Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 1/30/2026. Claims 1-8 are currently pending and have been examined. Claims 1-7 have been amended. Claims 8 have been newly entered. The 112f interpretations have been overcome by amendment.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Claim Rejection - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 1-7 are directed to a machine; claim 8 is directed to a process. Therefore, claims 1-8 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 1 and 8 recites at least the following limitations that are believed to recite an abstract idea:
Inputting an image of each of a plurality of contents into an image recognition process and outputting, from the image recognition process, an appearance feature vector as a content feature vector indicating a feature of an appearance for each of the plurality of contents;
storing the content feature vector for each of a plurality of contents, and store a user feature vector indicating a feature of a user, wherein the content feature vector and the user feature vector are allocated onto a same vector space;
acquiring information indicating a plurality of favorite contents selected by the user from the plurality of contents, and preference information indicating preference of the user according to each of the plurality of favorite contents, which is input by the user comparing the plurality of favorite contents with each other;
performing learning such that in the vector space indicating the content feature vector of the plurality of contents and the user feature vector, a position of the user feature vector and a position of the content feature vector of the plurality of favorite contents approach each other;
correcting the position of the user feature vector in the vector space by using weighting based on the preference information;
computing a score of each of the plurality of contents on the basis of a separation distance between the position of the user feature vector and the position of the content feature vector of the plurality of contents in the vector space; and
outputting a recommendation result of a content selected on the basis of the score.
The above limitations recite the concept of user preference calculation and customized recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 1-7 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user 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 exception.
In this instance, the claims recite the additional elements of:
A device comprising processing circuitry
A model that is a convolutional neural network
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort to monopolize the exception. For example, claims 2-7 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. Therefore, the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
A device comprising processing circuitry
A model that is a convolutional neural network
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim Rejection – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Gutnik et al (US 20190205481 A1), hereinafter Gutnik, in view of Snyder et al (US 20190130285 A1), hereinafter Snyder.
Regarding Claim 1, Gutnik discloses a recommendation device comprising processing circuitry (Gutnik: [0121]), configured to:
have a content feature vector [catalog-item vector] for each of a plurality of contents, and have a user feature vector [preference vector] indicating a feature of a user, wherein the content feature vector and the user feature vector are allocated onto a same vector space (Gutnik: “receive several references to catalog items … associated with metadata items. … metadata items associated with each reference may contain information about the respective catalog item… generate a catalog-item vector for each received reference. The catalog-item vector may be the same length as the user-preference vector…a catalog-item vector for the yellow curry in the above example may look like this:” [0053] – See Table 3, which recites dimensions for the catalog item including cuisine type and cost. – “generate a user-preference vector for each user. The user-preference vector may have N dimensions. Each dimension may correspond to a preference of the user. As an example and not by way of limitation, a user-preference vector may have 17 dimensions, as shown by Table 2 ” [0051] – See Table 2, which recites dimensions for user preferences including cuisine type and cost);
acquire information indicating a plurality of favorite contents selected by the user from the plurality of contents, and preference information indicating preference of the user according to each of the plurality of favorite contents, which is input by the user (Gutnik: “A user may provide information to the social-networking system 160 by updating a user profile associated with the user with information about the user (e.g.… interests, favorite movies, books, quotes and the like)” [0040] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – “determines that the user has specific dietary preferences (such as low-fat or high protein food), the social-networking system may uprank menu items that match the user's preferences. ” [0035]);
perform learning such that in the vector space indicating the content feature vector of the plurality of contents and the user feature vector, a position of the user feature vector and a position of the content feature vector of the plurality of favorite contents approach each other (Gutnik: “calculate the distances between the user-preference vector of the user and each of several catalog-item vectors corresponding to the different catalog items offered by the different vendors. These distances (or differences) may be calculated using any suitable method, including Hamming distance, cosine similarity … determine which catalog-item vectors are most similar to the user-preference vector for the user. Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. ” [0054] – See vector space as illustrated in Figure 4.);
correct the position of the user feature vector in the vector space by using weighting based on the preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052]);
compute a score of each of the plurality of contents on the basis of a separation distance between the position of the user feature vector and the position of the content feature vector of the plurality of contents in the vector space (Gutnik: “Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. ” [0054] – “calculated vector similarities may be used to calculate a score for each reference. ” [0055] – “order parameters may be determined by analyzing edge connections and affinity scores between the user node corresponding to the user and various catalog items. ” [0050]); and
output a recommendation result of a content selected on the basis of the score (Gutnik: “The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. The social-networking system may then generate recommendations for catalog items that correspond to the ranked references, and send the generated recommendations to a client system of the user in ranked order.” [0054]).
While Gutnik teaches generating a content feature vector and a user feature vector; and acquiring preference information which is input by the user, it does not specifically teach processing circuitry configured to input an image of each of a plurality of contents into an image recognition model that is a convolutional neural network (CNN) and output, from the image recognition model, an appearance feature vector as a content feature vector indicating a feature of an appearance for each of the plurality of contents; storing the vectors, or that preference information is input by the user comparing the plurality of favorite contents with each other.
However, Snyder teaches personalized query results (Snyder: [0016]), including
processing circuitry configured to input an image of each of a plurality of contents into an image recognition model that is a convolutional neural network (CNN) and output, from the image recognition model, an appearance feature vector as a content feature vector indicating a feature of an appearance for each of the plurality of contents (Snyder: “images for items belonging to all the categories of interest may be analyzed using a particular set of one or more machine learning models … and respective feature vectors of collection 751 may be extracted” [0056] – “ a deep neural network model may be used to obtain feature sets—for example, an embedding feature vector comprising 2048 numeric values, which represents a mapping of an image to a 2048-dimension space, may be obtained from a hidden layer of a convolutional neural network model. … the convolutional neural network model may have been trained for any of various purposes such as object recognition, image similarity detection and the like, and that the feature vector may be extracted from its hidden layer(s)” [0022] – See also [0051-0052]);
storing the vectors (Snyder: “feature vectors 752A-752D may be generated for items …and stored in a repository.” [0056]); and
that preference information is input by the user comparing the plurality of favorite contents with each other (Snyder: “Pairwise preferences regarding at least some pairs of the items may be derived or deduced from the feedback signals generated by consumers as they respond to items” [0060] – “The consumer has provided, by a left swipe 424, feedback indicating a dislike of rug image 420A, while right swipe 426 has been used to provide feedback indicating that the consumer likes rug image 420B. ” [0047] – “with respect to an image of a given item, … the consumer may affirmatively indicate that the item is liked or disliked ” [0021] – “if a consumer emphatically indicates positive feedback for a particular item, and is less enthusiastic but still positive about another item, a higher weight may be assigned to the more emphatic positive feedback signal in the score-generating model. ” [0024] – See also [0061] & Figure 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Gutnik would continue to teach a content feature vector and a user feature vector, and acquiring preference information which is input by the user, except that now it would also teach processing circuitry configured to input an image of each of a plurality of contents into an image recognition model that is a convolutional neural network (CNN) and output, from the image recognition model, an appearance feature vector as a content feature vector indicating a feature of an appearance for each of the plurality of contents; storing the vectors, and that preference information is input by the user comparing the plurality of favorite contents with each other, according to the teachings of Snyder. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to recommend items that truly meet user needs (Snyder: [0003]).
Regarding Claim 2, Gutnik/Snyder teach the recommendation device according to claim 1, wherein Gutnik teaches: wherein the processing circuitry is further configured to:
acquire aspect preference information as the preference information relevant to the aspect (Gutnik: “A user may provide information to the social-networking system 160 by updating a user profile associated with the user with information about the user (e.g.… interests, favorite movies, books, quotes and the like)” [0040] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – “determines that the user has specific dietary preferences (such as low-fat or high protein food), the social-networking system may uprank menu items that match the user's preferences. ” [0035]),
perform learning such that in an aspect vector space that is the vector space indicating the aspect feature vector of the plurality of contents, the position of the user feature vector and a position of the aspect feature vector of the plurality of favorite contents approach each other (Gutnik: “calculate the distances between the user-preference vector of the user and each of several catalog-item vectors corresponding to the different catalog items offered by the different vendors. These distances (or differences) may be calculated using any suitable method, including Hamming distance, cosine similarity … determine which catalog-item vectors are most similar to the user-preference vector for the user. Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. ” [0054] – See vector space as illustrated in Figure 4.), and
correct the position of the user feature vector in the aspect vector space by using weighting based on the aspect preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052]),
but does not specifically teach that the aspect of the content is an appearance.
However, Snyder teaches the aspect of the content is an appearance (Snyder: “images for items belonging to all the categories of interest may be analyzed using a particular set of one or more machine learning models … and respective feature vectors of collection 751 may be extracted” [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Snyder with Gutnik for the reasons identified above with respect to claim 1.
