Patent application EP21176417A (EP), filed 2021-05-28
Publication number: EP3916576A1
Title: GLOBAL ADDRESS PARSER
Abstract: The present disclosure pertains to parsing addresses using a deep learning model. The deep learning model may be trained using a plurality of reference address records and a plurality of modifications to the plurality of reference address records. In some implementations, the model can be a multi-layer bi-directional long short-term memory recurrent neural network model. The output of the model may include indications of an address component type for each character of the address. The output can be parsed based on the indications of the address component type to obtain a plurality of labeled address components for the address. The labeled address components can be compared to reference address records to determine a matched address record identifying a geographic location.
IPC codes: G06F  16/  2452            A I
G06F  16/    29            A I
G06F  40/   205            A I
G06F  40/   295            A I
G06N  20/    00            A I
G06N   3/    08            A I
G06Q  50/    32            A I
Claims: number: 1
text: A computer-implemented method, comprising:
number: 2
text: The computer-implemented method of claim 1, further comprising:
number: 3
text: The computer-implemented method of claim 1 or 2, wherein the plurality of modifications to the plurality of reference address records are based on one or more of address equivalence rules, typographical errors, and phonetic similarity rules.
number: 4
text: The computer-implemented method of any one of the preceding claims, further comprising:
applying a character embedding model to the address to obtain a character embedded address, wherein the address applied to the deep learning model is the character embedded address.
number: 5
text: The computer-implemented method of any one of the preceding claims, further comprising:
applying a classification function to the output to obtain a classified output, wherein the output parsed to obtain the plurality of labeled address components is the classified output.
number: 6
text: The computer-implemented method of any one of the preceding claims, further comprising:
generating a standardized address based on the matched address record, the standardized address formatted according to a plurality of standardization rules and indicating one or more address component types of the plurality of address component types.
number: 7
text: The computer-implemented method of any one of the preceding claims, wherein the deep learning model is a multi-layer bi-directional long short-term memory recurrent neural network model.
number: 8
text: A computer device, comprising:
one or more processors; and machine-readable medium coupled to the one or more processors and storing computer program code comprising sets instructions executable by the one or more processors to perform operations according to a method of any one of claims 1 to 7.
number: 9
text: One or more non-transitory computer-readable medium storing computer program code comprising sets of instructions to perform operations according to a method of any one of claims 1 to 7.
Office action: search_report, dated 2021-12-01
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: - RIGBY JASON: "AddressNet: How to build a robust street address parser using a Recurrent Neural Network", 5 December 2018 (2018-12-05), XP055850132, Retrieved from the Internet <URL:https://towardsdatascience.com/addressnet-how-to-build-a-robust-street-address-parser-using-a-recurrent-neural-network-518d97b9aebd> [retrieved on 20211011]
ref_type: npl
severity: background
ref_id: - RIGBY  JASON: "GitHub - jasonrig/address-net at 53b977c46435bb685ba36a225716b0be326cdd28", 5 December 2018 (2018-12-05), XP055850536, Retrieved from the Internet <URL:https://github.com/jasonrig/address-net/tree/53b977c46435bb685ba36a225716b0be326cdd28> [retrieved on 20211012]
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ref_type: npl
severity: background
