GB2583759A - Queuing reduction system and method - Google Patents

Abstract

A system and method is disclosed which receives information regarding a user selection of a first item or class of items, stores the information, and detects proximity of the user. A queuing area display 202, such as in a drive though restaurant, modifies a prominence of an indication associated with the first item or the class of items based on the information and in response to the proximity detection. The proximity of the user may be based on detecting a vehicle 2, and the information may be routine information. Also disclosed is a system and method which provides a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information.

Description

QUEUING REDUCTION SYSTEM AND METHOD

TECHNOLOGICAL FIELD

Embodiments of the present disclosure relate to a system and method. Some relate to a system and method for a drive-through queuing area display.

BACKGROUND

Many establishments exist which comprise queuing areas, such as drive-through restaurants, cafes, free workplace cafeterias, and non-food establishments. The establishments offer users a choice of items such as food items, other products, or services.

A board or display can be located in a queuing area to inform users of available choices. The available choices may change or their contents may be re-ordered regularly. If a large number of choices are on offer, small images and text may be required. For menu boards or displays, the text is associated provides information such as price, ingredients, calories, vegetarian/vegan suitability, or allergies/intolerance information. Drive-through restaurant menu boards often carry a very high density of information, which may change regularly.

Users often need to check menu boards to see whether items they want are available, or to take notice of changes. The high density of menu board information can cause queuing delays because users are physiologically only able to search through dense information at a certain rate. Changes in menu board layout cause involuntary distraction and hesitation.

The additional time spent checking dense menu boards increases queuing time. Increased queuing time increases physical and/or mental fatigue, and can increase vehicle emissions in the case of a drive-through restaurant. Delays in item preparation or picking also contribute to queuing time.

BRIEF SUMMARY

According to various, but not necessarily all, embodiments there is provided a system comprising at least one controller, the at least one controller comprising means for: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information and in response to the proximity detection. This personalization helps the user to find what they are likely to be looking for quickly. An advantage is reducing queuing delays to reduce fatigue, and vehicle emissions in the case of a drive-through.

According to various, but not necessarily all, embodiments there is provided a system comprising at least one controller, the at least one controller comprising means for: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information. This solves the same problem and provides the same advantage of reducing queuing delays to reduce fatigue, and vehicle emissions in the case of a drive-through. This is because the notification may instruct preparation or picking of items to meet forecast demand, by a human or automated preparer/picker. This reduces waiting times upstream of a collection point.

According to various, but not necessarily all, embodiments there is provided a method comprising: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information and in response to the proximity detection.

According to various, but not necessarily all, embodiments there is provided a drive-through system comprising the system described herein.

According to various, but not necessarily all, embodiments there is provided a method comprising: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and outputting a forecast demand notification to an output device associated with an item preparation or picking area, in response to the proximity detection According to various, but not necessarily all, embodiments there is provided a computer program that, when run on a computer, performs one or more of the methods described herein.

According to various, but not necessarily all, embodiments there is provided a system comprising at least one controller, the at least one controller comprising means for: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and controlling a queuing area display based on the information and in response to the proximity detection.

BRIEF DESCRIPTION

Some examples will now be described with reference to the accompanying drawings in which: Fig 1 illustrates an example of a method; Fig 2 illustrates an example of a system; Fig 3 illustrates an example network diagram of a system; Fig 4A illustrates a first example of controlling a queuing area display; Fig 4B illustrates a second example of controlling a queuing area display; Fig 4C illustrates a third example of controlling a queuing area display; and Fig 4D illustrates a fourth example of controlling a queuing area display; and Fig 5A illustrates an example of a controller; and Fig 5B illustrates an example of a computer readable storage medium.

DETAILED DESCRIPTION

Fig 1 illustrates a method 100, according to various, but not necessarily all examples of the disclosure.

In some examples, a user may optionally register an account, to opt-in for collection of their personal information. Therefore, the method 100 may start at block 102 with registering a user account.

At block 104, the method 100 comprises receiving information associated with user selection of a first item or class of items by the user. The information may be user selection information.

The user selection information may indicate what items were chosen during a first visit. In some examples, the user selection information may indicate a default item (e.g. favourite). In some examples, the user selection information may indicate a class of items, and therefore may comprise health requirement information (e.g. allergies) and/or dietary preference information (e.g. vegetarian). Alternatively or additionally, the information associated with selection of items may simply indicate a number of transactions associated with a user or vehicle, and may or may not identify specific items.

