WO2014123528A1

WO2014123528A1 - Avoiding the need for consumers to wait in line to make purchases

Info

Publication number: WO2014123528A1
Application number: PCT/US2013/025084
Authority: WO
Inventors: Timothy N. TROP
Original assignee: Individual
Current assignee: Individual
Priority date: 2013-02-07
Filing date: 2013-02-07
Publication date: 2014-08-14
Anticipated expiration: 2015-08-07

Abstract

In some embodiments, a user may be presented, on his or her mobile phone, with an order input user interface as soon as the user enters a retail establishment. Then the user can enter a selection or simply go with the default selection. The order is automatically communicated to the retail facility. There is no need for the user to wait in line and instead the user can sit anywhere the user wants or even proceed to another location and come back when the order is ready. This avoids the need for any user to wait in line and can in some embodiments reduce the order taking burden on the business. In some embodiments, the user is automatically charged for the transaction without ever waiting in line.

Description

AVOIDING THE NEED FOR CONSUMERS

TO WAIT IN LINE TO MAKE PURCHASES

Background

[0001 ] This relates generally to automated order taking.

[0002] Conventionally, a consumer who approaches a retail facility must get in line to make a purchase. A common example of this is purchasers lining up in the morning (when demand is high) to buy coffee at a coffee shop. They must queue up in line and wait their turn to make a purchase. Then they must wait for the order taker to enter the order and fulfill the order. Then they must make a payment and wait while that is completed. All this time the consumer is stuck in line with a number of other consumers. On the other hand, a business owner must incur a cost of employees who take the orders, process the orders and provide the service.

Brief Description Of The Drawings

[0003] Some embodiments are described with respect to the following figures:

Figure 1 is a flow chart for one embodiment of the present invention;

Figure 2 is a system depiction for one embodiment;

Figure 3 is a flow chart for another embodiment;

Figure 4 is a flow chart for still another embodiment;

Figure 5 is a flow chart for yet another embodiment; and

Figure 6 is still another flow chart for another embodiment.

Detailed Description

[0004] In some embodiments, a user may be presented, on his or her mobile phone, with an order input user interface as soon as the user enters a retail establishment. Then the user can enter a selection or simply go with a default selection. The order is automatically communicated to the retail facility. There is no need for the user to wait in line and instead the user can sit anywhere the user wants or even proceed to another location and come back when the order is ready. This avoids the need for any user to wait in line and can in some embodiments reduce the order taking burden on the business. In some embodiments, the user is

automatically charged for the transaction without ever waiting in line.

[0005] A line-free sequence 10, shown in Figure 1 may be implemented in software, firmware and/or hardware. Typically, it may be an application on the user's cellular telephone. In software and firmware embodiments, it may be implemented by computer executed instructions stored in one or more non-transitory computer readable media such as a magnetic, optical, or semiconductor storage. Thus in one typical embodiment, it can be stored in a memory chip executed on a processor within the user's cellular telephone.

[0006] However, the present invention is not necessarily limited to cellular telephones and may also be applicable to portable computers such as laptop computers and tablets and mobile Internet devices to mention some additional examples.

[0007] Initially, the user's mobile telephone detects a given location as indicated in block 12. The detection may be done in a number of different ways including determining the global positioning system coordinates of the cellular telephone and comparing those coordinates to global positioning system coordinates of known establishments. As another example, a wireless network available within the establishment may be detected and that may be used to identify the establishment.

[0008] Once the establishment has been identified, an appropriate server (that can communicate with that establishment) may be identified using a database within the mobile telephone or on a remote server or via the cloud. Thus as shown at block 14, the location of the device is correlated to a particular server. The server may be associated with the establishment in one embodiment. In another embodiment it may be associated with a service provider that provides the services for many different retail establishments. For example, the server may be associated with a cloud.

[0009] Next as indicated in block 15, the user may be authenticated by the server. For example, the user's cellular telephone number may be acquired or a subscriber identity module may be queried to authenticate the user, to give two examples.

[0010] Next, a selection screen may automatically be caused to appear on the user's device as indicated in block 16. Thus, in some embodiments, a menu or other list of services or products provided by that particular establishment may be presented for easy user selection of the service or product that the user actually wants. In one embodiment the menu screen is driven from the identified server.

[001 1 ] If no selection is received at diamond 18, a default selection (diamond 20) may be entered. For example, the user may indicate what the user typically wants at that retail facility when the user goes there. The input may be provided via a touch selection or a voice input, as two examples. Otherwise, the user selection on the selection screen may be transmitted to the server as indicated in block 22. The user's selection may be confirmed in block 24 and charges may be automatically charged to the user's account with that service or product provider or via a credit card or other payment modality.

