DETAILED ACTION
Notice to Applicant
1. The following is a 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.
Response to Amendment
4. In the response filed May 26, 2026, Applicant amended claims 1-9, and did not cancel any claims. No new claims were presented for examination.
5. Applicant's amendments to claims 1 and 7 are hereby acknowledged. The Claim Interpretation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 has been removed.
6. Applicant's amendments to claims 1, 8, and 9 are hereby acknowledged. The amendments are sufficient to overcome the previously issued rejection of claims 1-9 under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
7. Applicant's amendments to claims 1, 8, and 9 are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Examiner’s Note
8. The amendment document filed on 05/26/2026 is considered non-compliant because it has failed to meet the requirements of 37 CFR 1.121 or 1.4. As per 37 CFR 1.121, all claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of “currently amended,” and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text. In this case, claims 1, 8, and 9 were not submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. Specifically, claims 1 and 9 were amended to recite “determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout” and claim 8 was amended to recite “determining a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout.” However, the text of the added subject matter (i.e., a plurality of candidate wait places) is not shown by underlining the added text. In the interest of compact prosecution, the Examiner has addressed the amended limitation.
The limitation as presented in original claim 1 (Claims 12/31/2024) has been reproduced below:
PNG
media_image1.png
105
636
media_image1.png
Greyscale
The limitation as presented in amended claim 1 (Claims 05/26/2026) has been reproduced below:
PNG
media_image2.png
154
667
media_image2.png
Greyscale
Response to Arguments
9. Applicant's arguments filed May 26, 2026, have been fully considered.
10. Applicant submits “The Office Action characterizes the claims as directed to a "method of organizing human activity", managing personal behavior or interactions between people. Applicant respectfully disagrees.” [Applicant’s Remarks, 05/26/2026, page 9]
With particular respect to the §101 rejection, Applicant first argues with respect to Step 2A of the eligibility inquiry “the Office Action characterizes the claims as directed to a "method of organizing human activity", managing personal behavior or interactions between people. Applicant respectfully disagrees.” In response the Examiner maintains that claim 1 has been found to recite an abstract idea that falls into the “Certain methods of organizing human activity” by reciting limitations for managing personal behavior or relationships or interactions between people - including social activities, teaching, and following rules or instructions. The limitations reciting “collect order information regarding an item ordered by a customer; collect store status information regarding a status of a store; predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information and the store status information; determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout; and notify the customer of the determined wait place” are reasonably understood as setting forth activities of managing personal behavior or relationships or interactions between people, including following rules or instructions. Under Prong One of Step 2A, information is received and analyzed for purposes which relate to managing a commercial transaction and customer service interaction. The claim limitations constitute business related coordination of customer pickup activities and the management of interactions between a business and its customers, which are forms of organizing human activity.
As stated in MPEP 2106, the phrase “certain methods of organizing human activity” is used to describe concepts relating to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The claim, under its broadest reasonable interpretation, recites limitations within the Abstract idea grouping of “certain methods of organizing human activity.” Clearly, organizing human activities is applicable to the processes of coordinating customer order pickup activities. The claim limitations merely cover managing interactions between people, thus falling within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, when evaluated under Step 2A Prong One of the eligibility inquiry, the claims recite limitations falling within both the “Certain Methods of Organizing Human Activity” grouping as set forth in MPEP 2106. For the reasons above, this argument is found unpersuasive.
11. Applicant submits “Even assuming arguendo that any limitation of the claims could be characterized as reciting an abstract idea, the additional limitations integrate that idea into a practical application.” [Applicant’s Remarks, 05/26/2026, page 10]
In response to Applicant’s argument that “the additional limitations integrate that idea into a practical application,” it is noted that the additional elements in amended claim 1 are: a processor and a memory storing instructions, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
It is also noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no processor, memory, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to a determined wait place, which is not a technical result or improvement thereof.
Furthermore, the additional elements fail to integrate the abstract idea into a practical application because they 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.
