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.
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
with certain challenges in situations where the discoverability of an address is
15 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
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
SUMMARY
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
5 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
a simplified form as a prelude to the more detailed description that is presented
later.
10 It is an object of the invention to provide improved methods for generating a
unique identification for an address of an entity.
According to one aspect of the present invention, there is disclosed a computer
system including one or more processing modules. The computer system
15 further comprises one or more data storage modules operably coupled to the
one or more processing modules. The computer system also comprises at least
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
20 corresponding to an address of a second entity; compute a first correlation
6
between the first address data and the second address data; and update a unique
address parameter corresponding to the address of the first entity.
The independent claims define the invention in various aspects. The dependent
5 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
10 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
advantages upon reading the following detailed description, and upon viewing
15 the accompanying drawings.
7
DETAILED DESCRIPTION
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
5 evident, however, that the claimed subject matter may be practised without
these specific details. Also, in some instances, well-known features are omitted
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.
10 Reference will now be made to the drawings to describe the present invention in
detail. The implementations herein are described in terms of exemplary
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.
15
Fig. 1 is a block diagram schematically showing a system according to some
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
one or more processing module and at least one memory module coupled to the
20 one or more processing modules. The system may further include a networking
interface, to allow the system to operate in a networked environment supporting
8
connections to one or more remote computers, such as mobile devices and
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
5 laptops etc. via internet, WAN or other communication modes.
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
10 the processing module, for enabling the system to perform various functions.
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
15 in the art.
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
configured to store address data corresponding to an address of an entity. The
20 address data may include nationality, state, city, town, locality, sub-locality,
9
street, landmark, building, floor number, identification number, postal code, zip
code or any other address identifying parameter.
In an embodiment of the present invention, the storage of address data of
5 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
a plurality of hierarchical addressing blocks corresponding to at least one of:
nationality, state, city, town, locality, sub-locality, street, landmark, building,
10 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
relationship.
15 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
‘Delhi’ as city in their address data. Similarly, the hierarchical addressing block
named ‘Janpath’ shall list all the entities which have ‘Janpath’ as street name
20 in their address data.
10
In an embodiment of the present invention, the data storage module, as shown
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
5 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
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
10 address is lost or diminished.
Hence, the unique address parameter corresponds to atleast one such address of
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
15 case, the unique address parameter may correspond to more than one address of
the same entity, as will be shown in greater detail later.
The data storage module may also be configured to store location data
corresponding to an entity. The location data may include geocodes, geographic
20 coordinates such as latitude, longitude or elevation, natural area code, grid
references, or the like. The location data may further include a distance
11
parameter. The distance parameter along with geographical co-ordinates reflects
a probabilistic area, with centre as the geographical co-ordinate and the radius
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
5 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.
Referring now to Fig. 4, there is shown a unique address parameter system,
10 according to one embodiment of the present invention.
A first unique address parameter of a first entity is selected by the processing
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
15 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
corresponding to the first unique address parameter.
Consider the following first address data for the first entity:
20 HN-2134, ABC Enclave, PQR Colony, Sector 91, Gurgaon 122505, Haryana
12
The processing module may identify a plurality of hierarchical addressing
blocks from the first address data of the first entity. The hierarchical addressing
blocks may typically be: Country1, State1, City1, Locality1, Sub-Locality1,
5 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
techniques known in the art.
10
Hence, in the above example, the following hierarchical addressing blocks can
be identified from the address data:
State: Haryana
City: Gurgaon
15 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
plurality of addressing blocks identified from the address data of the first entity.
5 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
linkage to maximum number of entities or having linkage to maximum number
10 of entities whose distance parameters are lower than a threshold limit or any
combination thereof. The processor may also select the addressing block
randomly.
Hence, in the continuing example, the following addressing block can be
15 selected:
Sub-Locality: PQR Colony
The processing module may be instructed to select at least one second unique
address parameter for a second entity corresponding to the addressing block
20 selected by the processing module. The second entity may be selected
randomly, or based on rules such as, but not limited to, entity having distance
14
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
corresponding to the second unique address parameter.
