DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This communication is responsive to the amendment filed 2/24/2026.
Claims 1, 9, and 14 have been amended and no claims have been added and/or canceled.
Claims 1-18 are pending with claims 1, 9, and 14 as independent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process without significantly more. The claim 1 (and claim 9) recites “classifying the data files using at least the document information and a machine learning model to generate tabular data and non-tabular data, the machine learning model being trained using a plurality of document formats; transforming the tabular data into columns, rows and corresponding values using a stepwise vertical threshold defining a maximum acceptable distance between two vertically
adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities; transforming the non-tabular data into key and value pairs by identifying the entities in the data files as keys; defining a heuristic search area around the keys; and evaluating one or more entities within the heuristic search area based on content similarity, relative distance, and orientation to identify and map values corresponding to the keys. These underlined functions can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, therefore the claim is reciting a mental process.
This judicial exception is not integrated into a practical application because the mental process is merely applied using a general-purpose computer (a machine learning model), with the operations comprising insignificant extra-solution activity.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional underlined elements of accessing data files; extracting document information from the data files, wherein the document information includes entities comprising words and phrases grouped from text characters within the data files; storing the document information at a data storage; generating data objects using at least one of the transformed tabular data and the transformed non-tabular data; providing an output data file comprising the data objects; and updating the machine learning data using at least the output data file are insignificant extra-solution activity of mere data gathering.
These additional elements of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 2 is dependent on claim 1 and therefore inherits the same judicial exception recited in claim 1. Claim 2 also recites the operation of removing noise from the document information. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process.
The judicial exceptions recited in claims 1 and 2 are not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 3 is dependent on claim 1, and therefore inherits the same judicial exception recited in claim 1. Claim 3 also recites the additional element of the machine learning model comprises a transformer-based model, a bidirectional encoder, or a masked visual-language model, which is well-understood, routine or conventional activity, and therefore is insignificant extra-solution activity. The additional element of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination with the additional elements of claim 1, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 4 is dependent on claim 3, and therefore inherits the same judicial exception recited in claim 3. Claim 4 also recites the additional element of wherein the machine learning model is a fine tuned LayoutLM model, which is well-understood, routine or conventional activity, and therefore is insignificant extra-solution activity. The additional element of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination with the additional elements of claims 1 and 3, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 5 is dependent on claim 1 and therefore inherits the same judicial exception recited in claim 1. Claim 5 also recites the operation of classifying the data objects into tabular data or key-value pair data. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 4 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only
additional elements comprising insignificant extra-solution activity.
Claim 6 is dependent on claim 1 and therefore inherits the same judicial exception recited in claim 1. Claim 6 also recites the operation of generating a table using the
data objects, the table comprising a header that is based at least on the tabular data. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 6 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 7 is dependent on claim 6 and therefore inherits the same judicial exception recited in claim 6. Claim 7 also recites the operation of determining a column resolution and a row resolution based at least on the tabular data. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 7 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 8 is dependent on claim 1 and therefore inherits the same judicial exception recited in claim 1. Claim 8 also recites the operation of comparing the output data to
ground truth data or reference data. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 8 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only
additional elements comprising insignificant extra-solution activity.
Claim 10 is dependent on claim 9 and therefore inherits the same judicial exception recited in claim 9. Claim 10 also recites the operation of identifying error patterns
associated with the output data file. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 10 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only
additional elements comprising insignificant extra-solution activity.
Claim 11 is dependent on claim 9 and therefore inherits the same judicial exception recited in claim 9. Claim 11 also recites an additional element of modifying the machine learning model using at least the error patterns. The modifying of the machine learning is used to generally apply the abstract idea without limiting how the modifying of the machine learning functions. The limitation only recite the outcome of “error patterns” and “modifying the machine learning model” and without any details about how the outcomes are accomplished. The judicial exceptions recited in claim 11 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 12 is dependent on claim 9 and therefore inherits the same judicial exception recited in claim 9. Claim 12 also recites an additional element of modifying the data extraction module using at least the error patterns. The modifying of the extraction module is used to generally apply the abstract idea without limiting how the modifying of the extraction module functions. The limitation only recites the outcome of “error patterns” and “modifying the extraction module” and without any details about how the outcomes are accomplished. The judicial exceptions recited in claim 12 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 13 is dependent on claim 9 and therefore inherits the same judicial exception recited in claim 9. Claim 13 also recites the operation of classifying the data objects into tabular data or key-value pair data. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 13 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only
additional elements comprising insignificant extra-solution activity.
The claim 14 recites “grouping or de-grouping words and phrases found in financial documents; remove unwanted or irrelevant data from text data; extract document information from the documents and classify the extracted information into tabular data and non-tabular data wherein the document information includes entities comprising words and phrases grouped from text characters within the data files; transform the tabular data into columns, rows and corresponding values using a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities; transform the non-tabular data into key and value pairs by: identifying the entities in the data files as keys; defining a heuristic search area around the keys; and evaluating one or more entities within the heuristic search area based on content similarity, relative distance, and orientation to identify and map values corresponding to the keys. These underlined functions can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, therefore the claim is reciting a mental process.
