DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/08/2025 has been entered.
Status of Claims
Claims 1, 2, 4-10, and 12-22 submitted on 03/30/2026 are pending and have been examined. Claims 1, 4, 10, 12, and 15 have been amended. Claims 3 and 11 have been cancelled. Claims 21 and 22 are new.
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 .
Priority
Acknowledgment is made of applicant’s Provisional Application No. 63/504,959, filed on 05/30/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/20/2026 has been considered by the examiner.
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, 2, 4-10, and 12-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1, 2, and 4-9 are directed to a process, claims 10, 12-14, 21, and 22 are directed to a machine, and claims 15-20 are directed to an article of manufacture (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
a method comprising: identifying at least one candidate item as being relevant to at least one reference item;
providing a prediction, of a type of relationship that exists between the at least one candidate item and the at least one reference item in addition to relevancy of the at least one candidate item to the at least one reference item;
determining whether to present the at least one candidate item based on the relevancy of the at least one candidate item to the at least one reference item and the prediction of the type of relationship between the at least one candidate item and the at least one reference item, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one reference item; and
outputting a second candidate item from the at least one candidate item that is relevant to the at least one reference item and is not contextually incompatible with the at least one reference item.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction. Claims 10 and 15 recites similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1, 10, and 15 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
a method comprising: identifying at least one candidate item as being relevant to at least one reference item;
providing a prediction, by a context-sensitive classifier, of a type of relationship that exists between the at least one candidate item and the at least one reference item in addition to relevancy of the at least one candidate item to the at least one reference item;
determining whether to present the at least one candidate item in a user interface based on the relevancy of the at least one candidate item to the at least one reference item and the prediction of the type of relationship between the at least one candidate item and the at least one reference item, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one reference item; and
outputting a second candidate item from the at least one candidate item that is relevant to the at least one reference item and is not contextually incompatible with the at least one reference item.
Claim 10 and 15 include the same additional elements as claim 1. In addition to the additional elements of claim 1, claim 10 includes additional elements such as a computing system comprising:
a processor; and a memory storing instructions that, when executed by the processor, configure the computing system to. In addition to the additional elements of claim 1, claim 15 includes additional elements such as a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that when executed by a computer, cause the computer to.
The additional elements recited in claims 1, 10, and 15 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Fig. 8, ¶¶0091-0093, and ¶¶0104-0105).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1, 10, and 15 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1, 10, and 15 are acknowledged, claims 1, 10, and 15 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1, 10, and 15 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 10, and 15 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 10, and 15 are ineligible.
Dependent claims 2, 6, 9, 13, 17, and 20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 2, 6, 9, 13, 17, and 20 merely further define the abstract limitations of claims 1, 10, and 15 or provide further embellishments of the limitations recited in independent claims 1, 10, and 15. Claims 2, 6, 9, 13, 17, and 20 do not introduce any further additional elements. Thus, dependent claims 2, 6, 9, 13, 17, and 20 are ineligible.
Furthermore, it is noted that certain dependent claims recite additional elements supplemental to those recited in independent claims 1, 10, and 15: reference item embedding (claims 4, 5, 7, 8, 12, 14, 16, 18, 19, 21, and 22), candidate item embedding (claims 4, 5, 7, 8, 12, 14, 16, 18, 19, 21, and 22), multi-layer perceptron neural network (claims 5, 16, and 21), and two-tower neural network model (claims 5, 16, and 21). However, these elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 4, 5, 7, 8, 12, 14, 16, 18, 19, 21, and 22 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 4, 10, and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saad et al. (US 10,445,810 B1) in view of Al Jadda et al. (US 2021/0073891 A1 [previously cited])
Regarding Claim 1, Saad et al., hereinafter, Saad, discloses a method comprising: identifying at least one candidate item as being relevant to at least one user (Fig. 3; Col. 11, lines 15-22[FIG. 3 shows a flowchart outlining one exemplary embodiment of a method for recommending products in a specific category, based on a specific consumer profile]);
providing a prediction, by a context-sensitive classifier, of a type of relationship that exists between the at least one candidate item and the at least one user in addition to relevancy of the at least one candidate item to the at least one user (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]);
determining whether to present the at least one candidate item in a user interface based on the relevancy of the at least one candidate item to the at least one user and the prediction of the type of relationship between the at least one candidate item and the at least one user, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility… If, in step S335 it is determined that there is an incompatibility between the consumer and a product, control advances to step S340 and all of the products that are offered, which are incompatible with the consumer are removed from the list of selected offered products.]); and
outputting a second candidate item from the at least one candidate item that is relevant to the at least one user and is not contextually incompatible with the at least one user (Fig. 3[elements S335 and S370]; Col. 12, lines 15-17[Otherwise, if it is determined, in step S335, that none of the products are incompatible with the consumer, control jumps to step S345.] in view of Col. 13, lines 27-30[Then, in step S370, the sorted list of the remaining selected offered products is displayed or presented to the consumer.]).
