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 10 June 2026. Claims 2, 9, and 16 have been cancelled. Claims 13, 3, 8, 19, 15, and 17 have been amended. Claims 1, 3-8, 10-15, and 17-20 are pending.
Response to Remarks/Amendment
[3] Applicant's remarks filed 10 June 2026 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, 3-8, 10-15, 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 11 March 2026, Applicant provides the following remarks:
"…the Aug. Memorandum notes that ‘[a]n important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome," and further "cautioned not to oversimplify claim limitations’… "
Applicant further remarks:
“…claim 1, at least as amended, does not recite the abstract idea of a mental process. Specifically, the human mind cannot perform ‘determining, by a trained machine learning model, a predicted fulfillment time for the user request, wherein the predicted fulfillment time is based on at least the fulfillment order for the user request and ongoing fulfillment orders for the user request", perform predictions with a "trained machine learning model is trained based on historical data for a set of pickup locations over a period of time and has parameters that are adapted through active learning’…”
With respect to Examiner’s maintained conclusion that the claimed invention is directed to ineligible processes performable by Human Mental Processing, representative claim 8 retains limitations including:
“…receiving a user request including: i) element data, ii) a fulfillment location, and iii) a provisional fulfillment time; requesting…fulfillment data for the fulfillment location, the fulfillment data including at least a fulfillment order for the user request and ongoing fulfillment orders for the user request; in response to receiving the fulfillment data, determining…a predicted fulfillment time for the user request, wherein the predicted fulfillment time is based on at least the fulfillment order for the user request and ongoing fulfillment orders for the user request and defines a time for acquiring elements in the element data; determining whether a difference between the provisional fulfillment time and the predicted fulfillment time is within a fulfillment time threshold…”
As presented by amendment, claim 8 further includes/specifies:
[i] The previously recited “fulfillment predictor” is “…a trained machine learning model… wherein the trained machine learning model is trained based on historical data for a set of fulfillment locations over a period of time and has parameters that are adapted through active learning…”.
[ii] The previously recited “user request” is received “…from a user computing device…”. As amended, claim 8 further specifies the recited user computing device is “…for displaying, on the user computing device, a user interface that includes one or more options for adjusting the provisional fulfillment time…”.
Examiner respectfully submits that the steps/function added by amendment provide further clarity with respect to the technical elements engaged in the recited processes/functions. While the amendments clarify further aspects of the recited technical elements, Examiner respectfully maintains that the additional elements constitute generic technical processes/elements as further addresses below. Respectfully, absent further clarification of the processing steps executed by the recited model and/or computing device, one of ordinary skill in the art would be reasonably relied upon to estimate a fulfillment time for an order consisting of a specified set of items and further compare the determined time to a requested delivery time to make a determination as to whether the order could be fulfilled within the requested time using pen and paper and/or 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) or by utilizing a generic computing system as a tool to assist in the performance of the noted calculations and/or observations, determinations, or judgements (see at least MPEP 2106.05(f)).
Applicant remarks:
"…under Step 2A Prong Two at least because the claim as a whole integrates the alleged judicial exception of a mental process into a practical application. For example, Applicant's specification demonstrates to one of ordinary skill in the art that the claimed subject matter provides improvements in the technical field of managing and updating orders and predicting fulfillment times by a scalable solution that dynamically generates predicted fulfillment times in real-time thereby improving fulfillment time accuracy, allowing for faster fulfillment time determinations, and reducing overall computational needs… "
Applicant additionally remarks:
"…at least as amended, the claims recite significantly more than any mental process. Indeed,
the claims recite ‘specific limitation[s] other than what is well-understood, routine,
conventional activity in the field’ and ‘add unconventional steps that confine the claim
to a particular useful application’ as the prior art fails to teach or suggest the claimed subject matter, and the claimed subject matter allows for various technical advantages such as those noted above…the evidence indicates that the claims recite significantly more than any alleged abstract idea and, as shown above, the claims provide a practical application using unconventional methods that solve various technical problems. As such, Applicant respectfully submits that the claims, at least in amended form, are directed to statutory subject matter when analyzed in light of the preponderance of the evidence…"
In response, Examiner respectfully disagrees. With respect to considerations under Eligibility Step 2A prong 2 and Step 2B: (See MPEP 2106.04(d)):
As presented by amendment, additional technical elements of claim 8 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject are limited to: “trained machine learning model”, “user computing device”. Claim 8 further indicates, generally, that the claimed method is “computer-implemented” as designated in the preamble. Claims 1 and 15, directed to a system and computer-readable medium storing executable instructions introduce a “processor” and processor-executable “instructions” as engaged in a general manner in the performance of each of the recited steps/functions. With respect to these potential additional elements:
(1) The “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “user computing device” is identified as receiving transmitted fulfillment times and displaying/presenting interface elements to adjust fulfillment times, acknowledge an adjusted fulfillment time and request assistance. The user computing device is further identified as being “…for displaying, on the user computing device, a user interface that includes one or more options for adjusting the provisional fulfillment time…”.
