more other devices including other servers  532 , network attached storage devices, and so forth. 
     The data store  618  may include physical layout data  620 . The physical layout data  620  provides a mapping of physical locations within the physical layout of devices and objects such as the sensors  520 , inventory locations  514 , and so forth. The physical layout data  620  may indicate the coordinates within the facility  502  of an inventory location  514 , sensors  520  within view of that inventory location  514 , and so forth. For example, the physical layout data  620  may include camera data comprising one or more of a location within the facility  502  of a camera  520 ( 1 ), orientation of the camera  520 ( 1 ), the operational status, and so forth. Continuing example, the physical layout data  620  may indicate the coordinates of the camera  520 ( 1 ), pan and tilt information indicative of a direction that the field of view  528  is oriented along, whether the camera  520 ( 1 ) is operating or malfunctioning, and so forth. 
     In some implementations, the inventory management system  616  may access the physical layout data  620  to determine if a location associated with the event  524  is within the field of view  528  of one or more sensors  520 . Continuing the example above, given the location within the facility  502  of the event  524  and the camera data, the inventory management system  616  may determine the cameras  520 ( 1 ) that may have generated images of the event  524 . 
     The item data  622  comprises information associated with the items  504 . The information may include information indicative of one or more inventory locations  514  at which one or more of the items  504  are stored. The item data  622  may also include order data, SKU or other product identifier, price, quantity on hand, weight, expiration date, images of the item  504 , detail description information, ratings, ranking, and so forth. The inventory management system  616  may store information associated with inventory management functions in the item data  622 . 
     The data store  618  may also include sensor data  624 . The sensor data  624  comprises information acquired from, or based on, the one or more sensors  520 . For example, the sensor data  624  may comprise  3 D information about an object in the facility  502 . As described above, the sensors  520  may include a camera  520 ( 1 ), which is configured to acquire one or more images. These images may be stored as the image data  626 . The image data  626  may comprise information descriptive of a plurality of picture elements or pixels. Non-image data  628  may comprise information from other sensors  520 , such as input from microphones, weight sensors, and so forth. 
     User data  630  may also be stored in the data store  618 . The user data  630  may include identity data, information indicative of a profile, purchase history, location data, images of the user  516 , demographic data, and so forth. Individual users  516  or groups of users  516  may selectively provide user data  630  for use by the inventory management system  522 . The individual users  516  or groups of users  516  may also authorize collection of the user data  630  during use of the facility  502  or access to user data  630  obtained from other systems. For example, the user  516  may opt-in to collection of the user data  630  to receive enhanced services while using the facility  502 . 
     In some implementations, the user data  630  may include information designating a user  516  for special handling. For example, the user data  630  may indicate that a particular user  516  has been associated with an increased number of errors with respect to output data  526 . The inventory management system  616  may be configured to use this information to apply additional scrutiny to the events  524  associated with this user  516 . For example, events  524  that include an item  504  having a cost or result above the threshold amount may be provided to the associates for processing regardless of the determined level of confidence in the output data  526  as generated by the automated system. 
     The inventory management system  616  may include one or more of a location component  632 , identification component  634 , event-determination component  636 , inquiry component  638 , and checkout-eligibility component  640 , potentially amongst other components  656 . 
     The location component  632  functions to locate items or users within the environment of the facility to allow the inventory management system  616  to assign certain events to the correct users. That is, the location component  632  may assign unique identifiers to users as they enter the facility and, with the users&#39; consent, may locate the users throughout the facility  502  over the time they remain in the facility  502 . The location component  632  may perform this locating using sensor data  624 , such as the image data  626 . For example, the location component  632  may receive the image data  626  and may use facial-recognition techniques to identify users from the images. After identifying a particular user within the facility, the location component  632  may then locate the user within the images as the user moves throughout the facility  502 . Further, should the location component  632  temporarily “lose” a particular user, the location component  632  may again attempt to identify the users within the facility based on facial recognition, and/or using other techniques such as voice recognition, or the like. 
     Therefore, upon receiving the indication of the time and location of the event in question, the location component  632  may query the data store  618  to determine which one or more users were at or within a threshold distance of the location of the event at the particular time of the event. Further, the location component  632  may assign different confidence levels to different users, with the confidence levels indicating how likely it is that each corresponding user is the user that is in fact associated with the event of interest. 
     The location component  632  may access the sensor data  624  in order to determine this location data of the user and/or items. The location data provides information indicative of a location of an object, such as the item  504 , the user  516 , the tote  518 , and so forth. The location may be absolute with respect to the facility  502  or relative to another object or point of reference. Absolute terms may comprise a latitude, longitude, and altitude with respect to a geodetic reference point. Relative terms may include a location of 25.4 meters (m) along an x-axis and 75.2 m along a y-axis as designated by a floor plan of the facility  502 , 5.2 m from an inventory location  514  along a heading of 169°, and so forth. For example, the location data may indicate that the user  516 ( 1 ) is 25.2 m along the aisle  512 ( 1 ) and standing in front of the inventory location  514 . In comparison, a relative location may indicate that the user  516 ( 1 ) is 32 cm from the tote  518  at a heading of 73° with respect to the tote  118 . The location data may include orientation information, such as which direction the user  516  is facing. The orientation may be determined by the relative direction the user&#39;s  516  body is facing. In some implementations, the orientation may be relative to the interface device. Continuing the example, the location data may indicate that the user  516 ( 1 ) is oriented with a heading of 0°, or looking north. In another example, the location data may indicate that the user  516  is facing towards the interface device. 