5
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
entity may be chosen which has the following address data:
10 #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
address data. To compare the address data, the processing module may convert
15 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
processing module selects only those portions of the first and second address
20 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
Locality: Sector 91
5 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
for second address data: #325, DEF Society, JKL Street
16
The processing module may, then, generate a similarity score based on the
comparison between the first and the second address data. The processing
module may employ string comparison techniques, such as string metric, fuzzy
5 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
in house numbers, then the similarity score between the two is higher.
10
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
data are same. In such a case, the processing module may update the first
15 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 The processing module may repeat the above procedure by selecting a third
entity, a fourth entity, and so on.
17
The disclosure is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples of well
known computing systems, environments, and/or configurations that may be
5 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
electronics, network PCs, minicomputers, mainframe computers, and
distributed computing environments that include any of the above systems or
10 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
parks, grounds, forests.

We claim:

1. A system comprising:
5 one or more processing modules;
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 at least one of:
an address data corresponding to an address of at least
10 one entity;
wherein the address data is stored in form of
hierarchical addressing blocks, corresponding to
at least one of: nationality, state, city, town,
locality, sub-locality, street, landmark, building,
15 floor number, postal code, zip code and
identification number; and
a unique address parameter corresponding to atleast one
address of atleast one entity;
at least one memory module operatively coupled to the one or
20 more processing modules, wherein the at least one memory
19
module stores instructions which, when executed by the one or
more processing modules, causes the one or more processing
modules to,
select, from the one or more data storage modules, a first
5 address data corresponding to an address of a first entity;
convert the first address data into a first set of
hierarchical addressing blocks;
select, from the first set of hierarchical addressing blocks,
atleast one hierarchical addressing block;
10 select, from the one or more data storage modules, a
second address data corresponding to an address of a
second entity, based on the selected atleast one
hierarchical addressing block;
compare at least a portion of the first address data with at
15 least a portion of the second address data, to generate a
similarity score; and
update a first unique address parameter of the first
address data corresponding to the generated similarity
score.
20
20
2. The system, as claimed in claim 1, wherein the one or more processing
modules is further configured to convert the second address data into a
second set of hierarchical addressing blocks.
5 3. The system, as claimed in claim 2, wherein to generate the similarity
score, only those portions of the first and second address data are
compared which are lower in hierarchy than the selected atleast one
hierarchical addressing block.
10 4. The system, as claimed in claim 1, wherein the first unique address
parameter of the first address data is equal to a second unique address
parameter of the second address data when the generated similarity
score is above a threshold.
15 5. The system, as claimed in claim 1, wherein the first unique address
parameter of the first address data is different from a second unique
address parameter of the second address data when the generated
similarity score is lower than a threshold.
20 6. A method of generating a unique address parameter corresponding to a
first address data of a first entity, the method comprising:
converting the first address data into a first set of hierarchical
addressing blocks;
21
selecting, from the first set of hierarchical addressing blocks,
atleast one hierarchical addressing block;
selecting a second address data corresponding to an address of a
second entity, based on the selected atleast one hierarchical
5 addressing block;
comparing at least a portion of the first address data with at least
a portion of the second address data, to generate a similarity
score; and
updating a first unique address parameter of the first address data
10 corresponding to the generated similarity score.
7. The method, as claimed in claim 6, wherein the second address data is
converted into a second set of hierarchical addressing blocks.
15 8. The method, as claimed in claim 7, wherein to generate the similarity
score, only those portions of the first and second address data are
compared which are lower in hierarchy than the selected atleast one
hierarchical addressing block.
20 9. The method, as claimed in claim 7, wherein the method further
comprises:
22
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;
5 comparing the first set of unmatched hierarchical addressing
blocks with the second set of unmatched hierarchical
addressing blocks; and
generating the similarity score based on said comparison.