DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the communications filed on 10/19/2023.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the Subject Matter Eligibility Test for Products and Processes, the claims must be directed to one of the four statutory categories. All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), it is determined whether the claims are directed to a judicially recognized exception. Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 20 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
A computer system comprising: a processor; and
a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
receiving, at an online concierge system, a free-text query from a client device associated with a user of the online concierge system, wherein the free-text query describes one or more items included among one or more inventories of one or more retailers associated with the online concierge system and a set of constraints;
generating a prompt comprising the free-text query and a request to identify the one or more items and the set of constraints;
providing the prompt to a large language model to obtain a textual output;
extracting, from the textual output, the set of constraints and one or more item categories associated with the one or more items;
identifying a plurality of retailers based at least in part on a set of user data associated with the user;
for each retailer of the plurality of retailers:
identifying a set of items associated with each item category of the one or more item categories, wherein the set of items is included among an inventory of a corresponding retailer,
identifying, based at least in part on the set of constraints, a combination of items comprising a subset of the set of items associated with each item category of the one or more item categories, and
generating a score for the combination of items based at least in part on the set of user data associated with the user and a set of item data associated with each item included in the combination of items, wherein the score indicates a likelihood of conversion by the user for the combination of items;
ranking a plurality of combinations of items determined for the plurality of retailers based at least in part on the score computed for each combination of items; and
sending information describing a ranked set of the plurality of combinations of items for display to the client device associated with the user, wherein the sending causes the client device to display the ranked set of the plurality of combinations of items..
Certain methods of organizing human activity include:
fundamental economic principles or practices (including hedging, insurance, and mitigating risk)
commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; and business relations)
managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)
The limitations as emphasized, are a process that, under its broadest reasonable interpretation, covers a commercial interaction. That is, other than reciting that a user interface is generated from the list and products are displayed on the user interface, nothing in the claim element precludes the step from practically being performed by people. For example, “receiving, generating, providing, extracting, identifying, identifying, identifying, generating, ranking and sending” in the context of this claim encompasses advertising, and marketing or sales activities.
If a claim limitation, under its broadest reasonable interpretation, covers a commercial interaction but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Prong 2, it is determined whether the claim recites additional elements that integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application (NO).
The claim recites additional elements beyond the judicial exception(s), including:
A computer system comprising:a processor; and
a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
receiving, at an online concierge system, a free-text query from a client device associated with a user of the online concierge system, wherein the free-text query describes one or more items included among one or more inventories of one or more retailers associated with the online concierge system and a set of constraints;
generating a prompt comprising the free-text query and a request to identify the one or more items and the set of constraints;
providing the prompt to a large language model to obtain a textual output;
extracting, from the textual output, the set of constraints and one or more item categories associated with the one or more items;
identifying a plurality of retailers based at least in part on a set of user data associated with the user;
for each retailer of the plurality of retailers:
identifying a set of items associated with each item category of the one or more item categories, wherein the set of items is included among an inventory of a corresponding retailer,
identifying, based at least in part on the set of constraints, a combination of items comprising a subset of the set of items associated with each item category of the one or more item categories, and
generating a score for the combination of items based at least in part on the set of user data associated with the user and a set of item data associated with each item included in the combination of items, wherein the score indicates a likelihood of conversion by the user for the combination of items;
ranking a plurality of combinations of items determined for the plurality of retailers based at least in part on the score computed for each combination of items; and
sending information describing a ranked set of the plurality of combinations of items for display to the client device associated with the user, wherein the sending causes the client device to display the ranked set of the plurality of combinations of items..
.
These limitations are not indicative of integration into a practical application because:
The additional elements of claim 20 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than mere instructions to implement or apply the abstract idea on a generic computing hardware (or, merely use a computer as a tool to perform an abstract idea.) Specifically, the additional element of an online concierge system, a client device, a large language model, is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of connecting to a platform on a network) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements to no more than generally link the use of the judicial exception to a particular technological environment or field of use (such as computers or computing networks). For example, stating that the prompt is provided to a large language model to obtain textual output, only generally links the commercial interactions and management of relationships or interactions between people to a computer environment. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not integrate the exception into a practical application.
Additionally, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to i) reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, ii) apply the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, iii) effect a transformation or reduction of a particular article to a different state or thing, or iv) apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, the judicial exception is not integrated into a practical application.