Although Saad discloses identifying a candidate item that is relevant and contextually compatible with a user, Saad does not explicitly disclose relevant to a reference item, a relationship between an item and a reference item in addition to relevancy with a reference item, presenting based on the relevancy of a reference item and the relationship with a reference item, contextual incompatibility with a reference item, outputting a relevant reference item that is contextually compatible with a reference item.
However, Al Jadda et al., hereinafter, Al Jadda, teaches a candidate item being relevant and compatible with a reference item (Fig. 2[elements 202]; ¶0039[the server 118 may receive a product selection from a user, provide that product selection to the product recommendation system 106, receive one or more complementary product recommendations from the product recommendation system 106, and provide those complementary product recommendations to the user on a webpage or other interface portion respective of the product selected by the user.] in view of ¶0020[A “complementary product,” as used herein, includes products that relate to an anchor product in a meaningful way.]).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include a reference item as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 2, Saad in view of Al Jadda teaches the method of claim 1, Saad discloses further comprising: causing the at least one candidate item to be presented in the user interface when the at least one candidate item has a contextually compatible relationship with the at least one user (Fig. 3[elements S335 and S370]; Col. 12, lines 15-17[Otherwise, if it is determined, in step S335, that none of the products are incompatible with the consumer, control jumps to step S345.] in view of Col. 13, lines 27-30[Then, in step S370, the sorted list of the remaining selected offered products is displayed or presented to the consumer.]).
Although Saad discloses an item being presented when it is contextually compatible with a user, Saad does not explicitly disclose a reference item.
However, Al Jadda teaches a candidate item being relevant and compatible with a reference item (Fig. 2[elements 202]; ¶0039[the server 118 may receive a product selection from a user, provide that product selection to the product recommendation system 106, receive one or more complementary product recommendations from the product recommendation system 106, and provide those complementary product recommendations to the user on a webpage or other interface portion respective of the product selected by the user.] in view of ¶0020[A “complementary product,” as used herein, includes products that relate to an anchor product in a meaningful way.]).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include a reference item as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 4, Saad in view of Al Jadda teaches the method of claim 1, Saad discloses further comprising: prior to providing the prediction (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad discloses providing a prediction, Saad does not explicitly disclose creating a reference item embedding and creating a candidate item embedding.
However, Al Jadda teaches creating a reference and candidate item embeddings (Fig. 3; ¶0033[The text embedding generation module 110 may be configured to accept product information of a given product as input and to output embeddings (e.g., a vector description) respective of that product] in view of ¶¶0051-0054[The method 300 may further include a block 306 that includes generating text embeddings for a plurality of products (e.g., respective vector descriptions of text associated with each of those products)]).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 10, Saad discloses a computing system comprising: a processor; and a memory storing instructions that, when executed by the processor, configure the computing system to: identify at least one candidate item as being relevant to at least one user (Fig. 3; Col. 11, lines 15-22[FIG. 3 shows a flowchart outlining one exemplary embodiment of a method for recommending products in a specific category, based on a specific consumer profile] in view of Claim 1[A method for recommending, via a computer system having a processor and a memory, one or more products or services to a consumer]);
provide a prediction, by a context-sensitive classifier, of a type of relationship that exists between the at least one candidate item and the at least one user in addition to relevancy of the at least one candidate item to the at least one user (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]);
determine whether to present the at least one candidate item in a user interface based on the relevancy of the at least one candidate item to the at least one user and the prediction of the type of relationship between the at least one candidate item and the at least one user, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility… If, in step S335 it is determined that there is an incompatibility between the consumer and a product, control advances to step S340 and all of the products that are offered, which are incompatible with the consumer are removed from the list of selected offered products.]); and
output a second candidate item from the at least one candidate item that is relevant to the at least one user and is not contextually incompatible with the at least one user (Fig. 3[elements S335 and S370]; Col. 12, lines 15-17[Otherwise, if it is determined, in step S335, that none of the products are incompatible with the consumer, control jumps to step S345.] in view of Col. 13, lines 27-30[Then, in step S370, the sorted list of the remaining selected offered products is displayed or presented to the consumer.]).