At block 106, the method 100 comprises storing the information, for example Fig 3 shows information 224 associated with user selection of the first item or class of items in a database 222. Therefore, information associated with the first visit has been collected.

At block 108, the method 100 comprises detecting proximity of the user, for example in association with a second visit to the same site or to a different site that may be part of a same franchise. The second visit occurs at some unspecified time after the first visit.

At block 110, the method 100 comprises controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information 224 and in response to the proximity detection. The display may comprise a graphical user interface. This personalization helps the user to find what they are likely to be looking for quickly, among a large number of other indications of various items. In an example implementation, the method 100 may select an item or class based on the information 224. Each item or class can be associated with a probability score or order frequency based on the information. Items or classes can be displayed prominently when they are associated with an above-threshold probability or frequency (e.g. static or relative threshold).

At block 112, the method 100 comprises providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information. This reduces waiting times at an item collection point at the head of the queue.

Block 112 can be performed additionally to or alternatively from block 110.

After block 112, the method 100 may loop back to block 104 as new information 224 may have been received automatically or manually, based on what the user selected (ordered) on their current (second) visit. The new information 224 can be used to update the existing stored information 224. The user's new selections will either reinforce or diverge from their previous choices, and the method 100 can adapt accordingly to improve long-term accuracy. if a user stops ordering particular items or from a particular class of items, those items or classes may no longer be selected for prominent display when another item or class is later determined to have a higher probability or frequency. The other item or class may then be selected for prominent display.

Fig 2 illustrates an example of a system 1 for carrying out the method 100. The system 1 of Fig 2 is applied to a drive-through restaurant 200, comprising a queuing area for queuing vehicles. The queuing area display 202 is a drive-through queuing area display 202.

Information displayed on the queuing area display 202 is visible from at least a front of the queuing area. The display 202 may have a diagonal screen area of more than approximately 120 centimetres. The display 202 may be provided in a housing that is anchored to the ground. The display 202 may be external to the restaurant 200 and its housing may be configured for weather-resistance.

The illustrated drive-through restaurant 200 comprises an order point 206, a payment point 208, and a collection point 212. In some implementations, these points can be merged. The queuing area displays 202 are positioned proximal to or upstream of the order points 206.

In the illustration, two parallel order points 206 are provided via two parallel queuing lanes, however any number of parallel queuing lanes can be provided, or just one queuing lane. An example vehicle 2 is shown, in a queuing lane. The vehicle 2 can only travel one way. Kerbs defining the edges of the queuing lane may prevent the vehicle 2 from turning around.

The queuing lanes merge into one queuing lane for the payment point 208 and collection point 212, but this is not necessary for all implementations. The method 100 is also not limited to drive-through restaurants and can be applied to other drive-through establishments, other food establishments, and other non-food establishments whether drive-through or not, that employ queuing areas and queuing area displays.

In some examples, the system 1 can be retrofitted to an existing establishment without significant building construction or modification.

Since the establishment is a drive-through restaurant 200, the target users may be humans but they will arrive in their vehicles (e.g. automobiles) and will not necessarily exit their vehicles at any point during their visit. It is easier in this context to detect a vehicle than to directly detect a human.

Therefore, block 102 of the method 100 may comprise receiving vehicle information as part of the account setup. Vehicle information may comprise a vehicle number plate, for example, or any other detectable identifying characteristic. For example, as shown in Fig 3, the database 222 may store vehicle information 226. An association between the vehicle information 226 and the information 224 may be stored as part of the account.

In this example, detecting proximity of the user at block 108 of the method 100 comprises determining whether a detected vehicle has detected vehicle information that matches the vehicle information 226 of a particular user. In the event of a match, the correct stored information 224 associated with the user's item selections can be retrieved.

The system 1 may comprise a first detector 214 configured for vehicle number plate recognition. The first detector 214 may comprise an automatic number plate recognition (ANPR) camera. The ANPR camera may be less than 1.5m above the ground to provide a reasonable viewing angle. The ANPR camera may be laterally offset from a queuing lane to reduce the chance of obstruction by leading vehicles. Multiple ANPR cameras can be provided, for individual parallel queuing lanes. The ANPR camera may be pointed so that its monitored field of view is between an entry point to the queuing area (past the point of no return, for vehicles), and level with or upstream of the queuing area display 202.