In response to Applicant’s argument that “The present claims reflect technical improvements in restaurant order management,” it is noted the claim does not recite a technical improvement to restaurant order management systems themselves. Instead, it merely automates a business/data processing workflow. The claim does not recite a specific technological improvement to restaurant order management systems or computer functionality. The alleged improvement is directed to the efficiency of customer service management, not to a technical improvement in computer functionality or another technical field. The claim merely applies generic computing components to collect information, determine a wait location, and provide a notification in support of a business practice. For the reasons above, this argument is found unpersuasive.
12. Applicant submits “Just as the Desjardins claims reflected improvements to "how the machine learning model itself operates," Desjardins at 9, the present claims reflect improvements to how the wait- place determination algorithm itself operates. Specifically, the claims reflect improvements by tying the wait-place determination to specific structural inputs (provision time period and item provision form) drawn from concrete operational data sources (the order information and store status information).” [Applicant’s Remarks, 05/26/2026, page 8]
The Examiner respectfully disagrees. While Applicant cites the December 5, 2025 Memorandum regarding Ex Parte Desjardins, the rejection fully considers whether the additional elements in claim 1, including the processor and memory, integrate the abstract idea into a practical application. The claims recites generic computing components performing conventional data processing tasks without specifying a particular improvement in the functioning of the computer. Consistent with MPEP 2106.04(d)(1), 2106.05(a), and 2106.05(f), merely linking conventional computer elements to an abstract idea does not transform the claim into a patent eligible invention. Accordingly, when considered as a whole the claim does not recite a technological improvement under Ex Parte Desjardins and remains directed to an abstract idea. Applicant should amend the claim to clearly recite how the claim elements improve the functioning of the computer or solve a specific technological problem. Claim 1 does not recite any specific improvement to the functioning of a computer or other technology, as required under the framework articulated in cases such as Enfish. In Ex Parte Desjardins, the Board found eligibility where the claims and the Specification together describes a particularized technological improvement and the claims reflect how the improvement was achieved. In contrast, the present claim recites only results oriented steps (i.e., collect, predict, determine, notify) without specifying any concrete technological mechanism or change in how a computer operates.
Moreover, claim 1 does not recite any specific model or algorithm, that would demonstrate an improvement to a technical field, but instead broadly invokes generic processing to achieve the desired outcomes. Therefore, the claim does not integrate the abstract idea into a practical application, nor does it reflect a technological improvement comparable to that in Ex Parte Desjardins. For the reasons above, this argument is found unpersuasive.
13. Applicant submits “Even if any individual element of the claims were considered routine, the specific ordered combination recited in the claims (a processor and memory architecture that collects two distinct categories of operational data (order information and store status information), predicts a provision time period based on those inputs, determines a wait place from a plurality of candidate wait places based on both the predicted time period and an item provision form indicating dine-in or takeout, and notifies the customer of the determined wait place) is not well-understood, routine, and conventional.” [Applicant’s Remarks, 05/26/2026, page 11]
Specifically, regarding the rejection under 35 U.S.C. § 101, Applicant submits that “Even if any individual element of the claims were considered routine, the specific ordered combination recited in the claims (a processor and memory architecture that collects two distinct categories of operational data (order information and store status information), predicts a provision time period based on those inputs, determines a wait place from a plurality of candidate wait places based on both the predicted time period and an item provision form indicating dine-in or takeout, and notifies the customer of the determined wait place) is not well-understood, routine, and conventional.” As best understood by the Examiner, Applicant’s reliance on the Berkheimer Memo is based on Applicant’s misunderstanding of the Berkheimer decision, which is germane only to Step 2B eligibility inquiry into whether certain additional claim limitations are well-understood, routine, and conventional and the evidentiary requirements to support factual findings related thereto. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
Accordingly, the Examiner emphasizes that a §101 rejection, including one based on a judicial exception, does not hinge on whether or not any particular limitation or the entire claimed subject matter is directed to “well-understood, routine, and conventional activities.” Notably, a §101 rejection may be proper even none of the claim limitations are deemed well-understood, routine, and conventional. We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating §102 novelty.”); Intellectual Ventures LLC v. Symantec Corp., 838 F.3d 1307, 1315 (Fed. Cir. 2016) (same for obviousness) (Symantec).
