DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to the application filed on August 11, 2025. Claims 1-20 are pending at the time of examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 11, 2025 was considered by the examiner. See attached PTO-form 1449.
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.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Based on Supreme Court precedent, a method/process claim must (1) be tied to another statutory class of invention (such as a particular apparatus) (see at least Diamond v. Diehr, 450 U.S. 175, 184 (1981); Parker v. Flook, 437 U.S. 584,588 n.9 (1978); Gottschalk v. Benson, 409 U.S. 63, 70 (1972); Cochrane v. Deener, 94 U.S. 780, 787-88 (1876)) or (2) transform underlying subject matter (such as an article or materials) to a different state or thing (see at least Gottschalk v. Benson, 409 U.S. 63, 71 (1972)). A method/process claim that fails to meet one of the above requirements does not comply with the statutory requirements of 35 U.S.C. 101 for patent eligible subject matter. Here the claims fails to meet the above requirements because the steps are neither tied to another statutory class of invention (such as a particular apparatus) nor physically transform underlying subject matter (such as an article or materials) to a different state or thing. Applicant could overcome this rejection by specifying in the claim body that a disclosed device is responsible for the inventive steps in the method (assuming applicant has sufficient original support for such a claim).
Claims 1-2, 8-9, 10-11, 15, 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1, 17, 19 and 20 recite “obtaining a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtaining image feature data of each image based on the candidate interface and the one type of candidate content; predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; selecting, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation”.
Claims 10 and 18 recite “obtaining a plurality of sample images, wherein each sample image comprises one sample candidate interface and one type of sample candidate content presented by using the sample candidate interface; obtaining image feature data of each sample image based on the sample candidate interface and the one type of sample candidate content4n-the-plmtity-fam-le-images; predicting, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and adjusting the prediction model based on the degree of preference and historical click-through data of the sample user for the sample candidate content”.
The limitation of “obtaining a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtaining image feature data of each image based on the candidate interface and the one type of candidate content; predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data”, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a computer,” nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or manually performed, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using “a computer to perform the claims steps. The “computer” in these steps is recited at a high-level of generality (i.e., as a computer” performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim also recites the additional elements “selecting, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation” that are the insignificant extra-solution activity of data gathering and/or output, and can been understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)). Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. (see 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2A, Prong Two. See also MPEP 2106.04(II)(A)(2), MPEP 2106.04(d).
The claims 2, 6, 8-9, 11 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using “a computer” to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible. see 2019 Revised Patent Subject Matter Eligibility Guidance, Step 2B. See also MPEP 2106.05.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 of the instant application are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,423,368. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 21-40 are directed to
Instant application Patent (‘368)
Claim 1). A method comprising: obtaining a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtaining image feature data of each image based on the candidate interface and the one type of candidate content; predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; selecting, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation
1. A method, comprising: obtaining a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface, and each image comprises a plurality of regions; obtaining image feature data of each image, wherein the image feature data of each image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and selecting, based on the degree of preference, at least one of a candidate interface or candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation.
Claim 10). A method, comprising: obtaining a plurality of sample images, wherein each sample image comprises one sample candidate interface and one type of sample candidate content presented by using the sample candidate interface; obtaining image feature data of each sample image based on the sample candidate interface and the one type of sample candidate;
predicting, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and adjusting the prediction model based on the degree of preference and historical click-through data of the sample user for the sample candidate content.
9. A method, comprising: obtaining a plurality of sample images, wherein each sample image comprises one sample candidate interface and one type of sample candidate content presented by using the sample candidate interface, and each sample image comprises a plurality of regions; obtaining image feature data of each sample image, wherein the image feature data of each sample image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predicting, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and adjusting the prediction model based on the degree of preference and historical click-through data of the sample user for the sample candidate content.
17. A computer device, comprising one or more memories and one or more processors, wherein the one or more memories are coupled to the one or more processors and store computer-readable instructions for execution by the one or more processor to: obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface;
obtain image feature data of each image based on the candidate interface and the one type of candidate content;
predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and
select, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation.
15. A computer device, comprising one or more memories and one or more processors, wherein the one or more memories are coupled to the one or more processors and store computer-readable instructions for execution by the one or more processor to: obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface, and each image comprises a plurality of regions; obtain image feature data of each image, wherein the image feature data of each image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and select, based on the degree of preference, at least one of a candidate interface or candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation.
