Embodiments of the disclosure relate generally to the field of machine learning
and data interpretation. More particularly, embodiments of the disclosure relate
to a system, method and system for generating and assigning unique
identification to an address and optimizing the same.
10
BACKGROUND
Most of the earth’s population has a poorly defined addressing system, thus
having a poorly discoverable residence, property or business locations on a
map. Delivery service providers, including mail delivery providers, are faced
15 with certain challenges in situations where the discoverability of an address is
an issue. Misdirected mail is a source of inefficiency to the entity sending the
mail. Likewise the time and resources expended by the entity handling the mail
also represent lost resources. Delivery service providers handles millions of
mailpiece items in a calendar year, and the inefficiency and waste associated
20 with misdirected mail carries significant costs.
3
In poorly defined and unstructured addressing systems, consumers and other
participants independently identify and adopt addressing schemes according to
their own convenience. Much of the addresses are written with respect to a
landmark which typically lies between 50-1500 meters of the actual address
5 location. Weakly defined or haphazardly defined postal code schemes only add
to the inconvenience.
Such addressing schemes allow a significant amount of noise to creep into
written addresses. Automated systems or computer assisted systems find it
10 difficult to decode this noise and to resolve the addresses with reasonable
accuracy. Differently written versions of a single address may be resolved miles
apart from each other. Similarly, two different addresses, when poorly
described, may cause the automated system to resolve both to a same location.
15 A need, therefore, arises for a method and system to determine uniqueness of
written addresses among each other.
4
BRIEF DISCLOSURE OF THE DRAWINGS
Embodiments according to the claimed subject matter are described below with
reference to the drawings. The detailed description references the
5 accompanying figures. The same numbers can be used throughout the drawings
to reference like features and components. As used herein, like terms refer to
like elements throughout the description. It should be noted that views of
exemplary embodiments are merely to illustrate selected features of the
embodiment. The views qualitatively illustrate exemplary features of some
10 embodiments and, therefore, should not be interpreted as being drawn to scale.
Fig 1 is a block diagram schematically showing a system according to some
embodiments of the present invention.
Fig. 2 illustrates a data storage module according to some embodiments of the
15 present invention.
Fig. 3 illustrates a directed acyclic graph corresponding to address data stored in
the data storage module according to some embodiments of the present
invention.
Fig. 4 illustrates a unique identification generation system, according to one
20 embodiment of the present invention.
5
Fig. 5 illustrates a unique identification optimization system, according to one
embodiment of the present invention.
SUMMARY
5 The following presents a simplified summary in order to provide a basic
understanding of one or more aspects of the invention. This summary is not an
extensive overview of the invention, and is neither intended to identify key or
critical elements of the invention, nor to delineate the scope thereof. Rather, the
primary purpose of the summary is to present some concepts of the invention in
10 a simplified form as a prelude to the more detailed description that is presented
later.
It is an object of the invention to provide improved methods for generating a
unique identification for an address of an entity.
15
It is another object of the invention to provide improved methods for optimizing
a database containing unique identifications for a plurality of addresses of a
plurality of entities.
6
According to one aspect of the present invention, there is disclosed a computer
system including one or more processing modules. The computer system
further comprises one or more data storage modules operably coupled to the
one or more processing modules. The computer system also comprises at least
5 one memory module coupled to the one or more processing modules. The
processing modules may be configured to select a first address data
corresponding to an address of a first entity and a second address data
corresponding to an address of a second entity; compute a first correlation
between the first address data and the second address data; compute a second
10 correlation between a location data of the first entity and a location data of the
second entity; and update the location data corresponding to a location of the
first entity.
The independent claims define the invention in various aspects. The dependent
15 claims state selected elements of embodiments according to the invention in
various aspects.
This summary is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. This summary is not
20 intended to identify key features or essential features of the claimed subject
matter, nor is it intended to be used as an aid in determining the scope of the
claimed subject matter. Other methods, apparatus and systems are also
disclosed. Those skilled in the art will recognise additional features and
7
advantages upon reading the following detailed description, and upon viewing
the accompanying drawings.
DETAILED DESCRIPTION
5
For purposes of explanation, numerous specific details are set forth in order to
provide a thorough understanding of the claimed subject matter. It may be
evident, however, that the claimed subject matter may be practised without
these specific details. Also, in some instances, well-known features are omitted
10 or simplified to clarify the description of the exemplary implementations. In
some other instances, well-known features or units or circuits have been shown
in block diagram form in order avoid clutter due to unnecessary detailing.
Reference will now be made to the drawings to describe the present invention in
detail. The implementations herein are described in terms of exemplary
15 embodiments. However, it should be appreciated that individual aspects of the
implementations may be separately claimed and one or more of the features of
the various embodiments may be combined.
