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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 submissions filed on 09/10th/2025 have been entered.
Claim Objections
Claims 4 and 14 are objected to because of the following informalities: The claims recite maximize the second distance and minimize first distance. The claims should recite maximize the second distance and minimize the first distance. Appropriate correction is required.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 09/10th/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s argument, see REMARKS page 12-13 filed 09/10th/2025, regarding the rejection of claims 1-6, 8, 10-16, and 18-20 under 35 U.S.C. §101 have been considered and are not persuasive.
Applicant Argument:
As noted in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence (hereinafter "2024 Guidance"), the Office "must draw a distinction between a claim that 'recites' an abstract idea (and thus requires further eligibility analysis) and one that merely involves, or is based on, an abstract idea," under Step 2A, Prong 1. The present claims do not recite an abstract idea because they recite a neural network based complementary item recommendation, with operations including executing a first neural network on a multimodal embedding of the anchor item, executing a second neural network on a multimodal embedding of the positive item, executing a third neural network on a multimodal embedding of the negative item, determining a triplet loss function, in response to a determination that the first distance is greater than the second distance, re-training the first neural network, the second neural network, and the third neural network at least by: updating the at least one shared parameter based on a minimization of the triplet loss function to generate one or more updated parameters, and sharing the one or more updated parameters among the re-trained first neural network, the re-trained second neural network, and the re-trained third neural network. These operations do not fall into a mental process and cannot be performed practically by any human mind.
In addition, Applicant respectfully submits that even if, arguendo, Applicant's independent claims could recite an abstract idea, which the Office has not established and which Applicant does not concede, the claims are nevertheless patent eligible under Step 2A, Prong 2. As noted in the Memorandum to Technology Centers 2100, 2600, and 3600, Charles Kim, Deputy Commissioner for Patents, August 4, 2025 (hereinafter "2025 Memo"), "[a]n important consideration in determining whether a claim improves technology or a technical field is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome." The amended claim 1 here recites an improvement to a technical field ( existing item recommendation systems), because it covers a particular way to achieve a desired outcome (personalized recommendation using triplet neural networks). The particular way can be reflected at least in the features of "executing a first neural network on a multimodal embedding of the anchor item to determine a first position of the anchor item within the triplet network," "executing a second neural network on a multimodal embedding of the positive item to determine a second position of the positive item within the triplet network," "executing a third neural network on a multimodal embedding of the negative item to determine a third position of the negative item within the triplet network, wherein the first neural network, the second neural network, and the third neural network include at least one shared parameter," "determining a triplet loss function based on: a first distance between the first position and the second position, a second distance between the first position and the third position, and a minimum separation value," and "in response to a determination that the first distance is greater than the second distance, re-training the first neural network, the second neural network, and the third neural network at least by: updating the at least one shared parameter based on a minimization of the triplet loss function to generate one or more updated parameters, and sharing the one or more updated parameters among the re-trained first neural network, the re-trained second neural network, and the re-trained third neural network," as recited in the amended claim 1. These features in claim 1 integrate the alleged abstract idea into a practical application (neural network based complementary item recommendation) under Step 2A, Prong 2.
Examiner Response:
The examiner respectfully disagrees. The applicant is kindly asked to review the updated 101 rejection below for detailed analysis of the mental process steps. Furthermore, While the applicant argues that The amended claim 1 recites an improvement to the technical field of existing item recommendation systems, there is no improvement to the functioning of a computer nor to any other technology. At best, the claimed combination amounts to an improvement to the abstract idea of generating an embedding representing attributes, generating a node representative of an item, determining a position of an item within a space, determining a distance between two positions, sharing updated parameters among re-trained neural networks, generating a complimentary embedding space, generating a plurality of clusters each including a subset of items, determining a first cluster containing an anchor item selected by a user, computing a distance between the centers of clusters, sampling items within a cluster, ranking complementary items according to a ranked order based on distances between two items, generating a user preference from user click data, re-ranking complementary items according to a re-ranked order based on user preference data, or re-arranging complementary items on a user interface based on a re-ranked order rather than to an improvement on the functioning of a computer or to any other technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application.
