Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is responsive to the Application filed on 02/20/2026
Claims 21-22 and 24-41 are pending in this case. Claims 21, 39-40 have been currently amended. Claim 23 have been canceled.
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) 21-22 and 24-41 are rejected under 35 U.S.C. 103 as being unpatentable over Harvey et al. (Pub No.: 20140115007 A1), hereinafter referred to as Harvey, in view of Ishiyama et al. (US Patent No.10,540,548 B2), hereinafter referred to as Ishiyama and further in view of Majkowska et al. (US Patent No.8,583,648 B2), hereinafter referred to as Majkowska
With respect to claim 21, Harvey disclose:
A system for improving machine learning by using a template to generate a corpus of synthetic document for training a machine learning model, comprising: at least one processor (In Fig. 2 and paragraph [0077], Harvey discloses a processor configured to execute a synthetic data generator module.)
At least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising (In paragraph [0073], Harvey discloses that a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.)
Receiving a plurality of documents that includes sensitive data ((Under Broadest Reasonable Interpretation, "sensitive data" can be broadly interpreted as personal identifying information, medical information, etc. Thus, the Examiner interprets "sensitive data" as any information system that receives documents (i.e., information)) In Fig. 7 and paragraph [0024], Harvey discloses the received model of a dataset includes metadata comprising field names, field types and field values and a tree structure comprising decision branches and leaf nodes.)
Generating, based on identifying the at least one common input field in the plurality of documents, a template that is for [[a]] the document type of the plurality of documents (In paragraph [0024], Harvey discloses generating a reliable document structure based on common fields and positional analysis of similar information. The extracted information comprises generating base, leaf and tree common table expressions from the accessed metadata and tree structure. )
Extracting, based on generating the template, information from the at least one common input field (In paragraph [0024 & 0071], Harvey discloses identifying common input fields, analyzing information structures, obtaining information associated with those fields and using extracted field information to generate synthetic data.)
Analyzing at least one statistic associated with the extracted information to construct a data model that corresponds to each at least one common input field (In paragraph [0024], Harvey discloses identifying common input fields and analyzing information structures.)
Determining, based on the extracted information, expected data associated with the first cluster (In paragraph [0024 & 0048], Harvey discloses extracting information from common fields (e.g., first field )and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.
Generating, based on the expected data associated with the first cluster and one or more statistics of the extracted information, non-sensitive data using the data model (In paragraph [0024 & 0071], Harvey discloses extracting information from common fields and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.)
Generating the corpus of synthetic documents, for training the machine learning model, by inserting the non-sensitive data based on the at least one statistic into the template (In paragraph [0024 & 0071], Harvey discloses generating synthetic information, and using the generated information into reusable document structures/templates to generate synthetic documents, thereby teaching the claimed limitation.)
Training the machine learning model based on generating the corpus of synthetic documents by inserting the non-sensitive data into the template (In paragraph [0030], Harvey disclose training the decision tree model on generation of random synthetic test data)
With respect to claim 21, Harvey do not explicitly disclose:
determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for a set of corresponding pixels having a same position across multiple documents of the plurality of documents
determining a standard deviation of the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents
identifying at least one common input field based on determining the standard deviation of the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents
clustering, based on characteristics of the plurality of documents, the plurality of documents into a first cluster corresponding to a document type of the plurality of documents
However, it is known by Ishiyama to disclose:
Determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for a set of corresponding pixels having a same position across multiple documents of the plurality of documents (In Fig. 3 and Cols. 5–6, lines 61-3, Ishmael discloses calculating a statistical value for the distribution of luminance values, pixels existing at corresponding positions.)
Determining a standard deviation of the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents (In Fig. 3 and Cols. 5–6, lines 61-3, Ishmael discloses the statistical value represents standard deviation, variance, etc. of the luminance value.)
Identifying at least one common input field based on determining the standard deviation of the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents (In Fig. 3 and Col. 16, lines 36-42, Ishiyama disclose determining identicalness between the comparison object and the registered object by calculating the statistical value for the obtained luminance value.)