Because the step of transforming the tabular data and the non-tabular data into columns, rows, and corresponding values can reasonably be performed in human mind, the use of the stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities may be interpreted as information describing the mental process “transformation”.
This judicial exception is not integrated into a practical application because the mental process is merely applied using a general-purpose computer (computing system comprising hardware processors; a memory coupled to the one or more hardware processors), with the operations comprising insignificant extra-solution activity. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional underlined elements of a data output module configured to represent extracted data from the tabular data extraction rule module and the key value pair data extraction rule module to the users and updates other databases with the extracted information are insignificant extra-solution activities of mere post-processing. These additional elements of insignificant extra-solution activities and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 15 is dependent on claim 14 and therefore inherits the same judicial exception recited in claim 14. Claim 15 also recites the operation of scrape words, phrases, numbers, special characters and corresponding metadata from data files. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claim 15 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim 16 is dependent on claim 14, and therefore inherits the same judicial exception recited in claim 14. Claim 16 also recites the additional element of the content classification ML module is a fine-tuned transformer-based model, a bidirectional encoder, or a masked visual-language model, which is well-understood, routine or conventional activity, and therefore is insignificant extra-solution activity. The additional element of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination with the additional elements of claim 16, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 17 is dependent on claim 14, and therefore inherits the same judicial exception recited in claim 14. Claim 17 also recites the additional element of the content classification ML module comprises a fine-tuned Layout LM model, which is well-understood, routine or conventional activity, and therefore is insignificant extra-solution activity. The additional element of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination with the additional elements of claim 17, the additional elements do not provide an inventive concept, thus the claim is not eligible.
Claim 18 is dependent on claim 14 and therefore inherits the same judicial exception recited in claim 14. Claim 18 also recites an additional elements of a re-training module configured to integrate with the data extraction pipeline, automatically assess accuracy, generate reports, and provide feedback without manual
intervention. The additional elements are used to generally apply the abstract idea without limiting how the additional elements function. The limitations only recite the outcome of the operations of the additional elements operations and without any details about how the outcomes are accomplished. The judicial exceptions recited in claim 18 is not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claims 1-13 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bajaj et al. (US 2025/0103919, filed 12/01/2022, hereinafter as Bajaj). The effective filing date for application 18/974,093 is 12/01/2022.
Claim 1. A method for processing documents, the method comprising:
accessing data files; Bajaj discloses in [0035-0037] “a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification… data extraction may comprise an operation for identifying or recognizing data, such as recognizing text or image data using OCR or image recognition models and/or algorithms.” And in [0056-0061] “a user may submit a plurality of documents via a submission service, such as a customer user interface 104 or API module 106 of FIG. 1.” (emphasis added) examiner note: textual terms, header information of a document may be words and phrases such that an OCR, image recognition model and/or an algorithm may access documents, via submission service, submitted by a user,
extracting document information from the data files, wherein the document information includes entities comprising words and phrases grouped from text characters within the data files; Bajaj discloses in [0035-0037] “a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification… data extraction may comprise an operation for identifying or recognizing data, such as recognizing text or image data using OCR or image recognition models and/or algorithms.” And in [0056-0061] “a user may submit a plurality of documents via a submission service, such as a customer user interface 104 or API module 106 of FIG. 1.” (emphasis added) examiner note: textual terms, header information of a document may be words and phrases such that an OCR, image recognition model and/or an algorithm may access documents, via submission service, submitted by a user,
storing the document information at a data storage; Bajaj discloses in [0034 and 0068] “AI module 108 may select machine learning models and/or algorithms to pre-process the plurality of documents via PrePM 112 where AI module 108 may store the information collected in PrePM 112 into DB 110… The relevant sub-documents may be stored in a database, such as, DB 110 of FIG. 1.” (emphasis added),
classifying the data files using at least the document information and a machine learning model to generate tabular data and non-tabular data, the machine learning model being trained using a plurality of document formats; Bajaj discloses in [0035] “PrePM 112 may identify the file type and/or file extension in the plurality of documents where the file types may be word files (with .doc, docx, .docm, .and/or dotm extensions), text files (with .dat, .txt, .and/or .rtf file extensions), spreadsheet files (with .xls, .xlsx, and/or .xlsm file extensions), pdf files, and/or picture files (.jpeg, .png, .tiff, .bmp, .gif, .psd, and/or .raw file extensions)… PrePM 112 may further use rule-based splitting in AI module 108's machine learning models and/or algorithms to split files into multiple documents. For example, a document such as a business financial statement may include the businesses balance sheet, income statement, cash flow statement, statement of changes in shareholder equity, and statement of comprehensive income. AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user.” And in [0056-0061] “process 400 of extracting and classifying data 404 as input from a document 402 and converting the data 404 into a structured data output 414… document 402 may comprise, e.g., a single operating statement out of a plurality of submitted operating statements and/or other documents. AI module 108 in system 100 may select machine learning models and/or algorithms to process data 404. For example, data 404 may comprise unstructured data, semi-structured data, and/or structured data.” (emphasis added) examiner note: the AI module may categorize the split document information 402 to multiple documents such structured data (balance sheet such as data 410 and 412) and semi-structured and unstructured data 406 and 408 (non-tabular data). the document information 404 may include plurality of documents of different file types,
