DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/4/2026 has been entered.
Status of Claims
Claims 1-8, 10-17, and 19-20 remain pending, and are rejected.
Claims 9 and 18 have been cancelled.
Response to Arguments
Applicant’s arguments filed on 3/4/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive for at least the following rationale:
Applicant’s arguments filed on 3/4/2026 with respect to the rejection under 35 U.S.C. 101 have been fully considered, but are not persuasive.
Notably, on pages 1-2 of the Applicant’s Remarks, arguments are made that the claims address a technical problem in machine learning that arises from the types of training data used, such as “these different circumstances may provide different information related to the preference of customer for the different replacement items”, and leads to inaccurate predictions of which replacement items a user will actually accept. The Applicant argues that training data for machine learning models has uniform labeling that treats all user feedback equally, leading to the diminished prediction accuracy. On pages 2-3, arguments are made that the claims provide a specific technical solution by assigning different values to training data based on the type and context of user feedback and enable the machine learning model to learn contextually-appropriate weightings, improving prediction accuracy for replacement item acceptance. On pages 3-4, the Applicant argues that the claims integrate any alleged judicial exception into a practical application by improving machine learning technology by enabling the machine learning model to distinguish between different qualities of user acceptance signals based on the context in which feedback was provided using specific recitation of the label hierarchy structure.
Examiner respectfully disagrees. Problems of a type of data used does not represent a technical problem, but merely reflects a problem within the abstract idea. The claims are not changing how any machine learning functions or any technical functionality of machine learning, but merely utilizes different types of data to provide a more accurate output in context of the particular abstract idea. Providing different information related to the preference of customers for different replacement items is a sales and marketing activity, and is an abstract idea under certain methods of organizing human activity. Inaccurate predictions of which replacement items a user will actually accept is also a sales and marketing activity, and an abstract idea under certain methods of organizing human activity. The claims only provide a label or value for abstract data that is an input to a machine learning model (which the underlying technology is not disclosed or recited), and merely provides an output of the abstract idea using the input information. As such, it is also evident that the claims do not integrate the abstract idea into a practical application, as the machine learning model is merely applied to the abstract idea to calculate an output with a given input. The training is also recited in passing, merely reciting that the machine learning model is trained using the particular data of the abstract idea without any further description. The label hierarchy structure also does not constitute any technical improvement or significantly more than the abstract idea. The label hierarchy structure merely represents a further extension of the abstract idea in weighting information of the abstract idea, and is not changing any technical functionality of the computer, such as how a computer stores and retrieves information from memory. The claims are directed to the abstract idea of weighting information in order to make accurate predictions of replacement items that a user will accept, and does not provide any meaningful limitations of the additional elements beyond providing a general link to a computing environment.
In view of the above, the rejection under 35 U.S.C. 101 has been maintained below.
Applicant’s arguments filed on 3/4/2026 with respect to the rejection under 35 U.S.C. 103 have been fully considered, but are moot in light of new grounds of rejection. Applicant’s amendments necessitated new grounds of rejection.
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 1-8, 10-17, and 19-20 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1:
Claims 1-8 are directed to a method, which is a process. Claims 10-17 are directed to a non-transitory computer readable storage medium, which is an article of manufacture. Claims 19-20 are directed to a computer program product, which is an apparatus. Therefore, claims 1-8, 10-17, and 19-20 are directed to one of the four statutory categories of invention.
Step 2A (Prong 1):
Taking claim 19 as representative, claim 19 sets forth the following limitations reciting the abstract idea of determining a replacement item for an item in an order that is unavailable:
labeling each of the set of items according to a label hierarchy wherein the hierarchy includes a first type of user feedback received in response to a first type of stimulus associated with a first label value and a second type of user feedback received in response to a second type of stimulus associated with a second label value, wherein the first label value is greater than the second label value;
output a score that represents a likelihood that the user would accept a replacement item for an input order;
receiving an order placed by a user for a fulfillment by a picker at a physical location, wherein the order includes an ordered item that is unavailable for fulfillment by the picker at the physical location;
accessing a set of contextual features about the order placed by the user;
computing a score for each of a plurality of candidate replacement items by applying the set of contextual features;
selecting a candidate replacement item of the plurality of candidate replacement items based on the scores;
causing the selected candidate replacement item to be displayed as a suggested replacement item for the ordered item.
