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 .
Claim Objections
Claim 11 is objected to because of the following informalities: in the last limitation “generating extracted features” should read generating the extracted features. Appropriate correction is required.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1, 2, 8-11, 15-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 16 10 and 15 of U.S. Patent No. US 12190619 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the patent disclose all the features of the instant claims.
Re claim 1 claim 16 discloses A method comprising: receiving an image of text associated with an asset (see claim 13 “operations comprising: receiving an image of a document, the document associated with a transport of an asset” note that a document is an assets which contains text); generating first machine readable content representing the text ( see claim 13 “generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document” note that the machine readable content is generated using optical character recognition see also claim 13 “generating third machine readable content based at least in part on the first machine readable content and the second machine readable content” note that the first machine readable content of the instant claim corresponds to the third machine readable content); determining an address pattern based at least in part on one or more of a language associated with the first machine readable content, a location of origin of the assets, or a destination location for the assets (see claim 16 ” determining an address pattern based at least in part on one or more of a language associated with the third machine readable content, a location of origin of assets associated with the document or a destination location for the assets”); determining a location associated with the address based at least in part on the address pattern (see claim 16 “determining a location associated with the address based at least in part on the address pattern”); generating an address bounding box associated with the address; (see claim 16 “generating an address bounding box associated with the address”) and determining an address type based at least in part on content of the first machine readable content adjacent to the bounding box (see claim 16 ” and determining an address type based at least in part on content of the third machine readable content adjacent to the bounding box.”); and extracting the address from the first machine readable content (see claim 16 “wherein the extracted data includes an address and the generating the extracted data comprises” note that data extracted includes an address).
Re claim 2 Claim 16 discloses
further comprising: generating second machine readable content based at least in part on a first optical character recognition system, the second machine readable content representing the text; generating third machine readable content based at least in part on a second optical character recognition system, the third machine readable content representing the text (see claim 13 “generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document” note that the first content of the instant application corresponds to the third content of claim 16, and the second content corresponds to the first content of claim 16 and the third content corresponds to the second content of claim 16 );
generating the first machine readable content based at least in part on the second machine readable content and the third machine readable content; (see claim 16 “generating third machine readable content based at least in part on the first machine readable content and the second machine readable content;”)
generating a first classification for the text based at least in part on the first machine readable content and a first classification system ( see claim 13 generating a first classification for the document based at least in part on the third machine readable content and a first classification system”);
generating a second classification for the text based at least in part on the first machine readable content and a second classification system (see claim 13 “generating a second classification for the document based at least in part on the third machine readable content and a second classification system”);
generating an assigned classification for the text based at least in part on the first classification and the second classification (see claim 13 “generating an assigned classification for the document based at least in part on the first classification and the second classification”);
and generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification (see claim 13 “generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification”).
Re claim 8, claim 16 discloses preprocessing the image to align individual pages of the text with an upright vectors (see claim 13 “preprocessing the image to align individual pages of the document with an upright vectors”)
Re claim 9 claim 16 discloses identifying an imperfection within the image; determining a first bounding box associated with the imperfection; determining a second bounding box associated with content of the text; preforming at least one first operation on the first bounding box to reduce a visibility of the imperfection; and preforming at least one second operation on the second bounding box to increase a visibility of the content. ( see claim 13 “the preprocessing further comprising; identifying an imperfection within the image; determining a first bounding box associated with the imperfection; determining a second bounding box associated with content of the document; preforming at least one first operation on the first bounding box to reduce a visibility of the imperfection; and preforming at least one second operation on the second bounding box to increase a visibility of the content”
Re claim 10 claim 10 of the patent discloses
A method comprising: receiving an image of a document, the document associated with an asset; generating first machine readable content representing the document; (see claim 1 “receiving an image of a document; preprocessing the image to align individual pages of the document with an upright vectors; generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content” ” note that the third machine readable content of the patent corresponds to the first machine readable content of the instant application )
determining a content pattern based at least in part on the first machine readable content (see paragraph 10 “determine a content pattern based at least in part on the third machine readable content;” note that the third machine readable content of the patent corresponds to the first machine readable content of the instant application);
detecting a table within the first machine readable content based at least in part on a change between the content pattern and a pattern associated with content of the first machine readable content representing the table; (see claim 10 “detecting a table within the third machine readable content based at least in part on a change between the content pattern and a pattern associated with content of the third machine readable content representing the table”
)
determining a context of the table; and extraction features from the content of the first machine readable content representing the table, the extracted features organized as a virtual table (see claim 10” determining a context of the table; and extraction features from the content of the third machine readable content representing the table, the extracted features organized as the virtual table”).
