Apparatus and method for distance-based option data object filtering and modification
地域：
IL
IL Chicago
摘要
An apparatus, method, and computer program product are provided to filter and modify option data objects and weighted values associated with option data objects through the application of specific rule sets based on the relative density of option data objects within a particularized area. In some example implementations, option data objects and related parameters are parsed to identify locations associated with the option data object and a weighted value, such as a weighted value generated by a predictive model. Based at least in part on the location associated with the option data object, a determined location of a user of a mobile device, and location-specific distance criteria, the weighted value associated with the option data object may be modified to reflect distance-related option election probabilities.
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This application is a continuation of U.S. application Ser. No. 16/254,201, filed Jan. 22, 2019, which is a continuation of U.S. application Ser. No. 15/840,536, filed Dec. 13, 2017, which claims priority from U.S. Provisional Application No. 62/434,157, filed Dec. 14, 2016, which is incorporated by reference herein in its entirety.
An example embodiment relates generally to systems that allow for the filtering and modification of data objects in an efficient manner. Example implementations are particularly directed to systems, methods, and apparatuses for filtering and modifying option data objects and weighted values associated therewith based on one or more automatically derived distance measurements and related particularized rule sets.
Many users of mobile devices now rely heavily on the use of such mobile devices to identify and interact with systems and other entities that are located within a geographic area surrounding the user. The inventors of the invention disclosed herein have identified a number of technical and operational challenges to be addressed in such environments, and have developed the solutions disclosed herein to overcome such challenges.
權(quán)利要求
What is claimed is:1. An apparatus for performing distance-based option data object filtering for a plurality of option data objects associated with a geographic object space, the apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to perform operations configured to:for each option data object of the plurality of option data objects, determine a respective object location identifier, wherein the object location identifier corresponds to an object geographic location in the geographic object space; determine a plurality of object regions in the geographic object space, wherein each object region of the plurality of object regions is associated with a regional location identifier; and for each object-region pair associated with a respective option data object of the plurality of option data objects and a respective object region of the plurality of object regions, determine an object-region distance intensity prediction based on the object location identifier for the option data object and the regional location identifier for the object region; for each option data object of the plurality of option data objects, determine an object density prediction based on each object-region distance intensity prediction for a related region subset of the plurality of object regions that is associated with the option data object; and perform the distance-based option data object filtering for the plurality of option data objects based on each object density prediction for the plurality of option data objects. 2. The apparatus of claim 1 , wherein determining the object density prediction for a first option data object of the plurality of option data objects comprises:for each object region of the plurality of object regions, determining a regional density prediction based on each object-region distance intensity prediction for an object-region pair associated with the object region; and determining the object density prediction for the first option data object based on each regional density prediction for the related region subset that is associated with the first option data object. 3. The apparatus of claim 1 , wherein determining the object density prediction for a first option data object of the plurality of option data objects that is associated with a first object region of the plurality of object regions comprises:determining an initial object density value for the first option data object; determining whether the regional density prediction for the first object region exceeds the regional density prediction for an anchor region of the plurality of object regions; and determining the object density prediction for the first option data object based on whether the regional density prediction for the first object region exceeds the regional density prediction for the anchor region. 4. The apparatus of claim 3 , wherein determining the object density prediction for the first object region based on whether the regional density prediction for the first object region exceeds the regional density prediction for the anchor region comprises:in response to determining that the regional density prediction for the first object region fails to exceed the regional density prediction for the anchor region, modifying the initial object density value to generate the object density prediction for the first option data object. 5. The apparatus of claim 4 , wherein modifying the initial object density value to generate the object density prediction for the first option data object comprises lowering the initial object density value by a density scaling parameter for the first option data object. 6. The apparatus of claim 5 , wherein the density scaling parameter for the first option data object is determined based on a difference between a regional density difference between the regional density prediction for the first object region and the regional density prediction for the anchor region. 7. The apparatus of claim 3 , wherein:the anchor region comprises a user triangulation location identifier associated with an anchor user profile, and the user triangulation location identifier corresponds to a user triangulation location in the geographic object space. 8. The apparatus of claim 7 , wherein the user triangulation location identifier is determined based on a predicted travel destination for an ongoing user travel of the anchor user profile. 9. The apparatus of claim 7 , wherein the user triangulation location identifier is determined based on at least one user travel activity pattern associated with the anchor user profile. 10. The apparatus of claim 7 , wherein the user triangulation location is identified based on a detected triangulation location of a mobile device associated with the anchor user profile. 