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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 8 of U.S. Patent No. 12,183,106. Although the claims at issue are not identical, they are not patentably distinct from each other because representative patent claim 8 requires the additional elements (See the highlighted elements shown in the table below) not required by representative application claim 1. However, the conflicting claims are not patentably distinct from each other because:
The claims recite common subject matter;
Whereby representative application claim 1 which recite the open-ended transitional phrase "comprising", does not preclude the additional elements recited by representative patent claim 8, and
Whereby the elements of representative application claim 1 is fully anticipated by representative patent claim 8, and anticipation is "the ultimate or epitome of obviousness". (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
US Application No. 19/003,896
US Patent No. 12,183,106 B1
Claim 1. A non-transitory, processor-readable medium storing instructions that executed by a processor, cause the processor to:
Claim 8. A non-transitory, processor-readable medium storing instructions that executed by a processor, cause the processor to:
parse a document image to extract a plurality of subsets of characters from the plurality
of representations of characters to generate a text encoding for that document image, each subset of characters being associated with a structure type from a plurality of structure types;
receive a plurality of document images each including a plurality of representations of characters: parse each document image from the plurality of document images to extract a plurality of subsets of characters from the plurality of representations of characters to generate a text encoding for that document image, each subset of characters being associated with a structure type from a plurality of structure types;
extract a plurality of visual features from the document image to generate a visual
encoding, each visual feature from the plurality of visual features associated with at least one subset of characters from the plurality of subsets of characters;
for each document image from the plurality of document images, extract a plurality of visual features to generate a visual encoding for that document image, each visual feature from the plurality of visual features associated with at least one subset of characters from the plurality of subsets of characters;
generate a parsed document based on the text encoding and the visual encoding;
generate a plurality of parsed documents, each parsed document from the plurality of parsed documents uniquely associated with a document image from the plurality of document images and being based on the text encoding and the visual encoding for that document image;
identify a plurality of sections from the parsed document, each section from the plurality
of sections uniquely associated with a section type from a plurality of section types; and
for each parsed document from the plurality of parsed documents, identify a plurality of sections, each section from the plurality of sections uniquely associated with a section type from a plurality of section types;
apply a first trained machine learning model to a first section from the plurality of
sections to produce at least a first portion of a structured data file that identifies a feature of the first section in the document image; and
apply a second trained machine learning model to a second section from the plurality of sections to produce at least a second portion of the structured data file that identifies a feature
train a plurality of machine learning models to produce a plurality of trained machine learning models, each machine learning model from the plurality of machine learning models associated with one section type from the plurality of section types and trained using a portion of each parsed document that is from the plurality of parsed documents and that is associated with that section type; receive a first document image that is not from the plurality of document images: identify a first section in the first document image that is uniquely associated with a first section type from the plurality of section types; identify a second section in the first document that is uniquely associated with a second section type from the plurality of section types; apply a first trained machine learning model from the plurality of trained machine learning models to the first section in the first document image to produce at least a first portion of a structured data file that identifies a feature of the first section in the first document; and apply a second trained machine learning model from the second plurality of trained machine learning models to the second section in the first document image to produce at least a second portion of the structured data file that identifies a feature of the second section in the first document.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 15 recites the limitation apply a second trained machine learning model to a section from the plurality of sections to identify a feature of the section in the document image. This limitation would at least put the reader in doubt because it is unclear whether the feature of the section is identified after the plurality of sections are uniquely associated with a section type from a plurality of section types or before. Please clarify.
Since claims 16-20 depend on claim 15, they are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, for the same reason set forth above for claim 15.
Allowable Subject Matter
Claims 2-9 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.
Claims 10-14 are allowed. Claims 1-9 and 15-20 will also be allowed if applicant overcomes the rejection under obviousness double-patenting and35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, by way of an amendment and/or filing a terminal disclaimer.
