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 .
DETAILED ACTION
2. This action is in response to the original filing on 09/13/2024. Claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the abstract idea without significantly more.
Step 1, the claims are directed to a process, machine, and manufacture.
Step 2A Prong 1, Claims 1, 15, and 20 recite, in part
detecting an anomaly associated with an item selection made by a picker for fulfilling an order of a user (Mental processes, observation/evaluation, a person can examine what the customer ordered, examine what the picker selected, and determine that the selection is anomalous).
in response to detecting the anomaly, generating a prompt, the prompt comprising at least a chat log between the picker and the user (Mental processes, selecting/organizing information for the subsequent evaluation).
a description indicating whether the anomaly is attributable to the user (Mental processes, evaluation/judgment).
identifying, based on the output, that the item selection is not attributable to the user (Mental processes, evaluation/judgment).
Step 2A Prong 2, this judicial exception is not integrated into a practical application.
The additional elements:
a computer processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the computer processor, cause the computer processor to perform operations (mere instructions to apply the exception using a generic computer component).
an online system (generally linking the use of the judicial exception to a particular technological environment or field of use).
a machine-learned language model (mere instructions to apply the exception using computer/AI technology).
providing the prompt to the machine-learned language model for execution (mere instructions to apply the exception using computer/AI technology).
receiving, as output from the machine-learned language model (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
performing an automated action responsive to identifying that the item selection is not attributable to the use (insignificant extra-solution activity).
Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, either alone or in combination.
The additional elements:
a computer processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the computer processor, cause the computer processor to perform operations (mere instructions to apply the exception using a generic computer component).
an online system (generally linking the use of the judicial exception to a particular technological environment or field of use).
a machine-learned language model (mere instructions to apply the exception using computer/AI technology).
providing the prompt to the machine-learned language model for execution (mere instructions to apply the exception using computer/AI technology).
receiving, as output from the machine-learned language model (mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity).
performing an automated action responsive to identifying that the item selection is not attributable to the use (insignificant extra-solution activity).
Claims 2-14 and 16-19 provide further limitations to the abstract idea (Mental processes) as rejected in claims 1, 15, however, they do not disclose any additional elements that would amount to a practical application or significantly more than an abstract idea (data gathering/insignificant extra-solution activity and/or generic computer component).
Claim Rejections – 35 USC § 103
4. 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 of this title, 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.
5. Claims 1-6, 9-11, 14-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Van Horne et al. (U.S. Patent Application Pub. No. US 20210142391 A1) in view of Spiegel et al. (U.S. Patent Application Pub. No. US 20240249318 A1), and further in view of Mossoba et al. (U.S. Patent Application Pub. No. US 20200219105 A1).
Claim 1: Van Horne teaches a method comprising:
detecting an anomaly associated with an item selection (i.e. The online concierge system 102 receives 615 a selection of an item in the order from the PMA 112, the selection indicating that the order needs to be changed because there is an issue with the item. Issues may include the item being out of stock, lacking in quantity, of poor quality, or otherwise unavailable; para. [0044]) made by a picker for fulfilling an order of a user of an online system (i.e. The online concierge system 102 is configured to transmit orders received from customers 104 to one or more pickers 108. Each picker 108 fulfills an order received from the online concierge system 102 at one or more retailers 110, delivers the order to the customer 104, or performs both fulfillment and delivery; para. [022, 0043]);
in response to detecting the anomaly, generating a prompt for execution (i.e. In response to receiving the selection of the item from the picker 108, the online concierge system 102 displays 620 a set of template messages to the picker 108 via a picker order interface. The template messages each specify a different issue with the order that requires resolution from the customer 104. Examples include that the item is not available at the retailer 110, the item is of poor quality, the item is not available in the quantity ordered, and the item is not available in the type or brand ordered; para. [0044, 0045]), the prompt comprising at least a chat log between the picker and the user (i.e. Customers 104 may view their orders and communicate with pickers 108 regarding an issue with an item in an order using the customer ordering interface. For example, a customer 104 may respond to a message from a picker 108 indicating that an item cannot be retrieved for the order by selecting a replacement option for the item or requesting a refund via buttons on the customer ordering interface. Based on the chosen course of action, the customer ordering interface generates and displays a template message for the customer 104 to send to the picker 108. The customer 104 may edit the template message to include more information about the item or course of action and communicate back and forth with the picker 108 until the issue is resolved; para. [0031, 0035]);
