DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application filed on 6/25/2025.
Claims 1-20 are currently pending and have been examined.
Claim Objections
Claims 6, 9, and 20 are objected to because of the following informalities:
In Claim 6, “at least one of an average order completion time, a historical timeliness metric, a historical efficiency metric” should read “at least one of an average order completion time, a historical timeliness metric, or a historical efficiency metric.”
In Claim 9, “QSR return time” should read “quick-service restaurant (QSR) return time.”
In Claim 20, “…for fulfillment initiate preparation…” should read “…for fulfillment; initiate preparation…”
Appropriate correction is required.
Claim Interpretation
Claim 15 contains the following limitations: “generate, for the plurality of delivery persons, at least one dynamic driver metric based on real-time delivery data” and “update the batch score based on the at least one dynamic driver metric.” While the specification appears to provide no explanation of what is contemplated by “dynamic driver metric” nor any explanation of how such a “dynamic driver metric” might be used to update batch scores, these limitations are interpreted as having sufficient written description support based on the broadest reasonable interpretation of the term “dynamic driver metric” as including, e.g., “a respective current location of the plurality of delivery persons” used to generate the batch scores as claimed in Claims 2-3.
Claim Rejections – 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-7 and 10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6 contains the limitation “wherein the at least one historical delivery metric comprises at least one of an average order completion time, a historical timeliness metric, a historical efficiency metric,” Claim 5 (upon which Claim 6 depends) contains the limitation “wherein the deliverer data comprises, for the plurality of delivery persons, at least one historical delivery metric associated with the respective delivery person,” and Claim 2 (upon which Claim 6 depends) contains the limitation “generating the batch score based at least in part on the deliverer data.” These limitations, in conjunction as claimed, lack the requisite level of written description support in the original disclosure. While the original disclosure contains support for these limitations in isolation (e.g., generating a batch score based at least in part on deliverer data is disclosed in at least Paragraph 0038; deliverer data taking the form of at least one historical delivery metric is disclosed in at least Paragraph 0050; this at least one historical delivery metric taking the form of at least one of an average order completion time, a historical timeliness metric, or a historical efficiency metric is disclosed in at least Paragraphs 0005, 0031, 0049-0050, and 0057), the original disclosure provides no guidance as to how an average order completion time, a historical timeliness metric, or a historical efficiency metric might be used to generate a batch score, particularly in conjunction with how this batch score is claimed as being determined in Claim 1. Further, it would not be reasonably apparent to one of ordinary skill in the art at the time of filing how Applicant might contemplate this to be done from the high-level description found in the original disclosure. As such, one of ordinary skill in the art at the time of filing could not reasonably conclude that Applicant had possession of this functionality.
Claim 7 contains the limitation “wherein the deliverer data comprises a respective current workload of the plurality of delivery persons” and Claim 2 (upon which Claim 7 depends) contains the limitation “generating the batch score based at least in part on the deliverer data.” These limitations, in conjunction as claimed, lack the requisite level of written description support in the original disclosure. While the original disclosure contains support for these limitations in isolation (e.g., generating a batch score based at least in part on deliverer data is disclosed in at least Paragraph 0038; deliverer data taking the form of a respective current workload of the plurality of delivery persons is disclosed in at least Paragraphs 0034 and 0050), the original disclosure provides no guidance as to how this current workload might be used to generate a batch score, particularly in conjunction with how this batch score is claimed as being determined in Claim 1. Indeed, the only manner in which the original disclosure utilizes this workload is in the formation of batch permutations (see, e.g., Paragraphs 0034, 0038, 0047, and 0066), which is not the generation of batch scores as claimed but rather a separate step which, both as claimed and as described in the original disclosure, must occur prior to the generation of back scores. Further, it would not be reasonably apparent to one of ordinary skill in the art at the time of filing how Applicant might contemplate this to be done from the high-level description found in the original disclosure. As such, one of ordinary skill in the art at the time of filing could not reasonably conclude that Applicant had possession of this functionality.
Claim 10 contains the limitation “wherein the deliverer data comprises a respective carrying capacity of the plurality of delivery persons” and Claim 2 (upon which Claim 7 depends) contains the limitation “generating the batch score based at least in part on the deliverer data.” These limitations, in conjunction as claimed, lack the requisite level of written description support in the original disclosure. While the original disclosure contains support for these limitations in isolation (e.g., generating a batch score based at least in part on deliverer data is disclosed in at least Paragraph 0038; deliverer data may take the form of a respective carrying capacity of the plurality of delivery persons is disclosed in at least Paragraph 0049), the original disclosure provides no guidance as to how such carrying capacities might be used to generate a batch score, particularly in conjunction with how this batch score is claimed as being determined in Claim 1. Indeed, the only manner in which the original disclosure utilizes these capacities is in relation to assignments/re-assignments of batches and replacement orders (see, e.g., Paragraphs 0080, 0084-0085), which is not the generation of batch scores as claimed but rather a separate step(s). Further, it would not be reasonably apparent to one of ordinary skill in the art at the time of filing how Applicant might contemplate this to be done from the high-level description found in the original disclosure. As such, one of ordinary skill in the art at the time of filing could not reasonably conclude that Applicant had possession of this functionality.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11, and 20 contain variations on the limitation “generating, for the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss” (indicating generation of a singular batch score) as well as subsequent use of the plural term “the respective batch scores.” It is unclear as drafted how a plural term properly relates back to a singular term. For the purposes of this examination, the above quoted limitation will be interpreted as “generating, for each of the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss.” Consequently, each instance of the singular “the batch score” in dependent Claims 2, 15, and 16 are interpreted as “the batch scores.” Claims 2-10 and 12-19 are rejected due to their dependence upon Claims 1 and 11 respectively.
