Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1
Step 1 This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a processor and memory storing instructions that when executed cause the processor to perform at least one step. Thus, the claim is a system, which is one of the statutory categories of invention under 35 U.S.C. § 101.
Step 2A Prong 1 This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The limitation “a cost value is generated based on the output data and the training dataset” recites a Mathematical Concept. The limitation describes calculating values derived from input data to optimize model performance during execution. This falls within the abstract idea grouping of mathematical formulas, mathematical relationships, or calculations without a specific technological application.
The limitation “a determination is made whether the iterative training process is complete based on the cost value, wherein parameters of the natural language processing model are revised” recites a Mathematical Concept. The limitation involves evaluating mathematical conditions and adjusting variables to minimize an error metric in the system. This represents a mathematical optimization routine that sets forth an abstract idea without a structural or technological improvement.
“Unless it is clear that a claim recites distinct exceptions, such as a law of nature and an abstract idea, care should be taken not to parse the claim into multiple exceptions, particularly in claims involving abstract ideas.” MPEP 2106.04, subsection II.B. However, if possible, the examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the two steps above fall within the mathematical process grouping of abstract ideas and are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2 Prong 2 This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The additional elements:
a processor, a memory storing instructions
obtain from a database a training dataset comprising historical user session data,
output data is generated,
obtain one or more query messages having a natural language format;
generate a user request associated with one or more items by processing the one or more query messages using the natural language processing model, the user request including item information associated with the one or more items, a fulfillment type for managing the one or more items, and a data operation type for processing the item information, wherein:
the fulfillment type identifies a delivery option, a shipping option, or a pickup option;
the data operation type identifies a search option for searching the item information, a refinement option for filtering the item information, or an engagement option for adding the one or more items to a virtual shopping cart; and
in response to the user request:
identify an information source including a set of candidate information items associated with the item information based on the data operation type;
generate one or more information items from the set of candidate information items provided by the information source based on the fulfillment type;
identify a target user based on at least the data operation type;
generate instructions to display the one or more information items on an electronic device associated with the target user; and
transmit the instructions to the electronic device associated with the target user for display of the one or more information items.
MPEP 2106.05(a) Improvements to the Functioning of a Computer or to Any Other Technology or Technical Field:
The claim does not improve computer functionality beyond generic data processing tasks. It merely uses standard computing resources to execute abstract mathematical calculations and user interface instructions. No specific technical improvement to machine learning architecture is evident in the claimed system.
MPEP 2106.05(b) Particular Machine:
The processor and memory are recited at a high level of generality without specialized configuration. These components perform generic computer functions rather than acting as a particular machine tailored to the invention. Thus, they do not integrate the abstract idea into a practical application via specific hardware.
MPEP 2106.05(c) Particular Transformation:
The claim does not transform any article or object into a different state or thing. It processes data within a database and memory without altering physical objects in the real world. This lack of transformation indicates the steps remain abstract mathematical concepts implemented digitally.
MPEP 2106.05(e) Other Meaningful Limitations:
The additional elements do not add meaningful limitations to the claim as a whole. They are merely generic instructions for using standard computer components to perform data retrieval and display tasks. No specific technical solution is provided that solves a problem in a non-conventional way.
MPEP 2106.05(f) Mere Instructions to Apply an Exception:
The steps describe applying the mathematical concept of cost reduction to user session data. This application does not integrate the exception into a practical use case beyond standard software operations. It remains a mere instruction to apply the abstract idea using conventional tools.
MPEP 2106.05(g) Insignificant Extra-Solution Activity:
The claim recites obtaining queries and transmitting instructions as insignificant extra-solution activity. These steps are routine data handling tasks performed by generic electronic devices without specific technical constraints. They do not add enough practical application to overcome the abstract idea rejection.
MPEP 2106.05(h) Field of Use and Technological Environment:
The claim is directed toward a general e-commerce or shopping environment rather than a specialized technological field. It does not improve the underlying technology of that specific field but merely applies standard software logic to it. Thus, the field of use limitation fails to render the claim eligible.