Vehicles are not always driven by the same people. Therefore, a greater degree of certainty over the identity of the user at block 108 can be provided by using a first detector 214 that directly detects the human as opposed to the vehicle. A human detector can be provided alternatively or in addition to the ANPR camera. An example human detector includes a facial recognition detector, such as a camera with sufficient resolution for facial recognition. The camera's optical settings and orientation may be configured for detecting a face through a vehicle front windscreen. The camera may be no higher than 1.5m above the ground, so that faces are not obscured by a vehicle roof. A facial recognition algorithm may be employed to match an image from the camera with a photograph or other facial feature point information stored as personal information in the user's account. Another example human detector includes a portable user device (e.g. mobile phone) detector, because mobile phones are personal. The detector may comprise a receiver and may be configured to receive short range wireless local area network identifying information, for example. Alternatively, the detector may be a software module configured to receive an indication from a software application running on the device that the user is proximal, e.g. entered a geofence. Alternatively, a user could scan themselves in using RFID technology. Another human detection scheme comprises receiving information from the vehicle itself. A vehicle system may be configured to detect the identities of its occupants (e.g. via interior cameras, seat weight sensors, seat memory settings, etc) and to provide the relevant information via vehicle-to-infrastructure communication.

Many vehicles are multi-occupancy vehicles. The method 100 may therefore be configured, in response to block 108, to determine or estimate a number of transactions and/or users associated with the vehicle. The control of the queuing area display 202 can be adapted accordingly, to enable concurrent or consecutive personalization for each user in the vehicle. Deterministically ascertaining the number of users can be achieved using a human detector scheme as described above. Estimation may comprise determining, from stored information 224, how many transactions are associated with each visit by the vehicle. Information 224 associated with each transaction may be assigned to different putative user profiles associated with the vehicle. Determining that multiple transactions are associated with a same vehicle as opposed to different vehicles can be achieved using various schemes such as transaction timestamp monitoring and monitoring vehicle positions. The schemes could take into account information such as a number of seats in the vehicle, which could be stored or queried from a database using the vehicle number plate, or sensed. The schemes could take into account past transaction payment types. The schemes could take into account a stored indication that in the past, multiple transactions per visit have been associated with the same vehicle. The determination could be deterministic or probabilistic.

In order to work desirably, the system 1 may also be configured to determine when to prepare the queuing area display 202 for a next user. The system 1 could detect one or more of completion of an order request, payment, or exchange of items. Then, the queuing area display 202 may be prepared for a next user, by removing or resetting any personalized modification of item prominence.

Detecting completion of an order request may comprise a human or automated order-taker providing a command associated with completion of the order request. For example, the queuing area display 202 may be updated to show a subtotal, and/or to direct a user to continue to the next point 208. In some examples, a microphone and voice recognition may be employed. In some examples, a movement detector 216 for directly detecting movement of the user away from the queuing area display 202 and/or order point 206 may be employed, as shown in Fig 2. The movement detector 216 may comprise a camera or beam-breaking detector (e.g. light gate), pointed so that its monitoring area is proximal to (level with or slightly downstream of) the queuing area display 202.

Detecting payment may comprise determining that a transfer of funds has been registered.

The detection may be automatic, by integration of the system 1 with an automatic payment system at the payer or merchant end, and/or semi-automated, with detection of a human or automated cashier providing a command associated with completion of a transfer of funds. In some examples, the movement detector 216 can be employed and monitors an area proximal to the payment point 208. Detecting payment as a means for determining when to prepare the queuing area display 202 for a next user is useful if the order point 206 and payment point 208 are integrated or close. However, if the payment point 208 is significantly downstream of the order point 206, then this approach may not be the best approach because the queuing area display 202 may not be updated for the next user if the present user has not reached the payment point 208.

Detecting exchange of items can comprise detecting exchange of items via camera, or movement via a movement detector 216, or input from a human or automated item exchanger. Detecting exchange as a means for determining when to prepare the display 202 for a next user is useful if the order, payment and exchange points are integrated or close, but less so if the exchange point is downstream.

