DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to communications: RCE filed on 6/30/2025.
Claims 1-20 are pending. Claims 1, 11, and 20 are independent.
The previous rejection of claims 1-20 under 35 USC § 103 have been withdrawn in view of the amendment.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Paul et al. (US2021/0374360) in view of Belgacem et al. (“A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms”) and Hermanek et al. (US2022/0138191).
In regards to claim 1, Paul et al. substantially discloses a method comprising:
determining, from a workflow for generating an electronic document, fields that require definition in the electronic document (Paul et al. para[0033] ln1-9, determines field needed for customized digital document);
predicting, based on user input during the workflow, values for a first set of the fields (Paul et al. fig. 4 para[0033] ln9-13, predicts input for first set of fields based on user input);
generating for display a user interface showing predicted values for the first set of the fields and the second set of the fields, at least the second set of the fields is configured to be editable (Paul et al. para[0091] ln6-19, document displayed to user to edit for completeness and accuracy); and
generating the electronic document using confirmed values for each of the fields (Paul et al. para[0091] ln20-24, document is transmitted after accuracy is confirmed by user).
Paul et al. does not explicitly disclose inputting, into a supervised machine learning model, signals corresponding to a second set of the fields that do not intersect with the first set of the fields;
receiving, as output from the supervised machine learning model, one or more predicted values for each field of the second set of fields.
However Belgacem et al. substantially discloses inputting, into a supervised machine learning model, signals corresponding to a second set of the fields that do not intersect with the first set of the fields (Belgacem et al. pg12 section4.2 para6 ln1-7, receives input for a second set of fields independent from first set);
receiving, as output from the supervised machine learning model, one or more predicted values for each field of the second set of fields (Belgacem et al. pg14 section 4.3 para4 ln1-3, provides top predicted values for selected fields).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the document generation method of Paul et al. with the automatic form filling of Belgacem et al. in order to provide accurate suggestion of values for fields based on values in other fields in the form (Belgacem et al. pg3 section 1 para 8 ln 1-8).
Paul et al does not explicitly disclose the predicted values for the first set of the fields are predicted based on a threshold consistency between fields and values in a plurality of historical documents, the threshold consistency being defined by a predetermined threshold value indicating that a predetermined threshold value indicating that a predetermined value is associated with a predetermined field, and
the predicted values for the second set of fields are predicted based on a variation in the plurality of historical documents at lower confidence than the predicted values for the first set of fields, wherein the predicted values for the second set of fields are ranked in accordance with associated confidence scores determined for each predicted value in the predicted values for the second set of fields, wherein highest confidence scores are indicative of a highest likelihood that the predicted values for the second set of fields corresponding to the highest confidence scores will be confirmed for the corresponding second set of fields.
However Hermanek et al. substantially discloses the predicted values for the first set of the fields are predicted based on a threshold consistency between fields and values in a plurality of historical documents, the threshold consistency being defined by a predetermined threshold value indicating that a predetermined threshold value indicating that a predetermined value is associated with a predetermined field (Hermanek et al. para[0201], For a field, the domain updater 902 may identify the counter that is associated with the highest count and identify the value that is associated with the identified counter as the most common value for the field, para[0208], The domain updater may identify the second record object that is associated with a confidence score that exceeds a threshold), and
the predicted values for the second set of fields are predicted based on a variation in the plurality of historical documents at lower confidence than the predicted values for the first set of fields, wherein the predicted values for the second set of fields are ranked in accordance with associated confidence scores determined for each predicted value in the predicted values for the second set of fields, wherein highest confidence scores are indicative of a highest likelihood that the predicted values for the second set of fields corresponding to the highest confidence scores will be confirmed for the corresponding second set of fields (Hermanek et al. fig. 10 1004 and 1006 para[0233], confidence below threshold to match first set of fields, compares to second set of fields para[0236], Based on the comparison, the data processing system may identify the value that is associated with the highest counter value (e.g., count) for each of the fields (e.g., domain name, group entity name, postal code, etc.) of the second record objects. Accordingly, the data processing system may identify the most common value for each of the fields).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the document generation method of Paul et al. with the matching document generation of Hermanek et al. in order to predict values based on similar documents (Hermanek et al. para[0054]).
In regards to claim 2, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 1, wherein the supervised machine learning model is trained using a training set, the training set comprising: historical workflows for generating electronic documents; fields in electronic documents generated from the historical workflows; and values for the fields (Paul et al. para[0030] ln10-18).
In regards to claim 3, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 2, wherein the historical workflows correspond to at least one of: a type of the electronic document; a recipient of the electronic document; a user; and an entity of the user (Paul et al. para[0030] ln25-36).
In regards to claim 4, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 2, wherein the training set corresponds to a user, the user having permission to access the historical workflows and electronic documents generated from the historical workflows (Paul et al. fig. 2 204 para[0037] ln1-10) .
In regards to claim 5, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 2, further comprising determining the instructions corresponding to the second set of the fields based on variance of the fields in the electronic documents generated from the historical workflows in the training set (Paul et al. para[0082] ln1-13).
In regards to claim 6, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 1, further comprising receiving the instructions corresponding to the second set of fields as input (Paul et al. para[0082] ln1-13).
In regards to claim 7, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 1, further comprising:
for each field of the second set of fields, determining a confidence score for each predicted value (Belgacem et al. pg14 section 4.3 para4 ln1-3); and
displaying, via the user interface, a representation of the determined confidence score (Belgacem et al. pg15 section 4.3 para9 ln1-6).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the document generation method of Paul et al. with the automatic form filling of Belgacem et al. in order to provide accurate suggestion of values for fields based on values in other fields in the form (Belgacem et al. pg3 section 1 para 8 ln 1-8).
In regards to claim 8, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 7, further comprising:
for a subset of the second set of fields, receiving input for each predicted value (Paul et al. para[0091] ln14-23); and
generating the electronic document using the received input (Paul et al. para[0092] ln1-5).
In regards to claim 9, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 8, further comprising training the supervised machine learning model based on the received input (Belgacem et al. pg18 section5.1 para7 ln8-15).
It would have been obvious to one of ordinary skill in the art before the filing date of the invention to have combined the document generation method of Paul et al. with the automatic form filling of Belgacem et al. in order to provide accurate suggestion of values for fields based on values in other fields in the form (Belgacem et al. pg3 section 1 para 8 ln 1-8).
In regards to claim 10, Paul et al. as modified Belgacem et al. and Hermanek et al. substantially discloses the method of claim 1, wherein the input during the workflow comprises a selection of a series of actions corresponding to the electronic document (Paul et al. para[0081 ln5-16).
Claims 11-19 recite substantially similar limitations to claims 1-9. Thus claims 11-19 are rejected along the same rationale as claims 1-9.
Claim 20 recites substantially similar limitations to claim 1. Thus claim 20 is rejected along the same rationale as claim 1.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply the current rejection.
Conclusion
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/N.H/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141