Although Saad discloses identifying a candidate item that is relevant and contextually compatible with a user, Saad does not explicitly disclose relevant to a reference item, a relationship between an item and a reference item in addition to relevancy with a reference item, presenting based on the relevancy of a reference item and the relationship with a reference item, contextual incompatibility with a reference item, outputting a relevant reference item that is contextually compatible with a reference item.
However, Al Jadda teaches a candidate item being relevant and compatible with a reference item (Fig. 2[elements 202]; ¶0039[the server 118 may receive a product selection from a user, provide that product selection to the product recommendation system 106, receive one or more complementary product recommendations from the product recommendation system 106, and provide those complementary product recommendations to the user on a webpage or other interface portion respective of the product selected by the user.] in view of ¶0020[A “complementary product,” as used herein, includes products that relate to an anchor product in a meaningful way.]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include a reference item as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 12, Saad in view of Al Jadda teaches the computing system of claim 10, Saad discloses wherein the instructions further configure the computing system to: prior to providing the prediction (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad discloses providing a prediction, Saad does not explicitly disclose create a reference item embedding and create a candidate item embedding.
However, Al Jadda teaches creating a reference and candidate item embeddings (Fig. 3; ¶0033[The text embedding generation module 110 may be configured to accept product information of a given product as input and to output embeddings (e.g., a vector description) respective of that product] in view of ¶¶0051-0054[The method 300 may further include a block 306 that includes generating text embeddings for a plurality of products (e.g., respective vector descriptions of text associated with each of those products)]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 13, Saad in view of Al Jadda teaches the computing system of claim 10, Saad discloses wherein the instructions further configure the computing system to: determine at least one relationship feature as between the at least one user and the at least one candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]),
wherein the at least one relationship feature is stating a relationship between attributes of the at least one candidate item and the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility).
Although Saad discloses a relationship between a user and a product, Saad does not explicitly disclose relationship between an item and a reference item and an expression stating a relationship between attributes of an item and a reference item.
However, Al Jadda teaches a reference item and an expression stating a relationship between items (Fig. 2[element 206]; ¶0034[The product recommendation system 106 may further include an image feature vector generation module 112… accept one or more images of a product as input and to output a vector descriptive of the image, and thus descriptive of the product… The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on… image feature vectors respective of those products (e.g., image feature vectors generated by the image feature vector generation module 112).]; Examiner notes image similarity is comparable to a relationship feature wherein the feature is an expression stating a relationship).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include an expression stating a relationship between items as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 14, Saad in view of Al Jadda teaches the computing system of claim 13, Saad discloses wherein the instructions further configure the computing system to: the at least one relationship feature (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad discloses a relationship feature between a user and a product, Saad does not explicitly disclose to input the relationship into a classification layer along with a reference item embedding and a candidate item embedding.
However, Al Jadda teaches inputting a feature into a classification layer along with reference and candidate item embeddings (Fig. 2; ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products ( e.g., embeddings generated by the text embeddings generation module 110) and image feature vectors respective of those products ( e.g., image feature vectors generated by the image feature vector generation module 112).]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 15, Saad discloses a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that when executed by a computer, cause the computer to: identify at least one candidate item as being relevant to at least one user (Fig. 3; Col. 11, lines 15-22[FIG. 3 shows a flowchart outlining one exemplary embodiment of a method for recommending products in a specific category, based on a specific consumer profile] in view of Claim 1[A method for recommending, via a computer system having a processor and a memory, one or more products or services to a consumer]);
provide a prediction, by a context-sensitive classifier, of a type of relationship that exists between the at least one candidate item and the at least one user in addition to relevancy of the at least one candidate item to the at least one user (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]);
determine whether to present the at least one candidate item in a user interface based on the relevancy of the at least one candidate item to the at least one user and the prediction of the type of relationship between the at least one candidate item and the at least one user, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility… If, in step S335 it is determined that there is an incompatibility between the consumer and a product, control advances to step S340 and all of the products that are offered, which are incompatible with the consumer are removed from the list of selected offered products.]); and
output a second candidate item from the at least one candidate item that is relevant to the at least one user and is not contextually incompatible with the at least one user (Fig. 3[elements S335 and S370]; Col. 12, lines 15-17[Otherwise, if it is determined, in step S335, that none of the products are incompatible with the consumer, control jumps to step S345.] in view of Col. 13, lines 27-30[Then, in step S370, the sorted list of the remaining selected offered products is displayed or presented to the consumer.]).
