DETAILED ACTION
Status of Claims
This action is in reply to the Amendments filed on 12/04/2025.
Claims 7-8 and 16-17 have been cancelled.
Claims 21-24 are newly added.
Claims 1-6, 9-15, and 18-24 are rejected.
Claims 1-6, 9-15, and 18-24 are currently pending and have been examined.
Response to Amendment
Applicant’s amendment, filed 12/04/2025, has been entered. Claims 1-2, 11-12, and 19 have been amended.
Claim Objections
The claim objections from the prior Office Action have been withdrawn pursuant Applicant’s amendments.
Rejections under 35 U.S.C. §112(a)
The 35 U.S.C. §112(a) rejections have been withdrawn pursuant Applicant’s amendments.
Rejections under 35 U.S.C. §112(b)
The 35 U.S.C. §112(b) rejections have been withdrawn pursuant Applicant’s amendments.
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
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 1-6, 9-15, and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., law of nature, a natural phenomenon, or 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 (see MPEP 2106.03). All the claims are directed to one of the four statutory categories (YES).
Under Step 2A of the Subject Matter Eligibility Test, it is determined whether the claims are directed to a judicially recognized exception (see MPEP 2106.04). Step 2A is a two-prong inquiry.
Under Prong 1, it is determined whether the claim recites a judicial exception (YES). Taking Claim 1 as representative, the claim recites limitations that fall within the certain methods of organizing human activity groupings of abstract ideas, including:
-A method, performed at a computer system comprising a processor and a computer-readable storage medium, the method comprising:
-receiving, from a plurality of picker[s] client devices, images of one or more stock of an item available at each of a plurality of retailer locations, wherein the item is included in an order requested by a customer for fulfillment at an initial retailer location;
-applying a desirability model to attributes of stock of the item available at the plurality of retailer locations including the initial retailer location to output a retailer desirability score for each retailer location, wherein the desirability model is trained by:
-obtaining a set of training examples, each training example including attributes of the item offered by the retailer, and each training example having a label indicating whether the item is acceptable for inclusion in an order;
-applying the desirability model to each training example of the set to generated generate a predicted desirability score for the training example;
-scoring the predicted desirability score for the training example using a loss function and the label of the training example; and
-updating one or more parameters of the desirability model by backpropagation based on the scoring;
-identifying a candidate retailer location from the plurality of retailer locations having the retailer desirability score greater than the retailer desirability score of the initial retailer location;
-in response to identifying the candidate retailer location, transmitting a user interface to a customer client device for presentation of a notification that the candidate retailer location has the retailer desirability score greater than the retailer desirability score of the initial retailer location and an option on the user interface to modify the order to change fulfillment to be at the candidate retailer location;
-receiving, via the option on the user interface, input from the customer client device to modify the order to change fulfillment to be at the candidate retailer location;
-modifying the order for fulfillment at the candidate retailer location; and
-transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location
The above limitations recite the concept of recommending a retailer location to fulfill a customer's order based on item desirability. The above limitations fall within the “Certain Methods of Organizing Human Activity” and the "mathematical concepts" groupings of abstract ideas, enumerated in MPEP 2106.04(a)(2)(11). The claims are directed towards a system for recommending a retailer location to fulfill a customer's order, which represent a sales activity, a commercial or legal interaction. Additionally, the limitations reciting generating a score and scoring a variable using a loss function are directed towards mathematical relationships, formulas, and calculations.
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)
Mathematical concepts include:
mathematical relationships
mathematical formulas or equations
mathematical calculations
The limitations of obtaining a set of training examples, each training example including attributes of the item offered by the retailer, and each training example having a label indicating whether the item is acceptable for inclusion in an order; applying the desirability model to each training example of the set to generated generate a predicted desirability score for the training example; scoring the predicted desirability score for the training example using a loss function and the label of the training example; and identifying a candidate retailer location from the plurality of retailer locations having the retailer desirability score greater than the retailer desirability score of the initial retailer location; and modifying the order for fulfillment at the candidate retailer location are processes that, under their broadest reasonable interpretation, cover a commercial interaction. For example, “obtaining,” “applying,” “scoring,” “identifying,” and “modifying” in the context of this claim encompass advertising, and marketing or sales activities and “scoring,” in the context of this claim encompasses mathematical relationships, formulas, and calculations.
Similarly, the limitations of A method, performed at a computer system comprising a processor and a computer-readable storage medium, the method comprising: receiving, from a plurality of picker[s] client devices, images of one or more stock of an item available at each of a plurality of retailer locations, wherein the item is included in an order requested by a customer for fulfillment at an initial retailer location; applying a desirability model to attributes of stock of the item available at the plurality of retailer locations including the initial retailer location to output a retailer desirability score for each retailer location, wherein the desirability model is trained by: updating one or more parameters of the desirability model by backpropagation based on the scoring; in response to identifying the candidate retailer location, transmitting a user interface to a customer client device for presentation of a notification that the candidate retailer location has the retailer desirability score greater than the retailer desirability score of the initial retailer location and an option on the user interface to modify the order to change fulfillment to be at the candidate retailer location; receiving, via the option on the user interface, input from the customer client device to modify the order to change fulfillment to be at the candidate retailer location; and transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location are processes that, under their broadest reasonable interpretation, cover a commercial interaction. That is, other than reciting that the system is a computer system comprising a processor and a computer-readable storage medium, that the plurality of the pickers is a plurality of the picker client devices, that the desirability model is trained, that the one or more parameters are updated by backpropagation, that a user interface is transmitted for presentation, that the customer is a customer device, that the option is on the user interface, and that the one picker is one picker client device, nothing in the claim element precludes the step from practically being performed by people. For example, but for the “computer,” “processor,” “computer-readable storage medium,” “plurality of picker client devices,” “the picker client devices,” “is trained by,” “backpropagation,” “user interface,” “customer client device,” and “one picker client device” language, “receiving,” “applying,” “updating,” “transmitting,” “receiving,” and “transmitting” in the context of this claim encompasses advertising, and marketing or sales activities.
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).