Moreover, it is noted that the addition of non-conventional components to an abstract idea does not necessarily turn an abstraction into something concrete. Further, the Examiner points out that limitations that were found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception also include: adding the words “apply it” or equivalent with the judicial exceptions, or mere instruction to implement an abstract idea on a computer, simply appending well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, of the judicial exception. As described below, the claims of the instant application are drawn to an abstract idea. It is noted that for the role of a computer in a computer-implemented invention to be deemed meaningful, it must involve more than performance of "well-understood, routine and conventional activities previously known in the industry.”
Furthermore, it is noted that only those additional elements (analyzed under 2B) that are deemed “conventional” need to comply with Berkheimer. When elements are just part of “apply it” [abstract idea] on a computer, under MPEP 2106.05(f), no evidence is needed. Citations for conventionality to MPEP 2106.05 were already provided. Arguing abstract elements for Berkheimer is not persuasive. See BSG Tech, LLC v. Buyseasons, Inc., 899 F.3d 1281,1290 (Fed. Cir. 2018) states “Our precedent has consistently employed this same approach. If a claim’s only “inventive concept” is the application of an abstract idea using conventional and well-understood techniques, the claim has not been transformed into a patent-eligible application of an abstract idea. See, e.g., Berkheimer, 881 F.3d at 1370 (holding claims lacked an inventive concept because they “amount to no more than performing the abstract idea of parsing and comparing data with conventional computer components”). For the reasons above, this argument is found unpersuasive.
For the reasons above, in addition to the reasons provided in the updated §101 rejection below, Applicant’s amendment and supporting arguments are not sufficient to overcome the §101 rejection.
14. Applicant submits “Schwenker does not describe or reasonably suggest, as recited in Claim 1: A system comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: collect order information regarding an item ordered by a customer; collect store status information regarding a status of a store; predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information and the store status information; determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout; and notify the customer of the determined wait place.” [Applicant’s Remarks, 05/26/2026, page 12]
In response to the Applicant’s argument that “Schwenker does not describe or reasonably suggest, as recited in Claim 1: A system comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: collect order information regarding an item ordered by a customer; collect store status information regarding a status of a store; predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information and the store status information; determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout; and notify the customer of the determined wait place,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 05/26/2026, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
15. Applicant submits “Schwenker does not describe selection of a wait place based on “an item provision form indicating whether the ordered item is for dine-in or takeout”.” [Applicant’s Remarks, 05/26/2026, page 12]
In response to the Applicant’s argument that “Schwenker does not describe selection of a wait place based on “an item provision form indicating whether the ordered item is for dine-in or takeout”,” the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 05/26/2026, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
16. Applicant submits “ Schwenker also does not describe a "plurality of candidate wait places" of the type recited and elaborated upon in the present claims.” [Applicant’s Remarks, 05/26/2026, page 13]
In response to the Applicant’s argument that “Schwenker also does not describe a "plurality of candidate wait places”,” the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 05/26/2026, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
17. Applicant submits “Schwenker and Fox, either alone or in combination, do not describe or reasonably suggest wherein the memory further stores staff skill information regarding work skill of staff, the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item, as recited in Claim 5.” [Applicant’s Remarks, 05/26/2026, page 13]
In response to the Applicant’s argument that “Schwenker and Fox, either alone or in combination, do not describe or reasonably suggest wherein the memory further stores staff skill information regarding work skill of staff, the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item, as recited in Claim 5,” the Examiner notes the limitation being argued by Applicant as being newly amended to the claims in the response filed 05/26/2026, which has been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 5 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
18. Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 101
19. 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.
20. 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), method (claim 8), and non-transitory computer-readable medium (claim 9) are directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-9 is satisfied.