transforming the tabular data into columns, rows and corresponding values; Bajaj discloses in [0019] “Artificial intelligence systems may be used in this system to convert the plurality of documents from unstructured data, semi-structured data, and/or structured data into a structured data format. Furthermore, artificial intelligence systems may be used to transform the structured data into a new representation or presentation of data according to one or more customer needs.” And in [0056-0061] “PM 114 may extract and classify the data 406, 408, 410, and 412 into the above categories. AI module 108 may select machine learning models and/or algorithms to construct structured data output 414 in PostPM 116 in system 100… structured data output 414 may represent a final output product that is delivered to a user. The output product may comprise, e.g., one or more databases or collections of structured data files as discussed above. The structured data may thus be presented to a user in a format tailored according to the user's needs.” (emphasis added) examiner note: the data 404, which represents tabular data 410 and 412 and non-tabular data 406 and 408, be converted to structured data 414, which may represent tabular data and non-tabular data in the form of rows and columns,
using a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities; transforming the non-tabular data into key and value pairs by identifying the entities in the data files as keys; defining a heuristic search area around the keys; and evaluating one or more entities within the heuristic search area based on content similarity, relative distance, and orientation to identify and map values corresponding to the keys; Bajaj discloses in [0035] “PrePM 112 may further use rule-based splitting in AI module 108's machine learning models and/or algorithms to split files into multiple documents. For example, a document such as a business financial statement may include the businesses balance sheet, income statement, cash flow statement, statement of changes in shareholder equity, and statement of comprehensive income. AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user. For example, AI module 108 may use rule-based splitting based on the collected information from API module 106 and PrePM 112 stored in DB 110 to decide whether to split documents into additional documents. Rule-based splitting may comprise, e.g., using a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification.” And in [0052-0053] “extraction models typically perform better when their input data is more consistent. For instance, if there is greater consistency in the location on a page at which target information is found across all documents used for training, or if there is more consistency in the set of terms across all documents used for training (e.g. loan specific terms as compared to names as would appear in a death certificate), the model may more quickly and accurately establish relationships mapping inputs to outputs, and thus performance of the model is improved… tuning may comprise teaching a model how to identify or extract certain data, such as a patient identification number, by learning specific information about how the data is contained within the specific patient records that are being processed. Specific information may comprise, e.g. a location of the data within the document, associated object fields, contextual information, or a format of the patient identification number… the first through fourth image extraction models may comprise different classes of image extraction models, the same class of image extraction models that have been trained for different tasks, or may comprise identical text extraction models.” (emphasis added) examiner note: the system AI module may use rules to identify entities, e.g. textual terms, header information, metadata, format of structured data, image data and/or signature fields (keys). Learning specific information may indicate to find particular information, e.g. patient ID number, in particular area in the document and evaluate to whether the patient ID number found in the particular location of the document matches a class label based on learned information. Accordingly, the stepwise vertical threshold defining a maximum acceptable distance between tow vertically adjacent entities and the stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities may be rules applied by the AI module to identify format of the structured data, image data, signature fields, etc.,
generating data objects using at least one of the transformed tabular data and the transformed non-tabular data; Bajaj discloses in [0056-0064] the columns and rows of structured data 414 may represent objects generated by converting classified data 404,
providing an output data file comprising the data objects; and updating the machine learning data using at least the output data file. Bajaj discloses in [0056-0061] “a structured data output may be further refined before it is presented to a user, such as by a human review and/or model retraining process… process 600 for model retraining based on corrections from a human review user interface, such as HRUI 118 of FIG. 1. Process 600 may be used to retrain one or more of the plurality of machine learning models and/or algorithms to improve the accuracy and efficiency of, e.g., processing or post-processing such as data extraction, classification etc.” (emphasis added).
Claim 2. The rejection of the method of claim 1 is incorporated, wherein extracting document information further comprises removing noise from the document information. Bajaj discloses in [0054] “The post-processing module may perform a number of post-processing operations such as, e.g., scrubbing of model output data, standardization and restructuring, generation of output formats, generation of human review tasks, noise removal, and transmission of output data to further systems and modules.” (emphasis added).
Claim 3. The rejection of the method of claim 1 is incorporated, wherein the machine learning model comprises a transformer-based model, a bidirectional encoder, or a masked visual-language model. Bajaj discloses in [0025] “An AI-based natural language processing algorithm may process the free-form text description as the selection command, or it may utilize the free-form text description to, e.g., hone in on the user's needs and narrow the number of available menu selections.” (emphasis added) examiner note: the natural language processing (NLP) algorithm may be a masked visual-language model.
