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, 8, and 15 were amended. Claim(s) 2-7, 9-14, and 16-20 were previously/originally 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 24, 2026, has been entered.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments, see pages 11-16 Applicant’s Response, filed August 24, 2026, 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(s) 12-13, that the amended Independent Claim(s) 1, 8, and 15, do not fall within the revised Step 2A Prong 1 framework under the grouping of “Certain Methods of Organizing Human Activity.” Examiner, respectfully, disagrees.
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. Independent Claim(s) 1, 8, and 15 recite(s) “receiving an indication of a set of items from a user,” “computing a first ETA for delivery of the set of items to the user,” “generating a set of candidate ETAs based on the first ETAs,” “applying, an acceptance model to features representing the set of items from the user and the candidate ETA to predict a likelihood that the user will accept the candidate ETA for delivery of the set of items, wherein to process input features and generate probability values for multiple acceptance outcomes including acceptance of a standard ETA, acceptance of a prioritized ETA, and no acceptance, wherein configured to apply a probabilistic normalization function to normalize the probability values for the multiple acceptance outcomes into a probability distribution,” “applying a cost model to the candidate ETA to predict a cost of delivery of the set of items within the candidate ETA, wherein the cost model is configured to predict a multi-batch probability and a batch size for achieving the candidate ETA, and to predict a cost of delivery based on the multi-batch probability and the batch size, wherein the cost model are trained jointly such that a single set of trained hidden layer parameters is used to generate both the multi-batch probability and batch size prediction and the cost of delivery prediction, thereby reducing the computational resources and training data required to generate the candidate ETA’s cost of delivery relative to training separate single-task models for each prediction,” “selecting a first one of the candidate ETAs having a highest score,” and “presenting, to the user, the selected ETA as an option for delivery time for the set of items,” step(s)/function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., social activities and/or following rules or instructions) and/or fundamental economic principles/practices (e.g., hedging) and/or commercial or legal interactions (e.g., business relations).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found 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 customer(s), which, the entity is to receive a set of items for delivery to a user, which, the entity can determine the optimal ETA for a delivery. The entity can then provide the highest scored ETA option to a user. Thus, the claims are directed to the abstract idea of a business relation such as determining and presenting estimated time of arrival(s) for delivering an item to a user. 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. The courts determined that performing a resampled statistical analysis to generate a resampled distribution was merely a mathematical calculation.
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea are recited as “applying, an acceptance model to features representing the set of items from the user and the candidate ETA to predict a likelihood that the user will accept the candidate ETA for delivery of the set of items, wherein to process input features and generate probability values for multiple acceptance outcomes including acceptance of a standard ETA, acceptance of a prioritized ETA, and no acceptance, wherein configured to apply a probabilistic normalization function to normalize the probability values for the multiple acceptance outcomes into a probability distribution,” “applying a cost model to the candidate ETA to predict a cost of delivery of the set of items within the candidate ETA, wherein the cost model is configured to predict a multi-batch probability and a batch size for achieving the candidate ETA, and to predict a cost of delivery based on the multi-batch probability and the batch size, wherein the cost model are trained jointly such that a single set of trained hidden layer parameters is used to generate both the multi-batch probability and batch size prediction and the cost of delivery prediction,” and “computing, a score for the candidate ETA based at least in part on the probability values for the multiple acceptance outcomes and the cost of delivery,” step(s)/function(s) are merely mathematical concepts (e.g., 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 processing input features, which will then generate probability values for multiple acceptance outcomes and the probability values will be applied to a normalization function to normalize the values in via a probability distribution, thus at the very least training a mathematical function to normalize probability values using probability distribution is a mathematical calculation. Therefore, the claims are merely taking a neural network for determining probability values using a probability distribution, which at the very least a mathematical calculation thus abstract. Also, see 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); performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018); and MPEP 2106.04(a)(2). Therefore, the claim(s) recite at least an abstract idea of mathematical concepts. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mathematically. The claims would still fall under certain methods of organizing human activity, see the above analysis.
While, applicant argues, see applicant arguments on page(s) 13-14, that the limitations cannot be performed mentally. Examiner, respectfully, disagrees. As an initial matter, applicant’s claims were not analyzed under mental processes as applicant states. Examiner, merely explained if they were analyzed under mental processes how it possibly could apply based on applicants arguments in the office action, see the Final office action mailed on 04/22/2026 on page(s) 7-8. Thus, applicant’s claims would still fall under certain methods of organizing human activity and/or mathematical concepts, see the above analysis.