Under Step 2B, it is determined whether the claims recite additional elements that amount to significantly more than the judicial exception. The claims of the present application do not include additional elements that are sufficient to amount to significantly more than the judicial exception (NO).
In the case of system claim 20, taken individually or as a whole, the additional elements of claim 20 do not provide an inventive concept. As discussed above under step 2A (prong 2) with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed functions amount to no more than a general link to a technological environment.
Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Therefore, claim 20 does not provide an inventive concept and does not qualify as eligible subject matter.
Claim 1 is a method reciting similar functions as claim 20, and does not qualify as eligible subject matter for similar reasons.
Claim 11 is a computer program product comprising a computer readable storage medium reciting similar functions as claim 1, and does not qualify as eligible subject matter for similar reasons.
Claims 2-10 and 12-19 are dependencies of claims 1 and 11. The dependent claims do not add “significantly more” to the abstract idea. They recite additional functions that describe the abstract idea and only generally link the abstract idea to a particular technological environment, including:
generating a user embedding for the user based at least in part on the set of user data associated with the user; generating an item embedding for each item included in the combination of items based at least in part on the set of item data associated with a corresponding item; generating a dot product of the user embedding and the item embedding for each item included in the combination of items; aggregating the dot product of the user embedding and the item embedding for each item included in the combination of items; and generating the score for the combination of items based at least in part on the aggregated dot product of the user embedding and the item embedding for each item included in the combination of items. (additional sales and advertising activities, mathematical concepts)
Accordingly, the Examiner concludes that there are no meaningful limitations in the claim that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
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.
Claims 1, 3-8, 10-11, 13-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2015/0058154 A1 to APPLEYARD in view of U.S. Patent Application No. 2014/0092261 A1 to Goulart.
Regarding Claim 1, APPLEYARD discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
receiving, at an online concierge system, a free-text query from a client device associated with a user of the online concierge system, wherein the free-text query describes one or more items included among one or more inventories of one or more retailers associated with the online concierge system and a set of constraints; ([0041] During step 310, discount engine 154 and sentiment scoring engine 156 receive a shopping list of goods to purchase at optimization server 150 from user computer 120 (e.g., optimizing interaction 210, etc.). During step 312, discount engine 154 analyzes items on the received shopping list and searches for possible discount offerings from various brands, manufacturers, or retailers to compile a list of possible discounts that may be applied to the items (e.g., optimizing interactions 212, 214, etc.). [0011] comprehensive set of real-time factors such as preferences, retailers pricing, discounts, buying patterns, rating, consumer sentiments, environmental impact, and other criteria (constraints) [0024] person 102 can enter a shopping list of goods to purchase into a text input form of the loaded optimization web page displayed by the web browser program, )
generating a prompt comprising the free-text query and a request to identify the one or more items and the set of constraints; ([0024] the web browser program can transmit the shopping list of goods to purchase to optimization server 150. Subsequently, optimization server 150 can generate an optimized shopping list )
extracting, from the textual output, the set of constraints and one or more item categories associated with the one or more items; ([0010] Further, the ability to learn from shopper buying decisions to enhance the future shopping experiences (i.e., self-learning) further enhances the shopping experience. )
identifying a plurality of retailers based at least in part on a set of user data associated with the user; ([0009] The goods on the shopping list are accessed and analyzed by the optimization server. In particular, for each item on the shopping list, the optimization server queries all known data sources (e.g., retailers, service providers, manufactures, etc.) to retrieve available discounts for the item. The shopping list is then segmented into multiple sublists per retailer, based on the discounts, sentiment score by retailer, and product. )
for each retailer of the plurality of retailers: ([0028] one or more brick-and-mortar or online retailers)
identifying a set of items associated with each item category of the one or more item categories, wherein the set of items is included among an inventory of a corresponding retailer, identifying, based at least in part on the set of constraints, a combination of items comprising a subset of the set of items associated with each item category of the one or more item categories, and ([0041] During step 316, segment routing engine 158 groups items on the shopping list into shopping sub-lists based on discount information and sentiment scores, submits the shopping sub-lists to retailers for bidding, and receives bids (e.g., optimizing interactions 220, 222, etc.). )
generating a score for the combination of items based at least in part on the set of user data associated with the user and a set of item data associated with each item included in the combination of items, wherein the score indicates a likelihood of conversion by the user for the combination of items; ([0028] computes a sentiment score for each item and for each retailer. Segment routing engine 158 groups items on a shopping list, based on discount information (e.g., as provided by discount engine 154, etc.) and the customer sentiment scores (e.g., as provided by sentiment scoring engine 156, etc.), into shopping sub-lists and submits the shopping sub-lists to retailers for bidding (e.g., submits to retail servers 140, etc.).)
