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 Claim(s)
Claim(s) 1-20 were previously pending and were rejected in the previous office action. Claim(s) 1, 4, 11, and 14 were amended. Claim(s) 2-3, 5-10, 12-13, and 15-20 were left as originally/previously presented. Claim(s) 1-20 are currently pending and have been examined.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 19, 2025, has been entered.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments, see pages 9-15, of Applicant’s Response, filed February 19, 2025, with respect to 35 USC § 101 rejection of Claim(s) 1-20, have been fully considered but they are not persuasive.
First, Applicant argues, on page 12, that the amended Independent Claim(s) 1 and 11, do not fall within the revised Step 2A prong one framework under certain methods of organizing human activity and/or mathematical concepts. Examiner, respectively, disagrees with applicant’s arguments.
As an initial matter, courts have provided various sub groupings within organizing human activity grouping encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is also noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings, see MPEP 2106.04(a)(2)(II).
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea are recited as “receiving a plurality of requests including a plurality of alimentary combinations and a plurality of destinations, wherein each request specifies: an alimentary combination of the plurality of alimentary combinations; and a destination of the plurality of destinations,” “selecting an alimentary provider of a plurality of alimentary providers, wherein selecting the alimentary provider of the plurality of alimentary providers comprises: filtering the plurality of alimentary provides as a function of a user preference,” “comparing the plurality of alimentary combinations to alimentary provider data,” determining a plurality of assembly times, as a function of the projected nutritionally guided order volume, wherein determining the plurality of assembly times comprises training a process using training data correlating past nutritionally guided order volume inputs to past assembly time outputs, wherein the past assembly time outputs are specific to each alimentary provider of the plurality of alimentary providers, wherein the past assembly time outputs specific to each alimentary provider of the plurality of alimentary providers are calculated as a function of at least a facility size,” “selecting a runner route from a plurality of runner routes, for delivering at least one alimentary combination of the plurality of alimentary combinations, as a function of the plurality of assembly times,” “selecting the runner route,” “generating as a function of runner training data, wherein the runner data correlates a plurality of delivery parameter data to a plurality of runner route data, wherein the plurality of delivery parameter data includes data on a plurality of runners, data describing circumstances affecting each runner including compatibility of each runner with types of ingredients in combinations of alimentary elements, and a plurality of assembly time data,” “categorizing elements of the runner training data to generate a plurality of runner training data sets each containing a plurality of data entries categorizing the plurality of runners based on the compatibility of each runner with types of ingredients in the plurality of alimentary combinations,” “classifying the plurality of runners by selecting at least one categorized runner training data set of the plurality of training data sets as a function of the received plurality of alimentary combinations in the plurality of requests,” “selecting the runner route as a function of the classified plurality of runners, the plurality of assembly times and the selected at least one categorized runner training set using a runner route model,” “each runner route of the plurality of runner routes further includes: information related to a classified runner, an aggregation depot, a path from the alimentary provider of the plurality of alimentary providers to the aggregation depot, and a predicted estimated time of completion where the predicted estimated time of completion is configured as a function of at least a driving speed,” “generating a plurality of predicted routes as a function of a proximity of the plurality of destinations to the aggregation depot and the process, wherein each predicted route of the plurality of predicted routes comprises: a retrieval from the aggregation depot and at least a destination of the plurality of destinations,” “pairing a predicted route of the plurality of predicted routes with a courier, wherein pairing the predicted route further comprises: generating an objective function based on a plurality of objectives,” and “pairing, with the courier, the predicted route that optimizes the objective function,” step(s)/function(s) are merely certain methods of organizing human activity: commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., following rules or instructions).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found that that processing a credit application between a customer and dealer, where the business relation is the relationship between the customer and the dealer during the vehicle purchase was merely a commercial transaction, which, is a form of certain methods of organizing human activity. In this case, the claim(s) are similar to a business relationship between an entity and couriers, which, the entity receives a number of food request, which the entity can select a food provider for the requests. The entity can determine routes for the runners and couriers, which the can then select a route for the runner and courier. Therefore, the claims are directed to the abstract idea of a business relation such as selecting routes for the delivery of goods. Thus, applicant’s claims fall within at least the enumerated grouping of certain methods of organizing human activity.
