DETAILED ACTION
This Office Action is in response to the correspondence filed by the applicant on 1/7/2025.
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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The Information Statements (IDS) filed on 3/12/2025 have been accepted and considered in this office action and are in compliance with the provisions of 37 CFR 1.97.
Examiner’s Notes
Claims 15-20 recite, “A computer program product comprising a set of one or more computer readable storage media …" The specification describes the computer-readable medium, which is specifically limited to be non-transitory ([0033]). Thus, the claims are not directed to non-statutory subject matter.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1, 10, and 20 recite, “electronically parsing an electronic image to identify an irregular table, wherein table headers of the irregular table are identified; selecting an anchor column in the irregular table, the anchor column comprising column data, wherein the anchor column is selected based at least in part on coordinates of the column data and on vertical distances of the column data, wherein rows are determined based on vertical relationships of the vertical distances of the column data of the anchor column; separating the rows into snippets in accordance with the vertical relationships of the column data, wherein row data is extracted from the snippets; generating a new data structure such that the row data is combined in the new data structure; and causing an action to be performed in response to generating the new data structure.”
The limitations of the recited steps, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processing device” or “a processor”, nothing in the claim element precludes the step from practically being performed in the mind. For example, a person can look a table, align columns and rows to read data arranged in the rows and the columns, write the read data on a piece of paper and perform an action. The limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitations in the mind.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements –“a processor”. The additional elements in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the recited steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the recited steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Regarding the dependent claims, claims 2, 9, and 16 recite unstructured data; claims 3, 10, and 17 recite a plurality of candidate columns; claims 4, 11, and 18 recite an anchor column with a smaller variance; claims 5, 12, and 19 recite an anchor column with a greater number of the column data; claims 6, 13, and 20 recite enlarging ling spacings; and claims 7 and 14 recite performing an action.
Even though the disclosed invention is described in the specification as improving computer technology, the claim provides no meaningful limitations such that this improvement is realized. Therefore, the claim does not amount to significantly more than the abstract idea itself.
Accordingly, the limitations of the Claims, whether considered individually or as an ordered combination, are not sufficient to add significantly more to improve technological functionality. As such, Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
Claims 4-6, 11-13, and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 101 rejection is overcome.
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 of this title, 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 1-3, 7-10, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over SATHI (US 2022/0108108 A1), and in further view of BUISSSON (US 2019/0171704 A1).
REGARDING CLAIM 1, SATHI discloses a computer-implemented method comprising:
electronically parsing an electronic image to identify an irregular table, wherein table headers of the irregular table are identified (SATHI Par 48 – “The table data extraction process 400 receives 402 into a document image for a document that includes a table. Next, the document image can be processed to detect 404 objects within the document image. After the objects have been detected 404, a table, a table header and table header elements can be identified 406 from the detected objects. Next, columns for the table can be identified 408 using at least one or more of the objects that have been detected 404.”);
selecting an anchor column in the irregular table, the anchor column comprising column data, wherein the anchor column is selected based at least in part on coordinates of the column data (SATHI Par 64 – "FIG. 6C illustrates the exemplary document after columns that have table header elements that match an alias type have been identified. Such table header elements can be used to identify columns that can be used as denoting a row anchor pattern.”; Par 49 – “Thereafter, one or more of the identified columns can be selected 410 based on the table header elements. Next, row anchor objects can be determined 412. Row anchor objects are typically text, such as words or labels, or values that can be used to signal location of rows of the table. Then, a decision 414 can determine whether row anchor objects have been detected with confidence. When the decision 414 determines that row anchor objects have been detected with confidence, rows for the table can be identified 416 based on positions of the row anchor objects.”; Par 54 – “The other processing can proceed by selection 528 of a left-most column from the columns identified within the table. Then, the table data extraction process 500 can recognize 530 text within the left-most column. Thereafter, bounding word blocks can be formed 532 around each word from the recognized text. Rows for the table can then be identified 534 based on positions of the bounding word blocks. Finally, content from each resulting cell within the table can be extracted 536. Following the block 518, the table data extraction process 500 can end.”) [and on vertical distances of the column data], wherein rows are determined based on vertical relationships of the vertical distances of the column data of the anchor column (SATHI Par 67 – “FIG. 6F illustrates the exemplary document after the rows have been identified using the selected row anchors, such as those denoted in FIG. 6E or 6F. A given row can be identified as between the top left coordinate of one of the selected row anchors to the top left coordinate of the selected row anchor that is immediately below in the same column. In this example, six rows within the table have been identified, including a first row 644, a second row 646, a third row 648, a fourth row 650 and a fifth row 652.”);
