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 .
DETAILED CORRESPONDENCE
Status of Claims
Claims 1, 11 have been amended.
No claims have been cancelled.
No claims have been added.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
receiving product return data from a merchant indicating merchant-specific information on product returns to the merchant;
receiving an indication of a product return request from a customer;
determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data;
responsive to the likelihood of return rating exceeding a threshold, display extending a credit to the customer based on a value of the returned product and including an image and description of the returned product, an amount of the credit, and perform a product repurchase; and
enabling the customer to initiate the product repurchase using the credit,
wherein models include probability of a missed delivery for the merchant based on feature data specific to the merchant and a settlement defining expected time to settlement for the product returns to the merchant, the missed delivery providing a prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold.
The invention is directed towards the abstract idea of processing product returns and product purchasing/repurchasing/exchange (fundamental economic practice), which corresponds to “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by humans performing a return transaction, e.g., having a customer return a product to a third party serving as a facilitator between the customer and merchant and the third party processing the transaction and determining, based on collected information, whether to process the return and provide the customer with a credit to allow the customer to make another purchase.
The limitations of:
receiving product return data from a merchant indicating merchant-specific information on product returns to the merchant;
receiving an indication of a product return request from a customer;
determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data;
responsive to the likelihood of return rating exceeding a threshold, display extending a credit to the customer based on a value of the returned product and including an image and description of the returned product, an amount of the credit, and perform a product repurchase; and
enabling the customer to initiate the product repurchase using the credit,
wherein models include probability of a missed delivery for the merchant based on feature data specific to the merchant and a settlement defining expected time to settlement for the product returns to the merchant, the missed delivery providing a prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold,
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic apparatus comprising generic processing circuitry and a generic model. That is, other than reciting a generic apparatus comprising generic processing circuitry and a generic model nothing in the claim element precludes the step from practically being performed in the mind. For example, but for generic apparatus comprising generic processing circuitry and a generic model in the context of this claim encompasses a third party facilitate a return transaction between a customer and a merchant, as well as determining, based on information about the customer and merchant, whether to authorize the return and credit the customer. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic apparatus comprising generic processing circuitry and a generic model, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – a generic apparatus comprising generic processing circuitry to communicate information, as well as performing operations that a human can perform in their mind and/or using pen and paper, i.e. collect and review information about a customer and merchant, determine whether to process the return, and providing a credit to the customer to allow the customer to make another purchase. The generic apparatus comprising generic processing circuitry in the steps are recited at a high-level of generality (i.e., as a generic apparatus comprising generic processing circuitry can perform the insignificant extra solution steps of communicating information (See MPEP 2106.05(g) while also reciting that the a generic apparatus comprising generic processing circuitry is merely being applied to perform the steps that can be performed in the human mind and/or using pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using a generic processor executing computer code stored on a computer medium.
Although the claim recites “employ a trained model” and describing that the model includes a plurality of trained models, i.e. missed delivery classifier model and settlement model, the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or 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, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying (employing) a trained machine learning model is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being a machine learning model is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards determining whether a customer is entitled to a credit for returning a product based on collected information about the customer and merchant and issuing the credit to the customer. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the training and re-training (updating) are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with machine learning model(s). Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
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 claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a generic apparatus comprising generic processing circuitry and a generic model to perform the steps of:
receiving product return data from a merchant indicating merchant-specific information on product returns to the merchant;
receiving an indication of a product return request from a customer;
determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data;
responsive to the likelihood of return rating exceeding a threshold, display extending a credit to the customer based on a value of the returned product and including an image and description of the returned product, an amount of the credit, and perform a product repurchase; and
enabling the customer to initiate the product repurchase using the credit,
wherein models include probability of a missed delivery for the merchant based on feature data specific to the merchant and a settlement defining expected time to settlement for the product returns to the merchant, the missed delivery providing a prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold,
amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Additionally:
Claims 2, 4, 5 are directed towards human activities involved in a fundamental economic practice, in this case, dictating the party responsible for payment when issuing a product return.
Claim 3 is directed towards reciting generic technology at a high level of generality and applying it to the abstract idea, as was discussed above.
Claims 6, 7 are directed towards descriptive subject matter, in this case, describing what is included in the merchant-specific information.
Claim 8 is directed towards collecting and comparing information and reciting generic technology at a high level of generality and applying it to the abstract idea.
