DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
[2] This communication is in response to the amendment filed 11 November 2025. Claims 3, 6, 13, and 16 have been cancelled. Claims 1, 2, 4, 9, 11-12, 14, 19, and 20 have been amended. Claims 1-2, 4-5, 7-12, 14-15, and 17-20 are pending.
Response to Remarks/Amendment
[3] Applicant's remarks filed 11 November 2025 have been fully considered but they are not persuasive. The remarks will be addressed below in the order in which they appear in the noted response.
[i] In response to rejection(s) of claim(s) 1-20 (now claims 1-2, 4-5, 7-12, 14-15, and 17-20 as presented by amendment) under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 1 October 2025, Applicant provides the following remarks:
"…claim 1 addressed this technical deficiency by implementing dynamic, programmatic interfacing between multiple machine learning models…a first model is trained to infer measures of user preference…a second model is configured to predict levels of expertise of pickers in collecting a specified item. The claims system dynamically links these models such that the output of the first model, which is a predicted preference measure, is fed as contextual input to the second model to computer a joint inference identifying which picker is best suited to find the item…This dynamic model-to-model coordination produces a specific improvement to computer functionality and to the technical field of machine-learning…the result is an automated, data-driven decision process that makes predictions based on disparate data sources, which conventional, non-interfaces models are unable to do… "
In response, Examiner respectfully disagrees. With respect to considerations under Eligibility Step 2A prong 2 and Eligibility Step 2B: (See MPEP 2106.04(d)):
Representative claim 1 as presented by amendment recites additional elements of: “processor”, “online concierge system”, “user client device”, “first machine-learning model”, “second machine-learning model”, and “picker client device”. Claim 1 further indicates, generally, that the claimed method is “performed by “a processor and a computer-readable medium” as designated in the preamble. Claims 11 and 20, directed to a computer program product and system introduce a “processor” and processor-executable “instructions”. With respect to these potential additional elements:
(1) The “online concierge system”, “processor”, “computer-readable medium”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “user client device” is identified as sending an order.
(3) The “picker client device” is identified as receiving prompts and instructions to perform picking tasks.
(4) The “first machine-learning model” is identified as being “…trained by: receiving user data for a plurality of users of the online concierge system, wherein the user data includes historical orders by users, historical instances of items added to user's orders, and implicit information associated with users, the implicit information including user data associated with other users having at least a threshold measure of similarity to the user, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with an item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users…” and applied “…to predict a measure of preference of the user associated with each item category associated with the set of items based at least in part on the set of user data for the user….”
(5) The “second machine-learning model” is identified as being applied to “…the set of picker data and the predicted measure of preference of the user associated with the identified item to predict a level of expertise of the assigned picker in association with collecting the identified item:…” and further applied to “…the picker data of the sets of pickers and the predicted measure of preference of the user associated with the identified item to predict a level of expertise of each of the pickers in the set in association with collecting the identified item identifying an expert picker from the set of pickers, wherein the expert picker is associated with a highest level of expertise predicted for the pickers in the set…”.
With respect to the elements added by amendment and in particular to the differentiated actions and training of the first and second models, Examiner agrees that the contextual input of the predicted measure of preference of the user applied in the second model to select a picker having requisite expertise is derived from the output of the first model. However, Examiner as presently constructed, the claim is limited to providing a general identification of a “preference of the user with respect to the identified item” as an input to the second model. As presented, the claim fails to provide a functional or programmatic tie between the two models to provide a basis for the dynamic and programmatic model-to-model interface described in commentary by Applicant.
In present form, Examiner respectfully submits that the introduction of the second model operating using a preference for an item as an input is limited to the additional of a second, independent machine-learning model which is limited to a statement of inputs and desired predictive outputs of the model absent any description of functional or programmatic elements which differentiate the models from generic machine-learning processes applied to a specified set of inputs and recitation of a desired functionality or outputs. The instant recitations of “apply a model” and “training a model” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training. Accordingly, the recited machine-learning processes and associated training are reasonably understood to constitute generic machine-learning processes applied to a specified set of inputs and recitation of a desired functionality or outputs.
Examiner respectfully maintains that the claim limitations are limited to statements of intended results (e.g., orders and prompts are sent and received, data is retrieved and analyzed, predictions are made, pickers are identified and selected etc.) as associated with a respective “processor” or “model”. Beyond the general statement that the technical elements are engaged in the recite functions, generally, the limitations provide no further clarification with respect to the functions performed by the “processor” and “model” in producing the claimed result. A recitation of “by a processor” or “applying a model”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., orders and prompts/pick instructions); (2) storing and retrieving information and data from a generic computer memory (e.g., data); (3) displaying data on a generic computer display (e.g., instructions); and (4) mental observations using the obtaining information/data (e.g., identifying measures of preference and identifying pickers) (See MPEP 2106.05(f)).
NOTE: For Applicant’s benefit, Examiner respectfully notes paragraph [0065] which indicates that the expertise module may determine a threshold predicted level of expertise such that the threshold predicted level of expertise is proportional to the predicted measure of preference of the user. Amendments to clarify that the expertise threshold is a programmatic input to the second model that is dynamic and adjusted by the system/expertise model proportionally to the output preference measure output from the first model could assist in overcoming the maintained rejection under 35 U.S.C. 101. Applicant is encouraged to contact Examiner to discuss amendments to this effect and potentially expedite prosecution of the instant application.
