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 .
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 1-20 are rejected under 35 U.S.C. 101 because of the following reasons:
Claims 1, 16:
At Step 1:
The claim is directed to a "method" and “apparatus for” and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“extracting a media representation vector from media data and a text representation vector from a description text of the media data” recites a mental process because human mind can extract media representation vector from the media data by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“performing a knowledge retrieval in a knowledge graph according to the media representation vector, to obtain an entity sub-graph of the media data” recites a mental process because human mind can identify (performing a knowledge retrieval in a knowledge graph) according to a media vector an entity sub-graph of the media data by evaluation and judgement of data.
-“determining an entity representation vector of the entity sub-graph” recites a mental process because human mind can determine entity representation vector of the sub graph by evaluation and judgement of data.
-“performing a feature fusion processing on the media representation vector, the text representation vector, and the entity representation vector, to obtain a knowledge augmented vector” recites a because human mind can perform feature fusion processing by evaluation and judgment of data and/or mathematical concept and mathematical relationships
-“obtaining target media data based on the knowledge augmented vector, the knowledge augmented vector being a fused vector of the media representation vector, the text representation vector, and the entity representation vector, and” recites mental process because human mind can obtain media data based on vectors by evaluation and judgement of data and/or a mathematical concept and mathematical relationships.
-“recommending the target media data to a target object” recites a mental process because human mind can recommend target media data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a method of media data recommendation”, “an apparatus for media data recommendation, comprising processing circuitry configured to” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claims 2, 17:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“extracting the media representation vector from the media data
-“extracting the text representation vector from the description text of the media data
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“by using an image feature extraction model” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
-“by using a text feature extraction model” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“by using an image feature extraction model” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
-“by using a text feature extraction model” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 3, 18:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“extracting, when the media data is a video, features of a plurality of image frames in the video , to obtain the media representation vector” recites a mental process because human mind can extract media representation vector from the video data by evaluation/observation and judgment of data and/or mathematical concept or relationships.
-“and extracting, when the media data is an image, features of a plurality of image blocks in the image , to obtain the media representation vector” recites a mental process because human mind can extract media representation vector from the video data by evaluation and judgment of data and/or mathematical concept and mathematical relationships.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“ by using the image feature extraction model, See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
-“ by using the image feature extraction model,
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ by using the image feature extraction model, to obtain the media representation vector” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
-“ by using the image feature extraction model, particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claims 4, 19:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“retrieving, from the knowledge graph and based on the media representation vector, a plurality of target entities that are related to the media data” recites a mental process because human mind can identify/extract (retrieve) media data from the knowledge graph based on the vector by evaluation, observation and judgement of data.
-“determining the entity sub-graph of the media data based on the plurality of target entities and the knowledge graph” recites a mental process because human mind can determine the entity sub-graph of the media data based on target entities and knowledge graph by evaluation and judgement of data.
-“extracting features of a plurality of entities in the entity sub-graph, to obtain the entity representation vector” recites a mental process because human mind can extract features of a plurality of entities in the entity sub-graph to obtain the entity representation vector by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
Claims 5, 20:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and the retrieving the plurality of target entities comprises: obtaining initial entity vectors of a plurality of entities in the knowledge graph” is a mental process because human mind can identify/extract (retrieve) entity vectors for the knowledge graph by evaluation/observation and judgement of data.
-“retrieving candidate entities from the knowledge graph according to the initial entity vectors and the at least two image representation sub-vectors; and” is a mental process because human mind can identify/extract (retrieve) data from the knowledge graph based on vectors by evaluation/observation and judgement of data.
-“selecting, from the candidate entities, the plurality of target entities that are related to the media data” recites a mental process because human mind can select entities by evaluation/observation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the media representation vector comprises at least two image representation sub-vectors” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the media representation vector comprises at least two image representation sub-vectors” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining a correlation degree set of the at least two image representation sub-vectors according to the initial entity vectors and the at least two image representation sub-vectors, the correlation degree set comprising respective correlation degrees between the at least two image representation sub-vectors and the initial entity vectors” recites a mental process because human mind can determine a correlation degree of images by evaluation and judgement of data.