The recited limitations above set forth the abstract idea of determining a replacement item for an item in an order that is unavailable. These limitations amount to certain methods of organizing human activity, including commercial or legal interactions (e.g. advertising, marketing or sales activities or behaviors, etc.). The claims recite steps for accessing contextual features of a user and computing a score to determine what to suggest as a replacement item when an item is unavailable in an order (see specification [0003] disclosing the problem of appropriately selecting and suggesting replacement items according to the particular shopper and order), which is a sales activity and behavior.
Such concepts have been identified by the courts as abstract ideas (see: 2106.04(a)(2)).
Step 2A (Prong 2):
Examiner acknowledges that representative claim 19 recites additional limitations in the claims, such as:
a processor that executes instructions;
a non-transitory computer readable storage medium having instructions executable by the processor;
generating training data for a machine learning model;
training the machine learning model on the training data, wherein the machine learning model is trained to output a score;
applying the machine learning model to the set of contextual features;
Taken individually and as a whole, representative claim 19 does not integrate the recited judicial exception into a practical application of the exception. The additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Secondly, this is also because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement 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) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
While the claims recite a processor and a non-transitory computer readable storage medium, these elements are recited with a very high level of generality. The only description of the processor come in specification paragraph [0075], which merely discloses that the processor comprises one or more processing units that, individually or together, perform the steps of instructions. The only description for the computer readable storage medium is in specification paragraph [0075], which discloses the computer readable medium comprises one or more computer-readable media that comprises instructions to cause a processor to perform the steps of the instructions. As such, it is evident that these elements are any generic computing components that are merely being leveraged to implement that abstract idea with a computing device. The elements are also only briefly recited as executing the instructions to perform the limitations of the claims. The machine learning is also recited with a very high level of generality. The claims merely recite applying the model to provide an output without any detail to the underlying technology of machine learning. Furthermore, specification paragraph [0051] discloses where the machine learning model can be any of a large number of generic models. As such, it is evident that the machine learning model is any generic machine learning model that is merely applied to the abstract idea to provide a general link to a computing environment, and the claims are wholly directed to the abstract idea.
In view of the above, under Step 2A (prong 2), claim 19 does not integrate the recited exception into a practical application (see again: MPEP 2106.04(d)).
Step 2B:
Taken individually or as a whole, the additional elements of claim 19 do not provide an inventive concept (i.e. whether the additional elements amount to significantly more than the exception itself). As noted above, the additional elements recited in representative claim 19 are recited in a generic manner with a high level of generality and only serve to implement the abstract idea on a generic computer device. The claims result only in an improved abstract idea itself and do not reflect improvements to the functioning of a computer or another technology or technical field. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process ultimately amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Even when considered as an ordered combination, the additional elements of claim 19 do not add anything further than when they are considered individually.
In view of the above, representative claim 9 does not provide an inventive concept under step 2B, and is ineligible for patenting.
Regarding claim 1 (method), claim 1 recites at least substantially similar concepts and elements as recited in claim 19 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 1 is rejected under at least similar rationale as provided above regarding claim 19.
Regarding Claim 10 (non-transitory computer readable storage medium), claim 10 recites at least substantially similar concepts and elements as recited in claim 19 such that similar analysis of the claims would be readily apparent to one of ordinary skill in the art. As such, claims 10 is rejected under at least similar rationale as provided above regarding claim 19.
Dependent claims 2-8, 11-17, and 20 recite further complexity to the judicial exception (abstract idea) of claim 19, such as by further defining the algorithm for determining a replacement item for an item in an order that is unavailable. Thus, each of claims 2-8, 11-17, and 20 are held to recite a judicial exception under Step 2A (Prong 1) for at least similar reasons as discussed above.
Under prong 2 of step 2A, the additional elements of dependent claims 2-8, 11-17, and 20 also do not integrate the abstract idea into a practical application, considered both individually or as a whole. More specifically, dependent claims 2-8, 11-17, and 20 rely on at least similar elements as recited in claim 19. Any additional elements (e.g., a device of the picker (claim 7)) are recited only at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, this is also because the claims fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement 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) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Taken individually and as a whole, dependent claims 2-8, 11-17, and 20 do not integrate the recited judicial exception into a practical application of the exception under step 2A (prong 2).
Lastly, under step 2B, claims 2-8, 11-17, and 20 also fail to result in “significantly more” than the abstract idea under step 2B. The dependent claims recite additional functions that describe the abstract idea and use the computing device to implement the abstract idea, while failing to provide an improvement to the functioning of a computer, another technology, or technical field. The dependent claims fail to confer eligibility under step 2B because the claims merely apply the exception on generic computing hardware and generally link the exception to a technological environment.
