公开(公告)号：US08078656B1

公开(公告)日：2011-12-13

申请号：US10990884

申请日：2004-11-16

申请人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

发明人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

IPC分类号： G06F15/00

CPC分类号： G06T9/00

摘要： Methods and systems for decompressing data are described. The relative magnitudes of a first value and a second value are compared. The first value and the second value represent respective endpoints of a range of values. The first value and the second value each have N bits of precision. Either the first or second value is selected, based on the result of the comparison. The selected value is scaled to produce a third value having N+1 bits of precision. A specified bit value is appended as the least significant bit of the other (non-selected) value to produce a fourth value having N+1 bits of precision.

摘要翻译： 描述用于解压缩数据的方法和系统。 比较第一值和第二值的相对幅度。 第一值和第二值表示值范围的各个端点。 第一个值和第二个值每个都具有N位的精度。 基于比较的结果来选择第一或第二值。 所选值被缩放以产生具有N + 1位精度的第三值。 附加指定的位值作为其他（未选择）值的最低有效位，以产生具有N + 1位精度的第四值。

公开(公告)号：US07961195B1

公开(公告)日：2011-06-14

申请号：US10990900

申请日：2004-11-16

申请人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

发明人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

IPC分类号： G06T9/00 ,  G06F15/00 ,  G06T1/00

CPC分类号： G06T9/00 ,  G06T15/04

摘要： Methods and systems for compressing and decompressing data are described. A first value of N+1 bits and a second value of N+1 bits are reduced to strings of N bits each. The first and second strings of N bits are stored in a particular order relative to one another in a compression block. The particular order in which the first and second strings of N bits are stored in the compression block is used to derive a bit value that is then used in combination with one of the strings of N bits to reconstruct that string as N+1 bits.

摘要翻译： 描述用于压缩和解压缩数据的方法和系统。 N + 1位的第一值和N + 1位的第二值被减少到每个N位的字符串。 N位的第一和第二串以相对于彼此在压缩块中的特定顺序存储。 使用N位的第一和第二串存储在压缩块中的特定顺序用于导出一个比特值，然后与N个比特串中的一个组合使用，以将该字符串重构为N + 1比特。

公开(公告)号：US08918440B2

公开(公告)日：2014-12-23

申请号：US13324971

申请日：2011-12-13

申请人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

发明人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

IPC分类号： G06F15/00 ,  G06T9/00

CPC分类号： G06T9/00

摘要： Methods and systems for decompressing data are described. The relative magnitudes of a first value and a second value are compared. The first value and the second value represent respective endpoints of a range of values. The first value and the second value each have N bits of precision. Either the first or second value is selected, based on the result of the comparison. The selected value is scaled to produce a third value having N+1 bits of precision. A specified bit value is appended as the least significant bit of the other (non-selected) value to produce a fourth value having N+1 bits of precision.

摘要翻译： 描述用于解压缩数据的方法和系统。 比较第一值和第二值的相对幅度。 第一值和第二值表示值范围的各个端点。 第一个值和第二个值每个都具有N位的精度。 基于比较的结果来选择第一或第二值。 所选择的值被缩放以产生具有N + 1位精度的第三值。 附加指定的位值作为其他（未选择）值的最低有效位，以产生具有N + 1位精度的第四值。

公开(公告)号：US20120084334A1

公开(公告)日：2012-04-05

申请号：US13324971

申请日：2011-12-13

申请人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

发明人： Douglas H. Rogers ,  Gary C. King ,  Walter E. Donovan

IPC分类号： G06F17/10

CPC分类号： G06T9/00

摘要： Methods and systems for decompressing data are described. The relative magnitudes of a first value and a second value are compared. The first value and the second value represent respective endpoints of a range of values. The first value and the second value each have N bits of precision. Either the first or second value is selected, based on the result of the comparison. The selected value is scaled to produce a third value having N+1 bits of precision. A specified bit value is appended as the least significant bit of the other (non-selected) value to produce a fourth value having N+1 bits of precision.

摘要翻译： 描述用于解压缩数据的方法和系统。 比较第一值和第二值的相对幅度。 第一值和第二值表示值范围的各个端点。 第一个值和第二个值每个都具有N位的精度。 基于比较的结果来选择第一或第二值。 所选择的值被缩放以产生具有N + 1位精度的第三值。 附加指定的位值作为其他（未选择）值的最低有效位，以产生具有N + 1位精度的第四值。

公开(公告)号：US20180157636A1

公开(公告)日：2018-06-07

申请号：US15814349

申请日：2017-11-15

申请人： Robert J. Munro ,  Schuyler D. Erle ,  Tyler j. Schnoebelen ,  Brendan D. Callahan ,  Jessica D. Long ,  Gary C. King ,  Paul A. Tepper ,  Jason A. Brenier ,  Stefan Krawczyk

发明人： Robert J. Munro ,  Schuyler D. Erle ,  Tyler j. Schnoebelen ,  Brendan D. Callahan ,  Jessica D. Long ,  Gary C. King ,  Paul A. Tepper ,  Jason A. Brenier ,  Stefan Krawczyk

IPC分类号： G06F17/27

CPC分类号： G06F17/277 ,  G06F17/2715 ,  G06F17/2785

摘要： Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.