Furthermore, even if we assume, that applicant has some merit that the claims
cannot be performed by certain methods of organizing human activity. The courts have provided when determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. It is also important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." Similar to, SAP America, Inc. V. InvestPi, LLC, 890 F.3d 1016, 1022 (Fed. Cir. 2018), when the claims invoked two abstract categories and can be characterized fairly as reciting the combination of two ideas: training mathematical models by identifying relationships among numerical data and using the outputs of those models to determine a reward for the selection of a vehicle.
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea are recited as “computing a projected nutritionally guided order volume as a function of a process,” and “the past assembly time outputs specific to each alimentary provider of the plurality of alimentary providers are calculated as a function of at least a facility size,” step(s)/function(s) are merely mathematical concepts (e.g., mathematical relationships and/or mathematical calculations).
Here, the Claims are merely taking existing information and identifying relationships to generate additional information, which the focus on applicant’s claims are merely selecting certain information, analyzing that information, and then outputting those results based on the information to then optimize a function for the selected routes thus at the very least training mathematical models by identifying relationships among numerical data and using the outputs of those models to optimize a function for the selected route. Therefore, the claims are merely taking an algorithm for determining an optimal route for couriers, which at the very least a mathematical calculation thus abstract. Also, see using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); and organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014).
Examiner, also, notes that applicant’s specification provides that the machine learning process use mathematical algorithms to model mathematical relationships for solving an mathematical optimization function, see applicant’s specification paragraph(s) 0029-0030, 0033-0034, 0037, and 0080-0081. Thus, examiner disagrees with applicant’s argument(s) and applicant’s claims fall within at least the enumerated grouping of mathematical concepts. But, assuming that applicants don’t fall within mathematical concepts the claim(s) would still fall under certain methods of organizing human activity, see the above analyze.
Second, applicant argues, on page(s) 12-14 in applicant’s arguments, that the application is now integrated into a practical application. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, it is important to note that first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"), see MPEP 2106.04(d)(1). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Here, in this the specification discloses the machine learning is able to compute more accurate assembly times, see applicant’s specification Paragraph 0041. This is at best an improvement to the abstract idea itself rather than a technological improvement. First, the step(s) of accomplishing this desired improvement in the specification is made in blanket conclusory manner by merely providing the machine learning process that determines a plurality of assembly times as a function of the nutritionally guided order volume, applicant’s specification paragraph(s) 0041 and 0063-0064, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
Also, even if it is determined that the specification doesn’t set forth an improvement in a conclusory manner, the independent claim(s) that are being evaluated fail to reflect the disclosed step(s) of accomplishing such improvement listed by applicant. While applicant provides that the system is improved by providing a computationally robust approach that is akin to anomaly detection, that is, by preprocessing training data and removing incompatible data entries, which can subsequently be used for enabling efficient and accurate path selection predications using vehicle route guidance for provisioning of alimentary combinations, see applicants argument on page 12. However, the claims are not as narrowly claimed. There is nothing in the claims that provide how the system is able to identify incompatible data entries and remove those incompatible data entries. The claims fail to claim how the machine learning models are able to identify and detect incompatible data entries and how that improves the functioning of the computer, thus the limitations amount to no more than a recitation of the words “apply it. See, Versata Development Group, Inc. v.
SAP America, Inc., 793 F.3d 1306, 1332 (Fed. Cir. 2015) (citations omitted) ('"[T]he prohibition on patenting an ineligible concept cannot be circumvented by limiting the use of an ineligible concept to a particular technological environment").
Furthermore, merely improving the efficiency and accurate path selection predictions using vehicle route guidance for provisioning of alimentary combinations is at best an improvement to the business process (e.g., abstract idea) itself rather than a technological improvement. See, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). Thus, applicant’s argument is not persuasive.
Also, another important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP §2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.