As mentioned above, the method 100 may provide a forecast demand notification for an item preparer/picker. If the item preparer/picker is a human, then the system 1 may comprise an output device 204 such as a display. The display is located for viewing by an item preparer or picker or their supervisor, for example located in a kitchen area. The notification takes the form of instructions to prepare/pick the items which are forecast to be ordered by the user. The display 204 may be standalone or may display all of the orders received by the restaurant 200. The notification may or may not indicate whether some items are 'forecast based on the present method 100 and have not actually been ordered yet.

The forecast demand notification may be displayed within less than ten seconds of the vehicle entering the field of view of the first detector 214, or less than 3 seconds, to give as much lead time as possible and ensure the item is picked/prepared preemptively.

If the item preparer/picker is automated, the forecast demand notification may be a set of machine-readable instructions.

The forecast demand notification could be configured to minimize the chances or effects of false-predictions. Although many users' orders are predictable, it is often difficult to predict with certainty every time. However, in a fast-food context, French fries or other side orders may be required for almost every order. Likewise, some users may always select a particular portion size. Some users may always order a particular drink. Therefore, the forecast demand notification may be configured to instruct preemptive preparation of a specific class of items such as side order items (e.g. French fries and/or drink, or other low value items), but not the entire meal or high-value main-order items such as hamburgers. As a further feature, specific portion sizes could also be forecast. The feature could of course be extended to forecast entire meals or high-value items, depending on the amount of uncertainty. In some implementations, a probability scoring threshold could be defined based on a history of information 224. Machine learning algorithms could be trained to determine probabilities more accurately.

The forecast demand notification could be based on a determined or estimated number of transactions and/or users associated with the vehicle. A transaction means a record of a single payment having been made and/or a record of the identity of an item that has been exchanged.

Example methods for determining or estimating the number of transactions and/or users are described earlier.

If the forecast demand notification is only for side orders, then a basic estimation could be high-accuracy. For example, estimating how many transactions, on average, are performed with each visit by the vehicle, may yield an accurate prediction on side order demand. Specific user identities may not even be required. For instance, when a vehicle establishes a history of paying four times for four meals (e.g. group of friends or acquaintances), then the forecast demand notification may instruct a food preparer to prepare more side orders, up to four. When a vehicle establishes a history of ordering four meals and paying once (e.g. family), then the forecast demand notification may likewise instruct more side orders.

Fig 3 illustrates a network architecture for the system 1. One or more controllers 210 reside in the centre, which have access to a database 222 for querying and retrieving information. The controller 210 may perform the method 100.

The database 222 stores information including at least the information 224, the vehicle information 226, and optionally personal information 228. Personal information 228 includes, for example, at least one of: a name, a nickname; an address; an image of the user or an avatar; a dialect; or a personalized greeting message preference. The database 222 may store user accounts to store associations between the types of information. A group of associations may correspond to a particular user account. The database 222 may be integrated with the controller memory or may be separate.

Optional further information that can be stored by the database 222 includes one or more of: transaction value; payment method type; times/locations of visits; user feedback information (e.g. review); operator feedback information; operator identity information identifying who provided a service to the user; or weather information.

The database 222 and controller 210 may be local to the establishment, or remote. The database may be centralized or decentralized and/or distributed. The database 222 may optionally reside in a cloud layer. A remote implementation provides an advantage that the same service can be more readily administered across multiple sites (e.g. same franchise). The user will have the same experience at site A as at site B. Information 224 collected from site A can influence control of a queuing area display at site B. In the remote implementation, the system 1 can operate by transmitting data across a wide area network. The system 1 may encrypt at least information 224 and vehicle information 226 and personal information 228 for transmission.

The network inputs to the at least one controller 210 may come from at least one of: -a portable user device 3 (e.g. mobile phone), for example for setting up an account.

Information 224 could be manually provided as user feedback information. The user feedback information may indicate which items or classes have been selected as a preference, and/or as a record of what the user ordered. The device 3 could even be used for proximity detection at block 108 of the method 100, for instance when a software application 218 running on the device 3 detects, via location tracking (e.g. Global Positioning System), that a geofence is entered; -a detector such as the ANPR camera, for proximity detection at block 108 of the method 100. The detector provides an indication of the vehicle information 226 (e.g. the vehicle registration or the unprocessed image data). This triggers retrieval of information 224 as described above, for blocks 110 and 112 of the method 100; -a movement detector 216 such as a light gate, for detecting when the queuing area display 202 can be updated for the next user; -a transaction information source 220, such as a point of sale/merchant computer system. This source 220 may automatically passively monitor information 224 identifying what items and/or from what classes of items the user has ordered (selected).