公开(公告)号：US20160162458A1

公开(公告)日：2016-06-09

申请号：US14964522

申请日：2015-12-09

申请人： Robert J. Munro ,  Christopher Walker ,  Sarah K. Luger ,  Jason Brenier ,  Paul A. Tepper ,  Ross Mechanic ,  Andrew Gilchrist-Scott ,  Gary C. King ,  Brendan D. Callahan ,  Tyler J. Schnoebelen ,  Edgar Nunez ,  Haley Most

发明人： Robert J. Munro ,  Christopher Walker ,  Sarah K. Luger ,  Jason Brenier ,  Paul A. Tepper ,  Ross Mechanic ,  Andrew Gilchrist-Scott ,  Gary C. King ,  Brendan D. Callahan ,  Tyler J. Schnoebelen ,  Edgar Nunez ,  Haley Most

IPC分类号： G06F17/24 ,  G06F3/0482

CPC分类号： G06F17/241 ,  G06F3/0482 ,  G06F16/243 ,  G06F16/24532 ,  G06F16/285 ,  G06F16/288 ,  G06F16/3329 ,  G06F16/35 ,  G06F16/367 ,  G06F16/93 ,  G06F16/951 ,  G06F17/2241 ,  G06F17/272 ,  G06F17/2785 ,  G06F17/28 ,  G06F17/2809 ,  G06Q50/01

摘要： Methods and systems are disclosed for creating and linking a series of interfaces configured to display information and receive confirmation of classifications made by a natural language modeling engine to improve organization of a collection of documents into an hierarchical structure. In some embodiments, the interfaces may display to an annotator a plurality of labels of potential classifications for a document as identified by a natural language modeling engine, collect annotated responses from the annotator, aggregate the annotated responses across other annotators, analyze the accuracy of the natural language modeling engine based on the aggregated annotated responses, and predict accuracies of the natural language modeling engine's classifications of the documents.

摘要翻译： 公开了用于创建和链接被配置为显示信息并且接收由自然语言建模引擎进行的分类的确认的一系列接口以改进文档集合到分层结构中的组合的方法和系统。 在一些实施例中，接口可以向注释器显示由自然语言建模引擎识别的文档的潜在分类的多个标签，从注释器收集注释的响应，在其他注释器之间聚合注释的响应，分析 基于聚合注释响应的自然语言建模引擎，并预测自然语言建模引擎对文档分类的准确性。

公开(公告)号：US20160162456A1

公开(公告)日：2016-06-09

申请号：US14964517

申请日：2015-12-09

申请人： Robert J. Munro ,  Schuyler D. Erle ,  Christopher Walker ,  Sarah K. Luger ,  Jason Brenier ,  Gary C. King ,  Paul A. Tepper ,  Ross Mechanic ,  Andrew Gilchrist-Scott ,  Jessica D. Long ,  James B. Robinson ,  Brendan D. Callahan ,  Michelle Casbon ,  Ujjwal Sarin ,  Aneesh Nair ,  Veena Basavaraj ,  Tripti Saxena ,  Edgar Nunez ,  Martha G. Hinrichs ,  Haley Most ,  Tyler J. Schnoebelen

发明人： Robert J. Munro ,  Schuyler D. Erle ,  Christopher Walker ,  Sarah K. Luger ,  Jason Brenier ,  Gary C. King ,  Paul A. Tepper ,  Ross Mechanic ,  Andrew Gilchrist-Scott ,  Jessica D. Long ,  James B. Robinson ,  Brendan D. Callahan ,  Michelle Casbon ,  Ujjwal Sarin ,  Aneesh Nair ,  Veena Basavaraj ,  Tripti Saxena ,  Edgar Nunez ,  Martha G. Hinrichs ,  Haley Most ,  Tyler J. Schnoebelen

IPC分类号： G06F17/24 ,  G06F17/22 ,  G06F17/28

CPC分类号： G06F17/30598 ,  G06F3/0482 ,  G06F17/2241 ,  G06F17/241 ,  G06F17/272 ,  G06F17/2785 ,  G06F17/28 ,  G06F17/2809 ,  G06F17/30011 ,  G06F17/30401 ,  G06F17/30445 ,  G06F17/30604 ,  G06F17/30654 ,  G06F17/30705 ,  G06F17/30734 ,  G06F17/30864 ,  G06Q50/01

摘要： Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

摘要翻译： 提出了生成自然语言模型的方法。 该方法可以包括：摄取表示要由自然语言模型分析的文档的训练数据，生成包括至少两个主题节点的分层数据结构，训练数据将在该节点内被自然语言模型细分，选择多个 在要注释的训练数据中生成文档的注释提示，为每个文档生成关于所述文档的注释的注释提示，该注释指示所述文档中的至少两个主题节点中的哪个节点被分类，基于注释接收注释 提示; 以及使用自适应机器学习过程来生成所述自然语言模型，所述自适应机器学习过程被配置为根据所述分级数据结构的所述至少两个主题节点来确定所述注释中的模式如何根据所述训练数据中的文档被细分。

