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 .
DETAILED ACTION
1. This Office Action is in response to the Amendment filed on November 26, 2024, which paper has been placed of record in the file.
2. Claims 1-4, 6-16, and 19-20 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-4, 6-16, and 19-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Independent claim 1, which is illustrative of the all independent claims and analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method for order routing and redirection for fulfillment processing. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claimed invention relates to a method for order routing and redirection for fulfillment processing. Order details associated with an order that has not been placed are received. Potential establishments are identified that can satisfy the order details based on location data. Real-time order preparation times are obtained for the order details from the potential establishments. An optimal establishment is selected from the potential establishments based at least in part on the real- time order preparation times and the order is placed with the optimal establishment as an online order through an online service (see Specification para [0005]).
The claim recites the steps of: receiving order details with an order that has not been placed…, identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, placing the order with the first optimal establishment…, providing within the online service a real-time data integration feature that dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, business relations.
Moreover, the claim recites the steps of: identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, .dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, initiating and integrating, the method, via workflows within the order aggregation online services and establishment services, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
The claim recites “developing a predictive model that analyze the order details, location data, and real-time data and predicts time estimated for fulfillment of orders”, which are Mathematical relationships, falls within the Mathematical Concepts of abstract ideas (Mathematical relationships, formulas or equation, mathematical calculations). See MPEP 2106.04(a)(2), subsection III.
Accordingly, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list”, “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data.” The claim also recites that the steps of “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list”, “processing a machine-learning algorithm trained on historical order data…”, and “developing, by the machine-learning algorithm, a predictive model; “receiving order details with an order that has not been placed…, identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, placing the order with the first optimal establishment…, providing within the online service a real-time data integration feature that dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment” are performed by a processor.
The additional limitations “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Moreover, the recited APIs do not provide any improvements to the computer functionality, improvements to the network, improvements to the APIs, they are just merely used as general means for gathering, transmitting and receiving information. In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, they are just merely used as general means for collecting and outputting data. It is similar to other concepts that have been identified by the courts Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48; Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Further, the additional limitations “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list”, “processing a machine-learning algorithm trained on historical order data…”, and “developing, by the machine-learning algorithm, a predictive model; “receiving order details with an order that has not been placed…, identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, placing the order with the first optimal establishment…, providing within the online service a real-time data integration feature that dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment” are recited as being performed by a processor. The processor is recited at a high level of generality. In limitations receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list, the processor is used as a tool to perform the generic computer function of gathering, receiving and transmitting data. See MPEP 2106.05(f). In limitations “processing a machine-learning algorithm trained on historical order data…”, and “developing, by the machine-learning algorithm, a predictive model; “receiving order details with an order that has not been placed…, identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, placing the order with the first optimal establishment…, providing within the online service a real-time data integration feature that dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment”, the processor is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components a processor, a memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
The additional limitations “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The judicial exception of “generating a ranked listing for the potential establishments” is performed by “processing a machine-learning algorithm trained on historical order data…”, the judicial exception “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment” is performed by developing by the machine-leaning algorithm. The trained machine-learning algorithm is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, do not include any details about how the “generating a ranked listing” is accomplished. See MPEP 2106.05(f).
The additional elements of “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” limit the identified judicial exceptions generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, this type of limitations merely confine the use of the abstract idea to a particular technological environment (machine-learning algorithm) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application. Therefore, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As explained with respect to Step 2A, Prong Two, the additional elements of “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).
The additional elements “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list” were both found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the recitations of “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list” are recited at a high level of generality. These elements amount to collecting and transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: 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).
As discussed in Step 2A, Prong Two above, the recitation of the processor to perform limitations “receiving order details with an order that has not been placed…, identifying potential establishments that can satisfy the order details based on location data, obtaining real-time order preparation times…, generating a ranked listing for the potential establishments…, selecting a first optimal establishment from the ranked list…, placing the order with the first optimal establishment…, providing within the online service a real-time data integration feature that dynamically updating the ranked listing based on changes in traffic conditions and preparation time, evaluating preparation times estimated form establishments and delivery/pickup times based on traffic condition and availability of delivery personnel, scoring each establishment in a customer’s zone to rank them based on estimated fulfillment time, and selecting a particular establishment that provides shortest time form order fulfillment, permitting, via the method, users to initiate or place online orders for pickup and delivery by evaluating real-time data for establishment, selecting, via the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the order based on evaluation of the real-time data subsequent to placement of the order with the first optimal establishment”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
The Berkheimer Memorandum mandates that an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
(1) a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s);
(2) a citation to one or more of the court decisions discussed in MPEP § 2106.05(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s);
(3) a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); or
(4) a statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional element(s), which satisfies the requirements set forth in MPEP § 2144.03.
In this case, the present Specification described in para [0016] of using general-purpose computers and available commercial products to perform the method. Thus, the applicant provides (1) a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional elements.
Regarding independent claims 10 and 19, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 10 directed to method, independent claim 19 directed to a system, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Regarding dependent claims 2-4, 6-9, 11-16, and 20, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea.