(3) The “trained machine learning model” is identified in a general unspecified manner as engaged in the determination of fulfillments times, generation of predicted fulfillment times, and comparisons of times with respect to a threshold. As amended, the trained machine learning model “…is trained based on historical data for a set of fulfillment locations over a period of time and has parameters that are adapted through active learning…”.
Respectfully, the additional technical elements including the clarifying recitations and elements presented by amendment are limited to generic function of transmitting, receiving, and displaying information (user computing device) and generic training of a machine learning model where the limitations are restricted to inputs and outputs of an generic model (See MPEP 2106.05(f)). While Applicant contends that the recited technical elements constitute/perform functions that cannot be performed in the human mind, Examiner maintains that the present construction of the claims merely requires use of generic computing functions to perform the recited functions. 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 the identification of the determinations made by and training of the “trained machine learning model”, Examiner 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 “by the trained machine learning model” and “training the machine learning 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 and/or particular machine learning models. Accordingly, the recited machine-learning processes and associated training are reasonably understood to be generic machine learning processes.
Each of the above noted limitations states a result (e.g., users requests are received, fulfillment times are predicted based on the request and fulfillment data, and fulfilment times are compared in relation to a threshold, etc.) as associated with a respective “predictor” or “processor/instructions”. Beyond the general statement that the predictor and processor/instructions are engaged in the recited steps/functions, the limitations provide no further clarification with respect to the functions performed by the “predictor” and “processor/instructions” in producing the claimed result. A recitation of “by a predictor” or “by a processor”, 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., fulfillment data and fulfillment times); (2) storing and retrieving information and data from a generic computer memory (e.g., fulfillment data); (3) displaying data on a generic computer display (e.g., adjustment options and elements to confirm or adjust information); and (4) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., determining and comparing fulfillment times) (See MPEP 2106.05(f)).
In further 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.
Accordingly, claim 8 is reasonably understood to be conducting standard, and formally manually performed process of predicting fulfillment times for orders based on historical order fulfillment data and item data 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 and fail to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception and/or present significantly more than the recited abstract idea.
[ii] Applicant’s remarks directed to previous rejection(s) of claim(s) 1, 3-8, 10-15, and 17-20 under 35 U.S.C. 103 as being unpatentable as set forth in the previous Office Action mailed 11 March 2026 have been fully considered and are moot in light of newly added grounds of rejection responsive to the amendments to the subject claims. See revised rejection under 35 U.S.C. 103 presented below.
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.
[4] Claims 5-7, 12-14, and 19 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.
Claims 5-7, 12-14, and 19 each reference/recite "fulfillment predictor”. The term “fulfillment predictor” has been removed by the preceding lineage of these claims by amendment. Accordingly, there is insufficient antecedent basis for limitations reciting “the fulfillment predictor” in each of claims 5-7, 12-14, and 19.
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.
[5] Previous rejection(s) of claims 1-20 (now claims 1, 3-8, 10-15, and 17-20 as presented by amendment) is/are rejected 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 11 March 2026. 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, 3-8, 10-15, 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, 8, and 15 are directed to a system, a method, and non-transitory computer-readable storage medium, 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, 8, and 15 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 8, 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 fulfillment times for orders based on historical order fulfillment data and item data, which is reasonably considered to be method consisting of 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 8 recites:
“…receiving a user request including: i) element data, ii) a fulfillment location, and iii) a provisional fulfillment time; requesting…fulfillment data for the fulfillment location, the fulfillment data including at least a fulfillment order for the user request and ongoing fulfillment orders for the user request; in response to receiving the fulfillment data, determining…a predicted fulfillment time for the user request, wherein the predicted fulfillment time is based on at least the fulfillment order for the user request and ongoing fulfillment orders for the user request and defines a time for acquiring elements in the element data; determining whether a difference between the provisional fulfillment time and the predicted fulfillment time is within a fulfillment time threshold…”
Respectfully, absent further clarification of the processing steps executed by the recited “fulfillment predictor” (claims 1, 8, and 15) and/or “processor/instructions” (claims 1 and 15), one of ordinary skill, given requisite items inventory, availability, and location data, would readily be relied upon to determine or estimate a fulfillment time for an order consisting of a specified set of items and further compare the determined time to a requested delivery time to make a determination as to whether the order could be fulfilled within the requested time using pen and paper and/or 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).