The following is a statement of reasons for the indication of allowable subject matter regarding claims 10-14: the prior art to: Gu, et al. (US 2023/0154221 A1) disclose "systems for pretraining a document encoder model based on multimodal self cross-attention between the modes. A non-limiting exemplary method for training the model includes receiving image data that encodes a set of pretraining documents. A set of sentences may be extracted from the image data. A bounding box for each sentence may additionally be extracted. For each sentence of the set of sentences, a set of predicted features may be generated. The set of predicted features may be generated based on a gated-encoder model. The gated-encoder model may perform cross-attention between a set of masked-textual features for the sentence and a set of masked-visual features for the sentence. The set of masked- textual features may be based on a masking function and the sentence. The set of masked-visual features may be based on the masking function and the corresponding bounding box for the sentence. A document-encoder model may be pretrained. The pretraining may be based on the set of predicted features for each sentence of the set of sentences and one or more pretraining tasks. The one or more pretraining tasks may include at least one of masked sentence modeling, visual contrastive learning, or visual-language alignment." (See paragraph 0007); Geng (US 11,321,956 B1) discloses techniques for sectionizing documents based on visual and language models. In some embodiments, a document processing system may be configured to process documents in order to identify certain types of section headers in the documents. The document processing system may use a combination of a visual model and a language to identify these types of section headers in documents. For example, the document processing system can train a visual model trained to detect section headers in documents. The document processing system uses such a visual model to detect sections in a document that are predicted to be section headers (also referred to as candidate section headers). Unlike the visual model, the language model used by the document processing system does not require any training. Instead, the language model may include several components that are each configured to utilize a different natural language processing (NLP) technique. The language model uses one or more of these components to process candidate section headers to analyze text in candidate section headers and determine a type of the candidate section headers (See col. 5, lines 33-53); O'Gorman et al. (US 11,354,485 B1) at column 3, lines 25-51 states: "The resume analysis device 101 includes a memory 102, a communication interface 103, and a processor 104. The resume analysis device 101 can operate an image generator 105 and/or a statistical model 106 that together can generate resume document images from resume documents, and classify paragraphs of the set of resume document images by paragraph types (e.g., professional summary, experience timeline, skillsets, education history, publications, and/or the like). In some embodiments, the resume analysis device 101 can receive data including the resume documents from a data source(s). The data source(s) can include, for example, a scanner and/or an external hard drive (both optional; not shown), the compute device 160, the server 170, each operatively coupled to the resume analysis device 101. The resume analysis device 101 can receive the data in response to a user of the resume analysis device 101 and/or the compute device 160 providing an indication to begin training the statistical model 106 based on the set of resume document images or an indication to begin classifying paragraphs of the set of resume document image. The resume analysis device 101 can receive the resume documents that are often semi-structured and/or unstructured, generate resume document images, and identify and correlate entities (e.g., company names, individual names, skills, job titles, universities, etc.) in generic text of the resume document images to extract structured and machine-indexable data; and
Rahimov, et al. (US 2025/0165650) parse documents into text, images, and metadata. It then uses machine learning to detect obvious sensitive items such as names, account numbers, or IDs. It also tries to find sensitive information that is not explicit but can be inferred from context. For image content, it can run OCR to extract text and can also detect signatures and fingerprints directly. Once sensitive content is found, the system can redact it (See for example, Fig. 7 and the associated text).
In contrast, upon parsing a document image to extract a plurality of subsets of characters from the plurality of representations of characters to generate a text encoding for that document image, each subset of characters being associated with a structure type from a plurality of structure types, the instant invention: accesses metainformation associated with the document image; generates a parsed document based on the text encoding and the metainformation; identifies a plurality of sections from the parsed document, each section from the plurality
of sections uniquely associated with a section type from a plurality of section types. Thereafter, the instant invention: applies a first trained machine learning model to a first section from the plurality of sections to produce at least a first portion of a structured data file that identifies a feature of the first section in the document image; and applies a second trained machine learning model to a second section from the plurality of sections to produce at least a second portion of the structured data file that identifies a feature of the second section in the document image, as defined by independent claim 10. These elements in combination with all of the other elements of the claims are not disclosed or fairly suggested by the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US Patent Numbers: 11,321,956 and 11,354,485; and US Patent Application Publication Numbers: 2023/0154221 and 20250165650.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL G MARIAM whose telephone number is (571)272-7394. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mathew Bella can be reached at (571)272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL G MARIAM/Primary Examiner, Art Unit 2675
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