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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 an abstract idea without significantly more.
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) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-10 are directed to a process, claims 11-19 are directed to a manufacture, and claim 20 is directed to a machine. All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“receiving, at an online system … an order from … a user, the order including one or more items; accessing, by the … online system, a machine-learned model trained on a set of training data, the set of training data describing characteristics of a plurality of candidate locations, and characteristics of a plurality of previously received orders associated with the plurality of candidate locations, wherein the training comprises: identifying items in the plurality of previously received orders and information describing recent inventory records of the plurality of candidate locations; obtaining training examples comprising a plurality of order-location pairs, wherein each of the plurality of order-location pairs corresponds to a specific order in the plurality of received orders, a specific location in the plurality of candidate locations that is associated with the order, applying each of the training examples to the machine-learned model to predict a probability that the corresponding order is incompletely fulfilled at the corresponding location; and updating the machine-learned model based on comparisons between whether orders in the plurality of order-location pairs are completely fulfilled and the respective predictions generated by the machine-learned model; retrieving, by the … online system, information describing one or more candidate locations for fulfilling the order; determining, by … the online system, a probability of each of the one or more candidate locations incompletely fulfilling the received order, the probability of each of the one or more candidate locations incompletely fulfilling the received order determined by applying the trained machine-learned model to characteristics of the corresponding candidate location, characteristics of the received order, and information describing recent inventory records of the corresponding candidate location; selecting, by … the online system, a location of the candidate locations having a minimum probability of incompletely fulfilling the received order; transmitting, by the … online system, an instruction to fulfill the received order at the selected location to … one or more shoppers; receiving, by … the online system, from … the one or more shoppers data identifying the selected candidate location and the received order; identifying whether the received order is completely fulfilled; and updating the machine-learned model based on a comparison between whether the order is completely fulfilled and the respective prediction generated by the machine-learned model.” Claim 1.
With recited additional elements reserved for consideration under step 2A prong two, a careful analysis of the remaining limitations above, each on its own and all together combined, results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract idea falls within the grouping of abstract ideas described as certain methods of organizing human activity, for example commercial interactions (including marketing or sales activities or behaviors; business relations). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in the present claims are one or more processors, a client device of a user, and client devices of one or more shoppers. These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the client device of the user serves only as the identified source of an order (data) and performs no affirmatively claimed act; the client device a shopper serves only as a node standing in for the shopper, describing the source and target of transmitted data but also performing no affirmatively claimed action; and the processor applies a machine learned model to gathered data to produce a result, a probability. Because the processor merely applies a generic machine learning model distinguishable, if at all, only by the data used to train it and to which it is applied, the processor can only be understood as a generic processor processing data for the purposes of step 2A prong 2 analysis. The processor is otherwise broadly recited as performing other remaining steps in terms of the intended results of functionally nonspecific recitations.
The additional elements therefore do not integrate the judicial exception into a practical application because they amount to no more than instructions to apply the exception using generic computer components. The claim is otherwise entirely a recitation of abstract ideas. Additional elements do not improve the functioning of any computer or other technology or technical field, do not apply the judicial exception with or by use of a particular machine, do not transform or reduce a particular article to a different state or thing, and fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Claimed elements that are not abstract are identified broadly as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities, without reference to any particular acts or functions performed by any particularly identified machine, and without reference to its use in conjunction with any particular item of manufacture.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the claim are mere props supporting instructions to implement the abstract idea on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. No technical problem is indicated and the claims are not directed to a technical solution to such a problem. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that remaining dependent claims 2-10 and 12-19 do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe data and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-20 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Potentially Allowable Subject Matter
Claim 1-20 would be allowable if rewritten or amended to overcome the rejection under 35 U.S.C. 101 set forth in this Office action. The following is a statement of reasons for the indication of potentially allowable subject matter:
Independent claim 1 recites a method comprising inter alia:
“accessing, by the one or more processors of the online system, a machine-learned model trained on a set of training data, the set of training data describing characteristics of a plurality of candidate locations, and characteristics of a plurality of previously received orders associated with the plurality of candidate locations, wherein the training comprises: identifying items in the plurality of previously received orders and information describing recent inventory records of the plurality of candidate locations; obtaining training examples comprising a plurality of order-location pairs, wherein each of the plurality of order-location pairs corresponds to a specific order in the plurality of received orders, a specific location in the plurality of candidate locations that is associated with the order, applying each of the training examples to the machine-learned model to predict a probability that the corresponding order is incompletely fulfilled at the corresponding location; and updating the machine-learned model based on comparisons between whether orders in the plurality of order-location pairs are completely fulfilled and the respective predictions generated by the machine-learned model; … determining, by the one or more processors of the online system, a probability of each of the one or more candidate locations incompletely fulfilling the received order, the probability of each of the one or more candidate locations incompletely fulfilling the received order determined by applying the trained machine-learned model to characteristics of the corresponding candidate location, characteristics of the received order, and information describing recent inventory records of the corresponding candidate location; selecting, by the one or more processors of the online system, a location of the candidate locations having a minimum probability of incompletely fulfilling the received order; … receiving, by the one or more processors of the online system, from the client devices of the one or more shoppers data identifying the selected candidate location and the received order; identifying whether the received order is completely fulfilled; and updating the machine-learned model based on a comparison between whether the order is completely fulfilled and the respective prediction generated by the machine-learned model.”