Claim 2 contains the limitation “generating the batch score based at least in part on the deliverer data.” It is unclear as drafted whether this limitation is intended to relate back to the same generation of batch scores claimed in Claim 1 (upon which Claim 2 depends) or to indicate a separate generation of batch scores. Relatedly, it is further unclear whether the recited variable “the deliverer data” is to be somehow used in conjunction with the variables “one or more of a total delivery time, a total promise time miss, and a total hold time miss” (used to generate the batch scores in Claim 1), whether the variable “the deliverer data” is to replace/supplant these previously recited variables (in violation of 112(d)), or used in some other way. For the purposes of this examination, this limitation will be interpreted as “wherein the generating of the batch scores is further based at least in part on the deliverer data.” Claims 3-10 are rejected due to their dependence upon Claim 2.
Claim Rejections – 35 USC § 101
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claims 1, 11, and 20, the limitations of receiving a plurality of orders for delivery to a plurality of destinations; generating a plurality of permutations of the plurality of orders corresponding to different sequences of the plurality of destinations; estimating, for the plurality of permutations, one or more of a total delivery time, a total promise time miss, and a total hold time miss; generating, for the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss; generating a ranking of at least a portion of the plurality of permutations based on the respective batch scores; determining a top-ranked permutation based on the ranking; assigning orders in the top-ranked permutation to one of a plurality of delivery persons for fulfillment; initiating preparation of the orders in the top-ranked permutation in response to determining that an estimated arrival interval of the one of the plurality of delivery persons is within an estimated preparation interval for the orders in the top-ranked permutation; and dispatching delivery of the orders in the top -ranked permutation to the one of the plurality of delivery persons, as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of receiving a plurality of orders for delivery to a plurality of destinations; generating a plurality of permutations of the plurality of orders corresponding to different sequences of the plurality of destinations; estimating, for the plurality of permutations, one or more of a total delivery time, a total promise time miss, and a total hold time miss; generating, for the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss; generating a ranking of at least a portion of the plurality of permutations based on the respective batch scores; determining a top-ranked permutation based on the ranking; assigning orders in the top-ranked permutation to one of a plurality of delivery persons for fulfillment; initiating preparation of the orders in the top-ranked permutation in response to determining that an estimated arrival interval of the one of the plurality of delivery persons is within an estimated preparation interval for the orders in the top-ranked permutation; and dispatching delivery of the orders in the top -ranked permutation to the one of the plurality of delivery persons, as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of estimating, for the plurality of permutations, one or more of a total delivery time, a total promise time miss, and a total hold time miss; generating, for the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss; generating a ranking of at least a portion of the plurality of permutations based on the respective batch scores; determining a top-ranked permutation based on the ranking; and initiating preparation of the orders in the top-ranked permutation in response to determining that an estimated arrival interval of the one of the plurality of delivery persons is within an estimated preparation interval for the orders in the top-ranked permutation, as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of ______.
at least one processor; at least one memory including computer program code for one or more programs executable by the at least one processor; a computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon executable by at least one processor; and a plurality of orders. At least one processor; at least one memory including computer program code for one or more programs executable by the at least one processor; and a computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon executable by at least one processor, in the context of the claims as a whole, amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). A plurality of orders, in the context of the claims as a whole, amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claims are therefore directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claims, and thus the claims are not patent eligible.
Claims 2-10 and 12-19, describing various additional limitations to the method of Claim 1 or the apparatus of Claim 11, amount to substantially the same unintegrated abstract idea as Claims 1 and 11 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses obtaining deliverer data associated with the plurality of delivery persons (an abstract idea in the form of a certain method of organizing human activity and a mental process); and generating the batch score based at least in part on the deliverer data (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 3 discloses wherein the deliverer data comprises a respective current location of the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 4 discloses wherein the deliverer data comprises a respective transportation mode of the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 5 discloses wherein the deliverer data comprises, for the plurality of delivery persons, at least one historical delivery metric associated with the respective delivery person (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 6 discloses wherein the at least one historical delivery metric comprises at least one of an average order completion time, a historical timeliness metric, a historical efficiency metric (further defining the abstract idea set forth in Claim 5), which does not integrate the claim into a practical application.
Claim 7 discloses wherein the deliverer data comprises a respective current workload of the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 8 discloses wherein the deliverer data comprises a respective availability status of the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 9 discloses wherein the deliverer data comprises a respective availability status of the plurality of delivery persons wherein the deliverer data comprises an estimated QSR return time for the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 10 discloses wherein the deliverer data comprises a respective carrying capacity of the plurality of delivery persons (further defining the abstract idea set forth in Claim 2), which does not integrate the claim into a practical application.
Claim 12 discloses a respective permutation is associated with one of a plurality of delivery persons (an abstract idea in the form of a certain method of organizing human activity and a mental process); a respective order comprises at least one food item (further defining the abstract idea set forth in Claim 11); determine at least one of a plurality of temperature classifications associated with the at least one food item; and/or determine one of a plurality of container types associated with the delivery person (an abstract idea in the form of a certain method of organizing human activity and a mental process); and generate the total hold time miss based at least in part on the temperature classification and/or the container type (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 13 discloses the plurality of container types comprise paper container, passive insulated container, or active climate-controlled delivery container (further defining the abstract idea set forth in Claim 12), which does not integrate the claim into a practical application.
Claim 14 discloses wherein the plurality of temperature classifications comprise hot, ambient, cold, and frozen (further defining the abstract idea set forth in Claim 12), which does not integrate the claim into a practical application.
Claim 15 discloses generate, for the plurality of delivery persons, at least one dynamic driver metric based on real-time delivery data (an abstract idea in the form of a certain method of organizing human activity and a mental process); and update the batch score based on the at least one dynamic driver metric (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 16 discloses apply a first weight value to the total delivery time; and/or apply a second weight value to the total promise time miss; and/or apply a third weight value to the total hold time miss; and/or generate the batch score based on the weighted total delivery time, the weighted total promise time miss, and/or the weighted total hold time miss (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which does not integrate the claim into a practical application.
