# US 2019/0377624 A1 · Data validation

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- Number: US 2019/0377624 A1
- Document ID: US20190377624A1
- Status: Published application
- Kind: A1
- Application number: 16/002,516
- Filed: 2018-06-07
- Published: 2019-12-12
- Earliest claimed: 2018-06-07
- Assignees: Accenture Global Solutions Limited
- Applicants: Accenture Global Solutions Limited
- Inventors: Chung-Sheng LI; Emmanuel Munguia Tapia; Mohammad Ghorbani; Jingyun Fan; Priyankar Bhowal; David Clune; Sumraat Singh
- Web page: https://patentgrep.com/patent/US20190377624A1

## CPC

- G06F 11/079: {Root cause analysis, i.e. error or fault diagnosis (in a hardware test environment G06F11/22; in a software test environment G06F11/36)}
- G06F 11/0751: {Error or fault detection not based on redundancy (power supply failures G06F1/30; network fault management H04L41/06)}
- G06F 11/0772: {Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers}
- G06F 11/0787: {Storage of error reports, e.g. persistent data storage, storage using memory protection}
- G06F 11/0793: {Remedial or corrective actions (recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40)}
- G06F 40/279: Recognition of textual entities
- G06F 40/30: Semantic analysis
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

In an example, data, such as, a journal entry in a ledger, to be validated and associated supporting documents may be extracted. Further, an entity, indicative of a feature of the data may be extracted. Based on the extracted entity, one or more probable values for a field of the data may be determined. A probability score may be associated each of the probable values of the field. At least one of the probable values of the field may be compared with an actual value of the field of the data. Based on comparison, a notification indicative of a potential error in the data may generated. The data and historical data associated with the data may be processed, based on at least one of predefined rules and a machine learning technique, to detect an anomaly in the data, the anomaly being related to a contextual information associated with the data.

## Claims

### Claim 1

A validation system comprising:
a processor;
a data extractor coupled to the processor to,
extract data to be validated and associated supporting documents, the data including a journal entry in a ledger; and
extract an entity associated with the data using a natural language processing technique, the entity being indicative of a feature of the data;
a classifier coupled to the processor to,
determine a value for a field of the data, based on the extracted entity, the field of the data including information to be validated;
associate a probability score with the value of the field, the probability score being indicative of a likelihood of determination of the value being correct;
compare the value of the field with an actual value of the field of the data; and
based on comparison, generate a notification indicative of a potential error in the data; and
an anomaly detector coupled to the processor, the anomaly detector to,
process the data and historical data associated with the data, based on at least one of predefined rules and a machine learning technique; and
detect an anomaly in the data, based on the processing, the anomaly being related to contextual information associated with the data.

### Claim 2 (depends on 1)

The system as claimed in claim 1, wherein the system further comprises an augmentor coupled to the processor, the augmentor is to augment the extracted entity with augmentation data to determine features related to the extracted entity, the augmentation data including information pertaining to fields of the data from an external source.

### Claim 3 (depends on 2)

The system of claim 2, wherein the classifier is to process the extracted data using augmented extracted data to determine the value.

### Claim 4 (depends on 1)

The system as claimed in claim 1, wherein the anomaly detector comprises:
a semantic matcher to determine whether a description of the journal entry is semantically similar to corresponding historical data to detect a semantic anomaly;
an outlier detector to determine whether the journal entry is in a standard statistical range to detect an outlier anomaly; and
a rule based anomaly detector to determine whether a journal entry property fits into a pre-defined acceptable range to detect a rule based anomaly.

### Claim 5 (depends on 1)

The system of claim 1, wherein the system comprises a data generator coupled to the processor to generate the data to be validated, based on a supporting document.

### Claim 6 (depends on 1)

The system as claimed in claim 1, wherein the classifier is to:
ascertain whether the extracted entity and the field have a one to one mapping, based on the historical data associated with the extracted entity;
when the extracted entity and the field have the one to one mapping, select a corresponding value of the field as the value;
when the extracted entity and the field do not have the one to one mapping, obtain augmented extracted entity data; and
process the augmented extracted entity data to determine one or more values of the field.

### Claim 7 (depends on 1)

The system as claimed in claim 1, wherein the system further includes a notification analyzer to,
receive the notification indicating that the data includes one of the potential error and the anomaly;
generate a hypothesis providing an explanation for the potential error and the anomaly; and
provide a remedial action for correcting the potential error and the anomaly.