Second, Applicant argues, on page(s) 14-16, the invention provides that the application is now integrated into a practical application thus sufficient to amount to significantly more than the abstract idea. 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 case the specification merely discloses a solution to efficiently make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058. This is at best an improvement to the business process (e.g., abstract idea) itself rather than a technological improvement. The specification also discloses that the system offers several ETA options to the users, each option having a different speed/cost tradeoff, and the users may select the options that are most appropriate for their individual needs, such as their different tolerances for delay, and their different cost sensitivities. It can be difficult, however, for the concierge systems to select ETA options that will be most mutually beneficial both to the users and to the concierge system, see paragraph 0002. This is at best an improvement to the business process (i.e., determining optimal ETA times for item deliveries)(e.g., 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 efficiently making better estimate likelihoods, see paragraph(s) 0019 and 0058, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
Also, while the specification discloses the machine learning model is able to more accurately predict whether an ETA acceptance will occur, see applicant’s specification Paragraph(s) 0065. This is at best an improvement to the abstract idea itself (e.g., making accurate predictions regarding delivery ETA options) rather than a technological improvement.
Furthermore, similar to, Intellectual Ventures I LLC v. Capital One Bank, the court provided that merely “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. In this case, the judicial exception is not integrated into a practical application when efficiently make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058, since the appending generic computer functionality merely lends to speed or efficiency to the performance of an abstract concept doesn’t meaningfully limit the claim(s) thus as a whole applicant’s limitations merely describe how to generally “apply,” the concept(s) of an existing process of determining and presenting ETA options for the delivery of an item thus at best are mere instructions to apply the exception.
Here, in this case the specification discloses a solution to efficiently make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058. This is at best an improvement to the business process (e.g., abstract idea) itself rather than a technological improvement. The specification also discloses that the system offers several ETA options to the users, each option having a different speed/cost tradeoff, and the users may select the options that are most appropriate for their individual needs, such as their different tolerances for delay, and their different cost sensitivities. It can be difficult, however, for the concierge systems to select ETA options that will be most mutually beneficial both to the users and to the concierge system, see paragraph 0002. This is at best an improvement to the business process (i.e., determining optimal ETA times for item deliveries)(e.g., 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 efficiently making better estimate likelihoods, see paragraph(s) 0019 and 0058, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
Also, while the specification discloses the machine learning model is able to more accurately predict whether an ETA acceptance will occur, see applicant’s specification Paragraph(s) 0065. This is at best an improvement to the abstract idea itself (e.g., making accurate predictions regarding delivery ETA options) rather than a technological improvement.
Furthermore, similar to, Intellectual Ventures I LLC v. Capital One Bank, the court provided that merely “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. In this case, the judicial exception is not integrated into a practical application when efficiently make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058, since the appending generic computer functionality merely lends to speed or efficiency to the performance of an abstract concept doesn’t meaningfully limit the claim(s) thus as a whole applicant’s limitations merely describe how to generally “apply,” the concept(s) of an existing process of determining and presenting ETA options for the delivery of an item thus at best are mere instructions to apply the exception.
While applicant, also, states the architecture reduces the computational resources and training data required to generate the candidate ETA’s cost of delivery, relative to training separate single-task models for each prediction, see applicant’s arguments on page 14. However, applicant’s claim(s) are not as narrowly claimed. Nor does applicant’s specification or claims provide limitations of how the computational resources are being reduce, for example, how much RAM, GPU compute, or training time (e.g., computational resources) by using this multi-task architecture that will then physically enable the reduction of resources. Merely improving the abstract of efficiently determining and generating candidate ETA’s cost of delivery is not enough for showing an improvement to computer functionality
While applicant, on page 15 of applicants arguments, states that the architecture improves computational resources. Examiner, respectfully, disagrees. Applicant(s) specification nor amendments provide how this particular architecture reduces computational resources. Applicant explicitly provides that the architecture helps a particular picker handle all of the orders thereby reducing costs, which is at best is an improvement to the abstract idea itself (e.g., reducing delivery cost) rather than a technological improvement to the computer or deep neural network, see applicant’s specification paragraph(s) 0069-0070. 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, computing, generating, applying, generating, applying, normalizing, applying, predicting, predicting, computing, selecting, and presenting, ETA information using computer components that operate in their ordinary capacity (e.g., a computer system, processor, computer-readable medium, online concierge system, acceptance prediction model, multi-class classifier, deep neural network, multiple hidden layers, softmax layer, cost prediction model, deep neural network, input layer, second output layer, graphical user interface, plurality of hidden layers, shared multi-task architecture, and non-transitory computer-readable storage medium), which are no more than “applying,” the judicial exception.
