Patents by Inventor José Jimeno
José Jimeno has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11816889Abstract: Unsupervised learning for video classification. One or more features from one or more video clips are extracted using a spatial-temporal encoder. The one or more extracted features are processed, using a video instance discrimination task, to generate a classification label, the classification label indicating whether two of the video clips are from a same video. The one or more extracted features are processed, using a pair-wise speed discrimination task, to generate a comparison label, the comparison label indicating a relative playback speed between two given video clips. A search is performed in a video database for a video that is similar to a given video based on the comparison label.Type: GrantFiled: March 29, 2021Date of Patent: November 14, 2023Assignee: International Business Machines CorporationInventors: Chuang Gan, Dakuo Wang, Antonio Jose Jimeno Yepes, Bo Wu
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Patent number: 11734445Abstract: In an approach for providing a document access control based on document component layouts, a processor detects a layout of a document, the layout including one or more components of the document. A processor defines an access policy to access the one or more components based on the layout. A processor authorizes a request to access the one or more components based on the access policy and the layout. A processor retrieves the one or more components based on the access policy and the authorized request.Type: GrantFiled: December 2, 2020Date of Patent: August 22, 2023Assignee: International Business Machines CorporationInventors: Peter Zhong, Antonio Jose Jimeno Yepes, Lenin Mehedy
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Patent number: 11599711Abstract: Aspects of the present invention disclose a method for automatic delineation and extraction of tabular data in portable document format (PDF). The method includes one or more processors extracting metadata corresponding to tabular data in a text-based portable document format (PDF), wherein the metadata is associated with characters and border lines of the tabular data. The method further includes generating a graph structure corresponding to the tabular data in the text-based PDF based at least in part on the metadata. The method further includes generating a vector representation of the graph structure. The method further includes constructing a tree structure corresponding to the tabular data based at least in part on the vector representation.Type: GrantFiled: December 3, 2020Date of Patent: March 7, 2023Assignee: International Business Machines CorporationInventors: Peter Zhong, Antonio Jose Jimeno Yepes
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Publication number: 20220309278Abstract: Unsupervised learning for video classification. One or more features from one or more video clips are extracted using a spatial-temporal encoder. The one or more extracted features are processed, using a video instance discrimination task, to generate a classification label, the classification label indicating whether two of the video clips are from a same video. The one or more extracted features are processed, using a pair-wise speed discrimination task, to generate a comparison label, the comparison label indicating a relative playback speed between two given video clips. A search is performed in a video database for a video that is similar to a given video based on the comparison label.Type: ApplicationFiled: March 29, 2021Publication date: September 29, 2022Inventors: Chuang Gan, Dakuo Wang, Antonio Jose Jimeno Yepes, Bo Wu
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Patent number: 11380116Abstract: A computer-implemented method for using a machine learning model to automatically extract tabular data from an image includes receiving a set of images of tabular data and a set of markup data corresponding respectively to the images of tabular data. The method further includes training a first neural network to delineate the tabular data into cells using the markup data, and training a second neural network to determine content of the cells in the tabular data using the markup data. The method further includes, upon receiving an input image containing a first tabular data without any markup data, generating an electronic output corresponding to the first tabular data by determining the structure of the first tabular data using the first neural network and extracting content of the first tabular data using the second neural network.Type: GrantFiled: October 22, 2019Date of Patent: July 5, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Peter Zhong, Antonio Jose Jimeno Yepes, Elaheh Shafieibavani
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Publication number: 20220180044Abstract: Aspects of the present invention disclose a method for automatic delineation and extraction of tabular data in portable document format (PDF). The method includes one or more processors extracting metadata corresponding to tabular data in a text-based portable document format (PDF), wherein the metadata is associated with characters and border lines of the tabular data. The method further includes generating a graph structure corresponding to the tabular data in the text-based PDF based at least in part on the metadata. The method further includes generating a vector representation of the graph structure. The method further includes constructing a tree structure corresponding to the tabular data based at least in part on the vector representation.Type: ApplicationFiled: December 3, 2020Publication date: June 9, 2022Inventors: Peter Zhong, Antonio Jose Jimeno Yepes