ref_id: - ANONYMOUS: "PSMA Geocoded National Address File (G-NAF) - Datasets - data.gov.au", 10 April 2019 (2019-04-10), XP055850719, Retrieved from the Internet <URL:https://web.archive.org/web/20190410105546/https://data.gov.au/data/dataset/geocoded-national-address-file-g-naf> [retrieved on 20211013]
ref_type: npl
severity: background
ref_id: - SRIVASTAVA SHIVIN: "Extracting Addresses from Unstructured Text Using Bi-directional Recurrent Neural Networks", 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), IEEE, 17 November 2018 (2018-11-17), pages 1511 - 1513, XP033516191, [retrieved on 20190207], DOI: 10.1109/ICDMW.2018.00223
ref_type: npl
severity: background
ref_id: - ABID NOSHEEN ET AL: "DeepParse: A Trainable Postal Address Parser", 2018 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), IEEE, 10 December 2018 (2018-12-10), pages 1 - 8, XP033503504, [retrieved on 20190116], DOI: 10.1109/DICTA.2018.8615844
ref_type: npl
severity: background
ref_id: - MAGGE ARJUN ET AL: "Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature", BIOCOMPUTING 2019, 1 November 2018 (2018-11-01), pages 100 - 111, XP055849991, ISBN: 978-981-3279-82-7, DOI: 10.1142/9789813279827_0010
ref_type: npl
severity: background
ref_id: - SHARMA SHIKHAR ET AL: "Automated Parsing of Geographical Addresses: A Multilayer Feedforward Neural Network Based Approach", 2018 IEEE 12TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC), IEEE, 31 January 2018 (2018-01-31), pages 123 - 130, XP033345806, [retrieved on 20180409], DOI: 10.1109/ICSC.2018.00026
ref_type: npl
severity: background
ref_id: - Rigby Jason, "AddressNet: How to build a robust street address parser using a Recurrent Neural Network", (20181205), URL: https://towardsdatascience.com/addressnet-how-to-build-a-robust-street-address-parser-using-a-recurrent-neural-network-518d97b9aebd, (20211011), XP055850132 [X] 1-9 * page  3, paragraph  1 * * page  4, paragraph  3 * * page  7, paragraph  4  - page  9, paragraph  2 *
ref_type: npl
severity: novelty_destroying
ref_id: [ ]     - Rigby Jason, "GitHub - jasonrig/address-net at 53b977c46435bb685ba36a225716b0be326cdd28", (20181205), URL: https://github.com/jasonrig/address-net/tree/53b977c46435bb685ba36a225716b0be326cdd28, (20211012), XP055850536 [ ] * page  1, paragraph  1  - page  2, paragraph  1 * * page  2, paragraph  3 * * page  2, paragraph  l; table at the top of page 3 * * page  4, paragraphs  1-3, 6 * * page  4, paragraph  l  - page  5, paragraph  1 *
ref_type: npl
severity: background
ref_id: - Anonymous, "PSMA Geocoded National Address File (G-NAF) - Datasets - data.gov.au", (20190410), URL: https://web.archive.org/web/20190410105546/https://data.gov.au/data/dataset/geocoded-national-address-file-g-naf, (20211013), XP055850719 [A] 1-9 * page  1, paragraphs  1-2 *
ref_type: npl
severity: background
ref_id: - SRIVASTAVA SHIVIN, "Extracting Addresses from Unstructured Text Using Bi-directional Recurrent Neural Networks", 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW), IEEE, doi:10.1109/ICDMW.2018.00223, (20181117), pages 1511 - 1513, (20190207), XP033516191 [I] 1-9 * page  1512, column  l, paragraph  3  - column  r, paragraph  l *
ref_type: npl
severity: background
ref_id: - ABID NOSHEEN ET AL, "DeepParse: A Trainable Postal Address Parser", 2018 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), IEEE, doi:10.1109/DICTA.2018.8615844, (20181210), pages 1 - 8, (20190116), XP033503504 [I] 1-9 * page  2, column  r, paragraph  2  - page  4, column  l, paragraph  5 * * page  4, column  l, paragraph  l  - page  5, column  r, paragraph  1 * * page  5, column  r, paragraphs  2-4 *
ref_type: npl
severity: background
ref_id: - MAGGE ARJUN ET AL, "Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature", BIOCOMPUTING 2019, (20181101), doi:10.1142/9789813279827_0010, ISBN 978-981-3279-82-7, pages 100 - 111, XP055849991 [I] 1-9 * page  102, paragraph  l  - page  106, paragraph  3; figure 1 *
ref_type: npl
severity: background
Final disposition: pending (2021-12-01)