This patent is so general, it describes basically any data-mining software (that may or may not be distributed over multiple computers.)

Bullshit. Here are the two independent claims:

1. A system for large-scale processing of data, comprising: a plurality of processes executing on a plurality of interconnected processors; the plurality of processes including a master process, for coordinating a data processing job for processing a set of input data, and worker processes; the master process, in response to a request to perform the data processing job, assigning input data blocks of the set of input data to respective ones of the worker processes; each of a first plurality of the worker processes including an application-independent map module for retrieving a respective input data block assigned to the worker process by the master process and applying an application-specific map operation to the respective input data block to produce intermediate data values, wherein at least a subset of the intermediate data values each comprises a key/value pair, and wherein at least two of the first plurality of the worker processes operate simultaneously so as to perform the application-specific map operation in parallel on distinct, respective input data blocks; a partition operator for processing the produced intermediate data values to produce a plurality of intermediate data sets, wherein each respective intermediate data set includes all key/value pairs for a distinct set of respective keys, and wherein at least one of the respective intermediate data sets includes respective ones of the key/value pairs produced by a plurality of the first plurality of the worker processes; and each of a second plurality of the worker processes including an application-independent reduce module for retrieving data, the retrieved data comprising at least a subset of the key/value pairs from a respective intermediate data set of the plurality of intermediate data sets and applying an application-specific reduce operation to the retrieved data to produce final output data corresponding to the distinct set of respective keys in the respective intermediate data set of the plurality of intermediate data sets, and wherein at least two of the second plurality of the worker processes operate simultaneously so as to perform the application-specific reduce operation in parallel on multiple respective subsets of the produced intermediate data values.

9. A method of performing a large-scale data processing job, comprising: executing a plurality of processes on a plurality of interconnected processors, the plurality of processes including a master process for coordinating the large-scale data processing job for processing a set of input data, and worker processes; in the master process, in response to a request to perform the large-scale data processing job, assigning input data blocks of the set of input data to respective ones of the worker processes; in each of a first plurality of the worker processes, executing an application-independent map module to retrieve a respective input data block assigned to the worker process by the master process and to apply an application-specific map operation to the respective input data block to produce intermediate data values, wherein at least a subset of the intermediate data values each comprises a key/value pair, and wherein at least two of the first plurality of the worker processes operate simultaneously so as to perform the application-specific map operation in parallel on distinct, respective input data blocks; using a partition operator to process the produced intermediate data values to produce a plurality of intermediate data sets, wherein each respective intermediate data set includes all key/value pairs for a distinct set of respective keys, and wherein at least one of the respective intermediate data sets includes respective ones of the key/value pairs produced by a plurality of the first plurality of the worker processes; and in each of a second plurality of the worker processes, executing an application-independent reduce module to retrieve data, the retrieved data comprising at least a subset of the key/value pairs from a respective intermediate data set of the plurality of intermediate data sets and applying an application-specific reduce operation to the retrieved data to produce final output data corresponding to the distinct set of respective keys in the respective intermediate data set of the plurality of intermediate data sets, and wherein at least two of the second plurality of the worker processes operate simultaneously so as to perform the application-specific reduce operation in parallel on multiple respective subsets of the produced intermediate data values.

This very clearly only covers computations distributed over multiple systems (a plurality of processes executing on a plurality of interconnected processors) and is narrow enough that there are lots of parallel systems which don't fall under those claims.