Claims 8 recites technical elements 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 technical elements of claim 8 that potentially integrate the claimed ineligible subject matter into a practical application of the claimed subject are limited to: “fulfillment predictor”, “computing device”. Claim 8 further indicates, generally, that the claimed method is “computer-implemented” as designated in the preamble. Claims 1 and 15, directed to a system and computer-readable medium storing executable instructions introduce a “processor” and processor-executable “instructions” as engaged in a general manner in the performance of each of the recited steps/functions. With respect to these potential additional elements:
(1) The “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “computing device” is identified as receiving transmitted fulfillment times and displaying/presenting interface elements to adjust fulfillment times, acknowledge an adjusted fulfillment time and request assistance.
(3) The “fulfillment predictor”, alternatively referred to as a “pickup predictor” in claim 15, is identified in a general unspecified manner as engaged in the determination of fulfillments times, generation of predicted fulfillment times, and comparisons of times with respect to a threshold.
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); Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g); 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 determinations made by and training of the fulfillment/pickup predictor, Examiner 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 “by the predictor” and “training the predictor” (claims 12-13 an analogous system and CRM claims) 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 and/or particular machine learning models. Accordingly, the recited machine-learning processes and associated training are reasonably understood to be generic machine learning processes.
Each of the above noted limitations states a result (e.g., users requests are received, fulfillment times are predicted based on the request and fulfillment data, and fulfilment times are compared in relation to a threshold, etc.) as associated with a respective “predictor” or “processor/instructions”. Beyond the general statement that the predictor and processor/instructions are engaged in the recited steps/functions, the limitations provide no further clarification with respect to the functions performed by the “predictor” and “processor/instructions” in producing the claimed result. A recitation of “by a predictor” or “by a processor”, 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., fulfillment data and fulfillment times); (2) storing and retrieving information and data from a generic computer memory (e.g., fulfillment data); (3) displaying data on a generic computer display (e.g., adjustment options and elements to confirm or adjust information); and (4) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., determining and comparing fulfillment times) (See MPEP 2106.05(f)).
Accordingly, claim 8 is reasonably understood to be conducting standard, and formally manually performed process of predicting fulfillment times for orders based on historical order fulfillment data and item data 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 fulfillment times for orders based on historical order fulfillment data and item data 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 [0066]-[0077]. 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 “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “computing device” is identified as receiving transmitted fulfillment times and displaying/presenting interface elements to adjust fulfillment times, acknowledge an adjusted fulfillment time and request assistance.
(3) The “fulfillment predictor”, alternatively referred to as a “pickup predictor” in claim 15, is identified in a general unspecified manner as engaged in the determination of fulfillments times, generation of predicted fulfillment times, and comparisons of times with respect to a threshold.
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., fulfillment data and fulfillment times); (2) storing and retrieving information and data from a generic computer memory (e.g., fulfillment data); (3) displaying data on a generic computer display (e.g., adjustment options and elements to confirm or adjust information); and (4) performing repetitive calculations and/or mental observations using the obtaining information/data (e.g., determining and comparing fulfillment times).
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 fulfillment times for orders based on historical order fulfillment data and item data. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., predicting fulfillment times for orders based on historical order fulfillment data and item data, 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 fulfillment times for orders based on historical order fulfillment data and item data 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 1 and 15, 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 3-7, 10-14, and 117-20, 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.
For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Claim Rejections - 35 USC § 103
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.
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.