公开(公告)号：US07876332B1

公开(公告)日：2011-01-25

申请号：US11643558

申请日：2006-12-20

申请人： Christopher D. S. Donham ,  Edward A. Hutchins ,  Gary C. King ,  Michael J. M. Toksvig

发明人： Christopher D. S. Donham ,  Edward A. Hutchins ,  Gary C. King ,  Michael J. M. Toksvig

IPC分类号： G09G5/00

CPC分类号： G09G5/393 ,  G06T11/40 ,  G06T15/005 ,  G09G5/363

摘要： A computer-implemented graphics system that includes a rasterizer and a shader has a mode of operation in which primitive coverage information is generated for real sample locations and virtual sample locations for use in anti-aliasing. An individual pixel includes a single real sample location and at least one virtual sample location. In some instances, a primitive may cover only virtual sample locations and does not cover a real sample location. These instances can be identified in the coverage information sent from the rasterizer to the shader, so that the shader can determine whether or not it can write color information, depth information and/or stencil information for the real sample location to a framebuffer.

摘要翻译： 包括光栅化器和着色器的计算机实现的图形系统具有操作模式，其中为实际采样位置和用于抗锯齿的虚拟采样位置生成原始覆盖信息。 单个像素包括单个实际采样位置和至少一个虚拟采样位置。 在某些情况下，原语可能仅覆盖虚拟样本位置，并且不覆盖真实的样本位置。 可以在从光栅化器发送到着色器的覆盖信息中识别这些实例，使得着色器可以确定它是否可以将真实样本位置的颜色信息，深度信息和/或模板信息写入帧缓冲器。

公开(公告)号：US20160162468A1

公开(公告)日：2016-06-09

申请号：US14964526

申请日：2015-12-09

申请人： Schuyler D. Erle ,  Robert J. Munro ,  Brendan D. Callahan ,  Gary C. King ,  Jason Brenier ,  James B. Robinson

发明人： Schuyler D. Erle ,  Robert J. Munro ,  Brendan D. Callahan ,  Gary C. King ,  Jason Brenier ,  James B. Robinson

IPC分类号： G06F17/27 ,  G06F17/24

CPC分类号： G06F17/277 ,  G06F17/241 ,  G06F17/2785

摘要： Systems, methods, and apparatuses are presented for a trained language model to be stored in an efficient manner such that the trained language model may be utilized in virtually any computing device to conduct natural language processing. Unlike other natural language processing engines that may be computationally intensive to the point of being capable of running only on high performance machines, the organization of the natural language models according to the present disclosures allows for natural language processing to be performed even on smaller devices, such as mobile devices.

摘要翻译： 为训练有素的语言模型呈现系统，方法和装置，以有效的方式存储，使得训练语言模型可以在几乎任何计算设备中用于进行自然语言处理。 与其他只能在高性能机器上运行的计算密集型的自然语言处理引擎不同，根据本发明的自然语言模型的组织允许甚至在较小的设备上执行自然语言处理， 如移动设备。

公开(公告)号：US20160162467A1

公开(公告)日：2016-06-09

申请号：US14964525

申请日：2015-12-09

申请人： Robert J. Munro ,  Schuyler D. Erle ,  Tyler J. Schnoebelen ,  Brendan D. Callahan ,  Jessica D. Long ,  Gary C. King ,  Paul A. Tepper ,  Jason Brenier ,  Stefan Krawczyk

发明人： Robert J. Munro ,  Schuyler D. Erle ,  Tyler J. Schnoebelen ,  Brendan D. Callahan ,  Jessica D. Long ,  Gary C. King ,  Paul A. Tepper ,  Jason Brenier ,  Stefan Krawczyk

IPC分类号： G06F17/27

CPC分类号： G06F17/277 ,  G06F17/2715 ,  G06F17/2785

摘要： Methods, apparatuses, and systems are presented for generating natural language models using a novel system architecture for feature extraction. A method for extracting features for natural language processing comprises: accessing one or more tokens generated from a document to be processed; receiving one or more feature types defined by user; receiving selection of one or more feature types from a plurality of system-defined and user-defined feature types, wherein each feature type comprises one or more rules for generating features; receiving one or more parameters for the selected feature types, wherein the one or more rules for generating features are defined at least in part by the parameters; generating features associated with the document to be processed based on the selected feature types and the received parameters; and outputting the generated features in a format common among all feature types.

摘要翻译： 提出了使用用于特征提取的新型系统架构来生成自然语言模型的方法，装置和系统。 一种用于提取自然语言处理特征的方法，包括：访问从要处理的文档生成的一个或多个令牌; 接收用户定义的一个或多个特征类型; 从多个系统定义和用户定义的特征类型中接收对一个或多个特征类型的选择，其中每个特征类型包括用于生成特征的一个或多个规则; 为所选择的特征类型接收一个或多个参数，其中用于生成特征的所述一个或多个规则至少部分地由所述参数定义; 基于所选择的特征类型和接收到的参数来生成与要处理的文档相关联的特征; 并以所有特征类型中共同的格式输出生成的特征。