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
Taken individually or as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2B for at least similar rationale as discussed above regarding claim 19. Thus, dependent claims 2-8, 11-17, and 20 do not add “significantly more” to the abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-5, 7-8, 10, 13-14, 16-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable by Franey (US 20220237530 A1) in view of Agarwal (US 20230342365 A1), and in further view of Mehta (US 20170236434 A1).
Regarding Claim 1: Franey discloses a method comprising:
training the machine learning model on the training data, wherein the machine learning model outputs a score that represents a likelihood that the user would accept a replacement item for an input order; (Franey: [0074] – “The server or device can determine whether or not to select the substitute product using the distance metric or an acceptance probability score or a weighted score that is a combination of the distance metric and an acceptance probability score for a substitute product. The server can make the determination based in part on a customer preference (e.g., indicated in customer profile), store policy (e.g., policy for warehouse or company), and/or a policy for a particular product or type of product. For example, certain products may have limited available substitute products or a large number of potential substitute products and the server can assign a substitute policy to the respective product or product class indicating if substitutes can be selected using the distance metric, acceptance probability score or weighted score. In some embodiments, the server or device can use feedback, for example submitted responsive to being notified that the original product is unavailable, from the device, customer or picker to determine whether to use the distance metric, acceptance probability score or weighted score to select a substitute product”; Franey: [0041] – “The acceptance probability algorithm 164 can include one or more machine learning algorithms and/or artificial intelligence (AI) techniques to generate acceptance probability scores 160. In some embodiments, the inputs to the acceptance probability algorithm 164 can include, but are not limited to, product, product classes, data associated with previous substitutions accepted by a user or group of user, previous substitutions accepted by one or more other users (e.g., similar users, group profile), and/or product ranking data”).
receiving an order placed by a user for a fulfillment by a picker at a physical location, wherein the order includes an ordered item that is unavailable for fulfillment by the picker at the physical location; (Franey: [0052] – “a second product 244 may be unavailable at the second location 240. For example, the device 260 can determine that the second product 244 included in pick path 208 is not at the indicated second location 240 and can provide a notification 202 through the device 260 indicating a status of the product 244, for example, to a server or inventory database. The second product 244 may be sold out, stock depleted or otherwise not available at the second location 240 and/or at one or more other locations 240 (e.g., alternate locations) within the warehouse 204. The device 260 can generate and provide the notification 202 indicating that the product 244 is unavailable or otherwise unable to be selected at the warehouse 204 or update a status of the respective product 244 in an inventory database maintained at the device 260 using the notification”).
accessing a set of contextual features about the order placed by the user; (Franey: [0074] – “The server can make the determination based in part on a customer preference (e.g., indicated in customer profile), store policy (e.g., policy for warehouse or company), and/or a policy for a particular product or type of product. For example, certain products may have limited available substitute products or a large number of potential substitute products and the server can assign a substitute policy to the respective product or product class indicating if substitutes can be selected using the distance metric, acceptance probability score or weighted score. In some embodiments, the server or device can use feedback, for example submitted responsive to being notified that the original product is unavailable, from the device, customer or picker to determine whether to use the distance metric, acceptance probability score or weighted score to select a substitute product”).
computing a score for each of a plurality of candidate replacement items by applying a machine learning model to the set of contextual features; (Franey: [0075] – “The acceptance probability score can indicate a likelihood that a user or customer will accept or agree to the available substitute product. The server can execute the acceptance probability algorithm using input data associated with multiple possible available products (e.g., potential substitute products) for the missing product to generate acceptance probability scores for the possible available products for the missing product… The server can apply the acceptance probability algorithm (e.g., machine learning algorithm, artificial intelligence (AI) techniques) to the one or more substitute products to determine acceptance probability score for the potential substitute products identified for the missing product”).
selecting a candidate replacement item of the plurality of candidate replacement items based on the scores; (Franey: [0079] – “The server or device can select or identify a substitute product for the missing product using the weighted score. The server or device can compare the weighted scores for each of the identified available products or potential substitute products and select the substitute product having the highest weighted score as compared the weighted scores determined for the remaining available products or potential substitute products”).
causing the selected candidate replacement item to be displayed as a suggested replacement item for the ordered item. (Franey: [0083] – “The instructions can cause or instruct the device to insert the location of the selected substitute product in the revised pick path before or after the upcoming pick location that is positioned the minimum distance from the location of the substitute product to reduce the deviation from the originally planned pick path or minimize a change in a total distance of the pick path. The server can generate a new task (e.g., unit of work, pick task) for the substitute product and positioned within the pick path to include the new task, including, prior to or after the task corresponding to the minimum point or minimum distance. In some embodiments, the server can revise the pick path to insert the location of the substitute product in the revised pick path at the same position (e.g., same pick position) along the pick path as the original or missing product such that the order of pick locations of the pick path remains the same and the location of the missing product and respective pick order position is replaced by the location of the substitute product”).