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Publication number: 20220171871Abstract: In an approach for providing a document access control based on document component layouts, a processor detects a layout of a document, the layout including one or more components of the document. A processor defines an access policy to access the one or more components based on the layout. A processor authorizes a request to access the one or more components based on the access policy and the layout. A processor retrieves the one or more components based on the access policy and the authorized request.Type: ApplicationFiled: December 2, 2020Publication date: June 2, 2022Inventors: Peter Zhong, Antinio Jose Jimeno Yepes, Lenin Mehedy
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Patent number: 11244203Abstract: Methods, systems and computer program products for automatically generating structured training data based on an unstructured document are provided. Aspects include receiving an unstructured document and a corresponding structured document that includes labeled portions. Aspects also include generating a parsed document that has one or more extracted objects by applying a parsing tool to the unstructured document. Aspects also include identifying one or more matching extracted objects by applying a matching algorithm to the structured document and the parsed document. Each matching extracted object is an extracted object of the parsed document that corresponds to a labeled portion of the structured document. Aspects also include annotating a region of the unstructured document that corresponds to the bounding box of the respective matching extracted object with a respective label of the corresponding labeled portion of the unstructured document.Type: GrantFiled: February 7, 2020Date of Patent: February 8, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Peter Zhong, Antonio Jose Jimeno Yepes, Jianbin Tang
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Patent number: 11170031Abstract: A method, computer system, and a computer program product for automatically extracting and normalizing at least one mutant gene entity from at least one set of unstructured text is provided. The present invention may include extracting the unstructured text describing first and second entities. The present invention may then include identifying a specific first entity and a specific second entity. The present invention may also include associating the specific first and the specific second entities. The present invention may further include creating the mutant gene entity. The present invention may then include identifying at least one semantic relationship between the created mutant gene entity and one or more third entities. The present invention may further include storing the at least one set of data associated with the specific first and specific second entity, the semantic relationship, and the created mutant gene entity in a database.Type: GrantFiled: August 31, 2018Date of Patent: November 9, 2021Assignee: International Business Machines CorporationInventors: Richard L. Martin, Antonio Jose Jimeno Yepes, David Martinez Iraola, Alix Lacoste, Christine Schieber
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Publication number: 20210286989Abstract: Embodiments of the invention describe a computer-implemented method of analyzing an electronic version of a document. The computer-implemented method can include an architecture of machine learning sub-models that performs the global task of translating unstructured and semi-structured inputs into numerical representations that can be recognized and manipulated by a content-analysis (CA) sub-model without relying on brute force analysis. Embodiments of the invention achieve these results by separating the global task into auxiliary tasks and assigning each sub-model to at least one of the auxiliary tasks. The auxiliary tasks can include parsing the unstructured or semi-structured inputs into format types (e.g., lists, tables, figures, text, etc. of a PDF document), extracting features of the parsed document, and performing a computer-based CA on the extracted features.Type: ApplicationFiled: March 11, 2020Publication date: September 16, 2021Inventors: Peter Zhong, Antonio Jose Jimeno Yepes, Elaheh ShafieiBavani
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Publication number: 20210248420Abstract: Methods, systems and computer program products for automatically generating structured training data based on an unstructured document are provided. Aspects include receiving an unstructured document and a corresponding structured document that includes labeled portions. Aspects also include generating a parsed document that has one or more extracted objects by applying a parsing tool to the unstructured document. Aspects also include identifying one or more matching extracted objects by applying a matching algorithm to the structured document and the parsed document. Each matching extracted object is an extracted object of the parsed document that corresponds to a labeled portion of the structured document. Aspects also include annotating a region of the unstructured document that corresponds to the bounding box of the respective matching extracted object with a respective label of the corresponding labeled portion of the unstructured document.Type: ApplicationFiled: February 7, 2020Publication date: August 12, 2021Inventors: Peter Zhong, Antonio Jose Jimeno Yepes, Jianbin Tang
