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1. 发明授权

US11562453B2 Systems and methods for determining the impact of issue outcomes 有权
公开(公告)号：US11562453B2
公开(公告)日：2023-01-24
申请号：US16221410
申请日：2018-12-14
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Daniel Argyle
IPC分类号： G06Q50/26 , G06Q10/06 , G06F16/28 , G06F16/33 , G06F40/30 , G06F40/205 , G06F40/263 , G06N5/02 , G06Q50/18 , G06F3/0482 , G06F3/04847 , G06N5/04 , G06N20/00 , G06F16/95 , G06T11/20
摘要： A system for predicting and prescribing actions for impacting policymaking outcomes may include at least one processor configured to access first information scraped from the Internet to identify, for a particular pending policy, information about a plurality of policymakers slated to make a determination on the pending policy. The processor may parse the scraped first information to determine an initial prediction relating to an outcome of the pending policy. The processor may access second information to identify an action likely to change at least one of the initial prediction and the propensity of at least one policymaker, to thereby generate a subsequent prediction corresponding to an increase in a likelihood of achieving the desired outcome. The processor may display to the system user a recommendation to take the action in order to increase the likelihood of achieving the desired outcome.

2. 发明授权

US11316808B1 Use of machine-learning models in creating messages for advocacy campaigns 有权
公开(公告)号：US11316808B1
公开(公告)日：2022-04-26
申请号：US17453647
申请日：2021-11-04
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Daniel Argyle , Paul Matthew Ellender, Jr. , Anastassia Kornilova
IPC分类号： G06N20/20 , G06F40/40 , H04L51/02 , H04L51/52 , H04L51/18
摘要： An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include or use in creating messages for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes.

3. 发明申请

US20170308985A1 Systems and Methods for Correlating Comments and Sentiment to Policy Document Sub-Sections 审中-公开
公开(公告)号：US20170308985A1
公开(公告)日：2017-10-26
申请号：US15494371
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Brian Grom , Vladimir Eidelman , Daniel Argyle , Jervis Pinto , Manuela Rios
IPC分类号： G06Q50/26 , G06Q10/06 , G06F17/27
摘要： A text analytics system may ascertain sentiment about multi-sectioned documents and may associate the sentiment with particular sections. The system may include at least one processor configured to scrape the Internet for text data associated with comments expressed by a plurality of individuals about a common multi-sectioned document. The comments may not be not linked to a particular section. The at least one processor may be further configured to analyze the text data in order to determine a sentiment associated with each comment; apply an association analysis filter to the text data in order to correlate at least a portion of each comment with one or more sections of the multi-sectioned document; and transmit for display to the system user a visualization of the sentiment mapped to one or more sections of the multi-sectioned document.

4. 发明申请

US20170308984A1 SYSTEMS AND METHODS FOR STEERING AN AGENDA BASED ON USER COLLABORATION 审中-公开
公开(公告)号：US20170308984A1
公开(公告)日：2017-10-26
申请号：US15494300
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Brian Grom , Daniel Argyle , Jervis Pinto
IPC分类号： G06Q50/26 , G06Q10/06
摘要： A collaborative prediction system for altering predictive outcomes of dynamic processes may include a processor configured to store initial information about a specific dynamic process having a plurality of potentially differing outcomes, assign to the specific dynamic process a first likelihood of occurrence of at least one of the potentially differing outcomes, and receive, from a first system user, notification data, the notification data being associated with the specific dynamic process. The processor may be configured to transmit, based on the notification data, at least some of the stored initial information about the specific dynamic process to the second system user and the third system user, receive from the second system user, first additional information responsive to the at least some of the stored initial information about the specific dynamic process and impacting the specific dynamic process, and generate, based on the first additional information and the initial information, a second likelihood different from the first likelihood. The processor may further be configured to transmit to the first system user, the second system user, and the third user an indication of the second likelihood, receive from the third system user, second additional information impacting the specific dynamic process, generate, based at least in part on the second additional information, a third likelihood different from the first likelihood and the second likelihood, and transmit to the first system user, the second system user, and the third system user an indication of the third likelihood.

