DETAILED ACTION
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 .
Response to Amendment
This action is in response to applicant’s arguments and amendments filed 2/04/2026, which are in response to USPTO Office Action mailed 1/16/2026. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 13-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claim 1,
Claim 1 recites the following limitation(s): A method comprising: using at least a computer, correlating a data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not merely forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the data element belongs; Which recites a step of determining an association between features. The process of correlating is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a list of attributes such as a name, date of birth, license number and license class would be able to classify a document as representing a driver’s license based on these attributes being commonly associated with driver’s licenses. The claim nominally recites the use of “a computer”, however this element merely represents a generic computer device used to perform the abstract idea.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 1 recites the following additional elements:
ingesting data from within a datastore comprising: associating a plurality of features with a first tag; which encompasses a step of mere data gathering & outputting (e.g. the “associating” is described as part of a process of ingesting, which is a step of data gathering), which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element, storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the datastore is manipulated by the process of detecting and storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
ingesting data from within a datastore comprising: associating a plurality of features with a first tag; Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See Paragraph [0059], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. Page-level recognition model 416 comprises a machine learning classifier trained using feature vectors representing features of document start pages and document end pages for a plurality of document types. An input feature vector of an input page may be compared to a reference set of feature vectors representing the known classes to determine which of the reference set of feature vectors has a highest similarity to the input feature vector, i.e. associating a plurality of features with a first tag (e.g. a feature vector is associated with a document type);)
upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element, storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore. LORRAIN-HALE et al. (US PGPUB No. 2020/0301950; Pub. Date: Sep. 24, 2020) discloses the limitation at issue: See Paragraph [0043], (Disclosing a system for providing keyword suggestions to a user of a document during use of the document. Keyword suggestions correspond to tags for the document that are determined by examining contents of a document. A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata. Note [0027] wherein the system may provide functionality for automatic tagging suggestions of keywords during document use, i.e. upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element (e.g. Note FIG. 5 illustrating method 00 of identifying suggested keywords at step 520) , storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore (e.g. the system stores an entry in a tag index table including a pointer, tag data and other metadata).)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 2,
Claim 2 depends upon Claim 1, as such claim 2 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 2 further recites the following limitation(s):
associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a separate record associated with said second tag, the same data element, and a location of the same data element within the datastore. Which recites a step of determining an association between metadata tags based on criteria recited at a high degree of generality. The process of correlating is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a particular tag may be associated with other tags in a hierarchy such as a topic-subtopic relationship.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 2 recites the following additional elements:
wherein upon detecting each of the plurality of features within a same data element further comprises correlating the same data element within the data store to determine one or more of the second tags to associate therewith and associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a record associated with said second tag, the same data element, and a location of the same data element within the datastore. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the datastore is manipulated by the process of detecting and storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
wherein upon detecting each of the plurality of features within a same data element further comprises correlating the same data element within the data store to determine one or more of the second tags to associate therewith and associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a record associated with said second tag, the same data element, and a location of the same data element within the datastore. Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See FIG. 13 & Paragraph [0102], (FIG. 13 illustrates the method for classifying separated unclassified documents comprising step 1306 of applying a document-level classifier to an unclassified separated document stored at step 508 of the method illustrated in FIG. 5.) See Paragraph [0060], (Document-level recognition model 418 may be trained using feature vectors representing features of complete documents of various document types. A feature vector of an input document is compared to a reference set of feature vectors to determine a highest similarity to the input feature vector, i.e. wherein upon detecting each of the plurality of features within a same data element further comprises correlating the same data element within the data store (e.g. the input document is maintained in a data store) to determine one or more of the second tags to associate therewith (e.g. the document-level model 418 is used to determine a document type for an input document).) See FIG. 5 & Paragraph [0070], (FIG. 5 illustrates a method of classification and separation of document pages comprising step 504 of separating and classifying a first set of documents by the classification system. The classified document is then stored alongside extracted metadata. For example, an input page may be classified as a W-2 document according to the W-2 template and be stored as a separate document classified as a W-2 document alongside extracted content stored as metadata associated with the W-2 document, i.e. associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a record associated with said second tag, the same data element, and a location of the same data element within the datastore (e.g. Note [0066] wherein crawler 410 may crawl a location such as a folder and feed documents to an OCR pipeline 412 to extract content from a separated document 444 and provides the extracted content to content analytics module 414 for use by the document-level recognition model 418).)"
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 3,
Claim 3 depends upon Claim 2, as such claim 3 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 3 further recites the following limitation(s):
wherein the first tag and the second tags are stored in a hierarchical data structure. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the hierarchical data structure is a source of data manipulated by the storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
wherein the first tag and the second tags are stored in a hierarchical data structure. Kumar et al. (US PGPUB No. 2020/0387483; Pub. Date: Dec. 10, 2020) discloses the limitation at issue: See Paragraph [0017], (Disclosing a virtual file organization system configured to assign classification tags to files within a storage system 32.) See Paragraph [0019], A hierarchy of tags and scores could be generated wherein parent tags and child tags may be assigned with a stored file. Note FIG. 1 wherein storage system 32 comprises tag index 34, i.e. wherein the first tag and the second tags are stored in a hierarchical data structure.)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 4,
Claim 4 depends upon Claim 2, as such claim 4 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 4 further recites the following limitation(s):
wherein the first tag and the second tags are stored in an object-oriented data structure. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the object-oriented data structure is a source of data manipulated by the storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
wherein the first tag and the second tags are stored in an object-oriented data structure. GWOZDZ et al. (US PGPUB No. 2022/0019624; Pub. Date; Jan. 20, 2022) discloses the limitation at issue: See Paragraph [0081], (Disclosing a system for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, images and voice. The system may systematically classify and analyze a corpus of documents. Each document classification may be represented as a set of expressions composed to work with Lume Elements and data contained in the Lume.) See Paragraph [0061], (Document data and metadata are stored in a non-hierarchical fashion. FIG. 3 illustrates a plurality of individual Lume elements and their associated attributes, i.e. wherein the first tag and the second tags are stored in an object-oriented data structure.)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 5,
Claim 5 depends upon Claim 1, as such claim 5 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 5 further recites the following limitation(s):
associating a plurality of features with a third tag; Which recites a step of determining an association between metadata tags based on criteria recited at a high degree of generality. The process of correlating is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a particular tag may be associated with attributes, for example a “W-2 document” tag may be associated with a person’s name, income, social security number, etc.
correlating a second data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the second data element belongs; Which recites a step of determining an association between features. The process of correlating is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may, given a list of attributes such as a name, date of birth, license number and license class would be able to classify a document as representing a driver’s license based on these attributes being commonly associated with driver’s licenses.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 5 recites the following additional elements:
and upon detecting each of the plurality of features within the second data element, storing another record associated with the third tag, the second data element, and a location of the same data element within the datastore. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. storing data is a step of manipulating a particular data source) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
and upon detecting each of the plurality of features within the second data element, storing a record associated with the third tag, the second data element, and a location of the same data element within the datastore. Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See FIG. 5 & Paragraph [0070], (FIG. 5 illustrates a method of classification and separation of document pages comprising step 504 of separating and classifying a first set of documents by the classification system. The classified document is then stored alongside extracted metadata, i.e. upon detecting each of the plurality of features within the second data element, storing a record associated with the third tag, the second data element, and a location of the same data element within the datastore. (e.g. Note [0066] wherein crawler 410 may crawl a location such as a folder and feed documents to an OCR pipeline 412 to extract content from a separated document 444 and provides the extracted content to content analytics module 414 for use by the document-level recognition model 418).)