Franey does not explicitly teach generating training data for a machine learning model by:
determining a set of items that received feedback;
labeling each of the set of items according to a label hierarchy wherein the hierarchy includes a first type of user feedback received in response to a first type of stimulus associated with a first label value and a second type of user feedback received in response to a second type of stimulus associated with a second label value, wherein the first label value is greater than the second label value;
Notably, however, Franey does disclose a machine learning model to provide scores for the candidate replacement items (Franey: [0074-0075]), and receiving customer feedback regarding the substitute products (Franey: [0082]).
To that accord, Agarwal does teach determining a set of items that received feedback; (Agarwal: [0237] – “The method 2700 may then include training 2708 a model to relate product attribute-value pairs to an expected user sentiment (positive, skip, negative) using the scored product records and their attribute-value pairs. For example, training data may include a plurality of entries in which each entry includes a set of attribute-value pairs of a product record as an input and the score (1, 0, −1) as a desired output. The model may then be trained to provide an estimated score for a given input set of attribute-value pairs, or for a single attribute-value pair”).
Agarwal teaches determining a plurality of item that have received user feedback from scored product records for the training data (Agarwal: [0237]; see also: [0087]; [0130]; [0237]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Franey disclosing the system for determining substitute items for a picker fulfilling an order with the labeling of the set of items with a label hierarchy based on feedback as taught by Agarwal. One of ordinary skill in the art would have been motivated to do so in order to better predict consumer preferences and suitable products (Agarwal: [0003]).
Franey in view of Agarwal does not explicitly teach labeling each of the set of items according to a label hierarchy wherein the hierarchy includes a first type of user feedback received in response to a first type of stimulus associated with a first label value and a second type of user feedback received in response to a second type of stimulus associated with a second label value, wherein the first label value is greater than the second label value; Notably, however, Franey does teach receiving customer feedback regarding the substitute products (Franey: [0082]), and Agarwal does teach scoring the feedback according to the type of feedback (Franey: [0230]).
To that accord, Mehta does teach labeling each of the set of items according to a label hierarchy wherein the hierarchy includes a first type of user feedback received in response to a first type of stimulus associated with a first label value and a second type of user feedback received in response to a second type of stimulus associated with a second label value, wherein the first label value is greater than the second label value; (Mehta: [0039] – “It is noted that the ratings or other explicit behaviors of higher ranked and/or more historical active users can be weighted higher than the ratings of lower ranked and/or less historical active users. For example, a ‘Level 7’ ranked user's ‘like’ can be weighted higher with respect to calculating a score than a ‘Level 2’ ranked user's ‘like’. In one example, each user who consumes an educational content can be asked to rate the content on a difficulty level”; Mehta: [0041] – “explicit-user feedback and be weighted greater than implicit user actions in adjusting the score of the educational content”). In summary, responses are provided explicitly or implicitly, either by being directly asked or through implicit feedback during the content, in order to determine overall scores for the content.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Franey in view of Agarwal disclosing the system for determining substitute items for a picker fulfilling an order with the value of user feedback received in response to a first type of stimulus being greater than value of feedback of a second user feedback received in response to a second type of stimulus as taught by Mehta. One of ordinary skill in the art would have been motivated to do so in order to set a base score and current rating for content (Mehta: [0038]).
Regarding Claim 4: Franey in view of Agarwal and Mehta discloses the limitations of claim 1 above.
Franey further discloses wherein accessing the set of contextual features comprises accessing one or more user features describing characteristics of the user. (Franey: [0074] – “The server can make the determination based in part on a customer preference (e.g., indicated in customer profile), store policy (e.g., policy for warehouse or company), and/or a policy for a particular product or type of product”).
Regarding Claim 5: Franey in view of Agarwal and Mehta discloses the limitations of claim 1 above.