Re claim 11 Claim 10
discloses generating second machine readable content based at least in part on a first optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on a second optical character recognition system, the third machine readable content representing the document; generating the first machine readable content based at least in part on the second machine readable content and the third machine readable content; generating a first classification for the document based at least in part on the first machine readable content and a first classification system; generating a second classification for the document based at least in part on the first machine readable content and a second classification system; and generating an assigned classification for the document based at least in part on the first classification and the second classification (see claim 1 “generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content; generating a first classification for the document based at least in part on the third machine readable content and a first classification system; generating a second classification for the document based at least in part on the third machine readable content and a second classification system; generating an assigned classification for the document based at least in part on the first classification and the second classification;” note that the first content of the instant application corresponds to the third content of claim 10, and the second content corresponds to the first content of claim 10 and the third content corresponds to the second content of claim 10.)
generating extracted features from the first machine readable content is based at least in part on the assigned classification (see claim 1 “generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification”)
Re claim 15 Claim 15 of the patent discloses
A method comprising: receiving an image of a document, the document associated with an asset; generating first machine readable content representing the document; (see claim 13 “perform operations comprising: receiving an image of a document, the document associated with a transport of an asset; preprocessing the image to align individual pages of the document with an upright vectors, ….generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content” note that first content of the claim corresponds the third content of the patent)
determining a date format associated with the first machine readable content; detecting a first date within the first machine readable content based at least in part on the date format;
detecting a second date within the first machine readable content based at least in part on the date format; (see claim 15 “determining a date format based at least in part on one or more of a language associated with the third machine readable content, a location of origin of the assets, or a destination location for the assets; detecting the first date within the machine readable content based at least in part on the date format; detecting a second date within the machine readable content based at least in part on the date format”)
determining a modified date format based at least in part on the first date and the second date; detecting a third date within the first machine readable content based at least in part on the modified date format; and outputting the third date ( see claim 15 “determining a modified date format based at least in part on the first date and the second date; and detecting a third date within the machine readable content based at least in part on the modified date format” note that the determination of a third date step functionally outputs the date).
Re claim 16 Claim 15 of the patent discloses
generating second machine readable content based at least in part on a first optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on a second optical character recognition system, the third machine readable content representing the document; generating the first machine readable content based at least in part on the second machine readable content and the third machine readable content; “perform operations comprising: receiving an image of a document, the document associated with a transport of an asset; preprocessing the image to align individual pages of the document with an upright vectors, ….generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content” note that first content of the claim corresponds the third content of the patent)
generating a first classification for the document based at least in part on the first machine readable content and a first classification system; generating a second classification for the document based at least in part on the first machine readable content and a second classification system; generating an assigned classification for the document based at least in part on the first classification and the second classification; (see claim 13 “generating a first classification for the document based at least in part on the third machine readable content and a first classification system; generating a second classification for the document based at least in part on the third machine readable content and a second classification system; generating an assigned classification for the document based at least in part on the first classification and the second classification”) and generating extracted features from the first machine readable content is based at least in part on the assigned classification (see claim 13 “ generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification”).
