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 action is response to the communication filed on March 19, 2025. Claims 1-24 are pending.
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 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites (a) obtaining a user query relating to a construction specification document;(b) obtaining the construction specification document, wherein:(i) the construction specification document comprises a companion contractual document to construction drawings;(ii) the construction specification document is used in all phases of a construction project;(c) a Table of Content (ToC) page detection module autonomously classifying each page of the construction specification document as a table of content (ToC) or not ToC, wherein output from the classifying comprises ToC page classification results;(d) a text extraction module autonomously extracting text content from the construction specification document;(e) a document sectioning module autonomously:(i) outputting section titles based on the ToC page classification results and the extracted text content;(ii) sectioning the construction specification document into sections based on the section titles; and(f) processing the user query based on the sectioned construction specification document.
The claim recited the limitation of classifying, extracting, outputting, sectioning, and processing in steps (c), (d), (e), and (f) as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. User can mentally classify obtained page, extract text from document, section it (grouping), output the section title, and process it with the help of physical aid such as pen and paper. Hence, these limitations are mental process. See MPEP 2106.04(a)(2) III, B, If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand.").
The claim recites two additional elements: (a) and (b). The obtaining steps as recited amounts to mere data gathering for use in the detection step, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does 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 obtaining steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the ToC page detection module utilizes a computer vision model that leverages machine learning to classify each page, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 3 is dependent on claim 2 and includes all the limitations of claim 2. Therefore, claim 3 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of the machine learning leverages a you only look once (YOLO) classification architecture; and the YOLO classification architecture comprises a YOLO classification model that is trained on a set of pages sampled from a collection of generic specifications, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the text extraction module extracts the text content by: determining if the construction specification document is raster based or vector based; utilizing a vector text extraction module when the construction specification document is vector based; utilizing a raster text extraction module when the construction specification document is raster based; storing the extracted text content and metadata in a database, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the construction specification document is a portable document format (PDF) document, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the text extraction module further extracts metadata associated with the text content from the construction specification document, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the document sectioning module outputs the section titles by: matching text between ToC page text and non-ToC page text to generate first section title predictions;utilizing a header-footer parser to generate second section title predictions; combining the first section title predictions with the second section title predictions; and outputting the combined first section title predictions and second section title predictions as the section titles, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of performing text processing on text from each section, wherein the text processing splits, for each section, the extracted text content into multiple chunks; generating vector embeddings for the multiple chunks using a deep learning model; and storing the multiple chunks, corresponding vector embeddings, and metadata in an index; and wherein the user query is processed by querying the index, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 9 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 9 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of wherein the deep learning model comprises a vector embeddings model that takes the extracted text content as input and produces a vector representation as output, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 10 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 10 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of retrieving, using the extracted text content and corresponding vector embeddings, a top predefined number of matching chunks of the multiple chunks, from the index; re-ranking the retrieved matching chunks using a re-ranking algorithm that is based on relevancy of the matching chunks with respect to the user query; putting the user query and the re-ranked matching chunks together in a prompt; generating, using a large language model (LLM) a response to the user query using the prompt, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 11 is dependent on claim 8 and includes all the limitations of claim 8. Therefore, claim 11 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of using the extracted text content and corresponding vector embeddings, a top predefined number of matching chunks of the multiple chunks, from the index; summarizing the matching chunks at a specification section level, based on relevance to the user query; filtering and re-ranking the summaries based on the relevance to the user query to generate a final context; putting the user query and the final context together in a prompt; generating, using a large language model (LLM) a response to the user query using the prompt, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 12 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 12 recites the same abstract idea of optimizing queries of the construction specification. The claim recites the limitations of outputting a response from the processed user query, wherein the output response is displayed in a user interface with source section information, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer in the form of insignificant extra-solution activity which is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 13-24, they have similar limitations as of claims 1-12 above. Hence, they are rejected under the same rational as of claims 1-12 above.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-24 are rejected under 35 U.S.C. 103 as being unpatentable over Eveland et al. (Patent No. : US 9785638 B1) in the view of Miyamoto (Pub. No. : US 20090276693 A1)
As to claim 1 Eveland teaches a computer-implemented method for processing a construction domain query, comprising:
(a) obtaining a user query relating to a construction specification document (Column 3 lines 12-46, Column 5 lines 36-37: 2 shows a screen display 200 in which a user has entered query (“Tyvek”) in a search query field 210);
(b) obtaining the construction specification document (Column 5 lines 38-41: In response, the interface 140 has provided the user (via the screen display 200) with various information regarding the use of the Tyvek product in the construction industry (i.e., as reflected in spec documents)), wherein:
(i) the construction specification document comprises a companion contractual document to construction drawings (Column 3 lines 12-46: building permits, building permits drawings, inspection documents, and other information relevant to the user making a decision. These documents may include text, image data, and metadata);
(ii) the construction specification document is used in all phases of a construction project (Column 3 lines 12-46: construction projects, including but not limited to the name of the project, the companies involved with the project or building, construction labor and/or material bids, building labor and/or material quotes, … These documents may include text, image data, and metadata);
(e) a document sectioning module autonomously:
(i) outputting section titles based on the ToC page classification results and the extracted text content (Column 4 lines 3-6: the books may have a predefined uniform organizational structure if each of the books uses a uniform table of contents that specifies parts of the book, chapters within each part, and headings within each chapter);
(ii) sectioning the construction specification document into sections based on the section titles (Column 4 lines 34-40: section numbers and titles within each division, to follow in organizing information about a facility's construction requirements and associated activities. Each division contains a number of sections. Each section is divided into three parts—“general,” “products,” and “execution.” Each part is organized by a standardized system of articles and paragraphs);
(f) processing the user query based on the sectioned construction specification document (Column 5 lines 38-41: In response, the interface 140 has provided the user (via the screen display 200) with various information regarding the use of the Tyvek product in the construction industry (i.e., as reflected in spec documents)).