providing the prompt for execution (i.e. the online concierge system 102 may receive messages composed via a user interface of the picker mobile application 112 and transmit the messages to the customer mobile application 106 (or vice versa) via the communication engine 216. For example, a picker 108 may compose, via a picker order interface, a message indicating that an item of an order is not available. The online concierge system 102 may receive the message from the picker mobile application 112 and transmit the message to the customer mobile application 106 associated with the order; para. [0029]);
receiving, as output and based on at least the chat log, a description indicating whether the anomaly is attributable to the user (i.e. The customer 104 may interact with a customer ordering interface to indicate a course of action for the item, such as to remove the item from the order. The online concierge system 102 receives this information and transmits it in the form of a message to the picker mobile application 112 … The online concierge system 102 displays 715, via the customer ordering interface, a set of template messages the customer 104 may use to respond to the message from the picker 108. Each template message indicates a course of action for the item, such as to remove the item from the order and obtain a refund or to replace the item with a different item; para. [0029, 0048, 0049]); and
performing an automated action responsive to identifying that the item selection (i.e. The online concierge system 102 receives 720 a selection of one of the set of template messages from the customer 104. In this embodiment, the selection indicates that the customer 104 would like to replace the item with another item. In response to receiving one of a subset of the template messages as the selection 725, the online concierge system 102 displays 730, via the customer ordering interface, a set of replacement options and receives 735 a selection of one of the replacement options, which is inserted into the template message; para. [0049]).
Van Horne does not explicitly teach in response to detecting, generating a prompt for execution by a machine-learned language model; providing the prompt to the machine-learned language model for execution; receiving, as output from the machine-learned language model; identifying, based on the output from the machine-learned language model, that the item selection is not attributable to the user; and performing an action responsive to identifying that the item selection is not attributable to the user.
However, Spiegel teaches in response to detecting, generating a prompt for execution by a machine-learned language model, the prompt comprising at least a chat log between the picker and the user (i.e. the full conversation 410 is communicated to the LLM 338. For example, the entire conversation between the user and the chatbot system 300 is communicated to the Large Language Model (LLM) 338. This provides the LLM 338 with the full context of the interactive dialog, including the initial prompt from the user as well as any follow-up messages. By analyzing the complete exchange comprising the full conversation, the LLM 338 can better understand the intent and meaning behind the user's queries. Additionally, having access to the full conversation flow enables the LLM 338 to maintain continuity and generate responses that logically follow from previous parts of the dialog; para. [0139, 0141]); providing the prompt to the machine-learned language model for execution (i.e. the 300 receives the prompt 328. The chatbot system 300 uses the LLM 338 to generate the response 370; para. [0085-0087, 0139); receiving, as output from the machine-learned language model and based on at least the chat log, a description (i.e. The LLM 338 receives the prompt 328 and generates the raw response 362; para. [0087, 0139]); identifying, based on the output from the machine-learned language model (i.e. intent processing component 348 leverages machine learning models to infer the user's true intent and state of the conversation from the contextual signals; para. [0143, 0200, 0202]); and
performing an automated action responsive to identifying (i.e. the output selector component 342 detects whether the user is asking about purchasing diapers or requesting a Wikipedia definition. By extracting conversation details and detecting user intent, the intent processing component 348 enables downstream components to produce responses tailored to the user's needs; para. [0143]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Van Horne to include the feature of Spiegel. One would have been motivated to make this modification because it provides the full conversation to an LLM, rather than evaluating individual messages in isolation, enables the system to obtain a more holistic understanding of the conversation and to more accurately determine the user’s intent.