Re claim 17 claim 15 discloses identifying an imperfection within the image; determining a first bounding box associated with the imperfection; determining a second bounding box associated with content of the document; preforming at least one first operation on the first bounding box to reduce a visibility of the imperfection; and preforming at least one second operation on the second bounding box to increase a visibility of the content. (see claim 13 ”receiving an image of a document, the document associated with a transport of an asset; preprocessing the image to align individual pages of the document with an upright vectors, the preprocessing further comprising; identifying an imperfection within the image; determining a first bounding box associated with the imperfection; determining a second bounding box associated with content of the document; preforming at least one first operation on the first bounding box to reduce a visibility of the imperfection; and preforming at least one second operation on the second bounding box to increase a visibility of the content”).
Re claim 18 Claim 16 discloses wherein the first date format is determined based at least in part on a language associated with the first machine readable content (see claim 15 “determining a date format based at least in part on one or more of a language associated with the third machine readable content, a location of origin of the assets, or a destination location for the assets”).
Re claim 19 Claim 16 discloses wherein the first date format is determined based at least in part on a location of origin of the assets (see claim 15 “determining a date format based at least in part on one or more of a language associated with the third machine readable content, a location of origin of the assets, or a destination location for the assets”).
Re claim 20 Claim 16 discloses wherein the first date format is determined based at least in part on a destination location for the assets (see claim 15 “determining a date format based at least in part on one or more of a language associated with the third machine readable content, a location of origin of the assets, or a destination location for the assets”)
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.
Claim 1, 8, 10 12-15 and 18-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Re claim 1
A method comprising:; generating first machine readable content representing the text; ; determining a location associated with the address based at least in part on the address pattern; generating an address bounding box associated with the address; determining an address type based at least in part on content of the first machine readable content adjacent to the bounding box; and extracting the address from the first machine readable content
The limitation of receiving an image of text associated with an asset, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving and image in the context of this claim encompasses the user mentally looking at an image.
The limitation of determining an address pattern based at least in part on one or more of a language associated with the first machine readable content, a location of origin of the assets, or a destination location for the assets, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining and address pattern in the context of this claim encompasses the user mentally determining the address pattern.
The limitation of determining a location associated with the address based at least in part on the address pattern, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining a location in the context of this claim encompasses the user mentally determining the location.
The limitation of generating an address bounding box associated with the address, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, generating in the context of this claim encompasses the user mentally generating a bounding box.
The limitation of determining an address type based at least in part on content of the first machine readable content adjacent to the bounding box, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining an address type in the context of this claim encompasses the user mentally determining the address type.
The limitation of extracting the address from the first machine readable content, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, extracting the address type in the context of this claim encompasses the user mentally extracting the address.
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 claim only recites one additional element – generating first machine readable content representing the text. This element could be performed using the well known process of optical character recognition such that it amounts no more than limiting the claim to the well known field of optical character recognition. 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 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 generating first machine readable content representing the text amounts to no more than limiting the claim to the well-known field of Optical character recognition. Mere use of well-known optical character recognition cannot provide an inventive concept. The claim is not patent eligible.
Re claim 8 Claim 8 contains the same abstract idea as claim 9.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional element – generating first machine readable content representing the text and preprocessing the image to align individual pages of the text with an upright vectors. This element could be performed using the well known process of optical character recognition such that it amounts no more than limiting the claim to the well known field of optical character recognition. Furthermore, correcting a document to make it upright is well known preprocessing step see Sakamoto US 2009/0034848 See paragraphs 274 and 275 note that conventional known methods are used to correct vertical skew. This merely limits the claim to a well-known orientation/skew correction. 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 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 generating first machine readable content representing the text amounts to no more than limiting the claim to the well-known field of Optical character recognition. Further as discussed above correction of skew is well known. Mere instructions to perform optical character combines with the well-known concept of skew correction does constitute significantly more than the abstract idea. The claim is not patent eligible.
Re claim 10,
A method comprising:; generating first machine readable content representing the document;
determining a content pattern based at least in part on the first machine readable content; detecting a table within the first machine readable content based at least in part on a change between the content pattern and a pattern associated with content of the first machine readable content representing the table; determining a context of the table; and extraction features from the content of the first machine readable content representing the table, the extracted features organized as a virtual table.
The limitation of receiving an image of a document, the document associated with an asset, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving and image in the context of this claim encompasses the user mentally receiving the image.