The examiner notes that the limitation above amounts to a repetition of the process of determining a tag. The system of Yanamandra may determine page-level classifications as well as document-level classifications. The process of determining a "third tag" is identical to the process of determining a second tag is identical and therefore if a system is capable of determining multiple tags, then it is capable of determining first, second, third, fourth, etc. tags for a document.
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 6,
Claim 6 depends upon Claim 5, as such claim 6 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 6 further recites the following limitation(s):
when the first tag and the third tag are associated with the second data element, associating a fourth tag with the second data element, wherein the fourth tag is indicative of a status of the second data element. Which recites a step of determining an association between metadata tags based on criteria recited at a high degree of generality. The process of correlating is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a particular tag may be associated with other tags, such as in a hierarchical tagging system wherein a tag of a lower level is associated with tags of previous levels in the hierarchy.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 7,
Claim 7 depends upon Claim 1, as such claim 7 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 7 further recites the following limitation(s):
wherein the plurality of features taken together form an indication of purpose. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. an indication of a purpose comprising a set of features is a type of data) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
wherein the plurality of features taken together form an indication of purpose. SONG et al. (US PGPUB No. 2022/0121843; Pub. Date: Apr. 21, 2022) discloses the limitation at issue: See Paragraph [0071], (Disclosing a system for document recognition comprising a document type analyzer configured to analyze a similarity between a document feature vector of a target document having the same tag as that of a recognition target document and a document feature vector of the recognition target document, i.e. wherein the plurality of features taken together form an indication of purpose (e.g. a document type indicates a purpose of a document and is determined based on a comparison between features embodied as tags).)Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 8,
Claim 8 depends upon Claim 5, as such claim 8 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 8 further recites the following limitation(s):
when the first tag and the third tag are associated with the second data element, performing another tagging operation on the second data element, the another tagging operation associated with the first tag and with the third tag. Which recites a step of determining an association between tags in order to apply another tag to a particular data element. The process of “tagging” as presented in the claim is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a particular tag may be associated with other tags, such as in a hierarchical tagging system wherein a tag of a lower level is associated with tags of previous levels in the hierarchy and subsequently apply the new tag to a document based on the association.
Regarding dependent claim 13,
Claim 13 depends upon Claim 1, as such claim 13 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 13 further recites the following limitation(s):
analysing at least the same data element in dependence upon at least the first tag. Which recites a step of “analyzing” a data element recited at a high degree of generality. The process of “analyzing” as presented in the claim is considered to be an observation or evaluation, which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a tag corresponds to a document, such as determining that a W-2 document has been tagged as a W-2 document.
Based on the above, the claim is not patent eligible.
Regarding independent claim 14,
Claim 14 recites the following limitation(s):
A method comprising: using the at least a computer, determining a form of the first document; Which recites a step of determining a characteristic of a document recited at a high degree of generality. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may observe characteristics of a document such as a title, fields to be filled out, etc. and determine the type of document based on its characteristics.
based on the plurality of data elements and the form and using the at least a computer, determining a first tag for the first document; Which recites a step of determining a characteristic of a document recited at a high degree of generality. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may observe characteristics of a document such as a title, fields to be filled out, etc. and determine the type of document based on its characteristics such as determining that a document is a W-2 document if it is titled as a W-2 document and contains fields relating to a person’s name, social security, income, etc.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 14 recites the following additional elements:
ingesting data from within a datastore comprising: determining a plurality of data elements within a first document within a first data store; which encompasses a step of mere data gathering & outputting (e.g. the “determining” is described as part of a process of ingesting, which is a step of data gathering), which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
using the at least a computer, storing a record associated with the first tag and the first document; Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the datastore is manipulated by the process of storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
and using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. storing a plurality of tags is a step of manipulating a source of data) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
ingesting data from within a datastore comprising: determining a plurality of data elements within a first document within a first data store; Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See Paragraph [0013], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. The document-level recognition model is trained to recognize a document type.) See Paragraph [0036], (The classification system receives a set of input pages and processes said pages to create a set of structured documents and a set of unclassified pages to which the recognition models are applied.) See Paragraph [0057], (Content management system 420 comprises a data store configured with an associated set of documents and pages 430, i.e. ingesting data from within a datastore (e.g. document pages are provided as input from data store 430) comprising: determining a plurality of data elements within a first document within a first data store (e.g. Note [0049] wherein the process extracts and indexes keywords from documents.);)
and using the at least a computer, storing a record associated with the first tag and the first document; Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See Paragraph [0053], (Document capture system 302 may apply a template to an input document and store the page as a separate document classified based on the template, i.e. and storing a record associated with the first tag and the first document.)
and using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements. LORRAIN-HALE et al. (US PGPUB No. 2020/0301950; Pub. Date: Sep. 24, 2020) discloses the limitation at issue: See Paragraph [0043], (Disclosing a system for providing keyword suggestions to a user of a document during use of the document. Keyword suggestions correspond to tags for the document that are determined by examining contents of a document. A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata.) See FIG. 5 & Paragraph [0047], (FIG. 5 illustrates method 500 comprising step 550 of adding a tag to a document. The method may then determine if further modifications are to be made and returns to step 510, i.e. using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements (e.g. the step adding a tag may be repeated in order to store a plurality of tags).)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 15,
Claim 15 depends upon Claim 14, as such claim 15 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 15 recites the following limitation(s):
determining a first status of the first document; Which recites a step of determining a characteristic of a document recited at a high degree of generality. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may observe characteristics of a document and determine whether it has been filled out or not.
determining a second tag relating to the first status; Which recites a step of determining a characteristic of a document recited at a high degree of generality and determining a label associated with the observation. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may observe characteristics of a document and determine whether it has been filled out or not and subsequently label a document as “Filled out” if it was been filled out or “Pending” if it has not.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 15 recites the following additional elements:
storing a record associated with the second tag and the first document. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the datastore is manipulated by the process of storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
storing a record associated with the second tag and the first document. TAGUCHI (US PGPUB No. 2023/0095325; Pub. Date: Mar. 30, 2023) discloses the limitation at issue: See FIG. 6, (FIG. 6 illustrates a metadata storage unit configured to store metadata associated with a document which includes storing status metadata, i.e. storing a record associated with the second tag and the first document.)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 16,
Claim 16 depends upon Claim 15, as such claim 16 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 16 recites the following limitation(s):
providing a first process having a plurality of documents associated therewith; Which recites a step of performing a process recite at a high degree of generality over a corpus of documents. One of ordinary skill in the art may process a set of documents such as by providing signatures, making copies of documents, categorizing or sorting documents, etc.
based on the first tag and the first document, determining a first process instance associated with the first process and with which the first document is associated; Which recites a step of determining a process to be applied to a document based on a pair comprising a tag and document. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a document is tagged as a “W-2” and process the document as a financial document by the appropriate institution(s).
determining other documents associated with the first process instance having a plurality of documents associated therewith and associated with each other; Which recites a step of determining a group of documents associated with a first document. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a document is related such as by being subsequent pages of a same document, other documents associated with a particular name, documents of a same or similar subject, etc.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 16 recites the following additional elements:
upon a change of status of another document associated with the first process instance, changing a status of the first document. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. modifying a stored attribute is a step of modifying a data source), which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
upon a change of status of another document associated with the first process instance, changing a status of the first document. Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See FIG. 2C & Paragraph [0047], (FIG. 2B illustrates a GUI provided by content management system FIG. 2B. The content classification system 200 may identify, separate, classify and index a classified document 222, 224 and 225, while other pages may be considered "processed" including documents 226, 227, 228.) See Paragraph [0048], (Unclassified pages may be processed by the second stage classification and separation 104, i.e. upon a change of status of another document associated with the first process instance, changing a status of the first document (e.g. first stage classification and separation 102 is applied to a set of input document pages wherein certain pages of a document may be classified while other pages may be indicated as "processed" and subject to further classification steps, which describe changes in status).)