Claims 9, 10 are directed towards descriptive subject matter, in this case, describing what is included in the return rating.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for processing a return. Accordingly, the claims are not patent eligible.
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 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.
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 – 3, 6 – 13, 16 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kentris et al. (US PGPub 2021/0150616 A1) in view of Hammond et al. (US PGPub 2017/0039572 A1) in further view of The Selling Guys (How to get Amazon to reimburse you for lost and damaged inventory).
In regards to claims 1, 11, Kentris discloses (Claim 1) an apparatus for facilitating a product return transaction, the apparatus comprising processing circuitry configured to; (Claim 11) a method of facilitating a product return transaction, the method comprising:
receive product return data for a merchant indicating merchant-specific information on product returns to the merchant (¶ 227 – 233 wherein the system receives product return data from a merchant indicating merchant-specific information on the product, e.g., merchant provided the wrong size, damaged item, not what was expected, not what was advertised, or “other”.);
receive an indication of a product return request from a customer (¶ ¶ 227 – 233 wherein a customer provides the system with an indication of a product return request);
employ a machine learning module comprising a […] trained model[…] to determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data (¶ 248 wherein a model is trained to determine the likelihood of a return rating associated with the physical receipt of a returned product by comparing the received returned product with an image of the product to determine if there is an inconsistency with what was provided as a return reason by the customer);
responsive to the likelihood of return rating exceeding a threshold, provide instructions to a user device of the customer to generate an interface page at a display of the user device extending a [refund] to the customer based on a value of the returned product, the interface page including an image and description of the returned product, an amount of the [refund], and a button selectable by the customer to perform a product repurchase (¶ 84, 248 wherein, based on the determination of whether to reject the return, the system will extend a refund to the customer to do with whatever they please;
Fig. 8, 9, 10, 11, 12, 13, 16A, 16B, 17, 18, 19, 30; ¶ 225, 234, 235, 243, 247, 248, 253, 312 regarding a graphical user interface (GUI) that allows a user to initiate a return and send out the return, wherein the GUI includes selectable “buttons” to perform the aforementioned process. Additionally, the system, based on the information provided by the user, performs an analysis to approve the return request, inform the user of the refund/credit amount, and allow the user to make additional purchases.
In summary, Kentris provides a system and method that allows a customer to return and purchase products. This is accomplished by providing a GUI that includes various GUI elements that are selectable by the customer to perform the return and purchasing process. Kentris discloses that the user can perform, as a non-limiting example, a customer-to-customer return, thereby resulting in a plurality of customers utilizing the GUI to initiate the return of a product that they purchased, receive a refund for the return product, have the system approve the return based on the information provided by the user of why the return is being performed, issue a refund to a customer, and allowing a customer to purchase a product, wherein the payment can comprise the refund amount they were issued, e.g., the customer purchased a shirt for $20 dollars, returns the shirt, if approved, receives a refund for $20, and can make another purchase if they choose to using the funds that they have at their disposal, which would include the refunded amount); and
enable the customer to initiate the product repurchase using the [refund] responsive to selection of the button (The Examiner asserts that “enable” is not a positive recitation and is only simply stating that a customer is not forbidden to purchase another product using the refund they were provided.
With that said, there is nothing in Kentris that prohibits a customer to purchase another product with their refund, especially since ¶ 228, 229 disclose that the reason for the return is because the product was the wrong size and/or the product was damaged. Kentris discloses at ¶ 84, 248 that the system determines whether to accept or reject a return and, if it is returned, issuing a refund to the customer, thereby “enabling” the customer to use the refund to purchase a completely different product, the same product in the correct size, or exchange the product for an undamaged version of itself (which is a form of refund since, in the end, the customer is not purchasing or paying an additional cost for the undamaged product.
As was discussed above, provides a system and method that allows a customer to return and purchase products. This is accomplished by providing a GUI that includes various GUI elements that are selectable by the customer to perform the return and purchasing process. Kentris discloses that the user can perform, as a non-limiting example, a customer-to-customer return, thereby resulting in a plurality of customers utilizing the GUI to initiate the return of a product that they purchased, receive a refund for the return product, have the system approve the return based on the information provided by the user of why the return is being performed, issue a refund to a customer, and allowing a customer to purchase a product, wherein the payment can comprise the refund amount they were issued, e.g., the customer purchased a shirt for $20 dollars, returns the shirt, if approved, receives a refund for $20, and can make another purchase if they choose to using the funds that they have at their disposal, which would include the refunded amount),
[…], and
[…].