-A method, performed at a computer system comprising a processor and a computer-readable storage medium, the method comprising:
-receiving, from a plurality of picker client devices, images of one or more stock of an item available at each of a plurality of retailer locations, wherein the item is included in an order requested by a customer for fulfillment at an initial retailer location;
-applying a desirability model to attributes of stock of the item available at the plurality of retailer locations including the initial retailer location to output a retailer desirability score for each retailer location, wherein the desirability model is trained by:
-obtaining a set of training examples, each training example including attributes of the item offered by the retailer, and each training example having a label indicating whether the item is acceptable for inclusion in an order;
-applying the desirability model to each training example of the set to generated generate a predicted desirability score for the training example;
-scoring the predicted desirability score for the training example using a loss function and the label of the training example; and
-updating one or more parameters of the desirability model by backpropagation based on the scoring;
-identifying a candidate retailer location from the plurality of retailer locations having the retailer desirability score greater than the retailer desirability score of the initial retailer location;
-in response to identifying the candidate retailer location, transmitting a user interface to a customer client device for presentation of a notification that the candidate retailer location has the retailer desirability score greater than the retailer desirability score of the initial retailer location and an option on the user interface to modify the order to change fulfillment to be at the candidate retailer location;
-receiving, via the option on the user interface, input from the customer client device to modify the order to change fulfillment to be at the candidate retailer location;
-modifying the order for fulfillment at the candidate retailer location; and
-transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location
The additional elements of claim 1 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) as supported by paragraph [0102] of Applicant’s specification – “a software module is implemented with a computer program item comprising one or more computer-readable media storing computer program code or instructions, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. In some embodiments, a computer-readable medium comprises one or more computer-readable media that, individually or together, comprise instructions that, when executed by one or more processors, cause the one or more processors to perform, individually or together, the steps of the instructions stored on the one or more computer-readable media. Similarly, a processor comprises one or more processors or processing units that, individually or together, perform the steps of instructions stored on a computer-readable medium.” Specifically, the additional elements of a computer system, a processor, a computer-readable storage medium, a plurality of picker client devices, the picker client devices, training the desirability model, backpropagation, a user interface, a customer client device, and one picker client device are recited at a high-level of generality (i.e. as a generic processor performing the generic computer functions of receiving data, applying data, obtaining data, scoring data, updating data, identifying data, transmitting data, and modifying data) such that they amount do no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Further, the additional elements do 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). 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 claim 1, taken individually or as a whole, the additional elements of claim 9 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.
Claim 11 is a computer program product reciting similar functions as claim 1. Examiner notes that claim 11 recites the additional elements of a non-transitory computer readable storage medium having instructions encoded thereon, a processor, a plurality of picker client devices, the picker client devices, training the desirability model, backpropagation, a user interface, a customer client device, and one picker client device, however, claim 11 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Claim 19 is a system reciting similar functions as claim 1. Examiner notes that claim 19 recites the additional elements of one or more processors, a non-transitory computer readable storage medium having instructions encoded thereon, a plurality of picker client devices, the picker client devices, training the desirability model, backpropagation, a user interface, a customer client device, and one picker client device, however, claim 19 does not qualify as eligible subject matter for similar reasons as claim 1 indicated above.
Therefore, claims 1, 11, and 19 do not provide an inventive concept and do not qualify as eligible subject matter.
Dependent claims 2-6, 9-10, 12-15, 18, and 20-24, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. § 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2-6, 9-10, 12-15, 18, and 20-24 further fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas in that they recite commercial interactions. Claims 3-6, and 13-15 do not introduce new additional elements and as such are not indicative of integration into a practical application for at least similar reasons discussed above. Dependent claims 2, 9, 10, 12, 18, and 20-24, recite additional elements of a picker client device, a retailer computer system, a computer vision model, the plurality of picker client devices, a machine-learning model, the computer vision model being trained, and desirability model being an neural network, but similar to the analysis under prong two of Step 2A these additional elements are used as a tool to perform the abstract idea. As such, under prong two of Step 2A, claims 2-6, 9-10, 12-15, 18, and 20-24 are not indicative of integration into a practical application for at least similar reasons as discussed above. Thus, dependent claims 2-6, 9-10, 12-15, 18, and 20-24 are “directed to” an abstract idea. Next, under Step 2B, similar to the analysis of claims 1, 11, and 19, dependent claims 2-6, 9-10, 12-15, 18, and 20-24 when analyzed individually and as an ordered combination, merely further define the commonplace business method (i.e. recommending a retailer location to fulfill a customer's order based on item desirability) being applied on a general-purpose computer and, therefore, do not amount to significantly more than the abstract idea itself. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself. The analysis above applies to all statutory categories of invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Koch et. al. (U.S. 20200279191 Al), in view of Yee et al. (US 2021/0182930 A1), hereinafter Yee.
Regarding claim 1, Koch discloses a method, performed at a computer system comprising a processor and a computer-readable storage medium (Koch see at least: "The server system further comprises a processor associated with a machine learning model." and "Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein." [0178]), the method comprising:
-receiving images of one or more stock of an item available at each of a plurality of retailer locations, wherein the item is included in an order requested by a customer for fulfillment at an initial retailer location (Koch, see at least: "search results may display images, such as images 114-B and 114-C corresponding to merchant 114. Such images may include images of various order items, the merchant storefront or interior, or other relevant content." [0064]);
-applying a desirability model to attributes of stock of the item available at the plurality of retailer locations including the initial retailer location to output a retailer desirability score for each retailer location (Koch see at least: "a delivery logistics system is provided which includes a predictive merchant ranking model for generating scoring predictions for merchants. The system is configured to operate in a training mode to receive training datasets which include pairings between customer profiles and merchant profiles. The training data set may be used as a training corpus to train a predictive model to output scoring predictions based on the relevance of one or more merchants to a particular customer. In example embodiments, the predictive model may comprise a statistical machine learning model and/or a tree-based machine learning model." [0044] and "According to one aspect, the predictive merchant association model may include a neural network that is trained to reconstruct browsing contexts of merchants in a browsing session. A training corpus may include stored historical browsing sessions and a plurality of vector representations from the training corpus may be input as an input layer into a predictive merchant association model to generate a vector space, with each unique merchant assigned a corresponding vector in the space. The merchant vector representations may be positioned in the vector space such that associated merchants that share common characteristics are located in close proximity to one another in the vector space. Thus, the weighted vector space may represent the relationship between various latent features of the merchants, such as food type, order items, location, and convenience." [0111-0113]), wherein the desirability model is trained by:
-obtaining a set of training examples, each training example including attributes of the item offered by the retailer, and each training example having a label indicating whether the item is acceptable for inclusion in an order (Koch see at least: "The training set of feature values extracted for each pairing between a training customer profile and a training merchant profile includes a combination of one or more of the following: an average price range of merchants visited by the historical customer; an average delivery fee paid by the historical customer; an average item value of the historical merchant; an average delivery time of the historical merchant; and a number of intersecting tags between the training customer profile and the training merchant profile." [0019]);
-applying the desirability model to each training example of the set to generate a predicted desirability score for the training example (Koch see at least: "For each pairing between the available merchant and the customer, an input set of feature values may be extracted from the corresponding merchant profile and corresponding customer profile. An order score may then be determined based on the input set of feature values and the weighted coefficients determined in the training mode. The order score for a merchant may correspond to the probability that the customer will place an order from the merchant. Additional order models may be implemented to adjust the order score based on other customer activity information or other delivery logistics statistics. For example, customer preferences may be input by the customer and compared with characteristics of a merchant. The merchants may then be transmitted to the customer device to be displayed based on a ranking determined by the order score." [0047]);
