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 remarks the applicant filed on 07/31/2025.
Claims 1-20 stand rejected and are pending in this Office Action in which claims 1, 12 and 20 are independent.
Information Disclosure Statement
The information disclosure statements filed 07/31/2025 are in compliance with 37 CFR 1.97(c) and herein have been considered. Its corresponding PTO-1449 have been electronically signed as attached.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
Claims 1-20 are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-20 of prior U.S. Patent No. 12400469. This is a statutory double patenting rejection.
The following table shows the claims in Instant Application that are rejected by corresponding claim(s) in U.S. Patent No. 12400469, word by word.
Instant Application
U.S. Patent No. 12400469
1. A method comprising:
generating a plurality of image pages from a target document;
generating an image collage by assembling the plurality of image pages into a sequential image mosaic comprising a plurality of pixels;
extracting two-dimensional text and corresponding bounding boxes from the plurality of image pages; applying a structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on the corresponding bounding boxes to generate spatial-formatted text;
concatenating the spatial-formatted text to generate a sequential spatial-formatted text extraction;
applying a multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction;
comparing, using a nearest-neighbor model, the target artifact vector against each preexisting artifact vector in a set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors, wherein the corresponding artifact vector is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and
determining that a multimodal embedding distance between the corresponding artifact vector and the target artifact vector are within a threshold distance and, in response to determining, performing:
applying the corresponding metadata tag to the target document to generate a modified target document, and outputting the modified target document.
2. The method of claim 1, wherein: concatenating the spatial-formatted text to generate the sequential spatial-formatted text extraction comprises concatenating the spatial-formatted text from a first page, the spatial-formatted text is from a subsequent page, and a page break character separates the spatial-formatted text from the first page and separates the spatial-formatted text from the subsequent page.
3. The method of claim 1, wherein the corresponding artifact vector has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction.
4. The method of claim 1, further comprising: confirming whether the corresponding metadata tag matches a portion of the two-dimensional text.
5. The method of claim 4, further comprising: indicating, in response to the corresponding metadata tag not matching the portion of the two-dimensional text, that the corresponding metadata tag comprises a hallucination of a language model.
6. The method of claim 1, wherein the corresponding artifact vector is a preexisting artifact vector that is closest in a multimodal embedding space to the target artifact vector based on Euclidean distance.
7. The method of claim 1, wherein the corresponding artifact vector is a preexisting artifact vector that is closest in a multimodal embedding space to the target artifact vector based on nearest neighbor voting.
8. The method of claim 1, further comprising: flagging, in response to the multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within the threshold distance, the target document as an anomalous document.
9. The method of claim 8, further comprising taking additional steps in response to flagging the target document as the anomalous document, the additional steps comprising: applying a large language model to the target document to generate a created metadata tag; adding the created metadata tag to the target document to generate the modified target document; and outputting the modified target document.
10. The method of claim 1, further comprising:
applying optical character recognition to the plurality of image pages to extract the two-dimensional text.
11. The method of claim 1, wherein the image collage comprises a grid of the plurality of image pages, the grid in sequence from left-to-right, top-to-bottom.
12. A system comprising:
a server comprising a processor;
a data repository in communication with the processor, and
storing: a target document, the target document comprising two-dimensional text, an image collage comprising a sequential image mosaic having a plurality of pixels, spatial-formatted text comprising the two-dimensional text spatially formatted in one-dimension, a sequential spatial-formatted text extraction, a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, a set of preexisting artifact vectors,
wherein each of set of preexisting artifact vectors has an embedded representation, and is associated with a corresponding document, a threshold distance, and a modified target document;
a structure verbalizer which, when executed by the processor, is programmed to apply the structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on corresponding bounding boxes from a plurality of image pages to generate the spatial-formatted text;
a multimodal embedding model which, when executed by the processor, is programmed to apply the multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate the target artifact vector; and a server controller which, when executed by the processor, performs a computer-implemented method comprising:
generating the plurality of image pages from the target document,
generating the image collage by assembling the plurality of image pages into the sequential image mosaic, extracting the two-dimensional text and corresponding bounding boxes from the image pages, applying the structure verbalizer to the two-dimensional text to generate the spatial-formatted text, concatenating the spatial-formatted text to generate the sequential spatial-formatted text extraction, applying the multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate the target artifact vector, comparing, using a nearest-neighbor model, the target artifact vector against each preexisting artifact vector in the set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors,
wherein the corresponding artifact vector: has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, and is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and determining that a multimodal embedding distance between the corresponding artifact vector and the target artifact vector is within the threshold distance and, in response to determining, performing additional steps comprising: adding the corresponding metadata tag to the target document to generate the modified target document; and outputting the modified target document.