-“and selecting, according to the correlation degree set, the candidate entities that are related to the at least two image representation sub-vectors” recites a mental process because human mind can select according to a correlation degree set candidate entities by evaluation and judgement of data.
Claim 7:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining respective neighboring nodes of the plurality of target entities in the knowledge graph” recites a mental process because human mind can determine respective neighboring nodes of the knowledge graph based on evaluation and judgement of data.
-“determining extended entities according to the plurality of target entities and the respective neighboring nodes of the plurality of target entities” recites a mental process because human mind can determine extended entities of the neighboring nodes by evaluation and judgement of data.
-“determining relationships between the extended entities in the knowledge graph” recites a mental process because human mind can determine relationships between entities in the knowledge graph by evaluation and judgement of data.
-“determining the entity sub-graph of the media data according to the extended entities and the relationships between the extended entities” recites a mental process because human mind can determine entity sub graph by evaluation and judgement of data.
Claim 8:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“the retrieving the plurality of target entities comprises: retrieving, based on the media representation vector a, the plurality of target entities that are related to the media data” is a mental process because human mind can identify/extract (retrieve) the plurality of entities based on the vector by evaluation/observation and judgement of data.
-“the determining the entity sub-graph comprises: determining, based on the plurality of target entities and the knowledge graph and the entity sub-graph corresponding to the media data and” recites a mental process because human mind can determine based on the plurality of target entities and the knowledge graph, entity sub-graphs by evaluation and judgment of data.
-“the extracting the features comprises: extracting the features of the plurality of entities in the entity sub-graph , to obtain the entity representation vector” recites a mental process because human mind can extract entity sub-graphs to obtain vector by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“ by using a retrieval sub- model of a knowledge retrieval model,
-“by using a sub-graph construction network of the knowledge retrieval model,
-“by using a graph neural network of the knowledge retrieval model, F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“by using a sub-graph construction network of the knowledge retrieval model,
-“ by using a graph neural network of the knowledge retrieval model,
-“and by using a retrieval sub- model of a knowledge retrieval model,
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“concatenating the media representation vector, the text representation vector, and the entity representation vector” recites a mental process because human mind can concatenating vectors by evaluation and judgement of data.
-“adding a first separation element between the media representation vector and the text representation vector and a second separation element between the text representation vector and the entity representation vector during the concatenating, to obtain a concatenated vector” recites a mental process because human mind can add vectors during the concatenating by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“performing a feature fusion processing on the concatenated vector , to obtain the knowledge augmented vector” recites a mental process because human mind can perform feature fusion by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“ by using a knowledge augmented model,
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ by using a knowledge augmented model,at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data) and/or “Apply it” type limitation.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“performing a classification processing on the knowledge augmented vector, to obtain one or more interest types of the target object and” recites a mental process because human mind can perform classification on the knowledge augmented vector to obtain interest types by evaluation and judgement of data.
-“obtaining the target media data according to the one or more interest types” recites a mental process because human mind can obtain target media data according to interest types by evaluation and judgement of data.
Claim 11:
At Step 1:
The claim is directed to a "method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“extracting, , a first media training vector from first sample media data and a first text training vector from a first sample text of the first sample media data” recites a mental process because human mind can extract vectors by evaluation and judgment of data and/or mathematical concept and mathematical relationships.
-“performing, , a knowledge retrieval processing on the first media training vector and a knowledge graph to obtain a training sub-graph of the first sample media data” recites a mental process because human mind can perform a knowledge retrieval on the vectors and knowledge graph to obtain sub-graph of the sample media data by evaluation and judgement of data.
-“determining an entity training vector of the training sub-graph” recites a mental process because human mind can determine an entity training vector of the training sub-graph by evaluation and judgement of data.