18. (Currently Amended)A device, comprising one or more memories and one or more processors, wherein the one or more memories are coupled to the one or more processors and store computer-readable instructions for execution by the one or more processor to: obtain a plurality of sample images, wherein each sample image comprises one sample candidate interface and one type of sample candidate content presented by using the sample candidate interface; obtain image feature data of each sample image based on the sample candidate interface and the one type of sample candidate content in-he plurality-of sample- images; predict, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and adjust the prediction model based on the degree of preference and historical click-through data of the sample user for the sample candidate content
16. A device, comprising one or more memories and one or more processors, wherein the one or more memories are coupled to the one or more processors and store computer-readable instructions for execution by the one or more processor to: obtain a plurality of sample images, wherein each sample image comprises one sample candidate interface and one type of sample candidate content presented by using the sample candidate interface, and each sample image comprises a plurality of regions; obtain image feature data of each sample image, wherein the image feature data of each sample image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predict, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and adjust the prediction model based on the degree of preference and historical click-through data of the sample user for the sample candidate content.
19.) A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-readable instructions for execution by at least one processor to:
obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtain image feature data of each image based on the candidate interface and the one type of candidate content;
predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and select, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation.
17. A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-readable instructions for execution by at least one processor to: obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface, and each image comprises a plurality of regions; obtain image feature data of each image, wherein the image feature data of each image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and select, based on the degree of preference, at least one of a candidate interface or candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation.
20. A recommendation system, comprising a terminal device and a server, wherein the server comprises one or more first processors and one or more first memories coupled to the one or more first processors, and the terminal device comprises one or more second processors and one or more second memories coupled to the one or more second processors; and wherein: the one or more first memories store programming instructions for execution by the one or more first processors to: obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtain image feature data of each image based on the candidate interface and the one type of candidate content
predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and send, based on the degree of preference, target candidate content and metadata of a target candidate interface to the terminal device, wherein the terminal device displays the target candidate interface based on the metadata, and recommends the target candidate content to the target user by using the target candidate interface; and the one or more second memories store programming instructions for execution by the one or more second processors to: receive the target candidate content and the metadata of the target candidate interface from the server; and
display the target candidate interface based on the metadata, and recommend the target candidate content to the target user by using the target candidate interface.
18. A recommendation system, comprising a terminal device and a server, wherein the server comprises one or more first processors and one or more first memories coupled to the one or more first processors, and the terminal device comprises one or more second processors and one or more second memories coupled to the one or more second processors; and wherein: the one or more first memories store programming instructions for execution by the one or more first processors to: obtain a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface, and each image comprises a plurality of regions; obtain image feature data of each image, wherein the image feature data of each image comprises a plurality of local eigenvectors that correspond to the plurality of regions respectively; predict, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data; and send, based on the degree of preference, target candidate content and metadata of a target candidate interface to a terminal device, wherein the terminal device displays the target candidate interface based on the metadata, and recommends the target candidate content to the target user by using the target candidate interface; and the one or more second memories store programming instructions for execution by the one or more second processors to: receive target candidate content and metadata of a target candidate interface from the server; and display the target candidate interface based on the metadata, and recommend the target candidate content to a target user by using the target candidate interface.
After analyzing the language claim of the claims, it is clear that claims 1-20 of the instant application are merely an obvious variation of claims 1-18 of U.S. Patent No. 12,423,368. While claims 1-20 of the instant application is slightly broader than claims 1-18 of U.S. Patent No. 12,423,368, this difference is not enough to distinguish the two instant application claims and the patent claims. With respect to the language and the disclosure of the instant application not only fail to distinguish it from the Patent No. 12,423,368, but indicate that it is merely a subset of the Patent No. 12,423,368. These differences are not sufficient to render the claims patentably distinct, and therefore, claims 1-20 of the instant application are valid.
A later patent/application claim is not patentably distinct from an earlier claim if the later claim is anticipated by the earlier claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 8-9, 10, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US Patent No. 10,210,429) in view of Li et al. (US 2023/0004608 A1) and further in view of Bhotika et al. (US Patent No. 10,282,431).
