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 .
This action is responsive to the Amendment filed on 1/27/2026.
Claims 1, 2, 5-22 are pending in the case.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/27/2026 has been entered.
Response to Arguments
Applicant's arguments and amendments with regards to the 35 U.S.C. § 112(a) rejection of claim(s) 1, 2, 5-20, 22, have been fully considered and are persuasive. The 35 U.S.C. § 112(a) rejection of claim(s) 1, 2, 5-20, 22 is respectfully withdrawn.
Applicant's arguments and amendments with regards to the 35 U.S.C. § 112(b) rejection of claim(s) 1, 2, 5-20, 22, have been fully considered and are persuasive. The 35 U.S.C. § 112(b) rejection of claim(s) 1, 2, 5-20, 22 is respectfully withdrawn.
Applicant's arguments with respect to the 35 U.S.C. § 102 and 103 rejection of claim(s) 1, 2, 5-22 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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.
Claim(s) 1, 2, 5, 7-15, 17-22, is/are rejected under 35 U.S.C. 103 as being unpatentable over Cui (US 20200104898 A1), in view of Das Gupta (US 20230162258 A1).
Das Gupta was cited in the PTO-892 form dated 4/10/2025.
Regarding claim 1, Cui teaches a system comprising: one or more processors; and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to (Cui [150-152, 127-129, 133] Fig. 7 system processor executes instructions stored in system memory to perform method to train algorithm for complimentary items, training and determination of anchor-accessory relationships, may use methods 300, 400, 500 and 600):
determine one or more similar items similar to an item (Cui [54, 69, 70] other anchor products (items similar to anchor item (the item)) may be determined and product categories may be determined based on similar items);
determine one or more complementary items complementary to both the one or more similar items and the item (Cui [71-74, 79] based on product category and other anchor products- accessory (complementary) products and accessory categories are determined (based on manual relationships and/or text mining information may be used));
convert one or more complementary item signals into a form used by a machine learning algorithm by applying one or more labels to the one or more complementary items based on a rank of the one or more complementary items (Cui [75, 139] accessory products may be ranked based on multiple criteria and labels may be applied to accessory products based on ranking, Cui [36, 63, 150-154] item information including labels may be communicated to and from database and/or system server, communication may be through network communication (signal));
train the machine learning algorithm on the one or more labels (Cui [57, 133, 135, 139] based on labels- algorithm may be trained to learn aspects of existing anchor-accessory relationships);
receive a request to generate a customized graphical user interface (GUI) for the item (Cui [57, 61, 69] GUI for accessory products may be generated based on user request of search or selection of an anchor product));
coordinate displaying the customized GUI for the item using the machine learning algorithm (Cui [57, 62, 64, 112, 143] trained algorithm may be used to identify accessory products and GUI with accessory products may be displayed to user).
retrain, after training the machine learning algorithm on the one or more labels, the machine learning algorithm in real-time based on one or more of an interaction with the customized GUI, data being added to a training data set, or interaction data of a single user (Cui [57, 134-139, 148] further label data may be used to further train the machine learning algorithm on a continual basis, Cui [134-139] further label data used for training may be new data added to database, Cui [41, 56, 66, 128-134, 144] interaction data may be based on users’ (which would include a single user) interaction with customized GUI).
Cui does not specifically teach based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold
However Das Gupta teaches determine one or more complementary items complementary to both the one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold (Das Gupta [36-38] relationships for product similarity and complementary products is determined, product may be complementary to another product based on the number of times or the number of customers where (metric) the product was added to the cart when the another products was added first to the cart, based on relationships and thresholds- if Products A and A’ are similar, Products C and C’ are similar, then if C is complementary to A then C’ is complementary to A’, Das Gupta [35, 36, 39] items similar to user selected item may be determined, relationships between items may be used to train a model, Das Gupta [22, 37] allows the retailer to provide product recommendations that are more meaningful to the user, using existing product relationships to infer additional relationships and hidden links is more scalable and efficient, and increases the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Das Gupta of determine one or more complementary items complementary to both the one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold, into the invention suggested by Cui; since both inventions are directed towards determining complimentary items for a particular item, and incorporating the teaching of Das Gupta into the invention suggested by Cui would provide the added advantage of being more scalable and efficient, and increasing the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse, and the combination would perform with a reasonable expectation of success (Das Gupta [22, 35-39]).