The limitation of determining a content pattern based at least in part on the first machine readable content, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining in the context of this claim encompasses the user mentally determining the content pattern.
The limitation of detecting a table within the first machine readable content based at least in part on a change between the content pattern and a pattern associated with content of the first machine readable content representing the table, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, detecting a table in the context of this claim encompasses the user mentally detecting the table.
The limitation of determining a context of the table, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining a context in the context of this claim encompasses the user mentally determining a context.
The limitation of extraction features from the content of the first machine readable content representing the table, the extracted features organized as a virtual table., as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, extracting and organizing in the context of this claim encompasses the user mentally extracting the feature and organizing them into a mental virtual table.
The limitation of extracting the address from the first machine readable content, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, extracting the address type in the context of this claim encompasses the user mentally extracting the address.
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 claim only recites one additional element – generating first machine readable content representing the text. This element could be performed using the well known process of optical character recognition such that it amounts no more than limiting the claim to the well known field of optical character recognition. 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 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 generating first machine readable content representing the text amounts to no more than limiting the claim to the well-known field of Optical character recognition. Mere use of well-known optical character recognition cannot provide an inventive concept. The claim is not patent eligible.
Re claim 12
The limitations of wherein the table is a borderless table and the method further comprises: determining, within the first machine readable content, a geometric pattern indicative of a table, the geometric pattern determined with respect to a reminder of the content of the first machine readable content; determining, based at least in part on the geometric pattern, one or more bounding boxes associated with the table; and extraction features from the content of the first machine readable content representing the table based at least in part on the one or more bounding boxes, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining and extraction in the context of this claim encompasses the user mentally performing the above determination and extractions.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
Re claim 13
The limitations of determining the geometric pattern is based at least in part on a difference in an average word spacing and line spacing of the reminder of the first machine readable content and content of the first machine readable content representing the table, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining in the context of this claim encompasses the user mentally performing the above determinations.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
Re claim 14
The limitations of wherein the table is a bordered table and the method further comprises: detecting, within the first machine readable content, one or more boarders associated with the table, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, detecting in the context of this claim encompasses the user mentally detecting the border.
The limitations of analyzing a header section, footer section, spacing between row and columns of the table, determining, based at least in part on the one or more boarders, a header section of the table, a footer section of the table, spacing between row and columns of the table, one or more bounding boxes associated with the table, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, analyzing in the context of this claim encompasses the user mentally performing the analyzing.
The limitations of extraction features from the content of the first machine readable content representing the table based at least in part on the one or more bounding boxes, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, extraction in the context of this claim encompasses the user mentally performing the extraction.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
Re claim 15 The limitations of receiving an image of a document, the document associated with an asset, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, receiving in the context of this claim encompasses the user mentally looking at an image to receive it.
The limitations of determining a date format associated with the first machine readable content, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining in the context of this claim encompasses the user mentally making a determination of the date format.
The limitations of detecting a first date within the first machine readable content based at least in part on the date format; detecting a second date within the first machine readable content based at least in part on the date format, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, detecting a date in the context of this claim encompasses the user mentally detecting the first and second date.
The limitations of determining a modified date format based at least in part on the first date and the second date, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining in the context of this claim encompasses the user mentally determining a modified date format.
The limitations of detecting a third date within the first machine readable content based at least in part on the modified date format; and outputting the third date, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, detecting in the context of this claim encompasses the user mentally performing detecting and mentally outputting a third date.
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 claim only recites one additional element – generating first machine readable content representing the text. This element could be performed using the well known process of optical character recognition such that it amounts no more than limiting the claim to the well known field of optical character recognition. 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 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 generating first machine readable content representing the text amounts to no more than limiting the claim to the well known field of Optical character recognition. Mere use of well-known optical character recognition cannot provide an inventive concept. The claim is not patent eligible.