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.
[4] Previous rejection(s) of claims 1-20 (now claims 1-2, 4-5, 7-12, 14-15, and 17-20 as presented by amendment) under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more has/have not been overcome by the amendments to the subject claims and is/are maintained. The statement of rejection below is reiterated as originally presented in the previous Office Action mailed 1 October 2025. The present amendments and remarks are addressed above under “Response to Remarks/Amendment”.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-2, 4-5, 7-12, 14-15, and 17-20 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 11, and 20 are directed to a method, a non-transitory computer-readable storage medium, and a system, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 11, and 20 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 1, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item, which is reasonably considered to be method of Organizing Human Activity. In particular, the general subject matter to which the claims are directed evaluates an order and the associated items, estimates items which are highly preferred by the user/customer, and assigning picking tasks to pickers based on an estimated expertise the picker has with respect to the preferred items, which is an ineligible concept of Organizing Human Activity, namely: commercial interactions (e.g., marketing or sales activities or behaviors, and business relations); (3) managing interactions between people (e.g., following instructions).
In support of Examiner’s conclusion, Examiner respectfully directs Applicant’s attention to the claim limitations of representative claim 1. In particular, claim 1 includes:
“…receiving, from a user…an order comprising a set of items;…assigning a picker associated with the online concierge system to collect the set of items included in the order;…retrieving a set of picker data for the assigned picker;… retrieving picker data for a set of pickers associated with the online concierge system… and responsive to determining that the predicted level of expertise of the assigned picker associated with collecting the identified item is less than the threshold predicted level of expertise:… sending, to a picker…a prompt to assist the assigned picker with collecting the identified item…”
Considered as an ordered combination, the steps/functions of claim 1 are reasonably considered to be representative of the inventive concept and are further reasonably understood to be series of actions or activities directed to a general process of predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item, which is an ineligible concept of Organizing Human Activity (See MPEP 2106.04(a)(2)).
Further limitations are directed to ineligible processes/functions which are performable by Human Mental Processing and/or or by a human using pen and paper (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011).
The courts have previously identified subject matter limited to steps/processes performable by Human Mental Processing and/or by a human using pen and paper to be ineligible abstract ideas (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). Lastly, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2)).
With respect to functions/steps limited to processes performable by Human Mental Processing and/or by a human using pen and paper, representative claim 1 recites:
“…identifying an item of the set of items associated with at least a threshold predicted measure of preference…predicting a level of expertise of the assigned picker associated with collecting the identified item based at least in part on the set of picker data for the assigned picker… identifying an expert picker from the set of pickers based at least in part on a set of picker data for the expert picker …”
Respectfully, absent further clarification of the processing steps executed by the recited processor, system, and machine-learning model, one of ordinary skill given known attributes of pickers and a predicted measure of preference for items, would be capable of identifying pickers best suited to fill the order by employing by the human mental processing (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“a method that can be performed by human thought alone is merely an abstract idea and is not patent eligible under 35 U.S.C 101).
The technical elements identified in claim 1 and the recited functions constitute technical features which have been considered at each step of Examiner’s analysis but are determined to constitute generic computing structures executing generic computing functions previously identified by the courts, as further analyzed under Step 2A prong 2 and Step 2B below.
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Additional elements of claim 1 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject matter include: “processor”, “online concierge system”, “user client device”, “machine-learning model” and “picker client device”. Claim 1 further indicates, generally, that the claimed method is “performed by “a processor and a computer-readable medium” as designated in the preamble. Claims 11 and 20, directed to a computer program product and system introduce a “processor” and processor-executable “instructions”.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to the identification of the “machine-learning model” as being “…trained by: receiving user data for a plurality of users of the online concierge system, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with an item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users…” and applied “…to predict a measure of preference of the user associated with each item category associated with the set of items based at least in part on the set of user data for the user….”, Examiner notes that the claimed training steps occur external to the claimed method steps and are not reasonably part of the claimed invention as presented.
Examiner further notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register on 17 July 2024. In particular, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “apply a model” and “training a model” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training. Accordingly, the recited machine-learning processes and associated training are reasonably understood to constitute generic machine-learning processes applied to a specified set of inputs and recitation of a desired functionality or outputs.
Each of the above noted limitations states a result (e.g., orders and prompts are sent and received, data is retrieved and analyzed, predictions are made, pickers are identified and selected etc.) as associated with a respective “processor” or “model”. Beyond the general statement that the technical elements are engaged in the recite functions, generally, the limitations provide no further clarification with respect to the functions performed by the “processor” and “model” in producing the claimed result. A recitation of “by a processor” or “applying a model”, absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., orders and prompts/pick instructions); (2) storing and retrieving information and data from a generic computer memory (e.g., data); (3) displaying data on a generic computer display (e.g., instructions); and (4) mental observations using the obtaining information/data (e.g., identifying measures of preference and identifying pickers) (See MPEP 2106.05(f)).