-scoring the predicted desirability score for the training example using a loss function and the label of the training example (Koch see at least: "Each node in the decision tree may correspond to a feature and rules may be determined based on the value of the feature value in the training sets. For a particular training set of feature values, the GBM core order model may create a first optimal tree that performs the best on the particular first training set. A subsequent decision tree may then be added to optimize for the gradient of the loss function." [0143] Examiner notes that the value of the feature value equates to the score of the desirability score); and
-updating one or more parameters of the desirability model by backpropagation based on the scoring (Koch see at least: "In some embodiments, the processor is responsible for updating the parameters of each computational layer using algorithms, including but not limited to, a stochastic gradient descent algorithm and a backpropagation algorithm." [0175]);
-identifying a candidate retailer location from the plurality of retailer locations having the retailer desirability score greater than the retailer desirability score of the initial retailer location (Koch see at least: "The processor is further configured to determine a ranking order for each of the available merchant profiles according to corresponding primary order scores, and transmit at least a portion of the set of available merchants to the first customer device for display based on the ranking order." [0010]);
-in response to identifying the candidate retailer location, transmitting a user interface to a customer client device for presentation of a notification that the candidate retailer location has the retailer desirability score greater than the retailer desirability score of the initial retailer location and an option on the user interface to modify the order to change fulfillment to be at the candidate retailer location (Koch, see at least: "The processor is further configured to determine a ranking order for each of the available merchant profiles according to corresponding primary order scores, and transmit at least a portion of the set of available merchants to the first customer device for display based on the ranking order." [0010] and "Thus, the logistic regression version may be implemented to initially identify the subset of highest ranking merchant profiles. The GBM version may then refine the core order scores of the subset of merchant profiles. However, in some embodiments the GBM version of core order model may be implemented prior to the logistic regression version." [0128]);
-receiving, via the option on the user interface, input from the customer client device to modify the order to change fulfillment to be at the candidate retailer location (Koch see at least: "For example, a selection of one or more merchants may be received from a customer device with a request to view available items for order. Information corresponding to the selected merchants may be retrieved from database 216 and transmitted to the customer device." [0061] and "For example, a customer using an interface 300, may input search results and create browsing session 410 by selecting merchants 414, 318, 412, 110, and 112." [0067] and "In addition to a search request, additional browsing activity may also include information regarding selections, visits, and orders and other customer activity. Other customer activity may include items browsed, items added to a shopping cart, items removed from a shopping cart, registered complaints, ratings and reviews, etc. Such additional browsing activity may be used to update customer profile and merchant profile information at step 1003. As the, customer profile and merchant profile information is updated, it may be further processed to score subsequent available merchants based on subsequent search requests from the browsing activity transmitted by customer device 1020. In some embodiments, the updated customer profile and merchant profile information may be used to update order models 1009 of the merchant recommendation model at step 1009." [0171-173];
-modifying the order for fulfillment at the candidate retailer location (Koch see at least: "At 511, order data is received from the customer. The customer may place an order for selected items from a selected merchant from the current browsing session. The order data may include various information about the order, such as order items, number of items, total price of the order, as well as other relevant information regarding the restaurant and delivery route for the order." [0075]).
Koch does not explicitly disclose the receiving the images of one or more stock of the item available being from a plurality of picker client devices; and transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location.
Yee, however, teaches delivering grocery items (i.e. [0045]) including the known technique of receiving, from a plurality of picker client devices, images of one or more stock of an item available (Yee, see at least: “The remote shopping assistant 101 may include a camera 103 [i.e. from a plurality of picker client devices]. The camera may be any type of camera, such as a time-of-flight camera, action camera, point-and-shoot camera, light-field camera, digital SLR, etc. The camera 103 may be utilized to identify a location of a store that the remote shopping assistant 101 is located in, or to identify products in an aisle. The camera 103 may be utilized to identify various groceries or produce [i.e. receiving images of one or more stock of the item available]. For example, the camera may be utilized to distinguish and identify one fruit from another fruit, vegetable, or other item” [0016] and “Each shopper may have a personalized assistant that is assigned to shop for them [i.e. from a plurality of picker client devices]” [0015] and “the system may determine if a shopping assistant should select another item from the same batch. If the shopping assistant manually or automatically (e.g., via data retrieved from the sensors) identifies multiple grocery items [i.e. receiving images of one or more stock of the item available], it may determine that another selection can be made from the same batch. If it identifies the other items, the shopping assistant may send a notification to a user indicating that the other similar grocery items are available. For example, the system may be selecting a banana from a certain area of the store. The system may notice that other bananas are also located in that area, if the system recognizes the other bananas based on image data and image recognition patterning, the system may send a notification to a user indicating other bananas may be selected” [0040]); and
transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location (Yee, see at least: “The system may assign the assistant [i.e. transmitting the order to one picker client device] based on a distance from the delivery location, rating, compatibility with the shopper, store that the assistant is shopping at (e.g., shopping at the same store as the shopper wishes to purchase items) [i.e. located at the candidate retailer location for fulfillment of the order at the candidate retailer location], etc.” [0035]).This known technique is applicable to the method of Koch as they both share characteristics and capabilities, namely, they are directed to delivering grocery items.
It would have been recognized that applying the known technique of receiving, from a plurality of picker client devices, images of one or more stock of an item available, as taught by Yee, to the teachings of Koch would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of receiving, from a plurality of picker client devices, images of one or more stock of an item available, as taught by Yee, into the method of Koch would have been recognized by those of ordinary skill in the art as resulting in an improved method that would allow customers to gauge if they want to buy the produce today or to pass on the purchase (Yee, [0014]).
Additionally, it would have been obvious to one of ordinary skill in the art to include in the method, as taught by Koch, transmitting the order to one picker client device located at the candidate retailer location for fulfillment of the order at the candidate retailer location, as taught by Yee, 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 at the time of filing to modify Koch, to include the teachings of Yee, in order to allow customers to gauge if they want to buy the produce today or to pass on the purchase (Yee, [0014]).
Regarding Claim 11, which recites limitations directed towards 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 (Koch see at least: "Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions" [0178] and "a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors" [0040]. The limitations are otherwise parallel in nature to those addressed above for Claim 1 and are therefore rejected for those same reasons set forth above in Claim 1.
Regarding Claim 19, which recites limitations directed towards a system comprising: one or more processors; and a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by the one or more processors, cause the one or more processor to perform steps (Koch see at least: "Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions" [0178] and "a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors" [0040]. The limitations are otherwise parallel in nature to those addressed above for Claim 1 and are therefore rejected for those same reasons set forth above in Claim 1.
Claims 2-6, 9, 12-15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Koch, in view of Yee, in further view of Weaver (U.S. 20240054503 Al), hereinafter Weaver.
Regarding Claim 2, Koch in view of Yee teaches the method of Claim 1, Koch discloses wherein applying the trained desirability model to give feedback about an instance of the item offered by the retailer comprises: receiving, at the computer system, one or more additional images of the item (Koch see at least: "The training set of feature values extracted for each pairing between a training customer profile and a training merchant profile includes a combination of one or more of the following: an average price range of merchants visited by the historical customer; an average delivery fee paid by the historical customer; an average item value of the historical merchant; an average delivery time of the historical merchant; and a number of intersecting tags between the training customer profile and the training merchant profile." [0019] and "search results may display images, such as images 114-B and 114-C corresponding to merchant 114. Such images may include images of various order items, the merchant storefront or interior, or other relevant content." [0064]).