Eveland does not explicitly disclose but Miyamoto teaches (c) a Table of Content (ToC) page detection module autonomously classifying each page of the construction specification document as a table of content (ToC) or not ToC, wherein output from the classifying comprises ToC page classification results (paragraph [0115]: processing for creating a table of contents page in the above-described document processing system, wherein the table of contents insertion function is a function for creating a table of contents indicating the structure of the overall book file by the chapter structure of the book file);
(d) a text extraction module autonomously extracting text content from the construction specification document (paragraph [0009]: creat table of contents data that enumerates the extracted portion and a page number at which the extracted portion is arranged).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Eveland by adding above limitation as taught by Miyamoto to automatically generating a table of contents page (Miyamoto, see abstract).
As to claim 2 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Miyamoto teaches wherein the ToC page detection module utilizes a computer vision model that leverages machine learning to classify each page (paragraph [0044]).
As to claim 3 Eveland together with Miyamoto teaches a computer-implemented method according to claim 2. Miyamoto teaches wherein: the machine learning leverages a you only look once (YOLO) classification architecture; and the YOLO classification architecture comprises a YOLO classification model that is trained on a set of pages sampled from a collection of generic specifications (paragraph [0115]).
As to claim 4 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Eveland teaches wherein the text extraction module extracts the text content by: determining if the construction specification document is raster based or vector based; utilizing a vector text extraction module when the construction specification document is vector based; utilizing a raster text extraction module when the construction specification document is raster based; storing the extracted text content and metadata in a database (Column 5 lines 38-41).
As to claim 5 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Eveland teaches wherein the construction specification document is a portable document format (PDF) document (Column 3 line 47-67).
As to claim 6 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Eveland teaches wherein the text extraction module further extracts metadata associated with the text content from the construction specification document (Column 7 line 18-28).
As to claim 7 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Eveland teaches wherein the document sectioning module outputs the section titles by: matching text between ToC page text and non-ToC page text to generate first section title predictions;utilizing a header-footer parser to generate second section title predictions; combining the first section title predictions with the second section title predictions; and outputting the combined first section title predictions and second section title predictions as the section titles (Column 9 line 15-30).
As to claim 8 Eveland together with Miyamoto teaches a computer-implemented method according to claim 1. Eveland teaches performing text processing on text from each section, wherein the text processing splits, for each section, the extracted text content into multiple chunks; generating vector embeddings for the multiple chunks using a deep learning model; and storing the multiple chunks, corresponding vector embeddings, and metadata in an index; and wherein the user query is processed by querying the index (Column 7 line 20-35).
As to claim 9 Eveland together with Miyamoto teaches a computer-implemented method according to claim 8. Eveland teaches wherein the deep learning model comprises a vector embeddings model that takes the extracted text content as input and produces a vector representation as output (Column 15 line 20-30).
As to claim 10 Eveland together with Miyamoto teaches a computer-implemented method according to claim 8. Eveland teaches retrieving, using the extracted text content and corresponding vector embeddings, a top predefined number of matching chunks of the multiple chunks, from the index; re-ranking the retrieved matching chunks using a re-ranking algorithm that is based on relevancy of the matching chunks with respect to the user query; putting the user query and the re-ranked matching chunks together in a prompt; generating, using a large language model (LLM) a response to the user query using the prompt (Column 12 line 15-30).
As to claim 11 Eveland together with Miyamoto teaches a computer-implemented method according to claim 8. Eveland teaches retrieving, using the extracted text content and corresponding vector embeddings, a top predefined number of matching chunks of the multiple chunks, from the index; summarizing the matching chunks at a specification section level, based on relevance to the user query; filtering and re-ranking the summaries based on the relevance to the user query to generate a final context; putting the user query and the final context together in a prompt; generating, using a large language model (LLM) a response to the user query using the prompt (Column 10 line 30-50).
As to claim 12 Eveland together with Miyamoto teaches a computer-implemented method according to claim 8. Eveland teaches outputting a response from the processed user query, wherein the output response is displayed in a user interface with source section information (Column 11 line 20-35).
As to claims 13-24, they have similar limitations as of claims 1-12 above. Hence, they are rejected under the same rational as of claims 1-12 above.
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
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/MD I UDDIN/Primary Examiner, Art Unit 2169