Here, in this case applicant’s limitations merely receiving, selecting, comparing, computing, determining, training, outputting selecting, selecting, generating, categorizing, classifying, selecting, generating, pairing, pairing, generating, and pairing, respectively, determining assembly times and selecting/pairing routes to available vehicles for transporting goods using computer components that operate in their ordinary capacity (e.g., a machine learning process, a classifier, a classification algorithm, a runner machine learning model and a computing device), which are no more than “applying,” the judicial exception. Also, see a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and MPEP 2106.05(f).
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. The court also stated “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212.
Furthermore, in this case, there is no improvement to the machine learning process and/or the classification algorithm and/or the runner route machine learning model, merely providing an algorithm to be used in a delivery routing context is not enough to be considered significantly more. In fact, Applicant’s specification provides a list of well-known algorithms such as stochastic gradient descent, stochastic gradient descent with momentum, AdaGrad optimization, RMSProp, Adam, regression algorithm, neural net, greedy algorithm, or the like, thus any algorithm can be used to achieve such functions/steps, see applicant’s specification paragraph(s) 0037, 0064, 0078, and 0082.
While applicant argues the improvement in technology includes multi-stage provisions of machine-learning activities and transforming training data form a first form to a second form to create classified training data set that can be selectively and hence more efficiently processed, thereby desirably reducing downstream computational steeps, increasing accuracy and mitigating the associated carbon footprint, see applicants arguments on page 12.
It should be noted that a transformation that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more (or integrate a judicial exception into a practical application), see MPEP 2106.05(c)(5). Similar to, Intellectual Ventures I v. Capital One Fin. Corp., the court determined that collecting, displaying, and manipulating data was merely an abstract idea because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, see MPEP 2106.05(f).
In this case, applicant’s stating transforming training data from a first form to a second form is merely data gathering thus not providing significantly more. Furthermore, applicant’s claims merely recite receiving a plurality of requests including a plurality of alimentary combinations and a plurality of destinations (i.e., collecting). Selecting an alimentary provider of a plurality of alimentary providers (i.e., analyzing). Computing a projected nutritionally guided order volume (i.e., analyzing) and determine a plurality of assembly times (i.e., analyzing). The system will then select a runner route and generate runner training data (i.e., analyzing). The system can pair the predicted route with a courier (i.e., analyzing). The system can categorize elements of the runner training data and classify the plurality of runners by selecting at least one categorized runner training data (i.e., manipulating data). Thus, applicant merely claims receiving data, analyzing data, and manipulating that data, to determine courier and runner routes, however, without additional details of how the computer system function(s) are improved based on transforming (e.g., classification) data into a standardized or formatted data set. Thus, the lack of the additional details of how the computer performs these transformations and how these limitations are to achieve such result has been determined to be directed to an abstract idea. Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. Therefore, applicant’s arguments are not persuasive.
Third, Applicant argues, on page(s) 13-14, that the invention provides that the application is now integrated into a practical application thus sufficient to amount to significantly more than the abstract idea based on the ordered combinations similar to Example 47 since the limitations do not recite a mathematical concept. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, claim 3 of Example 47 is eligible because it recites an improvement in the technical field of network intrusion detection by taking proactive measures to remediate the danger by detecting the source address of potentially malicious packet in step (d), automatically dropping the malicious network packets in step (e), and blocking future traffic from the source address in step (f) to offer a specific computer security solution, see July 2024 Subject Matter Eligibility Examples. Thus, claim 3 of Example 47 recited steps that were determined to be additional elements rather than steps/features of the abstract idea recited in the claim, July 2024 Subject Matter Eligibility Examples. In fact, step (a) recited the use of specific mathematical calculations and steps (b) and (c) fall in the mental process groupings of abstract ideas, while steps (d)–(f) were found to be the non-abstract ideas.
In this case, applicant’s limitations are not as narrowly claimed as Claim 3 of Example 47. The limitations recited in Independent Claim(s) 1 and 11 are part of the abstract idea, which fall into one or more of the enumerated groupings (e.g., certain methods of organizing human activity and/or mathematical concepts), see the above analysis.