Regarding claim 11, Cui teaches a method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non- transitory computer-readable media, the method comprising (Cui [150-152, 127-129, 133] Fig. 7 system processor executes instructions stored in system memory to perform method to train algorithm for complimentary items, training and determination of anchor-accessory relationships, may use methods 300, 400, 500 and 600) :
determining one or more similar items similar to an item (Cui [54, 69, 70] other anchor products (items similar to anchor item (the item)) may be determined and product categories may be determined based on similar items);
determining one or more complementary items complementary to both the one or more similar items and the item (Cui [71-74, 79] based on product category and other anchor products- accessory (complementary) products and accessory categories are determined (based on manual relationships and/or text mining information may be used));
applying one or more labels to the one or more complementary items based on a rank of the one or more complementary items (Cui [75, 139] accessory products may be ranked based on multiple criteria and labels may be applied to accessory products based on ranking);
training a predictive algorithm on the one or more labels (Cui [57, 133, 135, 139] based on labels- algorithm may be trained to learn aspects of existing anchor-accessory relationships);
receiving a request to generate a customized graphical user interface (GUI) for the item (Cui [57, 61, 69] GUI for accessory products may be generated based on user request of search or selection of an anchor product));
coordinating displaying the customized GUI for the item using the predictive algorithm (Cui [57, 62, 64, 112, 143] trained algorithm may be used to identify accessory products and GUI with accessory products may be displayed to user).
Cui does not specifically teach based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold.
However Das Gupta teaches determining one or more complementary items complementary to both the one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold (Das Gupta [36-38] relationships for product similarity and complementary products is determined, product may be complementary to another product based on the number of times or the number of customers where (metric) the product was added to the cart when the another products was added first to the cart, based on relationships and thresholds- if Products A and A’ are similar, Products C and C’ are similar, then if C is complementary to A then C’ is complementary to A’, Das Gupta [35, 36, 39] items similar to user selected item may be determined, relationships between items may be used to train a model, Das Gupta [22, 37] allows the retailer to provide product recommendations that are more meaningful to the user, using existing product relationships to infer additional relationships and hidden links is more scalable and efficient, and increases the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Das Gupta of determining one or more complementary items complementary to both the one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold, into the invention suggested by Cui; since both inventions are directed towards determining complimentary items for a particular item, and incorporating the teaching of Das Gupta into the invention suggested by Cui would provide the added advantage of being more scalable and efficient, and increasing the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse, and the combination would perform with a reasonable expectation of success (Das Gupta [22, 35-39]).
Regarding claims 2 and 12, Cui and Das Gupta teach the invention as claimed in claims 1 and 11 respectively above.
Cui further teaches wherein applying the one or more labels to the one or more complementary items comprises: when the rank of the one or more complementary items is above a predetermined threshold, applying a positive label, of the one or more labels, to the one or more complementary items; and when the rank of the one or more complementary items is below the predetermined threshold, applying a negative label, of the one or more labels, to the one or more complementary items (Cui [56, 75-77, 135- 139] based on ranking of accessory association with anchor category, determination may be made whether the accessory is appropriate; based on whether the ranking or score is above or below a threshold- accessory is assigned a label (Y is X’s accessory) indicating whether or not the accessory is a recommended accessory for the anchor product).
Regarding claims 5 and 15, Cui and Das Gupta teach the invention as claimed in claims 1 and 11 respectively above.
Cui further teaches wherein machine learning algorithm is based on the one or more labels and one or more of... an item category...and item purchases (Cui [57, 133, 135, 139] algorithm is trained based on labels, Cui [54, 69, 70-75, 79, 136, 139] labels are determined based on anchor category and co-purchase ratios), as claimed in claim 5.
Cui further teaches wherein training the predictive algorithm comprises: training the predictive algorithm on the one or more labels and one or more of... an item category...and item purchases (Cui [57, 133, 135, 139] algorithm is trained based on labels, Cui [54, 69, 70-75, 79, 136, 139] labels are determined based on anchor category and co-purchase ratios), as claimed in claim 15.
Regarding claims 7 and 17, Cui and Das Gupta teach the invention as claimed in claims 1 and 11 respectively above.
