Source: http://www.google.ca/patents/US9396388
Timestamp: 2018-01-19 17:26:37
Document Index: 756326680

Matched Legal Cases: ['Application No. 200780001197', 'Application No. 2005', 'Application No. 04250855', 'Application No. 09770507', 'Application No. 10741580', 'Application No. 09770507', 'Application No. 03768631', 'Application No. 03768631', 'Application No. 03768631', 'Application No. 03768631', 'Application No. 04250855', 'Application No. 04250855', 'Application No. 04250855', 'Application No. 06721118', 'Application No. 10741580', 'Application No. 03768631', 'Application No. 2004', 'Application No. 2008', 'Application No. 2011', 'Application No. 2014', 'Application No. 096118505', 'Application No. 2001', 'Application No. 2008', 'Application No. 2001', 'Application No. 2009', 'Application No. 2004', 'Application No. 2005', 'Application No. 2008', 'Application No. 2008', 'Application No. 2011', 'Application No. 2014', 'Application No. 096118505', 'Application No. 200780001197', 'Application No. 200780001197']

Patent US9396388 - Systems, methods and computer program products for determining document validity - Google Patents
Computer program products include program code readable/executable by one or more processors, and configured to cause the processor(s) to: receive an image of a part or all of a document selected from a group consisting of: a gift card, an invoice, a bill, a receipt, a sales order, an insurance claim,...http://www.google.ca/patents/US9396388?utm_source=gb-gplus-sharePatent US9396388 - Systems, methods and computer program products for determining document validity
Publication number US9396388 B2
Application number US 14/283,156
Also published as US8774516, US20140079294, US20140254887
Publication number 14283156, 283156, US 9396388 B2, US 9396388B2, US-B2-9396388, US9396388 B2, US9396388B2
Inventors Jan Amtrup, Anthony Macciola, Steve Thompson
Patent Citations (494), Non-Patent Citations (259), Classifications (17), Legal Events (2)
US 9396388 B2
correct one or more OCR errors in the document using the complementary textual information, and
normalize data from the document prior to determining a validity of the document using at least one of the complementary textual information and one or more predefined business rules.
receive an image of a part or all of a document selected from a group consisting of: a gift card, an invoice, a bill, a receipt, a sales order, an insurance claim, a medical insurance document, and a benefits document;
identify one or more of:
data formatting specific to the sender,
wherein a processor used to accomplish at least one of performing the OCR, extracting the at least partial address, and identifying one or more of the textual information and the data formatting specific to the sender is a processor of a mobile device.
cprior: Cost associated with a specific combination of line-items. It is a heuristic cost containing prior
knowledge regarding the combination of line-items. For example the combination of line-items that
appear in consecutive order on the invoice is preferred over the combination of nonconsecutive line-
cline: The logarithmic sum of the probabilities of the line-items used for the current PMC set to be line-
items versus generic invoice lines. The probabilities are based on the different format of line-items
compared to generic invoice lines.
cextraction: The logarithmic sum of extraction probabilities of the tokens that have been assigned the
labels description, quantity, unit price and extended price for the current PMC set.
cOCR: The tokens assigned the labels quantity, unit price and extended price by the current PMC set have
to fulfill the constraint that quantity times unit price equals extended price. The cost cOCR is the
cost associated with fulfilling this algebraic constraint given the OCR confidences of the different
characters in these tokens.
csequence: This cost captures the prior knowledge that some sequences of line-item fields are more likely
than others. For example it is unlikely to observe on an invoice that extended price is the first line-
item field on a line-item followed by unit price, quantity and finally description, whereas the
sequence description, quantity, unit price and extended price is quite common for a line-item.
calignment: Cost that reflects the observation that line-item fields tend to be aligned vertically
Algorithm 1 Matching algorithm to find best association of line-items
to purchase order positions.
Require: Positions P for given invoice.
Require: Invoice I. I contains the tokens of the invoice together with
their (x,y) positions as well as their corresponding OCR and
extraction results.
1: I := updateInvoice(I) {Depending on additional external input update
information contained in I. For example user provided validation or
correction of line-item fields and OCR results.}
2: (M,setOfPMCs,cMAP,cPMC) := initializeMatchingHypothesis(P,I)
{The procedure initializeMatchingHypothesis elects an initial set of
PMCs setOfPMCs and determines its best mapping M to positions. It
returns the initial matching hypothesis (M,setOfPMCs) and its
cost cPMC and cMAP.}
3: bestMatch := (M,setOfPMCs) {Current best association of line-items
to positions.}
4: minCost := cPMC + cMAP {Current best cost associated with
bestMatch.}
5: while minCost improves sufficiently do
6: (cPMC,setOfPMCs) := nextPMC(cPMC,setOfPMCs.I)
{Generate next PMC set and its cost using stochastic annealing.}
7: (cMAP,M) := findMap(setOfPMCs) {Find best mapping M for
setOfPMCs and its cost cMAP using greedy search.}
8: c := cPMC + cMAP {Overall cost c of current matching hypothesis
given by setOfPMCs and M.}
9: if c < minCost then
10: minCost := c
11: bestMatch := (M,setOfPMCs)
13: updateAnnealingSchdedule( ) {Procedure that monitors the changes
in the individual costs that constitute the cost cPMC and their
relation with the overall cost c. It updates the annealing schedules
needed in the routine nextPMC accordingly.}
Algorithm 2 Routine nextPMC.
Require: Input PMC set setOfPMCs.
Require: Cost cPMC of setOfPMCs.
Require: Invoice I.
1: (modCombo,cost) := modifiedLineItemCombination(setOfPMCs.I)
{Procedure that randomly add/removes line-items and their
combination according to the cost cprior, cline and the annealing
schedule. It returns a modified combination modCombo of
line-items and the new cost for cprior and c line.}
2: (cPMC,setOfPMCs) := modifiedPMCs(setOfPMCs.I) {Procedure
that changes randomly labies of some of line-item fields
according to the cost cextraction, cOCR, csequence,
calignment and the annealing schedule. It returns
the modified set of PMCs setOfPMCs and its new cost cPMC.}
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International Classification G06K9/03, G06K9/18, G06K9/20, G06Q20/32, G06Q20/10, G06K9/00
Cooperative Classification G06K9/00523, G06K9/00483, G06Q20/3276, G06K9/183, G06Q20/10, G06K9/2063, G06K9/00469, G06K9/00442, G06K2209/01, G06K9/18, G06K9/03
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