1. A method for performing identity verification of an individual, the method comprising:
receiving an identification document from the individual;
receiving an indication of an environment or setting in which the identity verification is to be performed;
reading machine readable identification features of the identification document;
accessing a plurality of different data sources based on the machine readable identification features of the identification document and based on the environment or setting in which the identity verification is to be performed;
receiving geographic data and biographic data accessed from the plurality of different data sources;
selecting an authentication methodology to perform an analysis of the identification document and the received data based on one or more inferences made based on the environment or setting in which the identity verification is to be performed;
using the selected authentication methodology to analyze the identification document and the received data to generate a result providing an overridable determination of the individual’s identity;
combining validation scores of the analysis performed by the authentication methodology based on the plurality of different data sources to improve the confidence level of the result; and
automatically representing the result of analyzing the identification document and the received data with one or more indicators.
2. The method of claim 1, further comprising:
determining if the data received from one of the plurality of data sources is valid before analyzing the received data to verify the identity of the individual.
3. The method of claim 2, wherein the data received from the plurality of data sources includes biometric data.
4. The method of claim 2, wherein the data received from the plurality of data sources include one or more of demographic data, biographic data, transaction data, itinerary data, watchlist data for identifying undesirable individuals, data from data warehouses, or data from data services.
5. The method of claim 1, further comprising:
determining if the document is a genuine document, a counterfeit document, or a tampered document.
6. The method of claim 1, further comprising:
constructing a query to extract the data from one of the plurality of data sources.
7. The method of claim 1, wherein the selecting of the authentication methodology is based on a type of the identification document.
8. The method of claim 1, wherein the received data includes bad, missing, or conflicting data, and wherein the method further comprises:
automatically taking the bad, missing, or conflicting data into account during the analysis of the received data without need for user intervention.
9. A system for validating one or more objects, the system comprising:
a first computational device to receive information from the one or more objects, the information received including biometric data and non-biometric data;
a validation facility to validate the information received from the one or more objects, the validation facility configured to:
receive an indication of an environment or setting in which the validation is to be performed;
access a plurality of different data sources based on the information received and based on the environment or setting in which the validation is to be performed;
receive data accessed from the plurality of different data sources;
select a validation methodology to perform an analysis of the information received from the one or more objects, the validation methodology selected based on one or more inferences made based on the environment or setting in which the validation is to be performed;
use the selected validation methodology to analyze the information received from the one or more objects based on the data received from the plurality of different data sources;
combine validation scores of the analysis performed using the selected validation method based on the plurality of data sources to improve the confidence level of a result of the analysis; and
provide a plurality of outputs based on the analysis representing a determination on the validity of the one or more objects, each of the plurality of outputs having at least two states.
10. The system of claim 9, further comprising:
an interface configured to receive a request for data from the validation facility and forward the request in a format understandable by a data source identified in the request.
11. The system of claim 9, wherein the validation facility is extensible to add or remove components to add or remove functionality from the validation facility.
12. The system of claim 9, further comprising:
a storage device holding business rules that define what data conditions the validation facility should accept, what data conditions the validation facility should reject, and what data conditions the validation facility should take an action on to notify a party interested in validating the one or more objects.
13. The system of claim 9, further comprising:
an image acquisition apparatus.
14. The system of claim 9, further comprising:
a document reader apparatus.
15. The system of claim 9, wherein the one or more objects comprise an individual.
16. The system of claim 9, further comprising:
a second computational device to receive information from the one or more Objects, the information received includes biometric and non-biometric data.
17. The system of claim 16, wherein the second computational device comprises a server and the second computational device manages operation of the validation facility.
18. The system of claim 9, further comprising:
a user interface configured to allow a user to interact with the validation facility.
19. The system of claim 18, wherein the user interface is extensible to add or remove components to add or remove features that allow the user to interact with the validation facility.
20. The system of claim 18, wherein the user interface is configurable to enable communication between the user and the validation facility.
21. The system of claim 9, wherein the validation facility is configurable for a particular user preference.
22. A non-transitory computer readable storage medium holding computer readable instructions for performing a method to verify the identity of an individual, the method comprising:
reading machine readable identification features of an identification document received from the individual;
receiving an indication of an environment or setting in which the identity verification is to be performed;
accessing a plurality of different data sources based on the machine readable identification features of the identification document and based on the environment or setting in which the identity verification is to be performed;
receiving geographic data and biographic data accessed from the plurality of different data sources;
selecting an authentication methodology to perform an analysis of the identification document and the received data based on one or more inferences made based on the environment or setting in which the identity verification is to be performed;
using the selected authentication methodology to analyze the identification document and the received data to generate a result providing an overridable determination of the individual’s identity;
combining validation scores of the analysis performed by the authentication methodology based on the plurality of different data sources to improve the confidence level of the result; and
automatically representing the result of analyzing the identification document and the received data with one or more indicators.
23. The medium of claim 22, further comprising:
determining if the data received from one of the plurality of data sources is valid before analyzing the received data to verify the identity of the individual.
24. The medium of claim 23, wherein the data received from the plurality of data sources includes biometric data.
25. The medium of claim 23, wherein the data received from the plurality of data sources include one or more of demographic data, biographic data, transaction data, itinerary data, watchlist data for identifying undesirable individuals, data from data warehouses, and data from data services.