ranking a plurality of combinations of items determined for the plurality of retailers based at least in part on the score computed for each combination of items;and ([0012] the ranking can be based on cost, quantity, incentives, retailers rating, location, sentiments, or time. Each shopper can have different preferences and further refinement can be done by a ranking engine. Retailers can request the carbon footprint of the products they sell, and when the optimization server receives the retailer's bid it can use the carbon footprint to further refine the search results. Accordingly, the optimization engine can take a complex request and intelligently break it into a bundle of requests to be optimized. [0028] Each of the optimized shopping lists is optimized and ranked based on the shopper's preferences, the retailer and item sentiment scores, and the item's pricing. )
sending information describing a ranked set of the plurality of combinations of items for display to the client device associated with the user, wherein the sending causes the client device to display the ranked set of the plurality of combinations of items.. ([0041] During step 318, ranking engine 160 aggregates shopping sub-list bids, analyzes the results, generates one or more optimized shopping lists, and transmits the optimized shopping lists from optimization server 150 to user computer 120 (e.g., optimizing interactions 224, 226, etc.).)
But does not explicitly disclose providing the prompt to a large language model to obtain a textual output.
GOULART, on the other hand, teaches providing the prompt to a large language model to obtain a textual output. ([0058] At operation 376, the shopping list generation module 110 determines, for each alphanumeric string, one or more candidate items corresponding to the alphanumeric string. In some embodiments, the shopping list generation module 110 provides each alphanumeric string to a product language model 318. In some embodiments, the product language model 318 (discussed with respect to FIGS. 6 and 7) may be stored on the memory device 106 of the mobile computing device 10 or may be accessible to the shopping list generation module 110 over the network 30. The product language model 318 returns one or more candidate items corresponding to each alphanumeric string, and a score for each candidate item. As discussed, each candidate item can include a generic product or a specific product.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by APPLEYARD, the features, as taught by GOULART, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify APPLEYARD, to include the teachings of GOULART, in order to generate an electronic shopping list (GOULART, [0016]).
Regarding Claim 3, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein sending the information describing the ranked set of the plurality of combinations of items comprises sending one or more of: a set of items included in each combination of items, a total price associated with each combination of items, a retailer associated with each combination of items, a set of items associated with each combination of items that is not available, or a set of replacement items. ([0037] Each of the optimized shopping lists can include one or more brick-and-mortar or online retailers and items that person 102 should purchase from each. Each of the optimized shopping lists is optimized and ranked based on the preferences of person 102, the retailer and item sentiment scores, the item pricing, and other factors. )
Regarding Claim 4, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein identifying a plurality of retailers based at least in part on a set of user data comprises identifying a plurality of retailers based at least in part on one or more of: a preference of the user for each retailer of the plurality of retailers, a preference of the user for an attribute of an item, an order history associated with the user, or a location associated with the user.. ([0037] Each of the optimized shopping lists can include one or more brick-and-mortar or online retailers and items that person 102 should purchase from each. Each of the optimized shopping lists is optimized and ranked based on the preferences of person 102, the retailer and item sentiment scores, the item pricing, and other factors. )
Regarding Claim 5, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein extracting the set of constraints comprises extracting a budget.. ([0037] Each of the optimized shopping lists can include one or more brick-and-mortar or online retailers and items that person 102 should purchase from each. Each of the optimized shopping lists is optimized and ranked based on the preferences of person 102, the retailer and item sentiment scores, the item pricing, and other factors. [claim 3] wherein the user preference includes one or more of an environmental impact preference, a price preference, [claim 6] a dynamic price condition,)
Regarding Claim 6, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein generating the score for the combination of items comprises: generating a total price associated with the combination of items based at least in part on the set of item data associated with each item included in the combination of items; and generating the score for the combination of items based at least in part on the total price associated with the combination of items. ([0036] a bid is a simple total price for the entire shopping sub-list,,)
Regarding Claim 7, APPLEYARD in view of GOULART teaches the method of claim 6.