[0012] Finally, the user may receive an indication that his or her order is ready as indicated in block 26. This may be done by a text message or email, as two examples.

[0013] Referring next to Figure 2, a system depiction may involve a retail site 30 which may be identified by its global positioning system coordinates or by a wireless signal that is available at that site, as two examples. The mobile device 32 may have a wireless transmitter and receiver 44 that may communicate with that wireless transceiver 50 at the site 30. It may also communicate with the Internet over a wireless data connection for example using the cellular telephone network.

[0014] A mobile device 32 may include a display 42, a computer 40 with a processor and storage 58 and a transceiver 44 coupled to an antenna 46. The transceiver 44 may be able to communicate both over a cellular network and over one or more wireless protocols including Bluetooth, near field communications, and Wi-Fi cellular communications to mention some examples. [0015] The site 30 may include a computer 48 and a transceiver 50 coupled to an antenna 52 and a storage 59. The storage 59 (like the storage 58) may be a semiconductor, optical or magnetic storage. The transceiver 50 may be able to communicate over one or more wireless protocols including Bluetooth, near field communication, Wi-Fi and cellular communications.

[0016] Thus communications between a mobile device 32 and the site 30 may be over a variety of communication protocols including cellular, Bluetooth, near field communications, and Wi-Fi to mention a few examples. Thus in some cases communication directly with the site 30 may be possible and in other cases, communication over the Internet or directly with the site server 36 may be

implemented.

[0017] The site server 36 may include a storage 54. Likewise the user's server 38 may include a storage 56.

[0018] The user's device 32 may be any of a variety of mobile computing devices including a cellular telephone, a game playing device, a media playing device, a mobile Internet device, a laptop computer, a tablet computer, or even an e-book reader, to mention some examples.

[0019] Communications between the site 30 and the user's device 32 and other devices may be facilitated using the Internet 34 or the Internet coupled through a wireless network as two examples. The retail site 30 may have a server 36 also coupled to the Internet 34 and the user's server may also be coupled to the Internet 34.

[0020] Thus in some embodiments the product/service selection screen may be provided by either of the servers 36 or 38. For example, a retail chain may operate a server that can be linked to when it is determined that the user's mobile device is at a particular site. As another example, a service provider may provide the selection screen for authorized retail vendors who participate in the service provider's system. Other schemes may also be contemplated. [0021 ] In accordance with other embodiments, the system may actually be able to predict what the user may want at some particular time or place in the future. It may be able to do this by using programmed criteria that may be detected, triggers of various types, or by maintaining a history of activities and actions taken and machine learning from that history so as to be able to predict what the user may wish to do in similar circumstances in the future.

[0022] For example, while the above-described embodiment may automatically provide an order screen or default order based on a given location, in some embodiments it may also be possible to predict what the user wants under given circumstances. For example if a device has learned that the user always orders a particular type of coffee around a particular time, the system can suggest a proximate location to obtain that coffee at that time. It may be that the proximate location is a different one from the facility normally used by the consumer. For example, the user might typically go to a nearby coffee shop when the user is in town at about 7 a.m. But suppose the user is now traveling and is in a different city. The system could predict that at 7 a.m. the user may want to go to a particular brand of coffee shop and may find a local coffee shop, and suggest placing an order in accordance with the previously described embodiment.

[0023] Among the criteria that may be used to predict what the user may want in the future, which may be followed by automatically implementing the embodiment shown in Figure 1 , include a review of a history of actions taken by the user under similar circumstances, mashed up with criteria such as a current time, the current date, the day of the week, the month of the year, the location of the consumer, the route currently being taken by the consumer and/or other triggers. Other triggers include other triggers currently active on the user's mobile telephone.

[0024] For example, if the user has set an alarm to wake up at a given time, the user may be queried or may set a default so that the triggering of the alarm triggers an automatic order for breakfast, coffee or whatever, at a given amount of time after the alarm, or based on a subsequent given location of the user. For example, the system may trigger an alarm at 6 a.m. and the system may then determine that when the user gets to a certain location, an order for coffee may be implemented at the closest proximate coffee shop or the one that the user currently frequents. In this case, the order may be placed before the user even arrives at the coffee shop so that the order is ready when he or she arrives.

[0025] In still another embodiment, based on the location of the user on a given route, it can be learned over time, using computer learning software, that the user is on his or her way to a given retail facility to make a purchase. For example, if the user regularly drives down a given street at a given time and then goes to a coffee shop at a particular location along that route, at a particular time before arriving, the order may be automatically placed or the user may be given the opportunity to place an order so that the order will be ready when the user arrives.

