Office Action Predictor
Application No. 17/338,421

TRAINING A MODEL TO PREDICT TRAVEL DISTANCE BETWEEN TWO GEOGRAPHIC LOCATIONS

Non-Final OA §103§112
Filed
Jun 03, 2021
Examiner
TISSOT, ADAM D
Art Unit
3663
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Maplebear, Inc.(Dba Instacart)
OA Round
4 (Non-Final)
79%
Grant Probability
Favorable
4-5
OA Rounds
3y 1m
To Grant
99%
With Interview

Examiner Intelligence

79%
Career Allow Rate
540 granted / 680 resolved
Without
With
+21.9%
Interview Lift
avg trend
3y 1m
Avg Prosecution
25 pending
705
Total Applications
career history

Statute-Specific Performance

§101
7.9%
-32.1% vs TC avg
§103
54.3%
+14.3% vs TC avg
§102
14.2%
-25.8% vs TC avg
§112
20.2%
-19.8% vs TC avg
Black line = Tech Center average estimate • Based on career data

Office Action

§103 §112
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 . 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. At present, Applicant has submitted remarks in response to the latest Office action on 31 August 2025. Therein, Applicant amended claims 1, 7, 15 and 17-18; Applicant did not cancel or add any new claims. The submitted claims have been entered and are considered below. Response to Amendments/Arguments Applicant’s amendments and related arguments with respect to claims rejected under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 6, 14 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Independent claims 1, 7 and 15 define similar limitations that recite “wherein the location data in at least a subset of the pairs in the batch does not contain navigation information”. Dependent claims 6, 14 and 17 define a limitation that information is received from “a third party system generating navigation information for traveling from the starting location to the destination location”. It cannot be determined with certainty how the invention contains location data that both does not contain navigation information and also receives navigation information from a third party. Clarification is requested. This will be the only rejection of claims 6, 14 and 17 as an art rejection cannot reasonably reject an invention that both does not contain and contain an element simultaneously. 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. Claims 1-4, 7, 10-12, 15 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Meanor, et al. (U.S. Patent Publication No. 2022/0327482) in view of Putrevu, et al. (U.S. Patent No. 11,803,894) and in view of Morgan-Brown (U.S. Patent Publication No. 2019/0316924). For claim 1, Meanor discloses a distance prediction model stored on a non-transitory computer readable storage medium, wherein the distance prediction model is manufactured by a process comprising: obtaining training data comprising a plurality of examples, each example consisting of: a starting location, a destination location, and a Haversine distance between the starting location and the destination location (see para. 0071, 0076), a time for an object who travelled from the starting location to the destination to fulfill one of a plurality of past orders (see para. 0073), initializing the distance prediction model that comprises a plurality of layers of a neural network, where the distance prediction model is configured to receive a batch of pairs of origin locations and destination locations (see paras. 0075-0076), each pair comprising location data comprising an origin location, a destination location, and a Haversine distance between the origin location and the destination location (see paras. 0075-0076) and configured to generate a predicted distance for traveling to the destination locations in the batch in fulfilling a plurality of the past orders (see para. 0101; para. 0079, historical data equivalent to past orders) corresponding in the batch, wherein the location data in at least a subset of the pairs in the batch does not contain navigation information (see para. 0071, navigation information not disclosed as a type of information included); for each of a plurality of the examples of the training data: applying the distance prediction model to the starting location, the destination location, the Haversine distance between the starting location and the destination location (see paras. 0075-0080), and the time for the object who travelled from the starting location to the destination to fulfill one of the plurality of past orders (see paras. 0072-0073). Meanor does not disclose the specific “fulfillment” type of orders or a user that is a “picker” along with the other remaining limitations. A teaching from Putrevu discloses using time for training machine learning models (see col. 4:61 to col. 5:18; col. 19:35-48) that include the type of user that is a “picker” (see col. 1: 13-16) and how “fulfillment” orders differ from and substitute for point-to-point orders (see col. 19:35-48). It would have been obvious to modify Meanor with the teaching of Putrevu before the effective date of filing based on a reasonable expectation of success and motivation to improve ordering items through an online concierge system, and more specifically to allocation of shoppers and grouping of orders based on varying numbers of shoppers and orders across different time windows (see col. 1:8-13) or improve an online concierge system to identify a group of orders for a shopper to fulfill having the lowest overall cost to fulfill as a group (see col. 5:19-41). A teaching from Morgan-Brown discloses a label applied to each example comprising a distance traveled between the starting location and the destination location (see para. 0103); backpropagating one or more error terms obtained from one or more loss functions to update a set of parameters of the neural network, the backpropagating performed through the neural network and one or more of the error terms based on a difference between the label applied to the example and a predicted distance between the starting location and the destination location (see paras. 