Koch in view of Yee does not teach the images being from the one picker client device, and retrieving the desirability model; determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item; and transmitting a notification from the computer system to the picker client device, the notification based on the desirability score for the item and comprising a message indicating whether the item is suitable for inclusion in the order.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of wherein applying the trained desirability model to give feedback about an instance of the item offered by the retailer comprises: receiving, at the computer system, one or more additional images of the item, from the one picker client device (Weaver see at least: "the element 402 (e.g., QR code) is on a box of apples (an example perishable 106). A sign next to the perishable 106 also includes a message 404 that reads: "This is a ProofScore certified product. Scan the QR code on the product to lookup the ProofScore." This message 404 makes the user 140 aware of the availability of the ProofScore(s) 104(1) and related information 400 that can be accessed using his/her computing device 134." [0053] and "Although the element 402 is shown as a QR code in the example of FIG. 4, it is to be appreciated that the element 402 can take other forms, such as a radio frequency identification (RFID) tag, a near field communication (NFC) transmitter, or a picture that can be captured with a camera and analyzed using image analysis, or the like." [0054] and see Figs. 4-5 ( client device is used to take an image of the item or a code related to the item and" In any case, in response to the user 140 entering information and/or in response to the computing device 134 interacting with (e.g., scanning) the element 402 at the retail location 124, the computing device 134 may send, and the remote computing system 102 may receive, a request to access the score(s) 104 and/or information 400 associated with the perishable 106, the request including the identifier linked to the element 402 or to the information entered by the user 140." [0054] and "The computing system 102 is configured to process the sensor data 118 (perhaps in combination with the custody data 126) using one or more models 128 accessible to the computing system 102." [0033] and see Fig. 1 (the model is stored in the computing system, so it is being accessed or retrieved for use by the computing system));
retrieving the desirability model (Weaver see at least: "The computing system 102 is configured to process the sensor data 118 (perhaps in combination with the custody data 126) using one or more models 128 accessible to the computing system 102." [0033] and see Fig. 1 (the model is stored in the computing system, so it is being accessed or retrieved for use by the computing system));
determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item (Weaver see at least: "Although the element 402 is shown as a QR code in the example of FIG. 4, it is to be appreciated that the element 402 can take other forms, such as a radio frequency identification (RFID) tag, a near field communication (NFC) transmitter, or a picture that can be captured with a camera and analyzed using image analysis, or the like ... in response to the user 140 entering information and/or in response to the computing device 134 interacting with (e.g., scanning) the element 402 at the retail location 124, the computing device 134 may send, and the remote computing system 102 may receive, a request to access the score(s) 104 and/or information 400 associated with the perishable 106, the request including the identifier linked to the element 402 or to the information entered by the user 140. In response to receiving the request, the computing system 102 may cause the score(s) 104 and/or one or more interactive elements 406 (e.g., links, buttons, drop down elements, etc.) for accessing the information 400 to be displayed on a display of the computing device 134" [0054]); and
transmitting a notification from the computer system to the picker client device, the notification based on the desirability score for the item and comprising a message indicating whether the item is suitable for inclusion in the order (Weaver see at least: "As yet another example, the user 140 might wear smart glasses or a headset with a camera that automatically scans the element 402 and displays the score(s) 104" [0054] and see Figs. 4-5 for example of notification). These known techniques are applicable to the method of Koch in view of Yee as they both share characteristics and capabilities, namely, they are directed to a method for scoring items and retailers scoring items and retailers (i.e. [0013] and [0043])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that applying the known technique of receiving, at a computer system, one or more additional images of the item from the one picker client device; retrieving the desirability model; determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item and transmitting a notification from the computer system to the picker client device, the notification based on the desirability score for the item and comprising a message indicating whether the item is suitable for inclusion in the order, as taught by Weaver, to the teachings of Koch in view of Yee, would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, the addition of the teachings of Weaver, would have been recognized by those of ordinary skill in the art as resulting in an improved method that reduces food waste a provides incentives for retailer to improve their score, better serving the customer (Weaver see at least: [0018]).
Regarding Claim 3, the combination of Koch/Yee/Weaver teaches the method of Claim 2. Koch in view of Yee does not teach wherein the notification comprises a message that the item is suitable for inclusion in the order in response to the desirability score having at least a threshold value.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of wherein the notification comprises a message that the item is suitable for inclusion in the order in response to the desirability score having at least a threshold value (Weaver see at least: "A binary "fresh or spoiled" classification of a perishable 106 may be based on whether the FreshScore 104(2) satisfies a threshold. "Satisfying" a threshold, as used herein can mean meeting or exceeding the threshold, or strictly exceeding the threshold. For example, a threshold of 50 ( on a scale of 0 to 100) may be satisfied by a score 104 of 50, because 50 is equal to the threshold in this example. Alternatively, a threshold of 50 may be satisfied by a score 104 of 51, while a score 104 of 50 may not satisfy the threshold." [0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Weaver for the reasons identified above with respect to claim 2.
Regarding Claim 4, the combination of Koch/Yee/Weaver teaches the method of Claim 3. Koch in view of Yee does not teach wherein the notification comprises a message that the item is not suitable for inclusion in the order in response to desirability score having less than the threshold value.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of wherein the notification comprises a message that the item is not suitable for inclusion in the order in response to desirability score having less than the threshold value (Weaver see at least: "A binary "fresh or spoiled" classification of a perishable 106 may be based on whether the FreshScore 104(2) satisfies a threshold. "Satisfying" a threshold, as used herein can mean meeting or exceeding the threshold, or strictly exceeding the threshold. For example, a threshold of 50 (on a scale of 0 to 100) may be satisfied by a score 104 of 50, because 50 is equal to the threshold in this example. Alternatively, a threshold of 50 may be satisfied by a score 104 of 51, while a score 104 of 50 may not satisfy the threshold." [0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Weaver for the reasons identified above with respect to claim 3.
Regarding Claim 5, the combination of Koch/Yee/Weaver teaches the method of Claim 3. Koch further discloses
-wherein the value is based on a customer associated with the order and a category including the item (Koch see at least: "In some embodiments, pair features may correspond to the intersections of all available tags. In some embodiments, pair features may correspond to the intersections of tags within a particular category. In some embodiments, pair features may include an estimate of probability that the customer will order from the primary category of the merchant based on the merchants previously ordered from by the customer" [0102-0103]).
Koch in view of Yee does not teach the value, or desirability score, being a threshold value.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of the threshold value (Weaver see at least: "A binary "fresh or spoiled" classification of a perishable 106 may be based on whether the FreshScore 104(2) satisfies a threshold. "Satisfying" a threshold, as used herein can mean meeting or exceeding the threshold, or strictly exceeding the threshold." [0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Weaver for the reasons identified above with respect to claim 3.