Also, similar to, Intellectual Ventures I LLC v. Capital One Bank, which the courts stated merely 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. Here, applicant provides that the claims describe a specific way for a model and computational resource to improve efficiency make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058, however, the mere increase in efficiency of determining and presenting ETA options doesn’t demonstrate an improvement to the computer or any technological field but rather instructions to implement the claimed business process on a generic computer thus using the computer as a tool to merely perform the abstract idea.
Furthermore, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when merely gathering and analyzing information using conventional techniques and displaying the result(s). Here, in this case an indication of a set of items from a user is received (e.g., gathering). The system can compute a first ETA for delivery of the set of items and generate a set of candidate ETAs based on the first ETA (i.e., analyzing). The system will estimate a likelihood that the user will accept the candidate ETA for delivery by processing input features and generating probability values for multiple acceptance outcomes and applying a probabilistic function to normalize the probability values for the multiple acceptance outcomes into a probability distribution (e.g., analyzing). The system can also estimate a cost of delivery of the set of items within the candidate ETA and compute a score for the candidate ETA based on the probability values (e.g., analyzing). The system will then select the highest ETA score (e.g., analyzing). The system will then present the ETA as an option for delivery time for the set of items (e.g., displaying) thus merely gathering a user request for items, computing/estimating/generating ETA information, and then presenting the selected ETA options to a user are not sufficient to show an improvement in computers or technology of determining delivery ETA’s.
Also, while applicant provides that “…thereby reducing the computational resources and training data required…,” is merely intended use. The courts have provided that intended use of the claimed invention or a field of use limitation cannot integrate a judicial exception, see MPEP 2106.04(d)(2). Therefore, applicants’ argument(s) are not persuasive.
Third, Applicant argues on page(s) 15-16 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).
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, computing, generating, applying, generating, applying, normalizing, applying, predicting, predicting, computing, selecting, and presenting, ETA information using computer components that operate in their ordinary capacity (e.g., a computer system, processor, computer-readable medium, online concierge system, acceptance prediction model, multi-class classifier, deep neural network, multiple hidden layers, softmax layer, cost prediction model, deep neural network, input layer, shared multi-task architecture, second output layer, graphical user interface, and non-transitory computer-readable storage medium), which are no more than “applying,” the judicial exception.
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; Electric Power Group, LLC v. Alstom, S.A.; Intellectual Ventures I LLC v. Capital One Bank ; and TLI Communications) and how the limitations amount to mere instructions to apply an exception, see the above analysis in the argument section(s). Thus, the claims do not provide an improvement to the vehicle selection optimization.
Furthermore, on page(s) 15-16 of applicant’s arguments, applicant argues that the additional elements are not so widely prevalent as to render them conventional, routine, or well-understood since the prior art does not teach nor suggest the additional elements as evidenced by the Independent claim9s) being clear of the art under 35 USC 102 and 35 USC 103. Examiner respectfully disagrees. Therefore, applicants’ argument is not persuasive.
In Intellectual Ventures I v. Symantec Corp, found that 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 35 USC §101 categories of possibly patentable subject matter. Furthermore, the court in, Synopsys, Inc. v. Mentor Graphics Corp, stated that a claim for a new abstract idea is still an abstract idea the search for a 35 USC § 101 inventive concept is distinct from demonstrating 35 USC § 102 novelty rejection. And similar to the court in, BASCOM Global Internet v. AT&T Mobility LLC, the court stated that the search for a 35 USC § 101 inventive concept is also different from an obviousness analysis under 35 USC § 103. The lack of novelty under 35 USC § 102 or obviousness under 35 USC § 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 USC § 102 and 35 USC § 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 USC § 101. Examiner, respectfully, suggest that Applicant refer back to MPEP § 2106.05(d). Therefore, Applicant’s arguments are found to be unpersuasive.
