DETAILED ACTION
Notice to Applicant
1. The following is a NON-FINAL Office action upon examination of application number 19/006,305. Claims 1-9 are pending in this application, and have been examined on the merits discussed below.
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
3. Application 19/006,305 filed 12/31/2024 claims foreign priority to Japanese patent application 2024-005579, filed 01/17/2024.
Information Disclosure Statement
4. The information disclosure statements (IDS) filed on 12/31/2024 and 02/28/2025 have been acknowledged. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
5. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
6. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “an order information collection unit configured to,” “a status information collection unit configured to,” “a prediction unit configured to,” “a determination unit configured to,” “a notification unit configured to” in claim 1, and “an output unit configured to” in claim 7.
The claim limitations “an order information collection unit configured to,” “a status information collection unit configured to,” “a prediction unit configured to,” “a determination unit configured to,” “a notification unit configured to,” and “an output unit configured to” invoke 112(f). Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Specification describes that the “order information collection unit,” “status information collection unit,” “prediction unit,” “determination unit,” “notification unit,” and “output unit” are implemented by a computer (par. [0042]). Accordingly, the structure corresponding to the “order information collection unit,” “status information collection unit,” “prediction unit,” “determination unit,” and “notification unit” recited in claim 1 and the “output unit” in claim 7 are interpreted as being embodied as a generic computer programmed with software to perform the corresponding functions recited in these claims.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
7. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
9. Claim 1 recites the limitation “the wait place determination unit,” which lacks antecedent basis. The claim initially introduces only “a determination unit,” and does not introduce “a wait place determination unit.” It is unclear whether “the wait place determination unit,” refers to the previously recited “determination unit” or to a different determination unit, therefore rendering the claim indefinite. Appropriate correction is required.
10. Claims 1/8/9 recite the step of “notifying the wait place,” the step of “notifying the wait place,” is grammatically unclear because a “wait place” is not an entity capable of being notified. The claim fails to identify the recipient of the notification (i.e., the customer or store staff), and therefore renders the limitation unclear. For examination purposes, the step of “notifying the wait place” is interpreted as “notifying the customer of the wait place.” Appropriate correction is required.
11. Claim 9 recites “A computer program product for ordering an item to be executed in an order management device of performing order acceptance and settlement with respect to the item ordered by a customer, the computer program product causing a computer to perform a method comprising: collecting order information regarding an item ordered by a customer; collecting store status information regarding a status of a store; predicting…” The claim recites “a computer program product,” but does not positively recite any structural limitations for the product, such as a computer-readable medium or instructions stored thereon. Therefore, it is unclear what constitutes the claimed article. The term “computer program product” could encompass software per se, signal, or a storage medium, and the boundaries of the claimed subject matter are unclear. The claim recites that the computer program product is “for ordering an item to be executed in an order management device,” and also causes a computer “to perform a method.” It is unclear whether the claim is directed to a computer-readable medium storing instructions, a system including an order management device, or a method. Furthermore, shifting between one statutory category to another (i.e., computer program product to device to method) renders the claim scope ambiguous because it is unclear whether any steps are required to be executed by the device, or whether the claim merely requires possession of the device capable of performing the process (steps) for which it is configured. For purposes of examination, claim 9 will be interpreted as “a computer-readable medium storing instructions.” Appropriate correction is required.
12. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
13. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
14. Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-7) and method (claim 8) are directed to at least one potentially eligible category of subject matter (i.e., machine and process, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-8 is satisfied. Claim 9, however, is directed to “a computer program product.” The term “computer program product” is broad enough to encompass transitory propagating signals, such as carrier waves or signals embodying program instructions. Transitory propagating signals do not fall within one of the four statutory categories of invention because, as currently recited, the claimed computer program product could be embodied as software per se, a transitory signal, or any other non-tangible medium. The Specification does not clearly disclaim transitory embodiments. Thus, Step 1 of the Subject Matter Eligibility test for claim 9 is not satisfied because the computer program product encompasses transitory embodiments.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing order acceptance and settlement with respect to an item ordered by a customer, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions). With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: the item order system comprising: an order information collection unit configured to collect order information regarding an item ordered by a customer; a status information collection unit configured to collect store status information regarding a status of a store; a prediction unit configured to predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information collected by the order information collection unit and the store status information collected by the status information collection unit; a determination unit configured to determine a wait place where the customer waits until picking up the ordered item, based on the provision time period predicted by the prediction unit; and a notification unit configured to notify the wait place determined by the wait place determination unit. These steps are organizing human activity by managing interactions between people by following rules, or instructions. The claim recites limitations that fall under the “Certain Methods of Organizing Human Activity” abstract idea grouping because the limitations describe concepts related to managing customer behavior in a commercial setting by collecting order and store information, predicting a wait time, determining where a customer should wait, and notifying the customer accordingly.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Independent claims 8 and 9 recite similar limitations as those discussed above and are therefore found to recite the same or substantially the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: the item order system comprising: an order information collection unit configured to, a status information collection unit configured to, a prediction unit configured to, a determination unit configured to, and a notification unit configured to (claim 1), an order management device (claim 8), a computer program product, an order management device, and a computer (claim 9). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the “collecting” steps are evaluated as additional elements, these steps amount at most to insignificant pre-solution data gathering activity, which is not indicative of a practical application, as noted in MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: the item order system comprising: an order information collection unit configured to, a status information collection unit configured to, a prediction unit configured to, a determination unit configured to, and a notification unit configured to (claim 1), an order management device (claim 8), a computer program product, an order management device, and a computer (claim 9). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraph [0042, 0099]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
With respect to the collecting steps, these steps amount to insignificant extra-solution activity, which does not amount to a practical application (MPEP 2106.05(g)), nor add significantly more because such activity has been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-7 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-7 recite “wherein the order information is at least one of an order item, an order quantity, an order monetary amount, an item provision form, a desired item-pick-up time, and a terminal type,” “wherein the store status information is at least one of a time at which cooking of the ordered item is capable of being started, a congestion status indicating a congestion degree of the store, the number of waiting people at the wait place, and attendance information of store staff,” “wherein the wait place is any one of a pick-up counter where the customer picks up the ordered item, a customer seat where the customer eats or drinks the ordered item, an in-store wait space where the customer waits inside the store, and an outside-store wait space where the customer waits outside the store,” “store staff skill information regarding work skill of staff, and predicts a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information, the store status information, and the staff skill information,” “store information regarding a required time period taken until provision of the item in a predetermined period, and predicts the provision time period taken until provision of the ordered item to the customer, based on at least one of the order information, the store status information, and the information regarding the required time period,” “output information on the wait place,” however these limitations cover activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. The dependent claims recite additional elements of: a staff information memory configured to (claim 5), a memory configured to (claim 6), and an output unit configured to (claim 7). However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible. Even if the step for outputting is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 102
15. 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 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.
16. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
17. Claims 1-4 and 6-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Schwenker et al., Pub. No.: US 2024/0119400 A1, [hereinafter Schwenker].
