Abstract: A social-mobile-local (SML) system and environment includes user mobile devices, a distributed communications network over which the devices communicate, a means of sensing proximity between pairs of mobile devices, and one or more SML databases and programs resident on the user mobile devices, on remote computers, or both. Challenges addressed include prevention of “alert flooding,” privacy protection, credential verification, entering detailed data on mobile devices, power-saving, and improved quality in both the choice and the content of notifications.

Abstract: The most common automated search methods produce less-than-ideal results when searching online resumes, profiles, and the like (“biographies”) for the identities of people with a searcher-selected qualification (“candidates”). Keywords, their proximities, and their repetitions are less informative in biographies than in other informational documents. Similarly, chains of social connection (“referral paths”) do not always reveal the likelihood or ease of a searcher's introduction to a candidate. In both cases, the display order of results may be unrelated to any estimate of merit. To answer the question “Whom do I need and how do I reach them?” a classifier system uses heuristics or algorithms adapted to match the reactions of human experts on the selected qualifications. Terms in biographies, regardless of structure, are standardized and disambiguated for accurate comparisons, meaningful context is preserved, and biographies and referral paths are scored based on expected usefulness to the searcher.

Abstract: A social-mobile-local (SML) system and environment includes user mobile devices, a distributed communications network over which the devices communicate, a means of sensing proximity between pairs of mobile devices, and one or more SML databases and programs resident on the user mobile devices, on remote computers, or both. Challenges addressed include prevention of “alert flooding,” privacy protection, credential verification, entering detailed data on mobile devices, power-saving, and improved quality in both the choice and the content of notifications.

Abstract: The most common automated search methods produce less-than-ideal results when searching online resumes, profiles, and the like (“biographies”) for the identities of people with a searcher-selected qualification (“candidates”). Keywords, their proximities, and their repetitions are less informative in biographies than in other informational documents. Similarly, chains of social connection (“referral paths”) do not always reveal the likelihood or ease of a searcher's introduction to a candidate. In both cases, the display order of results may be unrelated to any estimate of merit. To answer the question “Whom do I need and how do I reach them?” a classifier system uses heuristics or algorithms adapted to match the reactions of human experts on the selected qualifications. Terms in biographies, regardless of structure, are standardized and disambiguated for accurate comparisons, meaningful context is preserved, and biographies and referral paths are scored based on expected usefulness to the searcher.

Abstract: The most common automated search methods produce less-than-ideal results when searching online resumes, profiles, and the like (“biographies”) for the identities of people with a searcher-selected qualification (“candidates”). Keywords, their proximities, and their repetitions are less informative in biographies than in other informational documents. Similarly, chains of social connection (“referral paths”) do not always reveal the likelihood or ease of a searcher's introduction to a candidate. In both cases, the display order of results may be unrelated to any estimate of merit. To answer the question “Whom do I need and how do I reach them?” a classifier system uses heuristics or algorithms adapted to match the reactions of human experts on the selected qualifications. Terms in biographies, regardless of structure, are standardized and disambiguated for accurate comparisons, meaningful context is preserved, and biographies and referral paths are scored based on expected usefulness to the searcher.