Regarding Claim 3, Gutnik/Snyder teach the recommendation device according to claim 1, wherein the processing circuitry is further configured to
have a detailed statement feature vector as the content feature vector indicating a feature of a detailed statement of each of the plurality of contents (Gutnik: “receive several references to catalog items … associated with metadata items. … metadata items associated with each reference may contain information about the respective catalog item… generate a catalog-item vector for each received reference. …a catalog-item vector for the yellow curry in the above example may look like this:” [0053] – See Table 3, which recites dimensions for the catalog item including cuisine type and cost.),
acquire detailed statement preference information as the preference information relevant to the detailed statement (Gutnik: “A user may provide information to the social-networking system 160 by updating a user profile associated with the user with information about the user (e.g.… interests, favorite movies, books, quotes and the like)” [0040] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – “determines that the user has specific dietary preferences (such as low-fat or high protein food), the social-networking system may uprank menu items that match the user's preferences. ” [0035]),
perform learning such that in a detailed statement vector space that is the vector space indicating the detailed statement feature vector of the plurality of contents, the position of the user feature vector and a position of the detailed statement feature vector of the plurality of favorite contents approach each other (Gutnik: “calculate the distances between the user-preference vector of the user and each of several catalog-item vectors corresponding to the different catalog items offered by the different vendors. These distances (or differences) may be calculated using any suitable method, including Hamming distance, cosine similarity … determine which catalog-item vectors are most similar to the user-preference vector for the user. Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. ” [0054] – See vector space as illustrated in Figure 4.), and
correct the position of the user feature vector in the detailed statement vector space by using weighting based on the detailed statement preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052]),
wherein Snyder further teaches that the vector is stored (Snyder: “feature vectors 752A-752D may be generated for items …and stored in a repository.” [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Snyder with Gutnik for the reasons identified above with respect to claim 1.
Regarding Claim 4, Gutnik/Snyder teach the recommendation device according to claim 1, wherein the processing circuitry is further configured to
acquire information indicating two of the favorite contents as the information indicating the plurality of favorite contents (Gutnik: “generate a user-preference vector for each user. The user-preference vector may have N dimensions. Each dimension may correspond to a preference of the user. As an example and not by way of limitation, a user-preference vector may have 17 dimensions, as shown by Table 2 ” [0051] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – See Table 2, which recites dimensions for user preferences including cuisine type and cost for a plurality of contents/products, e.g. multiple cuisines), and
correct the position of the user feature vector to a position that is an internally dividing point between the positions of the content feature vectors of the two favorite contents and considers the weighting based on the preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052] – See Also Figure 18, which further illustrates multiple preference nodes of a user).
Regarding Claim 5, Gutnik/Snyder teach the recommendation device according to claim 2, wherein the processing circuitry is further configured to
have a detailed statement feature vector as the content feature vector indicating a feature of a detailed statement of each of the plurality of contents (Gutnik: “receive several references to catalog items … associated with metadata items. … metadata items associated with each reference may contain information about the respective catalog item… generate a catalog-item vector for each received reference. …a catalog-item vector for the yellow curry in the above example may look like this:” [0053] – See Table 3, which recites dimensions for the catalog item including cuisine type and cost.),
acquire detailed statement preference information as the preference information relevant to the detailed statement (Gutnik: “A user may provide information to the social-networking system 160 by updating a user profile associated with the user with information about the user (e.g.… interests, favorite movies, books, quotes and the like)” [0040] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – “determines that the user has specific dietary preferences (such as low-fat or high protein food), the social-networking system may uprank menu items that match the user's preferences. ” [0035]),
perform learning such that in a detailed statement vector space that is the vector space indicating the detailed statement feature vector of the plurality of contents, the position of the user feature vector and a position of the detailed statement feature vector of the plurality of favorite contents approach each other (Gutnik: “calculate the distances between the user-preference vector of the user and each of several catalog-item vectors corresponding to the different catalog items offered by the different vendors. These distances (or differences) may be calculated using any suitable method, including Hamming distance, cosine similarity … determine which catalog-item vectors are most similar to the user-preference vector for the user. Similar vectors may have a high cosine similarity or a low vector difference. The social-networking system may rank the references based on the similarity between their respective catalog-item vectors and the user-preference vector. ” [0054] – See vector space as illustrated in Figure 4.), and
correct the position of the user feature vector in the detailed statement vector space by using weighting based on the detailed statement preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052]),
wherein Snyder further teaches that the vector is stored (Snyder: “feature vectors 752A-752D may be generated for items …and stored in a repository.” [0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Snyder with Gutnik for the reasons identified above with respect to claim 1.