Regarding dependent claim 17,
Claim 17 depends upon Claim 15, as such claim 17 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 17 recites the following limitation(s):
based on the first tag and the first document, determining other documents associated with the first document; Which recites a step of determining a group of documents associated with a first document. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a document is related such as by sharing a particular tag.
determining a sequence of the other documents and the first document for being matched against a known first process, the known first process including a documentary record of the known process comprising multiple documents; Which recites a step of determining a group of documents associated with a first document. The term “determine” is considered to be an observation or evaluation which are considered concepts performed in the human mind. For example, one of ordinary skill in the art may determine that a document is related such as by sharing a particular tag.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 17 recites the following additional elements:
storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. the datastore is manipulated by the process of storing) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements in the claim amount to no more than mere instructions applied to a generic computer environment. Mere instructions to apply a judicial exception using a generic computer environment cannot integrate a judicial exception into a practical application or provide an inventive concept.
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process. Khamkar et al. (US Patent No.: 11,481,823; Date of Patent: Oct. 25, 2022) discloses the limitation at issue: See Paragraph [0070], (Task performer information, which may include a merge document tag as in [0071], may be received from a task performer network at intake system 232 of FIG. 3.) See Paragraph [0072]-[0073], (Document merge component 306 may execute a task, such as a document merge, and may store the processed pages or information indicating the changes in document data store 312 of intake system 232, i.e. storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process.)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 18,
Claim 18 depends upon Claim 17, as such claim 18 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 18 recites the following limitation(s):
wherein the known first process is an offer and acceptance process. Which merely describes an intended field of use to which the method would be applied. Merely describing the context in which the invention is to be applied does not meaningfully limit the judicial exception and does not add significantly more.
Based on the above, the claim is not patent eligible.
Regarding independent claim 19,
Claim 19 recites the following limitation(s):
mapping a plurality of data fields onto the first standard form; Which recites a step of assigning fields to a document recited at a high degree of generality. One of ordinary skill in the art would be able to observe a document and determine the fields presented based on the layout of the document. For example, discrete sections of a document indicating a person enter their name, phone number, address, etc.
using at least a computer, identifying the data fields within an unstructured document; Which recites a step of determining which fields are present in a document recited at a high degree of generality. One of ordinary skill in the art would be able to observe a document and determine the fields presented based on the layout of the document. For example, discrete sections of a document indicating a person enter their name, phone number, address, etc.
tagging the unstructured document with the first tag; Which recites a step of associating a particular classification or type of document for an input document. One of ordinary skill in the art would be able to determine a category of a document based on its characteristics. For example, one of ordinary skill in the art may determine that a tag corresponds to a document, such as determining that a W-2 document has been tagged as a W-2 document.
The limitations, as drafted, comprise a process that, under its broadest, reasonable interpretation, cover the performance of the limitation in the mind.
Claim 19 recites the following additional elements:
providing a first tag relating to a first standard form; which encompasses a step of mere data gathering & outputting (e.g. providing a tag is a step of data gathering), which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
and then using at least a computer, automatically learning a format, content and location of data elements of the unstructured document for future use in identifying and tagging documents similar to the unstructured document. which encompasses a step of mere data gathering & outputting (e.g. the learning and storing of format, content and location data represents a step of data gathering), which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
providing a first tag relating to a first standard form; Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) discloses the limitation at issue: See Paragraph [0013], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. The document-level recognition model is trained to recognize a document type.) See Paragraph [0053], (Document capture system 302 applies structured document templates corresponding to structured document types and classifies the document according to the document type to which the template corresponds, i.e. providing a first tag relating to a first standard form;)
and then using at least a computer, automatically learning a format, content and location of data elements of the unstructured document for future use in identifying and tagging documents similar to the unstructured document for future use in identifying and tagging documents similar to the unstructured document. Duffy et al. (US PGPUB No. 2022/0365950; Pub. Date: Nov. 17, 2022) discloses the limitation at issue: See FIG. 4A & Paragraph [0033], (FIG. 4A illustrates method 400 of dynamically tagging a document via document tagging wizard DTW 222 comprising step 412 wherein the system may record and/or update tagging rules via a knowledge database of the system to learn to tag similar documents for a particular document type, i.e. automatically learning a format, ..., for future use in identifying and tagging documents similar to the unstructured document.)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
Regarding dependent claim 20,
Claim 20 depends upon Claim 19, as such claim 20 presents the same abstract idea of a mental process as identified in the discussion above.
Claim 20 recites the following limitation(s):
wherein the first standard form is an invoice comprising a source, a destination, a date and an invoice amount. Which encompasses a step of selecting a type or source of data to be manipulated (e.g. an invoice comprising a source, destination, date, etc. represents a form having various types of data) which represents insignificant extra-solution activity as described in MPEP 2106.05(g).
The additional elements, taken either alone or in combination do not result in the claim, as a whole, amount to significantly more than the judicial exception.
The following limitations represent elements that have been recognized as well-understood, routine, conventional activity within the field of computer functions:
wherein the first standard form is an invoice comprising a source, a destination, a date and an invoice amount. Stauber et al. (US Patent No.: 11,461,731; Date of Patent: Oct. 4, 2022) discloses the limitation at issue: See FIG. 5 & Col. 17, lines 17-29, (FIG. 5 illustrates an invoice processing flow 500 comprising step 504 wherein a shipment management system 100 may verify one or more fields of an invoice file to ensure that certain and/or required fields are included/populated in the invoice file and/or contain correct information. The system may verify the contents of at least the following fields: an origin field (i.e. a source), a destination field (i.e. a destination), one or more shipping details fields (e.g. Note Col. 15, lines 5-7 wherein shipping details may include timing, carrier, schedule, routes, cost, constraints, load, preferences, courier instructions, etc. (e.g. timing and cost information representing dates and invoice amounts).)
Therefore, the limitation may be recognized as well-understood, routine, conventional activity within the field of computer functions.
Based on the above, the claim is not patent eligible.
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) 1-2, 5, 9-10 and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) in view of LORRAIN-HALE et al. (US PGPUB No. 2020/0301950; Pub. Date: Sep. 24, 2020).
Regarding independent claim 1,
Yanamandra discloses a method comprising: ingesting data from within a datastore comprising: associating a plurality of features with a first tag; See Paragraph [0059], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. Page-level recognition model 416 comprises a machine learning classifier trained using feature vectors representing features of document start pages and document end pages for a plurality of document types. An input feature vector of an input page may be compared to a reference set of feature vectors representing the known classes to determine which of the reference set of feature vectors has a highest similarity to the input feature vector, i.e. associating a plurality of features with a first tag (e.g. a feature vector is associated with a document type);)
using at least a computer, correlating a data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not merely forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the data element belongs; See Paragraph [0053], (Document capture system 302 applies structured document templates corresponding to various document types to identify pages of an input document that match said template. For example, a W-2 template may be applied to a page such that the system may extract data from a page identified as a W-2 document such as the name of an individual, income or other data wherein the extracted data is stored as metadata of the W-2 document.) See Paragraph [0059], (Page-level recognition model 416 is a machine learning classifier trained using feature vectors to classify an input document, i.e. correlating a data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not merely forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the data element belongs;) Note Paragraph [0011] wherein the classification system includes a non-transitory computer-readable medium configured to perform the method , embodied by corresponding computer systems, apparatus, and computer programs.