As per claim 1, Schwenker teaches an item order system of performing order acceptance and settlement with respect to an item ordered by a customer, the item order system (paragraph 0002, discussing systems and methods for customer management and order preparation; paragraph 0022) comprising:
an order information collection unit configured to collect order information regarding an item ordered by a customer (paragraph 0029, discussing automated systems for receiving orders (e.g., an order terminal)…The quick service restaurant can include various components for receiving customer orders and delivering orders…; paragraph 0074, discussing that the order can be placed at any location, and the quick service restaurant (QSR) can receive the order from customer; paragraph 0075, discussing that the order is received by the QSR (e.g., by computer processor in communication with the customer via a mobile application); paragraph 0116, discussing receiving, from the customer, an order that includes at least one food item in need of preparation; paragraph 0032, discussing that the systems can include additional computer processors, user interfaces, storage media, cloud-based computing and/or storage, cloud-based location tracking and traffic monitoring, and/or communication device(s) for viewing, sending, receiving, and storing information electronically; paragraph 0084);
a status information collection unit configured to collect store status information regarding a status of a store (paragraph 0023, discussing that the system can determine an appropriate course of action based on the customer's needs. The system uses a machine-learning model to determine the optimal action based on customer's needs and current quick service restaurant (QSR) status (e.g., how busy the QSR is) [i.e., the quick service restaurant status corresponds to the status of a store]; paragraph 0037, discussing that the system can include one or multiple machine-learning models. The machine-learning models can be trained using inputs, such as information regarding the orders by customers as well as conditions of the QSR (quick service restaurant)…; paragraph 0095, discussing predicting the amount of time needed to prepare the first/second food item placed by first and second customers...This step can be performed by system (e.g., computer processor having machine-learning models)…The prediction can depend upon a variety of inputs/factors, such as the at least one item in the order, a time-of-day that the order was placed by first and second customers, respectively, a number of employees/staff members on duty at the QSR, which employees/staff members are on duty at the QSR, and a number of other orders currently pending at the QSR; paragraph 0033, discussing that the computer processor can be configured to receive information from the QSR, employees/staff members of the QSR, any components of system, a tracking/traffic system distant from system, and/or other sources in communication with the computer processor; paragraph 0032);
a prediction unit configured to predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information collected by the order information collection unit and the store status information collected by the status information collection unit (paragraph 0037, discussing that the system can include one or multiple machine-learning models. The machine-learning models can be trained using inputs, such as information regarding the orders by customers as well as conditions of the QSR (quick service restaurant), and outputs, such as the actual amount of time taken to prepare the orders. This information can be collected/recorded by the system during operation prior to the training of the machine-learning models. The inputs can be, but are not limited to, the food items in the orders, a time-of-day that the orders were placed by each customer, a number of employees/staff members on duty at the QSR when the order is received, which employees/staff members are on duty at the QSR when the order is received, a number of orders currently pending at the QSR when each order is received, an amount of ingredients in inventory necessary to prepare the order, and other information and conditions of the QSR; paragraph 0039, discussing that once trained, machine-learning models can receive the inputs and predict, for example, the amount of time taken by QSR (quick service restaurant) to prepare each order. The predicted time needed for preparation/completion of the orders can then be used by the system and QSR for customer management (e.g., directing customers to locations/positions to collect orders) and order preparation and optimization (e.g., preparing food items in each order at specific times so that the orders are ready when customers arrive at the QSR but the food items are still fresh/warm). While in operation, the machine-learning models can receive additional data sets (e.g., inputs/information regarding previous orders) as well as the actual amount of time needed to complete preparation of the orders to use in further refining/training machine-learning models. This additional refining/training can be done in real time as the previous inputs and outputs are determined and provided to machine-learning models. The additional refining/training can improve the accuracy of predictions by machine-learning models regarding the amount of time taken by the QSR to complete preparation of orders by customers; paragraphs 0055, 0075);