Regarding Claim 6, Koch in view of Yee, teaches the method of Claim 1. Koch further discloses:
-wherein applying the trained desirability model to give feedback about an instance of the item offered by the retailer comprises:
-retrieving an item catalog of items offered by a retailer (Koch, see at least: "a selection of one or more merchants may be received from a customer device with a request to view available items for order. Information corresponding to the selected merchants may be retrieved from database 216 and transmitted to the customer device” [0061] and “a merchant may be categorized into a price range based on the average price of items on its menu. In some embodiments, the merchant may be categorized into a price range based on the average price of main dishes on its menu” [0084]);
-determining, using the desirability model, a desirability score for the item based on attributes of the item from the retailer (Koch see at least: "a delivery logistics system is provided which includes a predictive merchant ranking model for generating scoring predictions for merchants. The system is configured to operate in a training mode to receive training datasets which include pairings between customer profiles and merchant profiles." [0044] and "The training set of feature values extracted for each pairing between a training customer profile and a training merchant profile includes a combination of one or more of the following: an average price range of merchants visited by the historical customer; an average delivery fee paid by the historical customer; an average item value of the historical merchant; an average delivery time of the historical merchant; and a number of intersecting tags between the training customer profile and the training merchant profile." [0019]);
-a set of items from the item catalog (Koch, see at least: "a selection of one or more merchants may be received from a customer device with a request to view available items for order. Information corresponding to the selected merchants may be retrieved from database 216 and transmitted to the customer device” [0061] and “a merchant may be categorized into a price range based on the average price of items on its menu. In some embodiments, the merchant may be categorized into a price range based on the average price of main dishes on its menu” [0084]) and
-generating a retailer desirability score for the retailer from the desirability score for the item (Koch see at least: "a delivery logistics system is provided which includes a predictive merchant ranking model for generating scoring predictions for merchants. The system is configured to operate in a training mode to receive training datasets which include pairings between customer profiles and merchant profiles." [0044] and "The training set of feature values extracted for each pairing between a training customer profile and a training merchant profile includes a combination of one or more of the following: an average price range of merchants visited by the historical customer; an average delivery fee paid by the historical customer; an average item value of the historical merchant; an average delivery time of the historical merchant; and a number of intersecting tags between the training customer profile and the training merchant profile." [0019]).
Koch in view of Yee does not teach determining desirability scores for each item of a set of items using desirability models stored in association with the items of the set; and generating the desirability scores for each item of the set of items.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of determining desirability scores for each item of a set of items using desirability models stored in association with the items of the set (Weaver see at least: "As shown in FIG. 3 , the FreshScoring module(s) 300 is configured to provide the sensor data 118 received from a sensor 116 associated with a perishable 106 as input to the trained machine learning model(s) 128(2), and to generate a score 104(2) (e.g., a FreshScore 104(2) associated with the perishable 106 as output from the trained machine learning model(s) 128(2)." [0052]); and
the known technique of generating a retailer desirability score for the retailer from the desirability score for the item and the desirability scores for each item of the set of items (Weaver see at least: "This type of score is sometimes referred to herein as a "ProofScore®." In some examples, the information associated with the perishable includes information about a "producer" of the perishable, such as information about a farmer, a grower, a manufacturer, a processor, a distributor, or the like." [0013] and "Freshness data 206(5) may include, without limitation, FreshScores generated by the computing system 102 in association with the perishables 106 produced by the producer and/or other data relating to the freshness of the producer's perishables 106." [0043] and Fig. 2 indicates that the freshness data used to generate the ProofScore). These known techniques are applicable to the method of Koch in view of Yee as they both share characteristics and capabilities, namely, they are directed to a method for scoring items and retailers scoring items and retailers (i.e. [0013] and [0043]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that applying the known technique of determining desirability scores for each item of a set of items using desirability models stored in association with the items of the set; and generating a retailer desirability score for the retailer from the desirability score for the item and the desirability scores for each item of the set of items, as taught by Weaver, to the teachings of Koch in view of Yee, would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, the addition of the teachings of Weaver, would have been recognized by those of ordinary skill in the art as resulting in an improved method that reduces food waste a provides incentives for retailer to improve their score, better serving the customer (Weaver see at least: [0018]).
Regarding Claim 9, Koch in view of Yee teaches the method of Claim 1, Koch further discloses:
-wherein applying the trained desirability model to give feedback about an instance of the item offered by the retailer comprises: receiving, at the computer system, one or more additional images of the item from a retailer computer system (Koch see at least: "The training set of feature values extracted for each pairing between a training customer profile and a training merchant profile includes a combination of one or more of the following: an average price range of merchants visited by the historical customer; an average delivery fee paid by the historical customer; an average item value of the historical merchant; an average delivery time of the historical merchant; and a number of intersecting tags between the training customer profile and the training merchant profile” [0019] and "search results may display images, such as images 114-B and 114-C corresponding to merchant 114. Such images may include images of various order items, the merchant storefront or interior, or other relevant content” [0064]).
However, Koch does not disclose retrieving the desirability model; determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item; generating a suggestion for the item based on the desirability score for the item; and transmitting the suggestion from the computer system to the retailer computer system.
Weaver, however, teaches scoring items and retailers (i.e. [0013] and [0043]), including the known technique of retrieving the desirability model (Weaver see at least: "The computing system 102 is configured to process the sensor data 118 (perhaps in combination with the custody data 126) using one or more models 128 accessible to the computing system 102." [0033] and see Fig. 1 (the model is stored in the computing system, so it is being accessed or retrieved for use by the computing system));
the known technique of determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item (Weaver see at least: "As shown in FIG. 3, the Fresh Scoring module(s) 300 is configured to provide the sensor data 118 received from a sensor 116 associated with a perishable 106 as input to the trained machine learning model(s) 128(2), and to generate a score 104(2) (e.g., a FreshScore 104(2) associated with the perishable 106 as output from the trained machine learning model(s) 128(2)." [0052]);
the known technique of generating a suggestion for the item based on the desirability score for the item (Weaver see at least: "A binary "fresh or spoiled" classification of a perishable 106 may be based on whether the FreshScore 104(2) satisfies a threshold. "Satisfying" a threshold, as used herein can mean meeting or exceeding the threshold, or strictly exceeding the threshold." [0036]); and
the known technique of transmitting the suggestion from the computer system to the retailer computer system (Weaver see at least:" In some examples, computing system 102 may be configured to generate reports, which may be sent to one or more users automatically and/or at the request of the user. FIG. 1 depicts an administrator (admin) dashboard 130, a partner dashboard 132, and user devices 134, which may be used by users (e.g., users 140) to access the computing system 102 and/or data and/or information provided by the computing system 102." [0037]). These known techniques are applicable to the method of Koch in view of Yee as they both share characteristics and capabilities, namely, they are directed to a method for scoring items and retailers scoring items and retailers (i.e. [0013] and [0043])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention that applying the known technique of retrieving the desirability model; determining, using the desirability model, a desirability score for the item based on attributes of the item, one or more of the attributes determined from the one or more additional images of the item; generating a suggestion for the item based on the desirability score for the item; and transmitting the suggestion from the computer system to the retailer computer system, as taught by Weaver, to the teachings of Koch in view of Yee, would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, the addition of the teachings of Weaver, would have been recognized by those of ordinary skill in the art as resulting in an improved method that reduces food waste a provides incentives for retailer to improve their score, better serving the customer (Weaver see at least: [0018]).
