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 .
Response to Amendment
This Final office action is responsive to Applicant’s amendment filed on
June 5, 2006. By the amendment, claims 1, 5, 10, 12, 16, and 20 were amended. Claims 1-20 are currently pending and under examination.
Response to Arguments
Applicant’s arguments with respect to the 35 U.S.C. §101 rejection filed on June 5, 2026 have been fully considered but they are not persuasive.
Applicant asserts on pages 11-12 of remarks that “the item sequence model is application-specific to the task of determining an optimized picking sequence for a picker. Further, as recited in amended independent claims 1, 12, and 20, the item sequence model receives the current location of the mobile device of the shopper as input. The location data is inherently related to a unique device (e.g., a mobile device of a shopper) that is related to the function of acquiring items at the source location. Similarly, the output of the item sequence model is related to the source location and the mobile device of the shopper. The item sequence model provides, as output, an updated suggested picking sequence to the mobile device that is tailored to a particular trip of a unique shopper at a specific location. Therefore, the claims integrate any abstract idea into a practical application”.
Applicant further argues on page 12 of remarks that “the claims are further eligible under Step 2A Prong 2 because they recite an improved way of training a machine learning model, which is expressly recognized as an improvement in computer functionality under updated MPEP § 2106.05(a), subsection I, example xiii. See Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential). Specifically, the item sequence model is trained using a particular methodology: historical order data is analyzed to derive a pairwise distance between each pair of aisles in the warehouse, and those pairwise distances are used as training inputs for the item sequence model. Like Ex Parte Desjardins, amended independent claims 1, 12, and 20 recite a non-generic application of a machine-learning model (item sequence model) to a dataset. The particular features learned by the item sequence model (e.g., inter-aisle traversal times derived from real fulfillment events), and the recited training steps equip the item sequence model to generate a picking sequence that minimizes shopper travel time from a given current location”.
In response, the Examiner respectfully disagrees. The claimed limitations pertaining to machine learning (item sequence model) amount merely to the very definition of (the training aspect of) the item sequence model. As such, the independent claims do not reflect any improvement in item sequence model (or in another technology/functioning of a computer), and the item sequence model limitations are merely generic computer elements.
In response, the instant independent claims use an artificial intelligence model or “an item sequence model” to generate the suggested picking sequence to minimize an amount of time a shopper would spend picking the list of items from the current location of the shopper, and providing, as input a list of remaining items in the delivery order that have not been picked and the deviated current location of the shopper” as found in independent claims 1 12 and 20, as these functions are not a technological implementation or improvement of a technological field.
Applicant is directed to e.g., McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F .3 d 1299, 1314-1315 (Fed. Cir. 2016) (finding claims not abstract because they "focused on a specific asserted improvement in computer animation").
Applicant is to be reminded that a system, apparatus, machine or method for performing business, however, novel, useful, or commercially successful, is not patentable apart from the means for making the system practically useful or carrying it out. The applicant is making use of generic devices to finally send or transmit the results to a computing device.
Accordingly, the additional elements (such as an item sequence model) do not improve (1) machine learning model (item sequence model), or (2) another technology or technical field. See Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(a)). Rather, the above-noted additional elements merely (1) apply the abstract idea on a computer; (2) include instructions to implement the abstract idea on a computer (computing device or system); or (3) use the computer as a tool to perform the abstract idea. See Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05. Therefore, the recited additional elements do not integrate the abstract idea into a practical application when reading the claims.
None of the steps, functions and/or elements recited in the claims provide, and nowhere in the applicant’s shows any description or explanation as to how the claimed item sequence model is intended to provide: (1) a “solution . . . necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks,” as explained by the Federal Circuit in DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257 (Fed. Cir. 2014); (2) “a specific improvement to the way computers operate,” as explained in Enfish, 822 F.3d at 1336; or (3) an “unconventional technological solution ... to a technological problem” that “improve[s] the performance of the system itself,” as explained in Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1299-1300 (Fed. Cir. 2016).
Accordingly, the applicant’s arguments in this respect are not convincing.
Double Patenting
The non-statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non-statutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and /n re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321 (d) may be used to overcome an actual or provisional rejection based on a non-statutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321 (b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http:/Awww.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based e-Terminal Disclaimer may be filled out completely online using web-screens. An e-Terminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about e-Terminal Disclaimers, refer to
http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-l.jsp.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11/763,229. Although the conflicting claims are not identical, they are not patentably distinct from each other because it is well settled that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before". in re Karlson, 136 USPQ 184 (CCPA 1963).
Claims 1-20 are rejected on the ground of non-statutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12/217,203. Although the conflicting claims are not identical, they are not patentably distinct from each other because it is well settled that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before". in re Karlson, 136 USPQ 184 (CCPA 1963).
The Double Patenting rejection is made FINAL.
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., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Claims 1-11 are drawn to method (i.e., a process), claims 12-19 are drawn to a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, and claim 20 is drawn to a system. As such, claims 1-20 are drawn to one of the statutory categories of invention.
The claim limitations in the abstract idea have been highlighted in non-bold and the “additional elements” in bold below.