Although Saad discloses identifying a candidate item that is relevant and contextually compatible with a user, Saad does not explicitly disclose relevant to a reference item, a relationship between an item and a reference item in addition to relevancy with a reference item, presenting based on the relevancy of a reference item and the relationship with a reference item, contextual incompatibility with a reference item, outputting a relevant reference item that is contextually compatible with a reference item.
However, Al Jadda teaches a candidate item being relevant and compatible with a reference item (Fig. 2[elements 202]; ¶0039[the server 118 may receive a product selection from a user, provide that product selection to the product recommendation system 106, receive one or more complementary product recommendations from the product recommendation system 106, and provide those complementary product recommendations to the user on a webpage or other interface portion respective of the product selected by the user.] in view of ¶0020[A “complementary product,” as used herein, includes products that relate to an anchor product in a meaningful way.]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include a reference item as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Claim(s) 5-9 and 16-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saad in view of Al Jadda in view of Wang et al. (US 2023/0394295 A1 [previously cited]).
Regarding Claim 5, Saad in view of Al Jadda teaches the method of claim 4 Saad discloses the candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein the context- sensitive classifier includes an embedding layer and a classification layer, wherein the classification layer is a multi-layer neural network, wherein the embedding layer is a neural network model that generates the reference item embedding and the candidate item embedding.
However, Al Jadda teaches a multi-layer neural network model that generates reference and candidate item embeddings (Fig. 1[elements 106 and 110]; ¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108], ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products]; Examiner notes that the product recommendation system is comparable to the classification layer, see also ¶0050 of the instant specification where it is noted that the classification layer provides a prediction of whether the candidate item is contextually compatible or not with the reference item; ¶0028[The functional modules 108, 110, 112, 114, 116 of the product recommendation system 106 may include a training module 108 that is configured to train one or more machine learning models] in view of ¶0050[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)]; Examiner notes that BERT is comparable to a multi-layer neural network; ¶¶0050-0054[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)… The method 300 may further include a block 306 that includes generating text embeddings for a plurality of products (e.g., respective vector descriptions of text associated with each of those products)]).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Although Saad discloses a candidate item, Saad in view of Al Jadda does not explicitly teach a multi-layer perceptron neural network and a two-tower neural network model.
However, Wang et al., hereinafter, Wang, teaches a two-tower model and a cascaded multilayer perceptron model (Figs. 2 and 3; ¶0015[In aspects, a two-tower model is applied in conjunction with a cascaded multilayer perceptron (MLP) model to enable adoption of variable combinations of input features resulting in more representative learned embeddings…The improved model provides relevant recommendations to the user to maximally increase user engagement.]).
The method of Wang is applicable to the method of Saad in view of Al Jadda as they share characteristics and capabilities, namely, they are targeted to content recommendation. 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 multi-layer neural network as taught by Saad in view of Al Jadda to include a two-tower and perceptron models as taught by Wang. One of ordinary skill in the art would have been motivated to expand the method of Saad in view of Al Jadda in order to improve recommendation quality and increase user engagement, thereby ultimately enhancing long term user experience (Abstract).
Regarding Claim 6, Saad in view of Al Jadda in view of Wang teaches the method of claim 5, Saad discloses further comprising: determining at least one relationship feature as between the at least one user and the at least one candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]),
wherein the at least one relationship feature is stating a relationship between attributes of the at least one candidate item and the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility).
Although Saad discloses a relationship between a user and a product, Saad does not explicitly disclose relationship between an item and a reference item and an expression stating a relationship between attributes of an item and a reference item.