13. The system of claim 12, wherein the computer-implemented method further comprises: concatenating the spatial-formatted text from a first page, the spatial-formatted text from a subsequent page and a page break character separating the spatial-formatted text from the first page and the spatial-formatted text from the subsequent page.
14. The system of claim 12, wherein the computer-implemented method further comprises: confirming whether the corresponding metadata tag matches a portion of the two-dimensional text.
15. The system of claim 14, wherein the computer-implemented method further comprises: indicating, in response to the corresponding metadata tag not matching the portion of the two-dimensional text, that the corresponding metadata tag comprises a hallucination of a language model.
16. The system of claim 12, wherein the computer-implemented method further comprises: flagging, in response to the multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within the threshold distance, the target document as an anomalous document; and performing, in response to flagging the target document as the anomalous document, additional steps comprising: applying a large language model to the target document to generate a created metadata tag; adding the created metadata tag to the target document to generate the modified target document; and outputting the modified target document.
17. The system of claim 12, wherein the computer-implemented method further comprises: applying optical character recognition to the plurality of image pages to extract the two-dimensional text.
18. The system of claim 12, wherein the image collage comprises a grid of the plurality of image pages.
19. The system of claim 18, wherein the grid is in sequence from left-to-right, top-to-bottom.
20. A method comprising:
generating a plurality of image pages from a target document;
generating an image collage by assembling the plurality of image pages into a sequential image mosaic comprising a plurality of pixels;
extracting two-dimensional text and corresponding bounding boxes from the plurality of image pages;
applying a structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on the corresponding bounding boxes to generate spatial-formatted text;
concatenating the spatial-formatted text to generate a sequential spatial-formatted text extraction;
applying a multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction;
comparing using a nearest-neighbor model to the target artifact vector against each preexisting artifact vector in a set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors,
wherein the corresponding artifact vector: has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, and is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and performing, in response to a multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within a threshold distance, additional steps comprising: flagging the target document as an anomalous document, applying a large language model to the target document to generate a created metadata tag, adding the created metadata tag to the target document to generate a modified target document, and outputting the modified target document.
1. A method comprising:
generating a plurality of image pages from a target document;
generating an image collage by assembling the plurality of image pages into a sequential image mosaic comprising a plurality of pixels;
extracting two-dimensional text and corresponding bounding boxes from the plurality of image pages; applying a structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on the corresponding bounding boxes to generate spatial-formatted text;
concatenating the spatial-formatted text to generate a sequential spatial-formatted text extraction;
applying a multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction;
comparing, using a nearest-neighbor model, the target artifact vector against each preexisting artifact vector in a set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors, wherein the corresponding artifact vector is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and
determining that a multimodal embedding distance between the corresponding artifact vector and the target artifact vector are within a threshold distance and, in response to determining, performing:
applying the corresponding metadata tag to the target document to generate a modified target document, and outputting the modified target document.
2. The method of claim 1, wherein: concatenating the spatial-formatted text to generate the sequential spatial-formatted text extraction comprises concatenating the spatial-formatted text from a first page, the spatial-formatted text is from a subsequent page, and a page break character separates the spatial-formatted text from the first page and separates the spatial-formatted text from the subsequent page.
3. The method of claim 1, wherein the corresponding artifact vector has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction.
4. The method of claim 1, further comprising: confirming whether the corresponding metadata tag matches a portion of the two-dimensional text.
5. The method of claim 4, further comprising: indicating, in response to the corresponding metadata tag not matching the portion of the two-dimensional text, that the corresponding metadata tag comprises a hallucination of a language model.
6. The method of claim 1, wherein the corresponding artifact vector is a preexisting artifact vector that is closest in a multimodal embedding space to the target artifact vector based on Euclidean distance.
7. The method of claim 1, wherein the corresponding artifact vector is a preexisting artifact vector that is closest in a multimodal embedding space to the target artifact vector based on nearest neighbor voting.
8. The method of claim 1, further comprising: flagging, in response to the multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within the threshold distance, the target document as an anomalous document.
9. The method of claim 8, further comprising taking additional steps in response to flagging the target document as the anomalous document, the additional steps comprising: applying a large language model to the target document to generate a created metadata tag; adding the created metadata tag to the target document to generate the modified target document; and outputting the modified target document.
10. The method of claim 1, further comprising:
applying optical character recognition to the plurality of image pages to extract the two-dimensional text.