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Publication number: 20210117668Abstract: A computer-implemented method for using a machine learning model to automatically extract tabular data from an image includes receiving a set of images of tabular data and a set of markup data corresponding respectively to the images of tabular data. The method further includes training a first neural network to delineate the tabular data into cells using the markup data, and training a second neural network to determine content of the cells in the tabular data using the markup data. The method further includes, upon receiving an input image containing a first tabular data without any markup data, generating an electronic output corresponding to the first tabular data by determining the structure of the first tabular data using the first neural network and extracting content of the first tabular data using the second neural network.Type: ApplicationFiled: October 22, 2019Publication date: April 22, 2021Inventors: PETER ZHONG, ANTONIO JOSE JIMENO YEPES, ELAHEH SHAFIEIBAVANI
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Publication number: 20200073995Abstract: A method, computer system, and a computer program product for automatically extracting and normalizing at least one mutant gene entity from at least one set of unstructured text is provided. The present invention may include extracting the unstructured text describing first and second entities. The present invention may then include identifying a specific first entity and a specific second entity. The present invention may also include associating the specific first and the specific second entities. The present invention may further include creating the mutant gene entity. The present invention may then include identifying at least one semantic relationship between the created mutant gene entity and one or more third entities. The present invention may further include storing the at least one set of data associated with the specific first and specific second entity, the semantic relationship, and the created mutant gene entity in a database.Type: ApplicationFiled: August 31, 2018Publication date: March 5, 2020Inventors: Richard L. Martin, Antonio Jose Jimeno Yepes, David Martinez Iraola, Alix Lacoste, Christine Schieber
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Publication number: 20180107451Abstract: Automatic scaling is performed on a floating point implementation of a DNN to perform scaling to a fixed point implementation. The DNN includes multiple layers in an order from a starting to an ending layer. The automatic scaling includes determining a scaling factor for each of multiple ones of the layers during training of the DNN. The scaling factor converts floating point numbers used for calculations in a layer into integer numbers to be used in the calculations. A scaling factor is determined for a selected layer, which is at a position in the order, based on scaling factors used in layers in the order prior to the position of the selected layer. The automatic scaling includes outputting the scaling factors for the multiple layers to be used for implementing the fixed point implementation of the DNN that uses integer calculations instead of floating point calculations.Type: ApplicationFiled: October 14, 2016Publication date: April 19, 2018Inventors: Stefan Harrer, Antonio Jose Jimeno Yepes, Filiz Isabel Kiral-Kornek, Benjamin Scott Mashford, Jianbin Tang
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Patent number: 8420130Abstract: A stable pharmaceutical composition of a didemnin compound, comprises firstly a lyophilized didemnin preparation including water-soluble material and secondly a reconstitution solution of mixed solvents.Type: GrantFiled: February 18, 1999Date of Patent: April 16, 2013Assignee: Pharma Mar S.A.Inventors: Bastiaan Nuijen, Jacob Hendrik Beijnen, Roland Elizabeth Cornelis Henrar, Andres Gomez, Jose Jimeno
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Patent number: 8119638Abstract: Et 743 is used in the preparation of a medicament for the treatment of the human body for cancer.Type: GrantFiled: June 28, 2007Date of Patent: February 21, 2012Assignee: Pharma Mar, S.A.Inventors: Esteban Cvitkovich, George Daniel Demetri, Cecilia Guzman, Jose Jimeno, Luis Lopez Lazaro, Jean Louis Misset, Chris Twelves, Daniel D. Von Hoff
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Publication number: 20100041594Abstract: Aplidine is active against cancer of the pancreas, including metastatic pancreatic cancer.Type: ApplicationFiled: October 23, 2009Publication date: February 18, 2010Applicant: Pharma Mar, S.A.Inventors: Ramón Mangues, Rubén Henriquez, José Jimeno
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Publication number: 20090227490Abstract: Aplidine and aplidine analogues are of use for the treatment of cancer, in particular in the treatment of leukemias and lymphomas, especially in combination therapies.Type: ApplicationFiled: April 30, 2009Publication date: September 10, 2009Applicant: Pharma Mar, S.A.U.Inventors: Joseph R. Bertino, Debabrata Barnejee, Saydam Guray, Jose Jimeno, Glynn Thomas Faircloth
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Patent number: 7576188Abstract: Aplidine and aplidine analogues are of use for the treatment of cancer, in particular in the treatment of leukemias and lymphomas, especially in combination therapies.Type: GrantFiled: March 12, 2004Date of Patent: August 18, 2009Assignee: Pharma Mar, S.A.U.Inventors: Joseph R. Bertino, Debabrata Barnejee, Saydam Guray, José Jimeno, Glynn Thomas Faircloth
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Publication number: 20070275942Abstract: Et 743 is used in the preparation of a medicament for the treatment of the human body for cancer.Type: ApplicationFiled: June 28, 2007Publication date: November 29, 2007Applicant: Pharma Mar S.A.Inventors: Esteban Cvitkovich, George Demetri, Cecilia Guzman, Jose Jimeno, Luis Lazaro, Jean Misset, Chris Twelves, Daniel Von Hoff