Claim 4. The rejection of the method of claim 3 is incorporated, wherein the machine learning model is a fine tuned LayoutLM model. Bajaj discloses in [0035] “PrePM 112 may also use the optical character recognition (OCR) available in AI module 108's machine learning models and/or algorithms to collect information about the layout, arrangement or other format of the plurality of documents.” (emphasis added) examiner note: the AI module 108’s machine learning model collects information about layout arrangement or format of the plurality of documents, which may indicates that the model may be layout LM model.
Claim 5. The rejection of the method of claim 1 is incorporated, further comprising classifying the data objects into tabular data or key-value pair data. Bajaj discloses in [0035] “AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user.” (emphasis added) examiner note: as can be seen in fig. 4, data 410 and 412 may be balance sheet file (data objects) transformed into tabular data in output document 414.
Claim 6. The rejection of the method of claim 1 is incorporated, further comprising generating a table using the data objects, the table comprising a header that is based at least on the tabular data. Bajaj discloses in [0035] “AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user.” (emphasis added) examiner note: as can be seen in fig. 4, data 410 and 412 may be balance sheet file (data objects) transformed into tabular data in output document 414, wherein output document 414 may comprise header such “Reimbursable” and/or “Revenue” as shown in document 414.
Claim 7. The rejection of the method of claim 6 is incorporated, further comprising determining a column resolution and a row resolution based at least on the tabular data. Bajaj discloses in [0060] “Document 402 may comprise one of a plurality of similar documents that are submitted for processing. For example, document 402 may comprise, e.g., a single operating statement out of a plurality of submitted operating statements and/or other documents. AI module 108 in system 100 may select machine learning models and/or algorithms to process data 404. For example, data 404 may comprise unstructured data, semi-structured data, and/or structured data. Data 404 may comprise various types of data such as, e.g., document title 406, revenue fields 408, month-to-date values 410, and year-to-date values 412. PM 114 may extract and classify the data 406, 408, 410, and 412 into the above categories. AI module 108 may select machine learning models and/or algorithms to construct structured data output 414 in PostPM 116 in system 100.” (emphasis added) examiner note: month-to-date column and year-to-date column in classified data 404 may be resolved to “Reported” column header and “Actual” column in output document 414. Similarly, revenue row data 408 corresponding to classified data 404 may be resolved to numerated row data in output document 414 as shown in fig. 4.
Claim 8. The rejection of the method of claim 1 is incorporated, further comprising comparing the output data to ground truth data or reference data. Bajaj discloses in [0041] “AI module 108 may obtain feedback from HRUI 118 and retrieve the collected information from PostPM 116 or the combination of API module 106, PrePM 112, PM 114, and/or PostPM 116 saved in DB 110 to compare the feedback received from HRUI 118 against the collected information from PM 114 or the combination of API module 106, PrePM 112, and/or PM 114… AI module 108 may learn from the feedback to later adjust its machine learning models and/or algorithms or optimize the selection of certain types of machine learning models and/or algorithms to optimize the customized new presentation of data or display of data.” (emphasis added) examiner note: the feedback may be ground truth data that may be utilized to optimize selected machine learning models.
Claim 9. A method for processing documents, the method comprising:
extracting document information from data files by a data extraction module wherein the document information includes entities comprising words and phrases grouped from text characters within the data files; Bajaj discloses in [0035-0037] “a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification… data extraction may comprise an operation for identifying or recognizing data, such as recognizing text or image data using OCR or image recognition models and/or algorithms.” And in [0056-0061] “a user may submit a plurality of documents via a submission service, such as a customer user interface 104 or API module 106 of FIG. 1.” (emphasis added) examiner note: textual terms, header information of a document may be words and phrases such that an OCR, image recognition model and/or an algorithm may access documents, via submission service, submitted by a user,
classifying the data files using at least the document information and a machine learning model to generate tabular data or non-tabular data, the machine learning model being trained using a plurality of document formats; Bajaj discloses in [0035] “PrePM 112 may identify the file type and/or file extension in the plurality of documents where the file types may be word files (with .doc, docx, .docm, .and/or dotm extensions), text files (with .dat, .txt, .and/or .rtf file extensions), spreadsheet files (with .xls, .xlsx, and/or .xlsm file extensions), pdf files, and/or picture files (.jpeg, .png, .tiff, .bmp, .gif, .psd, and/or .raw file extensions)… PrePM 112 may further use rule-based splitting in AI module 108's machine learning models and/or algorithms to split files into multiple documents. For example, a document such as a business financial statement may include the businesses balance sheet, income statement, cash flow statement, statement of changes in shareholder equity, and statement of comprehensive income. AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user.” And in [0056-0061] “process 400 of extracting and classifying data 404 as input from a document 402 and converting the data 404 into a structured data output 414… document 402 may comprise, e.g., a single operating statement out of a plurality of submitted operating statements and/or other documents. AI module 108 in system 100 may select machine learning models and/or algorithms to process data 404. For example, data 404 may comprise unstructured data, semi-structured data, and/or structured data.” (emphasis added) examiner note: the AI module may categorize the split document information 402 to multiple documents such structured data (balance sheet such as data 410 and 412) and semi-structured and unstructured data 406 and 408 (non-tabular data). the document information 404 may include plurality of documents of different file types,
transforming the tabular data into columns, rows and corresponding values
