Jeremy D Rishel

8543454, Sep 24, 2013

Feb 18, 2011

13/030940

Michael Ben Fleischman - Somerville MA, US
Deb Kumar Roy - Arlington MA, US
Jeremy Rishel - Maynard MA, US

Bluefin Labs, Inc. - Cambridge MA

G06Q 30/00

705 1444, 705 146, 705 1468

Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e. g. , as part of a ratings system.

8600984, Dec 3, 2013

Jul 13, 2012

13/548901

Michael Ben Fleischman - Somerville MA, US
Deb Kumar Roy - Arlington MA, US
Jeremy Rishel - Maynard MA, US
Anjali Midha - Winchester MA, US
Matthew Miller - Malden MA, US

Bluefin Labs, Inc. - Cambridge MA

G06F 17/30

707725, 707705, 707706, 707707, 707708, 707709, 707710, 707722, 709201, 709203, 709213, 709217

An affinity server estimates an affinity between two different time based media events (e. g. , TV, radio, social media content stream), between a time based media event and a specific topic, or between two different topics, where the affinity score represents an intersection between the populations of social media users who have authored social media content items regarding the two different events and/or topics. The affinity score represents an estimation of the real world affinity between the real world population of people who have an interest in both time based media events, both topics, or in a time based media event and a topic. One possible threshold for including a social media user in a population may be based on a confidence score that indicates the confidence that one or more social media content items authored by the social media user are relevant to the topic or event in question.

20040225671, Nov 11, 2004

Jun 30, 2003

10/611779

Andrew Carroll - Arlington MA, US
Jeremy Rishel - Boston MA, US

i2 Technologies US, Inc.

G06F007/00

707/101000

In one embodiment, a system is provided for executing bulk data transfers between persistent data stores in connection with an enterprise-level business workflow. A data integration server is coupled to one or more stores. Programmatic source interfaces are each associated with a source store, defined according to a source interface specification, and exposed within the server during a transfer in connection with an enterprise-level business workflow to enable the server to extract from its source store data entities for loading into any selected target stores during the transfer. Programmatic target interfaces are each associated with a target store, defined according to a target interface specification, and exposed within the server during a transfer in connection with an enterprise-level business workflow to enable the server to load into its target store data entities extracted from any selected source stores during the transfer. Each programmatic interface: (1) provides to its store an abstraction of transfer operations within the server such that custom code need not be developed in connection with its store to enable transfers between its store and any other particular stores; and (2) isolates from the server specific details associated with its store such that custom code need not be developed in connection with the server to enable transfers between its store and any other particular data stores.

20050120010, Jun 2, 2005

Nov 19, 2004

10/993405

Michael Philpott - Seymour IL, US
Eric Hiller - Waltham MA, US
R. Schrader - Ayer MA, US
Jeremy Rishel - Boston MA, US
Andrew Carroll - Arlington MA, US
Frank Azzolino - Acton MA, US

aPriori Technologies, Inc. - Concord MA

G06F017/30

707003000

In accordance with an embodiment of the invention, there is disclosed a method for determining costs within an enterprise. The method comprises determining a set of cost-drivers based on characteristics of three-dimensional design data of a manufacturable component; and providing, to a plurality of different enterprise functions, database access to cost data from any combination of: the set of cost-drivers, and a set of costs determined based on the set of cost-drivers. In another embodiment, there is disclosed a computer system for determining costs within an enterprise. The system comprises a database comprising a set of stored cost-drivers determined based on characteristics of three-dimensional design data of a manufacturable component; and a network capable of providing, to a plurality of different enterprise functions, access to cost data from any combination of: the set of cost drivers, and a set of costs determined based on the set of cost-drivers.

20110130855, Jun 2, 2011

Sep 29, 2010

12/893247

Michael L. Philpott - Seymour IL, US
Jeremy D. Rishel - Boston MA, US
Elisha Gallaudet - Arlington MA, US
Emmanuel Gerlovin - Wayland MA, US

G06F 19/00

700 99

In accordance with an embodiment of the invention, there is provided a method for determining an optimum manufacturing process routing. The method comprises electronically constructing a plurality of manufacturing process routings for a component to be manufactured. The electronically constructing for each manufacturing process routing of the plurality of manufacturing process routings comprises defining a recursive process routing tree for each manufacturing process routing, the recursive process routing tree comprising a plurality of nodes, each node of the plurality of nodes corresponding to a manufacturing process and comprising a branch node when the manufacturing process for a given node may be decomposed into a plurality of sub-processes for the given node. The method further comprises electronically determining an optimum process routing of the plurality of manufacturing process routings by evaluating each recursive process routing tree for the plurality of manufacturing process routings with reference to an electronic process routing variable to be optimized and determining the optimum process routing that minimizes the electronic process routing variable to be optimized. The electronic process routing variable to be optimized may be a cost for manufacturing the component, a production time for manufacturing the component, or a capacity used for manufacturing the component.

20130346330, Dec 26, 2013

Aug 26, 2013

13/975527

Deb Kumar Roy - Arlington MA, US
Jeremy Rishel - Maynard MA, US

BLUEFIN LABS, INC. - Cambridge MA

G06Q 50/00
G06Q 30/02

705319

Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e.g., as part of a ratings system.

20140052740, Feb 20, 2014

Oct 30, 2013

14/067589

Deb Kumar Roy - Arlington MA, US
Jeremy Rishel - Maynard MA, US
Anjali Midha - Winchester MA, US
Matthew Miller - Malden MA, US

Bluefin Labs, Inc. - Cambridge MA

G06F 17/30

707748

An affinity server estimates an affinity between two different time based media events (e.g., TV, radio, social media content stream), between a time based media event and a specific topic, or between two different topics, where the affinity score represents an intersection between the populations of social media users who have authored social media content items regarding the two different events and/or topics. The affinity score represents an estimation of the real world affinity between the real world population of people who have an interest in both time based media events, both topics, or in a time based media event and a topic. One possible threshold for including a social media user in a population may be based on a confidence score that indicates the confidence that one or more social media content items authored by the social media user are relevant to the topic or event in question.

20220335077, Oct 20, 2022

Apr 11, 2022

17/717887

- Cambridge MA, US
Deb Kumar Roy - Arlington MA, US
Jeremy Rishel - Maynard MA, US
Anjali Midha - Winchester MA, US
Matthew Miller - Malden MA, US

G06F 16/40
G06F 16/435
G06Q 50/00
G06Q 30/02

An affinity server estimates an affinity between two different time based media events (e.g., TV, radio, social media content stream), between a time based media event and a specific topic, or between two different topics, where the affinity score represents an intersection between the populations of social media users who have authored social media content items regarding the two different events and/or topics. The affinity score represents an estimation of the real world affinity between the real world population of people who have an interest in both time based media events, both topics, or in a time based media event and a topic. One possible threshold for including a social media user in a population may be based on a confidence score that indicates the confidence that one or more social media content items authored by the social media user are relevant to the topic or event in question.