Franey further discloses wherein accessing the set of contextual features comprises accessing one or more user features describing prior orders of the user. (Franey: [0075] – “the acceptance probability algorithm can consider or use as inputs the substitute products identified with respect to 412, products in a same product class as the missing product, data associated with previous substitutions accepted by the user, previous substitutions accepted by one or more other users (e.g., similar users, group profile), and/or product ranking data”).
Regarding Claim 7: Franey in view of Agarwal and Mehta discloses the limitations of claim 1 above.
Franey further discloses receiving an indication that the ordered item is unavailable for fulfillment by the picker at the physical location, wherein the indication is received from a device of the picker located at the physical location during the fulfillment of the order. (Franey: [0058] – “The second product 244 may be sold out, stock depleted or otherwise not available at the second location 240 and/or at one or more other locations 240 (e.g., alternate locations) within the warehouse 204. The picker 210 can provide the notification 202 through the device 260 indicating that the product 244 is unavailable or otherwise unable to be selected at the warehouse 204. The notification 202 can indicate that the product 244 is missing, unavailable at the indicated location 240, and/or that no inventory exists for the desired product 244 at one or more alternate locations 240. The picker 210 can continue to execute the instructions for the pick path 208 and generate one or more notifications 202 indicating a status of a remaining product 244 in the pick path 208 until the pick path 208 is completed or until each of the products 244 available to be selected are collected by the device 260 and picker”).
Regarding Claim 8: Franey in view of Agarwal and Mehta discloses the limitations of claim 1 above.
Franey further discloses wherein causing the selected candidate replacement item to be displayed comprises causing the selected candidate item to be displayed on a device of the picker. (Franey: [0083] – “the instructions can include a script or code that updates or revises the pick path and/or tasks indicated by the pick path to include the task for the substitute product and the substitute product location among the remaining tasks in the pick path. The instructions can cause or instruct the device to insert the location of the selected substitute product in the revised pick path before or after the upcoming pick location that is positioned the minimum distance from the location of the substitute product to reduce the deviation from the originally planned pick path or minimize a change in a total distance of the pick path. The server can generate a new task (e.g., unit of work, pick task) for the substitute product and positioned within the pick path to include the new task, including, prior to or after the task corresponding to the minimum point or minimum distance. In some embodiments, the server can revise the pick path to insert the location of the substitute product in the revised pick path at the same position (e.g., same pick position) along the pick path as the original or missing product such that the order of pick locations of the pick path remains the same and the location of the missing product and respective pick order position is replaced by the location of the substitute product”).
Regarding Claims 10 and 19: Claims 10 and 19 recite substantially similar limitations as claim 1. Therefore, claims 10 and 19 are rejected under the same rationale as claim 1 above.
Regarding Claim 13: Claim 13 recites substantially similar limitations as claim 4. Therefore, claim 13 is rejected under the same rationale as claim 4 above.
Regarding Claim 14: Claim 14 recites substantially similar limitations as claim 5. Therefore, claim 14 is rejected under the same rationale as claim 5 above.
Regarding Claim 16: Claim 16 recites substantially similar limitations as claim 7. Therefore, claim 16 is rejected under the same rationale as claim 7 above.
Regarding Claim 17: Claim 17 recites substantially similar limitations as claim 8. Therefore, claim 17 is rejected under the same rationale as claim 8 above.
Claims 2-3, 11-12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Franey (US 20220237530 A1), Agarwal (US 20230342365 A1), and Mehta (US 20170236434 A1), in view of Cho (US 20210233143 A1).
Regarding Claim 2: The combination of Franey and Agarwal and Mehta discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein accessing the set of contextual features comprises accessing one or more features based on other items in the order different from the ordered item. Notably, however, Franey does disclose determining a substitute product when an item in the order is unavailable (Franey: [0014]).
To that accord, Cho does teach wherein accessing the set of contextual features comprises accessing one or more features based on other items in the order different from the ordered item. (Cho: [0084] – “the order data may include customer context, store context, order ID and product context. In other examples, the order data can include other information. The customer context can include customer data sufficient to identify the customer, such as name, address, registration name, email, age, gender and the like. The store context can include store data sufficient to identify the store at which the customer will visit and/or retrieve the order such as, store region, store ID, address, store management information and the like. The order ID is a unique identification number used to identify the specific order of the customer. The product context can include product data or item data sufficient to identify the items purchased by the customer and included in the customer's order. The data obtained or received by the smart substitution computing device 102 is different from many existing or conventional process comparison tools in that the smart substitution computing device 102 obtains more data or more granular data that existing process comparison tools. For example, the smart substitution computing device obtains the unique order ID, customer context and store context rather than using cookies to identify an order. With this additional information, the smart substitution computing device 102 is able to filter and sample data (as will be further described) in order to reliably and effectively compare two operating conditions of the e-commerce platform”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Franey, Agarwal, and Mehta disclosing a system for determining substitute items for a picker fulfilling an order with the contextual features including items in the order different from the ordered item as taught by Cho. One of ordinary skill in the art would have been motivated to do so in order to improve customer satisfaction and sales when an item is unavailable (Cho: [0003]).
