Social-topical adaptive networking (STAN) system allowing for cooperative inter-coupling with external social networking systems and other content sources

Abstract

A cross-pollination subsystem is provided within a Social-Topical Adaptive Networking (STAN) system. The cross-pollination subsystem cooperatively interacts with external platforms to bring fresh cross-pollination data to topic nodes or on-topic Notes Exchange rings (TCONE's) of the STAN system. Users are provided with an interface that allows them to browse through the cross-pollinated topic space to thereby connect with online content sources that are likely to correspond to topics of interest currently on the minds of the STAN system users.

Description

1. FIELD OF DISCLOSURE[0001]The present disclosure of invention relates generally to online networking systems and uses thereof. The disclosure relates more specifically to social networking (SN) systems and in particular to cases where one or more social-topical networking (STN) systems are cooperatively coupled to other kinds of online networking and / or content providing systems.2A. CROSS REFERENCE TO CO-OWNED APPLICATION[0002]The following copending U.S. patent application is owned by the owner of the present application, and its disclosure is incorporated herein by reference in its entirety as originally filed:[0003](A) Ser. No. 12 / 369,274 filed Feb. 11, 2009 by Jeffrey A. Rapaport et al. and which is originally entitled, Social Network Driven Indexing System for Instantly Clustering People with Concurrent Focus on Same Topic into On Topic Chat Rooms and / or for Generating On-topic Search Results Tailored to User Preferences Regarding Topic.2B. CROSS REFERENCE TO PATENTS / PUBLICAT...

AI Technical Summary

Benefits of technology

[0021]In accordance with the present disclosure, an automated machine system (e.g., the STAN—2 system) is provided that automatically seeks to do something like that. However the here disclosed machine system does not operate by magic. Instead it automatically tries to disambiguate ambiguous first encodings (e.g., a primary keyword such as “bridge) by trying to collect additional and / or substantially contemporaneous further “encodings” that give more context to one or more primary, but often ambiguous encodings (e.g., the exemplary primary keyword, “bridge). The disambiguating other encodings may be automatically collected signals that indicate where the given user is in terms of time, space, planning and / or perhaps emotional moods. In one embodiment, a continuous flow of encodings surrounding the user either physically and / or logically are persistently being collected and analyzed by the STAN—2 system (when so-called “monitoring” is turned on) for the purpose of reducing or eliminating the ambiguity as to which specific topic the user currently has in mind (consciously or subconsciously). The persistently collected other “encodings” may include identifications of on-screen or off-screen other information that appears currently or in recent usage history of the user (in the dynamic flow of the user's recent life) to be correlated to current primary encodings (e.g., to the primary keyword, “bridge”). These additional encodings can help place the primary keyword(s) (e.g., “bridge”) in better context for the machine system and can thus enable the automated machine system (the STAN—2 system) to appear as if it has intuitively guessed what was on the user's mind without subjecting the user to an endless list of ambiguity reducing questions.

[0023]All these contextually enriching and automatically collected encodings acquired by the STAN—2 system increase the probability that the primary keyword, “bridge”, if so presented by that user at that time and place to his personal data processing device (e.g., his palm-held computer phone) is intended to mean that the user is thinking about a “bridge loan” and not about a bridge that crosses a river.

Problems solved by technology

However, even with those two determinations as givens, a term like “bridge” can be highly ambiguous.

However the here disclosed machine system does not operate by magic.

When the mind is forced to jump to an infrastructural level of detail, it can often lose track of the higher-level, substantive material it was concentrating on.

Images