Also, unlike claim 3 of Example 47, applicant’s limitations are more similar to claim 2 of Example 47. The machine learning algorithm here at best classifies candidate runner routes, which helps with more efficiently determining delivery routes for runners/couriers, which does not improve computer or neural network technology but at best merely improves the business process (e.g., abstract idea). Thus, not enough for integrating the underlying abstract ideas into a patent-eligible practical application. Therefore, applicants argument is not persuasive.
Fourth, Applicant argues on page 14-15 of applicants’ arguments, that the Claims are not well-understood, routine, or conventional activity and amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: (1) Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; (2) Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): (3) Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well- understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) Evaluate whether any additional element or combination of elements are other than what is well- understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d), see MPEP 2106.5(B)(II).
Examiner respectfully notes that in the Final Office Action mailed 11/21/2024 on page(s) 11-14 and 18-19, the Step 2B prong was used to analysis the previous Step 2A Prong Two additional elements that merely amounted to describing how to generally “apply,” the abstract idea in a computer environment thus Examiner carried over the identification of the additional elements and conclusions of the additional elements that were analyzed under Step 2A Prong Two, which the analysis also explained how the limitations were not an improvement to the technology.
As stated above, any claim elements that were identified as insignificant extra-solution activity should be reevaluated under Step 2B for determining if they are well- understood, routine, and conventional, however, since the additional elements were not categorized as insignificant extra-solution activity such analysis under well-understood, routine, and conventional was not required. However, even assuming arguendo, the claims needed to be analyzed under Berkheimer. In this case, the additional elements are well understood, routine, and conventional activity when applicant list various algorithms and states that the algorithms can include a stochastic gradient descent, stochastic gradient descent with momentum, AdaGrad optimization, RMSProp, Adam, regression algorithm, neural net, greedy algorithm, or the like, thus any algorithm can be used to achieve such functions/steps, thus well-understood, routine, and conventional algorithms are used, see applicant’s specification paragraph(s) 0037, 0064, 0078, and 0082.
It should also be noted that when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity, see MPEP 2106.05(d). In this case, examiner provided why these limitations are not sufficient to show an improvement (e.g., Affinity Labs v. DirecTv, Intellectual Ventures I v. Capital One Fin. Corp., Recentive Analytics, Inc. v. Fox Corp., and TLI Communications) and how the limitations amount to mere instructions to apply an exception, which do not amount to a particular improvement see the above analysis in the argument section(s). Thus, the claims do not provide an improvement to the machine learning algorithms.
Applicant also, argues that the limitations involve a novel and non-obvious method since the claims amount to an “inventive concept,” because they are not taught by the relevant art thus amounting to significantly more than a mere judicial exception. Examiner respectfully disagrees and notes that even if elements of the abstract idea could be considered novel/non-obvious, the search for an inventive concept should not be confused with a novelty or non-obviousness determination, see Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 and MPEP 2106.05(I). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016). Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101, see MPEP 2106.05(I). Therefore, applicants’ argument is not persuasive.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Independent Claim(s) 1 and 11 recites an entity receiving food order information, which, the entity can determine a route for the runner and courier to take based on order information and assembly time. Independent Claim(s) 1 and 11, as a whole recites limitation(s) that are directed to an abstract idea of certain methods of organizing human activity: commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions) and/or mathematical concept(s) (i.e., mathematical relationships and/or mathematical calculations).
