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 .
DETAILED ACTION
This communication is in response to the Application 18/539,956 filed on 11/07/2024.
Claims 1-20 have been examined and are pending. This Action is made Non-FINAL.
Information Disclosure Statement
The information disclosure statement (IDS), submitted on 02/27/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 9-14 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Admon, (US 2016/0232630), published on Aug. 11, 2016.
Regarding claim 1, Admon discloses a computer-implemented method to categorize a document (Figs. 1-2) comprising:
receiving a document (par. 0040; Fig. 2, step 205; receiving data file);
extracting text from the document (par. 0040; Fig. 2; steps 205-210; a data file having a first document containing text is received at the processor; document and the text therein is a sequence of characters);
using an artificial intelligence engine, identifying a document type corresponding to the document from among a plurality of document types (pars. 0041-0042; Fig. 2; step 210; identify document type; the processor may determine a document classification for the document; a document classification may be a document type, such as, e.g., an employment contract, lease, or other type of legal document, etc.; pars. 0025 and 0062; the system may employ artificial intelligence and machine learning to analyze documents);
accessing a database comprising a plurality of document type categories, each document type associated with a respective set of document type categories (pars. 0043-0045; Fig. 2; step 215; the system may be configured to access data associated with the document e.g. the data in a firm's document management systems);
identifying, from the database, a set of document type categories associated with the identified document type (pars. 0043-0045; Fig. 2; step 215; the system may be configured to access one or more legal information systems and/or databases (e.g., government database, etc.) to verify, e.g., that such state law exists, is still in effect, and/or that the document type may be governed by state law as opposed to federal law);
processing the extracted text to identify text corresponding to each of the set of document type categories associated with the identified document type (pars. 0046-0051 and Fig. 2; steps 235-250; “identify potential section classification”; the processor may identify one or more potential section classifications for one or more of the plurality of sections based on, e.g., information relating to a training set of signal representations, and/or based on the generated signal representation of the plurality of sections); and
generating an output comprising the identified text organized according to the set of document type categories (pars. 0072-0076; Fig. 2, steps 255 and 0270: “output recommended section classifications”; the processor may output one or more of the potential section classifications as one or more recommended section classifications for one or more of the plurality of sections, based, for example, on the assessing; par. 0079-0080; step 270, in some embodiments, the processor may generate a summary of a first document that has been analyzed.).
Regarding claim 2, Admon discloses the computer-implemented method of claim 1, wherein identifying, from the database, a set of document type categories associated with the identified document type comprises:
querying the database using the identified document type (pars. 0043-0045; Fig. 2; step 215; the system may be configured to access one or more legal information systems and/or databases (e.g., government database, etc.) to verify, e.g., that such state law exists, is still in effect, and/or that the document type may be governed by state law as opposed to federal law); and
receiving, from the database, the respective set of document type categories associated with the identified document type (pars. 0043-0045; Fig. 2; step 215; the system may be configured to access one or more legal information systems and/or databases (e.g., government database, etc.) to verify, e.g., that such state law exists, is still in effect, and/or that the document type may be governed by state law as opposed to federal law).
Regarding claim 3, Admon discloses the computer-implemented method of claim 1, wherein processing the extracted text comprises:
dividing the extracted text into segments (pars. 0046-0050; Fig. 2, steps 220-225; separate text into section (sectioning); the processor may section the first document into a plurality of sections of text);
for each segment:
providing the segment to the artificial intelligence engine (pars. 0057-0063; Fig. 2, step 235-245; implement sematic classification protocol; the processor may employ one or more machine learning algorithms, e.g., to automatically “learn” one or more classification rules/schemas from provided examples.); and
receiving, from the artificial intelligence engine, an identified document type category corresponding to the segment (pars. 0057-0063; Fig. 2; steps 235-245; obtain potential section classification; based on these statistics the machine learning algorithm may build a set rules (e.g., a “model” or “classifier”), that may determine how to classify future texts based on the information relating to the training sets).
Regarding claim 4, Admon discloses the computer-implemented method of claim 1, further comprising:
combining identified text corresponding to a same document type category into a composite segment (pars. 0057-0063; Fig. 2; steps 235-245; implement semantic classification protocol; generate sematic-based signal representation of each section); and
providing the composite segment to a summarization engine to generate a summary of the composite segment (pars. 0057-0063 and 0079-0080; Fig. 2; steps 250-270; access identified potential section classification and output recommended section classification; at step 270, the processor may generate a summary of a first document that has been analyzed).
Regarding claim 5, Admon discloses the computer-implemented method of claim 1, wherein the artificial intelligence engine utilizes a natural language model (par. 0062; the processor may employ one or more machine learning algorithms, e.g., to automatically “learn” one or more classification rules/schemas).
Regarding claim 6, Admon discloses the computer-implemented method of claim 1, wherein extracting text comprises converting the document to plain text (par. 0040; Fig. 2; steps 205-210; a data file having a first document containing text is received at the processor; document and the text therein is a sequence of characters).
Regarding claim 9, Admon discloses the computer-implemented method of claim 3, wherein providing the segment to the artificial intelligence engine further comprises encoding the segment (pars. 0057-0051; Fig. 2, step 240A-240C; for each of the plurality of sections, the processor may generate one or more signal representations; a signal representation of a section may be a vector representation).