5. 发明授权

US10692163B2 Systems and methods for steering an agenda based on user collaboration 有权
公开(公告)号：US10692163B2
公开(公告)日：2020-06-23
申请号：US15494300
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Brian Grom , Daniel Argyle , Jervis Pinto
IPC分类号： G06Q30/00 , G06Q50/26 , G06Q10/06 , G06F16/28 , G06F16/33 , G06F40/30 , G06F40/205 , G06F40/263 , G06N5/02 , G06Q50/18 , G06F3/0482 , G06F3/0484 , G06N5/04 , G06N20/00 , G06T11/20
摘要： A collaborative prediction system for altering predictive outcomes of dynamic processes may include a processor configured to store initial information about a specific dynamic process having a plurality of potentially differing outcomes, assign to the specific dynamic process a first likelihood of occurrence of at least one of the potentially differing outcomes, and receive, from a first system user, notification data, the notification data being associated with the specific dynamic process. The processor may be configured to transmit, based on the notification data, at least some of the stored initial information about the specific dynamic process to the second system user and the third system user, receive from the second system user, first additional information responsive to the at least some of the stored initial information about the specific dynamic process and impacting the specific dynamic process, and generate, based on the first additional information and the initial information, a second likelihood different from the first likelihood. The processor may further be configured to transmit to the first system user, the second system user, and the third user an indication of the second likelihood, receive from the third system user, second additional information impacting the specific dynamic process, generate, based at least in part on the second additional information, a third likelihood different from the first likelihood and the second likelihood, and transmit to the first system user, the second system user, and the third system user an indication of the third likelihood.

6. 发明授权

US10672092B2 Systems and methods for mapping to milestones in a policymaking process 审中-公开
公开(公告)号：US10672092B2
公开(公告)日：2020-06-02
申请号：US15494393
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Chris Simpson , Daryl Hok , John Zoshak , Anthony DeStefano , Jervis Pinto , Vladimir Eidelman
IPC分类号： G06Q50/26 , G06F16/00 , G06Q10/06 , G06F16/28 , G06F16/33 , G06F40/30 , G06F40/205 , G06F40/263 , G06N5/02 , G06Q50/18 , G06F3/0482 , G06F3/0484 , G06N5/04 , G06N20/00 , G06T11/20
摘要： A system for normalizing aggregated electronic data may include at least one processor and a memory. The processor may access data scraped from a plurality of local databases, each storing localized terms for temporal local milestones; for example, a first localized term for a first temporal milestone in a first locale may differ from a second localized term for a similar second temporal milestone in a second locale. The processor may store the temporal local milestones in a central database, determine a mapping of each of the localized terms for each temporal local milestone to a normalized term for each milestone, and identify, in a displayed timeline associated with the first locale, each associated temporal local milestone using the normalized term for each milestone. The processor may further identify, in a displayed timeline associated with the second locale, each associated temporal local milestone using the normalized term for each milestone.

7. 发明申请

US20170308976A1 SYSTEMS AND METHODS FOR PREDICTING FUTURE EVENT OUTCOMES BASED ON DATA ANALYSIS 审中-公开
公开(公告)号：US20170308976A1
公开(公告)日：2017-10-26
申请号：US15494390
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Brian Grom , Daniel Argyle , Jervis Pinto
IPC分类号： G06Q50/18 , G06N5/02
CPC分类号： G06Q50/26 , G06F3/0482 , G06F3/04847 , G06F16/288 , G06F16/3331 , G06F17/2705 , G06F17/275 , G06F17/2785 , G06N5/022 , G06N5/04 , G06N20/00 , G06Q10/06375 , G06Q50/18 , G06Q2230/00 , G06T11/206 , G06T2200/24
摘要： A hybrid prediction system may aggregate electronic data to identify and initially predict an outcome of a future event and subsequently update the initial prediction. The system may include at least one processor and a memory. The processor may access data scraped from the Internet. The data may be associated with at least one future event. The processor may further store the scraped data, determine, from the scraped data, an initial prediction of the outcome of the at least one future event, generate, from the scraped data, an initial likelihood indication associated with the initial prediction, and transmit the initial prediction and the initial likelihood indication to a device associated with one or more users. The processor may further receive proprietary information, store the proprietary information, determine, using the scraped data and the proprietary information, a subsequent likelihood indication, and transmit the subsequent likelihood indication to the device.