Claim Rejections - 35 USC § 101
101 Rejection
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-6, 8, 10-16, and 18-20 are rejected under 35 USC § 101 because the claimed invention is directed to non-statutory subject matter
Regarding Claim 1:
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis:
Claim 1 is directed to a system, which is directed to a machine/apparatus, one of the statutory categories.
Step 2A Prong One Analysis:
Claim 1 recites in part process steps which, under the broadest reasonable interpretation, are a series of mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process or a mathematical concept but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. The claim recites in part:
generate a multimodal embedding representative of the plurality of attributes for each of the plurality of items, wherein the multimodal embedding is configured to predict at least a subset of the received plurality of item attributes for each of the plurality of items As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a graph representing each item and its attributes.
generate a triplet network including a node representative of each of the plurality of items, wherein the triplet network is generated based on the multimodal embedding for each of the plurality of items at least by: As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a graph node for each item.
executing a first neural network on a multimodal embedding of the anchor item to determine a first position of the anchor item within the triplet network As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses the position of an anchor item within a cluster of items. Furthermore, the recitation of executing a first neural network is mere instructions to implement the exception using generic computer components (See MPEP 2106.05(f)).
executing a second neural network on a multimodal embedding of the positive item to determine a second position of the positive item within the triplet network As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses the position of an anchor item within a cluster of items. Furthermore, the recitation of executing a second neural network is mere instructions to implement the exception using generic computer components (See MPEP 2106.05(f)).
executing a third neural network on a multimodal embedding of the negative item to determine a third position of the negative item within the triplet network, wherein the first neural network, the second neural network, and the third neural network include at least one shared parameter As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses the position of an anchor item within a cluster of items. Furthermore, the recitation of executing a third neural network is mere instructions to implement the exception using generic computer components (See MPEP 2106.05(f)).
determining a triplet loss function based on: a first distance between the first position and the second position, a second distance between the first position and the third position, and a minimum separation value As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses determining a distance between a first item and multiple items.
sharing the one or more updated parameters among the re-trained first neural network, the re-trained second neural network, and the retrained third neural network As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses using the same parameter and value with multiple neural networks.
generate, based on the triplet network, a complimentary embedding space including embeddings of the plurality of items As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a list of recommendations along with their attributes from graph nodes.
generate, within the complimentary embedding space, a plurality of clusters each
including a subset of the plurality of items As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating groups containing items in the list of recommendations.
determine, among the plurality of clusters, a first cluster containing an anchor
item selected by a user As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses identifying a group of items recommended that that contains an item of interest.
compute a distance between a center of the first cluster and a center of each
of other clusters in the plurality of clusters As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses calculating the distance between the centers of two groups.
select, from the plurality of clusters, a plurality of nearest clusters to the first
cluster based on the computed distances As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses identifying a group of items that is closer to another group of items.
generate a plurality of complimentary items different from the anchor item by sampling items within the first cluster containing the anchor item and within each of the plurality of nearest clusters As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a list of recommended items by sampling multiple groups of potential items.
rank the plurality of complementary items according to a ranked order based on
distances between the anchor item and the plurality of complementary items As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses ranking the complementary items based on the similarity of an anchor item to the complementary items.
generate a user preference embedding from the user click data As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses creating a list of user preferences based on the user click data.
re-rank the plurality of complementary items according to a re-ranked order based on the user preference embedding As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses re-ranking the complementary items based on the user click data.
automatically rearrange the plurality of complementary items on the user interface based on the re-ranked order As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses rearranging the complementary items top to bottom in a list based on the re-ranked order.
Accordingly, the claim is directed to an abstract idea which is a judicial exception.