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, 8, and 15 recites an entity that is able to compute and generate candidate ETA’s. The entity can estimate a likelihood that a user will accept the candidate ETA for the delivery of the set of items and estimate a cost of delivery for the set of items. The entity can then compute a score for the candidate ETA based on the probability values and the cost of delivery. The entity can then select a candidate ETA for the user, which the user can then be provided with an ETA option for the delivery of the set of items. Independent Claim 1, 8, and 15, as a whole recite limitation(s) that are directed to an abstract idea(s) of certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., following rules or instructions) and/or fundamental economic principles/practices (e.g., mitigating risk and/or hedging) and/or commercial or legal interactions (e.g., business relations) and/or mathematical concepts (e.g., mathematical calculations).
Independent Claim(s) 1, 8, and 15, recite(s) “receiving an indication of a set of items from a user,” “computing a first ETA for delivery of the set of items to the user,” “generating a set of candidate ETAs based on the first ETAs,” “applying, an acceptance model to features representing the set of items from the user and the candidate ETA to predict a likelihood that the user will accept the candidate ETA for delivery of the set of items, wherein to process input features and generate probability values for multiple acceptance outcomes including acceptance of a standard ETA, acceptance of a prioritized ETA, and no acceptance, wherein configured to apply a probabilistic normalization function to normalize the probability values for the multiple acceptance outcomes into a probability distribution,” “applying a cost model to the candidate ETA to predict a cost of delivery of the set of items within the candidate ETA, wherein the cost model is configured to predict a multi-batch probability and a batch size for achieving the candidate ETA, and to predict a cost of delivery based on the multi-batch probability and the batch size, wherein the cost model ae trained jointly such that a single set of trained parameters is used to generate both the multi-batch probability and batch size prediction and the cost of delivery prediction thereby reducing the computational resources and training data required to generate the candidate ETA’s cost of delivery relative to training separate single-task models for each prediction,” “selecting a first one of the candidate ETAs having a highest score,” and “presenting, to the user, the selected ETA as an option for delivery time for the set of items,” step(s)/function(s) are merely certain methods of organizing human activity: managing personal behavior or relationships or interactions between people (e.g., following rules or instructions) and/or fundamental economic principles/practices (e.g., mitigating risk and/or hedging) and/or commercial or legal interactions (e.g., business relations).
Independent Claim(s) 1, 8, and 15, recite(s) “applying, an acceptance model to features representing the set of items from the user and the candidate ETA to predict a likelihood that the user will accept the candidate ETA for delivery of the set of items, wherein to process input features and generate probability values for multiple acceptance outcomes including acceptance of a standard ETA, acceptance of a prioritized ETA, and no acceptance, wherein configured to apply a probabilistic normalization function to normalize the probability values for the multiple acceptance outcomes into a probability distribution,” “applying a cost model to the candidate ETA to predict a cost of delivery of the set of items within the candidate ETA, wherein the cost model is configured to predict a multi-batch probability and a batch size for achieving the candidate ETA, and to predict a cost of delivery based on the multi-batch probability and the batch size, wherein the cost model is configured to predict a multi-batch probability and a batch size for achieving the candidate ETA, and to predict a cost of delivery based on the multi-batch probability and the batch size, wherein the cost model ae trained jointly such that a single set of trained parameters is used to generate both the multi-batch probability and batch size prediction and the cost of delivery prediction,” and “computing, a score for the candidate ETA based at least in part on the probability values for the multiple acceptance outcomes and the cost of delivery,” step(s)/function(s) can be considered as merely mathematical concepts (e.g., 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, 8, and 15, are similar to an entity determining an estimated time of arrival for a deliverer to bring a set of items to a customer. The mere recitation of generic computer components (Claim 1: a computer system, a processor, a computer-readable medium, an online concierge system, an acceptance prediction model, a multi-class classifier, a deep neural network, multiple hidden layers, a shared multi-task architecture, a plurality of hidden layers, a softmax layer, a cost prediction model, a deep neural network, an input layer, a second output layer, and a graphical user interface; Claim 8: a non-transitory computer-readable storage medium, one or more computer processors, a multi-class classifier, a deep neural network, softmax layer, multiple hidden layers, an acceptance prediction model, a cost prediction model, two output layers, a shared multi-task architecture, a plurality of hidden layers, a second output layer, and a graphical user interface; and Claim 15: a computer system, one or more computer processors, a non-transitory computer-readable storage, a multi-class classifier, a shared multi-task architecture, a plurality of hidden layers, a deep neural network, softmax layer, multiple hidden layers, an acceptance prediction model, a cost prediction model, an input layer, two output layers, a second output layer, and a graphical user interface) do not take the claims out of the enumerated group of certain methods of organizing human activity. Therefore, Independent Claim(s) 1, 8, and 15, recites the above abstract idea.