Yanamandra does not disclose the step wherein upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element, storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore.
LORRAIN-HALE discloses the step wherein upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element, storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore. See Paragraph [0043], (Disclosing a system for providing keyword suggestions to a user of a document during use of the document. Keyword suggestions correspond to tags for the document that are determined by examining contents of a document. A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata. Note [0027] wherein the system may provide functionality for automatic tagging suggestions of keywords during document use, i.e. upon detecting by the at least a computer each of the plurality of features in conjunction with a same data element (e.g. Note FIG. 5 illustrating method 00 of identifying suggested keywords at step 520) , storing by the at least a computer a record associated with the first tag, associated with the same data element, and storing within the record a location of the same data element within the datastore (e.g. the system stores an entry in a tag index table including a pointer, tag data and other metadata).)
Yanamandra and LORRAIN-HALE are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra to include the method of automatically determining document tags that may be associated with a document and stored in a tag index as disclosed by LORRAIN-HALE. Paragraph [0020] of LORRAIN-HALE discloses that the system may optimize the process of tagging a document by providing an easily accessible user interface element containing a list of intelligently suggested keywords to choose from, which eliminates the need for a user to manually tag documents, thus increasing accuracy and relevancy.
Regarding dependent claim 2,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra discloses the step of associating a plurality of second tags with the first tag, the second tags only occurring in some instances where the first tag has been associated with a data element, the second tags other than occurring when the first tag is other than associated with a data element; See Paragraph [0058], (Content classification system 400 may use page-level recognition model 416 to classify pages as a document "start page", "end page" or "other" which may generate an individual unclassified page. Document-level recognition model 418 may then be applied to an unclassified separated document to determine a document type for the selected document, i.e. associating a plurality of second tags with the first tag, the second tags only occurring in some instances where the first tag has been associated with a data element, the second tags other than occurring when the first tag is other than associated with a data element (e.g. the document type for a document is determined for individual unclassified documents which may have been classified as start pages, end pages, etc. by the page-level model 416;)
wherein upon detecting each of the plurality of features within a same data element further comprises correlating the same data element within the data store to determine one or more of the second tags to associate therewith. See FIG. 13 & Paragraph [0102], (FIG. 13 illustrates the method for classifying separated unclassified documents comprising step 1306 of applying a document-level classifier to an unclassified separated document stored at step 508 of the method illustrated in FIG. 5.) See Paragraph [0060], (Document-level recognition model 418 may be trained using feature vectors representing features of complete documents of various document types. A feature vector of an input document is compared to a reference set of feature vectors to determine a highest similarity to the input feature vector, i.e. wherein upon detecting each of the plurality of features within a same data element further comprises correlating the same data element within the data store (e.g. the input document is maintained in a data store) to determine one or more of the second tags to associate therewith (e.g. the document-level model 418 is used to determine a document type for an input document).)
Additionally, LORRAIN-HALE further discloses the step of associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a separate record associated with said second tag, the same data element, and a location of the same data element within the datastore. See Paragraph [0043], (A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata, i.e. associating the one or more second tags with the same data element by storing for each of the one or more of the second tags a separate record associated with said second tag, the same data element, and a location of the same data element within the datastore (e.g. additional tag entries may be stored in the tag index table by repeating method 500 of FIG. 5 for other keywords).)
Regarding dependent claim 5,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra further discloses the step of associating a plurality of features with a third tag; See Paragraph [0059], (Page-level recognition model 416 comprises a machine learning classifier trained using feature vectors representing features of document start pages and document end pages for a plurality of document types. An input feature vector of an input page may be compared to a reference set of feature vectors representing the known classes to determine which of the reference set of feature vectors has a highest similarity to the input feature vector, i.e. associating a plurality of features with a third tag (e.g. a feature vector is associated with a document type of a plurality of document types. Therefore, the system may determine first, second, third, etc. tags relating to a plurality of document types);)
correlating a second data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the second data element belongs; See Paragraph [0053], (Document capture system 302 applies structured document templates corresponding to various document types to identify pages of an input document that match said template. For example, a W-2 template may be applied to a page such that the system may extract data from a page identified as a W-2 document such as the name of an individual, income or other data wherein the extracted data is stored as metadata of the W-2 document.) See Paragraph [0059], (Page-level recognition model 416 is a machine learning classifier trained using feature vectors to classify an input document, i.e. correlating a data element within the datastore with the plurality of features, the plurality of features for occurring one in conjunction with another and not merely forming a single word or phrase, the plurality of features taken together forming an indication of at least one of a classification, purpose, or group to which the data element belongs;)
and upon detecting each of the plurality of features within the second data element, storing a record associated with the third tag, the second data element, and a location of the same data element within the datastore. See FIG. 5 & Paragraph [0070], (FIG. 5 illustrates a method of classification and separation of document pages comprising step 504 of separating and classifying a first set of documents by the classification system. The classified document is then stored alongside extracted metadata, i.e. upon detecting each of the plurality of features within the second data element, storing a record associated with the third tag, the second data element, and a location of the same data element within the datastore. (e.g. Note [0066] wherein crawler 410 may crawl a location such as a folder and feed documents to an OCR pipeline 412 to extract content from a separated document 444 and provides the extracted content to content analytics module 414 for use by the document-level recognition model 418).)
The examiner notes that the limitation above amounts to a repetition of the process of determining a tag. The system of Yanamandra may determine page-level classifications as well as document-level classifications. The process of determining a "third tag" is identical to the process of determining a second tag is identical and therefore if a system is capable of determining multiple tags, then it is capable of determining first, second, third, fourth, etc. tags for a document.
Regarding dependent claim 9,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra discloses the step wherein correlating is performed by a correlation engine, the correlation engine trained with a training data set comprising data elements and known tags for being associated with said known data elements. See Paragraph [0059], (Page-level recognition model 416 is a machine learning classifier trained using feature vectors to classify an input document. An input feature vector of an input page may be compared to a reference set of feature vectors representing the known classes to determine which of the reference set of feature vectors has a highest similarity to the input feature vector, i.e. wherein correlating is performed by a correlation engine, the correlation engine trained with a training data set comprising data elements and known tags (e.g. the known document types). for being associated with said known data elements (e.g. the features of the plurality of reference feature vectors).)
Regarding dependent claim 10,
As discussed above with claim 9, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra discloses the step wherein correlating includes a step of verifying correlation results. See FIG. 13 & Paragraph [0102], (FIG. 13 illustrates the method for classifying separated unclassified documents comprising step 1306 of applying a document-level classifier to an unclassified separated document stored at step 508 of the method illustrated in FIG. 5, i.e. wherein correlating includes a step of verifying correlation results (e.g. for a document that is still unclassified after the method of FIG. 5, the system applies a document-level classifier at step 1306).)