Step 2A Prong Two Analysis:
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
A system, comprising: a computing device which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive a plurality of item attributes for each of a plurality of items which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
receiving an anchor item, a positive item, and a negative item which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
in response to a determination that the first distance is greater than the second distance, re-training the first neural network, the second neural network, and the third neural network at least by: updating the at least one shared parameter based on a minimization of the triplet loss function to generate one or more updated parameters which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).present the plurality of complimentary items as recommendations to the user in the ranked order through a user interface which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
receive user click data of the user regarding the ranked plurality of
complementary items which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, the additional elements individually or in combination do no integrate the judicial exception into a practical application. Therefore, claim 1 is directed to a judicial exception.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements are:
A system, comprising: a computing device configured to which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
receive a plurality of item attributes for each of a plurality of items which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
receiving an anchor item, a positive item, and a negative item which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
in response to a determination that the first distance is greater than the second distance, re-training the first neural network, the second neural network, and the third neural network at least by: updating the at least one shared parameter based on a minimization of the triplet loss function to generate one or more updated parameters which are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
present the plurality of complimentary items as recommendations to the user in the ranked order through a user interface which amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
receive user click data of the user regarding the ranked plurality of
complementary items which is recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. The additional limitations of the dependent claims contain no additional elements that provide a practical application or amount to significantly more than the abstract idea and are addressed briefly below:
Dependent claim 2 recites in part:
generating the multimodal embedding for each of the plurality of items comprises: generating an embedding for each of the plurality of item attributes As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a list of attributes for each item.
combining the embeddings for the plurality of item attributes into an n- dimensional embedding As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a graph representing each of the items’ attributes.
converting the n-dimensional embedding to an m-dimensional embedding, wherein m is less than n. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses reducing the size of the embeddings.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 3 recites in part:
convert the n-dimensional embedding to the m-dimensional embedding. As drafted and under its broadest reasonable interpretation, the limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses reducing the size of the attributes embeddings. a contractive autoencoder is configured to is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 4 recites in part:
generating a node representative of each of the anchor item, positive item, and the negative item As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a triplet that includes an item purchased, an item that goes along with the purchased item, and an item that does not go along with the purchased item (e.g., cup, plate, pillow)
maximize the second distance and minimize first distance As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 5 recites in part:
the triplet loss function is calculated as: max(d(a, p) – d(a, n) + margin, 0) where a is a node position of the anchor item, p is a node position of the positive item, n is a node position of the negative item, d(a, p) is a Euclidean distance between the anchor item and the positive item, and d(a, n) is a Euclidean distance between the anchor item and the negative item. (mathematical concepts) Under the broadest reasonable interpretation, this limitation is a process step that covers Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim is directed to an abstract idea and does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Dependent claim 6 recites in part:
the node representative of each of the anchor item, the positive item, and the negative item is generated by a fully-connected (FC) neural network, a convolution neural network (CNN), or a combined FC/CNN network. This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, the claim does not recite any additional element that integrates the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 8 recites in part:
the plurality of clusters are generated by a k-means clustering process Under the broadest reasonable interpretation, these limitations are process steps that cover Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim is directed to an abstract idea and does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 10 recites in part:
the plurality of complementary items are re-ranked based on a probability distribution of each of the plurality of complimentary items with respect to the user preference embedding. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, the claim is directed to an abstract idea and dos not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Regarding Claim 11:
Claim 11 is directed to a non-transitory computer readable medium, which is directed to a machine/apparatus, one of the statutory categories. Claim 11 recites: “A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause a device to perform operations comprising:” which performs a process that has limitations similar to the limitations of claim 1. Thus claim 11 is rejected with the same rationale applied against claim 1. As performing an abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 11 remains subject matter ineligible.
Accordingly, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
For the reasons above, claim 11 is rejected as being directed to non-patentable subject matter under §101.
Dependent claim 12 recites in part:
generating the multimodal embedding for each of the plurality of items comprises: generating an embedding for each of the plurality of attributes As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a list of attributes for each item.
combining the embeddings for the plurality of attributes into an n- dimensional embedding As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a graph representing each of the items’ attributes.
