公开(公告)号：US20190050688A1

公开(公告)日：2019-02-14

申请号：US15673069

申请日：2017-08-09

Applicant: Intel Corporation

Inventor： Darshan Iyer ,  Nilesh K. Jain

IPC: G06K9/62

Abstract: Methods, apparatus, systems and articles of manufacture to analyze time series data are disclosed. An example method includes sub sampling time series data collected by a sensor to generate one or more candidate samples of interest within the time series data. Feature vectors are generated for respective ones of the one or more candidate samples of interest. Classification of the feature vectors is attempted based on a model. In response to a classification of one of the feature vectors, the classification is stored in connection with the corresponding candidate sample.

公开(公告)号：US20220108224A1

公开(公告)日：2022-04-07

申请号：US17554975

申请日：2021-12-17

Applicant: Intel Corporation

Inventor： Luis S. Kida ,  Nilesh K. Jain ,  Darshan Iyer ,  Ebrahim Al Safadi

IPC: G06N20/00

Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. Example instructions cause a processor to obtain dataset features indicative of a plurality of characteristics of an input dataset, rank, using multiple ranking algorithms, the dataset features, identify feature subsets for respective ones of the ranked dataset features, predict performance metrics based on the feature subsets, and select a final subset based on the predicted performance metrics.

公开(公告)号：US10956792B2

公开(公告)日：2021-03-23

申请号：US15673069

申请日：2017-08-09

Applicant: Intel Corporation

Inventor： Darshan Iyer ,  Nilesh K. Jain

IPC: G06K9/62 ,  G06K9/00 ,  A61B5/0452 ,  A61B5/00 ,  G06N3/08 ,  G06N3/02

Abstract: Methods, apparatus, systems and articles of manufacture to analyze time series data are disclosed. An example method includes sub sampling time series data collected by a sensor to generate one or more candidate samples of interest within the time series data. Feature vectors are generated for respective ones of the one or more candidate samples of interest. Classification of the feature vectors is attempted based on a model. In response to a classification of one of the feature vectors, the classification is stored in connection with the corresponding candidate sample.

公开(公告)号：US10373069B2

公开(公告)日：2019-08-06

申请号：US14866895

申请日：2015-09-26

Applicant: Intel Corporation

Inventor： Luis S. Kida ,  Nilesh K. Jain ,  Darshan Iyer ,  Ebrahim Al Safadi

IPC: G06N20/00

Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.

公开(公告)号：US20220161815A1

公开(公告)日：2022-05-26

申请号：US17434721

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Petrus J. Van Beek ,  Darshana D. Salvi ,  Mehrnaz Khodam Hazrati ,  Pragya Agrawal ,  Darshan Iyer ,  Suhel Jaber ,  Soila P. Kavulya ,  Hassnaa Moustafa ,  Patricia Ann Robb ,  Naveen Aerrabotu ,  Jeffrey M. Ota ,  Iman Saleh Moustafa ,  Monica Lucia Martinez-Canales ,  Mohamed Eltabakh ,  Cynthia E. Kaschub ,  Rita H. Wouhaybi ,  Fatema S. Adenwala ,  Jithin Sankar Sankaran Kutty ,  Li Chen ,  David J. Zage

IPC: B60W60/00 ,  B60W50/00 ,  G06T9/00

Abstract: According to one embodiment, an apparatus includes an interface to receive sensor data from a plurality of sensors of an autonomous vehicle. The apparatus also includes processing circuitry to apply a sensor abstraction process to the sensor data to produce abstracted scene data, and to use the abstracted scene data in a perception phase of a control process for the autonomous vehicle. The sensor abstraction process may include one or more of: applying a Sensor data response normalization process to the sensor data, applying a warp process to the sensor data, and applying a filtering process to the sensor data.

公开(公告)号：US20220126864A1

公开(公告)日：2022-04-28

申请号：US17434710

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Hassnaa Moustafa ,  Darshana D. Salvi ,  Suhel Jaber ,  Darshan Iyer ,  Mehrnaz Khodam Hazrati ,  Pragya Agrawal ,  Naveen Aerrabotu ,  Petrus J. Van Beek ,  Monica Lucia Martinez-Canales ,  Patricia Ann Robb ,  Rita Chattopadhyay ,  Jeffrey M. Ota ,  Iman Saleh Moustafa ,  Soila P. Kavulya ,  Karthik Reddy Sripathi ,  Mohamed Eltabakh ,  Igor Tatourian ,  Cynthia E. Kaschub ,  Rita H. Wouhaybi ,  Ignacio J. Alvarez ,  Fatema S. Adenwala ,  Cagri C. Tanriover ,  Maria S. Elli ,  David J. Zage ,  Jithin Sankar Sankaran Kutty ,  Christopher E. Lopez-Araiza ,  Magdiel F. Galán-Oliveras ,  Li Chen ,  Bahareh Sadeghi ,  Subramanian Anandaraj ,  Pradeep Sakhamoori