a determination unit configured to determine a wait place where the customer waits until picking up the ordered item, based on the provision time period predicted by the prediction unit (paragraph 0109, discussing that after the vehicle has been associated with the customer and the order by the customer, a process includes a step 812 of directing the vehicle to a location at which customer can receive the order. Step 812 can direct the vehicle to any location within parking lot, such as parking spots 12A, 12B, and 12C; drive-through lanes 13A and 13B; and general parking spots 14. The system can be aware of where vehicle is located and convey that information to the QSR so the QSR (e.g., employees/staff members) knows to what location to deliver the order. Step 812 can be performed such that the vehicle is directed to a location at which the customer will have to wait the least amount of time to receive his/her order. This optimal management/direction of vehicles can be determined by the computer processor. Directing the vehicle can be performed by notifying the customer via a notice on a mobile application, by a message and/or arrows on signs, and/or by other means, such as an audible message/notice; paragraph 0153, discussing that the method can further include that the directions for directing the customer into a waiting spot [i.e., a wait place] are dependent upon at least one of: whether the customer is a member of a loyalty program, an estimate wait time until delivery of the order, and whether the customer placed the order in advance of arriving at the quick service restaurant; paragraph 0162); and
a notification unit configured to notify the wait place determined by the wait place determination unit (paragraph 0049, discussing a sign displaying a welcome to customer to notify that specific customer that the display is directed at him/her…The sign can be located, for example, at entrance/exit to direct customer to a particular location to wait for the order of customer to be delivered (e.g., direct John Doe to parking spot 3 to collect his order). Finally, the sign shows the customer the estimated wait time until he/she will receive his/her order, which in this example is 30 seconds; paragraph 0071, discussing that the notice can be provided to the customer via a mobile application, on signs viewable by the customer, or by other means; paragraph 0109, discussing that after the vehicle has been associated with the customer and the order by the customer, a process includes a step 812 of directing the vehicle to a location at which customer can receive the order. Step 812 can direct the vehicle to any location within parking lot, such as parking spots 12A, 12B, and 12C; drive-through lanes 13A and 13B; and general parking spots 14. The system can be aware of where vehicle is located and convey that information to the QSR so the QSR (e.g., employees/staff members) knows to what location to deliver the order. Step 812 can be performed such that the vehicle is directed to a location at which the customer will have to wait the least amount of time to receive his/her order…Directing the vehicle can be performed by notifying the customer via a notice on a mobile application, by a message and/or arrows on signs, and/or by other means, such as an audible message/notice; paragraph 0152, discussing that the method can further include that the information specific to the customer displayed by the digital sign includes directions for directing the customer into a waiting spot).
As per claim 2, Schwenker teaches the item order system according to claim 1. Schwenker further teaches wherein the order information is at least one of an order item, an order quantity, an order monetary amount, an item provision form, a desired item-pick-up time, and a terminal type (paragraph 0116, discussing receiving, from the customer, an order that includes at least one food item [i.e., order item] in need of preparation; paragraph 0037, discussing that the inputs can be, but are not limited to, the food items (e.g., a hamburger, French fries, a strawberry milkshake) in the orders; paragraph 0111, discussing that the customer information and/or vehicle information can include any information specific to customer, such as the food items in the order placed by the customer; paragraph 0094).
As per claim 3, Schwenker teaches the item order system according to claim 1. Schwenker further teaches wherein the store status information is at least one of a time at which cooking of the ordered item is capable of being started, a congestion status indicating a congestion degree of the store, the number of waiting people at the wait place, and attendance information of store staff (paragraph 0023, discussing that the system uses a machine-learning model to determine the optimal action based on customer's needs and current quick service restaurant (QSR) status (e.g., how busy the QSR is); paragraph 0095, discussing predicting the amount of time needed to prepare the first/second food item placed by first and second customers...This step can be performed by system (e.g., computer processor having machine-learning models)…The prediction can depend upon a variety of inputs/factors, such as the at least one item in the order, a time-of-day that the order was placed by first and second customers, respectively, a number of employees/staff members on duty at the QSR [i.e., attendance information of store staff], which employees/staff members are on duty at the QSR, and a number of other orders currently pending at the QSR).
As per claim 4, Schwenker teaches the item order system according to claim 1. Schwenker further teaches wherein the wait place is any one of a pick-up counter where the customer picks up the ordered item, a customer seat where the customer eats or drinks the ordered item, an in-store wait space where the customer waits inside the store, and an outside- store wait space where the customer waits outside the store (paragraph 0109, discussing that after the vehicle has been associated with the customer and the order by the customer, a process includes a step 812 of directing the vehicle to a location at which customer can receive the order. Step 812 can direct the vehicle to any location within parking lot, such as parking spots 12A, 12B, and 12C [i.e., an outside- store wait space]; drive-through lanes 13A and 13B; and general parking spots 14. The system can be aware of where vehicle is located and convey that information to the QSR so the QSR (e.g., employees/staff members) knows to what location to deliver the order. Step 812 can be performed such that the vehicle is directed to a location at which the customer will have to wait the least amount of time to receive his/her order. This optimal management/direction of vehicles can be determined by the computer processor. Directing the vehicle can be performed by notifying the customer via a notice on a mobile application, by a message and/or arrows on signs, and/or by other means, such as an audible message/notice; paragraph 0153).
