DETAILED ACTION
This Final Office action is in response to Applicant’s Amendments filed on 07/31/2026. Claims 1-7 and 9-21 are pending. The effective filing date of the claimed invention is 11/21/2018.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 6-7, 9, 11, 15-18, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. US Pat No 9269103 to Kumar (“Kumar”) in view of US Pat No 8798646 to Wang (“Wang”).
With respect to claims 1, 6, 16, Kumar teaches the claimed system comprising:
one or more processors (see Kumar, e.g., Fig. 1 and 7); and
one or more non-transitory computer-readable media storing instructions executable by the one or more processors, wherein the instructions cause the one or more processors to perform acts comprising (see Kumar, e.g., Fig. 1 and 7):
causing, by one or more computing devices of a service provider, an electronic menu to be presented via a plurality of customer computing devices associated with a plurality of customers of a merchant (See Kumar, e.g. Fig. 1, BUYER(s) 132(1), 132(2…N) connected to processors, Fig. 2, 5 block 502. Col. 2, ln 15-20 As one example, a first buyer may use an application on a first buyer device to create a first order by selecting one or more items, such as food items, offered by a first merchant, such as a restaurant, col. 3 ln 30-35 present menu);
receiving, by the one or more computing devices and from a first merchant customer computing device of the plurality of customer one or more merchant computing devices, a first request indicating that a first instance of a first item of a plurality of items offered for sale by the merchant has been ordered by a first customer (see Kumar, e.g. Fig. 5, block 504, and Fig. 6, block 602);
determining, by the one or more computing devices, that an efficiency associated with adding a second item offered for sale by the merchant or a second instance of the first item in a batch with the first instance of the first item that has been ordered is above a threshold efficiency (see Kumar, e.g. col 2 and Figs 5-6, Fig. 6 blocks 606-616);
determining, by the one or more computing devices and based on location data indicative of a respective current location of individuals of the plurality of customer computing devices, a first subset of the plurality of customer computing devices that are within a geofence associated with a physical location of the merchant and a second subset of the plurality of customer computing devices that are outside of the geofence (Kumar uses GPS at col 12 ln 60-65, col. 14, ln. 1-10, As another example, the buyer application may communicate with a geo-fence set up for a particular area within which the first buyer device 132(1) is currently located, and the buyer application may send the invitation to participate in the combined order “only to other buyer devices determined to be within the geo-fenced area”. For instance, the particular area may have an open geo-fence that is accessible to determine any other buyer devices currently within the geo-fenced area, such as based on GPS location information, RFID communications, or the like (emphasis added). Kumar does not disclose the specifics of “a first subset of the plurality of customer computing devices that are within a geofence associated with a physical location of the merchant and a second subset of the plurality of customer computing devices that are outside of the geofence” See Wang obtains current mobile-device locations, including the GPS coordinates, obtains location data for merchant storefronts, establishes geofence boundaries around those points of interest, and determines whether a device is inside the boundary. See Wang, abstract, Fig. 2 blocks 205-230, and the discussion of merchant-storefront coordinates and user-device coordinates. Wang therefore supplies the merchant-location
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anchor missing from Kumar. Therefore it would have been obvious to one of ordinary skill in the geofencing management system before the effective filing date of the claimed invention to modify Kumar’s buyer device-merchant interaction to include such geofencing management systems of Wang, where the added benefit of the combination is that there can be a large geofence for a merchant as shown in Fig. 4 (to left), 401, and then there can be multiple different geofences 402..n within said geofence 402, where there can be a buyer in 402 – Merchant A and other buyers in the other geofence 402 Merchant B, C, where there are separate geofences that are not touching, thereby allowing different notifications to be sent to each user based on the specific geofence the buyer is in. See Wang, e.g., col 19 ln 1-20);
responsive at least in part to receiving the first request and determining that the efficiency is above the threshold efficiency, dynamically updating, by the one or more computing devices, the electronic menu to an updated electronic menu, wherein the updated electronic menu includes a promotion for the second item or the second instance of the first item (see Kumar, e.g. Fig. 3, 308, Fig. 4, 414 “ORDER WITHIN 5 MINUTES FOR DELIVERY DISCOUNT”, col 17, ln 60 – col 18 ln 10, col. 3 ln 29-40; Kumar Fig. 3, 308 dynamic update based on selection from Fig. 2.);
causing, by the one or more computing devices, the updated electronic menu to be presented via the first subset of the plurality of customer (See Kumar e.g. col 3 and Fig. 3, sends the combined-order invitation only to nearby or inside-geofence buyer devices. The recipient is then shown the merchant’s menu and can select the second item; Kumar’s geofence is not explicitly associated with the merchant. See Wang, above, and combination Fig. 2, blocks 225-230); and
refraining, by the one or more computing devices, from causing the updated electronic menu to be presented via the second subset of the plurality of customer computing devices (see Kumar, e.g., col 14, ln 1-10 - states that the invitation is sent “only” to devices determined to be inside the geofenced area. This affirmatively excludes outside devices).