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,” “computing,” “generating,” “applying,” processing,” “generating,” “applying,” “training,” “generating,” “normalizing,” “applying,” “predicting,” “computing,” “selecting,” and “presenting,” respectively, information in a computer environment. The limitations that amount to “apply it,” are as follows (Claim 1: a computer system, a processor, a computer-readable medium, an online concierge system, an acceptance prediction model, a multi-class classifier, a deep neural network, multiple hidden layers, a shared multi-task architecture, a plurality of hidden layers, a softmax layer, a cost prediction model, a deep neural network, an input layer, a second output layer, and a graphical user interface; Claim 8: a non-transitory computer-readable storage medium, one or more computer processors, a multi-class classifier, a deep neural network, softmax layer, multiple hidden layers, an acceptance prediction model, a cost prediction model, two output layers, a shared multi-task architecture, a plurality of hidden layers, a second output layer, and a graphical user interface; and Claim 15: a computer system, one or more computer processors, a non-transitory computer-readable storage, a multi-class classifier, a shared multi-task architecture, a plurality of hidden layers, a deep neural network, softmax layer, multiple hidden layers, an acceptance prediction model, a cost prediction model, an input layer, two output layers, a second output layer, and a graphical user interface). Examiner, notes that the computer system, processor, computer-readable medium, online concierge system, acceptance prediction model, multi-class classifier, deep neural network, multiple hidden layers, softmax layer, cost prediction model, deep neural network, input layer, shared multi-task architecture, plurality of hidden layers, second output layer, graphical user interface, and non-transitory computer-readable storage medium, 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 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, computing, generating, applying, training, generating, processing, generating, applying, normalizing, applying, predicting, computing, selecting, and presenting, ETA information using computer components that operate in their ordinary capacity (e.g., a computer system, processor, computer-readable medium, online concierge system, acceptance prediction model, multi-class classifier, deep neural network, multiple hidden layers, softmax layer, cost prediction model, shared multi-task architecture, plurality of hidden layers, deep neural network, input layer, second output layer, graphical user interface, and non-transitory computer-readable storage medium), which are no more than “applying,” the judicial exception.
Also, similar to, Intellectual Ventures I LLC v. Capital One Bank, which the courts stated merely 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. Here, applicant provides that the claims describe a specific way for a model and computational resource to improve efficiency make better estimate likelihoods that pickers will be willing to accept multiple customer orders, thus amortizing the time and effort required to fulfill each, see applicant’s specification paragraph(s) 0019 and 0058, however, the mere increase in efficiency of determining and presenting ETA options doesn’t demonstrate an improvement to the computer or any technological field but rather instructions to implement the claimed business process on a generic computer thus using the computer as a tool to merely perform the abstract idea.
Furthermore, similar to, TLI Communications, where the court found that there was no improvement upon computers or technology when merely gathering and analyzing information using conventional techniques and displaying the result(s). Here, in this case an indication of a set of items from a user is received (e.g., gathering). The system can compute a first ETA for delivery of the set of items and generate a set of candidate ETAs based on the first ETA (i.e., analyzing). The system will estimate a likelihood that the user will accept the candidate ETA for delivery by processing input features and generating probability values for multiple acceptance outcomes and applying a probabilistic function to normalize the probability values for the multiple acceptance outcomes into a probability distribution (e.g., analyzing). The system can also estimate a cost of delivery of the set of items within the candidate ETA and compute a score for the candidate ETA based on the probability values (e.g., analyzing). The system will then select the highest ETA score (e.g., analyzing). The system will then present the ETA as an option for delivery time for the set of items (e.g., displaying) thus merely gathering a user request for items, computing/estimating/generating ETA information, and then presenting the selected ETA options to a user are not sufficient to show an improvement in computers or technology of determining delivery ETA’s.