Claim 17 discloses receive, via a user interface, an adjustment to at least one of the first weight value, the second weight value, or the third weight value (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); apply the adjustment to the at least one of the first weight value, the second weight value, or the third weight value (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and generate updated batch scores for subsequent permutations based on the adjusted weight values (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 18 discloses a respective order comprises at least one food item (further defining the abstract idea set forth in Claim 11); and generate the third weight value based at least in part on a temperature classification of the at least one food item of the plurality of orders (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 19 discloses receive an additional order after preparation of the orders in the top-ranked permutation has occurred (an abstract idea in the form of a certain method of organizing human activity and a mental process); in response to determining that a proximity of a destination of the additional order is within a threshold range to a destination of at least one of the orders in the top-ranked permutation, update the top-ranked permutation to comprise the additional order (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and initiate preparation of the additional order (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-4, 8-9, 11, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (PGPub 20240337496) (hereafter, “Neumann”) in view of Riel-Dalpe et al (PGPub 20150262121) (hereafter, “Riel-Dalpe”).
Regarding Claim 1, Neumann discloses:
at least one processor (¶ 0018, 0067-0068; Fig. 8; computing device may include any computing device as described in this disclosure, including without limitation a microprocessor, etc.; computer system includes a processor);
at least one memory including computer program code for one or more programs, the at least one memory and the computer program code executable by the at least one processor (¶ 0063-0064, 0067-0069; Fig. 8; computer system includes a processor and a memory that communicate with each other, and with other components; memory may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) embodying any one or more of the aspects and/or methodologies of the present disclosure; aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software modules; such software may be a computer program product that employs a machine-readable storage medium; as used herein, a machine-readable storage medium does not include transitory forms of signal transmission);
a computer program product comprising at least one non-transitory computer-readable storage medium having computer program code stored thereon executable by at least one processor (¶ 0063-0064, 0069; memory may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) embodying any one or more of the aspects and/or methodologies of the present disclosure; aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software modules; such software may be a computer program product that employs a machine-readable storage medium; as used herein, a machine-readable storage medium does not include transitory forms of signal transmission);
receiving a plurality of orders for delivery to a plurality of destinations (¶ 0020, 0032, 0049, 0057; Fig. 7; computing device may receive a plurality of alimentary elements and a plurality of destinations, wherein receiving a plurality of alimentary elements 108 and a plurality of destinations may include receiving data corresponding to order placement time of alimentary elements, projected order completion time of alimentary elements, and alimentary element destination; an “alimentary element,” as used in this disclosure is any meal, grocery item, food element, or the like, that may be generated by a restaurant, cafeteria, fast food chain, grocery store, deli, or any place that would have a need for providing an alimentary item to a customer, client, patient, or individual; as used in this disclosure, “order placement time,” is a time at which a customer, client, or any individual places or has placed an order for an alimentary element, wherein placement of order may be the moment in time an order was place, or a pre-determined moment in time specified by the individual; a plurality of alimentary items may have an order time in order of when a restaurant received orders, for instance and without limitation an online queue, wherein customers may have also disclosed expected order time instructions that may differ from when the order was placed);
generating a plurality of permutations of the plurality of orders corresponding to different sequences of the plurality of destinations (¶ 0004, 0017, 0024, 0026, 0028, 0049-0050; Figs. 5-6, 7; the computing device then selects a candidate batching combination by generating and optimizing an objective function that groups alimentary elements based on their projected completion times and vendor locations; embodiments described in this disclosure establish alimentary combinations based on delivery location, and determine routes associated with orders based on the projected order completion times; computing device may compute, using a plurality of alimentary elements and a plurality of destinations, a candidate batching combination for a plurality of destinations as a function of an objective function, wherein generating a candidate batching combination further comprises a selection based on expected alimentary combination completion time and destination geolocation; a “candidate batching combination,” as used in this disclosure, is a batch of alimentary elements, for instance and without limitation a batch of meal orders, that are grouped according to an objective function, wherein the objective function is grouping elements based on the plurality of completion times and destination locations; the system may be configured to select a candidate batching combination of a plurality of candidate batching combinations; the objective function may accept an input of a plurality of alimentary elements and a plurality of destinations and generate an output of a candidate batching combination of a plurality of candidate batching combinations by grouping, batching, or otherwise dividing alimentary items into combinations for a delivery driver, drone, or any other suitable physical transfer apparatus, based on, for instance and without limitation, order placement time and/or destination locations; one or more tables of a path database may include, as a non-limiting example, an alimentary element table, which may include meals, grocery items, food elements, or the like, generated by a restaurant, cafeteria, fast food chain, grocery store, deli, and any associated data relating to an order by a customer, client, patient, or individual, including when the order was placed, what alimentary elements were in the order, and/or linked to other data such as the order destination geolocation data for an alimentary element, for use in determining projected alimentary combinations, batching, and/or other elements of data computing device 104 and/or system 100 may store, retrieve, and use to determine usefulness and/or relevance of data in determining projected alimentary combinations, batching instructions, etc.);
estimating, for the plurality of permutations, one or more of a total delivery time, a total promise time miss, and a total hold time miss (¶ 0017, 0020, 0051, 0059; embodiments described in this disclosure establish alimentary combinations based on delivery location, and determine routes associated with orders based on the projected order completion times; a “projected order completion time,” as used in this disclosure, refers to a projected alimentary element arrival time to an individual; minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch);
generating, for the plurality of permutations, a batch score based on the one or more of the total delivery time, the total promise time miss, and the total hold time miss (¶ 0025, 0028-0029, 0052-0053, 0059; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; computing device may assign variables relating to a set of parameters, which may correspond to score components as described above, calculate an output of mathematical expression using the variables, and select a pairing that produces an output having the lowest size, according to a given definition of “size,” of the set of outputs representing each of plurality of candidate alimentary combinations; generating the ranking combination further comprises generating a batching objective function of the plurality of batching combinations; batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations; batching objective function generates an output combination ranking candidate batching combination according to at least a target criterion; the target criterion may be to minimize the average time between the customer order time and the alimentary element reaching the order destination, wherein the ranking combination may describe the order in which alimentary elements reach a destination or plurality of destinations to minimize the average time; computing device may determine a combination ranking wherein determining the ranking may include generating a batching objective function of the plurality of batching combinations, wherein the batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations and the batching objective function generates an output ranking the candidate batching combination according to at least a target criterion, and selecting a candidate batching combination for which the output of the batching objective function most closely matches the at least a target criterion; target criterion may include minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch; objectives represented in a mapping algorithm and/or loss function may include minimization of delivery times, or minimization of average times of order completion times in excess of estimated or requested arrival times);
