Other

Cadreon LLC

Bhupendra Mohanlal Patel

Tushar Patel

Jagdish Mohanlal Patel

Nilesh Kuchekar

A humanoid batter toy system comprising: a baseball home plate comprising: a bottom plate, a top plate, wherein the set of gear transfers the torque force to the leg shaft. The baseball batter humanoid includes the leg shaft coupled to a hip gear in the baseball batter humanoid, wherein the hip gear receives the torque force uses the torque force to cause an upper torso of baseball batter humanoid to rotate a baseball bat in a batter swinging motion, wherein the metal rope is placed in a central manner within the upper torso portion and when metal wire rope is pulled, the shoulder rotation of the baseball batter humanoid occurs.

Human-powered mechanical toy batters

Embodiments of the invention improve over conventional or routine technologies by generating a separate data structure or organization, other than those temporary search files that are typically used, that facilitates the processing of the datasets. In one embodiment, instead of using sorted temp files or running sorting algorithms, a meta-join and/or meta-group-by indexes may be generated or created that provide a quick access to the records in the datasets. Moreover, embodiments of the invention eliminate the need to sort or pre-sort datasets before or during a query is conducted on the dataset. Aspects of the invention use, for example, the meta-join and/or meta-group-by indexes to pre-organize the data such that, when executing a query against the dataset, the query is executed against the meta-join and/or meta-group-by indexes.

Meta-join and meta-group-by indexes for big data

A graphical user interface showing relevant sliding scale cluster groups having a processor generating a ranking of Top N1 features of the seed dataset in order of frequency of their occurrence. The processor generates another ranking of Top N2 features of the seed dataset in order of frequency of their occurrence in correlation with their equivalent percentage in total dataset. The Top M co-related features is identified for each of the Top N (N1+N2) features that are present for the seed dataset. The processor, for each one in M&#xd7;N set, generates a sliding scale cluster via permutation of Top M features. The processor sorts each permutation based on closest occurrence match. The Seed Group Meta Bitmap Index is generated for the seed audience segment. For each cluster, the processor calculates the available amplification count from the Total Audience Bitmap Index, until the desired amplification is achieved.

System and method based on sliding-scale cluster groups for precise look-alike modeling

In one exemplary embodiment, a terminal user-interface (UI) client is configured to accept a user-scripted command from a user. The user-scripted command includes an operating system command or a user-customized command. The user-scripted command is passed to a distributed server management platform. A node tag input by a user is accepted. The node tag identifies at least one agent node in a server in the cloud-computing platform to execute the user-scripted command. A distributed server management platform that passes the user-scripted command to a group broker of an agent group comprising the agent node identified by the node tag, and to pass a command result to the terminal UI. A group broker to pass the user-scripted command to the agent node, and to pass a command result from the agent node to the distributed server management platform.

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Fremont, CA, United States of America

Bhupendra Mohanlal Patel