The MemeStore concept consists in using traits, as in personality traits, in the storing of data or memories, to build a knowledge base as a weighted graph. The criteria used in the storage of memories are traits, as well as, semantics, and relevance based on frequency.

The MemeStore concept is based on the way humans and animals store memories.
MemeStore is an adaptive knowledge base, which may be given a personality, through
the use of traits.

The whole concept is based on animals who evolved, with the imperative to survive and
reproduce. These imperatives are controlled through hormones and memories, creating
attractions and aversions -- related to danger and safety, danger being anything to do with
predation or any other type of harm; and safety being food, shelter, play, and reproduction --
which are stored as memories, and then drive certain types of behaviour; or predispose
the animal to certain types of behaviour.

A trait is associated with the concept of like or dislike, acceptability or unacceptability, affinity or conflict,  appealing or unappealing, attraction or aversion. Traits are incorporated into the framework of the knowledge base or meme store, as a single word, a sentence which includes the words or concepts of like, dislike, affinity, acceptability, attraction, aversion, repulsion, philia, phobia, hazard or safety; such as 'I like vegetables', or 'I like technology', a sentence which indicates a like or/and dislike, such as 'I am a vegan', or a sentence which indicates a modification in behaviour when processing commands, or responding to sensory stimuli, such as 'be lenient when ...', or 'be stubborn when ...'.

The MemeStore memory framework is based on a weighted graph; where each node/vertex in the knowledge base represents a word or concept; and each edge represents the weighted relationship between the vertices/nodes. The relevance of each edge is indicated by the numeric value of its weight.

The weight of the relationships, between the vertices, are made to vary, based on the data or information, made of text, audio, and images, that is processed by the knowledge base or meme store. The criteria used in attributing a weight to an edge are: first its semantic value, that is the value computed by examining the semantic type of the edge. Said types are isa, hasa, ako, likes, etc..
The second criterion is the similarity in concept where synonyms are attributed a higher weight than antonyms. The third criterion is relevance re-enforcement; either, by a person browsing the meme store, by the meme store itself, coming across the same related concept identified by the edge between the nodes, when reading or receiving sensory inputs.

The weight of the relationships, between the nodes, are also made to vary according to the list of traits that is assigned to the knowledge base or meme store. As data is processed, any concept relating to a like trait is attributed a higher weight; and any concept relating to a dislike trait is attributed a lower weight when it comes to search-engine relevance. All traits have an associated degree of relevance, which dictates how extracted concepts, which are related with said traits, are processed and stored. For adaptive search-engines used in systems such as robots; both likes and dislikes are treated equally when it comes to relevance; since their relevance level is associated with the concept of safety and danger.

The MemeStore is made of several modules. The modules are a character recognition (OCR) module,  a scanning/reading module, a personality trait/attribute module, a memory store module, a graph traversal module, a path overlay/memory trail module, a query/command module, a conversation module, , a search engine module, a semantic command mapping  module, a visualization module. and a reward system module.

The MemeStore uses our statistics-based text scanning and heuristics algorithm (CETE), using type disambiguation (patent pending), to read text as a person, and build it's own knowledge base, with the appropriately weighted links/edges. Rules on how to read dictionaries and thesauri are first incorporated into the reading module of the MemeStore.

The query module, creates a small graph of the entered keywords and their relationships. Concepts are extracted from the graph. The keyword graph is signatured, using its set of nodes, edges, and weights. The signatures are kept in a database, and are used when satisfying searches. Keyword graphs, capturing the same concept or concepts, will have similar signatures.

Path overlays (patent pending) are the set of nodes and edges, which are traversed when a query is issued. They are signatured and stored in a database. They are used when satisfying searches through the knowledge base.

The reward module, helps to reinforce the weight/relevance of a given memory. It uses sensory inputs, and the concept of emotions, to implement a feedback system, where retrieval of a memory triggers a set of behaviours; which in turn may trigger an emotional response. An emotional response serves in reinforcing a memory; and, can be triggered directly from the retrieval of said memory. In nature, emotions are associated with hormonal triggers and sensory inputs; in the MemeStore, they are simply viewed as a graph consisting of links between memories, behaviours, sensory inputs, and signals or triggers, akin to hormonal triggers, pointing to a set of behaviours to express.

The MemeStore has two types of memory stores. As for humans and animals, the types of memory stores are a permanent and a temporary one. Memories that are kept in the temporary store, can either be moved into the permanent store or deleted altogether. The criteria used to decide, when memories are moved to the permanent store or deleted, are based on how often the concepts they capture, are re-enforced by user interaction, assigned traits, or information processed by reading or sensors (i.e. heat, light, temperature, visual, etc.). All extracted concepts which are tagged, as dislikes, are always moved first to the temporary store, depending on their level of relevance.

The engine is built using propositional calculus, -- A proposition is a statement which may
be true or false -- and modeled using graph theory, and especially De Brujin sequences.
A level of fuzzy is added, consistng of the two meters dealing with like and dislike.
The propositional calculus engine, of  the MemeStore, can ultimately be burned onto an integrated circuit.

Our MemeStore PCG engine cannot be circumvented, by building a similar propositional calculus engine, using Bayes theorems, formal grammar or graph theory methodologies.

An adjunct to this patent is the ability MemeStore provides, which allows the examination of the evolution of the knowledge base over time, through the use of path overlays, or memory traces or trails; the paths that are traversed, through the knowledge base, when retrieving a memory.

An second adjunct to this patent is the data-mining ability MemeStore provides, which allows the extraction of interesting information, by examining the log/database of  network path and semantic signatures and associated data.

A third adjunct to this patent is the ability MemeStore has in retro-fitting old inference rule engines into its framework, and allow their sales as valuable knowledge bases. Medical, legal, and engineering knowledge bases can thus be retro-fitted.

A fourth adjunct to this patent is the ability MemeStore has in using traits to restrict certain updates in the knowledge base. For example, adding a stubborn trait to MemeStore, can prevent updates that are contrary to a primary trait. A primary trait, is a trait, that has a large percentage of associated links, based on a given threshold, in the knowledge base. An example of a stubborn trait is 'be stubborn when updating anything related to medical diagnosis', or 'be stubborn when updating traits related to eating preferences'. These types of restrictions are usually referred to as constraints., constraints on behaviour, on searches, on responses, etc..

A fifth adjunct to this patent is the ability MemeStore has in categorizing and storing sensory inputs as experiential memories; which are information, related to unusual or relevant sensor data, emanating from the particular task or activity being performed by an actor (i.e. robot, computer desktop, search engine, etc.) using a MemeStore. The relevance of experiential memories can also be categorized, based on the list of assigned traits.


Patent Pending

Tsert.inc/Tsert.Com