Regarding Claim 6, Gutnik/Snyder teach the recommendation device according to claim 2, wherein the processing circuitry is further configured to
acquire information indicating two of the favorite contents as the information indicating the plurality of favorite contents (Gutnik: “generate a user-preference vector for each user. The user-preference vector may have N dimensions. Each dimension may correspond to a preference of the user. As an example and not by way of limitation, a user-preference vector may have 17 dimensions, as shown by Table 2 ” [0051] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – See Table 2, which recites dimensions for user preferences including cuisine type and cost for a plurality of contents/products, e.g. multiple cuisines), and
correct the position of the user feature vector to a position that is an internally dividing point between the positions of the content feature vectors of the two favorite contents and considers the weighting based on the preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052] – See Also Figure 18, which further illustrates multiple preference nodes of a user).
Regarding Claim 7, Gutnik/Snyder teach the recommendation device according to claim 3, wherein the processing circuitry is further configured to
acquire information indicating two of the favorite contents as the information indicating the plurality of favorite contents (Gutnik: “generate a user-preference vector for each user. The user-preference vector may have N dimensions. Each dimension may correspond to a preference of the user. As an example and not by way of limitation, a user-preference vector may have 17 dimensions, as shown by Table 2 ” [0051] – “the user may post a photo of the food and say, “Just got take-out from Fortune Cookie, my favorite Chinese restaurant.” This may inform the social-networking system that the user likes Chinese food.” [0052] – See Table 2, which recites dimensions for user preferences including cuisine type and cost for a plurality of contents/products, e.g. multiple cuisines), and
correct the position of the user feature vector to a position that is an internally dividing point between the positions of the content feature vectors of the two favorite contents and considers the weighting based on the preference information (Gutnik: “update the user-preference vector associated with the first user … other affinity coefficients may be modified as well. In this example, those affinity coefficients that are adjusted may include the user's affinity coefficient for burritos, for Mexican food, or for any other suitable entity.” [0060] – “calculate the affinity coefficient by processing the user's actions on the online social network with respect to a particular entity or concept. Each action may be associated with a particular weight that is used to factor the overall affinity coefficient.” [0034] – “ordering food of a particular type may receive a weighting of 0.75, posting a photo of the food to the online social network may receive a weighting of 0.50, and leaving a positive rating of the food may receive a weighting of 0.25. Since the calculated affinity coefficient in this example is greater than the threshold affinity coefficient in this example, the social-networking system may enter a “1” in the vector space for Tex Mex food.” [0052] – See Also Figure 18, which further illustrates multiple preference nodes of a user).
Regarding Claim 8, the limitations of claim 8 are closely parallel to the limitations of claim 1 and are rejected on the same basis.
Response to Arguments
Applicant's arguments filed 1/30/2026 have been fully considered but are not persuasive.
Claim Rejections – 35 USC § 101
Applicant argues that the amendments make “clear that the claim as a whole does not correspond to a method [of] organizing human activity since the input an image and the transformation to an appearance vector by a convolutional neural network is a necessary aspect of the claimed features.”
Examiner disagrees. With reference to the rejection above, the claims recite steps which amount to a concept for user preference calculation and customized recommendations. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial interactions, e.g. sales activities/behaviors, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. As addressed in the rejection, the inputting and analysis of an image as claimed is part of this abstract idea for determining products to recommend, except for the high-level recitation of additional elements as analyzed in the subsequent steps of the 101 analysis.
Applicant further argues that the newly amended inputting step “makes it clear that a technological solution is being used to solve the problems in the conventional environment.” Applicant makes reference to Desjardins to argue that the claims similarly recite an improvement “at a proper level of specificity.”
Examiner disagrees. With reference to the rejection above, the additional elements are recited at a high level of generality, and are invoked as mere instructions to apply the abstract idea to a technological environment [MPEP 2106.05(f)], creating only a general linking to computer technology. Whereas Desjardins defines a specific technological solution and provides a specific, detailed solution rooted in particular technical parameters of machine learning technology, the additional elements of the instant claims, in contrast, at most provide the improved speed or efficiency inherent to general purpose computers, and do not integrate the abstract idea into a practical application [MPEP 2106.05(a)].
Claim Rejections – 35 USC § 103
Applicant’s arguments with respect to the claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20200302507 A1 teaches vector-distance analysis to determine user preferences for items to recommend
US 20230342833 A1 teaches vector-distance analysis to determine user preferences for items to recommend including using user feedback on specific items
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/T.J.S./Examiner, Art Unit 3689
/MARISSA THEIN/Supervisory Patent Examiner, Art Unit 3689