However, Mossoba teaches identifying, based on the output from the machine-learned model, that the item selection is not attributable to the user (i.e. The fraud analysis platform may determine the likelihood of the transaction being fraudulent based on whether any of the processed messages … If the fraud analysis platform determines that none of the processed messages is a confirmation message for the transaction, the fraud analysis platform may determine that the transaction is more likely to be fraudulent … the fraud analysis platform may train a machine learning model, such as a messaging account-based fraud analysis model, to determine a likelihood that the transaction is fraudulent based on the processed messages not including a confirmation message; para. [0051, 0055, 0062]); and performing an automated action responsive to identifying that the item selection is not attributable to the user (i.e. the fraud analysis platform may quickly determine a likelihood that the transaction is fraudulent in order to permit the fraud analysis platform to perform an action associated with a financial account of the user … the fraud analysis platform may freeze or place a hold on the financial account to prevent any additional, potential fraudulent transactions from occurring (e.g., until an investigation into whether the financial account has been compromised is performed); para. [0057, 0059, 0061, 0062]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Spiegel to include the feature of Mossoba. One would have been motivated to make this modification because it provides a technique for identifying unauthorized activity more quickly and permits responsive action to be taken sooner, thereby protecting the user and preventing further unauthorized activity.
Claim 2: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne does not explicitly teach wherein performing the automated action comprises at least one of: causing a client device of the user to display a notification to confirm whether the item is approved by the user; and providing another notification to a client device of the picker indicating that payment for the item selection is on hold pending approval of the user.
However, Mossoba further teaches wherein performing the automated action comprises at least one of: causing a client device of the user to display a notification to confirm whether the item selection is approved by the user; and providing another notification to a client device of the picker indicating that payment for the item selection is on hold pending approval of the user (i.e. the fraud analysis platform may send a prompt to the user to indicate whether the transaction was authorized or fraudulent (e.g., based on the user's knowledge); para. [0062, 0063]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Spiegel to include the feature of Mossoba. One would have been motivated to make this modification because it provides a technique for identifying unauthorized activity more quickly and permits responsive action to be taken sooner, thereby protecting the user and preventing further unauthorized activity.
Claim 3: Van Horne, Spiegel, Mossoba teach the method of claim 2. Van Horne does not explicitly teach determining a confidence score associated with the indication of the item selection as being not attributable to the user; and responsive to the confidence score being higher than a threshold, blocking the item selection without prompting the user for approval.
However, Mossoba further teaches determining a confidence score associated with the indication of the item selection as being not attributable to the user (i.e. The fraud analysis platform may determine a score, for a fraud analysis of a transaction, that is based on whether a messaging account receives a confirmation message for the transaction. The score may indicate a likelihood that the transaction is fraudulent … likelihood that the transaction is fraudulent; para. [0054, 0055]); and responsive to the confidence score being higher than a threshold (i.e. The threshold probability may be a static threshold (e.g., 50% likely fraudulent, 75% likely fraudulent, 90% likely fraudulent, and/or the like) or a transaction specific threshold that is based one or more characteristics of the transaction; para. [0028]), blocking the item selection made by the picker without prompting the user for approval (i.e. fraud analysis platform may perform one or more actions based on the determined likelihood that the transaction was fraudulent … the fraud analysis platform may freeze or place a hold on the financial account to prevent any additional, potential fraudulent transactions from occurring (e.g., until an investigation into whether the financial account has been compromised is performed); para. [0058, 0059]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Spiegel to include the feature of Mossoba. One would have been motivated to make this modification because it provides a technique for identifying unauthorized activity more quickly and permits responsive action to be taken sooner, thereby protecting the user and preventing further unauthorized activity.
Claim 4: Van Horne, Spiegel, Mossoba teach the method of claim 2. Van Horne further teaches wherein detecting the anomaly associated with the item selection comprises: identifying that a quantity or weight of an item picked by the picker for fulfilling the user's order does not match an ordered quantity or an ordered weight of an associated item included in the user's order (i.e. the item 410 is only available in smaller quantity than ordered; para. [0036-0038]).
Claim 5: Van Horne, Spiegel, Mossoba teach the method of claim 4. Van Horne further teaches wherein the description received as the output based on the chat log indicates an updated estimate of the ordered quantity or the ordered weight of the associated item included in the user's order (i.e. The customer ordering interface 500A also allows the customer 104 to alter the quantity of the item 510 using the quantity drop-down list 530, or, in some embodiments, by manually entering a quantity; para. [0040-0043]).
Van Horne does not explicitly teach the machine-learned language model.