As per claim 1, Cai discloses method, comprising:
obtaining a plurality of images, wherein each image comprises one candidate interface and one type of candidate content presented by using the candidate interface; obtaining image feature data of each image based on the candidate interface and the one type of candidate content (Col. 7, lines 9-15, as features include image features and textual features associated with users of the online system 100. In some embodiments, the training data store 145 includes predetermined information about a population of users of the online system 100 provided to the online system 100 or from another module of the online system 100, e.g., the action logger 115 or user profile store 140);
predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data, wherein input for the prediction model is determined based on the user feature data and the image feature data (col. 8, lines 13-30, 44-58, as the image class may refer to a plurality of image classes or one or more image classes comprising an amalgamation of objects, brands, professional sports teams, locations, etc. Each image class has multiples sets of image features commonly associated with that image class, e.g., distinct visual features associated male human faces); and selecting, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images (col. 13, lines 24-63, as based on visual features associated with the images 310A-310C and the textual features of the texts 320 describing the user, the ITC based prediction module 135 predicts the demographic information of the user 330, including age (35), gender (male), and interests (baseball, cars, and reading). The ITC based prediction module 135 provides the predictions to the online system 100, which updates the user profile associated with the user within the online system 100, and selects content items for display to the user based on the predictions. The selected content items are more likely more suitable to the user and have a higher click through rate by the user).
Cai does not explicitly teach, but Li teaches to perform recommendation (para. 0025, as the recommendation server 110 obtains the cover image features of the plurality of content cover images, and determines a user account feature of the user account based on the cover image features of the plurality of content cover images; and then, the recommendation server 110 determines an access probability value of the user account accessing a content to be recommended based on the cover image feature of the content to be recommended and the user account feature; finally, The recommendation server 110 provides the content to be recommended to the user account based on the access probability value. In practical applications). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of Cai to implement the steps of perform recommendation as taught by Li because it would provide accurately based on the image features of the content accessed by the personalized preferences of the user account.
As per claim 6, Cai further teaches wherein the image feature data of each image comprises a global eigenvector, and the global eigenvector represents the image (col. 11, lines 48-67).
As per claim 8, Li further teaches wherein the selecting, based on the degree of preference, at least one of a target candidate interface or target candidate content from candidate interfaces and candidate content that are comprised in the plurality of images, to perform recommendation comprises at least one of: selecting, based on the degree of preference, a type of candidate content from the candidate content comprised in the plurality of images as the target candidate content; or selecting, based on the degree of preference, a candidate interface from candidate interfaces of images that comprise the target candidate content as the target candidate interface, to recommend the target candidate content by using the target candidate interface (para. 0047).
As per claim 9, Li further teaches wherein after the selecting, based on the degree of preference, a candidate interface from candidate interfaces of images that comprise the target candidate content as the target candidate interface, the method further comprises: sending the target candidate content and metadata of the target candidate interface to a terminal device, so that the terminal device displays the target candidate interface based on the metadata, and recommends the target candidate content to the target user by using the target candidate interface (para. 0056).
As per claims 10, 17, 18-20, these independent claims recite several elements that are similar to the elements recited in claim 1, except in the context of a computer-readable medium, and a system, a method, respectively. Therefore, they are rejected at least for the same reasons as claim 1.
Claims 2, 6, 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al. (US Patent No. 10,210,429) in view of Li et al. (US 2023/0004608 A1) and further in view of Bhotika et al. (US Patent No. 10,282,431).
As per claim 2, Cai and Li do not teache, but Bhotika teaches wherein each image comprises a plurality of regions; and the image feature data of each image comprises a plurality of local eigenvectors, and each local eigenvector represents one region (col. 4, lines 45-55, as an attempt can be made to locate and/or grow connected regions of background color (or ranges of background color) from the corners or sides of the image towards the center of the image, to the extent possible. A connected component analysis, for example, can be utilized to attempt to connect the entire background region of the image, with the remaining region(s) being considered potential objects(s) of interest; col. 19, lines 44-64, as machine learning include principal component analysis (PCA), neural networks, support vector machines (SVM), inductive learning, adaptive boosting (Adaboost), deep learning, among others. In PCA, eigenvectors are computed on a training set of images known to include objects corresponding to a particular classification to determine the covariance matrix of the training set. Training images known to include the classified are projected onto “a classified object subspace” and clustered. The distance between a region of each training image known to include the classified object and the classified object subspace is computed for the training image). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of Cai and Li to implement the above steps as taught by Bhotika because it would improve of matching and/or recommendation process.
As per claim 6, Cai and Li do not teache, but Bhotika wherein the image feature data of each image comprises a global eigenvector, and the global eigenvector represents the image col. 4, lines 45-55, as an attempt can be made to locate and/or grow connected regions of background color (or ranges of background color) from the corners or sides of the image towards the center of the image, to the extent possible. A connected component analysis, for example, can be utilized to attempt to connect the entire background region of the image, with the remaining region(s) being considered potential objects(s) of interest; col. 19, lines 44-64, as machine learning include principal component analysis (PCA), neural networks, support vector machines (SVM), inductive learning, adaptive boosting (Adaboost), deep learning, among others. In PCA, eigenvectors are computed on a training set of images known to include objects corresponding to a particular classification to determine the covariance matrix of the training set. Training images known to include the classified are projected onto “a classified object subspace” and clustered. The distance between a region of each training image known to include the classified object and the classified object subspace is computed for the training image). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention was made to modify the teachings of Cai and Li to implement the above steps as taught by Bhotika because it would improve of matching and/or recommendation process.