     The identification component  634  is configured to identify an object. In one implementation, the identification component  634  may be configured to identify an item  504 . In another implementation, the identification component  634  may be configured to identify the user  516 . For example, the identification component  634  may use facial recognition techniques to process the image data  626  and determine the identity data of the user  516  depicted in the images by comparing the characteristics in the image data  626  with previously stored results. The identification component  634  may also access data from other sensors  520 , such as from an RFID reader, an RF receiver, fingerprint sensors, and so forth. 
     The event-determination component  636  is configured to process the sensor data  624  and generate output data  656 , and may include components described above. The event-determination component  636  may access information stored in the data store  618  including, but not limited to, event description data  642 , confidence levels  644 , or threshold values  646 . In some instances, the event-determination component  636  may be configured to perform some or all of the techniques described above with regards to the event-determination component  636 . For instance, the event-determination component  636  may be configured to create and utilize event classifiers for identifying events (e.g., predefined activity) within image data, potentially without use of other sensor data acquired by other sensors in the environment. 
     The event description data  642  comprises information indicative of one or more events  524 . For example, the event description data  642  may comprise predefined profiles that designate movement of an item  504  from an inventory location  514  with the event  524  of “pick”. The event description data  642  may be manually generated or automatically generated. The event description data  642  may include data indicative of triggers associated with events occurring in the facility  502 . An event may be determined as occurring upon detection of the trigger. For example, sensor data  624  such as a change in weight from a weight sensor  520  at an inventory location  514  may trigger detection of an event of an item  504  being added or removed from the inventory location  514 . In another example, the trigger may comprise an image of the user  516  reaching a hand toward the inventory location  514 . In yet another example, the trigger may comprise two or more users  516  approaching to within a threshold distance of one another. 
     The event-determination component  636  may process the sensor data  624  using one or more techniques including, but not limited to, artificial neural networks, classifiers, decision trees, support vector machines, Bayesian networks, and so forth. For example, the event-determination component  636  may use a decision tree to determine occurrence of the “pick” event  524  based on sensor data  624 . The event-determination component  636  may further use the sensor data  624  to determine one or more tentative results  648 . The one or more tentative results  648  comprise data associated with the event  524 . For example, where the event  524  comprises a disambiguation of users  516 , the tentative results  648  may comprise a list of possible user  516  identities. In another example, where the event  524  comprises a disambiguation between items  104 , the tentative results  648  may comprise a list of possible item identifiers. In some implementations, the tentative result  648  may indicate the possible action. For example, the action may comprise the user  516  picking, placing, moving an item  504 , damaging an item  504 , providing gestural input, and so forth. 
     In some implementations, the tentative results  648  may be generated by other components. For example, the tentative results  648  such as one or more possible identities or locations of the user  516  involved in the event  524  may be generated by the location component  632 . In another example, the tentative results  648  such as possible items  504  that may have been involved in the event  524  may be generated by the identification component  634 . 
     The event-determination component  636  may be configured to provide a confidence level  644  associated with the determination of the tentative results  648 . The confidence level  644  provides indicia as to the expected level of accuracy of the tentative result  648 . For example, a low confidence level  644  may indicate that the tentative result  648  has a low probability of corresponding to the actual circumstances of the event  524 . In comparison, a high confidence level  644  may indicate that the tentative result  648  has a high probability of corresponding to the actual circumstances of the event  524 . 
     In some implementations, the tentative results  648  having confidence levels  644  that exceed the threshold may be deemed to be sufficiently accurate and thus may be used as the output data  656 . For example, the event-determination component  636  may provide tentative results  648  indicative of the three possible items  504 ( 1 ),  504 ( 2 ), and  504 ( 3 ) corresponding to the “pick” event  524 . The confidence levels  644  associated with the possible items  504 ( 1 ),  504 ( 2 ), and  504 ( 3 ) may be 25%, 70%, 92%, respectively. Continuing the example, the threshold value  646  may be set such that confidence level  644  of  90 % are deemed to be sufficiently accurate. As a result, the event-determination component  636  may designate the “pick” event  524  as involving item  504 ( 3 ). 
     The inquiry component  638  may be configured to use at least a portion of the sensor data  624  associated with the event  524  to generate inquiry data  650 . In some implementations, the inquiry data  650  may include one or more of the tentative results  648  or supplemental data  652 . The inquiry component  638  may be configured to provide inquiry data  650  to one or more devices associated with one or more human associates. 
     An associate user interface is presented on the respective devices of associates. The associate may generate response data  654  by selecting a particular tentative result  648 , entering new information, indicating that they are unable to answer the inquiry, and so forth. 