using a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities; transforming the non-tabular data into key and value pairs by identifying the entities in the data files as keys; defining a heuristic search area around the keys; and evaluating one or more entities within the heuristic search area based on content similarity, relative distance, and orientation to identify and map values corresponding to the keys; Bajaj discloses in [0035] “PrePM 112 may further use rule-based splitting in AI module 108's machine learning models and/or algorithms to split files into multiple documents. For example, a document such as a business financial statement may include the businesses balance sheet, income statement, cash flow statement, statement of changes in shareholder equity, and statement of comprehensive income. AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user. For example, AI module 108 may use rule-based splitting based on the collected information from API module 106 and PrePM 112 stored in DB 110 to decide whether to split documents into additional documents. Rule-based splitting may comprise, e.g., using a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification.” And in [0052-0053] “extraction models typically perform better when their input data is more consistent. For instance, if there is greater consistency in the location on a page at which target information is found across all documents used for training, or if there is more consistency in the set of terms across all documents used for training (e.g. loan specific terms as compared to names as would appear in a death certificate), the model may more quickly and accurately establish relationships mapping inputs to outputs, and thus performance of the model is improved… tuning may comprise teaching a model how to identify or extract certain data, such as a patient identification number, by learning specific information about how the data is contained within the specific patient records that are being processed. Specific information may comprise, e.g. a location of the data within the document, associated object fields, contextual information, or a format of the patient identification number… the first through fourth image extraction models may comprise different classes of image extraction models, the same class of image extraction models that have been trained for different tasks, or may comprise identical text extraction models.” (emphasis added) examiner note: the system AI module may use rules to identify entities, e.g. textual terms, header information, metadata, format of structured data, image data and/or signature fields (keys). Learning specific information may indicate to find particular information, e.g. patient ID number, in particular area in the document and evaluate to whether the patient ID number found in the particular location of the document matches a class label based on learned information. Accordingly, the stepwise vertical threshold defining a maximum acceptable distance between tow vertically adjacent entities and the stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities may be rules applied by the AI module to identify format of the structured data, image data, signature fields, etc. The key-value pair data may be the specification that comprises entity name and location information such that format of structured data may be identified for classifying the input documents,
generating data objects using at least one of the transformed tabular data and the transformed non-tabular data; Bajaj discloses in [0056-0064] the columns and rows of structured data 414 may represent objects generated by converting classified data 404 as shown in fig. 4,
providing an output data file comprising the data objects; Bajaj discloses in [0056-0061] “a structured data output may be further refined before it is presented to a user, such as by a human review and/or model retraining process… process 600 for model retraining based on corrections from a human review user interface, such as HRUI 118 of FIG. 1. Process 600 may be used to retrain one or more of the plurality of machine learning models and/or algorithms to improve the accuracy and efficiency of, e.g., processing or post-processing such as data extraction, classification etc.” (emphasis added),
providing an accuracy assessment by comparing the output data file to reference data or ground truth data; Bajaj discloses in [0041] “AI module 108 may obtain feedback from HRUI 118 and retrieve the collected information from PostPM 116 or the combination of API module 106, PrePM 112, PM 114, and/or PostPM 116 saved in DB 110 to compare the feedback received from HRUI 118 against the collected information from PM 114 or the combination of API module 106, PrePM 112, and/or PM 114… AI module 108 may learn from the feedback to later adjust its machine learning models and/or algorithms or optimize the selection of certain types of machine learning models and/or algorithms to optimize the customized new presentation of data or display of data.” (emphasis added) examiner note: the feedback may be ground truth data that may be utilized to optimize selected machine learning models, and
modifying the machine learning model using at least the accuracy assessment. Bajaj discloses in [0041] “AI module 108 may obtain feedback from HRUI 118 and retrieve the collected information from PostPM 116 or the combination of API module 106, PrePM 112, PM 114, and/or PostPM 116 saved in DB 110 to compare the feedback received from HRUI 118 against the collected information from PM 114 or the combination of API module 106, PrePM 112, and/or PM 114… AI module 108 may learn from the feedback to later adjust its machine learning models and/or algorithms or optimize the selection of certain types of machine learning models and/or algorithms to optimize the customized new presentation of data or display of data.” (emphasis added) examiner note: the feedback may be ground truth data that may be utilized to optimize selected machine learning models.
Claim 10. The rejection of the method of claim 9 is incorporated, further comprising identifying error patterns associated with the output data file. Bajaj discloses in [0056] “If any information is inconsistent with an expectation, an error response may be issued to the API as illustrated below box 1.” And in [0064] “Process 600 may be used for retraining the machine learning models and/or algorithms to avoid such errors.” And in [0067] “When it is determined that the root cause of a correction does result from an error in an extraction model or a post-processing model, the model retraining process 600 may proceed to the further retraining steps 605-608 of annotation, validation, regression testing, and promotion.” (emphasis added) examiner note: the issue of errors and inconsistent information may be identified errors.