separating the rows into snippets in accordance with the vertical relationships of the column data, wherein row data is extracted from the snippets (SATHI Par 54 – “Then, the table data extraction process 500 can recognize 530 text within the left-most column. Thereafter, bounding word blocks can be formed 532 around each word from the recognized text. Rows for the table can then be identified 534 based on positions of the bounding word blocks. Finally, content from each resulting cell within the table can be extracted 536. Following the block 518, the table data extraction process 500 can end.”);
generating a new data structure such that the row data is combined in the new data structure (SATHI Par 46 – “The table block/object processing can process these components of the table. After the table block/object processing has been performed 322, or directly following the decision 320 when the table block/object is not been detected, the data extraction process can aggregate 324 processing results. Here, to the extent that the object detection has detected one or more information blocks, key-value blocks and/or table blocks/objects, the results from the processing thereof can be aggregated 324. The result of the aggregation 324 can be provided in a document data file. Following the aggregation 324, the data within the table provided in the document has been extracted and thus the data extraction and process 300 can end.”; Par 41 – “Additionally, the data extraction system 200 includes an aggregator 220. The aggregator 220 is coupled to the class A data extraction 210, the class B data extraction 214 and the class C data extraction 218 such that the extracted data from the various objects (or blocks) of the document can be aggregated together to form a document data file that is produced by the data extraction system 200 and contains all the extracted data for the document.”); and
causing an action to be performed in response to generating the new data structure (SATHI Par 46 – “The table block/object processing can process these components of the table. After the table block/object processing has been performed 322, or directly following the decision 320 when the table block/object is not been detected, the data extraction process can aggregate 324 processing results. Here, to the extent that the object detection has detected one or more information blocks, key-value blocks and/or table blocks/objects, the results from the processing thereof can be aggregated 324. The result of the aggregation 324 can be provided in a document data file. Following the aggregation 324, the data within the table provided in the document has been extracted and thus the data extraction and process 300 can end.”; Par 31 – “Software automation processes can perform a task or workflow that they are tasked with once or many times. As one example, a software automation process can locate and read data in a document, email, file, or other data source. As another example, a software automation process can connect with one or more Enterprise Resource Planning (ERP), Customer Relations Management (CRM), core banking, or other business systems to distribute data where it needs to be in whatever format is necessary.”).
SATHI does not explicitly teach the [square-bracketed] limitation.
BUISSON teaches the [square-bracketed] limitation. BUISSON discloses a method/system for extracting data from tables comprising: selecting an column data] (BUISSON Par 49 –“In the first step of the classification process, each line may be tokenized based on a specified spacing distance (e.g., more than two space characters), creating a set of tokenized words with a specified x (horizontal spacing) location and y (vertical spacing) location in the text. Then, the tokenized words are grouped into table columns, where each table column stores the tokenized words which share a location within a configured range.”; Par 51 – “To provide another example output 212 from the data zone column processing step 211, reference is now made to FIG. 5 which illustrates how extracted data zone columns 51 a, 52 a, 53 a, 54 a, 54 b, 55 a, 56 a are aligned with the closest header columns 51-56 for output as part of a candidate table 15 as shown in FIG. 1. With the first depicted column set 51 a having the same x-position as the first column header entry 51 (TEST NAME), the alignment process 51 b maps the column set 51 a to the corresponding x-position (e.g., x=17) of the first header 51.”);
separating the rows into snippets in accordance with the vertical relationships of the column data, wherein row data is extracted from the snippets (BUISSON Par 48 – “To the extent that the data zone processing step 211 identifies the table content as lines of text, the processing 211 should also group potential data zone columns together for alignment with the identified table headers. To this end, the input document 206 is next processed (step 3) at the data zone column processing step 211 to align columns and headers, such as by applying natural language processing (NLP) techniques to format the lines of text as table rows and columns by using a classification process to associate sets of words with average locations.”);
generating a new data structure such that the row data is combined in the new data structure (BUISSON Par 52 – “At the end of the data cleansing process step 213, the fully extracted, final table is stored as an output file 214 in a predetermined output table format, such as a comma-separated value (CSV) file. To provide an example output 214 from the data cleansing process step 213, reference is now made to FIG. 1 which shows an example extracted table 15 having a well formatted structure with a header row and one or more columns extracted from the input document 10.”); and
causing an action to be performed in response to generating the new data structure (BUISSON Par 53 – “In this well-formatted structure, the extracted table 15 can be processed by the Watson cognitive machine 101 to analyze the table data. For example, with a medical table 10 correctly detected and extracted, the Watson cognitive machine 101 can parse specific table cells and retrieve the critical health information that is mentioned or flagged as “abnormal” values, such as by applying an abnormal column detection processing step 216 to identify and process critical health information in the table (e.g., abnormal health results) using one or more abnormal indication rules 215 so that the final table output 217 is generated with abnormal result table values flagged and marked with an associated confidence factor. A rules file is used to specify what constitutes as abnormal table row for the specific domain, such as medical test.”).