[6] Claim(s) 1-20 (now claims 1, 3-8, 10-15, and 17-20 as presented by amendment) is/are rejected under 35 U.S.C. 103 as being unpatentable over Faturechi et al. (United States Patent Application Publication No. 2023/0245213 hereinafter ‘Faturechi’) in view of Wang et al. (United States Patent Application Publication No. 2023/0394420 hereinafter ‘Wang’), and further in view of in view of Neray et al. (United States Patent Application Publication No. 2025/0315786 hereinafter ‘Neray’).
With respect to (currently amended) claim 8, Faturechi discloses a computer-implemented method, comprising: receiving a user request from a user computing device including: i) element data, ii) a fulfillment location, and iii) a provisional fulfillment time (Faturechi et al.; paragraphs [0007]-[0008] [0027]-[0028]; See at least order received from user for specified items, a delivery location and warehouses/retailers form which the items are to be picked/obtained, and a time or time window for fulfillment/delivery of the order. These are reasonably a user request including elements/items, fulfillment locations, and an initial or provisional fulfillment time); in response to receiving the user request: requesting and receiving fulfillment data for the fulfillment location, the fulfillment data including at least a fulfillment order for the user request and ongoing fulfillment orders for the user request (Faturechi et al.; paragraphs [0007] [0010]-[0011] [0031]-[0032] [0039]; See at least predictions of time and fulfillment data for orders by predictive machine-learning models including an item availability model, i.e., reasonably a form of “trained machine learning model” that retrieves item availability for batches of orders, i.e., “ongoing fulfillment orders”); in response to receiving the fulfillment data, determining, by a trained machine learning model, a predicted fulfillment time for the user request (Faturechi et al.; paragraphs [0008] [0014]-[0015] [0036] [0042] [0060]; See at least prediction of fulfillment time for orders including component models, i.e., trained machine learning model to determine/forecast picking time for orders travel time item availability, warehouse location etc.) wherein the predicted fulfillment time is based on at least the fulfillment order for the user request and ongoing fulfillment orders for the user request and defines a time for acquiring elements in the element data (Faturechi et al.; paragraphs [0043] [0053] [0060]-[0064]; See at least predicative models including batch benefit model used to predict fulfillment time for an order in the context ongoing fulfillment of orders in batches based on order characteristics); determining whether a difference between the provisional fulfillment time and the predicted fulfillment time is within a fulfillment time threshold (Faturechi et al.; paragraphs [0009] [0017] [0036] [0055] [0066] [0076]; See at least determinations of adjustments/delays to selected time based on predicted batch processing benefits and comparisons of the adjusted time to a threshold probability that the order will be delivered late as a result of the delay time interval or time adjustment); and in accordance with a determination that the difference between the provisional fulfillment time and the predicted fulfillment time is outside the fulfillment time threshold, transmitting a delay interval to at least one computing device (Faturechi et al.; paragraphs [0036] [0052] [0071]-[0072] [0077]-[0078]; See at least adjustment of time including delay time interval. See further display of probability distribution of candidate time intervals and probabilities of the order being fulfilled after the time identified by the order for each candidate time interval. The system selects a candidate time interval having a minimum probability of the order being fulfilled later than the time identified by the order, i.e., a delay interval).
Claim 8 has been amended with respect to the functions of the “user computing device” to further include “…displaying, on the user computing device, a user interface that includes one or more options for adjusting the provisional fulfillment time…”
With respect to this element, Faturechi discloses a method including presenting/displaying, at the at least one user computing device, one or more options for adjusting the provisional fulfillment time (Faturechi et al.; paragraphs [0050]-[0052] [0077]-[0078]; See at least interface includes display of an estimated time of arrival for the order including an option for the user to select a short-term fulfillment options or option to select a discrete time interval).
With respect to the recitation of transmitting a predicted fulfillment time to at least one computing device, Faturechi discloses machine-learning analysis of an input order request time with respect to modelled analysis of batch orders, i.e., ongoing orders, to calculate candidate time intervals and associated probability thresholds of a late order associated with each of the candidate time intervals. A delay interval associated with the release of the order is calculated with respect to the selected delivery time associated with the order. Accordingly, Faturechi discloses comparing an input order delivery time to calculated probabilities of adjusted time intervals and associated thresholds associated with the time intervals to determine a permissible delay interval. While Faturechi is reasonably understood to transmit a system-selected time interval based on the time interval exceeding or not exceeding a threshold probability of the order being late, Faturechi is silent with respect to transmission and/or display of a predicted or estimated fulfillment time at a computing device.