However, Al Jadda teaches a reference item and an expression stating a relationship between items (Fig. 2[element 206]; ¶0034[The product recommendation system 106 may further include an image feature vector generation module 112… accept one or more images of a product as input and to output a vector descriptive of the image, and thus descriptive of the product… The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on… image feature vectors respective of those products (e.g., image feature vectors generated by the image feature vector generation module 112).]; Examiner notes image similarity is comparable to a relationship feature wherein the feature is an expression stating a relationship).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include an expression stating a relationship between items as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 7, Saad in view of Al Jadda in view of Wang teaches the method of claim 6, Saad discloses further comprising: the at least one relationship feature (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad discloses a relationship feature between a user and a product, Saad does not explicitly disclose to inputting the relationship into the classification layer along with the reference item embedding and the candidate item embedding.
However, Al Jadda teaches inputting a feature into a classification layer along with reference and candidate item embeddings (Fig. 2; ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products ( e.g., embeddings generated by the text embeddings generation module 110) and image feature vectors respective of those products ( e.g., image feature vectors generated by the image feature vector generation module 112).]).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 8, Saad in view of Al Jadda in view of Wang teaches the method of claim 5, Saad discloses the candidate item (Col. 4, lines 60-65[For example, the method for recommending products based on a specific consumer profile may be used to recommend any resource, product, good, or service to a consumer based on a specific consumer profile and/or product parameter.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein the embedding layer utilizes a language model to create the reference item embedding and embedding
However, Al Jadda teaches utilizing a language model to create item embeddings (¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108, or a portion of the model, in some embodiments. The text embedding generation module 110 may be configured to accept product information of a given product as input and to output embeddings ( e.g., a vector description) respective of that product]; Examiner notes that the text embedding generation module is comparable to a
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 9, Saad in view of Al Jadda in view of Wang teaches the method of claim 8, Saad discloses content items (Col. 4, lines 60-65[For example, the method for recommending products based on a specific consumer profile may be used to recommend any resource, product, good, or service to a consumer based on a specific consumer profile and/or product parameter.]).
Although Saad disclose content items, Saad does not explicitly disclose wherein the language model is trained on a dataset of advertisements relevant to content.
However, Al Jadda teaches a language model trained on a dataset of advertisements (Fig. 3; ¶0051[Embeddings may be generated with a machine learning model trained at block 304, based on product information (e.g., the product information stored in the product data database 104)]; Examiner notes that advertisement is comparable to product information).
The method of Al Jadda is applicable to the method of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include training a model on a dataset of advertisements as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the method of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 16, Saad in view of Al Jadda teaches the non-transitory computer-readable storage medium of claim 15, Saad discloses a candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein the context-sensitive classifier includes an embedding layer and a classification layer, wherein the classification layer is a multi-layer neural network, wherein the embedding layer is a neural network model that generates a reference item embedding and embedding.
However, Al Jadda teaches a multi-layer neural network model that generates reference and candidate item embeddings (Fig. 1[elements 106 and 110]; ¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108], ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products]; Examiner notes that the product recommendation system is comparable to the classification layer, see also ¶0050 of the instant specification where it is noted that the classification layer provides a prediction of whether the candidate item is contextually compatible or not with the reference item; ¶0028[The functional modules 108, 110, 112, 114, 116 of the product recommendation system 106 may include a training module 108 that is configured to train one or more machine learning models] in view of ¶0050[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)]; Examiner notes that BERT is comparable to a multi-layer neural network; ¶¶0050-0054[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)… The method 300 may further include a block 306 that includes generating text embeddings for a plurality of products (e.g., respective vector descriptions of text associated with each of those products)]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Although Saad discloses a candidate item, Saad in view of Al Jadda does not explicitly teach a multi-layer perceptron neural network and a two-tower neural network model.
However, Wang teaches a two-tower model and a cascaded multilayer perceptron model (Figs. 2 and 3; ¶0015[In aspects, a two-tower model is applied in conjunction with a cascaded multilayer perceptron (MLP) model to enable adoption of variable combinations of input features resulting in more representative learned embeddings…The improved model provides relevant recommendations to the user to maximally increase user engagement.]).
The system of Wang is applicable to the system of Saad in view of Al Jadda as they share characteristics and capabilities, namely, they are targeted to content recommendation. 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 multi-layer neural network as taught by Saad in view of Al Jadda to include a two-tower and perceptron models as taught by Wang. One of ordinary skill in the art would have been motivated to expand the system of Saad in view of Al Jadda in order to improve recommendation quality and increase user engagement, thereby ultimately enhancing long term user experience (Abstract).