Step 2B The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The ordered combination lacks unconventional architecture or steps because each element performs a well-understood, routine, and conventional function in data processing systems. Factual inquiries reveal that training models and calculating costs are conventional techniques in artificial intelligence development today. Consequently, the claim fails the Step 2B analysis as it does not provide an inventive concept sufficient to render the abstract idea patent-eligible under 35 U.S.C. § 101.
Claim 2 recites “wherein: the information source includes a plurality of data sets stored in one or more databases; the fulfillment type is selected from a delivery option associated with a first data set, a shipping option associated with a second data set, and a pickup option associated with a third data set; and the one or more information items are extracted based on at least one of the first data set, the second data set, and the third data set stored in the one or more databases based on the fulfillment type.” This additional limitation describes conventional database organization and data retrieval operations using standard digital storage infrastructure well-understood in computer science. Storing multiple datasets and selecting based on fulfillment type represents routine data management activity without any inventive concept that would render the claim patent eligible.
Claim 3 recites “wherein: the information source includes a database, each candidate information item is stored with a fulfillment indicator in the database; and the one or more information items are extracted from the database based on the fulfillment indicator of each of the one or more information items.” This limitation describes conventional database tagging and filtering operations using standard metadata fields to organize and retrieve data. Storing indicators and extracting records based on those indicators represents well-understood, routine database management activity that does not add significantly more than the abstract idea itself.
Claim 4 recites “wherein: the data operation type is selected from the search option or the refinement option; and a first user belonging to a first user group and providing the one or more query messages is identified as the target user in accordance with a determination that the data operation type is the search option or the refinement option.” This limitation describes conventional user identification and access control operations using standard membership grouping logic in computer systems. Identifying users based on group membership represents routine authentication or authorization activity that does not provide an inventive concept sufficient to render the claim patent eligible.
Claim 5 recites “wherein: the data operation type is selected from the engagement option; a first user belongs to a first user group and provides the one or more query messages; and at least a second user belonging to a second user group associated with the system is identified as the target user in accordance with a determination that the data operation type is the engagement option.” This limitation describes conventional multi-user identification operations using standard database queries to retrieve different users for display purposes. Under MPEP 2106.05(e) and Berkheimer standards, identifying alternative users based on data operation type represents routine computer functionality that does not add significantly more than the abstract idea.
Claim 6 recites “wherein: the data operation type includes a search option for which a database is identified as the information source, and the target user includes a first user who provides the one or more query messages; and the one or more information items are identified in a search of the database based on the item information and the fulfillment type, and displayed on a user interface as a search result to the first user.” This limitation describes conventional database searching and display operations using standard query mechanisms to retrieve and present results to users. Performing searches and displaying results represents well-understood computer functions that do not provide an inventive concept sufficient to render the claim patent eligible.
Claim 7 recites “wherein: the one or more query messages are obtained while the set of candidate information items is displayed on a user interface to a first user who provides the one or more query messages; the data operation type includes a refinement option for which the information source includes the user interface and the target user includes the first user; and the set of information items is filtered based on the fulfillment type to generate the one or more information items for display on the user interface.” This limitation describes conventional real-time filtering operations using standard software interfaces to process and update displayed content dynamically. Obtaining input during interface display and filtering results represents routine computer interaction functionality that does not add significantly more than the abstract idea under 35 U.S.C. § 101.
Claim 8 recites “wherein: the one or more query messages are obtained while the one or more information items are already displayed on a user interface to a first user who provides the one or more query messages, the set of candidate information items including the one or more information items; the data operation type includes an engagement option for which the information source includes the user interface and the target user includes a second user; and an engagement request is generated based on the one or more information items, and the one or more information items are displayed to the target user in response to the engagement request.” This limitation describes conventional multi-step interaction sequences using standard software interfaces where users provide input that triggers subsequent actions within the application. Generating requests from displayed content represents well-understood computer automation activity that does not add an inventive concept sufficient to render the claim patent eligible.