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: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to: collect order information regarding an item ordered by a customer; collect store status information regarding a status of a store; predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information and the store status information; determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period and an item provision form indicating whether the ordered item is for dine-in or takeout; and notify the customer of the determined wait place. 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 independent claim 8, it is noted that the claim does not recite additional elements (i.e., claim 8 is a method that recites several disembodied steps). Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) (“Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract”); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility “cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.”). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must “transform the nature of the claim” into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B. If there are no additional elements in the claim, then it cannot be eligible.
With respect to independent claims 1 and 9, the additional elements are: a processor and a memory storing instructions (claim 1), a non-transitory computer-readable medium storing instructions and a processor (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 independent claim 8, it is noted that the claim does not recite additional elements (i.e., claim 8 is a method that recites several disembodied steps). Accordingly, the subject matter encompassed by independent claim 1 fails to amount to a practical application or significantly more than the abstract idea itself.
With respect to independent claims 1 and 9, the additional elements are: a processor and a memory storing instructions (claim 1), a non-transitory computer-readable medium storing instructions and a processor (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 includes an order item, an order quantity, an order monetary amount, the item provision form, a desired item-pick-up time, and a terminal type,” “wherein the store status information includes 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 plurality of candidate wait, 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: the memory and the processor (claims 5-6), the instructions and the processor (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 § 103
21. 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.
22. 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.
23. 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.
24. 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.
25. Claims 1, 3-4, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Schwenker et al., Pub. No.: US 2024/0119400 A1, [hereinafter Schwenker], in view of Liguori et al., Pub. No.: US 2023/0169612 A1, [hereinafter Liguori], in further view of Jagolta et al., Pub. No.: US 2022/0351167 A1, [hereinafter Jagolta].
As per claim 1, Schwenker teaches a system (paragraph 0002, discussing systems and methods for customer management and order preparation; paragraph 0022) comprising:
a processor (paragraph 00033, discussing that the system can include a computer processor…; paragraph 0035, discussing that the computer processor can include one or multiple computer/data processor); and
a memory storing instructions that, when executed by the processor, cause the processor (paragraph 0036: “System 20 (including computer processor 22) can also include or function in association with machine-readable storage media 24. 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 signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, storage media can be entirely or in part a temporary memory, meaning that a primary purpose storage media is not long-term storage. Storage media, in some examples, is described as volatile memory, meaning that the memory, does not maintain stored contents when power to the system (or the component(s) where storage media are located) is turned off. Examples of volatile memories can include random access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories. In some examples, storage media can also include one or more machine-readable storage media. Storage media can be configured to store larger amounts of information than volatile memory. Storage media can further be configured for long-term storage of information. In some examples, storage media include non-volatile storage elements. Examples of such non-volatile storage elements can include magnetic hard discs, optical discs, flash memories and other forms of solid-state memory, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories) 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);
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);
predict a provision time period taken until provision of the ordered item to the customer, based on at least one of the order information and the store status 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; paragraphs 0055, 0075);
determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on the provision time period (paragraph 0049, discussing that FIG. 3A shows one example of sign 30A displaying a welcome to customer (e.g., John Doe) to notify that specific customer that the display is directed at him/her. Sign 30A in FIG. 3A 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); 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
notify the customer of the determined wait place (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).