APPLEYARD discloses wherein the total price associated with the combination of items is further based at least in part on a delivery fee associated with an order including the combination of items.. ([0012] Carbon footprint can be calculated and used as factor for analysis and optimization. Further, the carbon footprint is not limited to just a given product's carbon footprint, but also relates to the carbon footprint generated by the shopper and the retailer (e.g., the carbon footprint involved with shipping, transportation, and customer pickup, etc.). Generally, the optimization engine can generate an optimized shopping list based on the proximity of a brick-and-mortar retailer to a shopper, to offset a carbon footprint by directing the shopper to the brick-and-mortar retailer. )
Regarding Claim 8, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein generating the score for the combination of items comprises: predicting an availability of each item included in the combination of items at a retailer location associated with a corresponding retailer based at least in part on the set of item data associated with each item included in the combination of items; and generating the score for the combination of items based at least in part on the predicted availability of each item.. ([0025] Data servers 130 include one or more servers hosting reviews, feedback, or other sentiment data about goods available for purchase, about retailers, or both. For example, data servers 130 can include recall databases, service bulletin databases, product comment web pages, social media platforms, and other structured or unstructured data repositories about goods available for purchase and retailers. [0037] Each of the optimized shopping lists can include one or more brick-and-mortar or online retailers and items that person 102 should purchase from each. Each of the optimized shopping lists is optimized and ranked based on the preferences of person 102, the retailer and item sentiment scores, the item pricing, and other factors. ,)
Regarding Claim 10, APPLEYARD in view of GOULART teaches the method of claim 1.
APPLEYARD discloses wherein sending the information describing the ranked set of the plurality of combinations of items further causes the client device to display an option to add each combination of items to a shopping list associated with the user. ([0041] During step 320, customer data 162 receives the selected optimized shopping list as modified (if applicable) at optimization server 150 (e.g., optimizing interaction 228, etc.). (user selection of an optimized shopping list is interpreted as adding the combination of items to a shopping list))
Regarding Claim 11, APPLEYARD discloses a computer program product comprising a non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising:
receiving, at an online concierge system, a free-text query from a client device associated with a user of the online concierge system, wherein the free-text query describes one or more items included among one or more inventories of one or more retailers associated with the online concierge system and a set of constraints; ([0041] During step 310, discount engine 154 and sentiment scoring engine 156 receive a shopping list of goods to purchase at optimization server 150 from user computer 120 (e.g., optimizing interaction 210, etc.). During step 312, discount engine 154 analyzes items on the received shopping list and searches for possible discount offerings from various brands, manufacturers, or retailers to compile a list of possible discounts that may be applied to the items (e.g., optimizing interactions 212, 214, etc.). [0011] comprehensive set of real-time factors such as preferences, retailers pricing, discounts, buying patterns, rating, consumer sentiments, environmental impact, and other criteria (constraints) [0024] person 102 can enter a shopping list of goods to purchase into a text input form of the loaded optimization web page displayed by the web browser program, )
generating a prompt comprising the free-text query and a request to identify the one or more items and the set of constraints; ([0024] the web browser program can transmit the shopping list of goods to purchase to optimization server 150. Subsequently, optimization server 150 can generate an optimized shopping list )
extracting, from the textual output, the set of constraints and one or more item categories associated with the one or more items; ([0010] Further, the ability to learn from shopper buying decisions to enhance the future shopping experiences (i.e., self-learning) further enhances the shopping experience. )
identifying a plurality of retailers based at least in part on a set of user data associated with the user; ([0009] The goods on the shopping list are accessed and analyzed by the optimization server. In particular, for each item on the shopping list, the optimization server queries all known data sources (e.g., retailers, service providers, manufactures, etc.) to retrieve available discounts for the item. The shopping list is then segmented into multiple sublists per retailer, based on the discounts, sentiment score by retailer, and product. )
for each retailer of the plurality of retailers: ([0028] one or more brick-and-mortar or online retailers)
identifying a set of items associated with each item category of the one or more item categories, wherein the set of items is included among an inventory of a corresponding retailer, identifying, based at least in part on the set of constraints, a combination of items comprising a subset of the set of items associated with each item category of the one or more item categories, and ([0041] During step 316, segment routing engine 158 groups items on the shopping list into shopping sub-lists based on discount information and sentiment scores, submits the shopping sub-lists to retailers for bidding, and receives bids (e.g., optimizing interactions 220, 222, etc.). )
generating a score for the combination of items based at least in part on the set of user data associated with the user and a set of item data associated with each item included in the combination of items, wherein the score indicates a likelihood of conversion by the user for the combination of items; ([0028] computes a sentiment score for each item and for each retailer. Segment routing engine 158 groups items on a shopping list, based on discount information (e.g., as provided by discount engine 154, etc.) and the customer sentiment scores (e.g., as provided by sentiment scoring engine 156, etc.), into shopping sub-lists and submits the shopping sub-lists to retailers for bidding (e.g., submits to retail servers 140, etc.).)