Independent claims 11 and 20 recite equivalent limitations presented respectively in manufacture and machine claims.
The most relevant reference is Rao et al. (Patent No.: US 11,544,810 B2, prior Pub. No. US 2019/0236740 A1). Rao teaches predicting a probability that one of a plurality of items in a delivery order is available at a warehouse and generates an instruction to a picker based on the probability, but Rao has been disqualified as prior art based on the declarations of inventor Sharath Rao Karikurve and inventor Shishir Kumar Prasad under 37 CFR 1.130, filed August 11, 2023, in parent application 16/816,226, now U.S. Patent No. 12131358. As noted in applicant's Remarks filed August 11, 2023, in addition to the claimed subject matter having been disclosed directly by or obtained from inventors Sharath Rao Karikurve and Shishir Kumar Prasad, according to their declarations, applicant further stated that the parent application, now patent, was commonly owned with the prior art reference at the time of filing. The present application relies upon parent application 16/816,226, now U.S. Patent No. 12131358, for priority and therefore shares the same effective filing date and common ownership as of that date. The declarations of the inventors alone were and are in any case sufficient to disqualify the reference as prior art.
The most closely applicable prior art references are Agarwal et al. (Patent No. US 10,242,336 B1 ), Field-Darragh et al. (Pub. No. US 2016/0042315 A 1 ), and Kutzelnigg (nonpatent literature citation provided as item V on the attached form PTO-892).
Agarwal teaches determining an inventory level for an ordered item at a plurality of merchants and selecting a particular merchant within a geographic range of the customer based on the inventory level, but does not teach, suggest, anticipate, disclose, nor otherwise fairly and reasonably render obvious the limitations presently claimed in combination as recited above, and Field-Darragh teaches order intake, fulfillment, and inventory management, and the acquisition and processing of data related to the location and movement of merchandise within a store to assist customers or store employees to locate an item for purposes of fulfilling an order. Field-Darragh does not however teach, suggest, anticipate, disclose, nor otherwise fairly and reasonably render obvious the limitations presently claimed in combination as recited above in the steps presently claimed. Kutzelnigg discloses allocation of goods in a warehouse to minimize order picking expenses, but does not disclose, anticipate or fairly and reasonably render
obvious any of the above recited limitations.
Additional relevant prior art includes Chandra Sekar Rao (Patent No.: US 11,348,161 B2), MCCORRY et al. (Pub. No.: US 2017/0178221 A1), Oldridge et al. (Patent No.: US 10,387,795 B1), Phillips et al. (Patent No.: US 10,163,070 B1), Ripert et al. (Patent No.: US 10,818,186 B2), Seeger et al. (Patent No.: US 10,748,072 B1), Xiong et al. (Patent No.: US 10,438,164 B2), ZHUANG et al. (CA 3121006 C), and Glaeser (nonpatent literature citation provided as item U on the attached form PTO-892).
Chandra Sekar Rao teaches predicting the likelihood of an order being held in abeyance or suspended to avoid submitting an order that will not be promptly filled; MCCORRY teaches predicting the availability of an item in inventory but does not use machine learning, or any other technique recited in the above claim limitations; Oldridge teaches training a machine learning model, including automatically obtaining or rejecting updated training sets and models and implementing the resulting model, common machine learning elements, but does so to determine whether a user will upgrade service levels based at least in part on the attribute values logically associated with the respective user, which is not reasonably comparable to the present claimed subject matter. Phillips teaches a device determining a geographic location for delivery of a product and identifying locations capable of providing the product located near the geographic location with the device then identifying a courier to fulfill the order and a particular product location, but uses machine learning only to determine fulfillment time and scheduling using entirely different techniques and objectives than those claimed above. Ripert teaches optimizing selection of shopping agents for filling orders; Seeger teaches the use of machine learning model for statistical modeling and forecasting of demand for large inventories; Xiong teaches sensor data associated with an event being processed to determine results associated with the event; ZHUANG teaches determining probability of finding an item in a location within a warehouse in order to prompt users to provide information to other users to help locate the item, and Glaeser teaches using machine learning to optimize pick-up location configuration and schedule, and teaches how a retailer can optimize the mix of delivery zones and fulfillment models using data-driven analytics. None of these references, alone or in combination with each other or any other known references, disclose, anticipate or fairly and reasonably render obvious any of the presently claimed limitations recited above.
In light of the above and examiner’s overall review of the prior art it is examiner’s conclusion that the body of prior art currently known to the examiner does not alone or in combination disclose, anticipate, or otherwise fairly and reasonably render obvious the above noted features of the present method. It should be noted that this conclusion is based on the presence of all claimed features as they operate in conjunction rather than solely on any one feature or isolated group of features.
The most relevant applicable and nonduplicative prior art having thus been introduced, addressed, and distinguished, it is examiner’s position that the record is clear with regard to the reasons for indication of allowability of the claimed invention over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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, Marissa Thein can be reached at 571.272.6764. 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 L LEVINE/Primary Examiner, Art Unit 3689 February 21, 2026