Harvey and Ishiyama are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Harvey, with generating synthetic data from a model of a dataset as taught by Harvey, with comparison unit configured to compare the comparison object with a registered object on the basis of a statistical value for the luminance value of pixels as taught by Ishiyama. The motivation for doing so would have been to generate synthetic data that has the advantages that there are no costs of storing the synthetic data (See [0021] of Harvey.)
With respect to claim 21, Harvey in view of Ishiyama do not explicitly disclose:
clustering, based on characteristics of the plurality of documents, the plurality of documents into a first cluster corresponding to a document type of the plurality of documents
However, Majkowska is known to disclose:
Clustering, based on characteristics of the plurality of documents, the plurality of documents into a first cluster corresponding to a document type of the plurality of documents (In Col. 2, lines 42-53, Majkowska discloses that a server device may receive first label information regarding a first cluster that includes information identifying a first set of documents, where the first label information regarding the first cluster includes a first set of labels that are associated with the first cluster.)
Harvey in view of Ishiyama and Majkowska are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Majkowska, with clustering a first set of documents as taught by Majkowska. The motivation for doing so would have been determine whether the candidate clusters are semantically similar (See Col. 8, lines 45-46, of Majkowska.)
With respect to claim 22, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Ishiyama disclose:
The system of claim 21, wherein determining the distribution of positional values comprises generating the distribution of positional values (In Fig. 3 and Cols. 5–6, lines 61-3, Ishiyama discloses calculating a statistical value for the distribution of luminance values, pixels existing at corresponding positions.)
With respect to claim 24, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Ishiyama disclose:
The system of claim 21, wherein the operations further comprise: determining a statistical value of the distribution of positional values, wherein the at least one common input field is identified based on the statistical value (In Fig. 3 and Col. 16, lines 36–42, Ishiyama discloses determining identicalness between the comparison object and the registered object by calculating the statistical value for the obtained luminance value.)
With respect to claim 25, Harvey in view of Ishiyama and Majkowska disclose element of claim 24. In addition, Ishiyama disclose:
The system of claim 24, wherein the statistical value is associated with positional values of the set of corresponding pixels having, wherein the set of corresponding pixels comprises a number of adjacent pixels being great than or equal to a threshold value (In Cols. 9-10, lines 51-7, Ishiyama discloses that when the statistical value falls under a range of the predetermined threshold or is lower than the predetermined threshold, it can be determined that a registered image coincides with or is closely similar to a comparison image.)
With respect to claim 26, Harvey in view of Ishiyama and Majkowska disclose element of claim 24. In addition, Ishiyama disclose:
The system of claim 24, wherein the statistical value is a standard deviation (In Fig. 3 and Cols. 5–6, lines 61-3, Ishiyama discloses the statistical value represents standard deviation, variance, etc. of the luminance value.)
With respect to claim 27, Harvey in view of Ishiyama and Majkowska disclose element of claim 26. In addition, Ishiyama disclose:
The system of claim 26, wherein the at least one common input field is identified based on the standard deviation being greater than or equal to a threshold value (In Cols. 9-10, lines 51-7, Ishiyama discloses that when the statistical value falls under a range of the predetermined threshold or is lower than the predetermined threshold, it can be determined that a registered image coincides with or is closely similar to a comparison image.)
With respect to claim 28, Harvey in view of Ishiyama and Majkowska disclose element of claim 26. In addition, Ishiyama disclose:
The system of claim 26, wherein the operations further comprise: identifying, based on the standard deviation being below a threshold value, a common feature of multiple documents of the plurality of documents (In Cols. 9-10, lines 51-7, Ishiyama discloses whether this calculated statistical value of the luminance value falls under a range of a predetermined threshold or is lower than the predetermined threshold.)
With respect to claim 29, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Harvey disclose:
The system of claim 21, wherein the operations further comprise: identifying a common feature of multiple documents of the plurality of documents, wherein the template is generated to include the common feature (In paragraph [0024], Harvey discloses identifying common input fields and analyzing information structures.)