Schwenker does not explicitly teach determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on an item provision form indicating whether the ordered item is for dine-in or takeout. Liguori in the analogous art of ordering systems teaches this concept. Liguori teaches:
determine a wait place, where the customer waits until picking up the ordered item, out of a plurality of candidate wait places based on an item provision form indicating whether the ordered item is for dine-in or takeout (paragraph 0004, discussing systems, methods, and computer-readable storage media that support smart drive through and curbside delivery management. Aspects leverage cameras, computer vision, and machine learning/artificial intelligence to efficiently assign customers to various waiting locations (e.g., drive through lanes, parking spots, etc.) based on factors such as types of orders, customer priority, fulfillment rates at the waiting locations, and queue lengths at the waiting locations; paragraph 0005, discussing that a customer may place an order with a restaurant using online ordering via a mobile device application or the Internet, and upon arriving on site, the customer may access a customer interface device (e.g., a kiosk) to check-in...The input received by the customer interface device (e.g., the kiosk) is provided to a server or other computing device for processing and automatic assignment of the customer to a selected waiting location (e.g., a drive through lane or parking spot). The waiting location may be selected based on several factors, such as type of customer, requested delivery time, arrival time, type of order, queue lengths, order fulfillment speeds, or the like, as non-limiting examples; paragraph 0020, discussing that the system may be configured to manage drive through and/or curbside delivery of orders, including assigning customers to waiting locations; paragraph 0025, discussing that the location assignment engine may be configured to assign customers to one of a plurality of waiting locations, such as drive through lanes and/or parking spots, based on one or more factors such as customer type, order content, estimated waiting time, arrival time, queue lengths corresponding to the plurality of waiting locations, order fulfillment rates corresponding to the plurality of waiting locations, or the like; paragraph 0053, discussing that the location may include or correspond to a store or restaurant, a combination drive through and parking lot pickup location, or any other type of order pickup location that supports the operations described; paragraph 0019).
Schwenker is directed toward systems and methods for customer management and order preparation. Liguori relates to ordering systems and methods for managing order pickup. 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 Liguori 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 modifying Schwenker to include Liguori’s feature for including an item provision form indicating whether the ordered item is for takeout, in the manner claimed, would serve the motivation of efficiently assigning customers to various waiting locations (Liguori at paragraph 0004); 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.
The Schwenker-Liguori combination does not teach that the item provision form indicates whether the ordered item is for dine-in or takeout. However, Jagolta in the analogous art of order management systems teaches this concept. Jagolta teaches:
an item provision form indicating whether the ordered item is for dine-in or takeout (paragraph 0003, discussing that these systems further enable guests to place orders using devices outside of the restaurant, where the orders can be placed for dine-in, takeout, delivery by restaurant personnel, or delivery by third-party delivery services; paragraph 0036, discussing that the system may include a plurality of restaurants that each subscribe to a restaurant point-of-sale subscription service for automation of restaurant point-of-sale (POS) services including, but not limited to, displaying of menus, ordering and upsell of menu items by guests, routing of ordered items to kitchen staff for preparation, sequencing of ordering items through kitchens, capture and historical tracking of metadata corresponding to each order (e.g., staff assigned for preparation of ordered items; day, date, time, and season; kitchen conditions; short-term kitchen work load; true time for preparation of ordered items; dining option for order (e.g., takeout, dine in); pending incoming orders; local weather; and significant events, internal or external, that may impact order preparation times), acceptance of payments by guests and processing of those payments through corresponding credit card networks and financial institutions, and payment of charged amounts to the restaurants themselves; paragraphs 0071-0075, discussing that the order-level features according to the present invention may comprise: Dining option for the order, i.e., dine-in, takeout, or delivery; paragraph 0041).
The Schwenker-Liguori combination describes features related to order management. Jagolta relates to ordering systems and methods for managing order preparation. 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-Liguori combination with Jagolta 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 modifying the Schwenker-Liguori combination to include Jagolta’s feature for including an item provision form indicating whether the ordered item is for dine-in or takeout, in the manner claimed, would serve the motivation of ensuring timely preparation of orders and providing accurate order preparation/ready/pickup times to guests (Jagolta at paragraph 0004); 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.
As per claim 3, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein the store status information includes a time at which cooking of the ordered item is capable of being started, a congestion status indicating a congestion degree of the store, 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; paragraph 0053,discussing determining when system should begin preparation (or prompt an employee/staff member of QSR 10 to begin preparation) of an order placed by the customer; paragraph 0082, discussing that the process can include beginning preparation of the order; paragraphs 0055, 0058, 0073).