Regarding dependent claim 13,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra discloses the step of analysing at least the same data element in dependence upon at least the first tag. See FIG. 5 & Paragraph [0071], (FIG. 5 illustrates the method comprising step 506 of updating extracted content from a classified page as metadata. For example, name, income and other data associated with a W-2 form may be extracted and stored for a W-2 document classified in step 504, i.e. analyzing at least the same data element in dependence upon at least the first tag (e.g. the process of extracting metadata is a process that occurs for each input page following classification.)
Regarding independent claim 14,
Yanamandra discloses a method comprising: ingesting data from within a datastore comprising: using at least a computer, determining a plurality of data elements within a first document within a first data store; See Paragraph [0013], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. The document-level recognition model is trained to recognize a document type.) See Paragraph [0036], (The classification system receives a set of input pages and processes said pages to create a set of structured documents and a set of unclassified pages to which the recognition models are applied.) See Paragraph [0057], (Content management system 420 comprises a data store configured with an associated set of documents and pages 430, i.e. ingesting data from within a datastore (e.g. document pages are provided as input from data store 430) comprising: determining a plurality of data elements within a first document within a first data store (e.g. Note [0049] wherein the process extracts and indexes keywords from documents) ;)
using the at least a computer, determining a form of the first document; See Paragraph [0048], (Content classification system 200 may classify input document pages under a variety of document types. Examples are provided wherein one or more unclassified pages may be classified as a "Deed" while others may be classified as a "Mortgage", others still may be classified as "Other", i.e. determining a form of the first document (e.g. an input document may be classified according to a plurality of document types) ;)
based on the plurality of data elements and the form and using the at least a computer, determining a first tag for the first document; See FIG. 13 & Paragraph [0102], (FIG. 13 illustrates the method for classifying separated unclassified documents comprising step 1306 of applying a document-level classifier to an input document to determine a document type classification and confidence level. For example, an unclassified separated document may be determined by the recognition model to represent a mortgage document with an associated weightage of 86.9519 as in FIG. 2C, i.e. based on the plurality of data elements and the form, determining a first tag for the first document; (e.g. by determining a document type for the input unstructured document based on applying a template to an input document) ;)
and using the at least a computer, storing a record associated with the first tag and the first document; See Paragraph [0053], (Document capture system 302 may apply a template to an input document and store the page as a separate document classified based on the template, i.e. and storing a record associated with the first tag and the first document.)
Yanamandra does not disclose the step of using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements.
LORRAIN-HALE discloses the step of using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements. See Paragraph [0043], (Disclosing a system for providing keyword suggestions to a user of a document during use of the document. Keyword suggestions correspond to tags for the document that are determined by examining contents of a document. A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata.) See FIG. 5 & Paragraph [0047], (FIG. 5 illustrates method 500 comprising step 550 of adding a tag to a document. The method may then determine if further modifications are to be made and returns to step 510, i.e. using the at least a computer, storing in association with the first tag, a second other tag associated with at least one of the plurality of data elements (e.g. the step adding a tag may be repeated in order to store a plurality of tags).)
Yanamandra and LORRAIN-HALE are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra to include the method of automatically determining document tags that may be associated with a document and stored in a tag index as disclosed by LORRAIN-HALE. Paragraph [0020] of LORRAIN-HALE discloses that the system may optimize the process of tagging a document by providing an easily accessible user interface element containing a list of intelligently suggested keywords to choose from, which eliminates the need for a user to manually tag documents, thus increasing accuracy and relevancy.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 2 above, and further in view of Kumar et al. (US PGPUB No. 2020/0387483; Pub. Date: Dec. 10, 2020).
Regarding dependent claim 3,
As discussed above with claim 2, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein the first tag and the second tags are stored in a hierarchical data structure.
Kumar discloses the step wherein the first tag and the second tags are stored in a hierarchical data structure. See Paragraph [0017], (Disclosing a virtual file organization system configured to assign classification tags to files within a storage system 32.) See Paragraph [0019], (A hierarchy of tags and scores could be generated wherein parent tags and child tags may be associated with a stored file. Note FIG. 1 wherein storage system 32 comprises tag index 34, i.e. wherein the first tag and the second tags are stored in a hierarchical data structure.)
Yanamandra, LORRAIN-HALE and Kumar are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of storing document data and metadata in a hierarchical fashion as disclosed by Kumar. Paragraph [0003] of Kumar discloses that the use of classification tags may be leveraged to enhance traditional keyword searching and enforce policy restrictions of different types of documents. This represents an improvement in both user experience by improving document searching and security by promoting proper restrictions for different types of documents.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 2 above, and further in view of GWOZDZ et al. (US PGPUB No. 2022/0019624; Pub. Date; Jan. 20, 2022).
Regarding dependent claim 4,
As discussed above with claim 2, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein the first tag and the second tags are stored in an object-oriented data structure.
GWOZDZ discloses the step wherein the first tag and the second tags are stored in an object-oriented data structure. See Paragraph [0081], (Disclosing a system for analyzing and standardizing various types of input data such as structured data, semi-structured data, unstructured data, images and voice. The system may systematically classify and analyze a corpus of documents. Each document classification may be represented as a set of expressions composed to work with Lume Elements and data contained in the Lume.) See Paragraph [0061], (Document data and metadata are stored in a non-hierarchical fashion. FIG. 3 illustrates a plurality of individual Lume elements and their associated attributes, i.e. wherein the first tag and the second tags are stored in an object-oriented data structure.)
Yanamandra, LORRAIN-HALE and GWOZDZ are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of storing document data and metadata as Lume elements as disclosed by GWOZDZ. Paragraph [0066] of GWOZDZ outlines the following benefits of the Lume data model: Lume can be designed to be simple and only enforce basic requirements on users of the System. Interpretations and business logic are left to the users of the System rather than requiring declarative representations of both data and processes. The System can be designed to leave the modeling informal and to leave the details for implementations in the processing components. This allows Lume to maintain a very simple specification, and allows it to be extended for specific applications without impeding other applications.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 5 above, and further in view of Fleming et al. (US Patent No.: 11,676,204; Date of Patent: Jun. 13, 2023)
Regarding dependent claim 6,
As discussed above with claim 5, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein when the first tag and the third tag are associated with the second data element, associating a fourth tag with the second data element, wherein the fourth tag is indicative of a status of the second data element.
Fleming discloses the step wherein when the first tag and the third tag are associated with the second data element, associating a fourth tag with the second data element, wherein the fourth tag is indicative of a status of the second data element. See Col. 12, lines 56-67, (Disclosing a system for automated digital-property analysis including processing a document representing a digital property. The system maintains a trade-secret registry 118 configured to maintain records associated with the registration of digital property and include a record associated with a plurality of fields including one or more tags and a status identifier for the record, i.e. when the first tag and the third tag are associated with the second data element (e.g. the one or more tags of a record), associating a fourth tag with the second data element, wherein the fourth tag is indicative of a status of the second data element (e.g. the status identifier for the record is a data field of a record).)
Yanamandra, LORRAIN-HALE and Fleming are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of storing a plurality of fields associated with a document's characteristics as disclosed by Fleming. Col. 1, line 66 - Col. 2 line 4 of Fleming discloses that the system allows for registration of documents in a way that "establishes indicia of ownership, credibility of possession, or other information useful in assessing, protecting, insuring, or enforcing such property would be beneficial to such entities."
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 1 above, and further in view of SONG et al. (US PGPUB No. 2022/0121843; Pub. Date: Apr. 21, 2022).
