DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 19/067,414 was filed on February 28, 2025 and claims priority to U.S. Provisional Application No. 63/560,555 filed on March 1, 2024.
Status of the Claims
Claims 1-20 are currently pending. Claims 1, 11, 15, and 20 were amended in the reply filed March 4, 2026. No claims were added or cancelled.
Response to Arguments
101:
Applicant's arguments filed with respect to the rejection made under 35 U.S.C. § 101 have been fully considered but they are not persuasive. Applicant first argues that the claims “do not recite any mathematical concept for calculating probabilities but merely involve the use of mathematical concepts such as probability distributions” (Remarks p. 8). Examiner respectfully disagrees. A mathematical relationship may be expressed in words or using mathematical symbols (see MPEP 2106.04(a)(2)(I)(A)). In the claims, the probability distribution is a mathematical relationship between a set of possible outcomes/events and how likely each is to occur. The claims do not merely involve the use of a probability distribution, rather the probability distribution is explicitly recited as the basis for generating the range of estimated arrival times. As such, the claim recites a mathematical concept (see MPEP 2106.04(II)(A)(1) – Examiner notes the teeter-totter example distinguishing a claim that does recites a judicial exception, “A machine comprising elements that operate in accordance with F=ma” from one that does not, “A teeter-totter comprising an elongated member pivotably attached to a base member, having seats and handles attached at opposing sides of the elongated member”).
Applicant next argues that the claims do not recite certain methods of human activity because the “office does not identify which subgrouping of which element within a subgrouping that the claims are alleged to correspond” and “the Office has not provided an explanation as the why the [limitations] are directed towards a method of organizing human activity” (Remarks p. 9). Examiner respectfully disagrees and clarifies that receiving a request for delivery from a user and subsequently providing the user with an estimated time or delivery is sales activities or behaviors, and business relations (see MPEP 2106.04(a)(2)(II)(B)). Examiner notes paragraph [0002] of Applicant’s specification describing an end user ordering food from a restaurant for delivery, and paragraph [0060] describing checking out and providing payment for delivery items, which illustrates the sales and business aspects of the claims.
Applicant further argues that the additional elements in the claims integrate any judicial exception into a practical application by providing an improvement to the computer. Specifically, that “the combination of machine learning model and decision modules … improves system efficiency and reduces memory because the architecture enables a single machine learning module to accommodate multiple task-specific scenarios … without requiring separate, independently trained models for each scenario” (Remarks p. 10). Examiner respectfully disagrees. The improvements listed by Applicant are not improvements to a computer. Rather, they are improvements to how the abstract idea is performed. Any increase in system efficiency or reduction in memory is a result of improving the abstract idea – not from an actual improvement in the computer system (see MPEP 2106.05(a) “It is important to note, the judicial exception alone cannot provide the improvement”; see also, Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 15 "Finally, the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved.")
Lastly, Applicant argues that the claims are eligible under Step 2b because they contain limitations that are unconventional (Remarks p. 11-12). Examiner respectfully disagrees. Examiner notes that whether or not a claim includes well-understood, routine, conventional activities is only one of multiple considerations when determining whether additional elements amount to an inventive concept (see 2106.05 (A)). For the reasons discussed above and in the 101 rejection below, the claims do not amount to significantly more under Step 2B.
Accordingly, the rejection is maintained.
103:
Applicant's arguments filed with respect to the rejections made under 35 U.S.C. § 103 have been fully considered but are moot in view of the new grounds of rejection.
Examiner notes that, the “score” taught by Chen in [0057] is computed for “the various candidate ETAs” and corresponds to “the probability that a picker will be willing to accept an order with the given ETA” and is therefore a “probability distribution representing probabilities for delivery times” under the broadest reasonable interpretation. Furthermore, Examiner notes that Chen [Fig. 5] shows a user screenshot of an ETA range of “33-35min”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1
Claims 1-10 are directed to a method (i.e., a process), and claims 11-20 are directed to a computing device (i.e., device). Therefore, claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claims 1 and 11 recite, receiving, from an end user, a request for a delivery;
obtaining feature information including (1) retrieval information associated with a retrieval location from which an item is to be delivered by a transporter and (2) transporter information associated with a plurality of transporters that are currently active for the retrieval location;
generating a feature vector from the feature information;
generating, using the feature vector, a probability distribution representing probabilities for delivery times from the retrieval location;
providing the probability distribution to a decision layer that includes a plurality of decision modules, wherein each decision module of the plurality of decision modules is associated with a type of retrieval location and wherein the retrieval information includes an identifier corresponding to the type of retrieval location;
selecting a decision corresponding to the identifier;
generating a range of an estimated time of arrival of the transporter based on the probability distribution; and providing, to the end user, the range of an estimated time of arrival.