Claim 1 as a representative claim recites:
an online concierge system including one or more processors, a delivery order from a device of a customer, the delivery order containing a list of items;
tracking, by the online concierge system, a current location of a client device of a shopper within a warehouse where the order is to be fulfilled;
generating, by the online concierge system including one or more processors, a suggested picking sequence for picking the delivery order in a warehouse, wherein generating the suggested picking sequence comprises applying a trained item sequence model to the delivery order and the current location of the client device of the shopper, wherein the item sequence model is trained to generate the suggested picking sequence to minimize an amount of time a shopper would spend picking the list of items from the current location of the client device of the shopper, and wherein training the item sequence model comprises:
accessing data about a set of historical orders, wherein for each order in the set of historical orders, the data about the order comprises a duration between picking a first item in a first aisle and a second item in a second aisle in the order;
determining a pairwise distance between each pair of aisles in the warehouse based on the data about the set of historical orders; and
training the item sequence model based in part on the pairwise distance between each pair of aisles in the warehouse;
transmitting, the suggested picking sequence to a mobile device of the shopper, wherein the transmitting causes the mobile device of the shopper to display the list of items in the suggested picking sequence;
determining, based on the tracking, that the current location of the mobile device of the shopper deviates from a location corresponding to the suggested picking sequence;
providing, as input to the item sequence model, a list of remaining items in the delivery order that have not been picked and the deviated current location of the mobile device of the shopper;
receiving as output from the item sequence model, an updated suggested picking sequence; and
transmitting the updated suggested picking sequence to the mobile device of the shopper, wherein the transmitting causes the mobile device to display the list of remaining items in the suggested picking sequence.
Claim 2 further recites wherein determining the pairwise distance between each pair of aisles includes determining an average duration between timestamps of sequentially picked items from each pair of aisles in the set of historical orders.
Claim 3 further recites wherein training the item sequence model further includes:
removing outliers from the data about the set of historical orders, wherein the outliers include durations below a predefined threshold and durations above a predefined threshold.
Claim 4 further recites further recites wherein transmitting the suggested picking sequence to the mobile device of the shopper further includes displaying a next suggested item in the picking sequence in a visually prominent manner compared to remaining items in the picking sequence on the mobile device of the shopper.
Claim 5 further recites wherein generating the suggested picking sequence further includes:
constructing a distance graph from the warehouse, the distance graph including nodes representing aisles and edges representing the pairwise distance between the aisles.
Claim 6 further recites wherein constructing the distance graph includes supplementing a missing pairwise distance with an estimated pairwise distance estimated based on aisle adjacency.
Claim 7 further recites wherein the suggested picking sequence is further based on a warehouse floorplan layout stored in a database.
Claim 8 further recites wherein the data about the set of historical orders further includes:
a time of day when the historical order was fulfilled, and the item sequence model is trained to account for variations in picking times based on the time of day.
Claim 9 further recites wherein the suggested picking sequence is displayed on the mobile device of the shopper with an indication of an aisle for each item in the sequence.
Claim 10 further recites wherein tracking the current location of the client device of the shopper includes detecting the location of the client device using one or more of following location detection system:
global positioning system (GPS), Bluetooth beacons, WiFi beacons, and cellular triangulation.
Claim 11 further recites in response to determining that an item in the order is temporarily unavailable, updating the suggested picking sequence; and transmitting the updated suggested picking sequence to the mobile device of the shopper.
Claim 12 recites a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, cause the one or more processors to perform the steps of claim 1 above. See the claimed steps of method claim 1 above.
Claim 13 further recites wherein determining the pairwise distance between each pair of aisles includes determining an average duration between timestamps of sequentially picked items from each pair of aisles in the set of historical orders.
Claim 14 further recites removing outliers from the data about the set of historical orders, wherein the outliers include durations below a predefined threshold and durations above a predefined threshold.
Claim 15 further recites . The non-transitory computer readable storage medium of claim 12, wherein transmitting the suggested picking sequence to the mobile device of the shopper further includes displaying a next suggested item in the picking sequence in a visually prominent manner compared to remaining items in the picking sequence on the mobile device of the shopper.
Claim 16 further recites wherein generating the suggested picking sequence further includes: constructing a distance graph from the warehouse, the distance graph including nodes representing aisles and edges representing the pairwise distance between the aisles.
Claim 17 further includes wherein constructing the distance graph includes supplementing a missing pairwise distance with an estimated pairwise distance estimated based on aisle adjacency.
Claim 18 further recites wherein the suggested picking sequence is further based on a warehouse floorplan layout stored in a database.
Claim 19 further recites wherein the data about the set of historical orders further includes:
a time of day when the historical order was fulfilled, and the item sequence model is trained to account for variations in picking times based on the time of day.
Claim 20 recites a computing system, comprising: one or more processors; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the one or more processors, cause the one or more processors to perform steps of method claim 1. See claim 1 above
The claim limitations in the abstract idea have been highlighted in non-bold and the “additional elements” in bold above.