Kentris discloses a system and method of utilizing machine learning to determine whether a product return request should be accepted or rejected and, in the event that the return is accepted, extending a refund to the customer to do with what they will. Although the Examiner asserts that, given the scope of the claimed invention, that there is little to no distinction between a refund and credit since, ultimately, the customer is being provided funds to cover the purchase price of the product and using these funds to make a future purchase, the Examiner has provided Hammond to provide the more conservative interpretation of the term “credit”, e.g., “in store credit” and/or monetary amount (or equivalent) that is stored in an account, card, or the like that can be accessed and used by the customer for a future purchase. Additionally, although Kentris discloses the use of machine learning and referring to a rule to determine if a return should be authorized, Kentris fails to disclose all types of rules, i.e. defining an expected time to settlement or whether multiple models can be used.
To be more specific, Kentris fails to explicitly disclose:
employ a machine learning module comprising a plurality of trained models to determine a likelihood of return rating associated with physical receipt of a returned product associated with the product return request based on the product return data
responsive to the likelihood of return rating exceeding a threshold, provide instructions to a user device of the customer to generate an interface page at a display of the user device extending a credit to the customer based on a value of the returned product, the interface page including an image and description of the returned product, an amount of the credit, and a button selectable by the customer to perform a product repurchase;
enable the customer to initiate the product repurchase using the credit responsive to selection of the button,
wherein the plurality of trained models includes a missed delivery classifier model modeling probability of a missed delivery for the merchant based on feature data specific to the merchant used to train the missed delivery classifier model, and a settlement model defining expected time to settlement for the product returns to the merchant, the missed delivery classifier model providing a model prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold, and
wherein the machine learning module updates the plurality of trained models during operation.
However, Hammond, which is also directed towards managing returns and using machine learning to determine whether the return should be processed, further teaches that, upon the machine learning model authorizing a product return for a customer, the customer can be provided with various forms of compensation, such as, but not limited to, credit, to facilitate the exchange of the returned item. One of ordinary skill in the art would have found it obvious that any form of monetary compensation can be substituted with one another while still achieving the same predictable result of accepting a return from a customer and extending some sort of monetary compensation that can be applied to a future purchase. Hammond further teaches that this encourages a good customer who has returned a product to a retailer to spend the money they just received at that retailer.
(¶ 25, 37, 54, 89, 98)
With regards to whether a return should be authorized, Hammond teaches that a plurality of policies is in place that can affect the likelihood of return, such as, but not limited to, returning an item within a specified time period. One of ordinary skill in the art of product returns would have found it obvious that there is a plurality of different policies that govern whether a return should be authorized and that it is completely dependent on the entity authorizing the return to determine what policy to implement to govern this process. One ordinary skill in the art looking upon the teachings of Hammond would have found that one of those policies would include defining an expected time to settlement and that such a policy can be incorporated into the system and method of Kentris while still achieving the same predictable result of referring to a rule (policy) to determine whether a return should be authorized.
(¶ 42 – 47, 119)
With regards to utilizing a plurality of models, Hammond teaches that it would have been obvious for a machine learning model to implement one or more statistical models while still achieving the same predictable result of using machine learning to determine whether the return should be authorized, facilitate exchange of the returned item, and compensate a user. The Examiner asserts that one of ordinary skill in the art looking upon the teachings of Hammond would have found it obvious and within their ability to configure a machine learning model to be comprised of only a single model or multiple models and that the end result would still be the same. The Examiner asserts that substituting multiple models for a single model would yield the same predictable result and would simply be dependent on the available resources that one has at their disposal.
(¶ 94)
With regards to the types of models, i.e. settlement model and classifier model, Hammond teaches such models. As discussed above, Hammond teaches a fraud model architecture that implements one or more models used by a decision engine of the return authorization service and that the models accept information available at the time of a merchandise return transaction to provide an output on whether to authorize a return.
(¶ 94)
With that said, as was also discussed above, Hammond teaches that a plurality of policies is in place that can affect the likelihood of return, such as, but not limited to, returning an item within a specified time period and that one of ordinary skill in the art would have found that one of those policies would include defining an expected time to settlement to determine whether a return should be authorized, thereby resulting in the system being provided information to a model that includes settlement information and being a settlement model.