generating a ranking of at least a portion of the plurality of permutations based on the respective batch scores (¶ 0025, 0028-0029, 0052-0053, 0059; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; computing device may assign variables relating to a set of parameters, which may correspond to score components as described above, calculate an output of mathematical expression using the variables, and select a pairing that produces an output having the lowest size, according to a given definition of “size,” of the set of outputs representing each of plurality of candidate alimentary combinations; generating the ranking combination further comprises generating a batching objective function of the plurality of batching combinations; batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations; batching objective function generates an output combination ranking candidate batching combination according to at least a target criterion; the target criterion may be to minimize the average time between the customer order time and the alimentary element reaching the order destination, wherein the ranking combination may describe the order in which alimentary elements reach a destination or plurality of destinations to minimize the average time; computing device may determine a combination ranking wherein determining the ranking may include generating a batching objective function of the plurality of batching combinations, wherein the batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations and the batching objective function generates an output ranking the candidate batching combination according to at least a target criterion, and selecting a candidate batching combination for which the output of the batching objective function most closely matches the at least a target criterion; target criterion may include minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch; objectives represented in a mapping algorithm and/or loss function may include minimization of delivery times, or minimization of average times of order completion times in excess of estimated or requested arrival times);
determining a top-ranked permutation based on the ranking (¶ 0025, 0027-0029, 0033, 0052-0053, 0059; computing device may select alimentary element pairings so that scores associated therewith are the best score for each order and/or for each batch; in such an example, optimization may determine the combination of alimentary elements such that each delivery pairing includes the highest score possible; selected batching combination input may include a batch of alimentary elements ranked in order of when the alimentary elements may reach their destinations to minimize average delivery time; selecting a candidate batching combination for which the output of the objective function most closely matches the at least a target criterion may result in a combination ranking, wherein all alimentary elements within the candidate batching combination have a ranking that informs the order in which the alimentary elements are batched and/or delivered; selecting the candidate alimentary batching combination may include performing a greedy heuristic process on the objective function; the batching objective function solution target criterion further comprises minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch; the batching objective function may be the same as an objective function, which selects a candidate batching combination and/or a plurality of batching combinations based on the solution target criterion, for instance and without limitation, such as minimizing the average time between order placement time and completion of the order at the order destination);
assigning orders in the top-ranked permutation to one of a plurality of delivery persons for fulfillment (¶ 0028, 0031, 0052; Fig. 5; providing, to a user, batching instructions based on the selected batching combinations via a user device; batching instructions may correspond to which alimentary elements which are categorized into a batch for physical transfer by an individual courier, apparatus, or other physical transfer device, method, or the like; each batch is assigned only one physical transfer apparatus (e.g., a delivery driver, drone, or any other suitable physical transfer apparatus)); and
dispatching delivery of the orders in the top -ranked permutation to the one of the plurality of delivery persons (¶ 0056, 0062; Figs. 6-7; an exemplary embodiment of a computing device providing a path to the physical transfer apparatus may include providing geolocation data that corresponds to destination locations where the apparatus is expected to follow sent to a physical transfer device; physical transfer device may be a user device such as a smartphone, tablet, or other user device intended to be used by delivery driver or other personnel; computing device may provide, to physical transfer apparatus, a predicted path for the plurality of alimentary elements and the plurality of destination locations. Providing a path to physical transfer apparatus further comprises providing geolocation data that corresponds to destination locations where the apparatus is expected to follow sent to a user device).
Neumann does not explicitly disclose but Riel-Dalpe does disclose initiating preparation of the orders in response to determining that an estimated arrival interval of the one of the plurality of delivery persons is within an estimated preparation interval for the orders in the top-ranked permutation (¶ 0049, 0068-0070, 0107-0108, 0140-0144, 0191, 0196; Figs. 1, 18C, 19A; the method includes determining a pick-up estimated delay, the pick-up estimated delay representing a time delay estimated for the arrival of the available delivery vehicle for pick-up of the menu item; the method further comprising determining a recommended order preparation beginning time using the pick-up estimated delay and the preparation estimated delay; Figs. 18C and 19A illustrate the user device providing road navigation from the driver’s location to the destination which is the selected restaurant; wherein the sending a request for preparation is one of timed with the recommended order preparation beginning time and includes the order preparation beginning time; the dispatcher (e.g., an automated decision algorithm) submits the order to the associated restaurant at the right moment). Neumann additionally discloses wherein the orders are in the top-ranked permutation (¶ 0025, 0027-0029, 0033, 0052-0053, 0059; computing device may select alimentary element pairings so that scores associated therewith are the best score for each order and/or for each batch; in such an example, optimization may determine the combination of alimentary elements such that each delivery pairing includes the highest score possible; selecting a candidate batching combination for which the output of the objective function most closely matches the at least a target criterion may result in a combination ranking, wherein all alimentary elements within the candidate batching combination have a ranking that informs the order in which the alimentary elements are batched and/or delivered; selecting the candidate alimentary batching combination may include performing a greedy heuristic process on the objective function; the batching objective function may be the same as an objective function, which selects a candidate batching combination and/or a plurality of batching combinations based on the solution target criterion, for instance and without limitation, such as minimizing the average time between order placement time and completion of the order at the order destination).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the delivery-based timing techniques of Riel-Dalpe with the delivery batching system of Neumann because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Riel-Dalpe are applicable to the base device (Neumann), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 2, Neumann in view of Riel-Dalpe discloses the limitations of Claim 1. Neumann additionally discloses:
obtaining deliverer data associated with the plurality of delivery persons (¶ 0046, 0051; Fig. 5; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like; an exemplary embodiment of a predicted route is updated as a function of each alimentary element reaching its destination; after completing at least an order, the predicted path may change based on new calculations of the most optimal path based on minimizing the average time between the order completion time and predicted destination arrival for the remainder of the batch of alimentary elements; the predicted path may change to move the last destination closer to where a physical transfer apparatus may have originally departed for minimizing the time in accepting a second batching combination); and
generating the batch score based at least in part on the deliverer data (¶ 0046, 0051; Fig. 5; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like; an exemplary embodiment of a predicted route is updated as a function of each alimentary element reaching its destination; after completing at least an order, the predicted path may change based on new calculations of the most optimal path based on minimizing the average time between the order completion time and predicted destination arrival for the remainder of the batch of alimentary elements; the predicted path may change to move the last destination closer to where a physical transfer apparatus may have originally departed for minimizing the time in accepting a second batching combination).
