| 摘要 |
The technology disclosed herein provides accurate, targeted, building improvement content in a way that resonates with the user by considering the user's whole ecosystem. The technology uses details of the user's home, neighborhood, family, environmental and historical factors, goals, economic situation, and motivations and preferences to tailor content to a user's personal situation. A server or other computing device may accomplish this by receiving data from the client, a third party, or data local to the server; building modeling constructs based on these data sets such as a physics-based model of the building and a behavioral model of the user; operating these models relative to possible discrete building improvement content units; and using the results to determine personalized building improvement content for the user such as, for example, a webpage. |
| 主权项 |
1. A method, performed by a computing system, of providing personalized building related recommendations, the method comprising:
receiving, by the computing system, a first set of data elements representing features of a building; receiving, by the computing system, a second set of data elements representing user characteristics of a user; creating, by the computing system and using the first set of data elements, a building model; creating, by the computing system and using the second set of data elements, a behavioral model that comprises a probabilistic model wherein the probabilistic model is based on correlations identified between the user and one or more other users; choosing, by the computing system, potential recommendations; creating, by the computing system, one or more building scenarios,
wherein each particular building scenario, of the one or more building scenarios, is created by:
operating the building model under condition parameters that would be in place for the building when a selected set of the potential recommendations, corresponding to the particular building scenario, is implemented, andcomputing an amount of cost or energy consumption by the building when implementing the selected set of the potential recommendations corresponding to the particular building scenario; using, by the computing system, the behavioral model to assign a user confidence level to each selected potential recommendation, of one or more of the potential recommendations, wherein each user confidence level indicates an expected level to which the corresponding potential recommendation will be helpful, followed, or found valuable to the user; selecting, by the computing system, one or more elements from the potential recommendations, wherein each selected element of the one or more elements is selected based on (A) the amount of cost or energy consumption associated with the building scenarios that the selected element was used in creating and (B) the user confidence level assigned to the selected element; and creating and transmitting, by the computing system, the personalized building related recommendations, wherein the personalized building related recommendations include at least one of the selected one or more elements. |
