Interactive Silicon, Inc.

Cirrus Logic Inc.

Quickshift, Inc.

Arva Intelligence Corp.

Texas Instruments Corporation

Mossman Holdings LLC

Vpn Multicast Technologies, LLC

Meetrix Corporation

Other

S3 Incorporated, Inc.

Motorola Corporation

Dell USA L P

Intellectual Ventures I LLC

Meetrix Communications, Inc.

Thomas A Dye

Manuel Joseph Alvarez, Ii

Peter Dau Geiger

John A McEntire

Daniel P Wilde

Gautam P Vaswani

Thomas Dundon

Karl M Guttag

Richard D Simpson

Michael D Asal

Jerry R Van Aken

Derek Roskell

Neil Tebbutt

Thomas A Dundon

L Curtis Lehmann, Jr

Bradley A May

Goran Devic

Mark Alan Einkauf

Donn M Baumgartner

Mike Xudong Cui

Elijah Benjamin Hoffman

Christopher William Hardin Fedor

Leland David Bernstein

A system and method for determining optimal sampling parameters is described. The method gathers soil data from a soil data source, which is associated with a soil organic carbon (SOC) project area. The crop prediction engine then determines that the soil data is less than optimal, but that the soil data is sufficient to generate an optimal sampling plan. The method completes a Monte Carlo simulation, which generates an empirical sampling distribution. The optimal sampling plan is determined by defining a margin of error, which provides a deviation from a predictive analysis of measured soil chemistry for a plurality of collected soil samples, and performing Monte Carlo simulations that include one Monte Carlo simulation having a lowest sampling density that satisfies the margin of error. The optimal sampling plan has the lowest sampling density and includes one or more sampling locations.

Optimal sampling with soil stratification

A system and method for interpolating soil chemistry variables to different plots of land is described. A first interpolation training model includes a machine learning model that receives soil composition information. A distance field training model generates spatial predictors that are applied to the machine learning model. The first interpolation training model prioritizes spatial smoothing over accuracy. A second interpolation training model is also applied that includes a distance weighting training model that more greatly weighs interpolated soil composition information closer to a point of interpolation than interpolated soil composition information that is further away to the point of interpolation. The second interpolation training model prioritizes accuracy over spatial smoothing. The illustrative crop prediction engine estimates soil chemistry values at different locations with the first interpolation training model and the second interpolation training model.

Estimating soil chemistry at different crop field locations

A system and method for visualizing one or more crop response surfaces. The system includes a geospatial database associated with a crop prediction engine. The geospatial database receives soil composition information for plots of land. The crop prediction engine identifies covariates from the soil composition information, which has a feature matrix. The crop prediction engine generates a multi-dimensional covariate training data set from the covariates. The crop prediction engine then applies the multi-dimensional covariate training data set to a machine learning training model to generate at least one predictive crop-yield predictive model. The crop prediction engine ranks covariates having feature set interactions. Subsequently, the crop prediction engine determines a dominant crop-yield feature set interaction from the ranked covariates having feature set interactions. The crop prediction engine generates a crop response surface from the dominant crop-yield feature set interaction. The crop prediction engine then visualizes the crop response surface.

Determining crop-yield drivers with multi-dimensional response surfaces

A system and method for identifying ground types from one or more interpolated covariates. The method proceeds by accessing soil composition information for plots of land, in which the soil composition information includes measured soil sample results, environmental results, soil conductivity results or any combination thereof. The method continues by identifying covariates from the soil composition information. Subsequently, the method interpolates covariates associated with different locations with an interpolation training model. Voxels are generated that are each associated with interpolated covariates having a corresponding geographical location. The method trains a random forest training model with the interpolated covariates. The voxels traverse the trained random forest model to identify clusters of voxels that are co-associated. The method identifies a ground type by combining the co-associated clusters. Each ground type is associated with a crop zone, a soil fertility, or a farm management recommendation.

Identifying ground types from interpolated covariates

A system and method for predicting a crop yield for a type of seed in a location is described. The method includes receiving, at a client device, seasonal crop data for the type of seed, soil data associated with the location, and mapping data associated with the location. The soil data includes soil variables, and the location is represented by voxels. The seasonal crop data, the soil data and the mapping data are uploaded to a geospatial database associated with a crop prediction engine. A random forest prediction model is applied to the seasonal crop data, the soil data and mapping data in the geospatial database by the crop prediction engine, which then ranks covariates to determine one or more significant covariates. The crop prediction engine then re-applies the significant covariates to the random forest prediction model to predict the crop yield for the type of seed at the location.

Predicting crop yield with a crop prediction engine

A system and method for supporting a multi-participant voice conference call using PSTN and Internet networks is described. The method for supporting a multi-participant voice conference call includes receiving voice from a PSTN client. The method also includes receiving voice data from a moderator and from at least one remote client connected to the Internet. The method then proceeds to mix the voice data from the PSTN client with the voice data from the moderator into a first mixed voice data that is transmitted to the remote client that is connected to the Internet. The method also mixes the voice data from the moderator with the voice data from the remote client connected to the Internet into a second mixed voice data that is transmitted to the PSTN client.