Claim 11. The rejection of the method of claim 9 is incorporated, further comprising modifying the machine learning model using at least the error patterns. Bajaj discloses in [0067] “When it is determined that the root cause of a correction does result from an error in an extraction model or a post-processing model, the model retraining process 600 may proceed to the further retraining steps 605-608 of annotation, validation, regression testing, and promotion.” And in [0072] “the selective use of human review may help to minimize repetitive task fatigue that could introduce errors. Human reviewers may be able to focus on applying their subject matter expertise when reviewing the output data provided from a model inference, incorporating their corrections into the machine learning models and/or algorithms for retraining, allowing the machine learning models and/or algorithms to efficiently, quickly and effectively relearn in targeted scenarios.” (emphasis added) examiner note: the issue of errors and inconsistent information may be identified errors and retrain the machine learning models may be to modify them.
Claim 12. The rejection of the method of claim 9 is incorporated, further comprising modifying the data extraction module using at least the error patterns. Bajaj discloses in [0066] “at step 604 the plurality of machine learning models and/or algorithms may determine whether the root cause of the difference from step 602 was due to an action at an extraction model or due to a post-processing model… some pre-processing operations may comprise OCR issues, such as interpreting a numeral 8 as a capital letter “B.” A correction to this discrepancy may be disregarded for retraining purposes because the error did not result from a processing action within, e.g., a processing module or a post-processing module.” And in [0072](emphasis added) examiner note: the retraining to the extraction model may be modifying the data extraction module.
Claim 13. The rejection of the method of claim 9 is incorporated, further comprising classifying the data files into tabular data, key-value pair data, or value categories. Bajaj discloses in [0037] “PM 114 may be configured to extract a first data set from the plurality of documents based on, e.g., a plurality of machine learning models using the identification of the file type and the format of the plurality of documents…PM 114 may further be configured to classify the first data set based on the plurality of machine learning models using the identification of the file type and the format of the plurality of documents.” And in [0039] “PostPM 116 may be configured to reconstruct the first data set into a structured data set based on the plurality of machine learning models using the extraction and classification of the first data set. The structured data may be a tabular structure of the data extracted and classified in PM 114 based on AI module 108 retrieving collected information from API module 106, PrePM 112, and/or PM 114.” (emphasis added) examiner note: the first data set may be data files that has been classified as tabular data.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bajaj et al. (US 2025/0103919, filed 12/1/2022, hereinafter as Bajaj) in view of Muthu et al. (US 2023/0196813, published 6/22/2023, hereinafter as Muthu).
Claim 14. A machine learning based computing system for processing documents,
the machine learning (ML) based computing system comprising:
one or more hardware processors; and a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of modules in the form of programmable instructions executable by the one or more hardware processors, (Bajaj discloses in [0028] “System 100 may include a memory storing instructions and at least one processor to execute the instructions in IDP 102.”) and wherein the plurality of modules comprises:
a document acquisition module configured to handle the input of the data files in the computing system; Bajaj discloses in [0024-0025 and 0049] “Customer UI 104 may comprise a first user interface through which a plurality of documents may be received into IDP 102… At the start of process flow 300A, a user may submit a plurality of documents via a submission service 304 or other calling application, such as a customer user interface 104 or API module 106 of FIG. 1.” (emphasis added),
a noise removal module configured to remove unwanted or irrelevant data from text data; Bajaj discloses in [0054] “The post-processing module may perform a number of post-processing operations such as, e.g., scrubbing of model output data, standardization and restructuring, generation of output formats, generation of human review tasks, noise removal, and transmission of output data to further systems and modules.” (emphasis added),
a content classification ML module configured to extract document information from documents and classify the extracted information into tabular and non-tabular data wherein the document information includes entities comprising words and phrases grouped from text characters within the data files; Bajaj discloses in [0020] “system 100 may process data and/or information contained in a plurality of documents for one or more different business organizations or groups (e.g., a variety of financial service organizations). The plurality of documents for the one or more different business organizations or groups may include retail business documents, asset management documents, treasury management documents, commercial lending documents, corporate lending documents, health care documents, corporate financial documents, business transaction documents, invoices, receipts, and/or any other documents containing data.” And in [0030] “a customer may request that health care business documents be transformed into corporate financial documents and/or any other plurality of documents for different one or more business organizations or groups… The requested transformation may comprise, e.g., identifying, extracting, transforming, combining or converting this value information into corporate lending documents, such collateral statements for securing corporate loans.” And in [0035-0037] “a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification… data extraction may comprise an operation for identifying or recognizing data, such as recognizing text or image data using OCR or image recognition models and/or algorithms.” And in [0039] “PostPM 116 may be configured to reconstruct the first data set into a structured data set based on the plurality of machine learning models using the extraction and classification of the first data set. The structured data may be a tabular structure of the data extracted and classified in PM 114 based on AI module 108 retrieving collected information from API module 106, PrePM 112, and/or PM 114.” And in [0055] the plurality of documents may be routed to a subsequent machine learning model as selected and/or directed by a prior machine learning model in the process flow, such as by applying built-in logic to the classification or extraction results of the machine learning model. In some embodiments, the determination of the classification models to which documents are first routed may depend upon a calling application.” (emphasis added) examiner note: textual terms, header information of a document may be words and phrases such that an OCR, image recognition model and/or an algorithm may access documents, via submission service, submitted by a user,