-“performing, , a feature fusion processing on the first media training vector, the first text training vector, and the entity training vector to obtain a knowledge augmented training vector” recites a mental process because human mind can perform feature fusion of the training vector by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“determining a visual loss value and a language loss value according to the knowledge augmented training vector and a sample label of the first sample media data” recites a mental process because human mind can determine a visual loss value and language loss value according to the training vector and sample label by evaluation and judgement of data.
-“determining a knowledge retrieval loss value according to the knowledge augmented training vector and the training sub-graph” recites a mental process because human mind can
-“adjusting parameters of the one or more feature extraction models, the knowledge retrieval model, and the knowledge augmented model based on the visual loss value, the language loss value, and the knowledge retrieval loss value, to obtain an augmented vector extraction model, the augmented vector extraction model including the one or more feature extraction models, the knowledge retrieval model and the knowledge augmented model; and” recites a mental process because adjusting parameters based on the visual loss values and language loss value and the retrieval loss value to obtain models by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“determining a recommendation model based on the augmented vector extraction model and a classification model, the recommendation model including the augmented vector extraction model and the classification model”, recites a mental process because human mind can determine a recommendation model based on the vectors by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a method of media data recommendation”, which is all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“ by using one or more feature extraction models
-“ by using a knowledge retrieval model,
-“ by using a knowledge augmented model,
-“the recommendation model providing target media data to a target object based on media data, a description text of the media data and the knowledge graph” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“ by using one or more feature extraction models
-“ by using a knowledge retrieval model,
-“ by using a knowledge augmented model,
-“the recommendation model providing target media data to a target object based on media data, a description text of the media data and the knowledge graph” is well-understood, routine and conventional activities (WURC) as evidenced by the court cases cited in MPEP 2106.04(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or
"iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9"
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“the determining the visual loss value and the language loss value comprises: obtaining a masked sub-image augmented vector in the media augmented training vector” recites a mental process because human mind can determine loss values by obtaining augmented vectors in the media augmented training vector by evaluation and judgement of data.
-“determining the visual loss value according to the masked sub-image augmented vector and the masked sub-image label” recites a mental process because human mind can determine visual loss value according to the masked sub-image augmented vector and the masked sub-image label by evaluation and judgment of data.
-“performing a classification processing on the text augmented training vector to obtain a masked word prediction probability, and” recites a mental process because human mind can perform a classification processing on the text augmented training vector to obtain masked word prediction probability by evaluation and judgement of data.
-“determining the language loss value based on the masked word prediction probability and the masked word label” recites a mental process because human mind can determine the language loss value based on the masked word prediction probability and the label by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“the knowledge augmented training vector comprises a media augmented training vector and a text augmented training vector, and the sample label comprises a masked sub-image label and a masked word label and” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“the knowledge augmented training vector comprises a media augmented training vector and a text augmented training vector, and the sample label comprises a masked sub-image label and a masked word label and” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“the determining the knowledge retrieval loss value comprises: obtaining a positive entity sample pair from the training sub-graph, two entities in the positive entity sample pair having an entity relationship in the training sub-graph” recites a mental process because human mind can determine loss values by obtaining positive entity sample pair having an entity relationship in the training sub graph by evaluation and judgement of data.
-“determining a first score of the positive entity sample pair according to the entity augmented training vector” recites a mental process because human mind can determine scores by evaluation and judgement of data.
-“obtaining a negative entity sample pair from the training sub-graph, two entities in the negative entity sample pair having no entity relationship in the training sub-graph” recites a mental process because human mind can obtain negative sample pair by evaluation and judgement of data.
-“determining a second score of the negative entity sample pair according to the entity augmented training vector and” recites a mental process because human mind can determine a second score of the negative sample pair by evaluation and judgement of data.