Regarding Claim 12, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 2 and are therefore rejected for those same reasons set forth above in Claim 2.
Regarding Claim 13, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 3 and are therefore rejected for those same reasons set forth above in Claim 3.
Regarding Claim 14, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 5 and are therefore rejected for those same reasons set forth above in Claim 5.
Regarding Claim 15, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 6 and are therefore rejected for those same reasons set forth above in Claim 6.
Regarding Claim 18, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 9 and are therefore rejected for those same reasons set forth above in Claim 9.
Regarding Claim 20, which recites limitations directed towards training a desirability model to give feedback on a retailer, the limitations are parallel in nature to those addressed above for Claim 2 and are therefore rejected for those same reasons set forth above in Claim 2.
Claim 10 is rejected under 35 U.S.C.103 as being unpatentable over Koch in view of Yee in further view of Weaver, in further view Walker et. al. (U.S. 20130218700 Al), hereinafter Walker.
Regarding Claim 10, the combination of Koch/Yee/Weaver teaches the method of Claim 9,
The combination of Koch/Yee/Weaver does not teach wherein the suggestion specifies the retailer offering the item at a discount in response to the desirability score for the item being in a range of desirability scores.
Walker, however, teaches scoring a product based on its description (i.e. [0088]), including the known technique of the suggestion specifying the retailer offering the item at a discount in response to the desirability score for the item being in a range of desirability scores (Walker see at least: "The embodiment of the flexibility database 600 illustrated in FIG. 6B thereby allows retailer data such as inventory, sales, demand and subsidies to be used in creating a flexibility score which more accurately reflects a flexibility and a desirability of a particular description." [0130] and "Next, a score range 710 including the determined flexibility score is located in a portion of the product pricing database 700 corresponding to an appropriate category, and a sale price 720 corresponding to the located score range 710 is determined to be the sale price. "For example, the system 100 may communicate item attributes to a secondary server that may return with offers of related items but with more favorable attributes than those of the retailer of item 122. For example, other retailers may offer a related item 124 at a lower cost than the item 122 and so on." [0068]). This known technique is applicable to the method of the combination of Koch/Yee/Weaver as they both share characteristics and capabilities, namely, they are directed to scoring a product based on its description (Walker see at least: [0088]).
It would have been obvious to one of ordinary skill in the art that applying the known technique the suggestion specifying the retailer offering the item at a discount in response to the desirability score for the item being in a range of desirability scores, as taught by Walker, to the teachings of the combination of Koch/Yee/Weaver, would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, the addition of the teachings of Walker, would have been recognized by those of ordinary skill in the art as resulting in an improved method that ensures the user is provided with the most relevant items at a fair price (Walker see at least: [0022]).
Claims 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Koch, in view of Yee, in further view of Adato (U.S. 2021/0400195 Al), newly cited and hereinafter Adato.
Regarding Claim 21, Koch in view of Yee teaches the method of Claim 1.
Koch in view of Yee does not explicitly teach applying a computer vision model to the images of the stock of the item received from the plurality of picker client devices to extract attributes of the stock of the item available at the plurality of retailer locations, wherein the computer vision model is a machine-learning model.
Adato, however, teaches purchasing items in an online store (i.e. [0230]), including the known technique of applying a computer vision model to the images of the stock of the item received from the plurality of picker client devices to extract attributes of the stock of the item available at the plurality of retailer locations, wherein the computer vision model is a machine-learning model (Adato, see at least: “System 100 may also include an image processing unit 130 to execute the analysis of images captured by the one or more capturing devices 125, [i.e. applying a computer vision model to the images of the stock of the item] image processing unit 130 may include a server 135 operatively connected to a database 140 … image processing unit 130 may use any suitable image analysis technique including, for example, object recognition, object detection, image segmentation, feature extraction, optical character recognition (OCR), object-based image analysis, shape region techniques, edge detection techniques, pixel-based detection, artificial neural networks, convolutional neural networks, etc. [i.e. wherein the computer vision model is a machine-learning model] In addition, image processing unit 130 may use classification algorithms to distinguish between the different products in the retail store. In some embodiments, image processing unit 130 may utilize suitably trained machine learning algorithms and models to perform the product identification” [0119] and “Server 135 may access database 140 to detect and/or identify products. The detection may occur through analysis of features in the image [i.e. to extract attributes of the stock of the item available at the plurality of retailer locations] using an algorithm and stored data. The identification may occur through analysis of product features in the image according to stored product models. Consistent with the present embodiment, the term “product model” refers to any type of algorithm or stored product data that a processor may access or execute to enable the identification of a particular product associated with the product model. For example, the product model may include a description of visual and contextual properties of the particular product (e.g., the shape, the size, the colors, the texture, the brand name, the price, the logo, text appearing on the particular product, the shelf associated with the particular product, adjacent products in a planogram, the location within the retail store, etc.)” [0123] and “server 135 may receive image data acquired by store employees [i.e. received from the plurality of picker client devices]. In one implementation, a handheld device of a store employee (e.g., capturing device 125D) may display a real-time video stream captured by the image sensor of the handheld device” [0145]). This known technique is applicable to the method of Koch in view of Yee as they both share characteristics and capabilities, namely, they are directed to purchasing items in an online store.
It would have been recognized that applying the known technique of applying a computer vision model to the images of the stock of the item received from the plurality of picker client devices to extract attributes of the stock of the item available at the plurality of retailer locations, wherein the computer vision model is a machine-learning model, as taught by Adato, to the teachings of Koch in view of Yee would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such references into similar methods. Further, adding the modification of applying a computer vision model to the images of the stock of the item received from the plurality of picker client devices to extract attributes of the stock of the item available at the plurality of retailer locations, wherein the computer vision model is a machine-learning model, as taught by Adato, into the method of Koch in view of Yee would have been recognized by those of ordinary skill in the art as resulting in an improved method that would improve in-store execution by providing adequate visibility (Adato, [0228]).
Regarding Claim 22, the combination of Koch/Yee/Adato teaches the method of Claim 21.
Koch in view of Yee does not explicitly teach identifying a category of the item from an item catalog, wherein items in the item catalog are classified under a plurality of item categories; and selecting the computer vision model trained for the category of the item.
Adato, however, teaches purchasing items in an online store (i.e. [0230]), including the known technique of identifying a category of the item from an item catalog, wherein items in the item catalog are classified under a plurality of item categories (Adato, see at least: “a first product model may be used by server 135 to identify a product category (such models may apply to multiple product types, e.g., shampoo, soft drinks, etc.), [i.e. identifying a category of the item from an item catalog] and a second product model may be used by server 135 to identify the product type, product identity, or other characteristics associated with a product” [0123] and “a product type may refer to a particular category of product (e.g., soda, beer, sports drinks, etc.) [i.e. wherein items in the item catalog are classified under a plurality of item categories]” [0474]); and
the known technique of selecting the computer vision model trained for the category of the item (Adato, see at least: “image processing unit 130 may use classification algorithms to distinguish between the different products in the retail store. In some embodiments, image processing unit 130 may utilize suitably trained machine learning algorithms and models to perform the product identification [i.e. selecting the computer vision model trained for the category of the item]” [0119]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Adato for the reasons identified above with respect to claim 21.