Accordingly, claim 1 is reasonably understood to be conducting standard, and formally manually performed process of predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment. The claimed predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item benefits from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0024]-[0025]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
The claims specify that the above identified generic computing structures and associated functions/routines include:
(1) The “online concierge system”, “processor”, “computer-readable medium”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “user client device” is identified as sending an order.
(3) The “picker client device” is identified as receiving prompts and instructions to perform picking tasks.
(4) The “machine-learning model” is identified as being “…trained by: receiving user data for a plurality of users of the online concierge system, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with an item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users…” and applied “…to predict a measure of preference of the user associated with each item category associated with the set of items based at least in part on the set of user data for the user….”
While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., orders and prompts/pick instructions); (2) storing and retrieving information and data from a generic computer memory (e.g., data); (3) displaying data on a generic computer display (e.g., instructions); and (4) mental observations using the obtaining information/data (e.g., identifying measures of preference and identifying pickers).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of predicting a measure of preference a user has for items in an order and assigning the order to a picker based on a level of expertise of the picker associated with the preferred item benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information over a network buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 11 and 20, directed to an apparatus/system and computer-executable instructions stored on computer-readable media for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2, 4-5, 7-10, 12, 14-15, and 17-19, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
In accordance with all relevant considerations and aligned with previous findings of the courts, the technical elements imparted on the method that would potentially provide a basis for meeting a “significantly more” threshold for establishing patent eligibility for an otherwise abstract concept by the use of computer technology fail to amount to significantly more than the abstract idea itself. For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Allowable Subject Matter
[5] Claims 1-2, 4-5, 7-12, 14-15, and 17-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
Subject Matter Overcoming Art of Record
[6] The most closely applicable prior art of record was previously presented as ‘cited not applied’ to Grabovski et al. (United States Patent Application Publication No. 2014/0136255). Grabovski et al. provides system and method which selects and assigns order fulfillment tasks to workers including pickers in an online order fulfillment environment. The system and method analyze online purchase orders, determine tasks required to fulfill the order, and assigns tasks to workers including pickers to execute the fulfillment tasks. The inventive method and system further determine a complexity of the fulfillment tasks and selects pickers based on picker attributes including skill levels, certifications, and experience.
While Grabovski et al. is similar to the instant application in many respects, there are clear patentable distinctions. While Grabovski discloses assigning picking tasks to pickers on the basis of skill levels, certifications, and experience, Grabovski et al. fail to assess picker expertise with respect to a particular item/order category. By extension, the analysis of picker skills and experience fails to match pickers to a predicted measure of preference the customer may have to particular items within an order. The analysis of Grabovski does not include utilizing machine-learning to predict either a measure of a preference a customer has for items and predicting a level of expertise a picker has with respect to collecting the preferred item.
Accordingly, Grabovski et al. fail to disclose or otherwise render obvious at least:
“...receiving…an order comprising a set of items…accessing a machine-learning model trained to predict a measure of preference of the user associated with an item category, wherein the machine-learning model is trained by: receiving user data for a plurality of users of the online concierge system, receiving, for each user of the plurality of users, a label describing the measure of preference of a corresponding user associated with an item category, and training the machine-learning model based at least in part on the user data and the label for each user of the plurality of users; applying the machine-learning model to predict a measure of preference of the user associated with each item category associated with the set of items based at least in part on the set of user data for the user; identifying an item of the set of items associated with at least a threshold predicted measure of preference; assigning a picker associated with the online concierge system to collect the set of items included in the order; retrieving a set of picker data for the assigned picker; predicting a level of expertise of the assigned picker associated with collecting the identified item based at least in part on the set of picker data for the assigned picker; determining whether the predicted level of expertise of the assigned picker associated with collecting the identified item is less than a threshold predicted level of expertise; and responsive to determining that the predicted level of expertise of the assigned picker associated with collecting the identified item is less than the threshold predicted level of expertise: retrieving picker data for a set of pickers associated with the online concierge system, identifying an expert picker from the set of pickers based at least in part on a set of picker data for the expert picker, and sending, to a picker client device associated with the expert picker, a prompt to assist the assigned picker with collecting the identified item...”, as required by claims 1, 11, and 20.
Conclusion
[7] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Glaser et al., DISTRIBUTED DEVICE USAGE FOR PLANOGRAM GENERATION, United States Patent Application Publication No. 2023/0237431, paragraphs [0027]-[0033]: Relevant Teachings: Glaser discloses a system/method that includes steps/functions to direct and monitor actions of a picker and guiding a picker to retrieve items from a customer order.
Rajkhowa et al., SYSTEMS AND METHODS FOR RUSH ORDER FULFILLMENT OPTIMIZATION, United States Patent Application Publication No. 2022/0253792, paragraphs [0036]-[0040]: Relevant Teachings: Rajkhowa discloses a system/method that includes steps/functions to apply a predictive model to picker operations including computing an estimated picking time associated with an order based on picker attributes/availability.
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 ROBERT D RINES whose telephone number is (571)272-5585. The examiner can normally be reached M-F 9am - 5pm.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625