Regarding dependent claim 2, the claim simply refines the abstract idea by further reciting monitoring a progression of the order with the first optimal establishment; determining the second optimal establishment…, redirecting the order from the first optimal establishment to the second optimal…, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 3, the claim simply refines the abstract idea by further reciting providing an identifier for the second optimal establishment, an order number for the order, and an estimated fulfillment time to a customer that provided the order details, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 4, the claim recites the additional elements wherein receiving further includes receiving the order details from the online service after a customer provides the order details through the front-end workflow of online service, which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 6, the claim simply refines the abstract idea by further reciting defining a geographic zone from a fulfillment location…, and identifying the potential establishments as having site locations within the geographic zone based on the location data, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 7-8, the claims simply refine the abstract idea by further reciting wherein obtaining further includes: obtaining available delivery personnel and personnel locations…, and obtaining current traffic conditions between each of the site locations and the fulfillment location, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 9, the claim recites the additional elements providing identifiers for each of the potential establishments, the location data, the site locations, the fulfillment location, the current traffic conditions, and the real-time order preparation times as input to a trained machine-learning algorithm, which are used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “providing identifiers for each of the potential establishments… as input” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). Moreover, the claim recites the additional elements wherein receiving as output the ranked listing of the potential establishments sorted based on estimated fulfillment times with a least estimated fulfillment time listed first in the ranked listing, which are mere data gathering and outputting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and outputting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05 (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 11, the claim simply refines the abstract idea by further reciting redirecting the placed order to a different establishment selected from the ranked listing based on monitoring a progression of the placed order with the second optimal establishment, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 12, the claim simply refines the abstract idea by further reciting notifying the customer that a particular fulfillment location has changed from second optimal establishment to the different establishment…, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 13, the claim simply refines the abstract idea by further reciting notifying the customer of the second optimal establishment, the order details, an expected fulfillment time, and a confirmed fulfillment location, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 14-16, the claims simply refine the abstract idea by further reciting wherein obtaining further includes identifying the one or more fulfillment locations…, identifying the one or more fulfillment locations…, that fall under the category of Organizing Human Activity and Mental process groupings of abstract ideas as described above in the independent claim 1 above. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 20, the claim simply refines the abstract idea by further reciting obtaining delivery personnel locations for delivery personnel…, identifying the delivery address…, that fall under the category of Mental process grouping of abstract ideas as described above in the independent claim 1. Moreover, the claim recites the additional elements providing the delivery personnel locations as additional input to the machine-learning algorithm, which are used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “providing the delivery personnel locations as additional input” and do not include any details about how the solution is accomplished. See MPEP 2106.05(f). (See claim 1 above). Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Accordingly, claims 1-4, 6-16, and 19-20 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Novelty and Non-Obviousness
5. No prior arts were applied to the claims because the Examiner is unaware of any prior arts, alone or in combination, which disclose at least the limitations of “developing, by the machine-learning algorithm, a predictive model that analyze the order details, location data, and real-time data to predict time estimates to enable fulfillment of orders, evaluating the predictive model preparation times estimated for establishments and delivery/pickup time based on traffic conditions and availability of delivery personnel, scoring each establishment in a customer's zone to rank them based on estimated fulfillment time and selecting a particular establishment that provides shortest time for order fulfillment; and selecting, by the method, a second optimal establishment and an optimal delivery resource to enable fulfillment of the placed order based on evaluation of the real-time data subsequent to processing the placed order with the first optimal establishment” recited in the independent claims 1, 10, and 19.
Response to Arguments/Amendment
6. Applicant's arguments with respect to claims 1-4, 6-16, and 19-20 have been fully considered but are not persuasive.
Claim Rejections - 35 USC § 101
Claims 1-4, 6-16, and 19-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more (See details above).
In response to the Applicant’s arguments “The present claims are directly
analogous to those found eligible in Desjardins”, the Examiner respectfully disagrees submits that the claims in Desjardins were found patent-eligible because they recited specific improvement to how machine learning models function, in contrast, the machine model in the present claims is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions.
Training on Historical Data for Predictive Modeling:
The judicial exception of “generating a ranked listing for the potential establishments” is performed by “processing a machine-learning algorithm trained on historical order data…”, the judicial exception “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment” is performed by developing by the machine-leaning algorithm. The trained machine-learning algorithm is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, do not include any details about how the “generating a ranked listing” is accomplished. See MPEP 2106.05(f).
The additional elements of “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” also merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” limit the identified judicial exceptions generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, this type of limitations merely confine the use of the abstract idea to a particular technological environment (machine-learning algorithm) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Scoring and Ranking Mechanism: The amended claims recite that "each establishment is scored for a given order and the scores returned highest to lowest as output by the MLAs, with the highest score representing an establishment that has the shortest fulfillment time for a given order", which is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions, it simply just takes data as input and provides the output. Rather, these limitations only recite the outcome of “generating a ranked listing for the potential establishments”, do not include any details about how the machine learning model is accomplished. See MPEP 2106.05(f).