### Claim 8

A method comprising:
extracting data to be validated and associated supporting documents, the data including a journal entry in a ledger; and
extracting an entity associated with the data using a natural language processing technique, the entity being indicative of a feature of the data;
determining a value for a field of the data, based the extracted entity, the field of the data including information to be validated;
associating a probability score with the value of the field, the probability score being indicative of a likelihood of determination of the value being correct;
comparing the value of the field with an actual value of the field of the data;
based on comparison, generating a notification indicative of a potential error in the data;
processing the data and historical data associated with the data, based on at least one of predefined rules and a machine learning technique; and
detecting an anomaly in the data, based on the processing, the anomaly being related to contextual information associated with the data.

### Claim 9 (depends on 8)

The method as claimed in claim 8, wherein the method further comprises augmenting the extracted entity with augmentation data to determine features related to the extracted entity, the augmentation data including information pertaining to fields of the data from an external source.

### Claim 10 (depends on 9)

The method as claimed in claim 9, wherein the value for the field is determined based on the augmented extracted data.

### Claim 11 (depends on 8)

The method as claimed in claim 8, wherein processing the data and the historical data for detecting the anomaly comprises:
determining whether a description of the journal entry is semantically similar to corresponding historical data to detect a semantic anomaly;
determining whether the journal entry is in a standard statistical range to detect an outlier anomaly; and
determining whether a journal entry property fits into a pre-defined acceptable range to detect a rule based anomaly.

### Claim 12 (depends on 8)

The method as claimed in claim 8, wherein the method further comprises generating the data to be validated, based on a supporting document.

### Claim 13 (depends on 8)

The method as claimed in claim 8, wherein determining the value comprises:
ascertaining whether the extracted entity and the field have a one to one mapping, based on the historical data associated with the extracted entity;
when the extracted entity and the field have the one to one mapping, selecting a corresponding value of the field as the value;
when the extracted entity and the field do not have the one to one mapping, obtaining augmented extracted entity data; and
processing the augmented extracted entity data to determine one or more value of the field.

### Claim 14 (depends on 1)

The method as claimed in claim 1, wherein the method further comprises:
receiving the notification indicating that the data includes one of the potential error and the anomaly;
generating a hypothesis providing an explanation for the potential error and the anomaly; and
providing a remedial action for correcting the potential error and the anomaly.

### Claim 15

A non-transitory computer readable medium including machine readable instructions that are executable by a processor to:
extract data to be validated and associated supporting documents, the data including a journal entry in a ledger; and
extract an entity associated with the data using a natural language processing technique, the entity being indicative of a feature of the data;
determine a value for a field of the data, based the extracted entity, the field of the data including information to be validated;
associate a probability score with the value of the field, the probability score being indicative of a likelihood of determination of the value being correct;
compare the value of the field with an actual value of the field of the data;
based on comparison, generate a notification indicative of a potential error in the data;
process the data and historical data associated with the data, based on at least one of predefined rules and a machine learning technique; and
detect an anomaly in the data, based on the processing, the anomaly being related to contextual information associated with the data.

### Claim 16 (depends on 15)

The non-transitory computer readable medium as claimed in claim 15, wherein the value for the field is determined based on the augmented extracted data.

### Claim 17 (depends on 15)

The non-transitory computer readable medium as claimed in claim 15, wherein the processor is to augment the extracted entity with augmentation data to determine features related to the extracted entity, the augmentation data including information pertaining to fields of the data from an external source.

### Claim 18 (depends on 15)

The non-transitory computer readable medium as claimed in claim 15, wherein the processor is to
determine whether a description of the journal entry is semantically similar to corresponding historical data to detect a semantic anomaly;
determine whether the journal entry is in a standard statistical range to detect an outlier anomaly; and
determine whether a journal entry property fits into a pre-defined acceptable range to detect a rule based anomaly.

### Claim 19 (depends on 16)

The non-transitory computer readable medium as claimed in claim 16, wherein to determine the one or more probable the processor is to:
ascertain whether the extracted entity and the field have a one to one mapping, based on the historical data associated with the extracted entity;
when the extracted entity and the field have the one to one mapping, select a corresponding value of the field as the value;
when the extracted entity and the field do not have the one to one mapping, obtain augmented extracted entity data; and
process the augmented extracted entity data to determine one or more values of the field.

### Claim 20 (depends on 16)

The non-transitory computer readable medium as claimed in claim 16, wherein the processor is to:
receive the notification indicating that the data includes one of the potential error and the anomaly;
generate a hypothesis providing an explanation for the potential error and the anomaly; and
provide a remedial action for correcting the potential error and the anomaly.