[0026] In yet another example, calendar (e.g. personal information manager (PIM)) entries may be used to automatically place orders. For example, a calendar entry indicating a lunch at a given time may trigger an advance opportunity to place an order for lunch for take-out or in restaurant dining. That is, if a lunch is scheduled at 12:00 p.m., the system might (at a predetermined time before) offer the opportunity to place an order so that the order would be ready on arrival.

[0027] In another embodiment, time and location may be mashed up to make a determination of what it is that the user wants, particularly based on past history. Thus given the user's location or route at a given time, it may be determined that the user is in route to purchase coffee at a particular location. And the order may be placed automatically or a menu may be provided on the user's portable device to make a selection.

[0028] As still another example, the system may learn by analysis of history that the user always eats out at one of three locations on Friday night, at a particular time. Thus when the user reaches a particular location, it may determine which of the three locations the user is actually intending to visit and may place an order in advance. Or the system may give the user an opportunity, at a particular triggered time, to indicate which of the three locations the user wants to order from and may implement the automatic ordering process described in connection with Figure 1 . [0029] As another example, the system may determine a periodicity with which particular activities are done. For example the system may determine that the user gets a haircut every week, and when a week goes by and the user is proximate to his or her typical haircutting facility it may suggest getting a haircut and may automatically schedule the haircut appointment so that the user can immediately be serviced upon arrival. The determination of haircut frequency may be deduced from a location determination or an electronic (credit card) charge as two examples. Then the history and timing of these activities may be used to predict future activities at particular times or locations.

[0030] In accordance with some embodiments, a sequence 60, shown in Figure 3, for making a selection mash-up may be implemented in software, firmware and/or hardware. In software and firmware embodiments it may be implemented by computer readable instructions stored in a non-transitory computer readable medium such as a magnetic, optical or semiconductor storage. Again, in one embodiment, the instructions may be stored in a computer memory on the user's cellular telephone for execution by a processor contained within that telephone.

[0031 ] Referring to Figure 3, initially, a history of selections may be built up as indicated in block 62. These selections may be mashed up with criteria such as time, time of day, date of the year, days of the week, months in a year, locations, routes, and/or other triggers such as alarms and calendar entries to determine what it is that the user is likely to want, conceivably at a particular time or a particular location.

[0032] Then, in block 64, information may be received on one or more of the criteria on a real time basis. A check at diamond 66 determines whether any of the real time criteria match a selection that has been made previously in the recorded history. If so, the selection may be predicted in block 68. In some embodiments, this may mean implementing automatically the sequence of Figure 1 in one embodiment. Then the actual selection may be added to the history as indicated in block 70 to further provide more data and to further improve the accuracy of future selection predictions. [0033] Referring to Figure 4, in accordance with one embodiment, a set up sequence 72 may be used to set up a computer based device, such as a cellular telephone, to automatically contact a retailer and to automatically place an order under given conditions and circumstances. In some embodiments, the sequence may be implemented in software, firmware, or hardware. In software and firmware embodiments, it may be implemented by computer executed instructions stored in one or more non-transitory computer readable media, such as magnetic, optical, or semiconductor storage.

[0034] The set up sequence 72 begins by inputting a name of a retailer from whom the user may wish to purchase a product or service, as indicated in block 74. In some embodiments, a list of participating retailers or retailers that have been contacted in the past may be compiled automatically and as they are contacted by the user email or visiting their website, the retailer's contact information may be added automatically to the list. In another embodiment, every retailer whose website is contacted by the user may be added to the list for selection as an input in the sequence 72.

[0035] Then a location parameter may be input (block 76). In one embodiment, the location parameter may specify a particular location or even a distance from that retailer. For example, the distance parameter may specify that whenever you are within a given distance from any retail outlet for that retailer, the condition would be satisfied.

[0036] In addition, other parameters may be implemented, such as a time parameter, as indicated in block 78. The time parameter may indicate, in one embodiment, a time span when the condition is satisfied. For example, with respect to a retailer who provides coffee, the time span may be 6:00 to 8:00 a.m. in the morning.

[0037] Still other parameters that may be input including a day of the week parameter, as indicated in block 80. For example, a user may wish to purchase coffee on Monday through Friday, but not Saturday and Sunday. [0038] In one embodiment, a duration parameter may be indicated, as indicated in block 82. For example, the user may indicate that the duration parameter is for one month, a year, or any other duration, including permanent.