Regarding Claim 17, Saad in view of Al Jadda in view of Wang teaches the non-transitory computer-readable storage medium of claim 16, Saad discloses wherein the instructions further configure the computer to: determine at least one relationship feature as between the at least one user and the at least one candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]),
wherein the at least one relationship feature is stating a relationship between attributes of the at least one candidate item and the at least one user (Fig. 3; Col. 11, line 60 to Col. 12, line 3[In step S335, a determination is made as to whether the consumer's profile renders the consumer incompatible with any of the products. This may be accomplished, for example, by accessing information regarding the consumer in the specific consumer profile information stored in the consumer profile database 150. It should be appreciated that incompatibility of a consumer to a product may be determined to be a mental, physical, preferential, geographic, or other incompatibility).
Although Saad discloses a relationship between a user and a product, Saad does not explicitly disclose relationship between an item and a reference item and an expression stating a relationship between attributes of an item and a reference item.
However, Al Jadda teaches a reference item and an expression stating a relationship between items (Fig. 2[element 206]; ¶0034[The product recommendation system 106 may further include an image feature vector generation module 112… accept one or more images of a product as input and to output a vector descriptive of the image, and thus descriptive of the product… The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on… image feature vectors respective of those products (e.g., image feature vectors generated by the image feature vector generation module 112).]; Examiner notes image similarity is comparable to a relationship feature wherein the feature is an expression stating a relationship).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include an expression stating a relationship between items as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 18, Saad in view of Al Jadda in view of Wang teaches the non-transitory computer-readable storage medium of claim 17, Saad discloses wherein the instructions further configure the computer to: the at least one relationship feature (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad discloses a relationship feature between a user and a product, Saad does not explicitly disclose to input the relationship into the classification layer along with the reference item embedding and the candidate item embedding.
However, Al Jadda teaches inputting a feature into a classification layer along with reference and candidate item embeddings (Fig. 2; ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products ( e.g., embeddings generated by the text embeddings generation module 110) and image feature vectors respective of those products ( e.g., image feature vectors generated by the image feature vector generation module 112).]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 19, Saad in view of Al Jadda in view of Wang teaches the non-transitory computer-readable storage medium of claim 16, Saad discloses the candidate item (Col. 4, lines 60-65[For example, the method for recommending products based on a specific consumer profile may be used to recommend any resource, product, good, or service to a consumer based on a specific consumer profile and/or product parameter.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein the embedding layer utilizes a language model to create the reference item embedding and embedding
However, Al Jadda teaches utilizing a language model to create item embeddings (¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108, or a portion of the model, in some embodiments. The text embedding generation module 110 may be configured to accept product information of a given product as input and to output embeddings ( e.g., a vector description) respective of that product]; Examiner notes that the text embedding generation module is comparable to a
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 20, Saad in view of Al Jadda in view of Wang teaches the non-transitory computer-readable storage medium of claim 19, Saad discloses content items (Col. 4, lines 60-65[For example, the method for recommending products based on a specific consumer profile may be used to recommend any resource, product, good, or service to a consumer based on a specific consumer profile and/or product parameter.]).
Although Saad disclose content items, Saad does not explicitly disclose wherein the language model is trained on a dataset of advertisements relevant to content.
However, Al Jadda teaches a language model trained on a dataset of advertisements (Fig. 3; ¶0051[Embeddings may be generated with a machine learning model trained at block 304, based on product information (e.g., the product information stored in the product data database 104)]; Examiner notes that advertisement is comparable to product information).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include training a model on a dataset of advertisements as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Regarding Claim 21, Saad in view of Al Jadda teaches the computing system of claim 10, Saad discloses a candidate item (Fig. 3; Col. 11, lines 38-45[Then, in step S325, a determination is made as to whether the consumer desires that the offered products include one or more specific characteristics. This may be accomplished, for example, by accessing information regarding the consumer's other preferences, if included in the specific consumer profile information stored in the consumer profile database 150.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein: the context-sensitive classifier includes an embedding layer and a classification layer, the classification layer is a multi-layer neural network, and the embedding layer is a neural network model that generates a reference item embedding and embedding.