The network outputs related to commands from the controller 210 include the queuing area display 202 and/or the output device 204 for the forecast demand notification.

The system 1, as a whole, may comprise one or more of: the controller 210; the database 222; the first detector 214; the movement detector 216; the software application 218; the transaction information source 220; the queuing area display 202; the output device 204.

Although not shown, the system 1 could be further extended to receive additional information to improve performance. For example, context information such as weather information, traffic information, time/date information, may be received from a data source and processed. The method 100 may be dependent on context such as weather, traffic, day of week, month, time of day. The context may be associated with particular information, to determine a temporal pattern of selection choices. For instance, items associated with particular weather contexts could be assigned a higher probability score when the same weather context occurs.

The system 1 may also integrate features such as loyalty schemes, user feedback information such as a review/like/dislike, etc. Figs 4A to 4D provide different examples of a graphical user interface 400 for the queuing area display. The graphical user interface 400 comprises a number of representations 402, 404, 406, 408 of items that can be selected. Each representation comprises a number of indications (not shown) such as a picture; item information text (e.g. price, ingredients, calories, dietary preference information, and/or allergy information). For a drive-through restaurant, each item refers to a meal and/or to a specific meal component (e.g. fries, drink).

Figs 4A to 4D demonstrate ways in which the queuing area display 202 can be controlled to emphasize (increase) a prominence of an indication associated with the first item or class of items indicated by the information 224. The effect is to make it quicker for the user to find what they are predicted to be looking for, based on their information (history). The picture and/or item information text can be modified. Modifying a prominence of an indication may comprise modifying the indication directly (e.g. re-sizing, re-positioning, changing colour, rotation, animation), or displaying new information (e.g. text) proximal to the indication, or changing a group characteristic such as grouping indications of same-class items in a dedicated display area, and/or moving the other indications away, and/or removing the other indications.

In Fig 4A, a new message 410 is displayed. The message 410 is displayed proximal to a representation 402 of a recommended item. The relative close proximity of the message 410 to the representation 402 compared to the other representations 404, 406, 408 is what increases prominence of the representation 402 and therefore the item indications. The recommended item may represent the item which is usually ordered (e.g. most frequently ordered relative to other items). The message 410 may identify the item as being an item which is commonly/usually ordered. The representation 402 may be selected to be displayed in a different location and/or may be larger, compared to normal. Making the representation 402 larger may comprise making the picture larger and/or the item information text larger.

Other representations 404, 406, 408 may be displayed smaller and not proximal to the message, or temporarily not displayed at all.

In Fig 4B, indications associated with a class of items are made more prominent. In this example, but not necessarily in all examples, the class of items corresponds to a dietary preference, such as vegetarian or vegan. This can be achieved by selecting items to form a group 412 of adjacent/neighbouring items. The representations 402, 404 corresponding to those items can be displayed as a group 412. The group of items may be accompanied by a message 410 identifying a class to which those items belong or otherwise identifying that those items belong to a same group 412. The group 412 may be highlighted via a common visual characteristic, such as being inside a boundary, being inside a shaded area, having a same size that is different (e.g. larger) than usual, and/or being the only representations that are displayed. Other items outside the class may not share the common characteristic, or may temporarily be not displayed at all.

Fig 4C is a variant of Fig 4B in which the class of items corresponds to a health requirement, such as an allergy and/or intolerance requirement, such as gluten free or nut-free.

Fig 4D illustrates more clearly the concept of making a representation 402 of an item larger relative to other displayed representations 404, 406, 408 of other items. If the representation 402 becomes sufficiently large, the locations of other displayed representations 404, 406, 408 may be shifted to accommodate the larger size, or may be hidden.

Figs 4A to 4D represent a few of many possible ways in which prominence may be altered.

If the system 1 comprises a speaker associated with the queuing area display 202, audio output may also be controlled automatically, based on the information and in response to the proximity detection. The audio output may inform the user of at least one of the one or more recommended items, and/or may identify the class and/or may identify an item as being commonly/usually ordered. In some implementations, if a human order-taker is reading from a script, the script may be controlled automatically to provide the same result. The script may be displayed on a display, for example.

A personalized greeting may also be provided or scripted based on the personal information.