-“determining the knowledge retrieval loss value based on the first score and the second score” recites a mental process because human mind can determine a second score of the negative sample pair by evaluation and judgement of data.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“the knowledge augmented training vector further comprises an entity augmented training vector and” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“the knowledge augmented training vector further comprises an entity augmented training vector and” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“extracting a second media training vector from the second sample media data and a second text training vector from the second sample text; and” recites a mental process because human mind can extract media training vectors from the sample media by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
-“determining an image text comparison loss value according to the second media training vector, the second text training vector, the first media training vector, and the first text training vector” recites a mental process because human mind can determine loss value by evaluation and judgement of data
-“and the adjusting the parameters comprises adjusting the parameters of the one or more feature extraction models, the knowledge retrieval model, and the knowledge augmented model based on the visual loss value, the language loss value, the knowledge retrieval loss value, and the image text comparison loss value, to obtain the augmented vector extraction model” recites a mental process because human mind can adjust parameters of models to obtain augmented vector extraction model by evaluation and judgement of data and/or mathematical concept and mathematical relationships.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“the first sample media data and the first sample text belong to a sample set, and the sample set further comprises second sample media data and a second sample text of the second sample media data” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“the first sample media data and the first sample text belong to a sample set, and the sample set further comprises second sample media data and a second sample text of the second sample media data” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to language data).
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 15:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining a first similarity degree according to the first media training vector and the second text training vector” recites a mental process because human mind can determine similarity degree by evaluation and judgement of data.
-“determining a second similarity degree according to the first text training vector and the second media training vector” recites a mental process because human mind can determine similarity degree by evaluation and judgement of data.
-“and determining the image text comparison loss value according to the first similarity degree, the second similarity degree, a first similarity degree label for the first media training vector and the second text training vector, and a second similarity degree label for the first text training vector and the second media training vector” recites a mental process because human mind can determine loss value according to similarity degree by evaluation and judgement of data.
Prior art considerations
Prior arts are not cited for claims 1-20.
Prior art Huang et al. (US 2022/0222920) teaches text feature vector, an image feature vector and combining the vectors and recommend contents based on the click through rate in paragraphs [0053, 0095, 0171].
JI et al. (US 2020/0293874) teaches knowledge graph and subgraphs, vectors in paragraphs [0034, 0035, 0066].
Xiao et al. (US 2021/0216577) teaches knowledge graph and vectors [0031, 0038, 0070].
Wang et al. (US 2022/0147715) teaches fig. 8, processing fusion processing [0226, 0229].
Huang, Jianhui (WO 2021/223567A) teaches text vector and image vector and processing fusion on the vectors in page 3.
Prior arts of record do not explicitly teach “performing a knowledge retrieval in a knowledge graph according to the media representation vector, to obtain an entity sub-graph of the media data; determining an entity representation vector of the entity sub-graph; performing a feature fusion processing on the media representation vector, the text representation vector, and the entity representation vector, to obtain a knowledge augmented vector; obtaining target media data based on the knowledge augmented vector, the knowledge augmented vector being a fused vector of the media representation vector, the text representation vector, and the entity representation vector, and” in the independent claims 1 and 16 and the limitations “performing, by using a knowledge retrieval model, a knowledge retrieval processing on the first media training vector and a knowledge graph to obtain a training sub-graph of the first sample media data; performing, by using a knowledge augmented model, a feature fusion processing on the first media training vector, the first text training vector, and the entity training vector to obtain a knowledge augmented training vector; determining a visual loss value and a language loss value according to the knowledge augmented training vector and a sample label of the first sample media data; determining a knowledge retrieval loss value according to the knowledge augmented training vector and the training sub-graph; adjusting parameters of the one or more feature extraction models, the knowledge retrieval model, and the knowledge augmented model based on the visual loss value, the language loss value, and the knowledge retrieval loss value, to obtain an augmented vector extraction model, the augmented vector extraction model including the one or more feature extraction models, the knowledge retrieval model and the knowledge augmented model; and determining a recommendation model based on the augmented vector extraction model and a classification model, the recommendation model including the augmented vector extraction model and the classification model, the recommendation model providing target media data to a target object based on media data, a description text of the media data and the knowledge graph” in the independent claim 16.
Conclusion
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/FATIMA P MINA/Examiner, Art Unit 2159 /ALBERT M PHILLIPS, III/Primary Examiner, Art Unit 2159
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