[0039] As a result of providing the inputs in the set up sequence 72, a set of conditions may be established that, when satisfied, cause an alert to be generated to either initiate an automatic purchase or to offer the user an opportunity to make an automatic purchase. For example, if the time condition is 6:00 to 8:00 a.m. and day condition is Monday through Friday and the duration parameter is the next six months and the location parameter is within one mile and the retailer is a coffee shop, whenever the user is within that distance at that time from that retailer, an automatic purchase may be initiated.

[0040] Thus, referring to Figure 5, an execute sequence 84 may execute based on the satisfaction of the conditions set up in the set up sequence of Figure 4. In some embodiments, the execute sequence 84 may be implemented in software, firmware, or hardware. In software and firmware embodiments, it may be implemented by computer executed instructions stored in one or more non-transitory computer readable media, such as a magnetic, semiconductor, or optical storage media.

[0041 ] A check at diamond 86 determines whether the set up sequence's location parameter is satisfied. If so, a check at diamond 88 determines whether the time parameter from the set up sequence is satisfied. Next, the day of the week parameter is checked, as indicated in diamond 90. If the first three parameters are all satisfied, a check at diamond 92 determines whether the duration parameter is satisfied. If so, the order may be automatically placed, as indicated in block 94, or the user may be given an opportunity to automatically place the order in another embodiment. In one embodiment, a spoken request may be generated to ask if the user wants to place the order and the user's response may be received via voice recognition software.

[0042] In still another embodiment, in addition to providing information to the user, the particular selected retail facility may be provided with information. In some embodiments, this may be facilitated when the automatic ordering is implemented through the retailer's own website. In such case, the retailer may harvest information that may be used for its own planning and production purposes.

[0043] In some embodiments, a prepare sequence 96 shown in Figure 6 may be implemented in software, firmware, or hardware. In software and firmware embodiments, it may implemented by computer readable instructions stored in one or more non-transitory computer readable media, such as a magnetic, optical, or semiconductor storage. For example, it may be implemented on the server 36 or the computer 48 as two examples.

[0044] In one embodiment, the sequence begins, as indicated in block 98, by identifying a number of currently proximate customers. These may be customers whose cellular telephones automatically make contact with the retailer when they are within a given distance, in one embodiment, under other conditions. Then, the number of automatic orders may be cumulated, as indicated in block 99. For example, the number of automatic orders that have been placed under the conditions set forth in Figure 5 may be cumulated. Next, the number of advance orders may be cumulated, as indicated in block 100. Then the system may automatically determine when the users are expected to arrive, how many orders have been placed, and how many orders from proximate customers may be predicted to be placed. Based on this, advanced production parameters may be specified in block 102. For example, a hamburger restaurant chain may get advance information about how many users are likely to order a particular hamburger and may up the number of hamburgers that are being cooked in order to satisfy the demand as it arrives.

[0045] In some embodiments, a software production model may be used that may consider things such as number of staff, available equipment capacity, available materials, and other considerations to develop a model that predicts when production of each specific order would be completed based on the current conditions. This model may be used in conjunction with the incoming orders to predict, based on the current production rate, when future orders will be available. Then when a new order comes in, pursuant to the embodiment of Figure 1 for example, the system may use the production model to predict the time when the order will be done. In such case the user may get not only a confirmation that his or her order was received but also an estimated, available time for pick-up. As shown at block 104, an estimate of the order ready time may be provided to the customer to not only confirm receipt of the order but to help the customer plan when to arrive to receive the order.

[0046] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation encompassed within the present invention. Thus, appearances of the phrase "one embodiment" or "in an embodiment" are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be instituted in other suitable forms other than the particular embodiment illustrated and all such forms may be encompassed within the claims of the present application.

[0047] While the present invention has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous

modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the true spirit and scope of this present invention.

Claims

What is claimed is: 1 . A computer executed method comprising:

detecting a location of a computing device;

based on said location, accessing a web page related to said location, said web page providing information about a product or service offered at that location; receiving a selection of a product or a service on said device;

transmitting said selection for order fulfillment;

automatically accruing a charge for said selection; and

providing a notification of availability of an ordered service or product.

2. The method of claim 1 including automatically determining the location of the computing device and, based on that location, identifying a retailer proximate to that location.

3. The method of claim 2 including automatically locating a web page of said retailer.

4. The method of claim 2 including automatically locating a web page for a service provider to communicate with said retailer.

5. The method of claim 1 including determining when the device is within a predetermined distance from said retailer and automatically placing an order in response to being within that distance.