Re claim 18
The limitations of wherein the first date format is determined based at least in part on a language associated with the first machine readable content, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining based on language in the context of this claim encompasses the user mentally making determination by language.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
Re claim 19 The limitations of wherein the first date format is determined based at least in part on a location of origin of the assets, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining based on origin in the context of this claim encompasses the user mentally making the determination based on origin.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
Re claim 20 The limitations of wherein the first date format is determined based at least in part on a destination location for the assets, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, determining based on destination in the context of this claim encompasses the user mentally making the determination based on destination.
The analysis with respect to integration into an abstract idea and significantly more is not significantly changed from the claim which this claim depends.
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.
Claim(s) 10 and 14 is/are rejected under 35 U.S.C. 102(A)(1) as being anticipated by DENG US 20210158034 A1.
Re claim 10 and
A method comprising:
receiving an image of a document, the document associated with an asset (see 2 and 50 note that a PDF document associated with a journal is determined);
generating first machine readable content representing the document (see paragraph 50 “A stream of PDF content is decoded in units of pages by using a tool such as PDFBox, to obtain all parameter information related to text, including a character font (Font), a font size (FontSize), a character width (Width), a character height (Height), a character spacing (WidthOfSpacing), horizontal and vertical coordinates (X, Y) and a scale factor (XScale) thereof, and the like. A text order obtained by decoding PDF content in order is a normal reading order. Text state parameters have the following variation rule” note that pdf content is decoded);
determining a content pattern based at least in part on the first machine readable content (see paragraph 10 note that character information such as font size character width spacing ect. are determined and text line clustering is performed).
detecting a table within the first machine readable content based at least in part on a change between the content pattern and a pattern associated with content of the first machine readable content representing the table; (see paragraph 19 note that increases in font size or scale factor is used as a part of the table detection process)
determining a context of the table (see paragraph 63 note that a title line is determined);
and extraction features from the content of the first machine readable content representing the table, the extracted features organized as a virtual table (see paragraph 84 and 85 and figure 9 b note that the table data is extracted and organized an array i.e a virtual table).
Re claim 14 Deng discloses wherein the table is a bordered table and the method further comprises: detecting, within the first machine readable content, one or more boarders associated with the table (see paragraph 61 “a border of the table content is determined. Table data is marked till the border of the table content or the end of current-page characters. ““ see figure 9 a note that the tables bay have borders) analyzing a header section, footer section (see paragraph 65 note that header and footer text may be screened ) spacing between row (see paragraphs 75 and 76 row and line spacing is analyzed) and columns (see paragraph 70 note that the spacing between columns is analyzed) of the table; determining, based at least in part on the one or more boarders, a header section of the table, a footer section of the table, spacing between row and columns of the table, one or more bounding boxes associated with the table (see figure 3 paragraphs 66 and 68-70 note that cells i.e. bounding boxes are created for the table data note that this base on column spacing); and extraction features from the content of the first machine readable content representing the table based at least in part on the one or more bounding boxes (see paragraph 84 and 87 note that data is extracted according to the cells i.e. bounding boxes).
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, 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(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over DENG US 20210158034 A1 in view of Aggarwal US 20210073326 A1.
Re claim 12 Deng discloses
determining, within the first machine readable content, a geometric pattern indicative of a table, the geometric pattern determined with respect to a reminder of the content of the first machine readable content (see paragraph 19 note that the determine increases in font size or scale factor with respect to other content is used as a part of the table detection process);
determining, based at least in part on the geometric pattern, one or more bounding boxes associated with the table (see figure 3 paragraphs 66 and 68-70 note that cells i.e. bounding boxes are created for the table data);
and extraction features from the content of the first machine readable content representing the table based at least in part on the one or more bounding boxes (see paragraph 84 and 87 note that data is extracted according to the cells i.e. bounding boxes).