(¶ 42 – 47, 94, 119)
Hammond further teaches that the information associated with the return transaction is classified into different groups to assist with determining whether a return should be authorized by determining if a particular threshold for the merchant was exceeded. As a non-limiting example, the system collects and classifies information to determine a number of visits to stores, number of transactions having the same amount, number of stores visited within a particular time frame, number of requests, and etc. so that a score can be calculated and compared against a threshold to determine if the return should be authorized, thereby resulting in the system being provided information to a model that includes classified/categorized information and being a classifier model.
(¶ 94 – 97, 100 – 114)
With regards to updating the model, Hammond teaches that information used by the system can be updated and, accordingly, the machine learning model would be updated with this new information as this would provide a more accurate determination of whether to authorize compensation in response to a return.
(¶ 97)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention that since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself-that is in the substitution of credit, utilizing multiple models, and authorizing a return based on a policy directed to the expected time to settlement, as taught by Hammond, for a refund, a single model, and any of the other rules (e.g., damage to the item), respectively, as disclosed by Kentris.
Thus, the simply substitution of one known element for another producing a predictable result renders the claim obvious.
Additionally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to provide in the return and monetary compensation system and method of Kentris with the ability to offer different types of monetary compensation, such as, but not limited to, credit, as taught by Hammond, as this encourages a good customer who has returned a product to a retailer to spend the money they just received at that retailer.
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to try, by one of ordinary skill in the art, to pick a machine learning model comprised of multiple models, such as a classifier model and settlement model, as taught by Hammond, and incorporate it into the single machine learning model system and method of Kentris since there are a finite number of identified, predictable potential solutions (configuring a machine learning model with one or more models, such as a classifier model and settlement model) to the recognized need (configuring/training a model to determine a particular result based on available information to determine if a return should be authorized) and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success (the advantages, benefits, and required resources are known).
Finally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to update the machine learning process of Kentris, as taught by Hammond, as information is known to be updated over time and it would have been obvious and beneficial to, consequently, update the machine learning model as this would result in the machine learning providing better results over time.
The combination of Kentris and Hammond discloses a system and method of utilizing machine learning to determine whether a product return request should be accepted or rejected and, in the event that the return is accepted, extending a refund to the customer to do with what they will. Despite this, the combination of Kentris and Hammond fails to disclose all possible reasons of why a product return is performed, i.e. due to the probability of a missed delivery.
To be more specific, the combination of Kentris and Hammond fails to explicitly disclose:
wherein the plurality of trained models includes a missed delivery classifier model modeling probability of a missed delivery for the merchant based on feature data specific to the merchant used to train the missed delivery classifier model, and a settlement model defining expected time to settlement for the product returns to the merchant, the missed delivery classifier model providing a model prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold.
However, The Selling Guys, which discloses how a marketplace handles returns, further teaches that a return or refund can occur due to the probability of a missed delivery or lost product. The Selling Guys teaches that it is known for a marketplace to make mistakes when handling and processing purchased and returned products, such as, but not limited to, a missed delivery or lost product. The Selling Guys teaches that it would have been obvious for a seller to request payment in such scenarios since the error was due to the marketplace and not the seller and, consequently, the seller must be compensated as such fees can add up. The Selling Guys teaches that sellers are entitled to these reimbursements.
(Pages 1, 2, 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the product return system and method of the combination of Kentris and Hammond with the ability to include information pertaining to missed deliveries or products lost in transit, as taught by The Selling Guys, as it is known for the marketplace to make mistakes and such mistakes can be costly to a seller, thereby providing an avenue for a seller to request reimbursement due to an error by the marketplace.
In regards to claims 2, 12 , the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the processing circuitry is further configured to provide payment to the merchant for the product repurchase on behalf of the customer (Kentris – ¶ 52, 55, 82 wherein the online marketplaces serves as a payment gateway to handle transactions on behalf of the merchant, i.e. whenever a purchase is made by a customer, the payment goes to the merchant; Hammond – ¶ 25, 54 wherein the customer is provided with store credit to use at the merchant for the repurchase of a product).
In regards to claims 3, 13, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 2 (the method of claim 12), wherein the processing circuitry is further configured to update the plurality of trained models responsive to receipt of the returned product or failure to receive the returned product within a predetermined period of time (Hammond – Fig. 5; ¶ 24, 97, 115 wherein the model is updated when a product is returned as stored historical information to allow the model to determine if a particular behavior is being expressed and assist with the identification of fraudulent activity; ¶ 94 regarding multiple models, as was discussed above).