converting the n-dimensional embedding to an m-dimensional embedding, wherein m is less than n. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses reducing the size of the embeddings.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 13 recites in part:
convert the n-dimensional embedding to the m-dimensional embedding. As drafted and under its broadest reasonable interpretation, the limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses reducing the size of the attributes embeddings. a contractive autoencoder is configured to is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 14 recites in part:
generating a node representative of each of the anchor item, positive item, and the negative item As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses generating a triplet that includes an item purchased, an item that goes along with the purchased item, and an item that does not go along with the purchased item (e.g., cup, plate, pillow)
maximize the second distance and minimize first distance As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Dependent claim 15 recites in part:
the triplet loss function is calculated as: max(d(a, p) – d(a, n) + margin, 0) where a is a node position of the anchor item, p is a node position of the positive item, n is a node position of the negative item, d(a, p) is a Euclidean distance between the anchor item and the positive item, and d(a, n) is a Euclidean distance between the anchor item and the negative item. (mathematical concepts) Under the broadest reasonable interpretation, this limitation is a process step that covers Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim is directed to an abstract idea and does not recite additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Dependent claim 16 recites in part:
the node representative of each of the anchor item, the positive item, and the negative item is generated by a fully-connected (FC) neural network, a convolution neural network (CNN), or a combined FC/CNN network. This limitation amounts to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, the claim does not recite any additional element that integrates the abstract idea into a practical application or that amount to significantly more than the abstract idea.
Dependent claim 18 recites in part:
the plurality of clusters are generated by a k-means clustering process. Under the broadest reasonable interpretation, these limitations are process steps that cover Mathematical Concepts. If a claim, under its broadest reasonable interpretation, covers a mathematical concept, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
Accordingly, the claim does not recite any additional element that integrates the abstract idea into a practical application or that amount to significantly more than the abstract idea. This should be done for all claims.
Dependent claim 19 recites in part:
wherein the re-ranking is based on a probability distribution of each of the plurality of complimentary items with respect to the user preference embedding. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components.
Accordingly, the claim is directed to an abstract idea and the additional elements individually or in combination do no integrate the judicial exception into a practical application nor do they amount to significantly more than the judicial exception.
Regarding Claim 20:
Claim 20 is directed to a method, which is directed to a process, one of the statutory categories. Claim 20 recites: “A method comprising:” which performs a process that has limitations similar to the limitations of claim 1. Thus claim 20 is rejected with the same rationale applied against claim 1. As performing an abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 20 remains subject matter ineligible.
Accordingly, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
For the reasons above, claim 20 is rejected as being directed to non-patentable subject matter under §101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamamoto (US 10,691,762 B2)
“Yamamoto teaches a method of outputting a recommended item”
Brown (US 2015/0248720 Al)
“Brown teaches a method that computes a probability score indicating the probability that the user would prefer a particular item”
Lin (US 10,776,626 Bl)
“Lin teaches machine learning techniques for identifying collections of items that are visually complementary”
Bolivar (US 2011/0010324 Al)
“Bolivar teaches a method for making contextual recommendations to users on a network-based system”
Wilson (US 8,170,971 Bl)
“Wilson teaches a recommendation generator that builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues”
Asati (US 2018/0039824 Al)
“Asati teaches a method for multilevel clustering for a face recognition process”
Zhang (US 2013/0163661 A1)
“Zhang teaches a method for encoding video signals using example-based data pruning for improved video compression efficiency”
Ardite - US 2018/0349398 Al
“Ardite teaches a method to leverage the search or access activities of a core group of users to improve search functionality and performance of such search systems”
Rounthwaite - US 2010/0325133 Al
“Rounthwaite teaches a method to determine click distributions over search results selected by users with respect to a plurality of queries”
Jadhav - US20140214494A1
“Jadhav teaches a method for determining context aware information item recommendations for deals”
Rekhi - US20150269152A1
“Rekhi teaches a method for ranking recommendations within a set of recommendations ”
Patil - US 2015/0186535Al
“Patil teaches A method for determining an active persona of a user device”
Hassan - US 2016/0371376Al
“Hassan teaches a method for searching logical patterns in voluminous multi sensor data”
Ogasawara - US20090240358A1
“Ogasawara teaches recommending data suited to a user's mood at a moment or a situation selected”
Pilászy - US20120030159A1
“Pilászy teaches providing personalized item recommendations”
Hakkani-Tur - US20170372199A1
“Hakkani-Tur teaches a joint multi–domain recurrent neural network for spoken language understanding”
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128