Deng does not expressly disclose:
wherein the table is a borderless table
Aggarwal discloses wherein the table is a borderless table (see paragraph 6 “identifying at least one of a bordered table and a borderless table in the document using the image of the document. Furthermore, upon identifying the bordered table in the document, the method includes extracting tabular data in the identified bordered table using a first and a second set of pixel coordinates from the plurality of pixel coordinates, wherein the first and the second set of pixel coordinates corresponds to at least one row and at least one column in the image of the document respectively. Thereafter, upon identifying the borderless table in the document, the method includes determining a first set of document coordinates of at least one row of the borderless table by distinguishing the at least one row with at least one non-tabular row in the document”). The motivation to combine is “An issue with the existing data extraction techniques is the lack of ability to identify a borderless table in a document. Thus, existing documents scanning systems cannot be used for extracting tabular content not having a border” (see paragraph 03) and “further, the method includes identifying at least one of a bordered table and a borderless table in the document using the image of the document” (see paragraph 6). One of ordinary skill in the art could have easily adapted the method of Deng to detect borderless tables. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed inventio to combine Aggarwal and Deng to reach the aforementioned advantage.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miletzki US US 20040117192 A1 in view of Lecky US 9298997 B1.
Re claim 1 Miletzki discloses
A method comprising:
receiving an image of text associated with an asset (see paragraph 18 note that an image is created);
generating first machine readable content representing the text (see paragraph 22 note that OCR is performed see paragraph 24 note that OCR is performed based on language );
determining an address pattern based at least in part on one or more of a language associated with the first machine readable content, a location of origin of the assets, or a destination location for the assets (see paragraph 32 “the address block is determined using language-related layout models 11.1 to 11.n” note that the layout of the address is analyzed using a language dependent model );
determining a location associated with the address based at least in part on the address pattern (see paragraph 25 ” In unit 6, the address elements are determined and classified using syntax models 11. This employs packets, inter alia, use of individual keywords or designators such as "road", "number", "ZIP code" etc. which are searched for in the address. The hierarchy of the address elements such as <state>, <town>, <road>; <ZIP code>; etc. is therefore found” note that location elements are determined);
and extracting the address from the first machine readable content. (see paragraph 25 ” In unit 6, the address elements are determined and classified using syntax models 11. This employs packets, inter alia, use of individual keywords or designators such as "road", "number", "ZIP code" etc. which are searched for in the address. The hierarchy of the address elements such as <state>, <town>, <road>; <ZIP code>; etc. is therefore found” note that the elements of the address are extracted);
Miletzki does not expressly disclose
generating an address bounding box associated with the address;
determining an address type based at least in part on content of the first machine readable content adjacent to the bounding box;
Lecky discloses generating an address bounding box associated with the address; (see for example figure 4A and 6A and 6B and column 14 lines 45-65 note that a bounding box associated with the address is determined)
determining an address type based at least in part on content of the first machine readable content adjacent to the bounding box (see column 15 line 62-cilumn 16 line 10 “For example, where a registry or other record maintained in the database includes not only information regarding a plurality of signatures or patterns of bar codes that were previously observed within one or more images but also information regarding locations of alphanumeric characters or other symbols within a vicinity of such signatures or patterns in such images, the system may determine where such characters or symbols are typically located in absolute terms, or with respect to the locations or orientations of the bar codes.” Also See figure 4a note that a signature of barcode adjacent to the address are used to determine locations of important text, see also column 16 lines 45-67 “Similarly, where a region to the right of a Data Matrix code is known to infrequently include any text within a proximity thereof, then corresponding portions of labels including Data Matrix codes may be deemed unlikely to include alphanumeric characters, and a probability ranking of such portions may be defined accordingly. Moreover, the probability that a portion of an image includes alphanumeric characters need not be defined with reference to any one particular identifier; for example, many shipping labels frequently include information regarding an origin of an item to which the label is affixed (e.g., a name of a sender and his or her address) in an upper left corner thereof, while information regarding a destination for the item (e.g., a name of a recipient and his or her address) is frequently printed in a central portion of the label” note that the orientation of the objects is used to determine the type of address );
The motivation to combine is the system may determine where such characters or symbols are typically located in absolute terms, or with respect to the locations or orientations of the bar codes. (column 16 lines1-10). One of ordinary skill in the art could have easily modified the teachings of Miletzki with the teachings of Lecky to allow the system to determine the types of characters or addresses are located. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to combine Lecky and Miletzki to reach the aforementioned advantage.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miletzki US US 20040117192 A1 in view of Lecky US 9298997 B1 in further view of Balakrishnan US 20210124919 A1.
