View source for 17949575. CACHING OF TEXT ANALYTICS BASED ON TOPIC DEMAND AND MEMORY CONSTRAINTS simplified abstract (International Business Machines Corporation)

=CACHING OF TEXT ANALYTICS BASED ON TOPIC DEMAND AND MEMORY CONSTRAINTS=


==Organization Name==

[[:Category:International Business Machines Corporation|International Business Machines Corporation]]


[[Category:International Business Machines Corporation]]

==Inventor(s)==

[[:Category:Gandhi Sivakumar of Bentleigh (AU)|Gandhi Sivakumar of Bentleigh (AU)]][[Category:Gandhi Sivakumar of Bentleigh (AU)]]

[[:Category:Smitkumar Narotambhai Marvaniya of BANGALORE (IN)|Smitkumar Narotambhai Marvaniya of BANGALORE (IN)]][[Category:Smitkumar Narotambhai Marvaniya of BANGALORE (IN)]]

[[:Category:Vijay Ekambaram of Chennai (IN)|Vijay Ekambaram of Chennai (IN)]][[Category:Vijay Ekambaram of Chennai (IN)]]

[[:Category:Luke Peter Macura of Lucas (AU)|Luke Peter Macura of Lucas (AU)]][[Category:Luke Peter Macura of Lucas (AU)]]

==CACHING OF TEXT ANALYTICS BASED ON TOPIC DEMAND AND MEMORY CONSTRAINTS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17949575 titled 'CACHING OF TEXT ANALYTICS BASED ON TOPIC DEMAND AND MEMORY CONSTRAINTS

==Simplified Explanation==

The abstract describes a patent application for a system that analyzes user queries to identify a topic, maps it to a topic cluster in a text database, generates query demand data, and caches the text of the topic cluster based on available memory space.

* The system analyzes user queries to identify a topic using natural language processing (NLP).
* The identified topic is mapped to a topic cluster in a hierarchical model of a text database.
* Query demand data is generated based on user queries to indicate demand for the topic cluster.
* The system identifies the topic cluster as a topic-cache candidate based on the query demand data.
* It compares the memory required to store text associated with the topic cluster to available cache memory.
* The system caches the text of the topic cluster candidate if there is sufficient available cache memory space.

== Potential Applications ==
This technology could be applied in search engines, content recommendation systems, and information retrieval systems to improve efficiency and user experience.

== Problems Solved ==
This technology solves the problem of efficiently storing and retrieving text data based on user query demand, optimizing memory usage in caching systems.

== Benefits ==
The benefits of this technology include faster response times for user queries, improved system performance, and better utilization of cache memory resources.

== Potential Commercial Applications ==
Potential commercial applications of this technology include search engine optimization tools, content management systems, and online advertising platforms.

== Possible Prior Art ==
One possible prior art for this technology could be existing caching algorithms used in information retrieval systems or search engines to optimize data storage and retrieval processes.

=== Unanswered Questions ===

=== How does this technology handle updates to the cached text data? ===
The abstract does not mention how the system manages updates to the cached text data when new information is added or existing information is modified. 

=== What is the impact of caching on system performance and user experience? ===
The abstract does not provide information on how caching the text of topic clusters affects overall system performance and user experience in terms of response times and relevance of search results.


==Original Abstract Submitted==

An embodiment includes analyzing text content of a user query to identify via natural language processing (NLP) a query topic. The embodiment maps the query topic to a topic cluster at a node of a hierarchical model of a text database. The embodiment generates query demand data indicative of demand for the topic cluster based on user queries. The embodiment identifies the topic cluster as a topic-cache candidate based on the query demand data. The embodiment compares an amount of memory required for storing text associated with the first topic cluster to available cache memory. The embodiment caches the text of the topic cluster candidate upon determining that there is sufficient available cache memory space.



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