Fig. 1 is a block diagram schematically showing a system according to some
20 embodiments of the present invention. As shown, the system may comprise at
least one processing module, one or more data storage modules coupled to the
8
one or more processing module and at least one memory module coupled to the
one or more processing modules. The system may further include a networking
interface, to allow the system to operate in a networked environment supporting
connections to one or more remote computers, such as mobile devices and
5 computing devices. The networking interface may include a modem and a local
area network (LAN) interface. The modem and LAN interface may connect to
external communication devices, such as mobile phone, computer system and
laptops etc. via internet, WAN or other communication modes.
10 The memory module may be configured to store software used by the system
such as an operating system, applications program and associated database. The
memory module may further be configured to store instructions, executable by
the processing module, for enabling the system to perform various functions.
15 The system may also include an input-output interface which may include, but
not limited to, an interface for display, keyboard, mouse, keypad, speaker,
haptic device, microphone, camera or other input-output techniques well known
in the art.
20 Referring the Fig. 2, there is shown a data storage module according to some
embodiments of the present invention. The data storage module may be
9
configured to store address data corresponding to an address of an entity. The
address data may include nationality, state, city, town, locality, sub-locality,
street, landmark, building, floor number, identification number, postal code, zip
code or any other address identifying parameter.
5
In an embodiment of the present invention, the storage of address data of
entities in the data storage module may be implemented in form of a directed
acyclic graph. Referring to Fig.3, the directed acyclic graph corresponding to
address data stored in the data storage module is shown. The graph may include
10 a plurality of hierarchical addressing blocks corresponding to at least one of:
nationality, state, city, town, locality, sub-locality, street, landmark, building,
floor number, identification number, postal code, zip code or any other address
identifying parameter well known in the art. A first addressing block may be
connected to at least one second addressing block via a parent-child
15 relationship.
Each hierarchical addressing block may link to one or more entities whose
address data map on to the hierarchical addressing block. Hence, the
hierarchical addressing block named ‘Delhi’ shall list all the entities which have
20 ‘Delhi’ as city in their address data. Similarly, the hierarchical addressing block
10
named ‘Janpath’ shall list all the entities which have ‘Janpath’ as street name
in their address data.
In an embodiment of the present invention, the data storage module, as shown
5 in Fig. 2, may further be configured to store a unique address parameter
corresponding to atleast one address of atleast one entity. The unique address
parameter identifies an entity based on atleast one address of the entity. An
entity may have more than one address, which may occur due to a number of
reasons. As an example, different persons may write the same address
10 differently. In an instance, while writing an address, one person may include
landmark details while the other does not or there may be spelling differences,
intentional or unintentional. This induces complexity and the uniqueness of the
address is lost or diminished.
Hence, the unique address parameter corresponds to atleast one such address of
15 an entity. In one embodiment of the present invention, the system may be able
to identify two different addresses as belonging to the same entity. In such a
case, the unique address parameter may correspond to more than one address of
the same entity, as will be shown in greater detail later.
20 The data storage module may also be configured to store location data
corresponding to an entity. The location data may include geocodes, geographic
11
coordinates such as latitude, longitude or elevation, natural area code, grid
references, or the like. The location data may further include a distance
parameter. The distance parameter along with geographical co-ordinates reflects
a probabilistic area, with centre as the geographical co-ordinate and the radius
5 as the distance parameter, which contains the exact location of the entity.
Hence, the distance parameter is reflective of the error in the knowledge of
location of the entity. Therefore, a larger distance parameter translates to a
larger radius, which is interpreted as less preciseness in identifying the location
of the entity.
10
Referring now to Fig. 4, there is shown a unique address parameter system,
according to one embodiment of the present invention.
A first unique address parameter of a first entity is selected by the processing
15 module in accordance with the instructions stored in the memory module. The
unique address parameter of the first entity may be allotted by the processing
module according to an address data received by the processing module from a
remote communication device (not shown) in real time or it may be pre-stored
in data storage module. The processing module selects a first address data
20 corresponding to the first unique address parameter.
Consider the following first address data for the first entity:
12
HN-2134, ABC Enclave, PQR Colony, Sector 91, Gurgaon 122505, Haryana
The processing module may identify a plurality of hierarchical addressing
blocks from the first address data of the first entity. The hierarchical addressing
5 blocks may typically be: Country1, State1, City1, Locality1, Sub-Locality1,
Street1, Building1, House-Number1 and so on.
The identification of plurality of hierarchical addressing block may be
performed using data processing techniques such as, but not limited to, machine
learning, artificial intelligence, fuzzy learning, pattern matching or other
10 techniques known in the art.