Re claim 8 Miletzki and Lecky do not disclose “preprocessing the image to align individual pages of the text with an upright vectors.”
Balakrishnan discloses preprocessing the image to align individual pages of the document with an upright vectors (see paragraph 64 132 and figure 1f note that a page the document may be rotated to be upright); The motivation to combine is reliable determine the information in the documents see paragraph 64 40. One of ordinary skill in the art could have easily use the teachings of Balakrishnan to increase the accuracy and reliability of the data processing by preprocessing the image. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Lecky and Miletzki with Balakrishnan to reach the aforementioned advantage.
Allowable Subject Matter
Claim 2, 9, 11 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 any double patenting rejections overcome.
Re claim 2 Rubio US 8,787,681 Balakrishnan US 2021/0124919 and Weller US 2021/0081664 disclose the following
Rubio discloses receiving an image of a document; generating third machine readable content (see column 6 lines 5-15 note OCR is performed on the documents); generating a first classification for the document based at least in part on the third machine readable content and a first classification system (see column 7 lines 10-25 note that a plurality of classification filter (classification systems) are used to determine a class with a confidence values see column 6 lines 17 -45 note OCR data is used to generate tokens for use in the classification ); generating a second classification for the document based at least in part on the third machine readable content and a second classification system(see column 7 lines 10-25 note that a plurality of classification filter (classification systems) are used to determine a class with a confidence values see column 6 lines 17 -45 note OCR data is used to generate tokens for use in the classification ) ; generating an assigned classification for the document based at least in part on the first classification and the second classification (see column 7 lines 10-35 note that a final classification may be determined based on the group of classifications )
Rubio does not expressly disclose preprocessing the image to align individual pages of the document with an upright vectors; generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content.
Balakrishnan discloses preprocessing the image to align individual pages of the document with an upright vectors (see paragraph 64 132 and figure 1f note that a page the document may be rotated to be upright ); generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document (see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result); generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document (see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result);; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content(see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result);.
Weller discloses
generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification (see paragraph 22 24 and note that that a date format is determined and the date (key value) is extracted based on the language of the document i.e. a classification).
The examiner notes however that it would not be obvious to combine these references with Miletzki US 20040117192 A1 in view of Lecky US 9298997 B1 absent hindsight reasoning
Re claim 9 the prior art of record does not expressly disclose further comprising: identifying an imperfection within the image; determining a first bounding box associated with the imperfection; determining a second bounding box associated with content of the text; preforming at least one first operation on the first bounding box to reduce a visibility of the imperfection; and preforming at least one second operation on the second bounding box to increase a visibility of the content. In combination with the features of Miletzki US 20040117192 A1 in view of Lecky US 9298997 B1.
Re claim 11 Rubio US 8,787,681 Balakrishnan US 2021/0124919 and Weller US 2021/0081664 disclose the following
Rubio discloses receiving an image of a document; generating third machine readable content (see column 6 lines 5-15 note OCR is performed on the documents); generating a first classification for the document based at least in part on the third machine readable content and a first classification system (see column 7 lines 10-25 note that a plurality of classification filter (classification systems) are used to determine a class with a confidence values see column 6 lines 17 -45 note OCR data is used to generate tokens for use in the classification ); generating a second classification for the document based at least in part on the third machine readable content and a second classification system(see column 7 lines 10-25 note that a plurality of classification filter (classification systems) are used to determine a class with a confidence values see column 6 lines 17 -45 note OCR data is used to generate tokens for use in the classification ) ; generating an assigned classification for the document based at least in part on the first classification and the second classification (see column 7 lines 10-35 note that a final classification may be determined based on the group of classifications )
Rubio does not expressly disclose preprocessing the image to align individual pages of the document with an upright vectors; generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document; generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content.