Also, see the recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, applicant’s claims do not provide details as to how the computational resources are being reduce, for example, how much RAM, GPU compute, or training time (e.g., computational resources), by using this multi-task architecture.
Also, while applicant provides that “…thereby reducing the computational resources and training data required…,” is merely intended use. The courts have provided that intended use of the claimed invention or a field of use limitation cannot integrate a judicial exception, see MPEP 2106.04(d)(2). 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 practical application(s) of the abstract idea. Therefore, when viewed in combination these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the above abstract idea(s).
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. Therefore, the claims are ineligible.
Claim(s) 2-5, 7, 9-12, 14, and 16-19: The various metrics of Dependent Claim(s) 2-5, 7, 9-12, 14, and 16-19 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Independent Claim(s) 1, 8, and 15, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 6, 13, and 20: The limitations that amount to “apply it,” are the acceptance prediction model, recurrent neural network, a convolutional neural network, and a multi-layer perceptron. Examiner, notes that the acceptance prediction model, recurrent neural network, a convolutional neural network, and a multi-layer perceptron are generically claimed 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 task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception, see MPEP 2106.05(f)). Here, the additional elements is merely predicting information which is no more than “applying,” the judicial exception. For the reasons described above with respect to Claim(s) 6, 13, and 20, the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2-7, 9-14, and 16-20, 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 mere instructions to apply the exception using generic computer component(s), which, do not 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.
“Improving ETAs with multi-task models, deep learning, and probabilistic forecasts,” by Chi Zhang, Lewis Warne, Ziqi Jiang, Qingyang Xu, Hubert Jenq, Jianzhe Luo, and Pradeep Varma, March 12, 2024, (hereinafter Task). Task teaches a NextGen architecture that has a two-layer structure which decoples the decision making layer from the base prediction problem. This model streamlines ETA predictions across different customer touchpoints and delivery types within a singular, adaptable framework. The system uses different available features for predicting the ETAs. Task, also, teaches using a probabilistic distribution to determine actual outcomes. Examiner, respectfully, notes that the publication date fails to predate applicants priority date.
“DeepETA: How Uber Predicts Arrival Times Using Deep Learning,” by Tanmay Binayklya, Xinyu Hu, Olcay Cirit, February 9, 2022, (hereinafter Uber). Uber teaches a hybrid neural network architecture ETA prediction system that uses spatial and temporal features. The neural network architecture includes multiple layers, softmax, and loss functions.
“Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data,” by Claire Y.T. Chen, Edward W. Sun, Ming-Feng Chang, Yi-Bing Lin, Ann Oper Res. 2023 Apr 6:1-34, (hereinafter Travel). Travel teaches travel time prediction system using machine learning and big data. Travel, also, teaches the neural network includes hidden layers and an output layer. The neural networks includes loss functions that will determine information based on batch sampling.
“End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications,” by Arthur Cruz de Araujo and Ali Etemad, April 29, 2021, (hereinafter Parcel). Parcel teaches a DeepETA, spatial-temporal sequential neural network model for estimating travel (arrival) time for parcel delivery. Parcel, also, teaches using input features for the model. The neural network includes a mean squared error as a loss function, which the loss function uses batch sizes.
“Deciding Which Tasks Should Train Together in Multi-Task Neural Networks, by Christopher Fifty, October 25, 2021, (hereinafter Train). Train teaches a machine learning model that uses multi-task learning.
“Multi-task Predict-then-Optimize,” by Bo Tang and Elias B. Khalil, pp. 506-522, October 25, 2023, (hereinafter Predict). Predict teaches a multi-task end-to-end predict-then-optimize neural network that uses a loss function to predict travel times. The neural network includes hidden layers and task specific layers. Predict also, teaches a multi-task neural network that shares hidden layers across all tasks. Each task has different cost vectors, which the predictions are based on the input feature vectors. The model uses hyperparameters, learning rates, and batch sizes. Examiner, respectfully, notes that the publication date fails to predate applicants priority date.
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
