Patents by Inventor Wei-Hao Lin
Wei-Hao Lin 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).
-
Publication number: 20230296555Abstract: The present disclosure provides a method for preparing a biochip. The method includes steps of coating a chip with a first solution of biotin to form a biotin-coated chip, wherein the biotin in the first solution is in a first concentration ranging from 0.1 to 1 ?g/ml; providing the biotin-coated chip with a second solution of avidin to form an avidin/biotin-coated chip, wherein the avidin in the second solution is in a second concentration ranging from 0.1 to 100 ?g/ml; and providing the avidin/biotin-coated chip with a third solution of a biotinylated probe to form the biochip, wherein the biotinylated probed in the third solution is in a third concentration ranging from 1 to 3 ?g/ml.Type: ApplicationFiled: December 29, 2022Publication date: September 21, 2023Inventors: CHANG-FU KUO, LIAN-CHIN WANG, AO-HO HSIEH, MING-TANG CHIOU, WEI-HAO LIN
-
Patent number: 11734609Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: June 17, 2021Date of Patent: August 22, 2023Assignee: Google LLCInventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
-
Publication number: 20220013407Abstract: A semiconductor structure and method of forming the same are provided. The method includes: forming a plurality of mandrel patterns over a dielectric layer; forming a first spacer and a second spacer on sidewalls of the plurality of mandrel patterns, wherein a first width of the first spacer is larger than a second width of the second spacer; removing the plurality of mandrel patterns; patterning the dielectric layer using the first spacer and the second spacer as a patterning mask; and forming conductive lines laterally aside the dielectric layer.Type: ApplicationFiled: July 9, 2020Publication date: January 13, 2022Applicant: Taiwan Semiconductor Manufacturing Co., Ltd.Inventors: Yu-Hsin Chan, Jiing-Feng Yang, Kuan-Wei Huang, Meng-Shu Lin, Yu-Yu Chen, Chia-Wei Wu, Chang-Wen Chen, Wei-Hao Lin, Ching-Yu Chang
-
Patent number: 11093860Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: December 3, 2018Date of Patent: August 17, 2021Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Patent number: 11042809Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: May 16, 2016Date of Patent: June 22, 2021Assignee: Google LLCInventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
-
Patent number: 10515313Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.Type: GrantFiled: October 29, 2014Date of Patent: December 24, 2019Assignee: Google LLCInventors: Robert Kaplow, Wei-Hao Lin, Gideon S. Mann, Travis H. K. Green, Gang Fu, Robbie A. Haertel
-
Patent number: 10504024Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: GrantFiled: June 28, 2016Date of Patent: December 10, 2019Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Publication number: 20190147498Abstract: Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for increasing fill rate while maintaining average margin for a content serving system. Web properties associated with a publisher are identified, each web property including slots for inclusion of third party content, each slot having a reserve price which represents a minimum amount the publisher will accept for inclusion of the third party content in the slot when presented to viewers. Over a time period, an average margin is maintained for a serving system for the publisher. Bids that are valued at a price that is less than the reserve price plus a margin for the serving system are subsidized using a surplus account, based on accepted winning bids that are valued at a price that exceeds a sum of the reserve price plus compensation for the serving system.Type: ApplicationFiled: April 1, 2015Publication date: May 16, 2019Inventors: James Giles, Wei-Hao Lin
-
Patent number: 10157343Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: June 16, 2015Date of Patent: December 18, 2018Assignee: Google LLCInventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Publication number: 20160307099Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: ApplicationFiled: June 28, 2016Publication date: October 20, 2016Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Patent number: 9406019Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for score normalization. One of the methods includes receiving initial training data, the initial training data comprising initial training records, each initial training record identifying input data as input and a category as output. The method includes generating a first trained predictive model using the initial training data and a training function. The method includes generating intermediate training records by inputting input data of the initial training records to a second trained predictive model, the second trained predictive model generated using the training function, each intermediate training record having a score. The method also includes generating a score normalization model using a score normalization training function and the intermediate training records.Type: GrantFiled: February 1, 2013Date of Patent: August 2, 2016Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Patent number: 9342798Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: GrantFiled: June 4, 2014Date of Patent: May 17, 2016Assignee: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann
-
Patent number: 9239986Abstract: A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined.Type: GrantFiled: August 20, 2013Date of Patent: January 19, 2016Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Patent number: 9189747Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.Type: GrantFiled: July 20, 2012Date of Patent: November 17, 2015Assignee: Google Inc.Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
-
Patent number: 9185659Abstract: Exemplary embodiments are related to two-dimensional maximum power compensation. A method may include calibrating an output power level of a transmitter across a range of frequencies at a constant temperature. The method may further include characterizing the output power level of the transmitter for each temperature of a plurality of temperatures for each frequency of the range of frequencies.Type: GrantFiled: July 29, 2013Date of Patent: November 10, 2015Assignee: QUALCOMM IncorporatedInventors: Shrenik Patel, Orhan C Ozdural, Wei-Hao Lin, Pritesh Vora
-
Publication number: 20150186800Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.Type: ApplicationFiled: October 29, 2014Publication date: July 2, 2015Inventors: Robert Kaplow, Wei-Hao Lin, Gideon S. Mann, Travis H.K. Green, Gang Fu, Robbie A. Haertel
-
Patent number: 9070089Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.Type: GrantFiled: October 7, 2013Date of Patent: June 30, 2015Assignee: Google Inc.Inventors: Wei-Hao Lin, Travis H. K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Publication number: 20150170048Abstract: A computer-implemented method includes receiving, in a system of one or more computers, training data for predictive modeling, the training data including a plurality of categories; determining, by the system, one or more attributes of the training data; identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes; obtaining a utility function for the predictive model of the identified type, the utility function specifying importance of the plurality of categories relative to each other; and generating, based on the training data and the utility function, a predictive model of the identified type.Type: ApplicationFiled: August 12, 2011Publication date: June 18, 2015Inventors: Wei-Hao Lin, Travis H.K. Green, Robert Kaplow, Gang Fu, Gideon S. Mann
-
Publication number: 20150170049Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.Type: ApplicationFiled: July 20, 2012Publication date: June 18, 2015Inventors: Gideon S. Mann, Jordan M. Breckenridge, Wei-Hao Lin
-
Publication number: 20150170056Abstract: Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training predictive models. Multiple training data records are received that each include an input data portion and an output data portion. A training data type is determined that corresponds to the training data. For example, a training data type can be determined by inputting the output data portions into one or more trained predictive classifiers. In other example, the training data type can be determined by comparison of the output data portions to data formats. Based on the determined training data type, a set of training functions are identified that are compatible with the training data of the determined training data type. The training data and the identified set of training functions are used to train multiple predictive models.Type: ApplicationFiled: June 4, 2014Publication date: June 18, 2015Applicant: Google Inc.Inventors: Jordan M. Breckenridge, Travis H. K. Green, Robert Kaplow, Wei-Hao Lin, Gideon S. Mann