The Toronto-dominion Bank

Other

Paige Elyse Dickie

Denny Devasia Kuruvilla

John Jong-Suk Lee

Arun Victor Jagga

Vipul Kishore Lalka

Dino Paul D'Agostino

Robert Alexander McCarter

Nadia Moretti

Milos Dunjic

Rakesh Thomas Jethwa

Sarabjit Singh Walia

Dean C N Tseretopoulos

Dino Paul D'agostino

Robert Alexander Mccarter

Maksims Volkovs

Harjot Singh Panag

Dean Tseretopoulos

Ruby Walia

John Jong Suk Lee

Robert G Dickie

Gregory Richard Harper

Tomi Johan Poutanen

Jesse Cole Cresswell

Kin Kwan Leung

Yaqiao Luo

Michael W Van Eesbeek

Barum Rho

Atiyeh Ashari Ghomi

Chris Pearen

Kai Wang

Caitlin Messick

Lu Shu

Jianjin Dong

Valerie Velardo

Mark Andrew Hardy

Robert Jason Rousell

Bernard James Tarroza

Marc Philips

Michael W Van Eesbeck

Milo Dunjic

Dean Cn Tseretopoulos

The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained artificial-intelligence processes and contextual data. For example, an apparatus may generate an input dataset based on first interaction data and contextual data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.

Prediction of future occurrences of events using adaptively trained artificial-intelligence processes and contextual data

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Generating adaptive textual explanations of output predicted by trained artificial-intelligence processes

The present disclosure involves systems, software, and computer implemented methods for intercepting data exchange requests and automatically imposing a hold period while the data exchange is evaluated for a user profile initiating the request. One example system includes operations to detect a data exchange request prior to completion of the attempted data exchange, the data exchange request including information defining the attempted data exchange and associated wherein the attempted data exchange is associated with a particular user profile. A portion of the information is transmitted to a data exchange analysis system for execution of a user profile-specific data exchange analysis based on the information and a status of the user profile. A hold state is initiated for the data exchange request while the analysis is performed. The results of the analysis are presented and a confirmation to proceed is required before allowing the attempted data exchange to be processed.

Automatic digital personal assistant interjection for delaying data exchange outcomes

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, and processes that, among other things, dynamically authorize pre-stages data exchanges based on contextual data. For example, an apparatus may receive first data characterizing an initiation of a first exchange of data between a client device and a terminal device. Based on the first data, the apparatus may obtain second data that characterizes an expected initiation of a second exchange of data during a corresponding temporal interval, which may be specified relative to an initiation time of the first data exchange. The apparatus may generate and transmit, to a computing system, pre-authorization data that requests a pre-authorization of the second data exchange to a computing system. The pre-authorization data may include a portion of the second data and may instruct the computing system to pre-authorize the second data exchange in accordance with the second data.

Dynamic authorization of pre-staged data exchanges based on contextual data

A data management device, communication system and methods for tagging data in a data table and triggering automated action. A data management device receives from a first data provider one or more records of a data table, each record comprising a plurality of fields. The data table is associated with an account having one or more authorized users. The plurality of fields each has a value set by the first data provider. The data manger determines one or more tags for the one or more records in accordance with a plurality of autotagging rules. The data manger appends the one or more records to include one or more tag fields corresponding to the one or more tags for the one or more records. The appended records are stored in a database. One or more automated actions may be triggered based on one or more tag fields of one or more of the plurality of records.

Data management device, communication system and methods for tagging data in a data table and triggering automated actions

The disclosed exemplary embodiments include computer-implemented systems, apparatuses, devices, and processes that, among other things, dynamically provision exchanges of data based on detected relationships within processed image data. For example, a network-connected apparatus may receive, from a device, image data that identifies a plurality of individuals associated with an exchange of data. Based on an analysis of the image data, the apparatus may determine a value of a first characteristic associated with each of the individuals and generate relationship data characterizing a relationship between the individuals. The apparatus may also determine candidate values of parameters that characterize the data exchange based on portions of the first characteristic values and the relationship data, transmit the candidate parameter values to the device. An application program executed by the device may cause the device to present at least a portion of the candidate parameter values within a digital interface.

Dynamic provisioning of data exchanges based on detected relationships within processed image data

The present disclosure involves systems, software, and computer implemented methods for personalizing interactions within a conversational interface based on an input context. One example system performs operations including receiving a conversational input received via a conversational interface. The conversational input is analyzed to determine an intent and lexical personality score based on the input's characteristics. A set of responsive content is determined and includes a set of initial tokens representing an initial response. A set of synonym tokens associated with at least some of the initial tokens are identified, and at least one synonym token associated with a similar lexical personality score to the input is determined. At least one of the initial tokens are replaced with the determined synonym token to generate a modified version of the set of response content. The modified version of the response is then transmitted to a device in response to the input.

Conversational interface determining lexical personality score for response generation with synonym replacement

The present disclosure involves systems, software, and computer implemented methods for personalizing interactions within a conversational interface based on an input context. One example system performs operations including receiving a conversational input via a conversational interface associated with a particular user profile. The input is analyzed via a natural language processing engine to determine an intent and a personality input type. A persona response type associated with the determined personality input type is identified, and responsive content is determined. A particular persona associated with the particular user profile based on a related set of social network activity information associated with the user profile and that corresponds to the identified persona response type is identified. The responsive content is modified by applying persona-related content associated with the identified particular persona to generate a persona-associated response, and the persona-associated response is transmitted to a device associated with the particular user profile.

Persona-based conversational interface personalization using social network preferences

An energy generating device having a drum and at least one vane that is movable between a deployed state and a stowed state as the drum rotates to capture energy potential from flowing water and converting it to usable electrical and/or mechanical energy. The movable vanes can automatically retract when not in the flow of water and re-deploy when entering the flow of water over the drum. The present system can generate usable electricity or mechanical energy from slow but steadily flowing bodies of water without the need to dam, restrict, or alter the path of the water flow.

Method and apparatus for generating electricity

The present disclosure involves systems, software, and computer implemented methods for identifying locations associated with particular users in response to requests from a visitor or fellow user. One example system includes a communications module, a plurality of sensors located in a defined area configured to at least partially determine locations of at least one person, and a location database including location information received from the sensors associating particular persons with particular locations associated with particular sensors. A request for a location associated with a particular person is received from a first device. A current location associated with one of the plurality of sensors is determined from the location database, wherein the current location is also associated with the particular person. A response to request is generated including the current location and is transmitted to the first device.

Voice assistance direction

A data management device, communication system and methods for tagging data in a data table and triggering automated action. A data management device receives from a first data provider one or more records of a data table, each record comprising a plurality of fields. The data table is associated with an account having one or more authorized users. The plurality of fields each has a value set by the first data provider. The data manger determines one or more tags for the one or more records in accordance with a plurality of autotagging rules. The autotagging rules include heuristic rules learned from user behavior and based on one or more of the plurality of fields of the record. The data manger appends the one or more records to include one or more tag fields corresponding to the one or more tags for the one or more records. The appended records are stored in a database. One or more automated actions may be triggered based on one or more tag fields of one or more of the plurality of records.

Growing community of inventors

Toronto, Canada

Paige Elyse Dickie