0103-0115, equivalent to backpropagating); stopping the backpropagation after the one or more loss functions satisfy one or more criteria (see para. 0114, obvious to stop when neural network fit found); and storing the set of parameters of the layers of the distance prediction model on the computer readable storage medium as parameters of the distance prediction model (see para. 0100, installed equivalent to stored). It would have been obvious to modify Meanor with the teaching of Morgan-Brown before the effective date of filing based on a reasonable expectation of success and motivation to improve electric vehicle routing systems and determines at least one route in the road network from the start location to the destination location via the at least one waypoint and transmits data associated with the at least one determined route to the GUI module. With reference to claim 2, Meanor further teaches wherein the starting location comprises a starting latitude and a starting longitude (see para. 0076, haversine includes latitude and longitude). Referring to claim 3, Meanor further teaches wherein the destination location comprises a destination latitude and a destination longitude (see para. 0076, haversine includes latitude and longitude). Regarding claim 4, Meanor further discloses wherein the origin location comprises an origin latitude and an origin longitude (see para. 0076, haversine includes latitude and longitude). Claims 7 and 15 largely mirror the substantive elements of claim 1. Therefore, claims 7 and 15 are rejected based on the citations and reasoning provided above for claim 1. Claims 10-12 and 18-20 largely mirror the substantive elements of claims 2-4. Therefore, claims 10-12 and 18-20 are rejected based on the citations and reasoning provided above for claims 2-4. Claims 5, 8, 9, 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Meanor, et al. (U.S. Patent Publication No. 2022/0327482), Putrevu, et al. (U.S. Patent No. 11,803,894) and Morgan-Brown (U.S. Patent Publication No. 2019/0316924), as applied to claims 1, 7 and 15, in view of Yao, et al. (U.S. Patent No. 10,078,337). With reference to claim 5, Meanor does not explicitly disclose the claimed limitation. A teaching from Yao discloses wherein the distance traveled between the starting location and the destination location comprises distance information received from a client device that traveled from the starting location to the destination location (see col. 14:7-32). It would have been obvious to modify Meanor with the teaching of Yao before the effective date of filing based on a reasonable expectation of success and motivation to improve estimating trip duration, and in particular to estimating trip duration by using machine learning to combine historical trip data and real-time trip data (see col. 1:15-18). With reference to claim 8, Yao further teaches wherein select the set of combinations of origin locations and corresponding destination locations based on the predicted distances comprises: select combinations of origin locations and corresponding destination locations having less than a threshold position in a ranking based on predicted distances (see col. 7:64 to col. 8:7 and col. 11:52 to col. 12:34). Referring to claim 9, Yao further discloses wherein the non-transitory computer readable storage medium further has instructions encoded thereon that, when executed by the processor, cause the processor to: transmit the selected combinations of origin locations and corresponding destination locations to a third party system generating navigation information (see col. 14:7-32). Claims 13 and 16 are largely mirror the substantive elements of claim 5. Therefore, claims 13 and 16 are rejected based on the citations and reasoning provided above for claim 5. Conclusion Examiner previously stated at the end of the previous rejection that Applicant is considered to have implicit knowledge of the entire disclosure once a reference has been cited. The entire reference must be taken as a whole. This includes any teachings within the reference that have not been explicitly cited. The cited figures, columns and lines should not be considered the only relevant teachings. Taking the references as a whole, the art supports the new rejection of the currently amended claims. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM D TISSOT whose telephone number is (571)270-3439. The examiner can normally be reached 8:00-4:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Angela Ortiz can be reached at (571) 272-1206. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /ADAM D TISSOT/ Primary Examiner, Art Unit 3663
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Prosecution Timeline

Jun 03, 2021
Application Filed
Oct 31, 2024
Non-Final Rejection — §103, §112
Mar 17, 2025
Response Filed
Mar 28, 2025
Final Rejection — §103, §112
Aug 31, 2025
Request for Continued Examination
Sep 10, 2025
Response after Non-Final Action
Nov 20, 2025
Non-Final Rejection — §103, §112
Mar 23, 2026
Response Filed
Apr 03, 2026
Final Rejection — §103, §112 (current)

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Prosecution Projections

4-5
Expected OA Rounds
79%
Grant Probability
99%
With Interview (+21.9%)
3y 1m
Median Time to Grant
High
PTA Risk
Based on 680 resolved cases by this examiner