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) 1-20 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 18, 2026, has been entered.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant’s arguments, see pages 11-16 Applicant’s Response, filed October 31, 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(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,” “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 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,” 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 can not be performed mentally. Examiner, respectfully, disagrees. As an initial matter, applicant’s claims were not analyzed under mental processes as applicant states, see the Final office action mailed on 11/19/2025 on page(s) 22-25.
Assuming arguendo, that applicant has some merit that the claims cannot be performed within the grouping of certain methods of organizing human activity and/or mathematical concepts. The courts do not distinguish between mental processes that are performed by humans and claims that recite mental processes performed on a computer, see MPEP 2106.04(a)(2)(III). As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).
Similar to, Electric Power Group v. Alstom, S.A., when the court provided that a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps, which, were recited at a high level of generality such that they could practically be performed in the human mind.
Here, applicant’s claim limitations are recited at a high level of generality that can be performed in the human mind when the limitations recite receiving an indication of a set of items from a user (i.e., collecting). The system can compute first ETA for delivery of the set of items to the user, which the system will generate a set of candidate ETAs based on the first ETA (i.e., analyzing). The system will generate a set of candidate ETAs based on the first ETA (i.e., analyzing). The system can select a candidate ETA that has the highest score (i.e., analyzing). The system will then present the selected ETA as an option for delivery time for the set of items (i.e., displaying). Thus, collecting item information, which the system can use that information to determine candidate ETA’s for delivering an item to a user. The system can then rank those solutions, which the solutions can then be displayed to a user, is merely related to a mental processes. Therefore, the claim(s) recite at least an abstract idea of mental processes. However, even assuming arguendo, that applicant has some merit that the claims cannot be performed mentally. The 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 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 argues on page 15 of applicants arguments, that the machine learning bimodal architecture improves the system. Examiner, respectfully, disagrees. Applicant(s) specification nor amendments provide how this particular architecture of the deep-learning neural network is improved. In fact, applicant’s amendments do not detail how the layers of the deep neural network communicate with each other and how the deep neural network layers interact with the each other in a non-conventional and non-generic arrangement. Furthermore, applicant’s specification provides a list of well-known machine-learning neural networks such that the neural network can use any type of neural network method capable of making predictions, including, but not limited to, perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, transformers, deep neural networks (e.g., convolutional neural networks (CNNs), or recurrent neural networks (RNNs)), see applicant’s specification paragraph(s) 0047 and 0065. 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, 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 mere 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). Here, the limitations fail to provide how the results are being accomplished. The limitations lack the details as to how the softmax is able to apply the probabilistic normalization function to normalize the probability values thus the step(s)/function(s) lack how the results are being accomplished. Therefore, merely “applying,” the judicial exception.
Also, see "[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." See, Recentive Analytics, Inc. v. Fox Corp. Here, applicant(s) specification nor claim amendments provide how this particular architecture of the deep neural network using multiple layers and classifiers are improved. In fact, applicant’s specification provides a list of well-known machine-learning neural networks such that the neural network can include, but are not limited to, any type of neural network method capable of making predictions, including, but not limited to, perceptrons, multilayer perceptrons, convolutional neural networks, recurrent neural networks, sequence-to-sequence models, generative adversarial networks, transformers, deep neural networks (e.g., convolutional neural networks (CNNs), or recurrent neural networks (RNNs)), see applicant’s specification paragraph(s) 0047 and 0065.
Also, while applicant argues the particular architecture of the parallel deep-learning neural network amounts to an improvement. Examiner respectfully disagrees. Here, there is no indication in the Specification that the method steps and/or system functions, require any particularized structure. The recited components, i.e., the computer system, the processor, the computer-readable medium, the online concierge system, the acceptance prediction model, the multi-class classifier, the deep neural network, the multiple hidden layers, the softmax layer, the cost prediction model, the deep neural network, the input layer, the second output layer, the graphical user interface, and the non-transitory computer-readable storage medium, are merely combined in a generic manner, which do not operate in an unconventional manner to achieve an improvement in computer functionality. In fact, applicant’s specification discloses the architecture for multi-task learning is able to reduce cost for delivering a batch of items to users, see applicant’s specification Paragraph(s) 0068-0069. Also, while applicant argues on page 15 of applicants arguments, that the bimodal architecture improves accuracy and enables more precise ETA specific cost prediction. However, these are at best an improvement to the abstract idea itself (e.g., reducing delivery cost) rather than a technological improvement to the computer or deep neural network. Therefore applicant’s arguments are not persuasive.
Third, Applicant argues on page(s) 15-16, that the Claims are significantly more similar to Ex Parte Desjardins. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO). The ARP also found the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
However, applicant’s claims are not as narrowly claimed as Ex Parte Desjardins. In fact, applicant doesn’t recite how the deep neural networks layers communicate/interact together in an unconventional way with other components to improve the functioning of the computer. Furthermore, the claims recite the functional results (e.g., using a deep neural networks for determining delivery ETA’s) to be achieved rather than the implementation details of the deep neural network components. Thus “these claims in substance [are] directed to nothing more than the performance of an abstract business practice ... using a conventional computer. Such claims are not patent- eligible." See, the above analysis; also, see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014). Therefore, applicant’s arguments are not persuasive.
Fourth, Applicant argues on page 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 Non-Final Office Action mailed 07/30/2025 on page 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.
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, 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 ; TLI Communications; and Recentive Analytics, Inc. v. Fox Corp.) 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 15 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,” “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,” 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 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 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 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,” “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 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 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 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, 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, 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, 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 mere 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).
Also, see "[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." See, Recentive Analytics, Inc. v. Fox Corp. 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
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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