Verifying sensor data using embeddings

• US 10,111,043 B1
• Filed: 04/24/2017
• Issued: 10/23/2018
• Est. Priority Date: 04/24/2017
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Cirit, Fahrettin Olcay
• Primary Examiner(s): Bataille, Frantz
• Application Number:
  US15/495,686
• Publication Number:
  US 20180310135A1
• Time in Patent Office: 547 Days
• Field of Search: None
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/063114 Status monitoring or status determination for a person or group
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 30/018 Certifying business or products
• CPC Class Codes: G06Q 50/28 Logistics, e.g. warehousing, loading, distribution or shipping
• CPC Class Codes: G06Q 50/30 Transportation; Communications
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G08G 1/205 Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

A network system analyzes data samples using embeddings based on, for example, symbolic representations of the data samples or representations in latent dimension space. The network system coordinates providers who provide geographical location-based services to users. The network system may receive data samples from the client device of a provider. For instance, a sensor of the client device captures the data samples during a transportation service along a particular route. To verify that the data samples accurately indicate the location or movement of the provider, the network system can generate a test embedding representing the data samples and compare the test embedding with a reference embedding. The reference embedding is generated based on data samples captured for other similar services, e.g., corresponding to providers who also provided transportation services along the same particular route.

Claims

1. A method for verifying a trip for a user and a trip provider, the method comprising:

receiving, from a client device, data associated with sensor information of the client device and associated with a trip record, the trip record being associated with the trip for the user and the trip provider, the data including (i) a data sample for a set of sensors of the client device and (ii) one or more characteristics of the data sample;

generating a test embedding for the data sample, the test embedding using a plurality of latent dimensions that represent at least a portion of the data sample;

identifying a reference embedding for a set of reference characteristics, the set of reference characteristics corresponding to at least one of the one or more characteristics of the data sample, the reference embedding being based on a set of embeddings each using the plurality of latent dimensions that represent sensor data for a set of trip records different than the trip record, the set of trip records being associated with the set of reference characteristics;

determining a similarity score between the test embedding corresponding to the trip record and the reference embedding by comparing each latent dimension of the test embedding and a corresponding latent dimension of the reference embedding;

verifying, in response to the similarity score exceeding a threshold score, that the data sample was captured while the set of sensors were subject to the set of reference characteristics; and

in response to the verification that the data sample was captured while the set of sensors were subject to the set of reference characteristics, determining that the trip provider and the user traveled together along at least a portion of a route of the trip.

2. The method of claim 1, wherein the test embedding and the reference embedding are generated using a model trained based at least in part on feature vectors derived from data samples captured for the set of trip records.

3. The method of claim 1, wherein the portion of the data sample has a first duration in time, and wherein generating the test embedding comprises:

generating a plurality of embeddings for a plurality of sub-portions of the portion of the data sample, each of the sub-portions having a second duration in time less than the first duration in time; and

aggregating the plurality of embeddings.

4. The method of claim 1, wherein the user is associated with the client device and the trip record, wherein the set of trip records includes at least a sample trip taken by a second user, and wherein the set of embeddings includes a sample embedding representing sample sensor data captured for the sample trip by another set of sensors of another client device of the second user.

5. The method of claim 4, wherein the sample trip includes a plurality of routes, and further comprising:

determining a route of the plurality of routes traveled by both the user and the second user based at least in part on the similarity score.

6. The method of claim 1, wherein the set of trip records includes at least the trip record, and wherein the set of embeddings includes a sample embedding representing sample sensor data captured for the trip record by another sensor of the client device not included in the set of sensors.

7. The method of claim 1, wherein the one or more characteristics of the data sample includes at least one of:

an origin or destination location of the trip record, a route of the trip record, a type of the client device, or the user.

8. The method of claim 1, further comprising:

determining that the one or more characteristics of the data sample describe a geophysical event;

determining, for the trip record, a likelihood score that the geophysical event occurred based at least in part on the similarity score, and wherein the set of reference characteristics is associated with the geophysical event.

9. The method of claim 1, wherein the client device is transported in a vehicle, and wherein the method further comprises:

determining that the one or more characteristics of the data sample describe a safety incident;

determining that the vehicle was involved in the safety incident based at least in part on the similarity score.