With respect to claim 30, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Harvey disclose:
The system of claim 21, wherein the operations further comprise: extracting data from a document using the template (In paragraph [0024 & 0071], Harvey discloses identifying common input fields, analyzing information structures, obtaining information associated with those fields and using extracted field information to generate synthetic data. )
With respect to claim 31, Harvey in view of Ishiyama and Majkowska disclose element of claim 30. In addition, Ishiyama disclose:
The system of claim 30, wherein extracting data from the document using the template comprises performing a visual characterization process to input fields of the document (In Col. 4, lines 60–67, Ishiyama discloses a characteristic that the luminance value of pixels corresponding to the same normal vector on a surface of an object becomes the same value in the same image.)
With respect to claim 34, Harvey in view of Ishiyama and Majkowska disclose element of claim 33. In addition, Harvey disclose:
The system of claim 33, wherein the database associates synthetic documents with respective metadata (In paragraph [0024], Harvey discloses the metadata and tree structure within the received model and constructing of a database view from the extracted information comprises the generating base.)
With respect to claim 35, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Harvey disclose:
The system of claim 21, wherein the inserted non-sensitive data comprises only synthetic data and actual data (In paragraph [0024 & 0071], Harvey discloses generating synthetic information, and using the generated information into reusable document structures/templates to generate synthetic documents.)
With respect to claim 36, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Harvey disclose:
The system of claim 21, wherein the inserted non-sensitive data comprises only synthetic data (In paragraph [0024 & 0071], Harvey discloses generating synthetic information, and using the generated information into reusable document structures/templates to generate synthetic documents.)
With respect to claim 37, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Harvey disclose:
The system of claim 21, wherein the operations further comprise: extracting metadata from the plurality of documents (In paragraph [0024], Harvey discloses that the extraction of information from the received model comprises accessing the metadata and tree structure within the received model and constructing a database view from the extracted information comprises generating base, leaf and tree common table expressions from the accessed metadata and tree structure.)
storing the metadata in a database (In paragraph [0025], Harvey discloses that the data model does not contain any of the actual data which is stored in the array.)
With respect to claim 38, Harvey in view of Ishiyama and Majkowska disclose element of claim 21. In addition, Majkowska disclose:
The system of claim 21, wherein the machine learning model is configured to be used for a program to: identify handwritten information; identify typed information; identify an expected data type; or identify a document type (Examiner selects: identify a document type In Col. 2, lines 42-53, Majkowska disclose identifying a first set of documents.)
With respect to claim 39, Harvey disclose:
A method, comprising: receiving a plurality of documents ((Under Broadest Reasonable Interpretation, "sensitive data" can be broadly interpreted as personal identifying information, medical information, etc. Thus, the Examiner interprets "sensitive data" as any information system that receives documents (i.e., information)) In Fig. 7 and paragraph [0024], Harvey discloses the received model of a dataset includes metadata comprising field names, field types and field values and a tree structure comprising decision branches and leaf nodes.)
generating, based on identifying the at least one input field, a template that is for [[a]] the document type (In paragraph [0024], Harvey discloses generating a reliable document structure based on common fields and positional analysis of similar information. The extracted information comprises generating base, leaf and tree common table expressions from the accessed metadata and tree structure. )
extracting, based on generating the template, information from the at least one common input field (In paragraph [0024 & 0071], Harvey discloses identifying common input fields, analyzing information structures, obtaining information associated with those fields and using extracted field information to generate synthetic data.)
analyzing at least one statistic associated with the extracted information to construct a data model that corresponds to each at least one common input field (In paragraph [0024], Harvey discloses identifying common input fields and analyzing information structures. )
determining, based on the extracted information, expected data associated with the first cluster (In paragraph [0024 & 0071], Harvey discloses extracting information from common fields and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.)
generating, based on the expected data associated with the first cluster and one or more statistics of the extracted information, non-sensitive data using the data model (In paragraph [0024 & 0071], Harvey discloses extracting information from common fields and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.)
generating the corpus of synthetic documents, for training the machine learning model, by inserting the non-sensitive data based on the at least one statistic into the template (In paragraph [0024 & 0071], Harvey discloses generating synthetic information, and using the generated information into reusable document structures/templates to generate synthetic documents, thereby teaching the claimed limitation.)
training the machine learning model based on generating the corpus of synthetic documents by inserting the non-sensitive data into the template (In paragraph [0030], Harvey discloses the decision tree model for the generation of random synthetic test data.)