A personalized greeting may personalize a name, a nickname, a dialect, and/or the like.

If the queuing area display comprises a touch screen for receiving item selections, one or more shortcut software buttons may be controlled in accordance with the graphical user interface 400. For example, the sizes and/or positions of software buttons may be modified proportionally to the indications of the representations 402-408, so that touching the touch screen at the location of an emphasized indication will select the associated item. The software buttons may be visible or invisible.

It will be appreciated that the increased prominence of selected representations reduces queuing time because the representation has been selected based on an item or class of items that the user has looked for regularly in the past. In other words, it is easier for the user to find what they are determined to be looking for.

Fig 5A illustrates an example of a controller 210. Implementation of a controller 210 may be as controller circuitry. The controller 210 may be implemented in hardware alone, have certain aspects in software including firmware alone or can be a combination of hardware and software (including firmware).

As illustrated in Fig 5A the controller 210 may be implemented using instructions that enable hardware functionality, for example, by using executable instructions of a computer program 1206 in a general-purpose or special-purpose processor 1202 that may be stored on a computer readable storage medium (disk, memory etc) to be executed by such a processor 1202.

The processor 1202 is configured to read from and write to the memory 1204. The processor 1202 may also comprise an output interface via which data and/or commands are output by the processor 1202 and an input interface via which data and/or commands are input to the processor 1202.

The memory 1204 stores a computer program 1206 comprising computer program instructions (computer program code) that controls the operation of the apparatus 200 when loaded into the processor 1202. The computer program instructions, of the computer program 1206, provide the logic and routines that enables the apparatus to perform the methods illustrated in Fig 1. The processor 1202 by reading the memory 1204 is able to load and execute the computer program 1206.

The apparatus 200 therefore comprises: at least one processor 1202; and at least one memory 1204 including computer program code; the at least one memory 1204 and the computer program code configured to, with the at least one processor 1202, cause the apparatus 200 at least to perform one or more of the methods described herein.

As illustrated in Fig 5B, the computer program 1206 may arrive at the apparatus 200 via any suitable delivery mechanism 1208. The delivery mechanism 1208 may be, for example, a machine readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a Compact Disc Read-Only Memory (CD-ROM) or a Digital Versatile Disc (DVD) or a solid state memory, an article of manufacture that comprises or tangibly embodies the computer program 1206. The delivery mechanism may be a signal configured to reliably transfer the computer program 1206. The apparatus 200 may propagate or transmit the computer program 1206 as a computer data signal.

Computer program instructions for causing an apparatus to perform at least the following or for performing one or more of the methods described herein.

The computer program instructions may be comprised in a computer program, a non-transitory computer readable medium, a computer program product, a machine readable medium. In some but not necessarily all examples, the computer program instructions may be distributed over more than one computer program.

Although the memory 1204 is illustrated as a single component/circuitry it may be implemented as one or more separate components/circuitry some or all of which may be integrated/removable and/or may provide permanent/semi-permanent/ dynamic/cached storage.

Although the processor 1202 is illustrated as a single component/circuitry it may be implemented as one or more separate components/circuitry some or all of which may be integrated/removable. The processor 1202 may be a single core or multi-core processor.

References to 'computer-readable storage medium', 'computer program product', 'tangibly embodied computer program' etc. or a 'controller', 'computer', 'processor' etc. should be understood to encompass not only computers having different architectures such as single /multi-processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field-programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other processing circuitry. References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc. The blocks illustrated in Fig 1 may represent steps in a method and/or sections of code in the computer program 1206. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some blocks to be omitted.

The term 'comprise' is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising Y indicates that X may comprise only one Y or may comprise more than one Y. If it is intended to use 'comprise' with an exclusive meaning then it will be made clear in the context by referring to "comprising only one.." or by using "consisting".

In this description, reference has been made to various examples. The description of features or functions in relation to an example indicates that those features or functions are present in that example. The use of the term 'example' or 'for example or 'can' or may' in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus 'example', 'for example', can' or 'may' refers to a particular instance in a class of examples. A property of the instance can be a property of only that instance or a property of the class or a property of a sub-class of the class that includes some but not all of the instances in the class. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example as part of a working combination but does not necessarily have to be used in that other example.

Although examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims. For example, the method 100 could be implemented such that target users could be vehicles unregistered to any account, and their human occupants may be unmonitored. However, this method is less advantageous for vehicles driven by many different users such as rental cars or workplace cars.