Regarding Claim 3, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann additionally discloses wherein the deliverer data comprises a respective current location of the plurality of delivery persons (¶ 0046, 0051; Fig. 5; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like; an exemplary embodiment of a predicted route is updated as a function of each alimentary element reaching its destination; after completing at least an order, the predicted path may change based on new calculations of the most optimal path based on minimizing the average time between the order completion time and predicted destination arrival for the remainder of the batch of alimentary elements; the predicted path may change to move the last destination closer to where a physical transfer apparatus may have originally departed for minimizing the time in accepting a second batching combination).
Regarding Claim 4, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann additionally discloses wherein the deliverer data comprises a respective transportation mode of the plurality of delivery persons (¶ 0028, 0033, 0046, 0056; a delivery driver, drone, or any other suitable physical transfer apparatus; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like).
Regarding Claim 8, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann additionally discloses wherein the deliverer data comprises a respective availability status of the plurality of delivery persons (¶ 0028; an objective function may compute a candidate batching combination based upon a variety of other factors, including for instance the number of physical transfer apparatuses available, wherein how alimentary elements are batched to minimize average time may be affected by timing of pickup based on feasibility regarding the amount and availability of physical transfer apparatuses, among other factors).
Regarding Claim 9, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann does not explicitly disclose but Riel-Dalpe does disclose wherein the deliverer data comprises an estimated QSR return time for the plurality of delivery persons (¶ 0086, 0196, 0198; for each driver, the function determines the geographical position of the driver after completion of all his/her assignments; next the function determines the arrival time of each driver at restaurant).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 15, Neumann in view of Riel-Dalpe discloses the limitations of Claim 11. Neumann additionally discloses:
generate, for the plurality of delivery persons, at least one dynamic driver metric based on real-time delivery data (¶ 0046, 0051; Fig. 5; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like; an exemplary embodiment of a predicted route is updated as a function of each alimentary element reaching its destination; after completing at least an order, the predicted path may change based on new calculations of the most optimal path based on minimizing the average time between the order completion time and predicted destination arrival for the remainder of the batch of alimentary elements; the predicted path may change to move the last destination closer to where a physical transfer apparatus may have originally departed for minimizing the time in accepting a second batching combination); and
update the batch score based on the at least one dynamic driver metric (¶ 0025, 0028-0029, 0033, 0046, 0051-0053, 0059; Fig. 5; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; computing device may assign variables relating to a set of parameters, which may correspond to score components as described above, calculate an output of mathematical expression using the variables, and select a pairing that produces an output having the lowest size, according to a given definition of “size,” of the set of outputs representing each of plurality of candidate alimentary combinations; generating the ranking combination further comprises generating a batching objective function of the plurality of batching combinations; batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations; batching objective function generates an output combination ranking candidate batching combination according to at least a target criterion; the target criterion may be to minimize the average time between the customer order time and the alimentary element reaching the order destination, wherein the ranking combination may describe the order in which alimentary elements reach a destination or plurality of destinations to minimize the average time; computing device may determine a combination ranking wherein determining the ranking may include generating a batching objective function of the plurality of batching combinations, wherein the batching objective function is a mathematical function with a solution set including the plurality of candidate batching combinations and the batching objective function generates an output ranking the candidate batching combination according to at least a target criterion, and selecting a candidate batching combination for which the output of the batching objective function most closely matches the at least a target criterion; target criterion may include minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch; objectives represented in a mapping algorithm and/or loss function may include minimization of delivery times, or minimization of average times of order completion times in excess of estimated or requested arrival times; a destination machine-learning process may re-rank elements, for instance and without limitation, based upon predicted paths that further minimize average time between order of alimentary elements and alimentary elements reaching their destinations; in non-limiting illustrative examples, a destination machine-learning model may accomplish such a task by simulating routes between alimentary delivery points and calculating overall physical transfer time based on geolocations and other factors, such as traffic, weather, time of day, physical transfer method, and the like; a selected batching combination input may include a batch of alimentary elements ranked in order of when the alimentary elements may reach their destinations to minimize average delivery time; in non-limiting illustrative examples, a destination machine-learning process may alter or otherwise modify the ranking order to determine a predicted path that further minimizes the average time of delivery of a plurality of alimentary elements, for instance with a branched predicted route and/or rearranging the order of the rank after a first alimentary element has reached its destination).
Regarding Claim 16, Neumann in view of Riel-Dalpe discloses the limitations of Claim 11. Neumann additionally discloses apply a first weight value to the total delivery time; and/or apply a second weight value to the total promise time miss; and/or apply a third weight value to the total hold time miss; and/or generate the batch score based on the weighted total delivery time, the weighted total promise time miss, and/or the weighted total hold time miss (¶ 0025, 0034, 0039-0040; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; in some embodiments, pairings may be scored in a matrix for optimization, for instance and without limitation where columns represent order times and rows represent destination locations potentially paired therewith; each cell of such a matrix may represent a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; a “priority metric,” for the purpose of this disclosure, is a parameter that allows users to select a priority level for delivery based on the speed of delivery; users can pay more for a higher priority delivery, which may translate to a faster delivery speed compared to standard options; this priority metric may account for the urgency and time-sensitivity of the delivery, ranking orders to prioritize those with higher urgency; for instance, a high-priority delivery may be expedited using faster transfer methods such as drones or dedicated delivery personnel, while lower-priority deliveries may be grouped or batched together to optimize resources; by incorporating the priority metric, the destination machine-learning process can adjust the predicted path to ensure that higher-priority deliveries reach their destinations more quickly, effectively managing and optimizing delivery times to meet user expectations and service level agreements; in an embodiment, when the destination machine-learning process evaluates multiple candidate batching combinations, it may consider the priority metric to rank these combinations; specifically, a candidate batching combination that includes high-priority deliveries will be ranked higher than those with standard or lower-priority deliveries; this ranking is influenced by the priority level assigned to each order, with higher-priority orders receiving expedited handling; for instance, if a candidate batching combination includes several high-priority orders that require faster delivery, the machine-learning process may prioritize this combination; conversely, combinations with lower-priority orders may be batched together to optimize resources and minimize costs, using slower delivery methods if necessary; by incorporating the priority metric, the system can dynamically adjust the ranking of candidate batching combinations, ensuring that high-priority deliveries are handled with the urgency they require. This process helps in managing delivery schedules effectively, reducing overall delivery times for urgent orders, and maintaining customer satisfaction by meeting varied delivery expectations).