Hence, in the above example, the following hierarchical addressing blocks can
be identified from the address data:
State: Haryana
15 City: Gurgaon
Locality: Sector 91
Sub-Locality: PQR Colony
Building: ABC Enclave
House Number: 2134
13
The processing module may select at least one addressing block from the list of
the addressing blocks stored as the directed acyclic graph in the data storage
module. The processing module may select the addressing block based on the
5 plurality of addressing blocks identified from the address data of the first entity.
Hence, the processing module may select the addressing block corresponding to
City 1, or Locality 1 or Building 1 and so on. The processor may select the
addressing block based on rules such as, but not limited to, addressing block
having lower hierarchy in the directed acyclic graph, addressing block having
10 linkage to maximum number of entities or having linkage to maximum number
of entities whose distance parameters are lower than a threshold limit or any
combination thereof. The processor may also select the addressing block
randomly.
15 Hence, in the continuing example, the following addressing block can be
selected:
Sub-Locality: PQR Colony
The processing module may be instructed to select at least one second unique
20 address parameter for a second entity corresponding to the addressing block
selected by the processing module. The second entity may be selected
14
randomly, or based on rules such as, but not limited to, entity having distance
parameter lower than a threshold limit, entity which may serve as a landmark (a
monument or the like) or any combination thereof. The second entity may also
be selected randomly. The processing module selects a second address data
5 corresponding to the second unique address parameter.
In continuation with the above example, the processing module selects one
second entity from the list of entities whose address data incorporate the
hierarchical addressing block as Sub-Locality: PQR Colony. Hence, a second
10 entity may be chosen which has the following address data:
#325, DEF Society, JKL Street, PQR Colony, Sec-91, Gurgaon
The processing module may compare atleast portions of the first address data
and the second address data and generate a similarity score between the two
15 address data. To compare the address data, the processing module may convert
the second address data into a second set of hierarchical addressing blocks. The
processing module may select the complete address data for any of the first and
second address data for comparison. Preferably, the processing module selects
limited portions of the first and second address data. More preferably, the
20 processing module selects only those portions of the first and second address
data which are lower in hierarchy than the addressing block selected above.
15
In the above example, the processing module converts the address data of the
second entity into the following hierarchical addressing blocks:
City: Gurgaon
5 Locality: Sector 91
Sub-Locality: PQR Colony
Street Name: JKL Street
Building: DEF Society
House Number: 325
10
Now, given that the selected hierarchical addressing blocks was Sub-Locality:
PQR Colony, the processing module selects only those hierarchical addressing
blocks of the first and second address data which are lower in hierarchy from
the selected hierarchical addressing block.
15
Hence, the processing module may select the following portions of the first and
second address data for comparison:
for first address data: HN-2134, ABC Enclave
16
for second address data: #325, DEF Society, JKL Street
The processing module may, then, generate a similarity score based on the
comparison between the first and the second address data. The processing
5 module may employ string comparison techniques, such as string metric, fuzzy
searching, phonetic comparison, concept searching, semantic searching or other
techniques well known in the art. The similarity score reflects the similarity
between the selected portions of the first and second address data. Hence, if the
two address portions are very similar to each other, for example only differing
10 in house numbers, then the similarity score between the two is higher.
The processing module updates the first unique address parameter based on the
generated similarity score. If the generated similarity score is above a threshold
value, then it may be adjudged that the first address data and the second address
15 data are same. In such a case, the processing module may update the first
unique address parameter to be same as the second unique address parameter. If
the generated similarity score is below a threshold value, the processing module
may update the first unique address parameter to be different from the second
unique address parameter.
20
17
The processing module may repeat the above procedure by selecting a third
entity, a fourth entity, and so on.
In an another embodiment of the present invention, the processing module may
5 also compare the location data of the first and the second entity. The processing
module may select the location data corresponding to the first and second
unique address parameters. Referring to Fig. 5, the processing module may
compare the geographical co-ordinates and the distance parameters for both
unique address parameters with each other to find an overlap parameter value
10 which reflects the degree of overlap between the two locations.
In an embodiment of the invention, the processing module may render a
terrestrial map of at least a portion of the geographical area corresponding to
atleast one of the first location data and the second location data. The map may
15 be generated by the processor in real time, or it may be stored in the data
storage module, remote repository or the like. The map data may also be
received by the processing module from external sources such as Google Maps,
Open Street Maps or other third party services. The processing module may
overlay the location data of the first entity and the location data of the second
20 entity on the rendered map. The processing module may then calculate the
18
overlap parameter value between the first location data and the second location
data.
If the degree of overlap is very high, the processing module may identify both
5 unique address parameters to be corresponding to the same entity.