As per claim 6, Schwenker teaches the item order system according to claim 1. Schwenker further teaches further comprising a memory configured to store information regarding a required time period taken until provision of the item in a predetermined period (paragraph 0032, discussing that the systems can include additional computer processors, user interfaces, storage media, cloud-based computing and/or storage, cloud-based location tracking and traffic monitoring, and/or communication device(s) for viewing, sending, receiving, and storing information electronically; paragraph 0097, discussing comparing the amount of time until first the customer arrives at the QSR to the predicted amount of time needed to prepare the first/second food item. The comparison can result in three outcomes: 1) the amount of time until first customer arrives at the QSR can be greater than the predicted amount of time needed to prepare the food item; 2) the amount of time until the first customer arrives at the QSR can be equal to the predicted amount of time needed to prepare the food item; and 3) the amount of time until the first customer arrives at the QSR can be less than the predicted amount of time needed to prepare the food item. For the first two outcomes, there is enough time to remake the first food item for the first order by the customer, so process 700 can perform step 716, which is allocating the prepared first/second food item to the second order for the second customer. For the third outcome, there is not enough time to remake the first food item for the first order by the first customer, so step 722 can be performed, which is allocating the food item to the first order and delivering the food item in the first order to the first customer; paragraph 0112, discussing that the system can be configured to direct customers in vehicles that have yet to place an order into one of drive-through lanes 13A and 13B depending on if the customer is a loyalty or non-loyalty customer, which can be determined by whether the customer has signed up for the customer user profile. The system can be configured to prioritize loyalty customers by directing loyalty customers into a drive-through lane in which the estimated wait time is less than the other drive-through lane; paragraph 0162, discussing that the method can further include that the customer is directed to the waiting spot in response to the amount of time needed to complete preparation of the at least one food item being greater than three minutes),
wherein the prediction unit predicts the provision time period taken until provision of the ordered item to the customer, based on at least one of the order information, the store status information, and the information regarding the required time period (paragraph 0037, discussing that the system can include one or multiple machine-learning models. The machine-learning models can be trained using inputs, such as information regarding the orders by customers as well as conditions of the QSR (quick service restaurant), and outputs, such as the actual amount of time taken to prepare the orders. This information can be collected/recorded by the system during operation prior to the training of the machine-learning models. The inputs can be, but are not limited to, the food items in the orders, a time-of-day that the orders were placed by each customer, a number of employees/staff members on duty at the QSR when the order is received, which employees/staff members are on duty at the QSR when the order is received, a number of orders currently pending at the QSR when each order is received, an amount of ingredients in inventory necessary to prepare the order, and other information and conditions of the QSR; paragraph 0039, discussing that once trained, machine-learning models can receive the inputs and predict, for example, the amount of time taken by QSR (quick service restaurant) to prepare each order. The predicted time needed for preparation/completion of the orders can then be used by the system and QSR for customer management (e.g., directing customers to locations/positions to collect orders) and order preparation and optimization (e.g., preparing food items in each order at specific times so that the orders are ready when customers arrive at the QSR but the food items are still fresh/warm). While in operation, the machine-learning models can receive additional data sets (e.g., inputs/information regarding previous orders) as well as the actual amount of time needed to complete preparation of the orders to use in further refining/training machine-learning models. This additional refining/training can be done in real time as the previous inputs and outputs are determined and provided to machine-learning models. The additional refining/training can improve the accuracy of predictions by machine-learning models regarding the amount of time taken by the QSR to complete preparation of orders by customers).