Claim 9 recites “ wherein the instructions, when executed, cause the processor to: execute a user application including enabling display of a user interface; enable display of a voice assistant affordance item on the user interface, independently of content concurrently displayed on the user interface; and in response to detection of a user action on the voice assistance affordance item, obtaining an audio signal collected via a microphone, wherein a subset of the audio signal is converted to the one or more query messages.” This limitation describes conventional mobile or web interface elements including button displays and standard audio capture functionality using generic hardware components like microphones. Displaying UI affordances and capturing audio input represents well-understood digital automation that does not provide an inventive concept sufficient to render the claim patent eligible.
Claim 10 recites “wherein the instructions, when executed, cause the processor to: execute a user application including enabling display of a user interface associated with a user application, wherein the one or more query messages are entered by a first user on the user interface.” This limitation describes conventional text input functionality using standard graphical interfaces and keyboard or touch-based data entry mechanisms in computer applications. Displaying interfaces for manual user input represents well-understood routine activity that does not add significantly more than the abstract idea.
Claim 11 and 15 are similar to claim 1. The claims are rejected based on the same reason.
Claims 16-20 are similar to claims 2-6
Claim 12 depends on claim 11 and includes all the limitations of claim 11. Claim 12 recites “wherein the one or more query messages includes a sequence of two or more query messages in the natural language format, and the instructions to generate the user request further comprise instructions to: generate a context including a plurality of context terms by processing each of the sequence of two or more query messages using the natural language processing model separately, wherein the user request is generated based on the context.” This limitation is pre solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 13 depends on claim 11 and includes all the limitations of claim 11. Claim 13 recites “wherein the item information includes an item type, and each of the one or more information items represents a respective item of the item type and includes one or more of: a brand name, a quantity, a package size, a price, and an image of the respective item.” This limitation is pre solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Claim 14 depends on claim 11 and includes all the limitations of claim 11. Claim 14 recites “enable display of a user interface including a first information item; and while the first information item is displayed, determine that the item information recites an item without specifying an item type or an item name, wherein at least one of the one or more information items is generated based on the first information item.” This limitation is pre solution activities. The claim does not have any addition limitation that amount to significantly more than the abstract idea.
Response to Arguments
Section 35 U.S.C 101
Pg. 11-13, Applicant argues that “… Although Applicant disagrees with the rejection, as noted above, Applicant proposes to amend the independent claims to further establish their statutory nature. For example, as proposed, claim 1 is direct to a "system" that includes a "a non- transitory memory having instructions stored thereon" and "at least one processor operatively coupled to the non-transitory memory." The at least one process is configured to read the instructions to:
obtain from a database a training dataset comprising historical user session data;
execute an iterative training process using the training dataset to train a natural language processing model wherein, at each iteration of the iterative training process:
output data is generated;
a cost value is generated based on the output data and the training dataset; and
a determination is made whether the iterative training process is complete based on the cost value, wherein parameters of the natural language processing model are revised to reduce the cost value when the iterative training process is not complete;
obtain one or more query messages having a natural language format;
generate a user request associated with one or more items by processing the one or more query messages using the natural language processing model, the user request including item information associated with the one or more items, a fulfillment type for managing the one or more items, and a data operation type for processing the item information, wherein:
the fulfillment type identifies a delivery option, a shipping option, or a pickup option;
the data operation type identifies a search option for searching the item information, a refinement option for filtering the item information, or an engagement option for adding the one or more items to a virtual shopping cart; and
in response to the user request:
identify an information source including a set of candidate information items associated with the item information based on the data operation type;
generate one or more information items from the set of candidate information items provided by the information source based on the fulfillment type;
identify a target user based on at least the data operation type;
generate instructions to display the one or more information items on an electronic device associated with the target user; and
transmit the instructions to the electronic device associated with the target user for display of the one or more information items.
When considered under the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 4 (January 7, 2019) (hereinafter the "2019 Guidance"), claim 1 recites patentable subject matter. Moreover, claims 11 and 15 recite similar features and thus also recite patentable subject matter…”
Claim 1 remains ineligible under 35 U.S.C. § 101. Generic computer components constituted by the processor and memory do not integrate the exception into a practical application. The Applicant’s argument relies on standard system architecture rather than specific technical improvements to computer functionality.