In other words, SATHI already teaches selecting an anchor column among a plurality discovered columns based on the coordinates of the column data (e.g., selecting a left-most column data as an anchor for determining rows. BUISSON teaches discovering a plurality of potential columns based on vertical distances of the column data. Thus, the combination teaches selecting an anchor column at least in part on coordinates of the column data and on vertical distances of the column data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of SATHI to include considering vertical distances of column data for discovering potential columns, as taught by BUISSON.
One of ordinary skill would have been motivated to include considering vertical distances of column data for discovering potential columns, in order to accurately group column data so that columns are discovered accurately.
REGARDING CLAIM 2, SATHI in view of BUISSON discloses the computer-implemented method of claim 1.
SATHI further teaches extracting data from any document (SATHI Par 4 –“The document image can contain text which can be obtained by Optical Character Recognition (OCR) processing. While OCR processing of documents can recognize text contained therein, such process is not well suited to capture data from tables contained in the documents, such as invoices, purchase orders, or more generally tables, nor is such process well suited to capture values and associated descriptive labels contained in the documents.”). Since the method/system of SATHI is not limited to a structured document, SATHI implicitly suggest the irregular table comprising unstructured data as the row data.
BUISSON explicitly teaches wherein the irregular table comprises unstructured data as the row data (BUISSON Par 26 – “Once an input document 10 is received or scanned into a text format document (input arrow 13), a table detector engine 110 may detect unstructured table headers and data zones 11-12. In particular, the table detector engine 110 may include a header detection module 111 which is configured to detect unstructured table headers 11 by using semantic groups of table header terms to identify any type of lab results or other data in any table format without requiring any training or table structure knowledge.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of SATHI to include extracting data from unstructured data, as taught by BUISSON.
One of ordinary skill would have been motivated to include extracting data from unstructured data, in order to allow the method/system to process a variety of document so that a user can utilize the extracted data with a broader application.
REGARDING CLAIM 3, SATHI in view of BUISSON discloses the computer-implemented method of claim 1, wherein the anchor column is selected from a plurality of candidate anchor columns (SATHI Figs. 5A-5D; Par 65 – “FIG. 6D illustrates the exemplary document after the first column (surrounded by the bounding box 606) having the table header element “Quantity” is selected for providing row anchors for the rows of the table.”; Par 66 – “FIG. 6E illustrates the exemplary document after the sixth column (surrounded by the bounding box 606) having the table header element “Unit Price” is selected for providing row anchors for the rows of the table.”;).