The limitations stated above are processes/ functions that under broadest reasonable interpretation (e.g., determine ETA covers "certain methods of organizing human activity" (managing personal behavior or relationships or interactions between people and commercial or legal interactions and following rules or instructions) because the claims recite collecting, analyzing and outputting the result to a user. The claims recite concepts related to mathematical relationships (i.e. probability distribution) Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1 and 11 as a whole amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or "apply it" (or an equivalent).
Independent claims 1 and 11 recite the additional elements: computer, end user device, decision module, machine learning model, a plurality of transporter devices, computing device, one or more processors; and a computer readable medium coupled to the one or more processors. These are recited at a high-level of generality in the specification. (See specification: [0043] the transporters 108 may use transporter user devices 125, such as smart phones, tablet computers, wearable computing devices, laptops, or the like, as further enumerated elsewhere herein, and these transporter user devices 125 may have installed thereon the transporter application 127[0088] the decision modules may be machine learning models. [0112-115] A computer system can include desktop and laptop computers, tablets, mobile phones and other mobile devices. [0116-118] Any of the methods described herein may be totally or partially performed with a computer system including one or more processors, which can be configured to perform the steps. [0021] A "machine learning model" (ML model) can refer to a software module configured to be run on one or more processors to provide a classification or numerical value of a property of one or more samples), such that, when viewed as whole/ordered combination ( as shown in Fig.1), it amounts to no more than mere instruction to apply the judicial exception using generic computer components or "apply it" (See MPEP 2106.05(f).
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) "apply it" (or an equivalent), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of: (i) computer, end user device, decision module, machine learning model, a plurality of transporter devices, computing device, one or more processors; and a computer readable medium coupled to the one or more processors, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination (as shown in Fig. 1-2 and Fig.6), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea (e.g., using computers to communicate data). Claims 4-6 and 12-3 recite the elements " decoder" and " encoding using the machine learning model" which fail to integrate the abstract idea into a practical application because merely invoking the generic components as a tool to perform the abstract idea or "apply it". Thus, the claims are ineligible.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. Accordingly, the claims are not directed to significantly more than the exception itself. Claims 4-6 and 12-3 recite the elements " decoder" and " encoding using the machine learning model", that are recited at a high-level of generality (see specification: [0101] The machine learning model can select the probability distribution from the decoder for the current task [0077] The machine learning model 300 can encode feature information into a feature vector) these do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Therefore, the dependent claims are not eligible subject matter under § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103, which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 2, 10-11, 15-16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20240403812 A1) in view of Narayanam (US 2023/0096163 A1) in further view Koch (US 20200279191)
As per claim 1, Chen teaches:
A computer-implemented method comprising: receiving, from an end user device, a request for a delivery; (see at least: [0014] A customer uses the customer client device 100 to place an order with the online concierge system 140. An order specifies a set of items to be delivered to the customer. the order also specifies one or more retailers from which the ordered items should be collected.)
obtaining feature information including (1) retrieval information associated with a retrieval location from which an item is to be delivered by a transporter (see at least: [0014] the order also specifies one or more retailers from which the ordered items should be collected. [0066] the input features for step 406 include properties of customer orders, such as: certain real-time features, such as identifier (ID) of a warehouse at which the items will be picked up, a type of the warehouse,)
(2) transporter information associated with a plurality of transporter devices of transporters that are currently active for the retrieval location; (see at least: [0066] the input features for step 406 include a percentage of active shoppers, and an amount of shopper coverage)
Chen does not explicitly teach generating a feature vector from the feature information; and generating probabilities using the feature vector, however, this is taught by Koch ( see at least: [0045] The training set of feature values are then converted into a corresponding vector representation. The vector is then input along with corresponding known target outputs into the predictive model to generate weighted coefficients for each feature. Each vector and corresponding target output is used by the predictive model to adjust the weighted coefficients. [0047] An order score may then be determined based on the input set of feature values and the weighted coefficients determined in the training mode. The order score for a merchant may correspond to the probability that the customer will place an order from the merchant. See also [0124- 126]).