However, Spiegel further teaches the machine-learned language model (i.e. the full conversation 410 is communicated to the LLM 338. For example, the entire conversation between the user and the chatbot system 300 is communicated to the Large Language Model (LLM) 338. This provides the LLM 338 with the full context of the interactive dialog, including the initial prompt from the user as well as any follow-up messages. By analyzing the complete exchange comprising the full conversation, the LLM 338 can better understand the intent and meaning behind the user's queries. Additionally, having access to the full conversation flow enables the LLM 338 to maintain continuity and generate responses that logically follow from previous parts of the dialog; para. [0139, 0141]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Mossoba to include the feature of Spiegel. One would have been motivated to make this modification because it provides the full conversation to an LLM, rather than evaluating individual messages in isolation, enables the system to obtain a more holistic understanding of the conversation and to more accurately determine the user’s intent.
Claim 6: Van Horne, Spiegel, Mossoba teach the method of claim 5. Van Horne further teaches wherein the notification is provided to the client device of the user to confirm whether the item selection is approved by the user in response to determining that the quantity or weight of the item picked by the picker does not match the updated estimate of the ordered quantity or the ordered weight of the associated item included in the user's order (i.e. The customer ordering interface 500A also allows the customer 104 to alter the quantity of the item 510 using the quantity drop-down list 530, or, in some embodiments, by manually entering a quantity; para. [0040-0043]).
Claim 9: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne further teaches wherein detecting the anomaly associated with the item selection comprises: identifying that an item picked by the picker for fulfilling the user's order does not match an item included in the user's order (i.e. The online concierge system 102 displays 715, via the customer ordering interface, a set of template messages the customer 104 may use to respond to the message from the picker 108. Each template message indicates a course of action for the item, such as to remove the item from the order and obtain a refund or to replace the item with a different item. In some embodiments, the template message may include another remedy for the item, such as a request for information regarding available replacement options in-store from the picker 108. The customer ordering interface may also display the option for the customer 104 to change the quantity of the item; para. [0048]).
Claim 10: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne further teaches wherein the prompt generated for execution further comprises order data associated with the user's order (i.e. The picker order interface 400A displays a scrollable list of items 410 in the order 405, including the brand or type of the item 410, the quantity ordered, the price of the item 410, and an image of the item 410; para. [0036]).
Van Horne does not explicitly teach the machine-learned language model.
However, Spiegel further teaches the machine-learned language model (i.e. the full conversation 410 is communicated to the LLM 338. For example, the entire conversation between the user and the chatbot system 300 is communicated to the Large Language Model (LLM) 338. This provides the LLM 338 with the full context of the interactive dialog, including the initial prompt from the user as well as any follow-up messages. By analyzing the complete exchange comprising the full conversation, the LLM 338 can better understand the intent and meaning behind the user's queries. Additionally, having access to the full conversation flow enables the LLM 338 to maintain continuity and generate responses that logically follow from previous parts of the dialog; para. [0139, 0141]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Mossoba to include the feature of Spiegel. One would have been motivated to make this modification because it provides the full conversation to an LLM, rather than evaluating individual messages in isolation, enables the system to obtain a more holistic understanding of the conversation and to more accurately determine the user’s intent.
Claim 11: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne further teaches wherein historic chat logs between pickers and users (i.e. the online concierge system 102 may receive messages composed via a user interface of the picker mobile application 112 and transmit the messages to the customer mobile application 106 (or vice versa) via the communication engine 216; para. [0029, 0031, 0035]).
Van Horne does not explicitly teach the machine-learned language model.
However, Spiegel further teaches wherein the machine-learned language model is trained on historic chat logs between pickers and users (i.e. interactions with the chatbot system 300 across all features of the interactive system that hosts the chatbot system 300 improves the LLM 338 over time. Interaction data collected and used to train the LLM 338 include, but are not limited to, what is asked, follow-up questions, in application reporting of answers, in-application reactions to answers, forwarding of answers, sharing of answers, retention, frequency of use, .ad interactions, other searches, other actions, visual content of social network media content, camera rolls, user datastores of interactive application interactions, and the like; para. [0210, 0211]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Mossoba to include the feature of Spiegel. One would have been motivated to make this modification because it provides the full conversation to an LLM, rather than evaluating individual messages in isolation, enables the system to obtain a more holistic understanding of the conversation and to more accurately determine the user’s intent.
Claim 14: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne does not explicitly teach receiving feedback from the user indicating whether the description output from the machine-learned language model is accurate; and training the machine-learned language model based on the feedback from the user.
