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Canal Winchester, OH, United States of America

Ernst Wilhelm Spannhake, Ii

Average Co-Inventor Count = 3.27

ph-index = 2

The patent ph-index is calculated by counting the number of publications for which an author has been cited by other authors at least that same number of times.

Forward Citations = 6

Ernst Wilhelm Spannhake, IiMilan Shah (6 patents)Ernst Wilhelm Spannhake, IiThomas Francis Gianelle (6 patents)Ernst Wilhelm Spannhake, IiGirish Wali (1 patent)Ernst Wilhelm Spannhake, IiDeepali Tuteja (1 patent)Ernst Wilhelm Spannhake, IiPrasanth Babu Madakasira Ramakrishna (1 patent)Ernst Wilhelm Spannhake, IiErnst Wilhelm Spannhake, Ii (6 patents)Milan ShahMilan Shah (6 patents)Thomas Francis GianelleThomas Francis Gianelle (6 patents)Girish WaliGirish Wali (6 patents)Deepali TutejaDeepali Tuteja (6 patents)Prasanth Babu Madakasira RamakrishnaPrasanth Babu Madakasira Ramakrishna (4 patents)
..
Inventor’s number of patents
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Strength of working relationships

Company Filing History:

1. Citibank, N.a. (4 from 422 patents)

2. Citigroup Technology, Inc. (2 from 43 patents)


6 patents:

1. 12333395 - Systems and methods for cohort-based predictions in clustered time-series data in order to detect significant rate-of-change events

2. 12164525 - Systems and methods for aggregating time-series data streams based on potential state characteristics following aggregation

3. 12165035 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogonous time-series data

4. 11948065 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogeneous time-series data

5. 11868860 - Systems and methods for cohort-based predictions in clustered time-series data in order to detect significant rate-of-change events

6. 11704540 - Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogenous time series-data

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