8. 发明申请

US20170308795A1 SYSTEMS AND METHODS FOR PROVIDING A VIRTUAL WHIPBOARD 审中-公开
公开(公告)号：US20170308795A1
公开(公告)日：2017-10-26
申请号：US15494346
申请日：2017-04-21
申请人： FiscalNote, Inc.
发明人： Brian Grom , Daniel Argyle , John Zoshak , Vladimir Eidelman , Dan Maglasang
IPC分类号： G06N5/02 , G06Q50/26
CPC分类号： G06Q50/26 , G06F3/0482 , G06F3/04847 , G06F16/288 , G06F16/3331 , G06F17/2705 , G06F17/275 , G06F17/2785 , G06N5/022 , G06N5/04 , G06N20/00 , G06Q10/06375 , G06Q50/18 , G06Q2230/00 , G06T11/206 , G06T2200/24
摘要： A prediction system provided with an integrated communications interface may include at least one processor configured to scrape the Internet to identify a currently pending legislative bill and information about legislators slated to vote on the pending bill. The processor may parse the information to determine a tendency position for each legislator. The processor may transmit for display to a system user a virtual whipboard that groups legislators into a plurality of groups based on determined tendency positions. The processor may receive a selected one of the plurality of groups of legislators for a communication interaction based on the determined tendency position of the group and access a legislator database that includes legislative communication addresses of legislative personnel scraped from the Internet and divided into a plurality of legislative function categories and receive from the system user a selection of at least one of the plurality of legislative function categories.

9. 发明申请

US20230124041A1 Use of Machine-Learning Models in Creating Messages for Advocacy Campaigns 有权
公开(公告)号：US20230124041A1
公开(公告)日：2023-04-20
申请号：US17933836
申请日：2022-09-20
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Daniel Argyle , Paul Matthew Ellender, JR. , Megan McCoskey
IPC分类号： H04L51/02 , G06F40/40 , H04L51/18 , G06N20/20 , H04L51/52
摘要： An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

10. 发明申请

US20220191155A1 Use of Machine-Learning Models in Creating Messages for Advocacy Campaigns 有权
公开(公告)号：US20220191155A1
公开(公告)日：2022-06-16
申请号：US17652780
申请日：2022-02-28
申请人： FiscalNote, Inc.
发明人： Vladimir Eidelman , Daniel Argyle , Paul Matthew Ellender, JR. , Anastassia Kornilova
IPC分类号： H04L51/02 , G06N20/20 , H04L51/52 , H04L51/18 , G06F40/40
摘要： An advocacy system uses trained machine learning models to create messages that are sent to advocates or policymakers to achieve desired outcomes for an organization. Desired outcomes can include, for example: an advocate sending a message to a policymaker or legislative representative advocating in favor or the organization's position on an issue; a policymaker acting or voting in favor of the organization's position on an issue; or an advocate making a financial contribution to the organization. The machine learning models can be configured to select possible message characteristics or features that the system will include/use in creating/sending messages to/for individual senders and recipients. The machine learning models can be trained based on message characteristics, personal profile characteristics of senders/recipients, and outcomes from previously sent messages. Personal profile characteristics of senders/recipients can indicate correlations between certain message characteristics and certain outcomes of sending messages.

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