However, Spiegel further teaches receiving feedback from the user indicating whether the description output from the machine-learned language model is accurate; and training the machine-learned language model based on the feedback from the user (i.e. the chatbot system 300 provides for the reporting of answers in chat using chat reporting features of the interactive system hosting the chatbot system 300, and using the reporting to improve the LLM 338 by letting users provide the right response … Validation, refinement or retraining 612: This phase may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback; para. [0179, 0215, 0254]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne and Mossoba to include the feature of Spiegel. One would have been motivated to make this modification because it provides the full conversation to an LLM, rather than evaluating individual messages in isolation, enables the system to obtain a more holistic understanding of the conversation and to more accurately determine the user’s intent.
Claims 15, 16, and 18-20 are similar in scope to Claims 1, 2, 9, 10 and are rejected under a similar rationale.
6. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Van Horne in view of Spiegel, Mossoba, and further in view of Edwards et al. (U.S. Patent Application Pub. No. US 20120158529 A1).
Claim 7: Van Horne, Spiegel, Mossoba teach the method of claim 5. Van Horne does not explicitly teach identifying whether the quantity or weight of the item picked by the picker for fulfilling the user's order differs by more than a blocking threshold from the updated estimate of the ordered quantity or the ordered weight of the associated item included in the user's order; and responsive to identifying that the quantity or weight of the item picked by the picker differs by more than the blocking threshold, blocking the item selection made by the picker without prompting the client device of the user for approval.
However, Edwards teaches identifying whether the quantity or weight of the item picked by the picker for fulfilling the user's order differs by more than a blocking threshold (i.e. The item level security software uses several methods to either prevent or mitigate the effects of falsely detecting a misidentified item. One method is to create allowable item tolerance parameter for each item. This parameter is stored in the item database with the expected weight for each item. The allowable item tolerance parameter can be different for each item; para. [0038]) from the updated estimate of the ordered quantity or the ordered weight of the associated item included in the user's order (i.e. when the five pound bag of sugar is presented to the terminal 15, the item identifying device 28 identifies the item. Using the item's identity, the software 32 queries the item database to obtain an expected weight value and other information for the item. Some of the information is displayed on the display 16 and the item is placed on the security scales 46. The security scales 46 reports the new total weight to the software 32, which using the previous and new total weights calculate the actual weight of the item placed on the scales 46. The software 32 then uses the expected and actual weight to determine if the item was properly identified or if the item was misidentified; para. [0035, 0036]); and responsive to identifying that the quantity or weight of the item picked by the picker differs by more than the blocking threshold, blocking the item selection made by the picker without prompting the client device of the user for approval (i.e. If the item was determined to be misidentified, control passes to step 440 where a notification is sent to an assistant and all customer operations at the terminal 15 are stopped. Once the assistant has cleared the problem, the transaction continues to step 405; para. [0051]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne, Spiegel, and Mossoba to include the feature of Edwards. One would have been motivated to make this modification because it provides the use of an allowable deviation between actual and expected item weight reduces false item misidentification detections.
7. Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Van Horne in view of Spiegel, Mossoba, and further in view of Chen et al. (U.S. Patent Application Pub. No. US 20150088747 A1).
Claim 8: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne further teaches receiving an indication that the user interacted with a user interface (UI) element on a client device of the user to approve the item selection (i.e. FIG. 5A, a customer 104 may choose a template message 520 by interacting with buttons on the customer ordering interface 500A. The template messages 520 indicate possible courses of action, such as requesting a replacement option 525 for the item 510, requesting a new quantity of the item 510 (chosen via a quantity drop-down list 515), or requesting a refund 535 of the item 510, among other remedies for the item; para. [0040]).
Van Horne does not explicitly teach responsive to receiving the indication, approving payment for the item selection for the order.
However, Chen teaches receiving an indication that the user interacted with a user interface (UI) element on a client device of the user to approve the item selection (i.e. a consumer or other user may be able to view the transaction information. Approval may be requested from the user. In one example, the request for approval may be made via the user's mobile device; para. [0089]); and responsive to receiving the indication, approving payment for the item selection for the order (i.e. The mobile device may send notice of approval to an intermediary server. The notice of approval may initiate a transaction request at the intermediary server. The intermediary server may send a funds request to a financial institution. The financial institution may transfer the funds. In some instances, the financial institution may perform one or more assessment of whether the funds may be transferred prior to transferring the funds. For example, the financial institution may check that the user has sufficient funds in the user's account. If the transfer is a success, indication that the transfer was a success may be provided to the intermediary server. The intermediary server may inform the point of sale that the transaction was a success. The intermediary server may also provide a receipt for the transaction to the user, which may be accessible via a device of the user; para. [0090]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne, Spiegel, and Mossoba to include the feature of Chen. One would have been motivated to make this modification because it ensures the payment for an order is processed only after receiving the customer’s affirmative approval.
