Method and/or system for walking route recommendation

• US 9,803,988 B2
• Filed: 12/30/2014
• Issued: 10/31/2017
• Est. Priority Date: 12/30/2014
• Status: Expired due to Fees

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Inventors: Barbieri, Nicola, Quercia, Daniele
• Primary Examiner(s): OH, HARRY Y
• Application Number:
  US14/586,540
• Publication Number:
  US 20160187151A1
• Time in Patent Office: 1,036 Days
• Field of Search: 701540
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/3446 Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
• CPC Class Codes: G01C 21/3679 Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities G01C21/3611 takes precedence

Abstract

Methods and/or systems are provided that may be utilized to recommend a contextual walking route for a number of venues, for example.

Claims

1. A method of executing computer instructions on at least one computing device in which the at least one computing device includes at least one processor and at least one memory, the method for determining a walking route for a selection of venues in a navigation system comprising:

fetching the computer instructions from the at least one memory for execution on the at least one processor;

executing the computer instructions on the at least one processor of the last one computing device without human intervention;

storing in the at least one memory of the at least one computing device any results to be generated from the execution on the at least one processor of the to be executed computer instructions;

wherein the computer instructions to be executed comprises instructions for determining the walking route for the selection of venues;

wherein the executing the walking route determination instructions comprise;

generating a segment graph of the venues, the segment graph comprising a plurality of segments, wherein the plurality of segments of the segment graph have an associated weight; and

estimating the associated weight of the plurality of segments of the segment graph based at least in part on distance of the plurality of segments, number of turns between the plurality of segments and predicted usage of venues on the plurality of segments, the predicted usage being based at least in part on visits by individuals.

2. The method of claim 1, wherein estimating the predicted usage of venues on the plurality of segments is based at least in part on given contextual factors comprising time of day, whether a day is a weekday or weekend, or weather conditions.

3. The method of claim 2, wherein the contextual factors are determined by one or more factorization machine techniques, and the one or more factorization machine techniques include at least one of stochastic gradient descent, alternating least-squares, and/or Markov Chain Monte Carlo inference.

4. The method of claim 1, wherein generating the segment graph is based at least in part on angular segment processing techniques.

5. The method of claim 1, wherein estimating the predicted usage of venues is based at least in part on;

usage of venues on other segments adjacent to the plurality of segments with the venues; and

/or usage of venues on other segments crossing the plurality of segments with the venues.

6. The method of claim 1, further comprising estimating an associated weight of the plurality of segments of the segment graph based at least in part on angular change between the plurality of segments of the segment graph.

7. The method of claim 1, wherein estimating the distance of the plurality of segments on the walking route further comprises weighting the distance of the plurality of segments based at least in part on a popularity of venues located on the plurality of segments.

8. The method of claim 1, further comprising generating a contextual route recommendation based at least in part on values of the associated weight of the plurality of segments of the segment graph.

9. A system comprising:

at least one computing device;

the at least one computing device including at least one processor and at least one memory;

the at least one computing device to execute computer instructions without further human intervention;

the computer instructions to be executed having been fetched from the at least one memory for execution on the at least one processor; and

the at least one computing device to store in the at least one memory of the at least one computing device any results to be generated from the execution on the least one processor of the to be executed computer instructions;

wherein the computer instructions to be executed comprise instructions for execution of determination of a walking route for selected venues in a navigation system;

wherein walking route determination instructions to;

generate a segment graph of venues, the segment graph to comprise a plurality of segments, wherein segments of the segment graph to have an associated weight; and

estimate the associated weight of the plurality of segments of the segment graph, wherein the associated weight of the the plurality of segments to be based at least in part on distance of the plurality of segments, number of turns between the plurality of segments and predicted usage of venues on the plurality of segments, the predicted usage to be based at least in part on visits by individuals.

10. The system of claim 9, wherein the predicted usage of venues on the plurality of segments is to be based at least in part on given contextual factors, the contextual factors to comprise time of day, whether a day is a weekday or weekend, or weather conditions.

11. The system of claim 10, wherein the contextual factors are to be determined by one or more factorization machine techniques, and the one or more factorization machine techniques to include at least one of stochastic gradient descent, alternating least-squares, and/or Markov Chain Monte Carlo inference.

12. The system of claim 9, wherein the segment graph is to be based at least in part on angular processing techniques.

13. The system of claim 9, wherein the predicted usage of venues is to be based at least in part on usage of venues on other segments adjacent to the plurality of segments with the venues;

and/or usage of venues on other segments crossing the plurality of segments with the venues.

14. The system of claim 9, wherein the walking route determination instructions to:

estimate an associated weight of the plurality of segments of the segment graph to be based at least in part on angular change between the plurality of segments on the segment graph.

15. The system of claim 9, wherein the walking route determination instructions to:

weigh distance of the plurality of segments, based at least in part on, popularity of venues located on the plurality of segments.

16. An apparatus comprising:

means for fetching from at least one memory means computer instructions;

means for executing fetched computer instructions without further intervention;

means for storing in the at least one memory any results generated from the execution of computer instructions;

wherein the computer instructions comprise instructions for execution of determination of a walking route for selected venues in a navigation system;

further comprising;

means for generating a segment graph of the venues, the segment graph comprising a plurality of segments, wherein the plurality of segments of the segment graph have an associated weight; and

means for estimating the associated weight of the plurality of segments of the segment graph based at least in part on distance of the plurality of segments, number of turns between the plurality of segments and predicted usage of venues on the plurality of segments, the predicted usage being based at least in part on visits by individuals.

17. The apparatus of claim 16, wherein the means for estimating the predicted usage of venues on the plurality of segments comprises:

means for estimating usage of the venues on other segments adjacent to the plurality of segments with the venues; and

/or means for estimating usage of the venues on other segments crossing the plurality of segments with the venues.

18. The apparatus of claim 16, further comprising means for estimating an associated weight of the plurality of segments of the segment graph based at least in part on angular change between the plurality of segments of the segment graph.

19. The apparatus of claim 16, wherein the means for generating a segment graph comprises means for angular segment processing.

20. The apparatus of claim 16, wherein the means for estimating the associated weight of the plurality of segments being based at least in part on popularity of venues located on the plurality of segments.