Balakrishnan discloses preprocessing the image to align individual pages of the document with an upright vectors (see paragraph 64 132 and figure 1f note that a page the document may be rotated to be upright ); generating first machine readable content based at least in part on a first optical character recognition system, the first machine readable content representing the document (see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result); generating second machine readable content based at least in part on a second optical character recognition system, the second machine readable content representing the document (see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result);; generating third machine readable content based at least in part on the first machine readable content and the second machine readable content(see paragraph 286 note that multiple ocr engines are applied to the documents and then combined to determine a final result);.
Weller discloses
generating extracted data from the third machine readable content, the extracted data associated with one or more key value descriptors assigned based at least in part on the assigned classification (see paragraph 22 24 and note that that a date format is determined and the date (key value) is extracted based on the language of the document i.e. a classification ).
The examiner notes however that it would not be obvious to combine these references with DENG US 20210158034 A1 absent hindsight reasoning.
Claim 3-7 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. These claims depend from claim 2.
Claim 13 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101. Deng and Aggarwal disclose the elements of claim 11 but does not disclose wherein determining the geometric pattern is based at least in part on a difference in an average word spacing and line spacing of the reminder of the first machine readable content and content of the first machine readable content representing the table.
Claim 15, and 18-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and double patenting.
Re claim 15 A method comprising: receiving an image of a document, the document associated with an asset; generating first machine readable content representing the document; (see paragraph 17 “For example, extraction may comprise optical character recognition to identify and acquire character text, strings, and/or phrases from an electronic document”)determining a date format associated with the first machine readable content; Weller further discloses determining a date format based at least in part on one or more of a language associated with the third machine readable content, a location of origin of the assets, or a destination location for the assets; and detecting the first date within the machine readable content based at least in part on the date format (see paragraph 22 24 and note that that a date format is determined and the date is extracted based on the language of the document). The prior art of record does not disclose determining a modified date format based at least in part on the first date and the second date; detecting a third date within the first machine readable content based at least in part on the modified date format; and and outputting the third date.
Claims 18-20 contain the features of claim 15.
Claim 16 and 17 would be allowable if rewritten or amended to overcome the rejection(s) under double patenting. These claims contain the features of claim 15.
Cited Art
The following is a listing of art considered relevant but not cited above:
NEPOMNIACHTCHI US 20190188464 A1 discloses
Discloses [0026] At block 308, the extracted data can be validated using a variety of data validation techniques. As used herein, the term “validation” refers to the evaluation of data using rules and internally-consistent controls available within the mobile imaging process. These techniques can include, without limitation: validation that the information scanned from the PDF417 barcode matches the data obtained during data extraction, if available; validation that the information scanned using the barcode matches the data obtained during data extraction, if available; comparison of date fields to verify date format (This may be used to improve the data (for example, it is not possible to have a 13.sup.th month) or to validate the document (for example, exceptions would be flagged, such as expiration dates in the past, birthdates less than 16 years ago, birthdates over 100 years ago, etc.); validation that the expiration date is greater than today; validation that the date of birth is some date earlier than today; validation of data fields to known masks (example: zip code—(either XXXXX or XXXXX-XXXX) in the United States. Exceptions may be able to be corrected, by using a USPS database, or flagged as low-confidence); and validation of data fields to known minimum and maximum field lengths (ex. Validation of state field to defined set of 2-character abbreviations. Exceptions may be able to be corrected, by using a USPS database, or flagged as low-confidence). A myriad of other techniques for validation are possible in accordance with the scope of various embodiments. (see paragraph 26)
Campbell US 20140184843 A1 disclsoes A method of defining data patterns for object handling includes obtaining an image of an input data area, processing the image to obtain image data, and comparing the image data with a pattern, wherein the pattern identifies spatial information of corresponding pattern fields of the pattern. The method further includes determining a confidence level of the comparison of the image data according to a success in matching the image data with the pattern fields, comparing the confidence level with a confidence threshold associated with the pattern, and selecting the pattern. A pattern output associated with the selected pattern is identified, wherein the pattern output corresponds to a canonical return format, and the pattern output is applied to the image data. (see abstract)
Conclusion
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/SEAN T MOTSINGER/ Primary Examiner, Art Unit 2673
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