Claim 17 is similar in scope to Claim 8 and is rejected under a similar rationale.
8. Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Van Horne in view of Spiegel, Mossoba, and further in view of Di Capua et al. (U.S. Patent Application Pub. No. US 20240331006 A1).
Claim 12: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne does not explicitly teach wherein the machine-learned language model is trained on user-level features including at least one of the user's: prior replacement approval rate; absolute order volume for an item associated with the item selection; use of replacement policy specification at a store front; historic satisfaction with replacements; historic propensity to change item quantities or weights; or historic propensity to add incremental items to an order.
However, Di Capua teaches wherein the machine-learned model is trained on user-level features including at least one of the user's: prior replacement approval rate; absolute order volume for an item associated with the item selection; use of replacement policy specification at a store front; historic satisfaction with replacements; historic propensity to change item quantities or weights; or historic propensity to add incremental items to an order (i.e. the one or more models 107 may obtain user data 106 including user preferences, historical data 108 including previously requested grocery items, and merchant data 110, to determine a user preference suggestion for grocery items of a current delivery service order request. In some examples, the one or more models 107 may include a user preference model trained to determine the preferences of a user 125 … a user 125 may fail to indicate a replacement grocery item during the creation of the order request and the one or more models 107 of the network system 101 may determine that the user 125 selected a replacement grocery item for previous order requests and determine the replacement grocery item based on the previous order history. By way of example, the network system 101 may determine that, in a previous order, a user previously selected a high fat yogurt as a replacement item for full fat sour cream; para. [0058, 0154]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne, Spiegel, and Mossoba to include the feature of Di Capua. One would have been motivated to make this modification because the prior user preferences and satisfaction feedback can be used to update the model so that subsequent grocery item recommendations better reflect the particular user’s preferences.
9. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Van Horne in view of Spiegel, Mossoba, and further in view of Montague et al. (U.S. Patent Application Pub. No. US 20220237675 A1).
Claim 13: Van Horne, Spiegel, Mossoba teach the method of claim 1. Van Horne does not explicitly teach wherein the machine-learned language model is trained on item-level features including at least one of: historic chats mapped to orders including an item associated with the item selection; historic replacement rates associated with an item associated with the item selection; or historic likelihood to experience quantity adjustments for an item associated with the item selection.
However, Montague teaches wherein the machine-learned model is trained on item-level features including at least one of: historic chats mapped to orders including an item associated with the item selection; historic replacement rates associated with an item associated with the item selection; or historic likelihood to experience quantity adjustments for an item associated with the item selection (i.e. the machine learning computational model having been trained based on historic information for a plurality of past quantities associated with the first item and other items of the plurality of items … the buyer historic information 304 may include ordering trends 320, e.g., the buyer is ordering less of the same item for the same number of people than the buyer ordered during one or more previous orders. Furthermore, the merchant historic information 302 and the buyer historic information 304 may include additional types of information, with the foregoing being merely several examples of the types of information that may be used by the quantity determination program 170. Accordingly, while several types of information that may be used by the quantity determination program 170 are illustrated in this example, in other examples, other or additional types of information may be used by the quantity determination program 170, as discussed herein and/or as will be apparent to those of skill in the art having the benefit of the disclosure herein; claim 1 and para. [0076]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the combination of Van Horne, Spiegel, and Mossoba to include the feature of Montague. One would have been motivated to make this modification because the historical quantities and the frequency of prior surplus conditions for the same item are useful in predicting and correcting subsequent quantity values.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Abo Fool et al. (Pub. No. US 20240394787 A1), A computer-implemented method for online shopping is provided. The method includes providing a plurality of items for purchase, receiving a purchase order for at least one item selected by a user from among the plurality of items, and during at least part of the fulfillment of the purchase order, initiating a video conference with a user's device operated by the user.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/TAN H TRAN/Primary Examiner, Art Unit 2141
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