ranking a plurality of combinations of items determined for the plurality of retailers based at least in part on the score computed for each combination of items;and ([0012] the ranking can be based on cost, quantity, incentives, retailers rating, location, sentiments, or time. Each shopper can have different preferences and further refinement can be done by a ranking engine. Retailers can request the carbon footprint of the products they sell, and when the optimization server receives the retailer's bid it can use the carbon footprint to further refine the search results. Accordingly, the optimization engine can take a complex request and intelligently break it into a bundle of requests to be optimized. [0028] Each of the optimized shopping lists is optimized and ranked based on the shopper's preferences, the retailer and item sentiment scores, and the item's pricing. )
sending information describing a ranked set of the plurality of combinations of items for display to the client device associated with the user, wherein the sending causes the client device to display the ranked set of the plurality of combinations of items.. ([0041] During step 318, ranking engine 160 aggregates shopping sub-list bids, analyzes the results, generates one or more optimized shopping lists, and transmits the optimized shopping lists from optimization server 150 to user computer 120 (e.g., optimizing interactions 224, 226, etc.).)
But does not explicitly disclose providing the prompt to a large language model to obtain a textual output.
GOULART, on the other hand, teaches providing the prompt to a large language model to obtain a textual output. ([0058] At operation 376, the shopping list generation module 110 determines, for each alphanumeric string, one or more candidate items corresponding to the alphanumeric string. In some embodiments, the shopping list generation module 110 provides each alphanumeric string to a product language model 318. In some embodiments, the product language model 318 (discussed with respect to FIGS. 6 and 7) may be stored on the memory device 106 of the mobile computing device 10 or may be accessible to the shopping list generation module 110 over the network 30. The product language model 318 returns one or more candidate items corresponding to each alphanumeric string, and a score for each candidate item. As discussed, each candidate item can include a generic product or a specific product.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by APPLEYARD, the features, as taught by GOULART, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify APPLEYARD, to include the teachings of GOULART, in order to generate an electronic shopping list (GOULART, [0016]).
Claim 13 recites a system comprising substantially similar limitations as claim 3. The claim is rejected under substantially similar grounds as claim 3.
Claim 14 recites a system comprising substantially similar limitations as claim 4. The claim is rejected under substantially similar grounds as claim 4.
Claim 15 recites a system comprising substantially similar limitations as claim 5. The claim is rejected under substantially similar grounds as claim 5.
Claim 16 recites a system comprising substantially similar limitations as claim 6. The claim is rejected under substantially similar grounds as claim 6.
Claim 17 recites a system comprising substantially similar limitations as claim 7. The claim is rejected under substantially similar grounds as claim 7.
Claim 18 recites a system comprising substantially similar limitations as claim 8. The claim is rejected under substantially similar grounds as claim 8.