Regarding dependent claim 7,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein the plurality of features taken together form an indication of purpose.
SONG discloses the step wherein the plurality of features taken together form an indication of purpose. See Paragraph [0071], (Disclosing a system for document recognition comprising a document type analyzer configured to analyze a similarity between a document feature vector of a target document having the same tag as that of a recognition target document and a document feature vector of the recognition target document, i.e. wherein the plurality of features taken together form an indication of purpose (e.g. a document type indicates a purpose of a document and is determined based on a comparison between features embodied as tags).)
Yanamandra, LORRAIN-HALE and SONG are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of determining a document type based on document features as disclosed by SONG. Paragraph [0129] of SONG discloses that the system may analyze the similarity between documents to determine a type of document based on the plurality of previously stored documents and may subsequently store new types of documents as they are encountered.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) in view of ASBJORNSEN et al. (US PGPUB No. 2022/0156309; Pub. Date: May 19, 2022). Regarding dependent claim 8,
As discussed above with claim 5, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein when the first tag and the third tag are associated with the second data element, performing another tagging operation on the second data element, the another tagging operation associated with the first tag and with the third tag.
ASBJORNSEN discloses the step wherein when the first tag and the third tag are associated with the second data element, performing another tagging operation on the second data element, the another tagging operation associated with the first tag and with the third tag. See FIG. 2 & Paragraph [0050], (FIG. 2 illustrates a hierarchical schema of user-provided metadata tags wherein a media record may be tagged with at least one metadata tag from each tier. FIG. 2 illustrates at least 3 tiers T1, T2, T3 wherein a media record may be associated with a first tag at T1, a second Tag T2a, a third tag T2b, fourth tag T3a and so on until Tier "n" is reached, i.e. when the first tag and the third tag are associated with the second data element (e.g. a media record associated with tags T1 and T2a), performing another tagging operation on the second data element, the another tagging operation associated with the first tag and with the third tag (e.g. a record may be tagged with tag T3a which is associated with T1 and T2a).)
Yanamandra, LORRAIN-HALE and ASBJORNSEN are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the process of assigning tags to a media file over a plurality of tiers representing different subject granularities as disclosed by ASBJORNSEN. Paragraph [0049] of ASBJORNSEN discloses the following benefit associated with the hierarchical tagging of media files: a notable attribute of tagging all media records with a semantic tier-1 primary tag, and subtags, is the ability to perform media record searches without regard to their storage location or file name. A search for media records with a common primary tag can even span multiple storage locations with no impact on the user's involvement in performing the search.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 1 above, and further in view of Zagaynov et al. (US PGPUB No. 2023/0038097; Pub. Date; Feb. 9, 2023).
Regarding dependent claim 11,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra further discloses the step wherein the correlation engines trained with training data sets comprising data elements and known tags for being associated with said known data elements. See Paragraph [0059], (An input feature vector of an input page may be compared to a reference set of feature vectors representing the known classes to determine which of the reference set of feature vectors has a highest similarity to the input feature vector, i.e. the correlation engines trained with training data sets comprising data elements (e.g. documents having assocaited feature vectors) and known tags for being associated with said known data elements (e.g. the document type labels that are associated with the reference feature vectors).)
Yanamandra-LORRAIN-HALE does not disclose the step wherein correlating is performed by a plurality of correlation engines in parallel,
Zagaynov discloses the step wherein correlating is performed by a plurality of correlation engines in parallel, See FIG. 5 & Paragraph [0077], (Disclosing a method for document classification. FIG. 5 illustrates a method of document classification based on extracted visual words. Method 500 comprises step 530 of extracting a feature map from an input image followed by a step 540 of combining visual word vectors with the extracted feature map. The method then proceeds to step 550 of processing a combination of visual word vectors with a feature map. The method may be performed in parallel, i.e. wherein correlating is performed by a plurality of correlation engines in parallel.)
Yanamandra, LORRAIN-HALE and Zagaynov are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of classifying documents in parallel as disclosed by Zagaynov. Paragraph [0020] of Zagaynov discloses that the use of a neural network for document classification allows for automatic classification of input documents, thus improving both classification accuracy and the computational complexity as compared with various common systems and methods.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 1 above, and further in view of Khamkar et al. (US Patent No.: 11,481,823; Date of Patent: Oct. 25, 2022). Regarding dependent claim 12,
As discussed above with claim 1, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra-LORRAIN-HALE does not disclose the step wherein correlating includes a step of verifying correlation results in dependence upon a correlation engine, the correlation engine trained with a training data set comprising data elements, output data provided by the correlation engine in response to said data elements and known correct output data for said data elements.
Khamkar discloses the step wherein correlating includes a step of verifying correlation results in dependence upon a correlation engine, the correlation engine trained with a training data set comprising data elements, output data provided by the correlation engine in response to said data elements and known correct output data for said data elements. See FIG. 7A & Paragraph [0115], (Disclosing a method for assigning tasks between machine resources to facilitate collaborative text detection, text recognition and text retrieval in order to optimize system performance. FIG. 7A illustrates a process for training a vendor detection model that may be trained using images that indicate a type or classification or vendor for the document represented in each image of a set of images. Note [0045] wherein training system 230 may generate models and/or features for models for recognizing vendors associated with documents wherein a model may be a support vector machine (SVM) vendor detection algorithm wherein text entries for document fields may be retrieved and presented to a task performer to verify that the retrieved text entries match the text entries represented in the document, i.e. wherein correlating includes a step of verifying correlation results in dependence upon a correlation engine (e.g. the verification process of [0045]), the correlation engine trained with a training data set comprising data elements (e.g. Note [0115] the vendor detection models are trained using document images), output data provided by the correlation engine in response to said data elements (e.g. Note [0045] wherein an SVM vendor detection algorithm may determine a confidence metric generated in response to comparing an input image to a reference image used to train the model ) and known correct output data for said data elements (e.g. Note [0115] wherein the training set comprises images and data that indicates a type or classification or vendor for a corresponding document).)
Yanamandra, LORRAIN-HALE and Khamkar are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of tagging documents for merging and performing said merging as disclosed by Khamkar. Paragraph [0008] of Khamkar discloses that the system may facilitate collaborative text detection, text recognition, and text retrieval between machine resources (e.g., AI task performers, AI task validators) and human resources (e.g., task performers, task validators) which allows the system to optimize system performance optimize system performance along a variety of different performant dimensions specified by selection criteria, including, for example, improving system efficiency, reducing task performer idle time, reducing validation performer idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, in accordance with a cost structure, etc.
Claim(s) 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE as applied to claim 14 above, and further in view of TAGUCHI (US PGPUB No. 2023/0095325; Pub. Date: Mar. 30, 2023).
Regarding dependent claim 15,
As discussed above with claim 14, Yanamandra-LORRAIN-HALE discloses all of the limitations.
Yanamandra further discloses the step of determining a first status of the first document; See FIG. 2B & Paragraph [0048], (FIG 2B illustrates a GUI 220 that indicates the current status of a plurality of documents. A Classified Documents header on the GUI indicates a section for listing "Classified Documents" that have been classified such as W2 Form 222, another state of a document may be "Processed" such as Deed 228, i.e. determining a first status of the first document (e.g. the GUI indicates a status of a document);)
Yanamandra-LORRAIN-HALE does not disclose the step of determining a second tag relating to the first status;
and storing a record associated with the second tag and the first document.
TAGUCHI discloses the step of determining a second tag relating to the first status; See Paragraph [0044], (Disclosing an information processing system for managing documents, including requests to change a state or status of a document. Document management system 10 may process documents of an electronic contract system 30 based on a status of electronic data such as a document. A status of a document may be changed in response to a request such as a withdrawal request, i.e. determining a second tag relating to the first status (e.g. Note [0159] wherein a status of a document is considered metadata associated with a document);)
and storing a record associated with the second tag and the first document. See FIG. 6, (FIG. 6 illustrates a metadata storage unit configured to store metadata associated with a document which includes storing status metadata, i.e. storing a record associated with the second tag and the first document.)
Yanamandra, LORRAIN-HALE and TAGUCHI are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the process of storing status metadata for a document as disclosed by TAGUCHI. Paragraph [0102] of TAGUCHI discloses that the system may automatically manage execution of a workflow associated with a plurality of documents including keeping track of the various states of a document over the course of a workflow. The automation of a workflow represents an improvement in as it allows a user to not need to explicitly specify a workflow to start execution.
Regarding dependent claim 16,
As discussed above with claim 15, Yanamandra-LORRAIN-HALE-TAGUCHI discloses all of the limitations.
Yanamandra further discloses the step of providing a first process having a plurality of documents associated therewith; See FIG. 2A & Paragraphs [0040]-[0041], (FIG. 2A illustrates a GUI relating to the classification process comprising a first stage classification and separation and second stage classification and separation. First stage classification and separation 102 is performed over a set of input pages which stores classified, structured documents 140 with associated metadata under structured documents node 134, i.e. providing a first process having a plurality of documents associated therewith;)
based on the first tag and the first document, determining a first process instance associated with the first process and with which the first document is associated; See FIG. 2C & Paragraph [0049], (FIG. 2C illustrates a list of processed docs 220. Note [0040] wherein a document is considered processed after the first stage classification and separation 102 is performed on it, i.e. based on the first tag and the first document (e.g. an unstructured document is classified by process 102), determining a first process instance associated with the first process and with which the first document is associated (e.g. the listed document is associated with process 102, which is why it is presented in the GUI );)
determining other documents associated with the first process instance having a plurality of documents associated therewith and associated with each other; See FIG. 2C & Paragraph [0049], (FIG. 2C illustrates a list of processed docs 220. Note [0040] wherein a document is considered processed after the first stage classification and separation 102 is performed on it, i.e. determining other documents associated with the first process instance having a plurality of documents associated therewith and associated with each other (e.g. FIG. 2C illustrates a plurality of documents that have been processed);)
and upon a change of status of another document associated with the first process instance, changing a status of the first document. See FIG. 2C & Paragraph [0047], (FIG. 2B illustrates a GUI provided by content management system FIG. 2B The content classification system 200 may identify, separate, classify and index a classified document 222, 224 and 225, while other pages may be considered "processed" including documents 226, 227, 228.) See Paragraph [0048], (Unclassified pages may be processed by the second stage classification and separation 104, i.e. upon a change of status of another document associated with the first process instance, changing a status of the first document (e.g. first stage classification and separation 102 is applied to a set of input document pages wherein certain pages of a document may be classified while other pages may be indicated as "processed" and subject to further classification steps, which describe changes in status).)
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE and TAGUCHI as applied to claim 15 above, and further in view of Khamkar et al. (US Patent No.: 11,481,823; Date of Patent: Oct. 25, 2022).
Regarding dependent claim 17,
As discussed above with claim 15, Yanamandra-TAGUCHI discloses all of the limitations.
Yanamandra-TAGUCHI does not disclose the step wherein based on the first tag and the first document, determining other documents associated with the first document;
determining a sequence of the other documents and the first document for being matched against a known first process, the known first process including a documentary record of the known process comprising multiple documents;
storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process.
Khamkar discloses the step wherein based on the first tag and the first document, determining other documents associated with the first document; See Paragraph [0071], (Disclosing a method for assigning tasks between machine resources to facilitate collaborative text detection, text recognition and text retrieval in order to optimize system performance. The system comprises a document merge component 306 configured to scan task performer information for a merge document tag or other information indicating documents to be merged, i.e. based on the first tag and the first document, determining other documents associated with the first document;)
determining a sequence of the other documents and the first document for being matched against a known first process, the known first process including a documentary record of the known process comprising multiple documents; See Paragraph [0071], (A document merge component 306 may scan task performer information for a merge document tag or other information indicating documents to be merged. The merge document tag indicates one or more pages to be merged and/or one or more sets of pages to be merged, i.e. determining a sequence of the other documents and the first document for being matched against a known first process (e.g. the pages/set of pages having the merge document tag), the known first process including a documentary record of the known process comprising multiple documents (e.g. document merge component 306 performs a document merging operation on documents having a merge document tag or other information indicating documents to be merged, i.e. the merging process is a known process spanning multiple documents such as the pages and/or sets of pages);)
storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process. See Paragraph [0070], (Task performer information, which may include a merge document tag as in [0071], may be received from a task performer network at intake system 232 of FIG. 3.) See Paragraph [0072]-[0073], (Document merge component 306 may execute a task, such as a document merge, and may store the processed pages or information indicating the changes in document data store 312 of intake system 232, i.e. storing an indication of the sequence of other documents and the first document, the sequence forming at least part of an instance of the known first process.)
Yanamandra, LORRAIN-HALE, TAGUCHI and Khamkar are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE-TAGUCHI to include the method of tagging documents for merging and performing said merging as disclosed by Khamkar. Paragraph [0008] of Khamkar discloses that the system may facilitate collaborative text detection, text recognition, and text retrieval between machine resources (e.g., AI task performers, AI task validators) and human resources (e.g., task performers, task validators) which allows the system to optimize system performance optimize system performance along a variety of different performant dimensions specified by selection criteria, including, for example, improving system efficiency, reducing task performer idle time, reducing validation performer idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, in accordance with a cost structure, etc.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE, TAGUCHI and Khamkar as applied to claim 15 above, and further in view of YNION, JR. (US PGPUB No. 2020/0034788; Pub. Date: Jan. 30, 2020).
Regarding dependent claim 18,
As discussed above with claim 17, Yanamandra-LORRAIN-HALE-TAGUCHI-Khamkar discloses all of the limitations.
Yanamandra-LORRAIN-HALE-TAGUCHI-Khamkar does not disclose the step wherein the known first process is an offer and acceptance process.
YNION, JR discloses the step wherein the known first process is an offer and acceptance process. See Paragraph [0209], (Disclosing a method for real-time and online freight management. The disclosed platform validates booking information and presents applicable contracts to the service provider with regard to booking requests.) See FIG. 20 & Paragraph [0212], (FIG. 20 illustrates a workflow of a stage of booking request acceptance wherein a shipper may submit a booking request, service providers may offer their services and confirm the request from a shipper and generate service quotations, i.e. wherein the known first process is an offer and acceptance process.)
Yanamandra, LORRAIN-HALE, TAGUCHI, Khamkar and YNION, JR are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE-TAGUCHI-Khamkar to include the method of generating documents for processing real-time online freight management as disclosed by YNION, JR. Paragraph [0210] of YNION, JR discloses that the platform offers an independently unique, robust but streamlined workflows encapsulating services that are key to ensuring traceability, risk management and other key business requirements in order to ensure that shipment will be shipped, transported, paid and received by the buyer and involved parties.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra et al. (US PGPUB No. 2021/0286992; Pub. Date: Sep. 16, 2021) in view of LORRAIN-HALE et al. (US PGPUB No. 2020/0301950; Pub. Date: Sep. 24, 2020) and Duffy et al. (US PGPUB No. 2022/0365950; Pub. Date: Nov. 17, 2022).
Regarding independent claim 19,
Yanamandra discloses a method comprising: providing a first tag relating to a first standard form; See Paragraph [0013], (Disclosing a classification system for applying page-level and document-level recognition models to unclassified document pages. The document-level recognition model is trained to recognize a document type.) See Paragraph [0053], (Document capture system 302 applies structured document templates corresponding to structured document types and classifies the document according to the document type to which the template corresponds, i.e. providing a first tag relating to a first standard form;)
mapping a plurality of data fields onto the first standard form; See Paragraph [0053], (Document capture system 302 applies structured document templates corresponding to structured document types and classifies the document according to the document type to which the template corresponds. Document capture system 302 extract data from a classified document such as an individual name, income or other data, and stores the extracted data as metadata of a W-2 document, i.e. mapping a plurality of data fields onto the first standard form;)
using at least a computer, identifying the data fields within an unstructured document; See Paragraph [0053], (Document capture system 302 extract data from a classified document such as an individual name, income or other data, and stores the extracted data as metadata of the W-2 document, i.e. identifying the data fields (e.g. the extracted metadata corresponds to attributes of a document) within an unstructured document (e.g. the input document representing an unstructured document to which the classification model would be applied).)
tagging the unstructured document with the first tag; See FIG. 13 & Paragraph [0102], (FIG. 13 illustrates the method for classifying separated unclassified documents comprising step 1306 of applying a document-level classifier to an input document to determine a document type classification and confidence level. For example, an unclassified separated document may be determined by the recognition model to represent a mortgage document with an associated weightage of 86.9519 as in FIG. 2C, i.e. tagging the unstructured document with the first tag (e.g. by determining a document type for the input unstructured document) ;)
Yanamandra does not disclose the step of using at least a computer, automatically learning a format, content and location of data elements of the unstructured document for future use in identifying and tagging documents similar to the unstructured document.
LORRAIN-HALE disclose the step of using at least a computer, automatically learning a format, content and location of data elements of the unstructured document for future use, See Paragraph [0043], (Disclosing a system for providing keyword suggestions to a user of a document during use of the document. Keyword suggestions correspond to tags for the document that are determined by examining contents of a document. A tag may be added to a section of a document and scored in a tag index table along with a pointer to the particular section of the document alongside associated tagging metadata. Note [0027] wherein the system may provide functionality for automatic tagging suggestions of keywords during document use, i.e. automatically learning a format, content and location of data elements of the unstructured document for future use (e.g. by storing tag and pointer data in the tag index table such that future requests for tagging suggestions do not need to examine the same sections already stored in the tag index table.)
X and LORRAIN-HALE are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of X to include the method of automatically determining document tags that may be associated with a document and stored in a tag index as disclosed by LORRAIN-HALE. Paragraph [0020] of LORRAIN-HALE discloses that the system may optimize the process of tagging a document by providing an easily accessible user interface element containing a list of intelligently suggested keywords to choose from, which eliminates the need for a user to manually tag documents, thus increasing accuracy and relevancy.
However, Yanamandra-LORRAIN-HALE does not disclose the step of automatically learning a format, …, for future use in identifying and tagging documents similar to the unstructured document.
Duffy discloses the step of automatically learning a format, …, for future use in identifying and tagging documents similar to the unstructured document. See FIG. 4A & Paragraph [0033], (FIG. 4A illustrates method 400 of dynamically tagging a document via document tagging wizard DTW 222 comprising step 412 wherein the system may record and/or update tagging rules via a knowledge database of the system to learn to tag similar documents for a particular document type, i.e. automatically learning a format for future use in identifying and tagging documents similar to the unstructured document (e.g. tagging rules are applied to documents submitted over time).)
Yanamandra, LORRAIN-HALE and Duffy are analogous art because they are in the same field of endeavor, document processing. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE to include the method of automatically updating tagging rules as disclosed by Duffy. Paragraph [0033] of Duffy discloses that the system may perpetually improve by updating the corresponding algorithms for grouping documents.
Claim(s) 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanamandra in view of LORRAIN-HALE and Duffy as applied to claim 19 above, and further in view of Stauber et al. (US Patent No.: 11,461,731; Date of Patent: Oct. 4, 2022).
Regarding dependent claim 20,
As discussed above with claim 19, Yanamandra-LORRAIN-HALE-Duffy discloses all of the limitations.
Yanamandra-LORRAIN-HALE-Duffy does not disclose the step wherein the first standard form is an invoice comprising a source, a destination, a date and an invoice amount.
Stauber discloses the step wherein the first standard form is an invoice comprising a source, a destination, a date and an invoice amount. See FIG. 5 & Col. 17, lines 17-29, (FIG. 5 illustrates an invoice processing flow 500 comprising step 504 wherein a shipment management system 100 may verify one or more fields of an invoice file to ensure that certain and/or required fields are included/populated in the invoice file and/or contain correct information. The system may verify the contents of at least the following fields: an origin field (i.e. a source), a destination field (i.e. a destination), one or more shipping details fields (e.g. Note Col. 15, lines 5-7 wherein shipping details may include timing, carrier, schedule, routes, cost, constraints, load, preferences, courier instructions, etc. (e.g. timing and cost information representing dates and invoice amounts).)
Yanamandra, LORRAIN-HALE, Duffy and Stauber are analogous art because they are in the same field of endeavor, document recognition and classification. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Yanamandra-LORRAIN-HALE-Duffy to include the method of validating the presence and contents of fields of an invoice file as disclosed by Stauber. Col. 19, lines 52-62 of Stauber disclose that the validation allows the system to determine that a document does not contain a virus, verifying a format of the document, verifying that the document can be processed by the shipment management system, verifying that the data in the document can be extracted via the one or more processors, etc. The verification process represents an improvement in document processing by ensuring that an invoice file is able to be properly processed.
Regarding dependent claim 21,
As discussed above with claim 20, Yanamandra-LORRAIN-HALE-Duffy-Stauber discloses all of the limitations.
Yanamandra further discloses the step wherein automatically learning results in a process that identifies invoices in at least some different formats and document structures, each having data indicated in the first standard form. See Paragraph [0048], (Content classification system 200 may classify input document pages under a variety of document types. Examples are provided wherein one or more unclassified pages may be classified as a "Deed" while others may be classified as a "Mortgage", others still may be classified as "Other", i.e. wherein automatically learning results in a process that identifies invoices in at least some different formats and document structures (e.g. documents may be classified according to a plurality of document types), each having data indicated in the first standard form (e.g. each document type corresponds to particular features of a document).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2, 5-7, 14 and 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments modify the scope of the claimed invention and therefore necessitated the new grounds of rejection presented in this Office Action.
Applicant's arguments with regard to the rejection of claims 1-8 and 13-21 have been fully considered but they are not persuasive.
Regarding independent claim 1,
Applicant argues that claim 1 incorporates the subject matter of dependent claim 9, which was not previously rejected under 35 USC 101. However, claim 9 is currently pending in the present application and has not been incorporated into any of independent claims 1, 14 nor 19.
Therefore the rejection under 35 USC 101 is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ann J. Lo can be reached at (571) 272-9767. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FMMV/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159