a tabular data extraction rule module configured to transform the tabular data into columns, rows and corresponding values using a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities; a key value pair data extraction rule module configured to transform the nontabular data into key and value pairs by: identifying the entities in the data files as keys; defining a heuristic search area around the keys; and evaluating one or more entities within the heuristic search area based on content similarity, relative distance, and orientation to identify and map values corresponding to the keys; Bajaj discloses in [0035] “PrePM 112 may further use rule-based splitting in AI module 108's machine learning models and/or algorithms to split files into multiple documents. For example, a document such as a business financial statement may include the businesses balance sheet, income statement, cash flow statement, statement of changes in shareholder equity, and statement of comprehensive income. AI module 108 may use rules to identify the type of content in the business financial statement to independently determine to split this business financial statement into six different files being a balance sheet file, an income statement file, cash flow statement file, statement of changes in shareholder equity file, and statement of comprehensive income file. The splitting of the document into multiple files may allow AI module 108 to efficiently categorize the unstructured data, the semi-structured data, and/or the structured data in those files for efficient processing of the required information for presentation to a user. For example, AI module 108 may use rule-based splitting based on the collected information from API module 106 and PrePM 112 stored in DB 110 to decide whether to split documents into additional documents. Rule-based splitting may comprise, e.g., using a set of predefined or learned rules that relate a characteristic of a document (such as specific textual terms, header information, metadata, a format of structured data, the presence of image data or signature fields, etc.) with a particular classification.” And in [0052-0053] “extraction models typically perform better when their input data is more consistent. For instance, if there is greater consistency in the location on a page at which target information is found across all documents used for training, or if there is more consistency in the set of terms across all documents used for training (e.g. loan specific terms as compared to names as would appear in a death certificate), the model may more quickly and accurately establish relationships mapping inputs to outputs, and thus performance of the model is improved… tuning may comprise teaching a model how to identify or extract certain data, such as a patient identification number, by learning specific information about how the data is contained within the specific patient records that are being processed. Specific information may comprise, e.g. a location of the data within the document, associated object fields, contextual information, or a format of the patient identification number… the first through fourth image extraction models may comprise different classes of image extraction models, the same class of image extraction models that have been trained for different tasks, or may comprise identical text extraction models.” (emphasis added) examiner note: the system AI module may use rules to identify entities, e.g. textual terms, header information, metadata, format of structured data, image data and/or signature fields (keys). Learning specific information may indicate to find particular information, e.g. patient ID number, in particular area in the document and evaluate to whether the patient ID number found in the particular location of the document matches a class label based on learned information. Accordingly, the stepwise vertical threshold defining a maximum acceptable distance between tow vertically adjacent entities and the stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities may be rules applied by the AI module to identify format of the structured data, image data, signature fields, etc. The key-value pair data may be the specification that comprises entity name and location information such that format of structured data may be identified for classifying the input documents, and
a data output module configured to represent extracted data from the tabular data extraction rule module and key value pair data extraction rule module to the users and updates other databases with the extracted information. Bajaj discloses in [0068] “a human review annotator may annotate the correction for retraining. The human review annotator may locate the relevant document for the correction, split the document into the relevant sub-documents that include the change, and add the relevant sub-documents to a training set for the machine learning model and/or algorithm to be retrained. Some aspects of the annotation process may be automated and/or may have been performed prior to annotation. For example, a document may already have been split into relevant sub-documents during a pre-processing step. The relevant sub-documents may be stored in a database, such as, DB 110 of FIG. 1.” (emphasis added) examiner note: the human review annotator may be a user that makes change to relevant data in at least one sub-document and updates database 110 by adding the sub-document to the database.
Bajaj does not explicitly disclose
a document scraper module configured for parsing and scraping data from the document; a content processing module configured for grouping or de-grouping words and phrases found in financial documents. However, Muthu, in an analogous art, discloses in [0040] “For purposes of clarity, specific terms are defined below that are related to the system: (a) Chunk—individual phrase/word(s) of text identified in a OCR-ed document along with location and size; (b) Clustering—grouping of the chunks into phrases, sentences and paragraphs;… (g) Edge Padding Ratio—ratio of the empty region around a paragraph to the region with text, used to identify lines/phrases that appear adjacent to a paragraph but are not part of the paragraph and exclude them; and (h) Hanging Line Detection—detection of lines of text at beginning and end of a paragraph and including them in the cluster.” And in [0041] “Content clustering, whereby content of OCR-ed source documents is parsed and chunks are identified, is performed next… the chunks are then clustered with the modified DBSCAN-based clustering algorithm to find text groups/phrases and paragraphs.” (emphasis added).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Bajaj with the teaching of Muthu to provide “adaptive density-based spatial clustering on the first set of text chunks, thereby identifying clusters of text in the first content”. The advantage is to “solve the problems discussed above by providing a machine learning model that is designed to intelligently identify chunks of text, map the fields in the document, and extract values from complex tables.” Muthu [0004-0006].