However, as evidenced by Wang, it is well-known in the art to compare an initial order fulfillment time to a machine-learning model predicated delivery time for the order based on comparison using a threshold probability of order fulfillment exceeding the initial order fulfillment time (Wang et al.; paragraphs [0014]-[0016]; See at least prediction model determines a modification to the delivery time). Wang further discloses calculating an estimated delivery time based on the initial delivery order request time and the modification to the predicted delivery time and further transmitting the estimated delivery time to a user device for display on the device (Wang et al.; paragraphs [0017] [0061] [0065]; See at least calculation of estimated delivery time based on modelling/predicted delivery time and initial delivery time. See further transmission and display of estimated delivery time on client device).
With respect to the recited “trained machine learning model”, claim 8 has been further amended to specify “…wherein the trained machine learning model is trained based on historical data for a set of fulfillment locations over a period of time and has parameters that are adapted through active learning…”.
With respect to these elements, Faturechi discloses a predictive machine learning model (see late fulfillment model) that is trained on training data including time(s) to pick previous items. While Faturechi discloses a general input of time of day is included in the training datasets and further includes general availability of items at picking/retail locations, Faturechi fails to expressly state that the training process and training/historical data is directed to a set of fulfillment locations and periods of time and further includes adaptation of parameters through an active learning process.
However, as evidenced by Neray, it is well-known in the art to train predictive models to estimate delivery requirements and delivery times using training data including predicted availability of ordered items at multiple retail locations over discretized period of time (Neray et al.; paragraphs [0027]-[0028 [0044]-[0046] [0053] [0068] [0070] [0074]]; See at least predictive model for estimating order receipt and associated estimated item collection and delivery time trained using datasets partitioned by discrete time intervals and including item availability at multiple retail locations, past and current customer order data, i.e., ongoing orders, and picker data. See further active training and retraining of model by tuning parameters in a feedback loop)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the determination of fulfillment time intervals based on a determination that a threshold probability of the received order being fulfilled after the time identified in the received order of Faturechi by further including comparison of an initial order fulfillment time to a machine-learning model predicated delivery time using a threshold probability of order fulfillment exceeding the initial order fulfillment time and further transmitting the estimated delivery time to a user device for display on the device responsive to the comparison as taught by Wang. The instant invention is directed to a system and method of predicting and informing users of order fulfillment times based on modelled order fulfillment factors. As Faturechi disclose the use determination of fulfillment time intervals based on a determination that a threshold probability of the received order being fulfilled after the time identified in the received order in the context of a system and method for predicting and informing users of order fulfillment times based on modelled order fulfillment factors and Wang similarly discloses the utility of transmitting the estimated delivery time to a user device for display on the device responsive to a determination the probability in the context of a system and method for predicting and informing users of order fulfillment times based on modelled order fulfillment factors, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of providing predicted while reducing a probability of the order being fulfilled after a time identified to the user (Wang et al.; paragraph [0017]).
Regarding the combination that further includes Neray, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the general training of predictive order fulfillment model using training data including time of day for order fulfillment and general availability of items at picking/retail locations of Faturechi by further including actively training a predictive model for order delivery by employing active retraining by tuning model parameters in a feedback loop using training datasets partitioned by discrete time intervals and including item availability at multiple retail locations, past and current customer order data, and picker data as taught by Neray. The instant invention is directed to a system and method of predicting and informing users of order fulfillment times based on modelled order fulfillment factors. As Faturechi discloses the use of general training of predictive order fulfillment model using training data including time of day for order fulfillment and general availability of items at picking/retail locations in the context of a system and method for predicting and informing users of order fulfillment times based on modelled order fulfillment factors and Neray similarly discloses the utility of actively training a predictive model for order delivery by employing active retraining by tuning model parameters in a feedback loop using training datasets partitioned by discrete time intervals and including item availability at multiple retail locations, past and current customer order data, and picker data in the context of a system and method for predicting and informing users of order fulfillment times based on modelled order fulfillment factors, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of improving customer experience and picker scheduling by estimating delivery and pick times from multiple locations based on training predictive models using estimated times for order receipt and availability of matched order fulfillment agents.
Claim 9 is cancelled.