IPC: B60W60/00 ,  B60W40/09 ,  B60W50/16 ,  H04W4/40 ,  G06N20/00

Abstract: Sensor data is received from a plurality of sensors, where the plurality of sensors includes a first set of sensors and a second set of sensors, and at least a portion of the plurality of sensors are coupled to a vehicle. Control of the vehicle is automated based on at least a portion of the sensor data generated by the first set of sensors. Passenger attributes of one or more passengers within the autonomous vehicles are determined from sensor data generated by the second set of sensors. Attributes of the vehicle are modified based on the passenger attributes and the sensor data generated by the first set of sensors.

公开(公告)号：US20190340539A1

公开(公告)日：2019-11-07

申请号：US16513800

申请日：2019-07-17

Applicant: Intel Corporation

Inventor： Luis S. Kida ,  Nilesh K. Jain ,  Darshan Iyer ,  Ebrahim Al Safadi

IPC: G06N20/00

Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.

公开(公告)号：US20220126878A1

公开(公告)日：2022-04-28

申请号：US17434713

申请日：2020-03-27

Applicant: Intel Corporation

Inventor： Hassnaa Moustafa ,  Suhel Jaber ,  Darshan Iyer ,  Mehrnaz Khodam Hazrati ,  Pragya Agrawal ,  Naveen Aerrabotu ,  Petrus J. Van Beek ,  Monica Lucia Martinez-Canales ,  Patricia Ann Robb ,  Rita Chattopadhyay ,  Soila P. Kavulya ,  Karthik Reddy Sripathi ,  Igor Tatourian ,  Rita H. Wouhaybi ,  Ignacio J. Alvarez ,  Fatema S. Adenwala ,  Cagri C. Tanriover ,  Maria S. Elli ,  David J. Zage ,  Jithin Sankar Sankaran Kutty ,  Christopher E. Lopez-Araiza ,  Magdiel F. Galán-Oliveras ,  Li Chen

IPC: B60W60/00 ,  G05D1/00 ,  B60W50/14

Abstract: An apparatus comprising at least one interface to receive sensor data from a plurality of sensors of a vehicle; and one or more processors to autonomously control driving of the vehicle according to a path plan based on the sensor data; determine that autonomous control of the vehicle should cease; send a handoff request to a remote computing system for the remote computing system to control driving of the vehicle remotely; receive driving instruction data from the remote computing system; and control driving of the vehicle based on instructions included in the driving instruction data.

公开(公告)号：US10755198B2

公开(公告)日：2020-08-25

申请号：US15394711

申请日：2016-12-29

Applicant: Intel Corporation

Inventor： Darshan Iyer ,  Nilesh K. Jain

IPC: G06N20/00 ,  G06K9/62 ,  G06K9/00 ,  G06F16/35 ,  G06F17/16 ,  G06N3/063 ,  G06F16/28

Abstract: Methods, apparatus, and system determine if a data class in a plurality of data classes is separable, such as by determining an average intra-class similarity within each data class, inter-class similarity across all data classes in the plurality of data classes, and determining separability based on the average intra-class similarity relative to the inter-class similarity. Data classes determined to be highly variable may be removed. Pair(s) of data classes not separable from one another may be combined into one class or one of the data classes may be dropped. A hardware accelerator, which may comprise artificial neurons, accelerate performance of the data analysis.

公开(公告)号：US10747327B2

公开(公告)日：2020-08-18

申请号：US15195604

申请日：2016-06-28

Applicant: Intel Corporation

Inventor： Darshan Iyer ,  Nilesh K. Jain ,  Zhiqiang Liang

IPC: G06F3/0346 ,  G06F3/01 ,  G06K9/00 ,  G06K9/62

Abstract: Technologies for gesture recognition using downsampling are disclosed. A gesture recognition device may capture gesture data from a gesture measurement device, and downsample the captured data to a predefined number of data points. The gesture recognition device may then perform gesture recognition on the downsampled gesture data to recognize a gesture, and then perform an action based on the recognized gesture. The number of data points to which to downsample may be determined by downsampling to several different numbers of data points and comparing the performance of a gesture recognition algorithm performed on the downsampled gesture data for each different number of data points.