With respect to claim 39, Harvey do not explicitly disclose:
clustering, based on characteristics of the plurality of documents, the plurality of documents of a document type into a first cluster
determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for a set of corresponding pixels of the documents having a same position across multiple documents of the plurality of documents
identifying at least one input field based on determining the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents
However, it is known by Ishiyama to disclose:
Determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for a set of corresponding pixels of the documents having a same position across multiple documents of the plurality of documents (In Fig. 3 and Cols. 5–6, lines 61-3, Ishmael discloses calculating a statistical value for the distribution of luminance values, pixels existing at corresponding positions.)
Identifying at least one input field based on determining the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents (In Fig. 3 and Col. 16, lines 36-42, Ishiyama disclose determining identicalness between the comparison object and the registered object by calculating the statistical value for the obtained luminance value.)
Harvey and Ishiyama are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Harvey, with generating synthetic data from a model of a dataset as taught by Harvey, with comparison unit configured to compare the comparison object with a registered object on the basis of a statistical value for the luminance value of pixels as taught by Ishiyama. The motivation for doing so would have been to generate synthetic data that has the advantages that there are no costs of storing the synthetic data (See [0021] of Harvey.)
With respect to claim 39, Harvey in view of Ishiyama do not explicitly disclose:
clustering, based on characteristics of the plurality of documents, the plurality of documents of a document type into a first cluster
However, Majkowska is known to disclose:
Clustering, based on characteristics of the plurality of documents, the plurality of documents of a document type into a first cluster (In Col. 2, lines 42-53, Majkowska discloses that a server device may receive first label information regarding a first cluster that includes information identifying a first set of documents, where the first label information regarding the first cluster includes a first set of labels that are associated with the first cluster.)
Harvey in view of Ishiyama and Majkowska are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Majkowska, with clustering a first set of documents as taught by Majkowska. The motivation for doing so would have been determine whether the candidate clusters are semantically similar (See Col. 8, lines 45-46, of Majkowska.)
With respect to claim 40, Harvey disclose:
A system for determining synthetic information for documents, comprising: at least one processor (In Fig. 2 and paragraph [0077], Harvey discloses a processor configured to execute a synthetic data generator module.)
and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising: receiving a plurality of documents associated with respective individuals (In paragraph [0073], Harvey discloses that a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. ((Under Broadest Reasonable Interpretation, "sensitive data" can be broadly interpreted as personal identifying information, medical information, etc. Thus, the Examiner interprets "sensitive data" as any information system that receives documents (i.e., information)) In Fig. 7 and paragraph [0024], Harvey discloses the received model of a dataset includes metadata comprising field names, field types and field values and a tree structure comprising decision branches and leaf nodes.)
extracting data from the identified input fields (In paragraph [0024 & 0071], Harvey discloses identifying common input fields, analyzing information structures, obtaining information associated with those fields and using extracted field information to generate synthetic data.)
determining, based on the extracted data, expected values for the identified input field (In paragraph [0024 & 0071], Harvey discloses extracting information from common fields and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.)
generating a document template, for a document type, based on the identified input fields (In paragraph [0024], Harvey discloses generating a reliable document structure based on common fields and positional analysis of similar information. The extracted information comprises generating base, leaf and tree common table expressions from the accessed metadata and tree structure.)
analyzing at least one statistic associated with the extracted data to construct a data model that corresponds to each at least one input field (In paragraph [0024], Harvey discloses identifying common input fields and analyzing information structures.)
generating, based on the expected values associated with the first cluster and one or more statistics of the extracted information, data using the data model (In paragraph [0024 & 0071], Harvey discloses extracting information from common fields and statistically analyzes such information to generate synthetic information appropriate for documents of the same type, thereby determining expected associated with document grouping/template.)
generating a corpus of synthetic documents by populating the generated document template using the expected values (In paragraph [0024 & 0071], Harvey discloses generating synthetic information, and using the generated information into reusable document structures/templates to generate synthetic documents, thereby teaching the claimed limitation.)