With respect to claim 10, Faturechi discloses a method wherein presenting, at the at least one computing device, the one or more options for adjusting the provisional fulfillment time includes presenting: a first user interface element for acknowledging an adjusted fulfillment time; and a second user interface element for requesting assistance regarding completion of the user request (Faturechi et al.; paragraphs [0050]-[0052]; See at least display of an estimated time of arrival for the order. See further user interface options, i.e., elements, to select a short-term fulfillment option and a second option to select a discrete time interval. The two selection options are reasonably forms of assistance completing the order and acknowledging an adjusted delivery time, absent further clarification).
With respect to claim 11, Faturechi discloses a method further comprising: validating the user request before determining the predicted fulfillment time (Faturechi et al.; paragraphs [0031]-[0033]; See at least ordering interface and determination of item availability prior to fulfillments models predicting fulfillment time).
With respect to claim 12, Faturechi discloses a method further comprising: training the fulfillment predictor, wherein the fulfillment predictor is trained using historical data for a period of time (Faturechi et al.; paragraphs [0007] [0014] [0045]; See at least training datasets assembled from time intervals associated with previously fulfilled, i.e., historical orders), the historical data including: a set fulfillment locations, a set of user requests, a set of acquisition data for the set of user requests; and one or more fulfillment time windows (Faturechi et al.; paragraphs [0014] [0041] [0063]; See at least training datasets assembled to include multiple delivery and warehouse locations, delivery time metrics, multiple customer orders, time periods, and item delivery and availability metrics all associated with previous orders).
With respect to claim 13, Faturechi discloses a method wherein the historical data is sampled to decrease the set fulfillment locations and decrease a training time of the fulfillment predictor (Faturechi et al.; paragraphs [0037] [0039] [0041] [0045]-[0046]; See at least training datasets selected based on historical orders including historical availability of items at particular warehouses. See further selection of individual warehouses to generate training datasets relevant to a particular item-warehouse pair. This is reasonably a form of sampling available training data to refine training to a decreased set of fulfillment locations, i.e., warehouses).
With respect to claim 14, Faturechi discloses a method wherein the fulfillment predictor provides a value for the predicted time having an accuracy rate of a predefined threshold value within a predefined time window with respect to an actual pick completion time (Faturechi et al.; paragraphs [0036] [0045] [0071] [0076]-[0077]; See at least generation of candidate time intervals and associated thresholds of probabilities of an order being fulfilled after the time identified in the received order. See further probability determinations include historical pick completion times for items).
[7] Claims 1 and 15, directed to an enabling system and computer-readable medium storing computer-executable instructions differ from method claim 8 by referring to the order “elements” of claim 8 as order “items”. Claim 15 further refers to the “fulfillment locations” of claim 8 as “pickup locations” and the “fulfillment predictor” as a “pickup predictor”. With respect to these elements, the use of the terms order “items” and order “elements” are reasonably understood to be the same items/elements of a requested order to be fulfilled. With respect to the recitation of “pickup location” and “pickup predictor”, the fulfillment locations and predictive models of Faturechi are reasonably interchangeable with pickup locations and predictive models associated with specified pickup locations or warehouses.
Claims 1, 3-7, 15, and 17-20 otherwise substantially repeat the subject matter addressed above with respect to claims 8 and 10-14 as directed to the enabling system and computer-readable medium storing computer-executable instructions. With respect to these elements, Faturechi disclose enabling the disclosed method employing analogous systems and executable instructions (See at least Faturechi paragraphs [0026] [0045]-[0048][0081]-[0085]). Accordingly, claims 1, 3-7, 15, and 17-20 are rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claims 8 and 10-14.
Conclusion
[8] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cited PATENT Literature:
Dhesi et al., OPTIMIZED ORDER FULFILLMENT FROM MULTIPLE SOURCES, United States Patent Application Publication No. 2025/0104013, paragraphs [0055]-[0058]: Relevant Teachings: Dhesi discloses a system/method that includes steps/functions including a machine learning model and training process that tunes the model to predict fulfillment times from multiple locations.
Cheng et al., COMPUTER-BASED SYSTEMS CONFIGURED FOR MACHINE-LEARNING CONTEXT-AWARE CURATION AND METHODS OF USE THEREOF, United States Patent Application Publication No. 20240037417, paragraphs [0022]-[0024]: Relevant Teachings: Cheng discloses a system/method that includes steps/functions to leverage machine learning to predict a time to complete a specified activity where the model is trained using an active feedback and tuning process.
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.
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/ROBERT D RINES/Primary Examiner, Art Unit 3625
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