EP-4736077-A1 - MODELING A USER PREDISPOSITION BASED ON LOCATION DATA

Abstract

A method and system for modeling a user predisposition based on location data are provided. The method involves training at least one model to determine a user's predisposition for a particular behavior using location data for multiple users, inferring activity data from location data, and translating activity data and location data into time- varying and static behavior attributes for each user. The trained model is then used to predict predisposition for a particular behavior for an individual user by obtaining their location data, inputting it into the trained model, and receiving an assigned quantified predisposition for the particular behavior. The method can be applied to various behaviors such as purchase intent, hospitalization risk, job participation, job change, travel intent, residential relocation intent, and healthcare risk.

Inventors

• MACHA, MEGHANATH
• LI, BEIBEI
• HASHIM, Naseer

Assignees

• ZeroToOne.AI Inc.

Dates

Publication Date

20260506

Application Date

20240515

Claims (20)

1. CLAIMS What is claimed is: 1. A method of modeling a user predisposition based on location data, the method comprising: I. training at least one model to determine a user’s predisposition for a particular behavior or experience/condition, the training comprising: A) obtaining location data for multiple users; B) formatting location data into one or more trajectories for each user of multiple users; C) inferring activity data from location data; D) translating activity data and location data into time-varying and static behavior attributes for each user of the multiple users; and E) determining predisposition for a particular behavior or experience/condition comprising: for each user: modeling time-varying attributes; combining modeled time-varying attributes with static attributes; assigning a quantified predisposition for a particular behavior or experience/condition; and adjusting parameters based on results resulting in a trained model; and II. predicting predisposition for a particular behavior or experience/condition for an individual user comprising: A) obtaining location data for the individual user; B) inputting the location data into the at least one trained model produced by the training of at least one model; and C) receiving an assigned quantified predisposition for a particular behavior or experience/condition for the individual user from the trained model.
2. 2. The method claim 1, wherein the predisposition for a particular behavior or experience/condition is purchase intent. 4868-3590-9304, v.1
3. 3. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is hospitalization risk.
4. 4. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is job participation.
5. 5. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is job change.
6. 6. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is travel intent.
7. 7. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is residential relocation intent.
8. 8. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is healthcare risk.
9. 9. The method of claim 1, wherein the predisposition for a particular behavior or experience/condition is associated with an opportunity window.
10. 10. The method of claim 1, wherein the location data is geospatial data for a user hardware device over a period of time.
11. 11. The method of claim 1, wherein the location data for a user device is provided by a location provider/vendor.
12. 12. The method of claim 1, wherein the location data for a user is identified without using one or more of: personal identifying information (PII), demographic information, and socioeconomic information about the user. 4868-3590-9304, v.1
13. 13. The method of claim 1, wherein inferring activity data from location data further comprises: i) mapping location data to place types; ii) grouping place types into activity groups based on a function of the place type; and iii) transforming the one or more trajectories for the user into one or more activity- trajectories for the user using the activity groups.
14. 14. The method of claim 13, wherein the activity groups comprise one or more selected from the group comprising: hospital, health, necessity shopping, fitness, public transport, own transport, religious, recreation, travel, personal care, leisure shopping, unhealthy activities, restaurant, home, and work.
15. 15. The method of claim 1, wherein translating activity data and location data into behavior attributes for each user of the multiple users comprises: defining time-varying attributes; and defining static attributes.
16. 16. The method of claim 15, wherein defining time-varying attributes comprises: determining a lifestyle attribute; determining activity attributes; and determining mobility attributes.
17. 17. The method of claim 16, wherein determining a lifestyle attribute comprises using an unsupervised learning model that can identify similarities in activity patterns among users.
18. 18. The method of claim 17, wherein the unsupervised learning model comprises a clustering and dimension reduction model.
19. 19. The method of claim 17, wherein the unsupervised learning model comprises a Hidden Markov model.
20. 20. The method of claim 17, wherein the unsupervised learning model comprises an LDA and topic model. 4868-3590-9304, v.1

Description

PATENT APPLICATION FOR MODELING A USER PREDISPOSITION BASED ON LOCATION DATA BY MEGHANATH MACHA BEIBEI LI NASEER HASHIM CROSS-REFERENCE TO RELATED APPLICATION(S) [0001] This application claims priority to and the benefit of co-pending United States Provisional Application 63/523,874 filed June 28, 2023, for all subject matter common to both applications. The disclosure of said provisional application is hereby incorporated by reference in its entirety. FIELD OF THE INVENTION [0002] The present invention relates to the field of data analysis and machine learning, suitable for behavior modeling. In particular, the present invention relates to modeling user predispositions based on location data. BACKGROUND [0003] In recent years, the ubiquity of mobile devices and the rapid development of location-based services have led to exponential growth in the availability of location data. This data, which includes information about users' geographical positions and movements, has opened new possibilities for understanding and predicting human behavior. One area of interest is the modeling of user predispositions based on location data, which can provide valuable insights into users' preferences, habits, and potential future actions or experiences. [0004] Traditional methods of modeling user predispositions have often relied on self- reported data, such as surveys and questionnaires. However, these methods can be subject to various biases and inaccuracies, as users may not accurately recall or report their past 4868-3590-9304, v.1 behaviors and experiences. Moreover, self-reported data may not capture the full range of factors that influence users’ predispositions, such as environmental and contextual factors that can be derived from location data. [0005] Machine learning techniques have been increasingly employed to model user predispositions based on location data. These techniques can automatically learn patterns and relationships in the data, enabling the development of more accurate and robust models. However, the process of training machine learning models can be complex and computationally intensive, requiring the careful selection and tuning of model parameters to achieve optimal performance. Furthermore, current approaches can result in an inflexible model that makes it difficult to repurpose or adapt the model to other uses. [0006] Furthermore, the integration of time-varying and static behavior attributes in the modeling process can be challenging. Time-varying attributes, such as the frequency and duration of visits to specific locations, can change over time and may be influenced by various factors, such as users' schedules and preferences. Static attributes, on the other hand, represent more stable characteristics of users, such as their demographic information or home location. Combining these different types of attributes in a meaningful and effective way is crucial for developing accurate models of user predispositions. SUMMARY [0007] In light of these challenges, there is a need for a system to determine user predispositions based on location data that can effectively integrate time-varying and static behavior attributes, while also leveraging the power of machine learning techniques to automatically learn patterns and relationships in the data. The present invention enables the development of more accurate and robust systems to predict users' predispositions for particular behaviors or experiences/conditions with greater precision and reliability while maintaining the ability to adapt or repurpose the model to predict different behaviors, experiences, or behaviors as needed in a far more efficient manner. [0008] The present invention addresses this need by providing a method of determining user predispositions based on location data, which includes the steps of training at least one model to determine a user's predisposition for a particular behavior or experience/condition, and predicting predisposition for a particular behavior or experience/condition for an individual user. The method involves obtaining location data for multiple users, formatting 4868-3590-9304, v.1 the location data into trajectories, inferring activity data from the location data, translating the activity data and location data into time-varying and static behavior attributes, and determining predispositions for particular behaviors or experiences/conditions by modeling time-varying attributes, combining modeled time-varying attributes with static attributes, and assigning quantified predispositions. The method also includes adjusting model parameters based on results, resulting in a trained model, and using the trained model to predict predispositions for individual users based on their location data. [0009] Location data can be collected from various sources, such as GPS-enabled devices, Wi-Fi access points, and cell towers. This data can be used to create trajectories, which are sequences of geographical positions and ti