Training a machine learning model based on the corpus of synthetic documents (In paragraph [0030], Harvey discloses the decision tree model for the generation of random synthetic test data.)
With respect to claim 40, Harvey do not explicitly disclose:
clustering, based on characteristics of the documents, the plurality of documents of a document type into a first cluster
determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for [[the]] a set of corresponding pixels having [[the]] a same position across [[the]] multiple documents of the plurality of documents
identifying input fields of the plurality of documents based on determining the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents
However, it is known by Ishiyama to disclose:
Determining, based on clustering the plurality of documents into the first cluster, a distribution of positional values for [[the]] a set of corresponding pixels having [[the]] a same position across [[the]] multiple documents of the plurality of documents (In Fig. 3 and Cols. 5–6, lines 61-3, Ishmael discloses calculating a statistical value for the distribution of luminance values, pixels existing at corresponding positions.)
Identifying input fields of the plurality of documents based on determining the distribution of positional values for the set of corresponding pixels having the same position across the multiple documents of the plurality of documents (In Fig. 3 and Col. 16, lines 36-42, Ishiyama disclose determining identicalness between the comparison object and the registered object by calculating the statistical value for the obtained luminance value.)
Harvey and Ishiyama are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Harvey, with generating synthetic data from a model of a dataset as taught by Harvey, with comparison unit configured to compare the comparison object with a registered object on the basis of a statistical value for the luminance value of pixels as taught by Ishiyama. The motivation for doing so would have been to generate synthetic data that has the advantages that there are no costs of storing the synthetic data (See [0021] of Harvey.)
With respect to claim 40, Harvey in view of Ishiyama do not explicitly disclose:
clustering, based on characteristics of the documents, the plurality of documents of a document type into a first cluster
However, Majkowska is known to disclose:
Clustering, based on characteristics of the plurality of documents, the plurality of documents of a document type into a first cluster (In Col. 2, lines 42-53, Majkowska discloses that a server device may receive first label information regarding a first cluster that includes information identifying a first set of documents, where the first label information regarding the first cluster includes a first set of labels that are associated with the first cluster.)
Harvey in view of Ishiyama and Majkowska are analogous pieces of art because both references concern generating synthetic data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Majkowska, with clustering a first set of documents as taught by Majkowska. The motivation for doing so would have been determine whether the candidate clusters are semantically similar (See Col. 8, lines 45-46, of Majkowska.)
With respect to claim 41, Harvey in view of Ishiyama and Majkowska disclose element of claim 39. In addition, Ishiyama disclose:
The method of claim 39, further comprising: identifying one or more common features shared by the documents, wherein generating the template comprises: generating the template based on identifying the one or more common features and based on identifying the at least one input field (In Fig. 3 and Col. 16, lines 36-42, Ishiyama disclose determining identicalness between the comparison object and the registered object by calculating the statistical value for the obtained luminance value.)
Response to Arguments
Applicant's arguments filed 02/20/2026 have been fully considered, but are not persuasive.
Pertaining to rejection under 101
Rejections for claims 21-22 and 24-41 are withdrawn under 35 USC § 101.
Pertaining to rejection under 103
Thus, the applicant’s arguments in regard to the examiner’s rejections under 35 USC 103 are moot in view of the new grounds of rejection.
Conclusion
THIS ACTION IS MADE FINAL. 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 EVEL HONORE whose telephone number is (703)756-1179. The examiner can normally be reached Monday-Friday 8 a.m. -5:30 p.m.
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EVEL HONORE
Examiner
Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
Prosecution Insights