In case the degree of overlap is below a threshold, the processing module may
additionally compare at least portions of address data corresponding to the first
and the second entity. The processing module may employ string comparison
10 techniques, such as string metric, fuzzy searching, phonetic comparison,
concept searching, semantic searching or other techniques well known in the
art. Based on such address data comparison and the comparison of the location
data, the processing module may identify both unique address parameters to be
corresponding to the same entity. In such a case, the first and the second unique
15 address parameter are merged to form a single unique address parameter. This
reduces redundancy in the system and keeps the database optimized over time.
Hence, here a single unique address corresponding to one entity may
correspond to more than one address data of the one entity.
20 The disclosure is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples of well
19
known computing systems, environments, and/or configurations that may be
suitable for use with the disclosure include, but are not limited to, personal
computers, server computers, handheld or laptop devices, multiprocessor
systems, microprocessor-based systems, set top boxes, programmable consumer
5 electronics, network PCs, minicomputers, mainframe computers, and
distributed computing environments that include any of the above systems or
devices, and the like.
As used herein, the wording "entity" means any geographical area which may
include, but not limited to, buildings, structures, monuments, open areas such as
10 parks, grounds, forests.

We claim:

1. A system comprising:
one or more processing modules;
5 one or more data storage modules, operatively coupled to the one or
more processing modules, wherein the one or more data storage
modules are configured to store atleast one of:
a unique address parameter corresponding to atleast one address
of atleast one facility;
10 an address data corresponding to an address of atleast one
facilty; and
a location data corresponding to a location of atleast one facility,
wherein the location data comprises:
a geographical co-ordinate; and
15 a distance parameter;
atleast one memory module operatively coupled to the one or more
processing modules, wherein the atleast one memory module stores
instructions which, when executed by the one or more processing
modules, causes the one or more processing modules to,
20 select, from the one or more data storage modules, a first unique
address parameter corresponding to atleast one address of a first
facility and a second unique address parameter corresponding to
atleast one address of a second one facility;
21
select, from the one or more data storage modules, a first
location data corresponding to a location of the first facility and
a second location data corresponding to a location of the second
facility;
5 compare the first location data and the second location data to
generate an overlap parameter value; and
update atleast one of the first unique address parameter and the
second unique address parameter.
10 2. The system, as claimed in claim 1, wherein the distance parameter is
configured to reflect the location of the atleast one facility to lie in a circle
with center as the geographical co-ordinate and radius as the distance
parameter.
15 3. The system, as claimed in claim 1, wherein the one or more processing
modules is further configured to merge the first and the second unique
address parameter to form a single unique address parameter, if the overlap
parameter value is above a first threshold.
20 4. The system, as claimed in claim 1, wherein, in case the overlap parameter
value is above a second threshold, the one or more processing modules is
further instructed to:
22
select, from the one or more data storage modules, a first address data
corresponding to an address of the first facility and a second address
data corresponding to an address of the second facility;
compare at least portions of the first and second address data; and
5 update atleast one of the first unique address parameter and the second
unique address parameter.
5. The system, as claimed in claim 4, wherein the one or more processing
modules is further configured to convert the first address data into a first set
10 of hierarchical addressing blocks and the second address data into a second
set of hierarchical addressing blocks.
6. The system, as claimed in claim 1, wherein the one or more processing
modules further renders a terrestrial map of atleast a portion of the
15 geographical area corresponding to atleast one of the first location data and
the second location data.
7. A method for optimizing a unique address parameter database having a first
unique address parameter corresponding to atleast one address of a first
20 facility and a second unique address parameter corresponding to atleast one
address of a second facility stored therein, the method comprising:
23
selecting a first location data corresponding to a location of the first
facility and a second location data corresponding to a location of the
second facility;
comparing the first location data and the second location data to
5 generate an overlap parameter value; and
updating atleast one of the first unique address parameter and the second
unique address parameter.
8. The method, as claimed in claim 7, wherein the method further comprises
10 merging the first and the second unique address parameter to form a single
unique address parameter, if the overlap parameter value is above a first
threshold.
9. The method, as claimed in claim 7, wherein, in case the overlap parameter
15 value is above a second threshold, the method further comprises:
selecting a first address data corresponding to an address of the first
facility and a second address data corresponding to an address of the
second facility;
identifying a first portion of the first address data and a second portion
20 of the section address data;
comparing the first portion and the second portion with each other; and
updating atleast one of the first unique address parameter and the second
unique address parameter.
24
10. The method, as claimed in claim 9, wherein the method further comprises:
converting the first address data into a first set of hierarchical addressing
blocks and the second address data into a second set of hierarchical
5 addressing blocks; and
matching the first set of hierarchical addressing blocks with the second
set of hierarchical addressing blocks to identify a first set of unmatched
hierarchical addressing blocks and a second set of unmatched
hierarchical addressing blocks.