Step 2A Prong 1 shows claim limitations recite abstract ideas. Specifically, generating a cost value and determining whether the iterative training process is complete to revise model parameters constitute mathematical concepts. The calculation of the cost metric and the subsequent numerical revision of model parameters to optimize performance confirm the mathematical nature of these claim steps.
Additional elements, such as obtaining query messages and generating output data, are classified as additional elements. These steps merely apply the judicial exception using generic computer tools without meaningful integration into a practical application. The Applicant argues these features add technical weight, but they do not reflect an improvement to technology or a technical field.
Fulfillment types and data operation types identify delivery options, shipping options, or pickup options within the claim text. These are conventional business rules implemented on standard hardware without technological innovation beyond existing capabilities. The additional elements that do not integrate the exception into a practical application under Step 2A Prong 2. Consequently, they fail to provide significantly more than the abstract idea itself when considered with other limitations.
Transmitting instructions for display is another generic function found in modern electronic devices associated with target users. This step does not amount to improve computer functioning. The claim merely automates a manual business process using standard computing resources. Therefore, the rejection under 35 U.S.C. § 101 remains proper.
Pg. 13, Applicant argues that “… As an initial point, Applicant respectfully submits that the features of claim 1 cannot practically be performed in the human mind, or with pen and paper. Indeed, a human cannot "obtain from a database a training dataset comprising historical user session data" and "execute an iterative training process using the training dataset to train a natural language processing model," let alone where "at each iteration of the iterative training process: output data is generated [and] a cost value is generated based on the output data and the training dataset" in their mind or with pen and paper. As another example, a human cannot, in their mind or with pen and paper, "generate instructions to display the one or more information items on an electronic device associated with the target user" and "transmit the instructions to the electronic device associated with the target user for display of the one or more information items." Indeed, and as further described below, the claims solve technical problems that humans cannot manually or mentally address. As such, in alleging the claims merely recite a mental process, the Final Office Action appears to mischaracterize the claimed subject matter and/or improperly evaluate the claims at such a high level of generality thereby dismissing meaningful technical limitations…”
The Applicant argues that features cannot be performed in a human mind; however, the limitations generating cost values and determining iterative training completion to revise parameters are classified as Mathematical Concepts rather than mental processes. The additional elements do not integrate judicial exceptions into a practical application because they rely on generic computer functionality without a structural or technical improvement.
Generating output data, generating instructions to display items, and transmitting them are classified as additional elements. These steps merely apply the abstract idea using a general-purpose electronic device.
The Applicant asserts that claims solve technical problems humans cannot address, but the specification does not disclose a specific solution to a technological. Instead, the claim automates conventional data processing and shopping cart management functions using standard computing resources. Therefore, these features fail to amount to significantly more than the abstract idea itself.
Pg. 13, Applicant argues that “… Moreover, the Final Office Action alleges that the claims do not integrate the alleged abstract idea of a Mental Process into a practical application because "[t]here is no faster, more accurate, or more efficient computer hardware described [in the specification.]" See Final Office Action, p. 7. Applicant notes that, as indicated in the December Memorandum, technical improvements include "improvement[s] in the functioning of a computer, or an improvement to other technology or a technical field" including "machine learning technology." See Dec. Memo., pp. 2-3. Here, among other technical improvements, the natural language processing model is improved by the claimed iterative training process, thereby providing an improvement to machine learning technology. Indeed, the claimed iterative training process causes the natural language processing model to learn to generate user requests based on query messages, as claimed.
The limitations generating cost values and determining iterative training completion to revise parameters are classified specifically as Mathematical Concepts rather than mental process limitations. The claim includes generic mathematical calculations without specific technical implementation.
The Applicant argues that iterative training improves machine learning technology, but the claim language fails to disclose a particular solution to a technological problem. The specification does not describe how parameters are revised beyond reducing cost values without detailing specific algorithmic enhancements or technical mechanisms in the claims text.
The Applicant’s argument relies on the concept that machine learning improvements automatically confer eligibility. Generic training processes do not inherently improve computer functionality without specific technological constraints recited in the claims text.