11. The apparatus of claim 3 , wherein determining the regional density prediction for the first object region of the plurality of object regions comprises:applying a decreasing transformation to each first object-region distance intensity prediction for a first object-region pair that is associated with the first object region to generate one or more object-region inverse distance predictions for the first object-region pair; and determining the regional density prediction for the first object region based on the one or more object-region inverse distance predictions. 12. The apparatus of claim 11 , wherein determining the regional density prediction for the first object region based on the one or more object-region inverse distance predictions comprises determining the regional density prediction for the first object region based on a first summation of the one or more object-region inverse distance predictions. 13. A computer-implemented method for performing distance-based option data object filtering for a plurality of option data objects associated with a geographic object space, the computer-implemented method comprising:for each option data object of the plurality of option data objects, determining a respective object location identifier, wherein the object location identifier corresponds to an object geographic location in the geographic object space; determining a plurality of object regions in the geographic object space, wherein each object region of the plurality of object regions is associated with a regional location identifier; and for each object-region pair associated with a respective option data object of the plurality of option data objects and a respective object region of the plurality of object regions, determining an object-region distance intensity prediction based on the object location identifier for the option data object and the regional location identifier for the object region; for each option data object of the plurality of option data objects, determining an object density prediction based on each object-region distance intensity prediction for a related region subset of the plurality of object regions that is associated with the option data object; and performing the distance-based option data object filtering for the plurality of option data objects based on each object density prediction for the plurality of option data objects. 14. The computer-implemented method of claim 13 , wherein determining the object density prediction for a first option data object of the plurality of option data objects that is associated with a first object region of the plurality of object regions comprises:determining an initial object density value for the first option data object; determining whether the regional density prediction for the first object region exceeds the regional density prediction for an anchor region of the plurality of object regions; and determining the object density prediction for the first object region based on whether the regional density prediction for the first object region exceeds the regional density prediction for the anchor region. 15. The computer-implemented method of claim 14 , wherein:the anchor region comprises a user triangulation location identifier associated with an anchor user profile, and the user triangulation location identifier corresponds to a user triangulation location in the geographic object space. 16. The computer-implemented method of claim 14 , wherein determining the regional density prediction for the first object region based on the one or more object-region inverse distance predictions comprises determining the regional density prediction for the first object region based on a first summation of the one or more object-region inverse distance predictions. 17. The computer-implemented method of claim 13 , wherein determining the object density prediction for a first option data object of the plurality of option data objects comprises:for each object region of the plurality of object regions, determining a regional density prediction based on each object-region distance intensity prediction for an object-region pair associated with the object region; and determining the object density prediction for the first option data object based on each regional density prediction for the related region subset that is associated with the first option data object. 18. A computer program product for performing distance-based option data object filtering for a plurality of option data objects associated with a geographic object space, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:for each option data object of the plurality of option data objects, determine a respective object location identifier, wherein the object location identifier corresponds to an object geographic location in the geographic object space; determine a plurality of object regions in the geographic object space, wherein each object region of the plurality of object regions is associated with a regional location identifier; and for each object-region pair associated with a respective option data object of the plurality of option data objects and a respective object region of the plurality of object regions, determine an object-region distance intensity prediction based on the object location identifier for the option data object and the regional location identifier for the object region; for each option data object of the plurality of option data objects, determine an object density prediction based on each object-region distance intensity prediction for a related region subset of the plurality of object regions that is associated with the option data object; and perform the distance-based option data object filtering for the plurality of option data objects based on each object density prediction for the plurality of option data objects. 19. The computer program product of claim 18 , wherein determining the object density prediction for a first option data object of the plurality of option data objects that is associated with a first object region of the plurality of object regions comprises:determining an initial object density value for the first option data object; determining whether the regional density prediction for the first object region exceeds the regional density prediction for an anchor region of the plurality of object regions; and determining the object density prediction for the first option data object based on whether the regional density prediction for the first object region exceeds the regional density prediction for the anchor region. 20. The computer program product of claim 19 , wherein determining the object density prediction for the first option data object based on whether the regional density prediction for the first object region exceeds the regional density prediction for the anchor region comprises:in response to determining that the regional density prediction for the first object region fails to exceed the regional density prediction for the anchor region, modifying the initial object density value to generate the object density prediction for the first option data object.
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