Real-Time Adaptation: The claims recite dynamic updating of ranked listings based on real-time changes in traffic conditions and preparation times, which are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Integration with Multiple Data Sources: The claims recite the additional elements "providing within the online service a real-time data integration feature that dynamically updates the ranked listing based on changes in traffic conditions and preparation times", which are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, they are just merely used as general means for collecting and outputting data.
Moreover, these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, the machine learning models, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing. Accordingly, the amended claims are not integrated into a practical application.
In response to the Applicant’s arguments “The amended claims are patent-eligible under Alice/Mayo”, the Examiner respectfully disagrees submits that:
Technical Problem #1: Suboptimal Order Routing: While the specification described that "current ordering systems are mostly fixed based on customer selection and does not provide optimal experience to the customer. Customer often makes selection of a site based on proximity to their location or past order history but do not know if another site can provide faster service" (Specification, Background section), there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of updates the ranked listing based on changes in traffic conditions and preparation times rather than to any technology.
Technical Solution #1: Machine Learning-Based Dynamic Optimization:
The claims recite the additional elements of “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data”, which merely indicate a field of use or technological environment in which the judicial exception is performed. Although the additional elements “processing a machine-learning algorithm trained on historical order data…”, “developing, by the machine-learning algorithm, a predictive model”, and “wherein the machine-learning algorithm is trained on historical order data to produce a ranked listing of establishments when provided new order details, location details, and real-time data” limit the identified judicial exceptions generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, this type of limitations merely confine the use of the abstract idea to a particular technological environment (machine-learning algorithm) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Technical Problem #2: Inability to Adapt After Order Placement: While the specification described that that even after order placement, "certain issues can come up subsequently that can ruin the plan for a perfect meal as the order may be delayed due to traffic, delivery personnel availability, unforeseen circumstances at the selected site, etc." (Specification, Background section), there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of updates the ranked listing based on changes in traffic conditions and preparation times rather than to any technology.
Technical Solution #2: Continuous Monitoring and Redirection: The claims recite the features of: Continuously monitoring order progression with the selected establishment; Evaluating real-time data to identify better alternatives; Selecting a second optimal establishment and delivery resource when warranted; Enabling dynamic order redirection to optimize fulfillment, which do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, the machine learning models, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
Integration Architecture: The claims recite the additional limitations “receiving order details from application programming interfaces (APIs) via workflows”, “order details are received via a first API for a front-end workflow associated with an online service”, “placing the order…as an online order through the online service”, “presenting the ranked listing to a customer, receiving the first optimal establishment as a customer selection from the ranked list”, which are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). Moreover, the recited APIs do not provide any improvements to the computer functionality, improvements to the network, improvements to the APIs, they are just merely used as general means for gathering, transmitting and receiving information. In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting.
Real-Time Data Integration: The claims recite "a real-time data integration feature that dynamically updates the ranked listing based on changes in traffic conditions and preparation times", which are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g).
Machine Learning Training and Deployment: The claims recite the judicial exception of “generating a ranked listing for the potential establishments” is performed by “processing a machine-learning algorithm trained on historical order data…”, the judicial exception “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, which is performed by developing by the machine-leaning algorithm. The trained machine-learning algorithm is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, do not include any details about how the “generating a ranked listing” is accomplished. See MPEP 2106.05(f).
Predictive Modeling: The claims recite the additional elements "developing, by the machine-learning algorithm, a predictive model that analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment of orders", provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). The judicial exception of “generating a ranked listing for the potential establishments” is performed by “processing a machine-learning algorithm trained on historical order data…”, the judicial exception “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment” is performed by developing by the machine-leaning algorithm. The trained machine-learning algorithm is used to generally apply the abstract idea without placing any limits on how the machine-learning algorithm functions. Rather, these limitations only recite the outcome of “generating a ranked listing for the potential establishments” and “analyzes the order details, location data, and real-time data and predicts time estimates for fulfillment”, do not include any details about how the “generating a ranked listing” is accomplished. See MPEP 2106.05(f).
In conclusion: these additional elements do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, the machine learning models, or other technology. They just merely used as general means for performing the abstract idea. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing.
For the reason set forth above, the 101 rejection is maintained.
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 date of this final action.
8. Claims 1-4, 6-16, and 19-20 are rejected.
9. The prior arts made of record and not relied upon are considered pertinent to applicant's disclosure:
Mimassi (US 2022/0335381) discloses a system and method for automated order preparation and fulfillment timing.
Dhesi et al. (US 2025/0104013) disclose systems and methods of order fulfillment using a fulfillment engine, which optimizes the allocation of client or user orders to third party merchants for fulfillment through the use of linear programming or machine learning.
Rao Karikurve et al. (US 12,131,358) disclose in an online concierge system, a shopper retrieves items specified in an order by a customer from a retail location. The online concierge system optimizes order fulfillment by selecting a retail location for an order that is most time-efficient and that is most likely to have each of the item in the order available.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Nga B. Nguyen whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday, 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 April 15, 2026
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