Regarding Claim 20, APPLEYARD discloses A computer system comprising: a processor; and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, perform actions comprising:
receiving, at an online concierge system, a free-text query from a client device associated with a user of the online concierge system, wherein the free-text query describes one or more items included among one or more inventories of one or more retailers associated with the online concierge system and a set of constraints; ([0041] During step 310, discount engine 154 and sentiment scoring engine 156 receive a shopping list of goods to purchase at optimization server 150 from user computer 120 (e.g., optimizing interaction 210, etc.). During step 312, discount engine 154 analyzes items on the received shopping list and searches for possible discount offerings from various brands, manufacturers, or retailers to compile a list of possible discounts that may be applied to the items (e.g., optimizing interactions 212, 214, etc.). [0011] comprehensive set of real-time factors such as preferences, retailers pricing, discounts, buying patterns, rating, consumer sentiments, environmental impact, and other criteria (constraints) [0024] person 102 can enter a shopping list of goods to purchase into a text input form of the loaded optimization web page displayed by the web browser program, )
generating a prompt comprising the free-text query and a request to identify the one or more items and the set of constraints; ([0024] the web browser program can transmit the shopping list of goods to purchase to optimization server 150. Subsequently, optimization server 150 can generate an optimized shopping list )
extracting, from the textual output, the set of constraints and one or more item categories associated with the one or more items; ([0010] Further, the ability to learn from shopper buying decisions to enhance the future shopping experiences (i.e., self-learning) further enhances the shopping experience. )
identifying a plurality of retailers based at least in part on a set of user data associated with the user; ([0009] The goods on the shopping list are accessed and analyzed by the optimization server. In particular, for each item on the shopping list, the optimization server queries all known data sources (e.g., retailers, service providers, manufactures, etc.) to retrieve available discounts for the item. The shopping list is then segmented into multiple sublists per retailer, based on the discounts, sentiment score by retailer, and product. )
for each retailer of the plurality of retailers: ([0028] one or more brick-and-mortar or online retailers)
identifying a set of items associated with each item category of the one or more item categories, wherein the set of items is included among an inventory of a corresponding retailer, identifying, based at least in part on the set of constraints, a combination of items comprising a subset of the set of items associated with each item category of the one or more item categories, and ([0041] During step 316, segment routing engine 158 groups items on the shopping list into shopping sub-lists based on discount information and sentiment scores, submits the shopping sub-lists to retailers for bidding, and receives bids (e.g., optimizing interactions 220, 222, etc.). )
generating a score for the combination of items based at least in part on the set of user data associated with the user and a set of item data associated with each item included in the combination of items, wherein the score indicates a likelihood of conversion by the user for the combination of items; ([0028] computes a sentiment score for each item and for each retailer. Segment routing engine 158 groups items on a shopping list, based on discount information (e.g., as provided by discount engine 154, etc.) and the customer sentiment scores (e.g., as provided by sentiment scoring engine 156, etc.), into shopping sub-lists and submits the shopping sub-lists to retailers for bidding (e.g., submits to retail servers 140, etc.).)
ranking a plurality of combinations of items determined for the plurality of retailers based at least in part on the score computed for each combination of items;and ([0012] the ranking can be based on cost, quantity, incentives, retailers rating, location, sentiments, or time. Each shopper can have different preferences and further refinement can be done by a ranking engine. Retailers can request the carbon footprint of the products they sell, and when the optimization server receives the retailer's bid it can use the carbon footprint to further refine the search results. Accordingly, the optimization engine can take a complex request and intelligently break it into a bundle of requests to be optimized. [0028] Each of the optimized shopping lists is optimized and ranked based on the shopper's preferences, the retailer and item sentiment scores, and the item's pricing. )
sending information describing a ranked set of the plurality of combinations of items for display to the client device associated with the user, wherein the sending causes the client device to display the ranked set of the plurality of combinations of items.. ([0041] During step 318, ranking engine 160 aggregates shopping sub-list bids, analyzes the results, generates one or more optimized shopping lists, and transmits the optimized shopping lists from optimization server 150 to user computer 120 (e.g., optimizing interactions 224, 226, etc.).)
But does not explicitly disclose providing the prompt to a large language model to obtain a textual output.
GOULART, on the other hand, teaches providing the prompt to a large language model to obtain a textual output. ([0058] At operation 376, the shopping list generation module 110 determines, for each alphanumeric string, one or more candidate items corresponding to the alphanumeric string. In some embodiments, the shopping list generation module 110 provides each alphanumeric string to a product language model 318. In some embodiments, the product language model 318 (discussed with respect to FIGS. 6 and 7) may be stored on the memory device 106 of the mobile computing device 10 or may be accessible to the shopping list generation module 110 over the network 30. The product language model 318 returns one or more candidate items corresponding to each alphanumeric string, and a score for each candidate item. As discussed, each candidate item can include a generic product or a specific product.)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by APPLEYARD, the features, as taught by GOULART, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify APPLEYARD, to include the teachings of GOULART, in order to generate an electronic shopping list (GOULART, [0016]).