However, Al Jadda teaches a multi-layer neural network model that generates reference and candidate item embeddings (Fig. 1[elements 106 and 110]; ¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108], ¶0035[The product recommendation system 106 may also include a similarity determination module 114 configured to determine the similarity between any two products based on text embeddings respective of those products]; Examiner notes that the product recommendation system is comparable to the classification layer, see also ¶0050 of the instant specification where it is noted that the classification layer provides a prediction of whether the candidate item is contextually compatible or not with the reference item; ¶0028[The functional modules 108, 110, 112, 114, 116 of the product recommendation system 106 may include a training module 108 that is configured to train one or more machine learning models] in view of ¶0050[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)]; Examiner notes that BERT is comparable to a multi-layer neural network; ¶¶0050-0054[The method 300 may further include a block 304 that includes training a machine learning model with the training data. The machine learning model can be… a bidirectional encoder representation from transformer (BERT)… The method 300 may further include a block 306 that includes generating text embeddings for a plurality of products (e.g., respective vector descriptions of text associated with each of those products)]).
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Although Saad discloses a candidate item, Saad in view of Al Jadda does not explicitly teach a multi-layer perceptron neural network and a two-tower neural network model.
However, Wang teaches a two-tower model and a cascaded multilayer perceptron model (Figs. 2 and 3; ¶0015[In aspects, a two-tower model is applied in conjunction with a cascaded multilayer perceptron (MLP) model to enable adoption of variable combinations of input features resulting in more representative learned embeddings…The improved model provides relevant recommendations to the user to maximally increase user engagement.]).
The system of Wang is applicable to the system of Saad in view of Al Jadda as they share characteristics and capabilities, namely, they are targeted to content recommendation. 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 multi-layer neural network as taught by Saad in view of Al Jadda to include a two-tower and perceptron models as taught by Wang. One of ordinary skill in the art would have been motivated to expand the system of Saad in view of Al Jadda in order to improve recommendation quality and increase user engagement, thereby ultimately enhancing long term user experience (Abstract).
Regarding Claim 22, Saad in view of Al Jadda in view of Wang teaches the computing system of claim 21, Saad discloses the candidate item (Col. 4, lines 60-65[For example, the method for recommending products based on a specific consumer profile may be used to recommend any resource, product, good, or service to a consumer based on a specific consumer profile and/or product parameter.]).
Although Saad disclose a candidate item, Saad does not explicitly disclose wherein the embedding layer utilizes a language model to create the reference item embedding and embedding
However, Al Jadda teaches utilizing a language model to create item embeddings (¶0033[Text embedding generation module 110 may include the machine learning model trained by the training module 108, or a portion of the model, in some embodiments. The text embedding generation module 110 may be configured to accept product information of a given product as input and to output embeddings ( e.g., a vector description) respective of that product]; Examiner notes that the text embedding generation module is comparable to a
The system of Al Jadda is applicable to the system of Saad as they share characteristics and capabilities, namely, they are both targeted to recommending products. 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 contextual incompatibility between users and products as disclosed by Saad to include creating embeddings as taught by Al Jadda. One of ordinary skill in the art would have been motivated to expand the system of Saad in order to enable to user to find items that may be used together in an aesthetically or functionally complementary fashion (¶0003).
Response to Arguments
Applicant’s arguments on pages 8-11 of the remarks filed 03/30/2026, with respect to the previous 35
USC § 101 rejections have been fully considered but are not persuasive.
Applicant argues on pages 8-9 of the remarks that the amended claims are not directed to an abstract idea. Examiner respectfully disagrees. The MPEP enumerates groupings of abstract ideas, thereby synthesizing the holdings of various court decisions to facilitate examination. See MPEP 2106.04. Among the enumerated groupings is the Certain Methods of Organizing Human Activity grouping, which includes activity that falls within the enumerated sub-grouping of commercial or legal interactions, including subject matter relating to agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. With respect to the claim amendments, Examiner notes that the “context-sensitive classifier” and “user interface” have been analyzed as additional elements and accordingly are not analyzed under Step 2A, Prong 1. A method comprising: identifying at least one candidate item as being relevant to at least one reference item; providing a prediction, of a type of relationship that exists between the at least one candidate item and the at least one reference item in addition to relevancy of the at least one candidate item to the at least one reference item; determining whether to present the at least one candidate item based on the relevancy of the at least one candidate item to the at least one reference item and the prediction of the type of relationship between the at least one candidate item and the at least one reference item, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one reference item; and outputting a second candidate item from the at least one candidate item that is relevant to the at least one reference item and is not contextually incompatible with the at least one reference item as recited in amended claim 1 are all part of the abstract idea and represent Certain Methods of Organizing Human Activity. This is also illustrated in ¶¶0002-0003 of the specification, describing the invention as relating to shopping for items. These limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas, enumerated in the MPEP, in that they recite commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations). Specifically, the limitations of claim 1 represent sales activities and behaviors because the limitations recite providing recommendations. These are sales activities because they pertain to shopping (Spec: ¶¶0002-0003). Accordingly, these claims recite Certain Methods of Organizing Human Activity.
