US-12626154-B2 - Apparatus and a method for the generation of a judgment score

Abstract

An apparatus for the generation of a judgment score is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of judgment data from a user. The memory instructs the processor to identify a plurality of correction data as a function of the plurality of judgment data. The memory instructs the processor to generate one or more correction factors as a function of the plurality of correction data. The memory instructs the processor to identify a case group as a function of the one or more correction factors. The memory instructs the processor to generate a judgment score as a function of the case group.

Inventors

• Bruce Bryan

Assignees

• Bruce Bryan

Dates

Publication Date

20260512

Application Date

20231002

Claims (18)

1. 1 . An apparatus for the generation of a judgment score, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: generate a plurality of judgment data using a web crawler configured to generate a web query to search for at least a judgment record based on a time criteria and wherein an optical character recognition (OCR) is used to convert at least a portion of the plurality of judgment data into machine-encoded text, wherein converting the at least a portion of the plurality of judgment data into the machine-encoded text comprises converting images of text in the at least a portion of the plurality of judgment data into the machine-encoded text and further comprises: pre-processing the images of text by de-skewing at least one image component associated with the plurality of judgment data by applying a transform operation to the image components; and implementing an OCR algorithm comprising a matrix matching process by comparing pixels of the pre-processed images to pixels of a stored glyph on a pixel-by-pixel basis; identify a plurality of correction data as a function of the OCR-processed plurality of judgment data; generate one or more correction factors as a function of the plurality of correction data using a machine learning model comprising a correction classifier and further comprising: receiving correction training data, wherein the correction training data comprises an input layer of nodes comprising a plurality of wrongful convictions, one or more intermediate layers of nodes, and an output layer of nodes comprising a plurality of correction data; training, iteratively, the correction classifier using the correction training data, wherein training the correction classifier comprises: assigning weighted values to nodes in adjacent layers of the correction training data; and adjusting connections and the weighted values between the nodes in adjacent layers of the correction training data to produce a desired output based on at least user inputs indicating a sub-optimal performance received by the at least processor by performing an auditing process configured to compare outputs of the correction classifier to a convergence test to reconfigure a network of nodes; and generating the one or more correction factors as a function of the plurality of correction data using the trained correction classifier; wherein the at least a processor is further configured to: upsample the correction training data, using at least one of: a set of interpolation rules in order to predict interpolated data associated with the correction training data; a sample expander method for adding expander data associated with the correction training data; and a filter for filtering the correction training data in accordance with a frequency; downsample, using a compressor, the correction training data by removing an nth entry in a sequence of correction training data; identify a case group as a function of the one or more correction factors; and generate a judgment score as a function of the case group.
2. 2 . The apparatus of claim 1 , wherein the one or more correction factors comprises participant factors.
3. 3 . The apparatus of claim 1 , wherein the one or more correction factors comprises venue factors.
4. 4 . The apparatus of claim 1 , wherein the one or more correction factors comprises group factors.
5. 5 . The apparatus of claim 1 , wherein identifying the plurality of correction data comprises identifying one or more wrongful convictions as a function of the judgment data.
6. 6 . The apparatus of claim 1 , wherein identifying the plurality of correction data further comprises: determining an element of impropriety as a function of the plurality of judgment data; and identifying the plurality of correction data as a function of the element of impropriety.
7. 7 . The apparatus of claim 1 , wherein generating the one or more correction factors comprises generating the one or more correction factors as a function of a classification of one or more wrongful convictions into one or more correction categories.
8. 8 . The apparatus of claim 1 , wherein identifying the case group comprises classifying one or more traits of a case to the one or more correction factors.
9. 9 . A method for the generation of a judgment score, wherein the method comprises: generating, using at least a processor, a plurality of judgment data using a web crawler configured to generate a web query to search for at least a judgment record based on a time criteria and wherein an optical character recognition (OCR) is used to convert at least a portion of the plurality of judgment data into machine-encoded text, wherein converting the at least a portion of the plurality of judgment data into the machine-encoded text comprises converting images of text in the at least a portion of the plurality of judgment data into the machine-encoded text and further comprises: pre-processing the images of text by de-skewing at least one image component associated with the plurality of judgment data by applying a transform operation to the image components; and implementing an OCR algorithm comprising a matrix matching process by comparing pixels of the pre-processed images to pixels of a stored glyph on a pixel-by-pixel basis; identifying, using the at least a processor, a plurality of correction data as a function of the plurality of judgment data; generating, using the at least a processor, one or more correction factors as a function of the plurality of correction data using a machine learning model comprising a correction classifier and further comprising: receiving correction training data, wherein the correction training data comprises an input layer of nodes comprising a plurality of wrongful convictions, one or more intermediate layers of nodes, and an output layer of nodes comprising a plurality of correction data; training, iteratively, the correction classifier using the correction training data, wherein training the correction classifier comprises: assigning weighted values to nodes in adjacent layers of the correction training data; and adjusting connections and the weighted values between the nodes in adjacent layers of the correction training data to produce a desired output based on at least user inputs indicating a sub-optimal performance received by the at least processor by performing an auditing process configured to compare outputs of the correction classifier to a convergence test to reconfigure a network of nodes; and generating the one or more correction factors as a function of the plurality of correction data using the trained correction classifier; upsampling the correction training data, using at least one of: a set of interpolation rules in order to predict interpolated data associated with the correction training data; a sample expander method for adding expander data associated with the correction training data; and a filter for filtering the correction training data in accordance with a frequency; downsampling, using a compressor, the correction training data by removing an nth entry in a sequence of correction training data; identifying, using the at least a processor, a case group as a function of the one or more correction factors; and generating, using the at least a processor, a judgment score as a function of the case group.
10. 10 . The method of claim 9 , wherein the one or more correction factors comprises participant factors.
11. 11 . The method of claim 9 , wherein the one or more correction factors comprises venue factors.
12. 12 . The method of claim 9 , wherein the one or more correction factors comprises group factors.
13. 13 . The method of claim 9 , wherein identifying the plurality of correction data comprises identifying one or more wrongful convictions as a function of the judgment data.
14. 14 . The method of claim 9 , wherein identifying the plurality of correction data further comprises: determining an element of impropriety as a function of the plurality of judgment data; and identifying the plurality of correction data as a function of the element of impropriety.
15. 15 . The method of claim 9 , wherein generating the one or more correction factors comprises generating the one or more correction factors as a function of a classification of one or more wrongful convictions into one or more correction categories.
16. 16 . The method of claim 9 , wherein identifying the case group comprises classifying one or more traits of a case to the one or more correction factors.
17. 17 . The apparatus of claim 1 , further comprising at least one of: an anti-aliasing filter; an anti-imaging filter and a low-pass filter configured to clean an output associated with the compressor.
18. 18 . The method of claim 9 , further comprising cleaning, by least one of: an anti-aliasing filter; an anti-imaging filter and a low-pass filter, an output associated with the compressor.