Regarding claim 10, Admon discloses the computer-implemented method of claim 9, wherein encoding the segment comprises generating a vector representation of the segment (pars. 0057-0051; Fig. 2, step 240A-240C; for each of the plurality of sections, the processor may generate one or more signal representations; a signal representation of a section may be a vector representation).
Regarding claim 11, Admon discloses the computer-implemented method of claim 1, wherein generating the output further comprises formatting the identified text for readability (pars. 0079-0080; Fig. 2, steps 255 and 270; output recommended section classification and generate summary; a summary may include a summary of an entire set of text (e.g., an entire document). the system may provide a summary of specific elements and/or item existing in the text).
Regarding claim 12, Admon discloses the computer-implemented method of claim 11, wherein formatting comprises arranging the identified text into paragraphs based on the document type categories (pars. 0046-0047; Fig. 2, step 220; sectioning may include breaking the documents into paragraphs; sectioning may include designating each paragraph or group as a section based).
Regarding claim 13, Admon discloses the computer-implemented method of claim 1, further comprising training the artificial intelligence engine using a plurality of training documents (pars. 0062-0063; the information relating to a training set may include one or more statistics generated during prior analysis/examination of other sections of text in a training set having known signal representations).
Regarding claim 14, Admon discloses the computer-implemented method of claim 13, wherein training the artificial intelligence engine comprises supervised learning techniques (par. 0024; document analysis may be processed using supervised learning analysis—e.g., combining formalized know-how defined by expert lawyers with machine learning algorithms).
Regarding claim 17, Admon discloses the computer-implemented method of claim 1, wherein the output is generated as part of a cloud-implemented service (par. 0028; Fig. 1; system server may be any suitable computing device/system including cloud based computing systems capable of employing the systems and methods).
Regarding claim 18, Admon discloses the method of claim 1, wherein the artificial intelligence engine comprises an ensemble of natural language processing models (par. 0062; the processor may employ one or more machine learning algorithms, e.g., to automatically “learn” one or more classification rules/schemas).
Regarding claim 19, claim 19 is directed to a computing apparatus corresponding to the method recited in claim 1. Claim 19 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding claim 20, claim 20 is directed to least one non-transitory computer-readable storage medium corresponding to the method recited in claim 1. Claim 20 is similar in scope to claim 1 and is therefore rejected under similar rationale.
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.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Admon, (US 2016/0232630), published on Aug. 11, 2016, in view of Leal et al., (US 2018/0300315), published on Oct. 18, 2018.
Regarding claim 7, Admon discloses the computer-implemented method of claim 6.
Admon does not explicitly disclose converting the document to plain text comprises removing formatting and non-textual elements.
However, Leal discloses a system/method for automated document analysis using machine learning including the step of converting the document to plain text comprises removing formatting and non-textual elements (Leal: pars. 0034 and 0037-0038; removing tables or images appearing within a document).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Leal with the system/method of Admon. One would have been motivated to enable users to extract documents into semantic words for document classification (Leal: pars. 0037-0038).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Admon, (US 2016/0232630), published on Aug. 11, 2016, in view of Fan et al., (“Fan,” US 2024/0111941), published on Apr. 04, 2024.
Regarding claim 8, Admon discloses the computer-implemented method of claim 1.
Admon does not explicitly disclose wherein dividing the extracted text into segments further comprises removing stop words from each segment.
However, Fan discloses a method/system for analyzing documents wherein dividing the extracted text into segments further comprises removing stop words from each segment (Fan: pars. 0037and 0043; removing redundant text and “stop” words).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Fan with the system/method of Admon. One would have been motivated to remove redundant text or ‘stop’ words to properly analyze the context of documents (Fan: par. 0037)
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Admon, (US 2016/0232630), published on Aug. 11, 2016, in view of Kislal et al., (“Kislal,” US 2024/0012842), published on Jan. 11, 2024.
Regarding claim 15, Admon discloses the computer-implemented method of claim 1.
Admon does not explicitly disclose storing the output in a searchable format.
However, Kislal discloses a method/system to summarize and structuring of unstructured documents including the steps of storing the output in a searchable format (Kislal: pars. 0043-0045 and 0131-0134; perform ingestion operations to transform the document into a searchable document).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine teachings of Kislal with the system/method of Admon. One would have been motivated to transform unstructured documents to searchable format document (Kislal: pars. 0043-0045).
Regarding claim 16, Admon and Kislal disclose the computer-implemented method of claim 15.
Kislal further discloses receiving a search query and returning a portion of the output responsive to the search query (Kislal: pars. 0043-0045 and 0131-0134; performing a question-and-answer (Q-A) search on one or more documents (which may have been ingested to convert the one or more documents into respective one or more Q-A searchable documents) using the query set to produce answer data responsive to one or more questions included in the query set).
The motivation is the same that of claim 15 above.
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968))
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH K PHAM whose telephone number is (571)270-3230. The examiner can normally be reached Monday-Thursday from 8:00 AM to 6:00 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William L Bashore can be reached on (571) 272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LINH K PHAM/
Primary Examiner
Art Unit 2174