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the selection of the vector feature and feature for the same reasons its useful in Koch -namely, to provide consumers with meaningful and targeted options in a delivery platform ( par.4). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Chen further teaches generating, using a machine learning model, a probability distribution representing probabilities for delivery times from the retrieval location; ( see at least: [0057] The delivery ETA module 250 includes an ETA scoring module 310 that computes a score for the various candidate ETAs generated by the ETA candidate generation module 305.)
providing the probability distribution to a decision layer that includes a plurality of decision modules associated with different retrieval locations or types of retrieval locations, ( see at least: Fig. 3, 250 delivery ETA module [ decision layer] includes #305 ETA candidate generation module, 310 ETA scoring module, 315 ETA selection module [ plurality of decision modules])
Chen does not explicitly teach wherein each decision module of the plurality of decision modules is associated with a Here, the platform 120 may include a plurality of different AI models for a plurality of different shipping lanes. In this example, an AI model may correspond to only one of the shipping lanes; [0148] In some embodiments, the method may further include mapping the shipment to a predefined shipping lane from among a plurality of shipping lanes based on the origin location [a retrieval location] and the destination location. In some embodiments, the method may further include selecting the AI model from among a plurality of AI models [decision modules] based on the mapped predefined shipping lane from among the plurality of shipping lanes.
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine wherein each decision module of the plurality of decision modules is associated with a retrieval location -namely, to improve the accuracy of the machine learning model. Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
While Narayanam teaches a retrieval location, Narayanam does not teach, a type of retrieval location. However, Chen [0066] teaches a type of retrieval location. It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize a type of retrieval location as in Chen in the system executing the method of Narayanam with the motivation of enabling users to know when to expect their items as taught by Chen [0002] over that of Narayanam.
Chen further teaches and wherein the retrieval information includes an identifier corresponding to the type of the retrieval location; ( see at least: [0066] the input features for step 406 include properties of customer orders, such as: certain real-time features, such as identifier (ID) of a warehouse at which the items will be picked up, a type of the warehouse, an ID of a zone in which the warehouse is located, a distance from the customer to the warehouse).
Chen does not explicitly teach selecting a decision module corresponding to the identifier; However, this is taught by Narayanam ( see at least: [0089] In some embodiments, the shipping lane that is identified may control which AI model or models are used by the platform 120 to predict milestone events and also to predict charges; [0148] In some embodiments, the method may further include selecting the AI model from among a plurality of AI models based on the mapped predefined shipping lane from among the plurality of shipping lanes
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the selecting a decision module corresponding to the identifier -namely, to improve the accuracy of the machine learning model. Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Chen further teaches generating, by the decision module, a range of an estimated time of arrival of the transporter based on the probability distribution; ( see at least: Fig.5, [0055-56] The delivery ETA module 250 includes an ET candidate generation module 305 that generates a set of different candidate ETAs for further assessment. The ETA candidate generation module 305 generates multiple candidate ETAs for each of several different types of ETA. the ETA candidate generation module 305 may generate a range of candidate ETAs for both a standard ETA (e.g., representing an average ETA based on prior order deliveries, such as 65 minutes) and a prioritized ETA (representing an expedited ETA, which may be offered for a greater cost)).
and providing, to the end user device, the range of an estimated time of arrival on the end user device. ( see at least: Fig.5, [0069] FIG. 5 illustrates a user interface for a customer, in which the customer is presented with delivery options including a priority ETA 502 and a standard ETA 504,[0056], [0071] a range of candidate ETAs based on the base ETA (e.g., at various different time offsets from the base ETA).
As per claim 2, Chen in view of Narayanam and Koch teaches claim 1 as above. Chen further teaches :
the item has not been selected before receiving the request. ( see at least:[0015] A The ordering interface allows the customer to search for items that are available through the online concierge system 140 and the customer can select which items to add to a "shopping list." "shopping list," as used herein, is a tentative set of items that the user has selected for an order but that has not yet been finalized for an order ).
As per claim 10, Chen in view of Narayanam and Koch teaches claim 1 as above. Chen further teaches :
wherein the feature information includes aggregated features, categorical features, embedding features, and temporal features. ( see at least: [0066] the input features for step 406 include : past orders of that customer; features derived locally [aggregated features], type of the warehouse, [categorical features ] the day of the week of the event, and the hour of the day of the event [embedding features], whether delivery was assigned within a given recent time period (e.g., last 30 minutes), whether delivery was acknowledged within a given recent time period, whether delivery was assigned and acknowledged within a given recent time period, [temporal features])
As per claim 16, Chen in view of Narayanam and Koch teaches claim 11 as above. Chen further teaches :
wherein the request for delivery comprises the retrieval location. ( see at least: [0014] the order also specifies one or more retailers from which the ordered items should be collected. [0066] the input features for step 406 include properties of customer orders, such as: certain real-time features, such as identifier (ID) of a warehouse at which the items will be picked up, a type of the warehouse, )
As per claim 18, Chen in view of Narayanam and Koch teaches claim 11 as above. Chen further teaches :
wherein the item has been selected before receiving the request. ( see at least: Fig. 6, [0003] An online concierge system generates a set of candidate estimated times of arrival (ETAs) for delivery of a set of items being purchased by a user.)