The Applicant fails to include specific technical features regarding model architecture or data structures within your claim language. The generation of output data represents a conventional step. The claimed iterative process lacks the particularity required to distinguish it as an improvement over conventional machine learning techniques under Step 2A Prong 2. We maintain that these elements amount to mere instructions to implement the abstract idea on a computer without meaningful integration into a practical application.
Pg. 14, Applicant argues that “… For instance, Applicant's Specification states that, among other technical benefits, the claimed subject matter allows for the "process[ing of] user requests more independently without heavily involving user actions," demonstrating to one of ordinary skill in the art that the claimed subject matter reduces data processing associated with "heavily involve[ed] user actions." See Applicant's Specification, [0021]-[0022].2 The claimed subject matter achieves these and other technical benefits by using the "natural language processing model" that, as indicated above, is trained to process query messages and generate corresponding user requests that include item information, a fulfillment type, and a data operation type. Indeed, these query messages "enable[] intuitive and quick solution[s] to process information items," and "allow the users to express their fulfillment needs when the user application displays different functional pages, independently of information content presented for the user application “ id [0021]…”
The iterative training process describes steps that constitute a Mathematical Concept. This process sets forth steps that describe an abstract idea instead of providing technical improvements to computer functionality. Consequently, these limitations do not integrate the exception into a practical application because they rely on generic computing resources without meaningful constraints.
The specification paragraphs [0021]-[0022] regarding independent processing and reduced user actions as evidence of technical benefits. However, this specification does not provide specific technological improvements recited in the claim. The claims do not explicitly describe how these benefits are achieved through a particular solution to a technological problem.
The Applicant’s argument that natural language processing model training improves machine learning technology overlooks the lack of specific technical details in the claim language. Generic iterative training processes amount to applying an abstract idea on a computer rather than improving computer capabilities.
The additional elements, such as obtaining query messages or displaying instructions, are generic functions lacking an inventive concept under Step 2B analysis requirements.
Pg. 14-15, the Applicant argues that “… Further still, claim 1 provides for the generation of instructions to display one or more information items on an electronic device associated with a target user, and the transmission of the generated instructions to the electronic device for display of the one or more generated information items. As indicated above, the display of the information items can reduce user actions on the electronic device. These technical benefits are similar to Example 37 of the 2019 Guidance, where the claimed subject matter reduces the amount of icon manipulation a user would otherwise undergo. For instance, Example 37 states that "[i]f a computer user wants a non-typical arrangement of icons, the user would need to manually manipulate the icons on their display," where the claim "addresses this issue by providing a method for rearranging icons on a graphical user interface (GUI), wherein the method moves the most used icons to a position on the GUI." In other words, the process of manually manipulating icons on a display to achieve a desired non-typical arrangement requires additional time and effort by the user, which the claim addresses by providing a method that automatically arranges the icons based on the determined amount of use for each icon. Thus, similar to Example, 37, the subject matter of claim 1 is integrated into a practical application that provides, among other things, instructions to display information items that can reduce the amount of user actions that the electronic device may otherwise undergo.
As such, the Specification clearly notes the limitations of previous systems, and the claims themselves reflect improvements that address those limitations, illustrating the claims integrate any alleged abstract idea into a practical application. See, e.g., M.P.E.P. 2106.04(d)(a), 2106.05(a). For at least these reasons, the claims recite patent eligible subject matter…”
The Applicant compares the claim limitations to Example 37, but this analogy fails because the claims lack specific technical details regarding computer architecture improvements. The claim does not recite a particular solution to a technological problem. The comparison overlooks that Example 37 involves specific graphical user interface (GUI) manipulation logic that is entirely absent from your claim language.
Reducing user actions through automation often constitutes applying an abstract idea rather than improving technology or a technical field. The Applicant’s argument focuses on business efficiency or user-experience outcomes instead of specific enhancements to computer capabilities that would satisfy Step 2A Prong 2. Consequently, the claimed invention does not reflect an improvement in functioning as defined by existing eligibility standards.