As per claim 7, Schwenker teaches the item order system according to claim 1. Schwenker further teaches further comprising an output unit configured to output information on the wait place (paragraph 0071, discussing that the notice can be provided to the customer via a mobile application, on signs viewable by the customer, or by other means; paragraph 0109, discussing that after the vehicle has been associated with the customer and the order by the customer, a process includes a step 812 of directing the vehicle to a location at which customer can receive the order. Step 812 can direct the vehicle to any location within parking lot, such as parking spots 12A, 12B, and 12C; drive-through lanes 13A and 13B; and general parking spots 14. The system can be aware of where vehicle is located and convey that information to the QSR so the QSR (e.g., employees/staff members) knows to what location to deliver the order. Step 812 can be performed such that the vehicle is directed to a location at which the customer will have to wait the least amount of time to receive his/her order…Directing the vehicle can be performed by notifying the customer via a notice on a mobile application, by a message and/or arrows on signs, and/or by other means, such as an audible message/notice; paragraphs 0047, 0152).
Claim 8 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 8 Schwenker teaches an item order method to be performed in an order management device of performing order acceptance and settlement with respect to an item ordered by a customer (paragraph 0002, discussing systems and methods for customer management and order preparation; paragraph 0021, discussing that systems and related methods for use in association with a business, such as a quick service restaurant (also referred to herein as a “QSR”), to quickly deliver freshly prepared orders to multiple customers while reducing wait times and, if desired, prioritizing loyalty customers. The systems and methods can be directed at customer management and order preparation and delivery optimization; paragraph 0022, discussing that the disclosure provides systems and method for identifying and directing customer vehicles to parking spots/drive thru lanes without human intervention; paragraph 0032).
Claim 9 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 9 Schwenker teaches a computer program product for ordering an item to be executed in an order management device of performing order acceptance and settlement with respect to the item ordered by a customer, the computer program product causing a computer to perform a method (paragraph 0028, discussing that the system can include a computer processor (which can include a machine-learning model, storage media, and/or other components/capabilities)…; paragraph 0035, discussing that the computer processor can perform instructions stored within the storage media, and the computer processor can include storage media such that the computer processor is an all-encompassing component able to store instructions and perform the functions described; paragraph 0036, discussing that the system (including the computer processor) can also include or function in association with machine-readable storage media. In some examples, a machine-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signa…; paragraph 0032).
Claim Rejections - 35 USC § 103
18. 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 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.
19. 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 of this title, 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.
20. 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.
21. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
22. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Schwenker in view of Fox, Pub. No.: US 2022/0076362 A1, [hereinafter Fox].
As per claim 5, Schwenker teaches the item order system according to claim 1. Schwenker further teaches further comprising a staff information memory configured to store staff information (paragraph 0032, discussing that the systems can include additional computer processors, user interfaces, storage media, cloud-based computing and/or storage, cloud-based location tracking and traffic monitoring, and/or communication device(s) for viewing, sending, receiving, and storing information electronically; paragraph 0033, discussing that the computer processor can be configured to receive information from the quick service restaurant, employees/staff members of the QSR; paragraph 0037, discussing that the inputs can be, but are not limited to, the food items in the orders, a time-of-day that the orders were placed by each customer, a number of employees/staff members on duty at the QSR when the order is received, which employees/staff members are on duty at the QSR when the order is received (e.g., James is currently on duty and Jared is not on duty); paragraph 0085, discussing that predicting the amount of time needed to prepare the order placed by the customer. This step can be performed by the system…The prediction can depend upon a variety of inputs/factors, such as the at least one item in the order, a time-of-day that the order was placed by the customer, a number of employees/staff members on duty at the QSR, which employees/staff members are on duty at the QSR, and a number of other orders currently pending at the QSR; paragraph 0100),