REGARDING CLAIM 7, SATHI in view of BUISSON discloses the computer-implemented method of claim 1, wherein: the generating of the new data structure acts as a trigger to perform the action (SATHI Par 46 – “The table block/object processing can process these components of the table. After the table block/object processing has been performed 322, or directly following the decision 320 when the table block/object is not been detected, the data extraction process can aggregate 324 processing results. Here, to the extent that the object detection has detected one or more information blocks, key-value blocks and/or table blocks/objects, the results from the processing thereof can be aggregated 324. The result of the aggregation 324 can be provided in a document data file. Following the aggregation 324, the data within the table provided in the document has been extracted and thus the data extraction and process 300 can end.”; BUISSON also teaches the limitation: Fig. 8 –“Clean Table Data .. 307 -> Identify/Validate Flagged or Abnormal Table Data or Results 308; ”; Par 71 – “Once a final output table is generated, the table values may be processed to identify and/or validate flagged or abnormal table data or results at step 308.”; Par 53 – “In this well-formatted structure, the extracted table 15 can be processed by the Watson cognitive machine 101 to analyze the table data. For example, with a medical table 10 correctly detected and extracted, the Watson cognitive machine 101 can parse specific table cells and retrieve the critical health information that is mentioned or flagged as “abnormal” values, such as by applying an abnormal column detection processing step 216 to identify and process critical health information in the table (e.g., abnormal health results) using one or more abnormal indication rules 215 so that the final table output 217 is generated with abnormal result table values flagged and marked with an associated confidence factor. A rules file is used to specify what constitutes as abnormal table row for the specific domain, such as medical test.”); and the action comprises at least one of storing the new data structure to be accessible by a computer system, causing the new data structure to be displayed, causing an artificial intelligence engine to be trained with features generated from the new data structure, or causing the new data structure to be utilized by a search engine (SATHI Par 46 – “The table block/object processing can process these components of the table. After the table block/object processing has been performed 322, or directly following the decision 320 when the table block/object is not been detected, the data extraction process can aggregate 324 processing results. Here, to the extent that the object detection has detected one or more information blocks, key-value blocks and/or table blocks/objects, the results from the processing thereof can be aggregated 324. The result of the aggregation 324 can be provided in a document data file.”; BUISSON also teaches the limitation: BUISSON Fig. 8 –“Clean Table Data .. 307 -> Identify/Validate Flagged or Abnormal Table Data or Results 308; ”; Par 71 – “Once a final output table is generated, the table values may be processed to identify and/or validate flagged or abnormal table data or results at step 308.”; Par 53 – “In this well-formatted structure, the extracted table 15 can be processed by the Watson cognitive machine 101 to analyze the table data. For example, with a medical table 10 correctly detected and extracted, the Watson cognitive machine 101 can parse specific table cells and retrieve the critical health information that is mentioned or flagged as “abnormal” values, such as by applying an abnormal column detection processing step 216 to identify and process critical health information in the table (e.g., abnormal health results) using one or more abnormal indication rules 215 so that the final table output 217 is generated with abnormal result table values flagged and marked with an associated confidence factor. A rules file is used to specify what constitutes as abnormal table row for the specific domain, such as medical test.”; Par 72 – “An example sequence of steps may include a first step 321 to check for “range” columns in the table by using a dictionary-based approach with fuzzy matching logic, and then validating any abnormal result values as being out-of-range by extracting the range and result values from the table and then comparing them. Based on results from comparing the range and result values, a confidence level may be computed at step 322 for any detected abnormal values in the table, and then displayed with the highlighted abnormal value(s) in the final table.”).
REGARDING CLAIM 8, SATHI in view of BUISSON discloses a system comprising:
a memory comprising computer readable instructions; and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations (SATHI Fig. 11 – “CPU, processor, .. memory”) comprising: performing the steps of claim 1; thus, it is rejected under the same rationale.
Claim 9 is similar to claim 2; thus, it is rejected under the same rationale.
Claim 10 is similar to claim 3; thus, it is rejected under the same rationale.
Claim 14 is similar to claim 7; thus, it is rejected under the same rationale.
REGARDING CLAIM 15, SATHI in view of BUISSON discloses a computer program product comprising: a set of one or more computer-readable storage media; program instructions, collectively stored in the set of one or more storage media, for causing a processor set (SATHI Fig. 11 – “CPU, processor, .. memory”) to perform computer operations: the steps of claim 1; thus, it is rejected under the same rationale.
Claim 16 is similar to claim 2; thus, it is rejected under the same rationale.
Claim 17 is similar to claim 3; thus, it is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN C KIM whose telephone number is (571)272-3327. The examiner can normally be reached Monday to Friday 8:00 AM thru 4:00 PM EST.
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/JONATHAN C KIM/Primary Examiner, Art Unit 2655
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