With regard to claims 2 and 17, Kumar further discloses the efficiency comprises at least one of a cost savings (Kumar, Fig. 3, 308), a time savings, a reduction in spoilage, or an increase in customer satisfaction.
With regard to claims 3, 7, 18, Kumar further discloses determining that the efficiency is above the threshold efficiency comprises determining a reduction in at least one of a preparation time, a required ingredient, a quantity of employees, a cost (Kumar, Fig. 3 308), or spoilage.
With regard to claims 4 and 9, Kumar further discloses determining that the efficiency is above the threshold efficiency is based at least in part on inventory data maintained in an inventory database associated with the merchant and maintained by the service provider, and wherein the inventory data comprises, for an individual item of the plurality of items, item description, item price, item availability, item count, item portion size, item portion count, item discount (see e.g. Kumar, col 11, ln 28-42), item location, item image, or item characteristic.
With regard to claim 11, Kumar further discloses the promotion with delivery discount. Wang teaches “20% off coffee” at e.g. col. 10, ln 17-33). Therefore it would have been obvious to one of ordinary skill in the geofencing management system before the effective filing date of the claimed invention to modify Kumar’s buyer device-merchant interaction to include such geofencing management systems of Wang, where the added benefit of the combination is that there can be a large geofence for a merchant as shown in Fig. 4 (to left), 401, and then there can be multiple different geofences 402..n within said geofence 402, where there can be a buyer in 402 – Merchant A and other buyers in the other geofence 402 Merchant B, C, where there are separate geofences that are not touching, thereby allowing different notifications to be sent to each user based on the specific geofence the buyer is in. See Wang, e.g., col 19 ln 1-20);
With regard to claim 15, Kumar further discloses causing, by the one or more computing devices, an alert associated with availability of the promotion to be presented via at least one of the one or more merchant computing devices or the first subset of the plurality of one or more customer computing devices (Fig. 3).
With regard to claim 21, Kumar discloses determining the first subset of the plurality of customer computing devices is further based at least in part on reservation data (see Fig. 2).
Claim(s) 5, 14, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Wang, in view of US Pat Pub No 2010/0042506 to Ravenel.
With regard to claims 5, 14, and 20, Kumar further discloses
receiving, by the one or more computing devices, a second request indicating that the second item of the first item or the second instance of the first item has been requested (Kumar e.g. Fig 6, block 614);
determining, by the one or more computing devices, that the promotion is no longer available based at least in part on one of a lapse of a period or a lack of inventory of the second item or the second instance of the first item (Kumar e.g. col 15 ln 38-48, 5:20 examples, Fig. 6, block 614);
responsive at least in part to determining that the promotion is no longer available, dynamically updating, by the one or more computing devices, the updated electronic menu to a second updated electronic menu, wherein the second updated electronic menu lacks the promotion (Kumar discloses a GUI containing the discount, a countdown, and an express statement that the discount ends. Kumar does not teach the dynamically updating aspect. See Ravenel, [0027] [0046] [0055-56] [0058] Figs. 8-9 and 11; [0046] The digital menu display engine may be configured to update the digital menu display 116 in real time based on data received from the POS system 120 via the POS interface 302. In one embodiment, if the POS inventory data (stored in POS database 202) shows that inventory of a particular item 118 is depleted or otherwise unavailable, the digital menu display engine 408 may be configured to receive a message via the POS interface 302 and remove that item 118 from the digital menu display 116 in the QSR environment 100. Similarly, that item would be removed from the Customer Operated Kiosk 114 menu offerings. Therefore it would have been obvious to one of ordinary skill in the restaurant device art modify Kumar to include such dynamic update of available items on the menu, as this allows for the immediate updating the menu in accordance with various availabilities, as shown in Ravenel, where the benefit is that the data is “used to drive the customer-operated kiosk devices 114 in the QSR environment.” Ravenel [0055]); and
causing, by the one or more computing devices, the second updated electronic menu to be presented via the one or more merchant first subset of the plurality of customer computing devices (Kumar does not explicitly teach this limitation. See Wang, above, teaching determining devices into the merchant-associated geofence and sending content to those devices. Wang does not teach the post-expiration menu. See Ravenel at [0058-61] Fig. 13, blocks 1310-1312. Therefore it would have been obvious to one of ordinary skill in the restaurant device art modify Kumar to include such dynamic update of available items on the menu, as this allows for the immediate updating the menu in accordance with various availabilities, as shown in Ravenel, where the benefit is that the data is “used to drive the customer-operated kiosk devices 114 in the QSR environment.” Ravenel [0055]).
Claim(s) 10, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Wang, in view of US Pat Pub No 2018/0218346 to Renke.
With regard to claims 10 and 19, Kumar discloses wherein determining that the efficiency is above the threshold efficiency comprises identifying the second item or the second instance (Kumar, identifies a second item/order for combination with first order, including orders from the same or nearby merchants) based at least in part on a metric (Kumar uses threshold time and distance criteria, but not the claimed learned metric. See Renke, [0090] calculating a similarity score between a current ticket and historical split tickets, determining whether the similarity score satisfies a similarity threshold.) generated by a machine-learning mechanism being above a threshold metric (Renke, [0090] similarity threshold; [0091] published claim 11, Renke expressly uses nearest-neighbor analysis of historical split tickets. See Applicant’s Spec. [0020] [0061] where nearest neighbor is identified as machine learning. Under BRI, Renke similarity score/nearest neighbor process appears to satisfy the limitation ), wherein the machine-learning mechanism is trained with training data comprising transaction data associated with item sharing by a plurality of customers in purchases from a plurality of merchants (Renke [0089-90] uses historical ticket and split-ticket information as the reference dataset against which new tickets are compared. For a nearest-neighbor or “lazy learning” mechanism, that stored labeled dataset functions as the training set, Renke [0092] a first merchant and a second merchant, different from the first). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date would been motivated to modify Kumar’s combined-order system to employ Renke’s historical ticket-analysis and candidate-item identification technique. Kumar seeks to facilitate combined orders that reduce buyer expenses and permit multiple orders to be fulfilled with limited additional delivery effort. Renke teaches that historical ticket and split-ticket information received from POS devices associated with multiple merchants may be analyzed to provide intelligent item-splitting suggestions based on relationships between items and customers. Renke further teaches identifying candidate items by calculating a similarity score, determining that the score satisfies a similarity threshold, and identifying items from nearest-neighbor split tickets. Renke [0030]-[0032], [0089]-[0093]. A skilled artisan would therefore have applied Renke’s technique to Kumar’s system to identify, from among the items offered by the merchant, a second item or second instance having historical characteristics indicating that the item is suitable for sharing or combination. Doing so would predictably improve the relevance of Kumar’s promoted item and thereby increase the likelihood that another customer would accept the promotion and participate in the combined order, furthering Kumar’s stated objective of reducing the cost and additional fulfillment effort associated with separate orders.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Wang, in view of US Pat Pub No 2004/0177004 to Mueller.
With regard to claim 12, Kumar further discloses where the merchant menu is presenting through a GUI on buyer’s device. Kumar does not teach the digital menu billboard. See Mueller, [0045]. See Mueller, abstract, Fig. 2, [0011] [0045] [0057-58] [0062-63] [0127] supplying the digital board where menu is displayed for customers. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kumar to include such digital boards of Mueller to help multiple customers view the same menu, and “ensure that most or all of the customers who might have viewed the lower price on the menu board would not be charged the new, higher price once they reached the point-of-sale terminal.” Mueller [0164].
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar, Wang, in view of US Pat Pub No 2018/0084078 to Yan.
With regard to claim 13, Kumar teaches determining, by the one or more computing devices, a plurality of target customers (Kumar, identifies customers primarily based on proximity to first buyer or location; Wang uses geofences to identify customers; Yan [0004-5] determine whether a target user is likely to acquire a particular item; Yan [0016] expressly contemplates multiple client devices, [0017] applies the prediction and content-delivery process to users of those devices) that are likely to be interested in ordering the second item or the second instance of the first item (Yan e,g, [0035] [0054-55] Fig. 5, 560), wherein the determining the plurality of target customers is based at least in part on analyzing customer data (Yan [0022] [0023-27]) stored in a database associated with the service provider using a machine-trained model (Yan [0021-22], [0034-35] [0054-55]), wherein the first subset of the plurality of customer computing devices is further limited to respective customer computing devices causing the updated electronic menu to be presented comprises causing the updated electronic menu to be presented via customer computing devices of the plurality of target customers (Yan [0055] sends the item-related content to the target user’s client device only when the predicted likelihood exceeds a threshold). A person of ordinary skill would have been motivated to apply Yan’s machine-learning customer-selection technique to the customers identified by Kumar and Wang. Kumar seeks to induce additional customers to order a particular item or item instance so that their requests can be combined, while Wang geographically limits promotional content to customers associated with a merchant geofence. Yan teaches that indiscriminately sending item-related content to users who are unlikely to be interested wastes computing and networking resources and provides a poor user experience. Yan therefore analyzes stored user-profile and activity data using a trained machine-learning model, determines the likelihood that each user will acquire a particular item, and sends the item-related content to the user’s client device when the likelihood exceeds a threshold. Applying Yan’s technique to Kumar/Wang would predictably limit the updated menu to geographically eligible customers who are also likely to order the promoted item, thereby increasing the likelihood of obtaining Kumar’s combined-order efficiency while reducing irrelevant transmissions.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The examiner has withdrawn the 101 rejection based on the amendments provided.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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