Description

FIELD OF THE INVENTION The present invention generally relates to the field of data analysis. In particular, the present invention is directed to an apparatus and a method for the generation of a judgment score. BACKGROUND Identifying problematic or unreliable data in a dataset can be a challenge, particularly when the problematic data patterns are unknown. Furthermore, the use of data regarding outcomes related to the dataset to find problematic data patterns has not been satisfactorily investigated. Finally, a method for identifying instances of the problematic or unreliable data patterns within a large body of data and notifying stakeholders if an instance is detected is needed. SUMMARY OF THE DISCLOSURE In an aspect, an apparatus for the generation of a judgment score is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of judgment data from a user. The memory instructs the processor to identify a plurality of correction data as a function of the plurality of judgment data. The memory instructs the processor to generate one or more correction factors as a function of the plurality of correction data. The memory instructs the processor to identify a case group as a function of the one or more correction factors. The memory instructs the processor to generate a judgment score as a function of the case group. In another aspect, a method for the generation of a judgment score is disclosed. The method includes receiving, using at least a processor, a plurality of judgment data from a user. The method includes generating, using the at least a processor, one or more correction factors as a function of the plurality of correction data. The method includes identifying, using the at least a processor, a case group as a function of the one or more correction factors. The method includes generating, using the at least a processor, a judgment score as a function of the case group. These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein: FIG. 1 is a block diagram of an exemplary embodiment of an apparatus for the generation of a judgment score; FIG. 2 is a block diagram of an exemplary machine-learning process; FIG. 3 is a block diagram of an exemplary embodiment of a correction database; FIG. 4 is a diagram of an exemplary embodiment of a neural network; FIG. 5 is a diagram of an exemplary embodiment of a node of a neural network; FIG. 6 is an illustration of an exemplary embodiment of fuzzy set comparison; FIG. 7 is a flow diagram of an exemplary method for the generation of a judgment score; and FIG. 8 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof. The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. DETAILED DESCRIPTION At a high level, aspects of the present disclosure are directed to an apparatus and a method for the generation of a judgment score is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a plurality of judgment data from a user. The memory instructs the processor to identify a plurality of correction data as a function of the plurality of judgment data. The memory instructs the processor to generate one or more correction factors as a function of the plurality of correction data. The memory instructs the processor to identify a case group as a function of the one or more correction factors. The memory instructs the processor to generate a judgment score as a function of the case group. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for the generation of a judgment score is illustrated. Apparatus 100 includes a processor 104. Processor 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip