DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because “Unfiled forms” should be “Unfilled Forms” at the bottom right of Figure 7.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office Action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, Applicant will be notified and informed of any required corrective action in the next Office Action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
In ¶[0057], “vectors may indexed” should be “vectored may be indexed”.
In ¶[0068], “may match or associated response text strings” appears it should be “may match or associate response text strings”.
In ¶[0076], “aby an encoding algorithm” should be “by an encoding algorithm”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office Action.
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.
Claims 1, 3, 11 to 12, 15, 17 to 18, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tata et al. (U.S. Patent Publication 2021/0374395).
Regarding independent claims 1, 15, and 18, Tata et al. discloses a method, system, and computer program product for information extraction from form-like documents, comprising:
“generating a schema library for a set of forms” – to extract information from form-like documents, a document analysis system can identify a plurality of document types; each document type can be associated with a commonly received form-like document; a first document type can be an invoice, a second type can be a purchase order, etc. (“for a set of forms”); each document type can have an associated target schema; the target schema can include one or more expected fields, each field associated with a piece of information extracted in documents of that type (¶[0020]); target schemas can be determined prior to extraction of any text from the document; a target schema associated with a particular document can be determined prior to accessing the document (¶[0081]: Figure 2); here, a plurality of pre-established document types for form-like documents and associated target schemas are “a schema library for a set of forms”;
“obtaining a schema from the schema library by searching the schema library using the filled form” – a document type associated with the document can be determined by analysis of the document; once a plurality of text portions are extracted from a document, the document analysis system can determine based on the type associated with the document, the target schema associated with the document; based on the target schema, the document analysis system can determine one or more field types that are expected to be found in the document (¶[0021] - ¶[0022]); data access system 114 can determine a target schema for the document represented in the images; data access system 114 can select images associated with a particular target schema, e.g., processing a batch of similar document types; a document type, and therefore an associated target schema, associated with the document can be determined by analysis of the document (¶[0064] - ¶[0065]: Figure 1); here, a document with text portions is “the filled form”, and determining a document type for a document from a plurality of document types is “searching the schema library using the filled form”;
“providing the schema as a first input to a form extraction machine learning model” – a document analysis system can use a machine-learned model to generate the score for each candidate text portion; a document analysis system can select a candidate text portion to be assigned to the field in the target schema based, at least in part, on the generated score (¶[0023]); a machine-learned model can take, as input, information about the field type for which the candidate text portion is a candidate (¶[0024]); machine-learned model associated with the document analysis system can take information about the field type as input (¶[0025]); a score can be generated by a scorer system, either included as part of the machine-learned model or accessed by the document analysis system; a scorer system can take as input a candidate text portion and a target schema field it is associated with and produce a prediction score between 0 and 1 (“providing the schema as a first input”) (¶[0043]); scoring system 120 can use a machine-learned model to generate the score for each candidate text portion; scoring system 120 can select a candidate text portion to be assigned to the field in the target schema; a machine-learned model can take, as input, information about the field type for which the candidate text portion is a candidate (“a first input to a form extraction machine learning model”) (¶[0068] - ¶[0069]: Figure 1); here, taking a target schema field as input to a machine learning model is “providing the schema as a first input to a form extraction machine learning model”; that is, a machine learning model provides information extraction from form-like documents based on field types associated with a target schema; consequently, a machine learning model requires an input of the schema to determine field types to compare with candidate text portions;
“providing a computer-readable encoding of entries of the filled form as a second input to the form extraction machine learning model” – a computing system can generate a respective input feature vector for each candidate for the field type; a computing system can generate a respective candidate embedding for the candidate text portion (“a computer-readable encoding of entries in the filled form”) (Abstract); a system can include processing by the computer system using a machine-learned scoring model the respective input feature vector for each candidate text portion to generate a respective candidate embedding for the candidate text portion (¶[0007]); using a machine-learned model, a document analysis system can generate a score for each candidate text portion; a machine-learned model can generate one or more embeddings of the input data and generate scores by comparing the generated embeddings (¶[0025]); using a machine-learned model, the scoring system 120 can generate a score for each candidate text portion; a machine-learned model can generate one or more embeddings of the input data and generate scores by comparing the generated embeddings (¶[0070]: Figure 1); here, text portions represent “entries of the filled form” and a vector embedding of text portions of documents is “a computer-readable encoding of entries in the filled form”;
“receiving an output from the form extraction machine learning model, wherein the output from the form extraction machine learning model is an encoding of the form content in accordance with the schema” – information is extracted from structured documents including forms using a machine-learning model (¶[0001]); a method can include processing, by the computing system using a machine-learned scoring model, the respective input feature vector for each candidate text portion to generate a respective candidate embedding for the candidate text portion; a method can include determining, by the computing system, a respective score for each candidate text portion for the field type based at least in part on the respective candidate embedding for the candidate text portion; a method can include assigning, by the computing system, one or more of the candidate text portions to the field type based at least in part on the respective scores generated for the candidate text portions (¶[0007]); a machine-learned model associated with the document analysis system can take information about the field type as input; using this information, the machine-learned model can generate an embedding for the field type; the embedding can represent the characteristics that are expected of a text portion (¶[0025]); score model 216 can be included as a stage in a larger machine-learned model or an independent machine-learned model that is accessed by the document analysis system; score model 216 can take as input a candidate text portion and a target schema field 320 and produce, as output, a prediction score between 0 and 1; score 302 can be expected to be proportional to the likelihood that this candidate text portion is the correct value for that field in that document (¶[0087]: Figure 3); broadly, assigning a candidate text embedding to a field type is “an output from the form extraction machine learning model”, and is “an encoding of the form content in accordance with the schema”; that is, a machine-learned model takes input of text portions of a document and field types associated with a schema, and generates a candidate embedding that is “an encoding of the form content in accordance with the schema.”
Concerning claims 3, 17, and 20, Tata et al. discloses that a computing system can obtain an image of a document that contains a plurality of portions of text, and the computing system can generate a respective candidate embedding for the candidate text portion (Abstract); here, a document with portions of text is “the filled form” and embeddings of portions of text from an image of a document are “a textual encoding of the image of the filled form.”
Concerning claim 11, Tata et al. discloses these limitations because they are just a duplication of the limitations of the independent claim for a second schema; implicitly, information extraction is performed for a plurality of documents types associated with a plurality of target schemas, e.g., invoices, purchase orders, bills, and tax forms.
Concerning claim 12, Tata et al. discloses that form-like or “templatic” documents are common in many business workflows such as: invoices, purchase orders, bills, tax forms, and financial reports (¶[0002]); documents can include invoices, purchase orders, bills, tax forms, and financial reports (¶[0019]).
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.
Claims 2, 6 to 7, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tata et al. (U.S. Patent Publication 2021/0374395) in view of Sarrafzadeh et al. (U.S. Patent No. 11,763,075).
Concerning claims 2, 16, and 19, Tata et al. arguably discloses these limitations because a computing system can obtain an image of a document that contains a plurality of portions of text, the computing system can generate a respective input feature vector for each candidate for the field type, and the computing system can generate a respective candidate embedding for the candidate text portion (Abstract); a document type associated with the document can be determined by analysis of the document (¶[0021] - ¶[0022]); data access system 114 can access an image of a document from a database of image data 134; data access system 114 can determine a target schema for the document represented in the images (¶[0064] - ¶[0065]: Figure 1). That is, Tata et al. arguably discloses that “the schema library” is ‘searched’ based on an image of a document and that embedding vectors of text portions of the document are generated, so that “searching the schema library includes generating an embedding vector for the filled form by applying a computer-readable encoding of an image of the filled form to a schema identification model”. However, Tata et al. does not expressly disclose “searching the schema library”, even if this may be performed inferentially.
Concerning claims 2, 16, and 19, Sarrafzadeh et al. teaches a method of discovering templates for documents. The content and structure of a document are encoded into one or more embedding representations via a trained document encoder, and the embedding representations are compared to a plurality of template representations, each of the plurality of template representations being a representation of content and structure of the plurality of templates to identify one of the plurality of templates as corresponding to the document. (Abstract) A ‘template’ may refer to a pre-designed document that includes a specific structure and can include a resume template, a menu template, a business plan template, a business card template, and an invitation card template. (Column 4, Lines 62 to 67) Asset representation engine 118 may operate to convert one or more templates/sample documents into numerically dense vector embeddings. (Column 7, Lines 15 to 25: Figure 1) Each sample document in the sample documents library 160 may be encoded into one or more representations. Similarly, templates in the templates library 162 may be converted by the template representation model 150 to vector embeddings that can be used in efficient search and identification of template. (Column 10, Lines 36 to 41) Sarrafzadeh et al., then, teaches directly searching a template library (“searching the schema library”) and includes using embedding vectors of documents. An objective is to provide an improved system and method of discovering templates for a document that reduces time and effort to locate a correct template. (Column 1, Lines 21 to 35) It would have been obvious to one having ordinary skill in the art to search a template library that includes generating an embedding vector for a document as taught by Sarrafzadeh et al. to identify a schema of a document in Tata et al. for a purpose of providing improved discovery of templates for a document that reduces time and effort.
Concerning claim 6, Sarrafzadeh et al. teaches that asset representation engine 118 may operate to convert one or more templates/sample documents into numerically dense vector embeddings. (Column 7, Lines 15 to 25: Figure 1) Each sample document in the sample documents library 160 may be encoded into one or more representations. Similarly, templates in the templates library 162 may be converted by the template representation model 150 to vector embeddings that can be used in efficient search and identification of template. (Column 10, Lines 36 to 41) Sarrafzadeh et al., then, teaches that templates are encoded into vector embeddings (“generating a set of embedding vectors for the set of forms by applying a set of computer-readable encodings of a set of images of a set of unfilled forms to a schema library generation model”). That is, Sarrafzadeh et al.’s templates can be encoded as vector embeddings in a same way as documents are encoded into vector embeddings from images in Tata et al.
Concerning claim 7, Sarrafzadeh et al. teaches that a modular construction of the framework used to identify and match templates and/or sample documents provides significant advantages for operating, analyzing, maintaining, and periodically updating the underlying models. Document type classifier 170 may be independently updated to incorporate new document types (“generating, using the schema library generation model . . . and after receiving the output, an updated schema library” and “wherein the updated schema library includes an additional schema generated using an unfilled version of an additional form and the schema library generator model”). This can occur independent of some of the other models, thus providing an efficient mechanism for maintaining and updating the system. Furthermore, new templates and/or new document libraries can be added at any time without affecting any of the elements involved in the online phase. (Column 12, Line 63 to Column 13, Line 8) Implicitly, updating a template library is “without retraining the schema library” due to the modular construction and independent updating, and there is no description of a requirement for “retraining” in Sarrafzadeh et al.
Claims 4 to 5 are rejected under 35 U.S.C. 103 as being unpatentable over Tata et al. (U.S. Patent Publication 2021/0374395) in view of Inavalli et al. (U.S. Patent Publication 2024/0232204).
Concerning claim 4, Tata et al. arguably discloses “obtaining a set of structured or semi-structured data from the filled form” and “providing the set of structured or semi-structed data as a third input to the form extraction machine learning model” because candidate text portions are associated with field types. (Abstract) Candidate text portions for the invoice date field in an invoice are the dates that occur in that document. (¶[0031]) Arguably, a date field and a date in an invoice are “structured or semi-structured data from the filled form”. That is, a text portion of a filled document is associated with a field type to generate “structured or semi-structured data from the filled form”. However, Tata et al. does not expressly disclose “structured or semi-structured data from the filled form”.
Concerning claim 4, Inavalli et al. teaches combining structured and semi-structured data for explainable artificial intelligence. A machine-learning model is trained using a training set that combines both structured (tabular) data and semi-structured (text) data. Explainable AI refers to systems and methods for generating explanations for the output of machine-learning models. (Abstract) Semi-structured data refers to data that has some structure, but does not have a fixed schema. Responses to a survey that asks for textual answers to four different questions are semi-structured data. (¶[0015]) The original tabular data may be combined with the converted text data to generate unified structured data. The unified structured data may be provided to a tabular explanation model, which can generate an explanation that is based both on the text data and the tabular data. (¶[0020]) Inavalli et al., then, teaches combining structured data of a table and semi-structured data of free-form text to generate explanations of information in a table using a machine-learning model. An objective is to provide explainable artificial intelligence that reveals the method used to determine the output. (¶[0001] - ¶[0002]) It would have been obvious to one having ordinary skill in the art to generate structured or semi-structured data as input to a machine learning model as taught by Inavalli et al. to extract information from a filled form in Tata et al. to provide explainable artificial intelligence that reveals the method used to determine the output.
Concerning claim 5, Tata et al. discloses “the computer-readable encoding of the entries of the filled form is a textual encoding of an image of a filled form” because a computing system can obtain an image of a document that contains a plurality of portions of text and can generate a respective input feature vector and a respective candidate embedding for the candidate text portion. (Abstract) Inavalli et al. teaches that text data may be converted to tabular data using vector embeddings to convert data from human-readable text to a form usable by a machine learning model. (¶[0019]) Here, “the schema and the set of structured or semi-structured data are in text format and are human-readable” at least before they are converted using vector embeddings.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tata et al. (U.S. Patent Publication 2021/0374395) in view of Lammel (U.S. Patent Publication 2008/0201351).
Tata et al. discloses schema of document types, but does not disclose “the schema is stored using extensible markup language schema definitions or document type definitions” and “the schema is manipulated using extensible markup language path language or extensible markup language query.” However, Lammel teaches automatic transformations for style normalization of schemas. (Abstract) Specifically, Lammel teaches XML Schema is a W3C (World Wide Web Consortium) standard that defines a schema definition language for an XML data model. Schema definitions (e.g., a type definition such as CustomerType that describes structure of information regarding each Customer) can be employed to validate content and structure of XML instance documents. The type information supplied in an XML schema document can also be used to check XML queries for correctness, and optimize XML queries and XML storage. (¶[0003]) Input XML schema 112 describes a structure of an XML document, wherein schema definitions (e.g., a type definition such as DataType that describes the structure of information regarding data) can be employed to validate the content and the structure of XML instance documents. An XML schema document supplies an XML document that can be expressed in a different ways and styles -- e.g., different from table and column definitions of a relational database system. The type information supplied in an XML schema document can also be employed to check XML queries for correctness, and optimize XML queries and XML storage. (¶[0025]: Figure 1) Lammel, then, teaches extensible markup language (XML) definitions are a standard type of data structure that can be used for representing documents and for providing an extensible markup language query to search for documents. An objective is to use a standard that enables validation of content and structure of a document. (¶[0003]) It would have been obvious to one having ordinary skill in the art to provide schemas in Tata et al. that are structured as extensible markup language schema definitions and that can be queried by an extensible markup language query as taught by Lammel for a purpose of validating content and structure of an XML document.
Claims 13 to 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tata et al. (U.S. Patent Publication 2021/0374395) in view of Rodriguez et al. (U.S. Patent Publication 2020/0159820).
Tata et al. discloses “the computer-readable encoding of the entries of the filled form comprises a textual encoding of an image of the filled form” because a computing system can obtain an image of a document that contains a plurality of portions of text and can generate a respective input feature vector and a respective candidate embedding for the candidate text portion. (Abstract) However, Tata et al. does not provide the specific image types of “the image of the filled form is one of a Joint Photographic Experts Group (JPEG) image, a Portable Network Graphics (PNG) image, a Graphics Interchange Format (GIF) image, a Bitmap (BMP) image, or a Scalable Vector Graphics (SVG) image.” Similarly, Tata et al. does not provide the specific output type of the form extraction machine learning model “is a structured javascript object notations format.”
Rodriguez et al. teaches extracting structured information from a document containing filled form images by extracting textual information and a hierarchy in a blank form, and using this information to extract and understand the content of the filled form. (Abstract) Specifically, structured information is extracted from a document containing filled form images with typical scanned images have file extensions of JPG, BMP, PDF, and PNG. (¶[0054]: Figure 3) Each page is stored in an output JSON file. (¶[0056]: Figure 3) The form may be configured to generate a JSON file 350. (¶[0059]: Figure 3) Rodriguez et al., then, teaches extracting information from a filled form that is received as an image with file extensions of “a Photographic Experts Group (JPEG) image, a Portable Network Graphics (PNG) image, a Bitmap (BMP) image” and that an output format of information extracted from a document is “a structured javascript object notation format.” Here, ‘JSON’ is an abbreviation for “javascript object notation”. An objective is to provide an automatic tool able to analyze scanned filled form documents. (¶[0009]) It would have been obvious to one having ordinary skill in the art to obtain an image of a document in Tata et al. with an input format that includes JPEG, PNG, or BMP and to provide an output format of JSON as taught by Rodriguez et al. for a purpose of providing an automatic tool able to analyze scanned filled form documents.
Claims 8 to 9 are rejected under 35 U.S.C. 103 as being unpatentable over Tata et al. (U.S. Patent Publication 2021/0374395) in view of Sarrafzadeh et al. (U.S. Patent No. 11,763,075) as applied to claims 1 and 6 above, and further in view of Bondugula et al. (U.S. Patent Publication 2020/0034419).
Tata et al. discloses “generating an embedding vector for the filled form by applying a computer-readable encoding of an image of the filled form to a schema identification model” because a computing system can obtain an image of a document that contains a plurality of portions of text and can generate a respective input feature vector and a respective candidate embedding for the candidate text portion. (Abstract) Similarly, Tata et al. discloses “providing the set of candidate schemas and the image of the filled form to a schema selection machine learning model” and “receiving an output from the schema selection machine learning model, wherein the output from the schema selection machine learning model is the schema” because a document analysis system can receive an image of a document, a document type associated with the document can be determined by analysis of the document, and once a plurality of text portions are extracted from a document, the document analysis system can determine based on the type associated with the document, the target schema associated with the document; based on the target schema, the document analysis system can determine one or more field types that are expected to be found in the document (¶[0021] - ¶[0022]); data access system 114 can access an image of a document from a database of image data 134; data access system 114 can determine a target scheme for the document represented in the images (¶[0064] - ¶[0065]: Figure 1). However, Tata et al. does not disclose “conducting a nearest neighbor analysis using a set of embedding vectors and the embedding vector” and “obtaining a set of candidate schemas from the schema library based on the nearest neighbor analysis”. That is, Tata et al. discloses determining a schema by analysis of a document, but does not determine a candidate schema based on a nearest neighbor analysis.
Bondugula et al. teaches text classification by searching a stored, pre-trained corpus defining embedding vectors for selected documents to produce nearest neighbor vectors for each vector embedding. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection. (Abstract) An objective is to perform text classification with models having greater accuracy. (¶[0003]) It would have been obvious to one having ordinary skill in the art to obtain candidate schemas in Tata et al. by searching for documents by nearest neighbor selections of vector embeddings as taught by Bondugula et al. for a purpose of performing text classification with models having greater accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure.
Sinha et al., Biegert et al., Tillberg et al., and Perot et al. disclose related prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN LERNER whose telephone number is (571) 272-7608. The examiner can normally be reached Monday-Thursday 8:30 AM-6:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN LERNER/Primary Examiner
Art Unit 2658 June 16, 2026
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