Applicant argues on pages 9-11 of the remarks the amended claims integrate the abstract idea into a practical application. Examiner respectfully disagrees. The MPEP sets forth, in Step 2A Prong Two, that a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole "integrates the recited judicial exception into a practical application of that exception." The evaluation of Prong Two requires the use of the considerations (e.g. improving technology, effecting a particular treatment or prophylaxis, implementing with a particular machine, etc.) identified by the Supreme Court and the Federal Circuit, to ensure that the claim as a whole integrates [the] judicial exception into a practical application [that] will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.' In the instant case, the claims include additional elements such as a “context-sensitive classifier” and “user interface.” While these elements are recited, they are merely peripherally incorporated in order to implement the abstract the idea. Put another way, these additional elements are merely used to apply the abstract idea of providing recommendation based on product analysis in a technological environment without effectuating any improvement or change to the functioning of the additional elements or other technology. Applicant’s disclosure does not articulate or suggest how these additional elements function, individually or in combination, in any manner other than using generic functionality nor does the disclosure articulate how the elements provide a technical improvement. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they merely amount to using the computing components as a tool to perform the abstract idea.
Applicant cites to ¶0017 on page 9 of the remarks to provide support. However, Examiner notes that ¶0017 is also part of the abstract idea. Determining contextually relevant item pairs in order to generate product recommendations is part of the abstract idea and does not amount to a technical improvement. Furthermore, determining a type of relationship that exists between items in addition to relevancy is part of the abstract idea and does not show a technical improvement.
Applicant further cites to Desjardins and argues that the instant claims are analogous to Desjardins. Examiner respectfully disagrees. Desjardins reflected an improvement to machine learning model functionality and its specification described the prior art and how the invention improved storage reductions and training efficiencies recited in the claims that demonstrated eligibility (see MPEP 2106.05(a)(I)). Unlike the claims in Desjardins, the additional elements of Applicant’s claims do not pertain to an “improvement” to the functioning of a computer or to another technology (see MPEP 2106.04(a) and 2106.05(a)). The additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to 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 linking the use of the judicial exception to a particular technological environment.
Applicant further argues on pages 10-11 of the remarks that the amended claims provide a technical improvement and cites to ¶0078 of the instant specification. Examiner respectfully disagrees. A method comprising: identifying at least one candidate item as being relevant to at least one reference item; providing a prediction, of a type of relationship that exists between the at least one candidate item and the at least one reference item in addition to relevancy of the at least one candidate item to the at least one reference item; determining whether to present the at least one candidate item based on the relevancy of the at least one candidate item to the at least one reference item and the prediction of the type of relationship between the at least one candidate item and the at least one reference item, wherein the type of relationship is that a first candidate item of the at least one candidate item is contextually incompatible with the at least one reference item; and outputting a second candidate item from the at least one candidate item that is relevant to the at least one reference item and is not contextually incompatible with the at least one reference item as recited in amended claim 1 are all part of the abstract idea. Merely using generic and high-level components such as a “context-sensitive classifier,” “user interface,” “an embedding layer and a classification layer,” “a multi-layer perceptron neural network,” and “a two-tower neural network model that generates the reference item embedding and the candidate item embedding” does not overcome the rejection, refer to Fig. 8, ¶0057, ¶0087, and ¶0131 in applicant’s specification for high level and generic components.
Accordingly, Examiner maintains that the invention is directed to a judicial exception without
significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the 35 USC §101 rejections are maintained.
Applicant’s arguments on pages 11-14 of the remarks filed 03/30/2026, with respect to the previous 35 USC § 103 rejections have been fully considered but are moot in view of the new 103 rejection of the amended claims.
Conclusion
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/AHOORA LADONI/Examiner, Art Unit 3689
/ANNA MAE MITROS/Examiner, Art Unit 3689
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