Regarding Claim 17, Neumann in view of Riel-Dalpe discloses the limitations of Claim 16. Neumann additionally discloses:
receive, via a user interface, an adjustment to at least one of the first weight value, the second weight value, or the third weight value (¶ 0018, 0034; a “priority metric,” for the purpose of this disclosure, is a parameter that allows users to select a priority level for delivery based on the speed of delivery; computing device may interface or communicate with one or more additional devices via a network);
apply the adjustment to the at least one of the first weight value, the second weight value, or the third weight value (¶ 0025, 0034, 0039-0040; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; in some embodiments, pairings may be scored in a matrix for optimization, for instance and without limitation where columns represent order times and rows represent destination locations potentially paired therewith; each cell of such a matrix may represent a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; a “priority metric,” for the purpose of this disclosure, is a parameter that allows users to select a priority level for delivery based on the speed of delivery; users can pay more for a higher priority delivery, which may translate to a faster delivery speed compared to standard options; this priority metric may account for the urgency and time-sensitivity of the delivery, ranking orders to prioritize those with higher urgency; for instance, a high-priority delivery may be expedited using faster transfer methods such as drones or dedicated delivery personnel, while lower-priority deliveries may be grouped or batched together to optimize resources; by incorporating the priority metric, the destination machine-learning process can adjust the predicted path to ensure that higher-priority deliveries reach their destinations more quickly, effectively managing and optimizing delivery times to meet user expectations and service level agreements; in an embodiment, when the destination machine-learning process evaluates multiple candidate batching combinations, it may consider the priority metric to rank these combinations; specifically, a candidate batching combination that includes high-priority deliveries will be ranked higher than those with standard or lower-priority deliveries; this ranking is influenced by the priority level assigned to each order, with higher-priority orders receiving expedited handling; for instance, if a candidate batching combination includes several high-priority orders that require faster delivery, the machine-learning process may prioritize this combination; conversely, combinations with lower-priority orders may be batched together to optimize resources and minimize costs, using slower delivery methods if necessary; by incorporating the priority metric, the system can dynamically adjust the ranking of candidate batching combinations, ensuring that high-priority deliveries are handled with the urgency they require. This process helps in managing delivery schedules effectively, reducing overall delivery times for urgent orders, and maintaining customer satisfaction by meeting varied delivery expectations); and
generate updated batch scores for subsequent permutations based on the adjusted weight values (¶ 0025, 0034, 0039-0040; generation of an objective function may include generation of a function to score and/or weight factors to achieve a combination score for each feasible pairing of alimentary elements; in some embodiments, pairings may be scored in a matrix for optimization, for instance and without limitation where columns represent order times and rows represent destination locations potentially paired therewith; each cell of such a matrix may represent a score of a pairing of the corresponding alimentary batch destinations and/or order completion times to the corresponding route, wherein the alimentary batches are selected based on routes with minimizing order completion times; a “priority metric,” for the purpose of this disclosure, is a parameter that allows users to select a priority level for delivery based on the speed of delivery; users can pay more for a higher priority delivery, which may translate to a faster delivery speed compared to standard options; this priority metric may account for the urgency and time-sensitivity of the delivery, ranking orders to prioritize those with higher urgency; for instance, a high-priority delivery may be expedited using faster transfer methods such as drones or dedicated delivery personnel, while lower-priority deliveries may be grouped or batched together to optimize resources; by incorporating the priority metric, the destination machine-learning process can adjust the predicted path to ensure that higher-priority deliveries reach their destinations more quickly, effectively managing and optimizing delivery times to meet user expectations and service level agreements; in an embodiment, when the destination machine-learning process evaluates multiple candidate batching combinations, it may consider the priority metric to rank these combinations; specifically, a candidate batching combination that includes high-priority deliveries will be ranked higher than those with standard or lower-priority deliveries; this ranking is influenced by the priority level assigned to each order, with higher-priority orders receiving expedited handling; for instance, if a candidate batching combination includes several high-priority orders that require faster delivery, the machine-learning process may prioritize this combination; conversely, combinations with lower-priority orders may be batched together to optimize resources and minimize costs, using slower delivery methods if necessary; by incorporating the priority metric, the system can dynamically adjust the ranking of candidate batching combinations, ensuring that high-priority deliveries are handled with the urgency they require. This process helps in managing delivery schedules effectively, reducing overall delivery times for urgent orders, and maintaining customer satisfaction by meeting varied delivery expectations).
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe and Iacono et al (PGPub 20180096414) (hereafter, “Iacono”).
Regarding Claim 5, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann does not explicitly disclose but Iacono does disclose wherein the deliverer data comprises, for the plurality of delivery persons, at least one historical delivery metric associated with the respective delivery person (¶ 0043; courier profile information may include (i) delivery history information for a courier indicating an average amount of time for the courier to perform deliveries (e.g., an average amount of time per mile, a total average amount of travel time, etc.), (ii) information indicating whether or not the courier is on-time for delivery pick-up and/or drop-off, etc., (iii) vehicle information indicating a vehicle or type of vehicle that is used by the courier to transport items (e.g., a bike, car, van, truck, etc.), (iv) historical location information indicating where the courier is typically located (e.g., a home address, an establishment where the courier is located more than a particular amount of time, etc.), and so on).
