Cognitive distributed network

Patent metadata

Field	Value
Publication number	US-10713574-B2
Application number	US-201414249679-A
Country	US
Kind code	B2
Filing date	Apr 10, 2014
Priority date	Apr 10, 2014
Publication date	Jul 14, 2020
Grant date	Jul 14, 2020

Abstract

Approaches are provided for answering an inquiry of a cognitive distributed network. An approach includes receiving the inquiry at the cognitive distributed network. The approach further includes determining a classification for the inquiry based on natural language of the inquiry. The approach further includes classifying the inquiry as a single question class. The approach further includes determining, by at least one computing device, a type of introspection to be used by the cognitive distributed network on the inquiry. The approach further includes generating an answer to the inquiry based on the determined type of introspection.

First claim

What is claimed is:

1. A method for answering an inquiry of a cognitive distributed computer network, comprising:
generating, by a computing device, an evidence based ranked hypothesis for a training inquiry;
comparing, by the computing device, the evidence based ranked hypothesis for the training inquiry with an actual answer from a key;
minimizing errors between the evidence based ranked hypothesis for the training inquiry with the actual answer from the key by using a Newton Raphson learning algorithm and changing weights of each type of feature value within a logistic regression algorithm;
receiving the inquiry at an introspective module of the computing device in the cognitive distributed computer network;
determining, by the computing device, a classification for the inquiry based on natural language of the inquiry;
classifying, by the computing device, the inquiry as a single question class;
determining, by the introspective module of the computing device, a type of introspection to be used by the cognitive distributed computer network on the inquiry to generate an answer to the inquiry, the type of introspection being based on an amount of detail provided in the inquiry;
suggesting, by the computing device, related terms which are related to the inquiry based on the amount of detail provided in the inquiry;
adjusting, by the computing device, a threshold of a precision oriented introspection algorithm that has the determined type of introspection for generating the answer to the inquiry by using related terms, from a previous inquiry, outside a predetermined threshold of the precision oriented introspection algorithm;
minimizing, by the computing device, false positive responses using the adjusted threshold of the precision oriented introspection algorithm;
generating, by the computing device, the answer to the inquiry using natural text and a cognitive cloud visualization which comprises a graphical chart that shows a predictive cloud using unstructured information management architecture based on the determined type of introspection, minimizing errors between the evidence based ranked hypothesis for the training inquiry with the actual answer from the key, and minimizing a number of false positive responses obtained using the adjusted threshold of the precision oriented introspection algorithm; and
provisioning and allocating cloud computing resources for the cognitive distributed computer network based on the received inquiry, the generated answer to the inquiry, and the predictive cloud using unstructured information management architecture by applying predictive analytics and forecasting.

2. The method of claim 1 , wherein the inquiry is received in response to a human wanting to discover a health of the cognitive distributed network through a visual interface that takes input from the human using the natural language.

3. The method of claim 1 , wherein the determining the classification for the inquiry is based on key words and phrases within the natural language of the inquiry such that the inquiry is classified as the single question class in response to the inquiry including predetermined phrases or the inquiry is classified as a conversational class in response to the inquiry pertaining to a string of inquiries.

4. The method of claim 1 , wherein the determined type of introspection is selected from the group consisting of recall, precision and average, and the determined type of introspection is used to analyze, formulate, and generate the answer to the inquiry using a collection of information and resources available to the computing device.

5. The method of claim 1 , wherein a service provider at least one of creates, maintains, deploys and supports a computer infrastructure that includes the computing device.

6. The method of claim 1 , wherein steps of claim 1 are provided by a service provider on at least one of a subscription, advertising, and fee basis.

7. The method of claim 1 , further comprising a forecasting module which uses symbiosis to predict fault tolerance and disk failures, and minimizing false negative responses using a midterm introspection algorithm, projecting to a middle, and replying to additional information using a past or predicted performance of the cognitive distributed computer network in view of the projected middle.

8. A computer program product for answering an inquiry of a cognitive distributed computer network, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable and executable by a computing device to cause the computing device to:
generate, by the computing device, an evidence based ranked hypothesis for a training inquiry;
compare, by the computing device, the evidence based ranked hypothesis for the training inquiry with an actual answer from a key;
minimize errors between the evidence based ranked hypothesis for the training inquiry with the actual answer from the key by using a Newton Raphson learning algorithm and changing weights of each type of feature value within a logistic regression algorithm;
receive, by an introspective module of the computing device in the cognitive distributed computer network, the inquiry;
determine, by the computing device, a classification for the inquiry;
classify, by the computing device, the inquiry as a conversational class;
determine, by the computing device, whether the inquiry pertains to past performance or future performance of the cognitive distributed computer network;
in response to determining the inquiry pertains to the past performance, apply, by the computing device, natural language processing to the inquiry to determine how the cognitive distributed computer network was performing in the past using an ontology mapping module by mapping introspection tags to analytics correlating at least to a natural language;
in response to determining the inquiry pertains to the future performance, apply, by the computing device, the natural language processing to the inquiry to determine how the cognitive distributed computer network will be performing in the future using a forecasting module which simulates ahead of horizon metrics and forecasting by applying the natural language processing to questions or conversation correspondence;
suggest, by the computing device, related terms which are related to the inquiry based on the amount of detail provided in the inquiry;
adjust, by the computing device, a threshold of a recall oriented introspection algorithm by using related terms for generating a reply to the inquiry, from a previous inquiry, outside a predetermined threshold of the recall oriented introspection algorithm;
use, by the computing device, the adjusted threshold of the recall oriented introspection algorithm to minimize false negative responses;
reply, by the computing device, to the inquiry using natural text and a cognitive cloud visualization which comprises a graphical chart that shows how a predictive cloud is using unstructured information management architecture based on the determination of how the cognitive distributed computer network was performing in the past or will be performing in the future, minimized errors between the evidence based ranked hypothesis for the training inquiry with the actual answer from the key, and the use of the adjusted threshold of the recall oriented introspection algorithm; and
provision and allocate cloud computing resources for the cognitive distributed computer network based on the received inquiry, the reply to the inquiry, and the predictive cloud using unstructured information management architecture by applying predictive analytics and forecasting,
wherein the key comprises a trainin

Assignees

• IBM

Inventors

• Baughman Aaron K
• Harrison Thomas B
• O'Connell Brian M
• Pearthree Herbert D

Classifications

• G06F16/3329

  Natural language query formulation · CPC title

• G06N3/126

  Evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F40/279

  Recognition of textual entities · CPC title