10. A method for verifying a trip for a user and a trip provider, the method comprising:

receiving, from a client device of the user, data associated with sensor information of the client device and associated with a trip record, the trip record being associated with the trip for the user and the trip provider, the data including (i) a data sample for a set of sensors of the client device and (ii) one or more characteristics of the data sample;

generating a test embedding for the data sample, the test embedding using a plurality of latent dimensions that represent at least a portion of the data sample;

identifying a reference embedding for a set of reference characteristics, the set of reference characteristics corresponding to at least one of the one or more characteristics of the data sample, the reference embedding being based on a set of embeddings each using the plurality of latent dimensions that represent sensor data for a set of trip records different than the trip record, the set of trip records being associated with the set of reference characteristics, the reference embedding generated using a model trained based at least in part on feature vectors derived from data samples captured for the set of trip records;

determining a similarity score between the test embedding corresponding to the trip record and the reference embedding by comparing each latent dimension of the test embedding and a corresponding latent dimension of the reference embedding;

verifying that the similarity score exceeds a threshold score; and

in response to the verification that the similarity score exceeds the threshold score, determining that the user and the trip provider traveled together along at least a portion of a route of the trip using the data sample.

11. The method of claim 10, further comprising:

determining a different route along which only the user or the trip provider traveled for the trip.

12. A computer program product comprising a non-transitory computer readable storage medium having instructions, for verifying a trip for a user and a trip provider, encoded thereon that, when executed by one or more processors, cause the one or more processors to:

receive, from a client device, data associated with sensor information of the client device and associated with a trip record, the trio record being associated with the trip for the user and the trip provider, the data including (i) a data sample for a set of sensors of the client device and (ii) one or more characteristics of the data sample;

generate a test embedding for the data sample, the test embedding using a plurality of latent dimensions that represent at least a portion of the data sample;

identify a reference embedding for a set of reference characteristics, the set of reference characteristics corresponding to at least one of the one or more characteristics of the data sample, the reference embedding being based on a set of embeddings each using the plurality of latent dimensions that represent sensor data for a set of trip records different than the trip record, the set of trip records being associated with the set of reference characteristics;

determine a similarity score between the test embedding corresponding to the trip record and the reference embedding by comparing each latent dimension of the test embedding and a corresponding latent dimension of the reference embedding;

verify, in response to the similarity score exceeding a threshold score, that the data sample was captured while the set of sensors were subject to the set of reference characteristics; and

in response to the verification that the data sample was captured while the set of sensors were subject to the set of reference characteristics, determine that the trip provider and the user traveled together along at least a portion of a route of the trip.

13. The non-transitory computer readable storage medium of claim 12, wherein the test embedding and the reference embedding are generated using a model trained based at least in part on feature vectors derived from data samples captured for the set of trip records.

14. The non-transitory computer readable storage medium of claim 12, wherein the portion of the data sample has a first duration in time, and wherein generating the test embedding comprises:

generating a plurality of embeddings for a plurality of sub-portions of the portion of the data sample, each of the sub-portions having a second duration in time less than the first duration in time; and

aggregating the plurality of embeddings.

15. The non-transitory computer readable storage medium of claim 12, wherein the user is associated with the client device and the trip record, wherein the set of trip records includes at least a sample trip taken by a second user, and wherein the set of embeddings includes a sample embedding representing sample sensor data captured for the sample trip by another set of sensors of another client device of the second user.

16. The non-transitory computer readable storage medium of claim 15, wherein the sample trip includes a plurality of routes, and having further instructions that when executed by the one or more processors cause the one or more processors to:

determining a route of the plurality of routes traveled by both the user and the second user based at least in part on the similarity score.

17. The non-transitory computer readable storage medium of claim 12, wherein the set of trip records includes at least the trip record, and wherein the set of embeddings includes a sample embedding representing sample sensor data captured for the trip record by another sensor of the client device not included in the set of sensors.

18. The non-transitory computer readable storage medium of claim 12, wherein the one or more characteristics of the data sample includes at least one of:

an origin or destination location of the trip record, a route of the trip record, a type of the client device, or the user.

19. The non-transitory computer readable storage medium of claim 12, having further instructions that when executed by the one or more processors cause the one or more processors to:

determine that the one or more characteristics of the data sample describe a geophysical event;

determine, for the trip record, a likelihood score that the geophysical event occurred based at least in part on the similarity score, and wherein the set of reference characteristics is associated with the geophysical event.

20. The non-transitory computer readable storage medium of claim 12, having further instructions that when executed by the one or more processors cause the one or more processors to:

determine that the one or more characteristics of the data sample describe a safety incident;

determine that the vehicle was involved in the safety incident based at least in part on the similarity score.