The rationale to combine Neumann and Riel-Dalpe remains the same as for Claim 1. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the historical deliverer data considerations of Iacono with the delivery batching system of Neumann and Riel-Dalpe because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Iacono are applicable to the base device (Neumann and Riel-Dalpe), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 6, Neumann in view of Riel-Dalpe discloses the limitations of Claim 5. Neumann does not explicitly disclose but Iacono does disclose wherein the at least one historical delivery metric comprises at least one of an average order completion time, a historical timeliness metric, a historical efficiency metric (¶ 0043; courier profile information may include (i) delivery history information for a courier indicating an average amount of time for the courier to perform deliveries (e.g., an average amount of time per mile, a total average amount of travel time, etc.), (ii) information indicating whether or not the courier is on-time for delivery pick-up and/or drop-off, etc., (iii) vehicle information indicating a vehicle or type of vehicle that is used by the courier to transport items (e.g., a bike, car, van, truck, etc.), (iv) historical location information indicating where the courier is typically located (e.g., a home address, an establishment where the courier is located more than a particular amount of time, etc.), and so on).
The rationale to combine remains the same as for Claim 5.
Claims 7 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe and Ditaranto et al (US 11636563) (hereafter, “Ditaranto”).
Regarding Claim 7, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann does not explicitly disclose but Ditaranto does disclose wherein the deliverer data comprises a respective current workload of the plurality of delivery persons (Column 2, lines 44-59; Column 7, lines 8-22; Column 9, lines 57-46; a centralized scheduling system may track and record currently assigned loads for drivers; the log may include loads that are in progress and/or currently assigned to the drivers; the recommendation component may compare the schedule of the drivers with the available loads on the load board to determine load(s) for drivers; that is, by analyzing the schedules of the drivers, the loads on the load board, as well as the capacity and/or capabilities of the carriers, the recommendation component may match loads on the load board with the drivers).
The rationale to combine Neumann and Riel-Dalpe remains the same as for Claim 1. It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the deliverer capacity/load considerations of Ditaranto with the delivery batching system of Neumann and Riel-Dalpe because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Ditaranto are applicable to the base device (Neumann and Riel-Dalpe), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 10, Neumann in view of Riel-Dalpe discloses the limitations of Claim 2. Neumann does not explicitly disclose but Ditaranto does disclose wherein the deliverer data comprises a respective carrying capacity of the plurality of delivery persons (Column 7, lines 8-22; Column 27, lines 40-63; the recommendation component may compare the schedule of the drivers with the available loads on the load board to determine load(s) for drivers; that is, by analyzing the schedules of the drivers, the loads on the load board, as well as the capacity and/or capabilities of the carriers, the recommendation component may match loads on the load board with the drivers; the dispatcher may track the load board in real-time for loads that may be undertaken based on a current availability and/or capacity of the driver).
The rationale to combine remains the same as for Claim 7.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe and Neeld (PGPub 20210208558) (hereafter, “Neeld”).
Regarding Claim 12, Neumann in view of Riel-Dalpe discloses the limitations of Claim 11. Neumann additionally discloses:
a respective permutation is associated with one of a plurality of delivery persons (¶ 0028, 0031, 0052; Fig. 5; providing, to a user, batching instructions based on the selected batching combinations via a user device; batching instructions may correspond to which alimentary elements which are categorized into a batch for physical transfer by an individual courier, apparatus, or other physical transfer device, method, or the like; each batch is assigned only one physical transfer apparatus (e.g., a delivery driver, drone, or any other suitable physical transfer apparatus););
a respective order comprises at least one food item (¶ 0020; computing device may receive a plurality of alimentary elements and a plurality of destinations, wherein receiving a plurality of alimentary elements and a plurality of destinations may include receiving data corresponding to order placement time of alimentary elements, projected order completion time of alimentary elements, and alimentary element destination; an “alimentary element,” as used in this disclosure is any meal, grocery item, food element, or the like, that may be generated by a restaurant, cafeteria, fast food chain, grocery store, deli, or any place that would have a need for providing an alimentary item to a customer, client, patient, or individual); and
determine at least one of a plurality of temperature classifications associated with the at least one food item; and/or determine one of a plurality of container types associated with the delivery person (¶ 0022, 0033; a restaurant vendor may prepare and package hot meals, a grocery store vendor might pack fresh produce and other groceries, etc.; other suitable methods for physical exchange may include bicycles for eco-friendly, short-distance deliveries or specialized food trucks equipped to maintain specific temperature conditions during transit).
Neumann does not explicitly disclose but Neeld does disclose generate the total hold time miss based at least in part on the temperature classification and/or the container type (¶ 0005, 0020-0021, 0050, 0078; shipping containers may be temperature-controlled; shipped goods may be classified as having a required storage temperature; wherein the remote access server is configured to: (i) identify, within the set of sensor data, an area of the container in which the temperature exceeded a configured threshold; and (ii) provide a notification to a user associated with the container describing the temperature, and the period of time for which the temperature exceeded the configured threshold).
The rationale to combine Neumann and Riel-Dalpe remains the same as for Claim 1. One of ordinary skill in the art would have been motivated to include the delivery temperature-based monitoring, recording, and communication techniques of Neeld with the delivery batching system of Neumann and Riel-Dalpe to provide notification of non-compliance with temperature requirements for temperature-sensitive goods and provide an opportunity to intervene and mitigate damages (see, e.g., Paragraphs 0005, 0007, and 0078 of Neeld).
Regarding Claim 13, Neumann in view of Riel-Dalpe and Neeld discloses the limitations of Claim 12. Neumann additionally discloses the plurality of container types comprise paper container, passive insulated container, or active climate-controlled delivery container (¶ 0033; other suitable methods for physical exchange may include bicycles for eco-friendly, short-distance deliveries or specialized food trucks equipped to maintain specific temperature conditions during transit).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe, Neeld, and Kuo (PGPub 20070150373) (hereafter, “Kuo”).