Regarding Claim 23, the combination of Koch/Yee/Adato teaches the method of Claim 21. Koch further discloses:
-aggregating the features of the stock of the item based on each retailer location (Koch, see at least: “the weighted vector space may represent the relationship between various latent features of the merchants, such as food type, order items, location, and convenience [i.e. wherein applying the computer vision model comprises: aggregating the features of the stock of the item based on each retailer location]” [0113]).
Koch in view of Yee does not explicitly teach applying the computer vision model comprising: applying the computer vision model to each image of the stock of the item to extract visual features characterizing the stock of the item; and the features being the visual features extracted from the image.
Adato, however, teaches purchasing items in an online store (i.e. [0230]), including the known technique of applying the computer vision model comprising: applying the computer vision model to each image of the stock of the item to extract visual features characterizing the stock of the item (Adato, see at least: “Database 140 may include product type model data 240 (e.g., an image representation, a list of features, a model obtained by training machine learning algorithm using training examples, an artificial neural network, and more) that may be used to identify products in received images [i.e. wherein applying the computer vision model comprises:]; contract-related data 242 (e.g., planograms, promotions data, etc.) that may be used to determine if the placement of products on the store shelves and/or the promotion execution are consistent with obligations of retail store 105; catalog data 244 (e.g., retail store chain's catalog, retail store's master file, etc.) that may be used to check if all product types that should be offered in retail store 105 are in fact in the store, if the correct price is displayed next to an identified product, etc.; inventory data 246 [i.e. applying the computer vision model to each image of the stock of the item to extract visual features characterizing the stock of the item] that may be used to determine if additional products should be ordered from suppliers 115” [0134]); and
the known technique of the visual features extracted from the image (Adato, see at least: “Embodiments of the present disclosure further include determining at least one characteristic of the product for determining the type of the product. As used herein, the term “characteristic of the product” refers to one or more visually discernable features attributed to the product [i.e. the visual features extracted from the image]. Consistent with the present disclosure, the characteristic of the product may assist in classifying and identifying the product. For example, the characteristic of the product may be associated with the ornamental design of the product, the size of the product, the shape of the product, the colors of the product, the brand of the product, a logo or text associated with the product (e.g., on a product label), and more” [0116]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Adato for the reasons identified above with respect to claim 21.
Regarding Claim 24, the combination of Koch/Yee/Adato teaches the method of Claim 23. Koch further discloses:
-wherein the desirability model is a neural network (Koch, see at least: “Process 700 may be implemented using a neural network [i.e. wherein the desirability model is a neural network] comprising one or more computational layers” [0107]), and wherein applying the desirability model comprises:
-generating a feature vector for each retailer location based on the aggregated features for the retailer location (Koch, see at least: “Various processes may be implemented to convert merchants into vector representations [i.e. generating a feature vector for each retailer location]” [0108] and “the weighted vector space may represent the relationship between various latent features of the merchants, such as food type, order items, location, and convenience [i.e. based on the aggregated visual features for the retailer location]” [0113]); and
-inputting the feature vector into the desirability model to output the desirability score (Koch, see at least: “Using this weighted vector space [i.e. inputting the feature vector into the desirability model], the neural network 700 can learn the relationship between merchants, such as between merchant 112 and merchant 116. Thus, the weighted vector space may represent the relationship between various latent features of the merchants, such as food type, order items, location, and convenience” [0113] and “The training data set may be used as a training corpus to train a predictive model to output scoring predictions [i.e. to output the desirability score] based on the relevance of one or more merchants to a particular customer” [0044]).
Koch in view of Yee does not explicitly teach the features being visual features.
Adato, however, teaches purchasing items in an online store (i.e. [0230]), including the known technique of visual features (Adato, see at least: “Embodiments of the present disclosure further include determining at least one characteristic of the product for determining the type of the product. As used herein, the term “characteristic of the product” refers to one or more visually discernable features attributed to the product [i.e. visual features]. Consistent with the present disclosure, the characteristic of the product may assist in classifying and identifying the product. For example, the characteristic of the product may be associated with the ornamental design of the product, the size of the product, the shape of the product, the colors of the product, the brand of the product, a logo or text associated with the product (e.g., on a product label), and more” [0116]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Koch in view of Yee with Adato for the reasons identified above with respect to claim 21.
Response to Arguments
Rejections under 35 U.S.C. §101
Applicant argues that (1) under Step 2A, Prong Two, the additional elements integrate any alleged judicial exception into a practical application, and (2) under Step 2B, the additional elements are non-routine and unconventional activity that amount to an inventive concept. Applicant argues that the recited limitation cannot be characterized as directed to an excluded method of organizing human activity abstract idea. These steps relate to transmission and receipt of digital data over computer systems and devices, and the presentation and operation of graphical user interfaces. Although the context of the claimed invention describes order fulfillment and actions that may be taken by users ( e.g., pickers and the customer), the steps themselves recite technical limitations surrounding computer functionality by the computer system. For example, though the step of "receiving, from a plurality of picker client devices," images of one or more stock of an item available at each of a plurality of retailer locations ... " contextually recites "pickers" and "retailers," the limitation as a whole is directed to data receipt of images from a plurality of client devices disparately located across a plurality of locations. As another example, though the step of "transmitting a user interface to a customer client device ... " may present information relevant to the customer's order, thus in the context of order modification, the express step of transmission of the user interface for presentation on the customer client device is a technical limitation that should be deemed an additional element. In effect, precluding the entirety of such limitations as an excluded method of organizing human activity is erroneous (Remarks, pages 15-17).
Examiner respectfully disagrees. As detailed in both the prior and the current rejections, the recited additional elements are identified and analyzed under Prong 2 of Step 2A. The additional elements recited in the claims, such as computer systems, devices, and graphical user interfaces, 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). Accordingly, the claims are not integrated into a practical application.
Applicant further argues that the additional elements, embodied in the above-noted limitations, sufficiently integrate any of the alleged judicial exceptions into a practical application under Step 2A, Prong Two of the eligibility framework. The MPEP provides that "limitations the courts have found indicative that an additional element ( or combination of elements) may have integrated the exception into a practical application include: an improvement in the functioning of a computer, or an improvement to other technology or technical field .... " MPEP § 2106.04(d). Such is the case with the present claims. The additional elements relate to a technological improvement in crowdsourcing images of stock of an item available across a plurality of locations. The additional elements relate to receiving images of stock of an item from a plurality of client devices disparately located across a plurality of locations. By leveraging the plurality of client devices to obtain image data of item stock across the plurality of locations, the online system can compare desirability of item stock across the plurality of locations. Upon identifying better desirability of item stock at one location over another, the computer system generates and deploys the graphical user interface for modifying the user's order. Without such innovation, the system is limited and has no up-to-date information on the stock of an item across the locations. This limited insight creates a technological problem, which is solved by the claimed invention. In sum, the recited technological solution thereby integrates the alleged judicial exception into a practical application-supporting a finding of eligibility under Step 2A, Prong Two (Remarks, pages 17-18).