Claim 11, 15 recite similar limitations as claims 1 and 10, therefore they are rejected over the same rationales.
Claim(s) 3, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20240403812 A1) in view of Narayanam ( US 20230096163 A1) in view Koch ( US 20200279191) in further view of Hueser (US 20230317066 A1) in view of Reda ( US 2023/0105829 A1)
As per claim 3, Chen in view of Narayanam and Koch teaches claim 1 as above. Chen further teaches :
wherein the probability distribution includes (1) a single delivery time with the highest probability (see at least: abstract, [0069] Returning to FIG. 3, the delivery ETA module 250 includes an ETA selection module 315 that selects one or more "best" ETAs from among the candidate ETAs. The "best" ETAs are considered to be those with the highest scores, as determined by the ETA scoring module 310, where "highest" scores refer to those indicating greatest favorability (e.g., the score domain could be [0, 1], with 0 representing the most favorable scores and 1 representing the least favorable scores in some embodiments, or 1 representing the most favorable scores and 0 the least favorable scores in other embodiments)
Chen does not explicitly teach (2) an uncertainty, however this is taught by Reda ( see [0041] the probability output by the machine-learned item availability model 216 includes a confidence score. The confidence score may be the error or uncertainty score of the output availability probability and may be calculated using any standard statistical error measurement.)
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the uncertainty feature for the same reasons its useful in Reda-namely, to determine the confidence score (par.41). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim 19 recites similar limitations as claim 3, therefore its rejected over the same rationales.
As per claim 20, Chen in view of Narayanam and Koch and Reda teaches claim 19 as above. Chen further teaches :
wherein the single delivery time is within the range of an estimated time of arrival (see at least: abstract, One or more of the highest-scoring ETAs are selected and provided to the user, who may then approve one of the ETAs for use with delivery of the user's set of items )
Claim(s) 4 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 20240403812 A1) in view of Narayanam ( US 20230096163 A1) in view Koch ( US 20200279191) in further view of Hueser (US 20230317066 A1)
As per claim 4, Chen in view of Narayanam and Koch teaches claim 1 as above. Chen further teaches :
the machine learning model ( see at least: [0048])
Chen does not explicitly teach a plurality of decoders, wherein each decoder is associated with a different task, and wherein a task flag is provided to the machine learning model based on a selection or lack of selection in a user interface by the end user device. However, this is taught by Hueser ( see at least: abstract [0020] A system can use various machine learning models, such as those configured in an encoder-decoder architecture, to process user inputs. Som example systems use multiple encoder-decoder pairs, where each encoder-decoder pair is configured to perform a different task. For example, a first encoder-decoder pair may be configured to perform intent classification for a first domain, a second encoder-decoder pair may be configured to perform intent classification for a second domain, a third encoder-decoder pair may be configured to perform NER for the first domain, etc. [0154])
It would have been obvious for one ordinary skilled in the art before the effective filing date of present invention to combine the decoders feature for the same reasons its Hueser in Reda-namely, to reduce use of time and resources with respect to runtime and training operations ( par.25). Moreover, this is merely a combination of old elements in the art. In the combination, no element would serve a purpose other than it already did independently, and one skilled in the art would have recognized that the combination could have been implemented through routine engineering producing predictable results.
Claim 12 recites similar limitations as claim 4, therefore its rejected over the same rationales.
Prior Art
The closest prior arts, cited below, do not teach or suggest the recited claims 5-9, 13-14 and 17 in any reasonable combination.
Wright (US 2023/0061754 A1)
Bialynicka-Birula (US 20140330741 A1)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Judkins ( US 20250139570 A1)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEPHANIE S WALLICK whose telephone number is (703)756-1081. The examiner can normally be reached M-F 10am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shannon Campbell can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.S.W./Examiner, Art Unit 3628
/SHANNON S CAMPBELL/ Supervisory Patent Examiner, Art Unit 3628
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