26. The medium of claim 22, further comprising:
determining if the document is a genuine document, a counterfeit document, or a tampered document.
27. The medium of claim 22, further comprising:
constructing a query to extract the data from one of the plurality of data sources.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A recommendation apparatus, comprising:
adjustment information storage means for generating and storing adjustment information, the adjustment information mapping a value, resulting from multiplying a component similarity between a component in user preference information and a component in item preference information by a weight coefficient, to a target of an evaluation value determined in response to a type of an operation performed by a user on an item, the user preference information representing a preference of the user and constructed as a vector containing a plurality of components and the item preference information generated based on metainformation of each item serving as a target for recommendation and containing components of the same number as the number of components of the user preference information;
multiple regression analysis means for calculating each weight coefficient on a per user basis through a multiple regression analysis based on a plurality of pieces of adjustment information, each component of the user preference information being multiplied by the respective weight coefficient;
weight coefficient storage means for storing the calculated weight coefficient on a per user basis, each component of the user preference information being multiplied by the respective calculated weight coefficient in the calculation of the evaluation value; and
recommendation item identifying means for identifying the item to be recommended to the user based on each component of the user preference information and the item preference information and the evaluation value calculated based on the weight coefficient responsive to the component.
2. The recommendation apparatus according to claim 1, wherein the adjustment information generated within a predetermined duration of time serves as a target of the multiple regression analysis.
3. The recommendation apparatus according to claim 2, wherein the adjustment information is generated and stored at every predetermined cyclic period, wherein the weight coefficient is calculated on a per user basis, and wherein the calculated weight coefficient is stored on a per user basis, each component of the user preference information being multiplied by the respective calculated weight coefficient in the calculation of the evaluation value.
4. The recommendation apparatus according to claim 1, further comprising adjustment information deleting means for deleting the adjustment information used in the multiple regression analysis.
5. The recommendation apparatus according to claim 4, wherein the adjustment information deleting means deletes a predetermined percentage ratio of the adjustment information in the order of generation of from old to young age.
6. The recommendation apparatus according to claim 4, wherein the adjustment information comprises the number of uses determined by the type of operation of the user, and
wherein the adjustment information deleting means deletes the adjustment information that has been used in the multiple regression analysis by the number of uses.
7. A recommendation method, comprising the steps of:
generating and storing adjustment information, the adjustment information mapping a value, resulting from multiplying a component similarity between a component in user preference information and a component in item preference information by a weight coefficient, to a target of an evaluation value determined in response to a type of an operation performed by a user on an item, the user preference information representing a preference of the user and constructed as a vector containing a plurality of components and the item preference information generated based on metainformation of each item serving as a target for recommendation and containing components of the same number as the number of components of the user preference information;
calculating each weight coefficient on a per user basis through a multiple regression analysis based on a plurality of pieces of adjustment information, each component of the user preference information being multiplied by the respective weight coefficient;
storing the calculated weight coefficient on a per user basis, each component of the user preference information being multiplied by the respective calculated weight coefficient in the calculation of the evaluation value; and
identifying the item to be recommended to the user based on each component of the user preference information and the item preference information and the evaluation value calculated based on the weight coefficient responsive to the component.
8. A computer program for causing a computer to execute the steps of:
generating and storing adjustment information, the adjustment information mapping a value, resulting from multiplying a component similarity between a component in user preference information and a component in item preference information by a weight coefficient, to a target of an evaluation value determined in response to a type of an operation performed by a user on an item, the user preference information representing a preference of the user and constructed as a vector containing a plurality of components and the item preference information generated based on metainformation of each item serving as a target for recommendation and containing components of the same number as the number of components of the user preference information;
calculating each weight coefficient on a per user basis through a multiple regression analysis based on a plurality of pieces of adjustment information, each component of the user preference information being multiplied by the respective weight coefficient;
storing the calculated weight coefficient on a per user basis, each component of the user preference information being multiplied by the respective calculated weight coefficient in the calculation of the evaluation value; and
identifying the item to be recommended to the user based on each component of the user preference information and the item preference information and the evaluation value calculated based on the weight coefficient responsive to the component.
9. A recording medium storing the computer program according to claim 8.
10. A recommendation apparatus, comprising:
an adjustment information storage unit generating and storing adjustment information, the adjustment information mapping a value, resulting from multiplying a component similarity between a component in user preference information and a component in item preference information by a weight coefficient, to a target of an evaluation value determined in response to a type of an operation performed by a user on an item, the user preference information representing a preference of the user and constructed as a vector containing a plurality of components and the item preference information generated based on metainformation of each item serving as a target for recommendation and containing components of the same number as the number of components of the user preference information;
a multiple regression analysis unit calculating each weight coefficient on a per user basis through a multiple regression analysis based on a plurality of pieces of adjustment information, each component of the user preference information being multiplied by the respective weight coefficient;
a weight coefficient storage unit storing the calculated weight coefficient on a per user basis, each component of the user preference information being multiplied by the respective calculated weight coefficient in the calculation of the evaluation value; and
a recommendation item identifying unit identifying the item to be recommended to the user based on each component of the user preference information and the item preference information and the evaluation value calculated based on the weight coefficient responsive to the component.