Examiner respectfully disagrees. Crowdsourcing images of stock of an item available across a plurality of locations is not a technical field and providing up-to-date information on the stock of an item across the locations is not a technical solution. The additional elements recited in the claims, such as computer systems, devices, and graphical user interfaces, 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). Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field.
Applicant further argues that the additional elements are non-routine, unconventional, and not well understood activity in the technological field, thereby amounting to an inventive concept supporting eligibility under Step 2B. The MPEP provides that "an examiner should determine that an element ( or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry" (italics for emphasis). MPEP § 2106.0S(d). The additional elements are not so widely prevalent. Wide prevalence requires a high degree of understanding among artisans in the field, such that it need not be described in detail. See id, further referencing 35 U.S.C. § l 12(a). The additional elements are not so widely prevalent as to render them conventional, routing, or well understood. Critically, the prior art does not teach nor suggest the additional elements, specifically the steps on prompting picker client devices to capture image data of item stock across the retailer locations, as further discussed below under the arguments against the rejections under 35 U.S.C. § 103. Accordingly, the additional elements further support finding of eligibility under Step 2B, in addition to Step 2A, Prong Two (Remarks, pages 18-19).
Examiner respectfully disagrees. The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually. Additionally, as is described in the MPEP 2106.05(II) (i.e. “Thus, in Step 2B, examiners should: … Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant”), step 2B considers whether additional elements concluded to be insignificant extra-solution activity in Step 2A are more than well-understood, routine, conventional activity in the field. Examiner did not identify any of the additional elements as insignificant extra-solution activity in Step 2A so there weren’t elements to be evaluated in terms of whether they are more than well-understood, routine, conventional activity in the field.
Additionally, the courts consider the determination of whether a claim is eligible (which involves identifying whether an exception such as an abstract idea is being claimed) to be a question of law, not a question of novelty. While the second step of the Alice analysis does consider whether the functions being performed are well-understood, routine, or conventional (and therefore, not considered to amount to significantly more), the question of whether a claim is eligible under 101 is based on determining whether an abstract idea has been claimed, and whether the additional elements amount to significantly more than the abstract idea. Even considered as an ordered combination (as a whole), the additional elements do not add anything significantly more than when considered individually.
Accordingly, the claims are ineligible.
Applicant further argues that Claims 11 and 19 recite analogous limitations to claim 1. Accordingly, the reasons advanced above with respect to claim 1 cross-apply to claims 11 and 19. Remaining claims, by virtue of their dependency on claims 1, 11, or 19, incorporate the additional elements of their intervening base claim, which support eligibility under Step 2A, Prong Two, and/or under Step 2B. Likewise, the remaining claims are also directed to patent eligible subject matter (Remarks, page 19).
Examiner respectfully disagrees. As detailed in the response to arguments above, claim 1 is not eligible. Similarly, 11 and 19 as well as the dependent claims are ineligible.
Applicant further argues that newly added claims 21-24 detail the application of another machine learning model, i.e., the "computer vision model," which extracts the attributes of the item stock from the images received from the client devices. Claims 21-24, in conjunction with the limitations of claim 1 amount to further technological improvements in the particular architecting of the machine-learning models, further bolstering eligibility of claims 21-24 (Remarks, page 19).
Examiner respectfully disagrees. Merely utilizing a computer vision model and client devices does not improve machine learning technology itself, nor does it improve client device technology. Accordingly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to reflect an improvement in the functioning of a computer or an improvement to another technology or technical field and the claims are ineligible.
Rejections under 35 U.S.C. §103
Applicant argues that Koch does not teach nor suggest the claimed limitation of "receiving, from a plurality of picker client devices, images of one or more stock of an item available at each of a plurality of retailer locations, wherein the item is included in an order requested by a customer for fulfillment at an initial retailer location" as nothing in this description describes the act of receiving images from picker client devices across a plurality of retailer locations and the images exampled in this description do not describe images of stock of an item available across the plurality of retailer locations. On either of these distinctions, Koch fails to teach the claimed limitation (Remarks, pages 19-21).
Examiner respectfully disagrees. Koch discloses receiving images across a plurality of retailer locations as it discloses a plurality of different images of order items [i.e. images of one or more stock of an item available] that correspond to particular merchants [i.e. available across the plurality of retailer locations] (see Koch, [0064]). Order items are items available for order (i.e. in stock and available for order). Yee is brought in to modify the receiving of images of Koch to be received from a plurality of picker client devices. Specifically, Yee teaches remote shopping assistants that are assigned to particular shoppers and these remote shopping assistants utilizing a camera to capture images [i.e. from a plurality of picker client devices] of various groceries or produce and sending these images to the system to identify the grocery items [i.e. receiving images of one or more stock of the item available] (see Yee, [0016], [0015], and [0040]). Accordingly, Koch in view of Yee teach this feature.
Applicant further argues that Koch does not teach nor suggest the claimed limitation of "applying a desirability model to attributes of stock of the item available at the plurality of retailer locations including the initial retailer location to output a retailer desirability score for each retailer location." Koch's model is identifying optimal pairings of customers to merchants through extracting latent features for customers and merchants, then scoring similarities in the latent features. The claimed limitation applies a model to solely attributes of item stock at a retailer location, not to latent features of the retailer location. Accordingly, Koch fails to teach or suggest the claimed limitation (Remarks, pages 21-22).
Examiner respectfully disagrees. Initially, Examiner points out that the claims don't recite that the model is applied solely to attributes of item stock at a retailer location. Additionally, Koch discloses that latent features of the merchant input into the model include food type [i.e. applying a desirability model to attributes of stock of the item] (see Koch, [0044] and [0111-0113]). Accordingly, Koch in view of Yee teach this feature.
Applicant further argues that Claims 11 and 19 recite analogous limitations to claim 1. As such, the arguments against the§ 103 rejection with respect to claim 1 cross-apply to claims 11 and 19. Remaining claims, by virtue of their dependency on claims 1, 11, or 19, are likewise non-obvious under§ 103 over the cited references (Remarks, page 22).
As detailed in the response to arguments above, claim 1 is not allowable as the argued features are taught by the cited references. Accordingly, claims 11 and 19, as well as, the dependent claims are not allowable.
Applicant further argues that none of the cited references teach or suggest the limitations in newly added claims 21-24 (Remarks, page 22).
Examiner respectfully disagrees. As detailed in the rejection above, the combination of Koch/Yee/Adato (Adato being newly cited) teach the newly added claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
-Fotso et al. (US 2022/0327583 A1) teaches numerical representations of merchant services in a vector space.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ARIELLE E WEINER/ Primary Examiner, Art Unit 3689