Independent Claim(s) 1 and 11, recites “receiving a plurality of requests including a plurality of alimentary combinations and a plurality of destinations, wherein each request specifies: an alimentary combination of the plurality of alimentary combinations; and a destination of the plurality of destinations,” “selecting an alimentary provider of a plurality of alimentary providers, wherein selecting the alimentary provider of the plurality of alimentary providers comprises: filtering the plurality of alimentary provides as a function of a user preference,” “comparing the plurality of alimentary combinations to alimentary provider data,” determining a plurality of assembly times, as a function of the projected nutritionally guided order volume, wherein determining the plurality of assembly times comprises training a process using training data correlating past nutritionally guided order volume inputs to past assembly time outputs, wherein the past assembly time outputs are specific to each alimentary provider of the plurality of alimentary providers, wherein the past assembly time outputs specific to each alimentary provider of the plurality of alimentary providers are calculated as a function of at least a facility size,” “selecting a runner route from a plurality of runner routes, for delivering at least one alimentary combination of the plurality of alimentary combinations, as a function of the plurality of assembly times,” “selecting the runner route,” “generating as a function of runner training data, wherein the runner data correlates a plurality of delivery parameter data to a plurality of runner route data, wherein the plurality of delivery parameter data includes data on a plurality of runners, data describing circumstances affecting each runner including compatibility of each runner with types of ingredients in combinations of alimentary elements, and a plurality of assembly time data,” “categorizing elements of the runner training data to generate a plurality of runner training data sets each containing a plurality of data entries categorizing the plurality of runners based on the compatibility of each runner with types of ingredients in the plurality of alimentary combinations,” “classifying the plurality of runners by selecting at least one categorized runner training data set of the plurality of training data sets as a function of the received plurality of alimentary combinations in the plurality of requests,” “selecting the runner route as a function of the classified plurality of runners, the plurality of assembly times and the selected at least one categorized runner training set using a runner route model,” “each runner route of the plurality of runner routes further includes: information related to a classified runner, an aggregation depot, a path from the alimentary provider of the plurality of alimentary providers to the aggregation depot, and a predicted estimated time of completion where the predicted estimated time of completion is configured as a function of at least a driving speed,” “generating a plurality of predicted routes as a function of a proximity of the plurality of destinations to the aggregation depot and the process, wherein each predicted route of the plurality of predicted routes comprises: a retrieval from the aggregation depot and at least a destination of the plurality of destinations,” “pairing a predicted route of the plurality of predicted routes with a courier, wherein pairing the predicted route further comprises: generating an objective function based on a plurality of objectives,” and “pairing, with the courier, the predicted route that optimizes the objective function,” step(s)/function(s) are merely certain methods of organizing human activity: commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Also, Independent Claim(s) 1 and 11, recites “computing a projected nutritionally guided order volume as a function,” and “wherein the past assembly time outputs specific to each alimentary provider of the plurality of alimentary providers are calculated as a function of at least a facility size,” step(s)/function(s) are merely mathematical concept(s) (i.e., mathematical relationships and/or mathematical calculations). Furthermore, As, explained in the MPEP and the October 2019 update, where a series of step(s) recite judicial exceptions, examiners should combine all recited judicial exceptions and treat the claim as containing a single judicial exception for purposes of further eligibility analysis. (See, MPEP 2106.04, 2016.05(II) and October 2019 Update at Section I. B.).
For instance, in this case, Independent Claim(s) 1 and 11, are similar to an entity receiving food orders from customers and delivery destination information. The entity will then be able to determine a volume of orders based on past orders, which, the entity will then be able to determine assembly times for the orders. The entity will then select a route for the runners and couriers to deliver the orders to customers based on assembly times. The mere recitation of generic computer components (Claim 1: a first machine-learning process, a classifier, a classification algorithm, and a runner route machine learning model; and Claim 11: a computing device, a classifier, a classification algorithm, a runner route machine learning model, and a first machine-learning process) do not take the claims out of the enumerated group of certain methods of organizing human activity. Therefore, Independent Claim(s) 1 and 11, recites the above abstract idea(s).
Step 2A Prong 2: This judicial exception is not integrated into a practical
application because the claims as a whole describes how to generally “apply,” the
concept(s) of “receiving,” “selecting,” “comparing,” “computing,” “determining,” “selecting,” “selecting,” “generating,” “categorizing,” “classifying,” “selecting,” “generating,” “filtering,” “pairing,” “pairing,” “generating,” and “pairing,” information in a computer environment, respectively. The limitations that amount to “apply it,” are as follows (Claim 1: a first machine-learning process, a classifier, a classification algorithm, and a runner route machine learning model; and Claim 11: a computing device, a classifier, a classification algorithm, a runner route machine learning model, and a first machine-learning process). Examiner, notes that the first machine-learning process, classifier, classification algorithm, runner route machine learning model; and computing device, respectively, are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv., the court has held that certain additional elements are not integrated into a practical application or provide significantly more when the additional elements merely use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) thus they do no more than merely invoke computers or machinery as a tool to perform an existing process, which, amounts to no more than “applying,” the judicial exception, MPEP 2106.05(f). Here, the above additional elements are not integrated into a practical application or provide significantly more when they are merely receiving, selecting, filtering, comparing, computing, determining, selecting, generating, categorizing, classifying, selecting, generating, pairing, pairing, generating, and pairing, determining assembly times and selecting/pairing routes to available vehicles for transporting goods using computer components that operate in their ordinary capacity (e.g., a machine learning process and a computing device), which are no more than “applying,” the judicial exception. Also, see a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and MPEP 2106.05(f).