The paragraphs [0021]-[0022] describe general benefits regarding independent processing and reduced user effort. The claims recite judicial exceptions without integrating specific technological improvements. These paragraphs disclose conventional business results rather than the specific algorithmic enhancements required for eligibility.
Generic instructions for displaying information items are conventional computer functions. These additional elements amount to mere instructions to implement an existing process using standard computing resources without meaningful integration. Transmission of data does not constitute a technological solution distinct from generic computer operations.
Pg. 15, Applicant argues that “… Additionally, 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. See M.P.E.P. § 2106.05; see also supra. (indicating that the claims recite novel and non-obvious subject matter). Moreover, the claimed subject matter is confined to a "particular useful application" that includes the generation of user requests by processing query messages using a natural language processing model that is iteratively trained to revise its parameters, and allows for various technical advantages, as noted above.
Accordingly, Applicant respectfully submits that independent claims 1, 11, and 15 are directed to patent-eligible subject matter, and respectfully request the reconsideration and withdrawal of the rejection of these claims under 35 U.S.C. § 101. Further, as claims 2-10, 12-16, and 18-20 depend from independent claims 1, 11, and 15, these claims are directed to statutory subject matter for at least those reasons set forth above for these independent claims, and for further reasons recited therein.
Therefore, Applicant respectfully requests that the rejection of claims 1-20 under 35 U.S.C. §101 be withdrawn…”
The Applicant argues that the claims are eligible because the prior art fails to teach these steps, which confines the claim to a particular useful application. However, novelty and non-obviousness under 35 U.S.C. 102 and 103 are not the standards for eligibility under 35 U.S.C. 101. A claim can be novel but still remain patent-ineligible if it lacks an inventive concept.
The Applicant also argues that processing query messages with an iteratively trained model makes the claim eligible. However, limitations generating a cost value and determining completion to revise parameters are Mathematical Concepts. These steps set forth an abstract idea, and the claim lacks specific algorithmic details or structural constraints required to integrate this exception under Step 2A Prong 2.
The remaining claim features, including the generation of output data, are conventional computer functions classified as additional elements. These steps merely apply the mathematical concept using standard computing tools. They do not add unconventional steps or provide an inventive concept under Step 2B.
Dependent Claims:
Because independent claims 1, 11, and 15 do not overcome the judicial exception, dependent claims 2–10, 12–14, and 16–20 fail for the same reasons. The additional limitations in the dependent claims merely add conventional data retrieval parameters or business options that do not supply an inventive concept. The rejection is maintained.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure
U.S. Patent 11928107 – Mostafa discloses a method includes: receiving, by a computing device, a natural language search query; determining, by the computing device, a filtering phrase in the natural language search query using a natural language understanding model; encoding, by the computing device, the filtering phrase; retrieving, by the computing device, a plurality of encoded columns; for each of the plurality of encoded columns, the computing device determining a similarity score based on a similarity between the encoded filtering phrase and the encoded column; and outputting, by the computing device, a column corresponding to an encoded column of the plurality of encoded columns having a highest similarity score.
U.S. Pub 2023/0401622 – Rao discloses systems and methods of generating a fulfillment interface are disclosed. A request for a fulfillment interface is received from a first device. The request identifies a plurality of items for an order. Fulfillment options are determined for each of the items and a first fulfillment plan including a first fulfillment option for a first set of items and a second fulfillment option for a second set of items is generated. The first and second fulfillment options are generated based on the fulfillment options of the corresponding set of items and a customer intent. The fulfillment interface including the first fulfillment plan is generated and transmitted to the first device for display. The interface includes a first portion identifying the first fulfillment option and each of the first set of items and a second portion identifying the second fulfillment option and each of the second set of items.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAU HAI HOANG whose telephone number is (571)270-5894. The examiner can normally be reached 1st biwk: Mon-Thurs 7:00 AM-5:00 PM; 2nd biwk: Mon-Thurs: 7:00 am-5:00pm, Fri: 7:00 am - 4:00pm.
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HAU HAI. HOANG
Primary Examiner
Art Unit 2154
/HAU H HOANG/ Primary Examiner, Art Unit 2154
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