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公开(公告)号：US11410220B2
公开(公告)日：2022-08-09
申请号：US16814241
申请日：2020-03-10
Applicant: Samsung Electronics Co., Ltd.
Inventor： Tong Yu , Yilin Shen , Hongxia Jin
Abstract: Described is a system for providing improved exploration for an interactive recommendation system by leveraging intuitive user feedback. The recommendation system may provide images of recommend items and receive user feedback preferences in the form of a natural language expression. Traditional techniques for interactive recommendation systems typically rely on restricted forms of user feedback such as binary relevance responses, or feedback based on a fixed set of relative attributes. In contrast, the recommendation system described herein introduces a new approach to interactive image recommendation (or image search) that enables users to provide feedback via natural language, allowing for a more natural and effective interaction. The recommendation system may be based on formulating the task of natural-language-based interactive image recommendation as a reinforcement learning problem, and reward the recommendation system for improving the rank of the target image during each iterative interaction.
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82.发明授权公开(公告)号：US11341945B2
公开(公告)日：2022-05-24
申请号：US16704600
申请日：2019-12-05
Applicant: Samsung Electronics Co., Ltd.
Inventor： Peter M. Bretan , Larry P. Heck , Hongxia Jin
IPC: G10H1/00 , G06N3/04 , G06K9/62 , G06F16/638
Abstract: A method includes receiving a non-linguistic input associated with an input musical content. The method also includes, using a model that embeds multiple musical features describing different musical content and relationships between the different musical content in a latent space, identifying one or more embeddings based on the input musical content. The method further includes at least one of: (i) identifying stored musical content based on the one or more identified embeddings or (ii) generating derived musical content based on the one or more identified embeddings. In addition, the method includes presenting at least one of: the stored musical content or the derived musical content. The model is generated by training a machine learning system having one or more first neural network components and one or more second neural network components such that embeddings of the musical features in the latent space have a predefined distribution.
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公开(公告)号：US20220147843A1
公开(公告)日：2022-05-12
申请号：US17096316
申请日：2020-11-12
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor： Dalkandura Arachchige Kalpa Shashika Silva Gunaratna , Vijay Srinivasan , Sandeep Nama , Christian Koehler , Hongxia Jin
IPC: G06N5/02 , G06T7/11 , G06F40/284 , G06F40/211 , G06N7/00 , H04N19/176
Abstract: Computer-based content understanding can include segmenting an image into a plurality of blocks, wherein each block includes textual information from the image. For each block of the plurality of blocks, encoded feature data is generated by encoding visual information of the block and visual information of one or more neighboring blocks from the plurality of blocks and encoded textual data is generated by encoding the textual information of the block and the textual information of the one or more neighboring blocks. Further, using an entity class prediction model, one or more tokens of the block are classified into one or more entity classes based on a combination of the encoded textual data and the encoded feature data. A plurality of entities can be extracted from the image based on the entity classes of the plurality of blocks.
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公开(公告)号：US20220114479A1
公开(公告)日：2022-04-14
申请号：US17090542
申请日：2020-11-05
Applicant: Samsung Electronics Co., Ltd.
Inventor： Changsheng Zhao , Yilin Shen , Hongxia Jin
Abstract: A machine learning method using a trained machine learning model residing on an electronic device includes receiving an inference request by the electronic device. The method also includes determining, using the trained machine learning model, an inference result for the inference request using a selected inference path in the trained machine learning model. The selected inference path is selected based on a highest probability for each layer of the trained machine learning model. A size of the trained machine learning model is reduced corresponding to constraints imposed by the electronic device. The method further includes executing an action in response to the inference result.
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公开(公告)号：US20210279606A1
公开(公告)日：2021-09-09
申请号：US16813510
申请日：2020-03-09
Applicant: Samsung Electronics Co., Ltd.
Inventor： Vijay Srinivasan , Christian Koehler , Hongxia Jin
Abstract: Systems and methods are described for adding new attributes to entities of a knowledge base. A plurality of correlations may be identified between the new attribute and existing attributes of the entities using a rule-based model, such that attribute rules may be associated with each identified correlation exceeding a predetermined confidence threshold. An unstructured data model may then be applied to the knowledge base to identify unstructured data associated with each entity of the plurality of entities correlated to presence of the new attribute. Then a meta learner model may be applied to identify weights for each attribute rule and the identified unstructured data. After the weights have been set for the meta learner model, the meta learner model may then be applied to each entity in the knowledge base to accurately identify entities having the new attribute.
