Morgan Stanley has filed a patent for a system and method to predict future events based on prior occurrences. The system groups a time series dataset by time and date fields, determines factors based on the occurrences, and uses these factors to predict future events for a particular user in real-time. The predicted events are then displayed on a screen. GlobalData’s report on Morgan Stanley gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Morgan Stanley, retail trading platforms was a key innovation area identified from patents. Morgan Stanley's grant share as of September 2023 was 91%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting future events based on prior occurrences
A recently filed patent (Publication Number: US20230267520A1) describes a method for predicting future events for a specific user based on their prior occurrences. The method involves receiving real-time timeseries data of the user's prior occurrences, grouping them by time or date fields, and determining the span between each occurrence and the most recent occurrence with the same field. The method also involves determining a set of factors for each field, based on the number of occurrences and the respective span. These factors are used to predict one or more desired future events for the user, which are then outputted in real-time to a display.
The patent also mentions that the method can be applied to various fields, including transactions. In the case of prior transactions, the method involves grouping them by date and tagging them with the most recent occurrence of the same merchant. Factors for each merchant are then determined, including the number of transactions, average and variance of transaction amounts, average day span between transactions, and variance in the day of the week or month the transactions occurred. Based on these factors, the method predicts a projected repeat transaction amount, transaction interval, or both for each merchant.
The patent further describes a non-transitory computer program product that includes instructions for executing the method. The program receives real-time timeseries data of prior occurrences, groups them by time or date fields, determines the span between each occurrence and the most recent occurrence with the same field, and determines a set of factors for each field. These factors are then used to predict desired future events for the user, which are outputted in real-time to a display.
Overall, this patent presents a method for real-time prediction of future events based on a user's prior occurrences. It offers a way to group and analyze data, determine relevant factors, and make predictions for specific users. The method can be applied to various fields, including transactions, and has the potential to provide valuable insights and predictions for users.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