Regarding Claim 14, Neumann in view of Riel-Dalpe and Neeld discloses the limitations of Claim 12. Neumann additionally discloses wherein the plurality of temperature classifications comprise hot, ambient, and cold (¶ 0022; a restaurant vendor may prepare and package hot meals, a grocery store vendor might pack fresh produce and other groceries, and a deli vendor could provide sandwiches and salads). Neumann does not explicitly disclose but Kuo does disclose wherein the plurality of temperature classifications comprise hot, ambient, cold, and frozen (Abstract; ¶ 0003, 0008, 0040; Figs. 4, 7, 8; a system for delivering multi-temperature goods; room temp foods, refrigerating foods, and frozen foods; ambient temperature foods; a plurality of multi-temperature insulated containers shown in FIG. 4, so as to temporarily store various consigned goods of different storage temperatures, e.g., frozen food, hot food, cold food, fresh food).
The rationale to combine Neumann, Riel-Dalpe and Neeld remains the same as for Claim 12. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the food delivery temperature classifications of Kuo with the delivery batching system of Neumann, Riel-Dalpe, and Neeld because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Kuo are applicable to the base device (Neumann, Riel-Dalpe, and Neeld), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe and Smith et al (PGPub 20250356305, claiming the benefit of Provisional Application 63648982) (hereafter, “Smith”).
Regarding Claim 18, Neumann in view of Riel-Dalpe discloses the limitations of Claim 16. Neumann additionally discloses a respective order comprises at least one food item (¶ 0020; computing device may receive a plurality of alimentary elements and a plurality of destinations, wherein receiving a plurality of alimentary elements and a plurality of destinations may include receiving data corresponding to order placement time of alimentary elements, projected order completion time of alimentary elements, and alimentary element destination; an “alimentary element,” as used in this disclosure is any meal, grocery item, food element, or the like, that may be generated by a restaurant, cafeteria, fast food chain, grocery store, deli, or any place that would have a need for providing an alimentary item to a customer, client, patient, or individual).
Neumann additionally discloses generate the third weight value based at least in part on a temperature-related factor (¶ 0063, 0125; Tables III and IV; unique characteristics of each passive thermal shipping system should be determined, e.g., actual temperature weight factor). Neumann does not explicitly disclose but Smith does disclose wherein the temperature-related factor is a temperature classification of the at least one food item of the plurality of orders (¶ 0022, 0033; a restaurant vendor may prepare and package hot meals, a grocery store vendor might pack fresh produce and other groceries, etc.; other suitable methods for physical exchange may include bicycles for eco-friendly, short-distance deliveries or specialized food trucks equipped to maintain specific temperature conditions during transit).
The rationale to combine Neumann and Riel-Dalpe remains the same as for Claim 1.
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the delivery-based weighting techniques of Smith with the delivery batching system of Neumann and Riel-Dalpe because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Smith are applicable to the base device (delivery batching system of Neumann and Riel-Dalpe), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Neumann in view of Riel-Dalpe and Roberts et al (PGPub 20140180954) (hereafter, “Roberts”).
Regarding Claim 19, Neumann in view of Roberts discloses the limitations of Claim 11. Neumann additionally discloses receive an additional order after preparation of the orders in the top-ranked permutation has occurred (¶ 0051; alternatively or additionally in non-limiting examples, if a new batch of alimentary elements has been queued at a location, a computing device determining a predicted path may factor in minimizing the time between, for instance and without limitation, arriving at a final destination of a batch and returning to a restaurant location; in such an example, the predicted path may change to move the last destination closer to where a physical transfer apparatus may have originally departed for minimizing the time in accepting a second batching combination).
Neumann does not explicitly disclose but Roberts does disclose in response to determining that a proximity of a destination of the additional order is within a threshold range to a destination of at least one of the orders, update a bundle to comprise the additional order (¶ 0041, 0055; Fig. 2; the operation sequence commences by generating order bundles; the unit described herein as an order bundle can be defined in various ways, for example, an order bundle can be defined as a group of orders that have sources situated at locations within a particular proximity and have destinations situated at locations within another particular proximity; in some cases multiple destination points are proximally located (as shown, see "D1," "D2," and "D3" near destination region 264)). Neumann additionally discloses wherein the at least one of the orders is in the top-ranked permutation; wherein the bundle is the top-ranked permutation (¶ 0025, 0027-0029, 0033, 0051-0053, 0059; computing device may select alimentary element pairings so that scores associated therewith are the best score for each order and/or for each batch; in such an example, optimization may determine the combination of alimentary elements such that each delivery pairing includes the highest score possible; selected batching combination input may include a batch of alimentary elements ranked in order of when the alimentary elements may reach their destinations to minimize average delivery time; selecting a candidate batching combination for which the output of the objective function most closely matches the at least a target criterion may result in a combination ranking, wherein all alimentary elements within the candidate batching combination have a ranking that informs the order in which the alimentary elements are batched and/or delivered; selecting the candidate alimentary batching combination may include performing a greedy heuristic process on the objective function; the batching objective function solution target criterion further comprises minimizing the average time between the order placement time and the projected order completion time for the plurality of alimentary elements in the batch; the batching objective function may be the same as an objective function, which selects a candidate batching combination and/or a plurality of batching combinations based on the solution target criterion, for instance and without limitation, such as minimizing the average time between order placement time and completion of the order at the order destination).
The rationale to combine Neumann and Riel-Dalpe remains the same as for Claim 1. One of ordinary skill in the art would have been motivated to include the delivery batching determination techniques of Roberts with delivery batching system of Neumann and Riel-Dalpe to improve the efficiency and costs of delivery operations, e.g., deliver the orders with fewer overall ship units (see at least Paragraph 0011 of Roberts).
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20200134762 – “Order Group Allocation Method and Device,” Chen et al, disclosing a system for score-based matching and bundling of orders for delivery
Paul et al, An Optimization Framework for On-Demand Meal Delivery System, 2020 IEEE Int’l Conference on IEEM, pgs. 822-826, disclosing systems and techniques for batching orders for delivery to minimize the delivery costs and delays in order delivery times
Conclusion
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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