6. The method of claim 1 including automatically identifying a plurality of locations for one retailer.

7. The method of claim 1 including placing said order depending on the time of day.

8. The method of claim 1 including placing the order based on the day of the week.

9. The method of claim 1 including placing the order based on the device's route of travel.

10. The method of claim 1 including using a calendar on said device to determine whether to place the order.

1 1 . The method of claim 1 including placing the order based on a past history of order placements.

12. The method of claim 1 including using an alarm set on said device to determine when to place an order.

13. One or more computer readable media storing instructions to cause a computer to perform a sequence comprising:

detecting a location of a computing device;

transmitting said selection for order fulfillment;

automatically accruing a charge for said selection; and

providing a notification of availability of an ordered service or product.

14. The media of claim 13, said sequence including automatically determining the location of the computing device and, based on that location, identifying a retailer proximate to that location.

15. The media of claim 14, said sequence including automatically locating a web page of said retailer.

16. The media of claim 14, said sequence including automatically locating a web page for a service provider to communicate with said retailer.

17. The media of claim 13, said sequence including determining when the device is within a predetermined distance from said retailer and automatically placing an order in response to being within that distance.

18. The media of claim 13, said sequence including automatically identifying a plurality of locations for one retailer.

19. The media of claim 13, said sequence including placing said order depending on the time of day.

20. The media of claim 13, said sequence including placing the order based on the day of the week.

21 . The media of claim 13, said sequence including placing the order based on the device's route of travel.

22. The media of claim 13, said sequence including using a calendar on said device to determine whether to place the order.

23. The media of claim 13, said sequence including placing the order based on a past history of order placements.

24. The media of claim 13, said sequence including using an alarm set on said device to determine when to place an order.

25. An apparatus comprising:

a transceiver; and

a computer, coupled to said transceiver, to perform a sequence comprising: detecting a location of a computing device;

based on said location, accessing a web page related to said location, said web page providing information about a product or service offered at that location;

receiving a selection of a product or a service on said device;

transmitting said selection for order fulfillment;

automatically accruing a charge for said selection; and

providing a notification of availability of an ordered service or product.

26. The apparatus of claim 25, said sequence including automatically determining the location of the computing device and, based on that location, identifying a retailer proximate to that location.

27. The apparatus of claim 26, said sequence including automatically locating a web page of said retailer.

28. The apparatus of claim 26, said sequence including automatically locating a web page for a service provider to communicate with said retailer.

29. The apparatus of claim 25, said sequence including determining when the device is within a predetermined distance from said retailer and automatically placing an order in response to being within that distance.

30. The apparatus of claim 25, said sequence including automatically identifying a plurality of locations for one retailer.

31 . The apparatus of claim 25, said sequence including placing said order depending on the time of day.

32. The apparatus of claim 25, said sequence including placing the order based on the day of the week.

33. The apparatus of claim 25, said sequence including placing the order based on the device's route of travel.

34. The apparatus of claim 25, said sequence including using a calendar on said device to determine whether to place the order.

35. The apparatus of claim 25, said sequence including placing the order based on a past history of order placements.

36. The apparatus of claim 25, said sequence including using an alarm set on said device to determine when to place an order.

37. A computer executed method comprising:

receiving automatically placed orders from consumers; and

analyzing said orders to manage production of products based on the number of orders and the predicted arrival times of said consumers.

38. The method of claim 37 including receiving orders from customers while they are en route to a retail facility.

39. The method of claim 37 including receiving information about the present location of said consumers.

40. The method of claim 37 including receiving information useful to predict order placement.

41 . The method of claim 37 including using a software production model to predict a time when an order will be ready and notifying a consumer of an order ready time.

42. One or more non-transitory computer readable media storing instructions executed by a computer to perform a sequence comprising:

receiving automatically placed orders from consumers; and

43. The media of claim 41 , said sequence including receiving orders from customers while they are en route to a retail facility.

44. The media of claim 41 , said sequence including receiving information about the present location of said consumers.

45. The media of claim 41 , said sequence including receiving information useful to predict order placement.

46. An apparatus comprising:

a computer to receive automatically placed orders from consumers, analyze said orders to manage production of products based on the number of orders and the predicted arrival times of said consumers; and

a storage coupled to said processor.

47. The apparatus of claim 45, said processor to receive orders from customers while they are en route to a retail facility.

48. The apparatus of claim 45, said processor to receive information about the present location of said consumers.

49. The apparatus of claim 45, said processor to receive information useful to predict order placement.

50. The apparatus of claim 45 including a cellular telephone.