Cui further teaches wherein, to coordinate displaying the customized GUI, the one or more processors are to: apply one or more feature weights determined by the machine learning algorithm to one or more features of the item (Cui [45, 106, 112] compatibility rules are considered by algorithm, rules may include attribute priority and whether required or not (feature weight)); and
coordinate displaying the customized GUI using the one or more feature weights as applied to the one or more features of the item (Cui [57, 62, 64, 112, 143] algorithm may use feature weights to identify accessory products to recommend, and GUI with accessory products may be displayed to user), claimed in claim 7.
Cui further teaches wherein coordinating displaying the customized GUI using the predictive algorithm comprises: applying one or more feature weights determined by the predictive algorithm to one or more features of the item (Cui [45, 106, 112] compatibility rules are considered by algorithm, rules may include attribute priority and whether required or not (feature weight)); and
coordinating displaying the customized GUI using the one or more features weights as applied to the one or more features of the item (Cui [57, 62, 64, 112, 143] algorithm may use feature weights to identify accessory products to recommend, and GUI with accessory products may be displayed to user), claimed in claim 17.
Regarding claim 8 and 18, Cui and Das Gupta teach the invention as claimed in claims 1 and 11 respectively above.
Cui further teaches wherein the customized GUI comprises a GUI displaying items complementary to the item (Cui [29] accessory product recommendations displayed may be products that are complementary to the anchor item).
Regarding claim 9 and 19, Cui and Das Gupta teach the invention as claimed in claims 8 and 18 respectively above above.
Cui further teaches wherein the item and the items complementary to the item have no historical data linking them together (Cui [71-74, 79] accessory products and accessory categories are determined based on product category and other anchor products (i.e. not the specific anchor item and there need not be any specific information linking the anchor product and the accessory product(s)).
Regarding claims 10 and 20, Cui and Das Gupta teach the invention as claimed in claims 1 and 11 respectively above.
Cui further teaches wherein the computing instructions are further configured to run on the one or more processors and cause the one or more processors to: coordinate displaying a second customized GUI using the machine learning algorithm, as re- trained (Cui [57, 62, 64, 112, 143, 148] retrained algorithm may be used to provide GUI to user), as claimed in claim 10.
Cui further teaches receiving one or more interactions with the customized GUI; retraining the predictive algorithm on the one or more interactions (Cui [49, 102, 148] based on user interactions- algorithm may be retrained); and
coordinating displaying a second customized GUI using the predictive algorithm, as re- trained (Cui [57, 62, 64, 112, 143, 148] retrained algorithm may be used to provide GUI to user), as claimed in claim 20.
Regarding claim 21, Cui teaches a non-transitory, computer-readable medium comprising instructions that, when executed by a processing resource, causes the processing resource to (Cui [150-152, 127-129, 133] Fig. 7 system processor executes instructions stored in system memory to perform method to train algorithm for complimentary items, training and determination of anchor-accessory relationships, may use methods 300, 400, 500 and 600) :
determine one or more complementary items complementary to both an item and one or more similar items (Cui [71-74, 79] based on product category and other anchor products- accessory (complementary) products and accessory categories are determined (based on manual relationships and/or text mining information may be used));
train a machine learning algorithm on one or more labels that are applied to the one or more complementary items (Cui [54, 69, 70] other anchor products (items similar to anchor item (the item)) may be determined and product categories may be determined based on similar items, Cui [71-74, 79] based on product category and other anchor products- accessory (complementary) products and accessory categories are determined (based on manual relationships and/or text mining information may be used), Cui [75, 139] accessory products may be ranked based on multiple criteria and labels may be applied to accessory products based on ranking, Cui [57, 133, 135, 139] based on labels- algorithm may be trained to learn aspects of existing anchor-accessory relationships);
coordinate displaying, using the machine learning algorithm, a customized graphical user interface (GUI) for the item (Cui [57, 62, 64, 112, 143] trained algorithm may be used to identify accessory products and GUI with accessory products may be displayed to user, Cui [57, 61, 69] GUI for accessory products may be generated based on user request of search or selection of an anchor product)).
retrain, after training the machine learning on the one or more labels, the machine learning algorithm based on one or more of an interaction with the customized GUI or data being added to a training data set (Cui [57, 134-139, 148] further label data may be used to further train the machine learning algorithm on a continual basis, Cui [134-139] further label data used for training may be new data added to database, Cui [41, 56, 66, 128-134, 144] interaction data may be based on users’ (which would include a single user) interaction with customized GUI, interaction information may be filtered (weighted)).