Claims 2, 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2015/0058154 A1 to APPLEYARD in view of U.S. Patent Application No. 2014/0092261 A1 to Goulart in view of U.S. Patent Application No. 2025/0094898 A1 to Saurav.
Regarding Claim 2, APPLEYARD in view of GOULART teaches the method of claim 1.
However the combination of APPLEYARD and GOULART does not explicitly teach generating a user embedding for the user based at least in part on the set of user data associated with the user; generating an item embedding for each item included in the combination of items based at least in part on the set of item data associated with a corresponding item; generating a dot product of the user embedding and the item embedding for each item included in the combination of items; aggregating the dot product of the user embedding and the item embedding for each item included in the combination of items; and generating the score for the combination of items based at least in part on the aggregated dot product of the user embedding and the item embedding for each item included in the combination of items..
SAURAV, on the other hand, teaches generating a user embedding for the user based at least in part on the set of user data associated with the user; generating an item embedding for each item included in the combination of items based at least in part on the set of item data associated with a corresponding item; generating a dot product of the user embedding and the item embedding for each item included in the combination of items; aggregating the dot product of the user embedding and the item embedding for each item included in the combination of items; and generating the score for the combination of items based at least in part on the aggregated dot product of the user embedding and the item embedding for each item included in the combination of items. ([0071] In some embodiments, the combined model 396 may be used to combine the seller embeddings generated by the seller model 392 and the item embeddings generated by the item model 394, to form item-seller combinations each with an associated affinity score. In some examples, for each item-seller pair or combination, the combined model 396 may perform a dot product operation based on the embeddings of the item and the seller in the pair or combination, to compute an affinity score representing a degree of affinity between the item and the seller. A higher affinity score represents a higher degree of affinity between the item and the seller. An affinity indicates how close the embedding of the seller is to the embeddings of the items present in the marketplace, by considering the catalog of the seller. [0104] the retrieval model 601 may generate a list of items each of which has a higher-than-threshold affinity score when being paired with the query seller. )
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by APPLEYARD and GOULART, the features as taught by SAURAV, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of SAURAV, in order to generate a recommendation list (SAURAV, [0001]).
Claim 12 recites a system comprising substantially similar limitations as claim 2. The claim is rejected under substantially similar grounds as claim 2.
Claims 9, 19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application No. 2015/0058154 A1 to APPLEYARD in view of U.S. Patent Application No. 2014/0092261 A1 to Goulart in view of U.S. Patent Application No. 2024/0037863 A1 to Cocchiarella.
Regarding Claim 9, APPLEYARD in view of GOULART teaches the method of claim 8.
However the combination of APPLEYARD and GOULART does not explicitly teach further comprising: determining a percentage of items included in the combination of items associated with at least a threshold predicted availability at the retailer location; and selecting the ranked set of the plurality of combinations of items based at least in part on the percentage of items included in the combination of items associated with at least the threshold predicted availability at the retailer location..
Cocchiarella, on the other hand, teaches further comprising: determining a percentage of items included in the combination of items associated with at least a threshold predicted availability at the retailer location; and selecting the ranked set of the plurality of combinations of items based at least in part on the percentage of items included in the combination of items associated with at least the threshold predicted availability at the retailer location.. ([0051] The confidence score may be the error or uncertainty score of the probability of availability and may be calculated using any standard statistical error measurement. In some embodiments, the confidence score is based in part on whether the item-warehouse pair availability prediction was accurate for previous delivery orders (e.g., if an item was predicted to be available at a warehouse 210 and was not found by a shopper 208 or was predicted to be unavailable but was found by the shopper 208).)
It would have been obvious to one of ordinary skill in the art to include in the method, as taught by APPLEYARD and GOULART, the features as taught by Cocchiarella, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. It further would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination, to include the teachings of Cocchiarella, in order to provide their customers with options to suit their personal needs and tastes (Cocchiarella, [0002]).
Claim 19 recites a system comprising substantially similar limitations as claim 9. The claim is rejected under substantially similar grounds as claim 9.
Conclusion
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/MICHELLE T KRINGEN/Primary Examiner, Art Unit 3689