     The supplemental data  652  comprises information associated with the event  524  or that may be useful in interpreting the sensor data  624 . For example, the supplemental data  652  may comprise previously stored images of the items  504 . In another example, the supplemental data  652  may comprise one or more graphical overlays. For example, the graphical overlays may comprise graphical user interface elements such as overlays depicting indicia of an object of interest. These indicia may comprise highlights, bounding boxes, arrows, and so forth, that have been superimposed or placed atop the image data  626  during presentation to an associate. 
     The inquiry component  638  processes the response data  654  provided by the one or more associates. The processing may include calculating one or more statistical results associated with the response data  654 . For example, statistical results may include a count of the number of times associates selected a particular tentative result  648 , determination of a percentage of the associates that selected a particular tentative result  648 , and so forth. 
     The inquiry component  638  is configured to generate the output data  656  based at least in part on the response data  654 . For example, given that a majority of the associates returned response data  654  indicating that the item  504  associated with the “pick” event  524  is item  504 ( 5 ), the output data  526  may indicate that the item  504 ( 5 ) was picked. 
     The inquiry component  638  may be configured to selectively distribute inquiries to particular associates. For example, some associates may be better suited to answering particular types of inquiries. Performance data, such as statistical data about the performance of the associates, may be determined by the inquiry component  638  from the response data  654  provided by the associates. For example, information indicative of a percentage of different inquiries in which the particular associate selected response data  654  that disagreed with the majority of associates may be maintained. In some implementations, test or practice inquiry data  650  having a previously known correct answer may be provided to the associate for training or quality assurance purposes. The determination of the set of associates to use may be based at least in part on the performance data. 
     By using the inquiry component  638 , the event-determination component  636  may be able to provide high reliability output data  656  that accurately represents the event  524 . The output data  656  generated by the inquiry component  638  from the response data  654  may also be used to further train the automated systems used by the inventory management system  616 . For example, the sensor data  624  and the output data  656 , based on response data  654 , may be provided to one or more of the components of the inventory management system  616  for training in process improvement. Continuing the example, this information may be provided to an artificial neural network, Bayesian network, and so forth, to further train these systems such that the confidence level  644  and the tentative results  648  produced in the future for the same or similar input is improved. Finally, as  FIG. 6  illustrates, the servers  532  may store and/or utilize other data  658 . 
     The checkout-eligibility component  640 , meanwhile, may perform some or all of the operations described above with reference to processes  200 ,  300 , and  400 . For instance, the checkout-eligibility component  640  may store indications, for each of respective users within a facility, whether the respective user is currently eligible to exit through an AC exit location or otherwise eligible to exit a facility with one or more items without performing manual checkout of the items. The checkout-eligibility component  640  may change these respective indications based on predefined events that occur within the facility, as described above. For instance, a user that is identified upon entry to the facility may be initially eligible, but thereafter may be deemed ineligible upon performing an event at an inventory location. Conversely, a user that is not identified upon entry and, thus, initially ineligible may become eligible upon being identified by the identification component  634 . The checkout-eligibility component  640  may also generate and output the notification data, discussed above. 
     Embodiments may be provided as a software program or computer program product including a non-transitory computer-readable storage medium having stored thereon instructions (in compressed or uncompressed form) that may be used to program a computer (or other electronic device) to perform processes or methods described herein. The computer-readable storage medium may be one or more of an electronic storage medium, a magnetic storage medium, an optical storage medium, a quantum storage medium, and so forth. For example, the computer-readable storage media may include, but is not limited to, hard drives, floppy diskettes, optical disks, read-only memories (ROMs), random access memories (RAMs), erasable programmable ROMs (EPROMs), electrically erasable programmable ROMs (EEPROMs), flash memory, magnetic or optical cards, solid-state memory devices, or other types of physical media suitable for storing electronic instructions. Further, embodiments may also be provided as a computer program product including a transitory machine-readable signal (in compressed or uncompressed form). Examples of machine-readable signals, whether modulated using a carrier or unmodulated, include, but are not limited to, signals that a computer system or machine hosting or running a computer program can be configured to access, including signals transferred by one or more networks. For example, the transitory machine-readable signal may comprise transmission of software by the Internet. 
     Separate instances of these programs can be executed on or distributed across any number of separate computer systems. Thus, although certain steps have been described as being performed by certain devices, software programs, processes, or entities, this need not be the case, and a variety of alternative implementations will be understood by those having ordinary skill in the art. 
     Additionally, those having ordinary skill in the art readily recognize that the techniques described above can be utilized in a variety of devices, environments, and situations. Although the subject matter has been described in language specific to structural features 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. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims. 
     While the foregoing invention is described with respect to the specific examples, it is to be understood that the scope of the invention is not limited to these specific examples. Since other modifications and changes varied to fit particular operating requirements and environments will be apparent to those skilled in the art, the invention is not considered limited to the example chosen for purposes of disclosure, and covers all changes and modifications which do not constitute departures from the true spirit and scope of this invention.