Claim 15. The rejection of the machine learning based computing system of claim 14 is incorporated, wherein the document scraper module is configured to scrape words, phrases, numbers, special characters and corresponding metadata from data files. Bajaj discloses in [0037] “the AI module 108 may select a single machine learning model and/or algorithm to perform the data extraction and classification based on information saved in DB 110 from API module 106 and/or PrePM 112. For example, data extraction may comprise an operation for identifying or recognizing data, such as recognizing text or image data using OCR or image recognition models and/or algorithms. Classification may comprise sorting the extracted information into predetermined or learned categories.” And in [0053] “The model may be provided with further training data based specifically on the format or content of the customer's health care patient records to tune the model to the specific requested task. For example, tuning may comprise teaching a model how to identify or extract certain data, such as a patient identification number, by learning specific information about how the data is contained within the specific patient records that are being processed.”
Claim 16. The rejection of the machine learning based computing system of claim 14 is incorporated, wherein the content classification ML module is a fine-tuned transformer-based model, a bidirectional encoder, or a masked visual-language model. Bajaj discloses in [0025] “An AI-based natural language processing algorithm may process the free-form text description as the selection command, or it may utilize the free-form text description to, e.g., hone in on the user's needs and narrow the number of available menu selections.” (emphasis added) examiner note: the natural language processing (NLP) algorithm may be a masked visual-language model.
Claim 17. The rejection of the machine learning based computing system of claim 14 is incorporated, wherein the content classification ML module comprises a fine-tuned Layout LM model. Bajaj discloses in [0035] “PrePM 112 may also use the optical character recognition (OCR) available in AI module 108's machine learning models and/or algorithms to collect information about the layout, arrangement or other format of the plurality of documents.” (emphasis added) examiner note: the AI module 108’s machine learning model collects information about layout arrangement or format of the plurality of documents, which may indicates that the model may be layout LM model.
Claim 18. The rejection of the machine learning based computing system of claim 14 is incorporated, further comprises a re-training module configured to integrate with the data extraction pipeline, automatically assess accuracy, generate reports, and provide feedback without manual intervention. Bajaj discloses in [0037] “AI module 108 may construct a table reporting the accuracy, precision, and confidence level of the extracted and classified data processed in PM 114 for each machine learning model and/or algorithm used for PM 114. For example, accuracy may refer to the proportion of a set of data values that are correctly extracted or identified. Precision may characterize the consistency of an extraction of classification operation, such as how closely a set a of similar extractions match each other. A confidence level may represent a probability that an extracted or classified value or class of values is correct. The table reporting the accuracy, precision, and confidence level associated with each of the machine learning models and/or algorithms may allow AI module 108 to learn about the strengths and weaknesses of each of the machine learning models and/or algorithms.” (emphasis added) examiner note: the learning about the strength and weaknesses may be based on assessing accuracy and provided feedback.
Response to Arguments
Applicant's arguments filed 2/24/2026 have been fully considered but they are not persuasive.
Argument: with regard to the claim rejections based 35 USC 101, Applicant argues “claim 1 requires transforming tabular data using a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities… The human mind does not calculate spatial "stepwise" vertical or horizontal thresholds representing maximum acceptable distances in a digital file to read a table, nor does it define mathematical heuristic search areas and calculate orientation degrees to map a key to a value. Because the claimed steps are rooted in specific spatial and geometric calculations that cannot be practically performed in the human mind, the claims do not recite a mental process. Therefore, Applicant requests that the 35 U.S.C. § 101 rejection of the claims as allegedly being directed toward an abstract idea of a mental process be reconsidered and withdrawn.”
Response: the underlined function of “transforming the tabular data into columns, rows and corresponding values” has been interpreted as a function that can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, therefore the claim is reciting a mental process. The use of “a stepwise vertical threshold defining a maximum acceptable distance between two vertically adjacent entities and a stepwise horizontal threshold defining a maximum acceptable distance between two horizontally adjacent entities” describes the mental process of the “transforming” function and , accordingly, the limitation recites a mental process.
Argument: with regard to argument regarding the claim rejections based 35 USC 102(a)(2), Applicant argues “First, Bajaj fails to disclose transforming tabular data using "stepwise vertical thresholds" and "stepwise horizontal thresholds" to dictate the maximum acceptable distances between adjacent entities to construct tabular rows and columns… Second, Bajaj completely fails to disclose mapping non-tabular keys to values by defining a "heuristic search area" around an identified key… Third, Bajaj does not teach evaluating entities based on "orientation" rules within a search area to map corresponding values.”
Response: Applicant argument appears to be based on amendment to the independent claims. Accordingly, rejections have been found in the reference of Bajaj as detailed above in the corresponding claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHAMED I NAZAR whose telephone number is (571)270-3174. The examiner can normally be reached 10 am to 7 pm Mon-Fri.
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/AHAMED I NAZAR/Examiner, Art Unit 2178 4/1/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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