Voice conference call using PSTN and internet networks

A multi-participant conference system and method is described. The multi-participant system includes a PSTN client, at least one remote client and a first participant client. The PSTN client communicates audio data and the remote clients communicate audio-video data. The first participant client includes a voice over IP (VoIP) encoder, a VoIP decoder, a first audio mixer, and a second audio mixer. The VoIP encoder compresses audio data transported to the PSTN client. The VoIP decoder then decodes audio data from the PSTN client. The first audio mixer mixes the decoded audio data from the PSTN client with the audio-video data from the first participant into a first mixed audio-video data stream transmitted to the remote client. The second audio mixer mixes the audio-video data stream from the first participant with the audio-video data stream from each remote client into a second mixed audio transmitted to the PSTN client.

Audio-video multi-participant conference systems using PSTN and internet networks

An integrated memory controller (IMC) may sit on the main CPU bus or a high speed system peripheral bus and couple to system memory. The IMC may use a lossless data compression and decompression scheme for improved performance. The IMC may also include microcode for specific decompression of particular data formats such as digital video and digital audio. Compressed data may be decompressed in the IMC and stored into system memory or saved in the system memory in compressed format. Internal memory mapping may allow for format definition spaces which may define the format of the data and the data type to be read or written. Software overrides may be placed in applications software in systems that desire to control data decompression at the software application level.

Memory controller including a hardware compression and decompression engine for managing system memory and graphical operations

As prior art systems fail to produce end-to-end transport and routing mechanisms capable of secure, accurate, and timely delivery of real-time media, the present invention prescribes the method and process to facilitate server-less, IP based sessions across all of public and private network infrastructure without regard for network hardware or carrier makeup. The method and process claimed herein defines the application of well known standards in a unique fashion so as to facilitate transportation of TCP and UDP packets associated with a real-time multicast session in a secure manner while achieving unencumbered access through firewalls and across multiple carrier, public networks through IPSec based virtual networking.

Applying multicast protocols and VPN tunneling techniques to achieve high quality of service for real time media transport across IP networks

A method which allows standard telephone users to audio conference with video conferencing participants over IP networks in a private secure environment. A dial-out is performed from one or more conference client terminals bridging audio between the Internet and the PSTN networks. The process uses a mixed mode hybrid network architecture for call set-up, initialization and teardown including the method to mix audio at the desktop terminal instead of in a general purpose server as in the prior art. The method conferences video and audio between multiple clients and include audio from a standard telephone network within the conference. A virtual private network connects all of the IP clients together including the voice over IP server used to transcode the proprietary audio into the H.323 standard for transport into the telephony network.

Media based collaboration using mixed-mode PSTN and internet networks

A method and system for allowing a processor or I/O master to address more system memory than physically exists are described. A Compressed Memory Management Unit (CMMU) may keep least recently used pages compressed, and most recently and/or frequently used pages uncompressed in physical memory. The CMMU translates system addresses into physical addresses, and may manage the compression and/or decompression of data at the physical addresses as required. The CMMU may provide data to be compressed or decompressed to a compression/decompression engine. In some embodiments, the data to be compressed or decompressed may be provided to a plurality of compression/decompression engines that may be configured to operate in parallel. The CMMU may pass the resulting physical address to the system memory controller to access the physical memory. A CMMU may be integrated in a processor, a system memory controller or elsewhere within the system.

System and method for managing compression and decompression of system memory in a computer system

An integrated memory controller (IMC) preferably sits on the main CPU bus or a high speed system peripheral bus and couples to system memory. The IMC may use a lossless data compression and decompression scheme for improved performance. The IMC may also include microcode for specific decompression of particular data formats such as digital video and digital audio. Compressed data may be decompressed in the IMC and stored into system memory or saved in the system memory in compressed format. Internal memory mapping may allow for formal definition spaces which may define the format of the data and the data type to be read or written. Software overrides may be placed in applications software in systems that desire to control data decompression at the software application level.

A network device, also referred to as the Compression Enhanced Network Processor (CENP), with embedded parallel (or fast serial) compression and/or decompression capability. The network device may be a network processor based multi-ported switch, bridge, router, hub, or other device. The CENP may provide improved data density, efficiency and bandwidth for each port of a multi-port network switch. In one embodiment, the CENP may comprise a network processor core, a memory management unit, a memory buffer (e.g., an SRAM memory buffer), and a system memory. The CENP may comprise a compression and decompression engine. In one embodiment, the memory management unit comprises the compression and decompression engine, and thus may be referred to as a Compression Enhanced Memory Controller Unit (CEMCU).

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Austin, TX, United States of America

Thomas A Dye