Step 2A, Prong One:
Regarding claims 1, 12 and 20, other than reciting an online concierge system including one or more processors, a client device and a mobile device (claim 1), non-transitory computer readable storage medium having instructions encoded thereon that, when executed by one or more processors, a client device and a mobile device (claim 12), a computing system, comprising one or more processors, and a non-transitory computer readable storage medium having instructions encoded thereon that when executed by the one or more processors (claim 20), the claim limitations merely cover commercial interactions, including business relations, thus falling within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Applicant is directed to In re Grams, 888 F .2d 835, 837 n.1 (Fed. Cir. 1989) in stating that ("Words used in a claim operating on data to solve a problem can serve the same purpose as a formula."); see also Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (noting that analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are essentially mental processes. Applicant is also directed to the 84 Fed. Reg. at 52 (listing exemplary mental processes including observations, evaluations, and judgments.
The claim limitations of the dependent claims also fall within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas. Thus the dependent claims recite an abstract idea.
Under Step 2A Prong Two, the eligibility analysis evaluates whether the claims as a whole integrates the recited judicial exception into a practical application of the exception. This judicial exception is not integrated into a practical application. The claims include more processors, a client device and a mobile device. The more processors, a client and a mobile device in the steps are recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. As a result, the claims are directed to an abstract idea.
The claims 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 elements of a computing device and memory amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The dependent claims described above do not recite additional limitations that are sufficient to amount to significantly more than the abstract idea. A more detailed abstract idea remains an abstract idea.
Under step 2B of the analysis, the claims include, inter alia, one or more processors, a client and a mobile device.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
There isn't any improvement to another technology or technical field, or the functioning of the computer itself. Moreover, individually, there are not any meaningful limitations beyond generally linking the abstract idea to a particular technological environment, i.e., implementation via a computer system. Further, taken as a combination, the limitations add nothing more than what is present when the limitations are considered individually. There is no indication that the combination provides any effect regarding the functioning of the computer or any improvement to another technology.
In addition, as discussed in Paragraph 0063 of the specification, "an apparatus for performing the operations. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium, which includes any type of tangible media suitable for storing electronic instructions and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable, mobile, or fixed electronic device". As such, this disclosure supports the finding that no more than a general purpose computer, performing generic computer functions, is required by the claims.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank Int'/ et al., No. 13-298 (U.S. June 19, 2014).
As a result of the above analysis, claim 1, as well as claims 12 and 20, do not appear to be patent eligible under 101.
Dependent claims 2-11, and 13-19 recite additional elements that merely narrow the previously recited abstract idea. When viewed as a whole, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)).
NOTE: Currently there are no outstanding prior art rejections under 35 USC § 102 or 35 USC§ 103.
Claims 1-20 would be allowable if overcome the 35 USC § 101 rejection.
Regarding claims 1, 12 and 20, prior art of record fails to teach or suggest:
“determining a pairwise distance between each pair of aisles in the warehouse based on the data about the set of historical orders; and training the item sequence model based in part on the pairwise distance between each pair of aisles in the warehouse; transmitting, by the one or more processors, the suggested picking sequence to a mobile device of the shopper, wherein the transmitting causes the mobile device of the shopper to display the list of items in the suggested picking sequence; responsive to determining, based on the tracking, that the current location of the client device of the shopper deviates from a location corresponding to the suggested picking sequence, applying the trained item sequence model to remaining items in the delivery order that has not been picked and the current location of the client device of the shopper to generate an updated suggested picking sequence”.
Johnson et al. (US Application No. 20190138978) teach a method for grouping a plurality of orders in an order queue of each order including one or more items and each item being associated with a physical location in a warehouse. Determine a physical location in the warehouse of each item in the plurality of orders, establish at least one cluster region, each cluster region including at least one item from the respective order and grouping the plurality of orders based on the physical locations of the cluster regions in the warehouse to form at least one order set.
Asaria et al (US Application 20130317642) teach a warehouse employee walks through a warehouse to pick various products in a combined pick list. By the time they have picked all the products on the combined pick list, they will have the products necessary to fulfil the orders that were used to generate the combined pick list. Asaria further teaches an itinerary generation module that determines a sequence for picking the products on the list based upon a warehouse information.
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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ahmann WO (2018068026 A1) teaches a successful application of an
automation of a picker-to-goods vehicle to a fully automated order fulfillment system where the goods the picker takes from the warehouse shelves in an aisle are automatically transported from that pick location to their ultimate warehouse destination, thus allowing the picker to proceed immediately to the next pick location as opposed to delivering the picked item to a minimum of the end of the aisle as current implementations dictate has, heretofore, not been addressed and that is the foundation of the present invention's solution. This includes the automated retrieval of said goods from the picker's location and the software system to coordinate all the required actions in an optimal sequence.
Jain (US Publication No. 2020/0118061) teaches a method and system for
determining the optimal packing sequence, maximum capacity utilization of the transport units is achieved.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Romain Jeanty whose telephone number is (571) 272-6732. The examiner can normally be reached M-F 9:00AM to 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on 571 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RJ/
/ROMAIN JEANTY/Primary Examiner, Art Unit 3624
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