Regarding Claim 3: The combination of Franey, Agarwal, and Mehta, in view of Cho, discloses the limitations of claim 2 above.
The combination does not explicitly teach wherein computing a score for each of a plurality of candidate replacement items includes applying a bag-of-words model with the set of contextual features based on other items in the order different from the ordered item. Notably, however, Franey does disclose determining a substitute product when an item in the order is unavailable (Franey: [0014]).
To that accord, Cho does teach wherein computing a score for each of a plurality of candidate replacement items includes applying a bag-of-words model with the set of contextual features based on other items in the order different from the ordered item. (Cho: [0084] – “the order data may include customer context, store context, order ID and product context. In other examples, the order data can include other information. The customer context can include customer data sufficient to identify the customer, such as name, address, registration name, email, age, gender and the like. The store context can include store data sufficient to identify the store at which the customer will visit and/or retrieve the order such as, store region, store ID, address, store management information and the like”; Cho: [0062] – “The smart substitution computing device 102 can use any suitable methodology to determine a similarity between the ordered item and each substitute item in a pool of possible substitute items. For example, the smart substitution computing device 102 can identify similarities in words between a title or description of an ordered item and a title or description of a substitute item”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Franey, Agarwal, and Mehta disclosing a system for determining substitute items for a picker fulfilling an order with the computing a score including applying a word model with the set of contextual features as taught by Cho. One of ordinary skill in the art would have been motivated to do so in order to improve customer satisfaction and sales when an item is unavailable (Cho: [0003]).
Regarding Claims 11 and 20: Claims 11 and 20 recite substantially similar limitations as claim 2. Therefore, claims 11 and 20 are rejected under the same rationale as claim 2 above.
Regarding Claim 12: Claim 12 recites substantially similar limitations as claim 3. Therefore, claim 12 is rejected under the same rationale as claim 3 above.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable by the combination of Franey (US 20220237530 A1), Agarwal (US 20230342365 A1), and Mehta (US 20170236434 A1), in view of Subbarayan (US 20180183737 A1).
Regarding Claim 6: The combination of Franey, Agarwal, and Mehta discloses the limitations of claim 1 above.
The combination does not explicitly teach wherein accessing the set of contextual features includes accessing features describing a real-time conversation with the user based on natural-language processing of the real-time conversation. Notably, however, Franey does disclose accessing customer preference information from the customer profile (Franey: [0074]).
To that accord, Subbarayan does teach wherein accessing the set of contextual features includes accessing features describing a real-time conversation with the user based on natural-language processing of the real-time conversation. (Subbarayan: [0022] – “the commerce system analyzes the message(s) to determine a context of the conversation. For instance, the commerce system can analyze messages using natural language processing to identify a product mentioned or referred to in the messages. The commerce system can identify the product based on a description of the product and information stored by the merchant for the product. Analyzing messages using natural language processing allows users flexibility in describing products and/or obtaining information about products offered by the merchant”; Subbarayan: [0023] – “The commerce system can then recommend the product to the user as a message from the messaging bot within the conversation flow of the communications session. Providing suggestions to the user can aid the user in identifying a desired product or an alternate product provided by the merchant”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of the combination of Franey, Agarwal, and Mehta disclosing a system for determining substitute items for a picker fulfilling an order with the accessing contextual features by using natural-language processing on a conversation as taught by Subbarayan. One of ordinary skill in the art would have been motivated to do so in order to aid the user in identifying a desired product (Subbarayan: [0023]).
Regarding Claim 15: Claim 15 recites substantially similar limitations as claim 6. Therefore, claim 15 is rejected under the same rationale as claim 6 above.
Conclusion
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/T.J.K./ Examiner, Art Unit 3689
/VICTORIA E. FRUNZI/ Primary Examiner, Art Unit 3689 6/8/2026
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