Cui does not specifically teach based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold.
However Das Gupta teaches determine one or more complementary items complementary to both an item and one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold (Das Gupta [36-38] relationships for product similarity and complementary products is determined, product may be complementary to another product based on the number of times or the number of customers where (metric) the product was added to the cart when the another products was added first to the cart, based on relationships and thresholds- if Products A and A’ are similar, Products C and C’ are similar, then if C is complementary to A then C’ is complementary to A’, Das Gupta [35, 36, 39] items similar to user selected item may be determined, relationships between items may be used to train a model, Das Gupta [22, 37] allows the retailer to provide product recommendations that are more meaningful to the user, using existing product relationships to infer additional relationships and hidden links is more scalable and efficient, and increases the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Das Gupta of determine one or more complementary items complementary to both an item and one or more similar items and the item based on a complementary metric for a complementary item, of the one or more complementary items, and the item satisfying a threshold, into the invention suggested by Cui; since both inventions are directed towards determining complimentary items for a particular item, and incorporating the teaching of Das Gupta into the invention suggested by Cui would provide the added advantage of being more scalable and efficient, and increasing the reach of potential recommendations to items where data representative of complementary or similar products may otherwise be sparse, and the combination would perform with a reasonable expectation of success (Das Gupta [22, 35-39]).
Regarding claims 22, Cui and Das Gupta teach the invention as claimed in claim 11 above.
Cui further teaches retraining, after training the predictive algorithm on the one or more labels applied to the one or more complementary items, the predictive algorithm based on an interaction with the customized GUI retrain, after training the machine learning algorithm on the one or more labels, the machine learning algorithm in real-time based on one or more of an interaction with the customized GUI, data being added to a training data set, or interaction data of a single user, associated with the customized GUI, that is weighted (Cui [57, 134-139, 148] further label data may be used to further train the machine learning algorithm on a continual basis, Cui [134-139] further label data used for training may be new data added to database, Cui [41, 56, 66, 128-134, 144] interaction data may be based on users’ (which would include a single user) interaction with customized GUI, interaction information may be filtered (weighted)).
Claims 6, 16, are rejected under 35 U.S.C. 103 as being unpatentable over Cui (US 20200104898 A1) in view of Das Gupta (US 20230162258 A1), and further in view of Shamir (US 20250077934 A1, e.f.d 10/13/2021).
Regarding claim 6, Cui and Das Gupta teach(es) the invention as claimed in claim 1 above. Cui does not specifically teach wherein the machine learning algorithm is configured to use listwise ranking loss.
However Shamir teaches wherein the machine learning algorithm is configured to use listwise ranking loss (Shamir Fig. 3, [4, 6, 26, 38, 65] algorithm is for providing recommendations based on current resource (item), listwise ranking loss may be used to “result in a more efficient implementation”).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Shamir of wherein the machine learning algorithm is configured to use listwise ranking loss, into the invention suggested by Cui and Das Gupta; since both inventions are directed towards providing recommendations based on a current resource, and incorporating the teaching of Shamir into the invention suggested by Cui and Das Gupta would provide the added advantage of using listwise ranking loss to “result in a more efficient implementation”, and the combination would perform with a reasonable expectation of success (Shamir Fig. 3, [4, 6, 26, 38, 65]).
Regarding claim 16, Cui and Das Gupta teach(es) the invention as claimed in claim 1 above. Cui does not specifically teach wherein the predictive algorithm uses listwise ranking loss.
However Shamir teaches wherein the predictive algorithm uses listwise ranking loss (Shamir Fig. 3, [4, 6, 26, 38, 65] algorithm is for providing recommendations based on current resource (item), listwise ranking loss may be used to “result in a more efficient implementation”).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Shamir of wherein the predictive algorithm uses listwise ranking loss, into the invention suggested by Cui and Das Gupta; since both inventions are directed towards providing recommendations based on a current resource, and incorporating the teaching of Shamir into the invention suggested by Cui and Das Gupta would provide the added advantage of using listwise ranking loss to “result in a more efficient implementation”, and the combination would perform with a reasonable expectation of success (Shamir Fig. 3, [4, 6, 26, 38, 65]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Levitan (US 8290818 B1) discloses providing complementary item recommendations based on association with substitute items.
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146