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13.
Also, similar to, Intellectual Ventures I v. Capital One Fin. Corp., the court determined that collecting, displaying, and manipulating data was merely an abstract idea because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, see MPEP 2106.05(f). In this case, applicant’s stating transforming training data from a first form to a second form is merely data gathering thus not providing significantly more. Furthermore, applicant’s claims merely recite receiving a plurality of requests including a plurality of alimentary combinations and a plurality of destinations (i.e., collecting). Selecting an alimentary provider of a plurality of alimentary providers (i.e., analyzing). Computing a projected nutritionally guided order volume (i.e., analyzing) and determine a plurality of assembly times (i.e., analyzing). The system will then select a runner route and generate runner training data (i.e., analyzing). The system can pair the predicted route with a courier (i.e., analyzing). The system can categorize elements of the runner training data and classify the plurality of runners by selecting at least one categorized runner training data (i.e., manipulating data). Thus, applicant merely claims receiving data, analyzing data, and manipulating that data, to determine courier and runner routes, however, without additional details of how the computer system function(s) are improved based on transforming (e.g., classification) data into a standardized or formatted data set. Thus, the lack of the additional details of how the computer performs these transformations and how these limitations are to achieve such result has been determined to be directed to an abstract idea. Also, see gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48. Each of the above limitations simply implement an abstract idea that is no more than mere instructions to apply the exception using a generic computer component, which, is not a practical application of the abstract idea.
Step 2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted previously, the claims as a whole merely describe how to generally “apply,” the abstract idea in a computer environment. Thus, even when viewed as a whole, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible.
Claim(s) 2-7, 9, 12-17, and 19: The various metrics of Dependent Claim(s) 2-7, 9, 12-17, and 19 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Claim(s) 1 and 11, respectively, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 8 and 18: The additional limitation “filtering,” is further directed to a certain method of organizing human activity, as described in Independent Claim(s) 1 and 11. The recitation of “filtering the plurality of alimentary providers comprises filtering the plurality of alimentary providers as a function of the dietary restriction,” step(s)/function(s) falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, MPEP 2106.05(f). Here, the above additional elements merely filtering, location information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim 3 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 10 and 20: The additional limitation “filtering,” is further directed to a certain method of organizing human activity, as described in Independent Claim(s) 1 and 11. The recitation of “filtering the plurality of alimentary providers comprises filtering the plurality of alimentary providers as a function of the feedback data and the user preference,” step(s)/function(s) falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, MPEP 2106.05(f). Here, the above additional elements merely filtering, location information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim 3 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2-10 and 12-20 above 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 abstract idea into a practical application, the additional element(s) in the dependent claim(s) above are no more than either mere instructions to apply the exception using generic computer component(s), which, doesn’t provide an inventive concept. Therefore, Claim(s) 1- 20 are not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Food safety for food delivery,” March 26, 2020, (hereinafter Food). Food teaches a system that allows a user to request food orders and specify allergen information they may have. Food, further, teaches that all food must be delivered to consumers in a way that ensures that it does not become unsafe or unfit to eat. The food needs to be refrigerated and packed in an insulated box with coolant gel or a cool bag. The food should also be packaged securely and stored allergen-free meals separately in transit to avoid contamination though any spillages.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN A HEFLIN whose telephone number is (571)272-3524. The examiner can normally be reached 7:30 - 5:00 M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628