Schwenker does not explicitly teach teaches wherein the store status information includes the number of waiting people at the wait place. However, Liguori in the analogous art of ordering systems teaches this concept. Liguori teaches:
wherein the store status information includes the number of waiting people at the wait place (paragraph 0004, discussing systems, methods, and computer-readable storage media that support smart drive through and curbside delivery management. Aspects leverage cameras, computer vision, and machine learning/artificial intelligence to efficiently assign customers to various waiting locations (e.g., drive through lanes, parking spots, etc.) based on factors such as types of orders, customer priority, fulfillment rates at the waiting locations, and queue lengths at the waiting locations; paragraph 0063, discussing that the drive through feed may include still images or video from one or more image capture devices, such as cameras, positioned on-site to capture images of one or more drive through lanes…In some implementations, additional information may be overlaid or displayed adjacent to the drive through feed. The additional information may include counts of vehicles (e.g., queue lengths) for the drive through lanes, threshold or maximum capacities of each drive through lane, a total count of vehicles in all of the drive through lanes, and a maximum capacity of all of the drive through lanes, as non-limiting examples).
Schwenker is directed toward systems and methods for customer management and order preparation. Liguori relates to ordering systems and methods for managing order pickup. 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 Liguori 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 modifying Schwenker to include Liguori’s feature for including wherein the store status information includes the number of waiting people at the wait place, in the manner claimed, would serve the motivation of efficiently assigning customers to various waiting locations (Liguori at paragraph 0004); 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.
As per claim 4, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein the plurality of candidate wait places include 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).
The Schwenker-Liguori combination does not teach wherein the plurality of candidate wait places include a pick-up counter where the customer picks up the ordered item, a customer seat where the customer eats or drinks the ordered item, and an in-store wait space where the customer waits inside the store. However, Jagolta in the analogous art of order management systems teaches this concept. Jagolta teaches:
wherein the plurality of candidate wait places include a pick-up counter where the customer picks up the ordered item, a customer seat where the customer eats or drinks the ordered item, and an in-store wait space where the customer waits inside the store (paragraph 0003, discussing that these systems further enable guests to place orders using devices outside of the restaurant, where the orders can be placed for dine-in, takeout, delivery by restaurant personnel, or delivery by third-party delivery services; paragraph 0004, discussing that orders that take too long to prepare test a diner's patience, particularly if the diner is waiting for a pickup order, but also if they are forced to sit at their table or in a pickup waiting area for a long period of time; paragraphs 0071-0075, discussing that the order-level features according to the present invention may comprise: Dining option for the order, i.e., dine-in, takeout, or delivery).
The Schwenker-Liguori combination describes features related to order management. Jagolta relates to ordering systems and methods for managing order preparation. 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-Liguori combination with Jagolta 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 modifying the Schwenker-Liguori combination to include Jagolta’s feature for including wherein the plurality of candidate wait places include a pick-up counter where the customer picks up the ordered item, a customer seat where the customer eats or drinks the ordered item, and an in-store wait space where the customer waits inside the store, in the manner claimed, would serve the motivation of ensuring timely preparation of orders and providing accurate order preparation/ready/pickup times to guests (Jagolta at paragraph 0004); 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.
As per claim 6, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein the memory further stores 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); and
the instructions further cause the processor to predict 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, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein the instructions further cause the processor 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 the Schwenker-Liguori-Jagolta combination teaches a method (Schwenker, 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 the Schwenker-Liguori-Jagolta combination teaches a non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to (Schwenker, 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).
26. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Schwenker in view of Liguori, in view of Jagolta, in further view of Fox, Pub. No.: US 2022/0076362 A1, [hereinafter Fox].