As per claim 11 and 15 have similar limitations as recited in claims 2 and 6; therefore, they are rejected under the same subject matter.
Allowable Subject Matter
Claim 3-5, 7, 12-14, 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art of record fails to teach wherein the predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data comprises: for each image, obtaining N word vectors based on the candidate content in the image, wherein each word vector represents one word in the candidate content, and N is a positive integer; for each word vector, calculating, by using a model of an attention mechanism, an attention weight of each of the plurality of local eigenvectors based on the word vector and the plurality of local eigenvectors, wherein the attention weight indicates a degree to which the target user pays attention to a region represented by a respective local eigenvector when the target user reads a word represented by each word vector; performing fusion on each word vector and the plurality of local eigenvectors based on the attention weight of each of the plurality of local eigenvectors to obtain a first fusion eigenvector, wherein one first fusion eigenvector is correspondingly obtained for each word vector; and predicting, by using the prediction model, the degree of preference of the target user for each image based on a user eigenvector and N first fusion eigenvectors corresponding to the N word vectors, wherein the input for the prediction model is determined based on the user eigenvector and the N first fusion eigenvectors, and the user eigenvector represents the user feature data of the target user, as recited in claim 3.
The prior art of record fails to teach wherein the predicting, by using a prediction model, a degree of preference of a target user for each image based on user feature data of the target user and the image feature data comprises: for each image, obtaining a content eigenvector based on the candidate content in the image, wherein the content eigenvector represents the candidate content; determining a weight of the content eigenvector and a weight of the global eigenvector based on the content eigenvector and the global eigenvector; performing fusion on the content eigenvector and the global eigenvector based on the weight of the content eigenvector and the weight of the global eigenvector to obtain a third fusion eigenvector; and predicting, by using the prediction model, the degree of preference of the target user for each image based on user eigenvector and the third fusion eigenvector, wherein the input for the prediction model is determined based on the user eigenvector and the third fusion eigenvector, and the use eigenvector represents the user feature data of the target user, as recited in claim 7.
The prior art of record fails to teach wherein the predicting, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data comprises: for each sample image, obtaining N word vectors based on the sample candidate content in each sample image, wherein each word vector represents one word in the sample candidate content and N is a positive integer; for each word vector, calculating, by using a model of an attention mechanism, an attention weight of each of the plurality of local eigenvectors based on the word vector and the plurality of local eigenvectors, wherein the attention weight indicates a degree to which the sample user pays attention to a region represented by a respective local eigenvector when the sample user reads a word represented by each word vector; performing fusion on each word vector and the plurality of local eigenvectors based on the attention weight of each of the plurality of local eigenvectors to obtain a first fusion eigenvector, wherein one first fusion eigenvector is correspondingly obtained for each word vector; an predicting, by using the prediction model, the degree of preference of the sample user for each sample image based on a user eigenvector and N first fusion eigenvectors corresponding to the N word vectors, wherein the input for the prediction model is determined based on the user eigenvector and the N first fusion eigenvectors, and the user eigenvector represents the user feature data of the sample user, as recited in claim 12.
The prior art of record fails to teach wherein the predicting, by using a prediction model, a degree of preference of a sample user for each sample image based on user feature data of the sample user and the image feature data comprises: for each sample image, obtaining a content eigenvector based on the sample candidate content in each sample image, wherein the content eigenvector represents the sample candidate content; determining a weight of the content eigenvector and a weight of the global eigenvector based on the content eigenvector and the global eigenvector; performing fusion on the content eigenvector and the global eigenvector based on the weight of the content eigenvector and the weight of the global eigenvector to obtain a third fusion eigenvector; and predicting, by using the prediction model, the degree of preference of the sample user for each sample image based on a user eigenvector and the third fusion eigenvector, wherein the input for the prediction model is determined based on the user eigenvector and the third fusion eigenvector, and the user eigenvector represents the user feature data of the sample user, as recited in claim 16.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEBBIE M LE whose telephone number is (571)272-4111. The examiner can normally be reached 9:00-5:00.
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/DEBBIE M LE/Primary Examiner, Art Unit 2168 July 11, 2026
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