Abrégé

Introduced are systems and methods that enable modification of logs in multiple off-line databases. Multiple off-line devices can mistakenly associate different respondents with the same identification (ID) unique to the system. When the multiple off-line devices synchronize with each other, or synchronize with a server hosting the central database, the software running on the off-line devices, or on the server detects that the modified logs come from different respondents, and the software assigns two different IDs unique to system to the logs. In another embodiment, multiple off-line devices can mistakenly associate the same respondent with two different IDs unique to the system. When the multiple off-line devices synchronize with each other or with the server, the software running on the off-line devices, or the server detects that the modified logs come from the same respondent, and the software assigns the logs to the same ID unique to system.

Classes IPC  ?

• G06F 16/23 - Mise à jour
• G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet

Abrégé

Presented here are system and methods for collecting information from devices, such as sensors, that are not necessarily connected to the Internet. Multiple sensors are distributed in a geographic area. The sensors power up every 10 minutes to gather data about the environment and then power down to save battery. A collecting device, i.e., a device attached to a moving object, traverses the geographic area containing the sensors, and continuously sends wake-up signals into the environment. When a sensor is within 20 feet of the collecting device, and receives the wake-up signal, the sensor uploads the gathered data to the collecting device. Subsequently, when the collecting device establishes an Internet connection, the collecting device uploads the gathered data to a central database.

Classes IPC  ?

• H04W 52/02 - Dispositions d'économie de puissance
• G01D 11/24 - Boîtiers
• G01D 9/02 - Enregistrement de valeurs mesurées produisant un ou plusieurs enregistrements des valeurs d'une seule variable

Abrégé

Presented here is a system for automatic conversion of data between various data sets. In one embodiment, the system can obtain a data set, can analyze associations between the variables in the data set, and can convert the data set into a canonical data model. The canonical data model is a smaller representation of the original data set because insignificant variables and associations can be left out, and significant relationships can be represented procedurally and/or using mathematical functions. In one embodiment, part of the system can be a trained machine learning model which can convert the input data set into a canonical data model. The canonical data model can be a more efficient representation of the input data set. Consequently, various actions, such as an analysis of the data set, merging of two data sets, etc. can be performed more efficiently on the canonical data model.

Classes IPC  ?

• G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
• G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
• G06F 7/00 - Procédés ou dispositions pour le traitement de données en agissant sur l'ordre ou le contenu des données maniées
• G06F 9/46 - Dispositions pour la multiprogrammation

Abrégé

Presented here is a system for automatic conversion of data between various data sets. In one embodiment, the system can obtain a data set, can analyze associations between the variables in the data set, and can convert the data set into a canonical data model. The canonical data model is a smaller representation of the original data set because insignificant variables and associations can be left out, and significant relationships can be represented procedurally and/or using mathematical functions. In one embodiment, part of the system can be a trained machine learning model which can convert the input data set into a canonical data model. The canonical data model can be a more efficient representation of the input data set. Consequently, various actions, such as an analysis of the data set, merging of two data sets, etc. can be performed more efficiently on the canonical data model.

Classes IPC  ?

• G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
• G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
• G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques
• G06F 19/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des applications spécifiques (spécialement adaptés à des fonctions spécifiques G06F 17/00;systèmes ou méthodes de traitement de données spécialement adaptés à des fins administratives, commerciales, financières, de gestion, de surveillance ou de prévision G06Q;informatique médicale G16H)
• H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole

Abrégé

The technology presented here enables low skilled administrators to design a hierarchical survey, low skilled field agents to collect answers to the hierarchical survey, and low skilled field managers to manage and monitor the progress of the field agents. The hierarchical surveys designed can be complex hierarchical surveys comprising multi-stage sampling units. The graphical user interfaces presented to the users are easy to use, and hide the complexity of the hierarchical survey. The user devices can communicate with each other to transmit the hierarchical surveys and the answers received to the hierarchical surveys using peer-to-peer networks, in environments where there is low, or no Internet connectivity.

Classes IPC  ?

• G06Q 30/00 - Commerce