As per claim 2, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein the order information includes an order item, an order quantity, an order monetary amount, the 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,…, payment information used by customer to pay for the order,…, and/or any other information; paragraph 0029, discussing that QSR (quick service restaurant) 10 can be any business that accepts orders from customers, whether in person or distant from QSR 10, and prepares those orders for delivery to/pick-up by customers. QSR 10 can be, for example, a quick service restaurant that is configured to accept orders from customers distant from QSR 10 via mobile application, a website on the internet, telephone, or other means understood by one of skill in the art. Additionally, QSR 10 can accept orders from customers in person with customers being within drive-through lanes, at parking spots 12A-12C, within QSR 10 at an order terminal, and/or via other means; paragraph 0084, discussing that the customer can place the order via the mobile application, an online website for QSR 10, telephone to QSR 10, in person within QSR 10, or by other means. The order can be placed at any location, and QSR 10 can receive the order from the customer when the customer is at any location. Other configurations in which QSR 10 is one of multiple franchisees of an overall company/franchise can automatically or prompt the customer to select the location of QSR 10 to which the order is to be placed; paragraph 0094).
Schwenker does not explicitly teach wherein the order information includes an order quantity and a desired item-pick-up time. Liguori in the analogous art of ordering systems teaches:
wherein the order information includes an order quantity (paragraph 0005, discussing that a customer may place an order with a restaurant using online ordering via a mobile device application or the Internet, and upon arriving on site, the customer may access a customer interface device (e.g., a kiosk) to check-in...The input received by the customer interface device (e.g., the kiosk) is provided to a server or other computing device for processing and automatic assignment of the customer to a selected waiting location (e.g., a drive through lane or parking spot). The waiting location may be selected based on several factors, such as type of customer, requested delivery time, arrival time, type of order, queue lengths, order fulfillment speeds, or the like, as non-limiting examples; paragraph 0035, discussing that the selected location is a waiting location that is selected from a plurality of waiting locations. For example, the selected location may be a selected drive through lane of a plurality of drive through lanes, a selected parking spot from a plurality of parking spots, or the like. To illustrate waiting location selection, the location assignment engine may assign the customer to a drive through lane that provides a certain type of orders or items based on the order information, or to a drive through lane that corresponds to the fastest fulfilment time based on current measurements, or to closer parking spot based on the order information including a large number of items, as non-limiting examples; paragraph 0040, discussing that the server may estimate a waiting time at the selected location. The estimated waiting time may be estimated based on queue length at selected location, order details (e.g., quantity of items in an order, types of items in an order), measured or estimated fulfillment rates at the selected location, staff at the selected location, other information, or a combination thereof).
Schwenker is directed toward systems and methods for customer management and order preparation. Liguori relates to ordering systems and methods for managing order pickup. 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 Liguori 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 modifying Schwenker to include Liguori’s feature for including wherein the order information includes an order quantity, in the manner claimed, would serve the motivation of efficiently assigning customers to various waiting locations (Liguori at paragraph 0004); 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.
The Schwenker-Liguori-Jagolta combination does not explicitly teach wherein the order information includes a desired item-pick-up time. However, Fox in the analogous art of order management systems teaches this concept. Fox teaches:
wherein the order information includes an order quantity and a desired item-pick-up time (paragraph 0150, discussing that the method may additionally include a step of receiving a time selection for the items to be prepared. For example, the one or more customers can select a designated pickup time from the restaurant to retrieve the entirety of the selections made by the one or more customers. In response to the time selection, in some embodiments, the method may additionally include a step of transmitting the time selection to the computing device associated with the restaurant to cause the items to be prepared at the time).
The Schwenker-Liguori-Jagolta combination describes features related to customer and order management. Fox describes systems and methods for coordinating ordering between mobile devices. 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 the Schwenker-Liguori-Jagolta combination 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 modifying the Schwenker-Liguori-Jagolta combination to include Fox’s feature for including wherein the order information includes a desired item-pick-up time, in the manner claimed, would serve the motivation of providing at least more time-efficient pick up of orders by customers (Fox at paragraph 0014); 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.
27. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Schwenker in view of Liguori, in view of Jagolta, in view of Fox, Pub. No.: US 2022/0076362 A1, [hereinafter Fox], in further view of Mozafarian et al., Pub. No.: US 2021/0264502 A1, [hereinafter Mozafarian].