wherein the prediction unit predicts a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information, the store status information, and the staff skill information (paragraph 0037, discussing that the system can include one or multiple machine-learning models. The machine-learning models can be trained using inputs, such as information regarding the orders by customers as well as conditions of the QSR (quick service restaurant), and outputs, such as the actual amount of time taken to prepare the orders. This information can be collected/recorded by the system during operation prior to the training of the machine-learning models. The inputs can be, but are not limited to, the food items in the orders, a time-of-day that the orders were placed by each customer, a number of employees/staff members on duty at the QSR when the order is received, which employees/staff members are on duty at the QSR when the order is received, a number of orders currently pending at the QSR when each order is received, an amount of ingredients in inventory necessary to prepare the order, and other information and conditions of the QSR; paragraph 0039, discussing that once trained, machine-learning models can receive the inputs and predict, for example, the amount of time taken by QSR (quick service restaurant) to prepare each order. The predicted time needed for preparation/completion of the orders can then be used by the system and QSR for customer management (e.g., directing customers to locations/positions to collect orders) and order preparation and optimization (e.g., preparing food items in each order at specific times so that the orders are ready when customers arrive at the QSR but the food items are still fresh/warm). While in operation, the machine-learning models can receive additional data sets (e.g., inputs/information regarding previous orders) as well as the actual amount of time needed to complete preparation of the orders to use in further refining/training machine-learning models. This additional refining/training can be done in real time as the previous inputs and outputs are determined and provided to machine-learning models. The additional refining/training can improve the accuracy of predictions by machine-learning models regarding the amount of time taken by the QSR to complete preparation of orders by customers).
Schwenker does not explicitly teach further comprising a staff information memory configured to store staff skill information regarding work skill of staff. However, Fox in the analogous art of order management systems teaches this concept. Fox teaches:
further comprising a staff information memory configured to store staff skill information regarding work skill of staff (paragraph 0007, discussing that for many restaurants the preparation time significantly varies between menu items, and order completion time is determined by multiple dynamic variables: staffing levels, staff position training, staff skill levels, prior orders in progress, inventory on hand, order size, order complexity, and by the longest preparation time of any one item on an order; paragraphs 0069-0075, discussing that the wait time for as soon as possible (ASAP) orders and time slots allowed for future orders is based on an algorithm that factors multiple variables. Variables include (but are not limited to): a. ASAP or promised time(s) of prior orders and the current production progress of each of those prior orders, b. Order size, c. Order item complexity, d. Production staff levels, e. Delivery staff levels, f. Skill levels of staff members; paragraph 0085, discussing that the data may be updated as often as needed and there are means for updating the data. This management or administrative unit allows for input, for changes in data storage, and for receiving data output. Temporary, calculated, and intermediate calculation values are stored in a data storage unit, and can be accessed and written to by the data processing unit. For example, the preparation time is affected by staff levels and skill levels in the algorithm; paragraph 0094, discussing that the system will access in-unit schedules and staff positions and skill levels to determine team's productive capacity at any given time interval on any given day).
Schwenker is directed toward systems and methods for customer management and order preparation. Fox relates to drive-through, pick up, and delivery ordering and delivery systems and methods for restaurants. Therefore, they are deemed to be analogous as they both are directed towards solutions for order management. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Schwenker with Fox because the references are analogous art because they are both directed to solutions for order management, which falls within applicant’s field of endeavor (system and method for ordering items), and because Schwenker to include Fox’s feature for including a staff information memory configured to store staff skill information regarding work skill of staff, in the manner claimed, would serve the motivation of providing a more accurate order ready time, instead of either being inconvenienced by an over-ambitious estimate that is too short and requires the customer to wait longer than expected, or by an overly-conservative estimate that unnecessarily discourages the customer from ordering (Fox at paragraph 0027); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rosas-Maxemin et al., Pub. No.: US 2022/0237713 A1 – describes directing the user to a waiting zone by presenting a set of navigation instructions to the user.
Pugh et al., Pub. No.: US 2016/0171592 A1 – describes a customer message including instructions to advance to the waiting area to await delivery of the customer's ordered item(s).
Zhang, Tianhua, et al. "An approximation of the customer waiting time for online restaurants owning delivery system." Journal of Systems Science and Complexity 32.3 (2019): 907-931 – describes that careful design of the meal preparation and order delivery systems is needed to avoid excessive customer waiting time between ordering and delivery.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625