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Patent Agency Ranking
公开(公告)号：US11094317B2
公开(公告)日：2021-08-17
申请号：US16404012
申请日：2019-05-06
Applicant: Samsung Electronics Co., Ltd.
Inventor： Yilin Shen , Xiangyu Zeng , Yu Wang , Hongxia Jin
IPC: G10L15/22 , G10L15/18 , G10L15/06 , G10L15/183 , G10L15/16
Abstract: An electronic device for training a machine learning model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to train a classification layer of the model. To train the classification layer, the at least one processor is configured to receive, by the classification layer, one or more language contexts from an utterance encoder layer and to classify, by the classification layer, at least one portion of an utterance into an information type among a plurality of information types. The at least one processor may be further configured to jointly train a slot filling layer and an intent detection layer of the model.
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公开(公告)号：US20210241050A1
公开(公告)日：2021-08-05
申请号：US16900664
申请日：2020-06-12
Applicant: Samsung Electronics Co., Ltd.
Abstract: A method, an electronic device and computer readable medium for entity-relationship embeddings using automatically generated entity graphs instead of a traditional knowledge graph are provided. The method includes receiving, by a processor, an input text. The method also includes identifying a primary entity, a secondary entity and a context from the input text, wherein the context comprises a relationship between the primary entity and the secondary entity. The method additionally includes generating, by the processor, an entity context graph based on the primary entity, the secondary entity, and the context by: extracting, from the context, one or more text segments comprising a plurality of words describing one or more additional relationships between the primary entity and the secondary entity, and generating a plurality of context triples from the one or more text segments, each of the plurality of context triples defining a respective relationship between primary entity and the secondary entity.
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公开(公告)号：US20200257962A1
公开(公告)日：2020-08-13
申请号：US16273973
申请日：2019-02-12
Applicant: Samsung Electronics Co., Ltd.
Inventor： Yue Deng , KaWai Chen , Yilin Shen , Hongxia Jin
Abstract: Systems and methods are described for converting input content. A first model may convert input content to an output content that exhibits one or more desired properties. A second model may determine if the conversion meets a desired quality of conversion using a discriminating function. The discriminating function may determine a difference between properties of the output content and properties of desired content, where the difference corresponds to the success of the conversion applying the desired properties. Updated control data may be generated by a third model using information from the second model, where the updated control data may be used by the first model to reduce the determined difference. After updated control data has been generated, the foregoing steps may be repeated based upon the updated control data. One of a plurality of different actions may be determined in response to the difference.
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公开(公告)号：US20200043480A1
公开(公告)日：2020-02-06
申请号：US16404012
申请日：2019-05-06
Applicant: Samsung Electronics Co., Ltd.
Inventor： Yilin Shen , Xiangyu Zeng , Yu Wang , Hongxia Jin
IPC: G10L15/18 , G10L15/06 , G10L15/183 , G10L15/22
Abstract: An electronic device for training a machine learning model includes at least one memory and at least one processor coupled to the at least one memory. The at least one processor is configured to train a classification layer of the model. To train the classification layer, the at least one processor is configured to receive, by the classification layer, one or more language contexts from an utterance encoder layer and to classify, by the classification layer, at least one portion of an utterance into an information type among a plurality of information types. The at least one processor may be further configured to jointly train a slot filling layer and an intent detection layer of the model.
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公开(公告)号：US20190354578A1
公开(公告)日：2019-11-21
申请号：US16236886
申请日：2018-12-31
Applicant: Samsung Electronics Co., Ltd.
Inventor： Yilin Shen , Avik Ray , Hongxia Jin
Abstract: A system receives a phrase that includes at least one tagged object and generates instantiated phrases by instantiations of each tagged object in the phrase. The system generates lists of natural language phrases by corresponding paraphrases of each of the instantiated phrases. The system generates ordered lists of natural language phrases by ordering natural language phrases in each list of natural language phrases based on occurrences of each natural language phrase. The system generates annotated natural language phrases by using each tagged object in the phrase to annotate the ordered lists of natural language phrases or an enhanced set of natural language phrases that is based on the ordered lists of natural language phrases.
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