11. The method of claim 1, wherein the image collage comprises a grid of the plurality of image pages, the grid in sequence from left-to-right, top-to-bottom.
12. A system comprising:
a server comprising a processor;
a data repository in communication with the processor, and
storing: a target document, the target document comprising two-dimensional text, an image collage comprising a sequential image mosaic having a plurality of pixels, spatial-formatted text comprising the two-dimensional text spatially formatted in one-dimension, a sequential spatial-formatted text extraction, a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, a set of preexisting artifact vectors,
wherein each of set of preexisting artifact vectors has an embedded representation, and is associated with a corresponding document, a threshold distance, and a modified target document;
a structure verbalizer which, when executed by the processor, is programmed to apply the structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on corresponding bounding boxes from a plurality of image pages to generate the spatial-formatted text;
a multimodal embedding model which, when executed by the processor, is programmed to apply the multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate the target artifact vector; and a server controller which, when executed by the processor, performs a computer-implemented method comprising:
generating the plurality of image pages from the target document,
generating the image collage by assembling the plurality of image pages into the sequential image mosaic, extracting the two-dimensional text and corresponding bounding boxes from the image pages, applying the structure verbalizer to the two-dimensional text to generate the spatial-formatted text, concatenating the spatial-formatted text to generate the sequential spatial-formatted text extraction, applying the multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate the target artifact vector, comparing, using a nearest-neighbor model, the target artifact vector against each preexisting artifact vector in the set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors,
wherein the corresponding artifact vector: has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, and is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and determining that a multimodal embedding distance between the corresponding artifact vector and the target artifact vector is within the threshold distance and, in response to determining, performing additional steps comprising: adding the corresponding metadata tag to the target document to generate the modified target document; and outputting the modified target document.
13. The system of claim 12, wherein the computer-implemented method further comprises: concatenating the spatial-formatted text from a first page, the spatial-formatted text from a subsequent page and a page break character separating the spatial-formatted text from the first page and the spatial-formatted text from the subsequent page.
14. The system of claim 12, wherein the computer-implemented method further comprises: confirming whether the corresponding metadata tag matches a portion of the two-dimensional text.
15. The system of claim 14, wherein the computer-implemented method further comprises: indicating, in response to the corresponding metadata tag not matching the portion of the two-dimensional text, that the corresponding metadata tag comprises a hallucination of a language model.
16. The system of claim 12, wherein the computer-implemented method further comprises: flagging, in response to the multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within the threshold distance, the target document as an anomalous document; and performing, in response to flagging the target document as the anomalous document, additional steps comprising: applying a large language model to the target document to generate a created metadata tag; adding the created metadata tag to the target document to generate the modified target document; and outputting the modified target document.
17. The system of claim 12, wherein the computer-implemented method further comprises: applying optical character recognition to the plurality of image pages to extract the two-dimensional text.
18. The system of claim 12, wherein the image collage comprises a grid of the plurality of image pages.
19. The system of claim 18, wherein the grid is in sequence from left-to-right, top-to-bottom.
20. A method comprising:
generating a plurality of image pages from a target document;
generating an image collage by assembling the plurality of image pages into a sequential image mosaic comprising a plurality of pixels;
extracting two-dimensional text and corresponding bounding boxes from the plurality of image pages;
applying a structure verbalizer to spatially format the two-dimensional text in one-dimension with spatial information based on the corresponding bounding boxes to generate spatial-formatted text;
concatenating the spatial-formatted text to generate a sequential spatial-formatted text extraction;
applying a multimodal embedding model to the plurality of pixels of the image collage and the sequential spatial-formatted text extraction to generate a target artifact vector comprising an embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction;
comparing using a nearest-neighbor model to the target artifact vector against each preexisting artifact vector in a set of preexisting artifact vectors to identify a corresponding artifact vector from the set of preexisting artifact vectors,
wherein the corresponding artifact vector: has a similar embedded representation that is similar to the embedded representation of the plurality of pixels and the sequential spatial-formatted text extraction, and is associated with a corresponding document, the corresponding document having a corresponding metadata tag; and performing, in response to a multimodal embedding distance between the corresponding artifact vector and the target artifact vector not being within a threshold distance, additional steps comprising: flagging the target document as an anomalous document, applying a large language model to the target document to generate a created metadata tag, adding the created metadata tag to the target document to generate a modified target document, and outputting the modified target document.
“Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U.S. Court of Customs and Patent Appeals.
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
Examiner has cited particular columns and line numbers in the references 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 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 entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mr. Aleksandr Kerzhner can be reached on 571-270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
June 18, 2026
Prosecution Insights