In regards to claims 6, 16, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the merchant-specific information includes merchant fraud rate information (Hammond – ¶ 23, 24, 59, 132, 143 wherein the merchant-specific information includes merchant fraud rate information).
In regards to claims 7, 17, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the merchant-specific information includes a probability of missed delivery (The Selling Guys – Pages 1, 2, 4 – wherein The Selling Guys teaches that it is known for a marketplace to make mistakes when handling and processing purchased and returned products, such as, but not limited to, a missed delivery or lost product. The Selling Guys teaches that it would have been obvious for a seller to request payment in such scenarios since the error was due to the marketplace and not the seller and, consequently, the seller must be compensated as such fees can add up. The Selling Guys teaches that sellers are entitled to these reimbursements).
In regards to claims 8, 18, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the processing circuitry is further configured to perform a customer check to determine a limit to the credit based on the customer check (Kentris – ¶ 84; Hammond – ¶ 89, 152 wherein the system performs a customer check to determine a limit to the credit based on the customer check).
In regards to claims 9, 19, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the likelihood of return rating further includes an upsell expectation value (Hammond – ¶ 25, 89 wherein the likelihood of return rating further includes an upsell expectation value by allowing the customer to use the credit to purchase another product at the retailer that the product was returned at, as well as allowing for the credit to be used to pay for a portion of a future transaction).
In regards to claims 10, 20, the combination of Kentris, Hammond, and The Selling Guys discloses the apparatus of claim 1 (the method of claim 11), wherein the likelihood of return rating further includes an expected time to settlement (Hammond – ¶ 42 – 47, 119 wherein a plurality of policies are in place that can affect the likelihood of return, such as, but not limited to, returning an item within a specified time period).
______________________________________________________________________
Claims 4, 5, 14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kentris et al. (US PGPub 2021/0150616 A1) in view of Hammond et al. (US PGPub 2017/0039572 A1) in view of The Selling Guys (How to get Amazon to reimburse you for lost and damaged inventory) in further view of Facebook (Facebook Marketplace Policies).
In regards to claims 4, 14, the combination of Kentris, Hammond, and The Selling Guys discloses a system and method of managing returns and payment to and from a customer and merchant. Despite this, the combination of Kentris, Hammond, and The Selling Guys fails to disclose whether the facilitation system receives payment from a merchant responsive to the merchant receiving the returned produce.
To be more specific, the combination of Kentris, Hammond, and The Selling Guys fails to explicitly disclose:
the apparatus of claim 3 (the method of claim 13), wherein the processing circuitry is further configured to receive payment from the merchant based on the product repurchase responsive to the merchant obtaining the receipt of the returned product.
However, Facebook, which also discloses an online marketplace for facilitating the purchasing, selling, and returning/refunding of products, further teaches that it is well-known in the art for a marketplace to require the merchant to pay the marketplace when a return/refund is processed. Facebook teaches that a return/refund can be in favor of a seller or buyer and, in cases where it is in favor of the buyer, the marketplace will deduct the refund from the seller’s account since the marketplace is simply serving as a facilitator and not actually the seller of the product, thereby resulting in the seller having to provide the refund. One of ordinary skill in the art would have found it obvious that a marketplace defines specific policies that must be adhered to and, consequently, that it would have been obvious that it is the seller and not the marketplace who is responsible for the paying back the marketplace when the marketplace is crediting the customer.
(For support see: Page 1, “Support & Refunds”; “Responding to a Chargeback”; Pages 3 – 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the return management system and method of the combination of Kentris, Hammond, and The Selling Guys with the ability to require the merchant to pay the facilitator platform, i.e. marketplace, as taught by Facebook, when the facilitator is processing and handling the return transaction on behalf of the merchant as the merchant is the seller and party responsible for the product and a marketplace would have policies in place dictating as much.
In regards to claims 5, 15, the combination of Kentris, Hammond, and The Selling Guys discloses a system and method of managing returns and payment to and from a customer and merchant. Despite this, the combination of Kentris, Hammond, and The Selling Guys disclosing that it is well-known in the art for the customer to not be responsible for any payment to a facilitator/marketplace or return of the product (Kentris – ¶ 92, 94), the combination of Kentris, Hammond, and The Selling Guys fails to disclose whether the facilitation system receives payment from a merchant responsive to the merchant receiving the returned produce.