Further, the method 100 could be implemented for a non-drive-through establishment, in which the queueing area may comprise a standing queuing area, rather than a vehicle queuing lane.

Features described in the preceding description may be used in combinations other than the combinations explicitly described above.

Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not.

Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not.

The term 'a' or 'the' is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a/the Y indicates that X may comprise only one Y or may comprise more than one Y unless the context clearly indicates the contrary. If it is intended to use 'a' or the' with an exclusive meaning then it will be made clear in the context. In some circumstances the use of at least one' or 'one or more' may be used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer and exclusive meaning.

The presence of a feature (or combination of features) in a claim is a reference to that feature or (combination of features) itself and also to features that achieve substantially the same technical effect (equivalent features). The equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. The equivalent features include, for example, features that perform substantially the same function, in substantially the same way to achieve substantially the same result.

In this description, reference has been made to various examples using adjectives or adjectival phrases to describe characteristics of the examples. Such a description of a characteristic in relation to an example indicates that the characteristic is present in some examples exactly as described and is present in other examples substantially as described.

Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance it should be understood that the Applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not emphasis has been placed thereon.

I/we claim:

Claims (20)

1. CLAIMS1. A system comprising at least one controller, the at least one controller comprising means for: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information and in response to the proximity detection.
2. 2. The system of claim 1, configured to emphasize a prominence associated with the first item or class of items.
3. 3. The system of claim 1 or 2, wherein the information comprises one or more of: user routine information associated with a routine of user selection of items; health requirement information; or dietary preference information.
4. 4. The system of claim 1, 2 or 3, the at least one controller comprising means for: receiving vehicle information; and wherein the detecting proximity of the user comprises determining whether a detected vehicle has matching detected vehicle information.
5. 5. The system of claim 4, the at least one controller comprising means for: determining or estimating a number of users associated with the vehicle; and wherein the controlling a queuing area display is based on information associated with each user associated with the vehicle.
6. 6. The system of any preceding claim, the at least one controller comprising means for: providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information.
7. 7. The system of claim 6, wherein the forecast demand notification is based on a determined or estimated number of users and/or transactions associated with a vehicle.
8. 8. The system of claim 7, wherein the number of users and/or transactions is estimated based on the information indicating a number of users and/or transactions associated with each visit by the vehicle.
9. 9. The system of any preceding claim, the at least one controller comprising means for: using a detector to monitor a portion of a queuing area downstream of an entry point to the queuing area and level with or upstream of the queuing area display, for the detecting a proximity of the user.
10. 10. The system of claim 9, wherein the detector is configured for vehicle number plate recognition and/or facial recognition and/or portable user device detection. 15
11. 11. The system of any preceding claim, the at least one controller comprising means for: using a movement detector to monitor a portion of the queuing area proximal to the queuing area display, for detecting movement of the user away from the queuing area display; and preparing the queuing area display for a next user based on detection of movement of the user away from the queuing area display.
12. 12. The system of any preceding claim, the at least one controller comprising means for: detecting one or more of completion of an order request, payment, or exchange of items; and preparing the queuing area display for a next user based on the detection.
13. 13. The system of any preceding claim, applied to a drive-through restaurant, wherein the queuing area display is a drive-through queuing area display configured to present a menu.
14. 14. The system of any preceding claim, wherein the information is received via user feedback information and/or via passive monitoring.
15. 15. A system comprising at least one controller, the at least one controller comprising means for: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information.
16. 16. The system of any preceding claim, comprising one or more detectors for detecting proximity and/or movement of the user.
17. 17. A method comprising: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information and in response to the proximity detection.
18. 18. A method comprising: receiving information associated with user selection of a first item or class of items by a user; storing the information; detecting proximity of the user; and providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information.
19. 19. A computer program that, when run on a computer, performs: causing receiving information associated with user selection of a first item or class of items by a user; causing storing the information; causing detecting proximity of the user; and causing controlling a queuing area display to modify a prominence of an indication associated with the first item or the class of items, based on the information and in response to the proximity detection.
20. 20. A computer program that, when run on a computer, performs: causing receiving information associated with user selection of a first item or class of items by a user; causing storing the information; causing detecting proximity of the user; and causing providing a forecast demand notification for an item preparer or picker, in response to the proximity detection and in dependence on the information.