As per claim 5, the Schwenker-Liguori-Jagolta combination teaches the system according to claim 1. Schwenker further teaches wherein: the memory further stores 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); and
the instructions further cause the processor 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, 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 store staff skill information regarding work skill of staff, the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item. Fox in the analogous art of order management systems teaches:
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).
The Schwenker-Liguori-Jagolta combination describes features related to customer and order management. 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 the Schwenker-Liguori-Jagolta combination 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 modifying the Schwenker-Liguori-Jagolta combination to include Fox’s feature for including storing 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.
The Schwenker-Liguori-Jagolta-Fox combination does not explicitly teach the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item. However, Mozafarian in the analogous art of order management systems teaches this concepts. Mozafarian teaches:
the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item (paragraph 0001, discussing The present invention relates to an electronic menu, ordering, and payment system and method. More specifically, the present invention relates processing of customer food and beverage orders, routing orders to kitchen, bar, and wait staff, and accepting electronic payments; paragraph 0064, discussing that all employee and customer interactions will be monitored. In one example, an order is placed by a patron, the item is then sent to the kitchen the system will monitor all processing and pick up times by each staff member and section. For instance, how long, it took the chef to prepare, how long it took the server to pick up the order take it to the table etc. If there are any errors along the way for ex, a server takes longer than usual to pick up the food or makes a mistake this will all be noted in their employee profile which will allow manage to monitor. Also, for instance if a customer makes a complaint such as the food is burnt, this will be noted in the profile that the chef overcooked the product (again recorded in their employee profile). This allows retail operators to monitor staff behavior and customer interactions in a unique way that allows them to make appropriate changes when needed to maximize efficiency and customer satisfaction).
The Schwenker-Liguori-Jagolta-Fox combination describes features related to customer and order management. Mozafarian relates to ordering systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for ordering systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Schwenker-Liguori-Jagolta-Fox combination with Mozafarian 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 modifying the Schwenker-Liguori-Jagolta-Fox combination to include Mozafarian’s feature for including the staff skill information including a time period for a store clerk to carry the ordered item to a customer seat after completion of cooking of the ordered item, in the manner claimed, would serve the motivation of improving the ordering experience (Mozafarian at paragraph 0037); 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.
Gautreaux et al., Pub. No.: US 2024/0177220 A1 – describes that the order router selects a target store to place the online order based on current in-store customer counts for customers waiting to place in-store orders at the terminals of the candidate stores and based on current online order counts from pending online orders at the candidate stores.
Musk et al., Pub. No.: US 2021/0374885 A1 – describes a restaurant on-demand location and order management system. Further describes that in some cases, the available menu items may be provided based on one or more parameters associated with a user that is accessing the menu. In some cases, additionally or alternatively, the available menu items may be provided based on a time of day, a day of the week, whether the order is dine-in, take out, or delivery, availability of items at the restaurant or current specials at the restaurant, or any combinations thereof.
Kelly et al., Pub. No.: US 2019/0279181 A1 – describes a designated waiting area for customers who have placed their order through the kiosk or mobile application.
Fox et al., Pub. No.: US 2023/0419388 A1 – describes that a customer is directed to circle the restaurant building and enter a waiting area, such as non-sequential waiting spaces, non-sequential queue lanes, parking spaces, etc.
Chochiang, Kitsiri, Pkaypreak Ung, and Narongrit Bunsaman. "One stop restaurant service application." 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2020 – describes that One Stop Restaurant Service application is designed and developed for the restaurant business. The application can handle all steps required in the restaurant management business including taking the customer queue, calling the queue, deleting the queue, managing the no-show customer, taking the order, monitoring the order status, managing the cooking order for the chefs, managing the order serving queue, calculating the total amount of each table, handling the payment, and conducting the customer satisfaction survey at the end.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DARLENE GARCIA-GUERRA whose telephone number is (571) 270-3339. The examiner can normally be reached M-F 7:30a.m.-5:00p.m. EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian M. Epstein can be reached on (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
Prosecution Insights