To be more specific, the combination of Kentris, Hammond, and The Selling Guys fails to explicitly disclose:
the apparatus of claim 2 (the method of claim 12), wherein providing the payment to the merchant does not establish any responsibility for the customer to pay a party facilitating the product return transaction, and only establishes a responsibility for the merchant to pay the party in response to the merchant obtaining the physical receipt of the returned product.
However, Facebook, which also discloses an online marketplace for facilitating the purchasing, selling, and returning/refunding of products, further teaches that it is well-known in the art for a marketplace to require the merchant to pay the marketplace when a return/refund is processed. Facebook teaches that a return/refund can be in favor of a seller or buyer and, in cases where it is in favor of the buyer, the marketplace will deduct the refund from the seller’s account since the marketplace is simply serving as a facilitator and not actually the seller of the product, thereby resulting in the seller having to provide the refund. One of ordinary skill in the art would have found it obvious that a marketplace defines specific policies that must be adhered to and, consequently, that it would have been obvious that it is the seller and not the marketplace who is responsible for the paying back the marketplace when the marketplace is crediting the customer.
(For support see: Page 1, “Support & Refunds”; “Responding to a Chargeback”; Pages 3 – 4)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate into the return management system and method of the combination of Kentris, Hammond, and The Selling Guys with the ability to require the merchant to pay the facilitator platform, i.e. marketplace, as taught by Facebook, when the facilitator is processing and handling the return transaction on behalf of the merchant as the merchant is the seller and party responsible for the product and a marketplace would have policies in place dictating as much.
Response to Arguments
Applicant's arguments filed 12/23/2025 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The applicant argues that the following subject matter renders the claim patent eligible:
“the missed delivery classifier model providing a model prediction to a decision threshold estimator configured to determine a decision threshold for determining the probability of the missed delivery via a merchant-specific threshold.”
However, the Examiner respectfully disagrees.
The Examiner asserts that the applicant’s statement that the additional elements, namely, missed delivery classifier model, settlement model, and decision threshold estimator, are directed towards the recitation of generic technology that has been recited at a high level of generality and applying it to the abstract idea. Moreover, the claimed invention is not improving these models or estimator or resolving an issue that arose in this technology, but, again, reciting generic technology at a high level of generality and applying it to the abstract idea. This is further evidenced by the fact that the claimed invention is applying already “trained” models. Although the claimed invention later recites that the models are updated, the Examiner asserts that this, too, has been recited at a high level of generality and is akin to the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
With regards to “well-understood, routine, and conventional”, the Examiner asserts that no such analysis was provided in the rejection and, therefore, the applicant’s reliance on this analysis does not apply and is, therefore, unpersuasive.
The Examiner asserts that the amendments and the applicant’s corresponding arguments of generating an interface to display information and interface elements is insufficient to overcome the rejection. The Examiner asserts that the amendments and arguments are unpersuasive because they are directed towards the recitation of generic technology recited at a high level of generality to perform the extra-solution activity of displaying information, as well as receiving input using generic graphical user interface (GUI) elements. The Examiner asserts that the claimed invention is not directed towards improving GUI technology, resolving an issue that arose in GUI technology, or deeply rooted in GUI technology, but reciting and applying generic GUI technology. The claimed invention does not rise to the level of CoreWireless, which improved upon how a GUI fundamentally functions. The claimed inventions interface generation to display specific information is similar to a human writing down information using pen and paper, in this case, an image and description of a returned product (drawing the product and, if necessary, providing a description associated with the product) and a credit amount. The button is nothing more than a generic GUI button that requires a human to “press” to perform a product repurchase and is akin to a customer informing or instructing a merchant to use their credit for a purchase.
The Examiner asserts that the claimed invention is not improving technology, resolving an issue that arose in technology, or deeply rooted in technology, but reciting generic technology at a high level of generality and applying it to the abstract idea of processing product returns and product purchasing/repurchasing/exchange (fundamental economic practice), e.g., having a customer return a product to a third party serving as a facilitator between the customer and merchant and the third party processing the transaction and determining, based on collected information, whether to process the return and provide the customer with a credit to allow the customer to make another purchase
Rejection under 35 USC 103
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Kovalova et al. (US PGPub 2025/0315775 A1) – which is directed to product deliveries and return/refund system
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 1/14/2026