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 the Application filed on 5/2/2024. Claims 1-20 are pending in the case.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Balzer et al. (US 20190044829 A1, hereinafter Balzer) in view of Pretorius et al. (US 20190102371 A1, hereinafter Pretorius).
As to independent claim 1, Balzer teaches a system
a server comprising one or more processors and a memory storing an orchestrator bot (“FIG. 8 illustrates a logic flow diagram of a method to perform bot network orchestration. Process 800 may be implemented on a computing device, server, or other system. An example computing device configured to execute an orchestration service to perform bot network orchestration comprises a communication interface to facilitate communication between the computing device, at least one server, and a client device. The example server may also comprise a memory to store instructions, and one or more processors coupled to the memory.” Paragraph 0096) and a plurality of task bots, wherein each of the plurality of task bots is configured to execute an action (“The orchestrator 118 may provide instructions to the plurality of bots associated with aspects of the service request, where each bot may be selected based on an aspect of the service request.” paragraph 0026),
wherein the orchestrator bot, when executed by the one or more processors, causes the one or more processors to:
invoke
execute the subset of task bots according to the execution sequence (“At operation 830, the processors may be configured to provide instructions to the plurality of bots associated with aspects of the service request, where each bot is selected based on an aspect of the service request…At operation 840, the processors may be configured to receive responses from the plurality of bots associated with the aspects of the service request.” Paragraph 0098-0099).
Balzer does not appear to expressly teach a system for generating an immigration petition, comprising
obtain petitioner data and a case type classification value as input;
invoke from a case type repository, based on the case type classification value,{…} an execution sequence;
wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
pre-process the petitioner data for injection into one or more digital forms, and
generate, from the one or more digital forms, the immigration petition configured for printing on a paper of a given size.
Pretorius teaches a system for generating an immigration petition (Fig. 1 illustrates an immigration application creation), comprising
obtain petitioner data (“The creation of immigration applications often involves information drawn from potentially many different sources. For example, different individuals may provide document drafts. As another example, information may be retrieved from a database or other repository.” Paragraph 0023) and a case type classification value as input (“FIG. 3 illustrates an example of an immigration application template 300, configured in accordance with one or more embodiments. According to various embodiments, a template may be associated with one or more immigration application types.” Paragraph 0059);
invoke from a case type repository, based on the case type classification value an execution sequence (“In the specific example shown in FIG. 3, the immigration application template 300 is associated with an H-1B visa application.” Paragraph 0061),
pre-process the petitioner data for injection into one or more digital forms (“an automated process may provide some or all of the information for filling a template field by retrieving information from any of various data sources such as a database or data storage medium. For example, the information for filling a template field may be retrieved from one or more pre-existing documents such as an I-9 form.” Paragraph 0072,0023), and
generate, from the one or more digital forms, the immigration petition configured for printing on a paper of a given size (“Alternately, or additionally, the immigration application information may be printed for physical submission or transmitted to a client machine.” Paragraph 0077, printing on a given size, i.e. default printer paper sizes are typically Letter 8.5" x 11").
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 the system of Balzer to comprise a system for generating an immigration petition, comprising obtain petitioner data and a case type classification value as input; invoke from a case type repository, based on the case type classification value, a task bot chain, wherein the task bot chain identifies one or more task bots selected from the plurality of task bots to create a subset of task bots to implement an execution sequence; pre-process the petitioner data for injection into one or more digital forms, and generate, from the one or more digital forms, the immigration petition configured for printing on a paper of a given size. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 2, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
determine if any of the one or more digital forms is expired by comparing a revision date on the digital form to a revision date of an available digital form on a website, and
responsive to determining that any of the one or more digital forms is expired, download the available digital form from the website.
Pretorius teaches determine if any of the one or more digital forms is expired by comparing a revision date on the digital form to a revision date of an available digital form on a website (“update an application with corrective information and replace a previous version of the application with the updated one…the updating of an application may be performed by an automated process.” Paragraph 0098-0099), and
responsive to determining that any of the one or more digital forms is expired, download the available digital form from the website (“replace a previous version of the application with the updated one.” Paragraph 0098).
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: determine if any of the one or more digital forms is expired by comparing a revision date on the digital form to a revision date of an available digital form on a website, and responsive to determining that any of the one or more digital forms is expired, download the available digital form from the website. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 3, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
generate, using a trained machine learning language model, a cover letter and a support letter.
Pretorius teaches generate, using a trained machine learning language model, a cover letter (“At 313, …a cover letter may be created by an automated process to create a merged document from information such as a blank form and data retrieved from a database.” paragraph 0063) and a support letter (At 314,316, 318 supporting documentation).
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: generate, using a trained machine learning language model, a cover letter and a support letter. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 4, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
complete one or more web forms on a website using the petitioner data.
Pretorius teaches complete one or more web forms on a website using the petitioner data (“The creation of immigration applications often involves information drawn from potentially many different sources. For example, different individuals may provide document drafts. As another example, information may be retrieved from a database or other repository. As yet another example, individuals may provide free-form text responses.” Paragraph 023).
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: complete one or more web forms on a website using the petitioner data. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 5, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
download a petitioner travel history from a website.
Pretorius teaches “an automated process may provide some or all of the information for filling a template field by retrieving information from any of various data sources such as a database or data storage medium.” Paragraph 0072, this implies that the automated process may retrieve or download any data from various data sources).
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: download a petitioner travel history from a website. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 6, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to:
insert signatures into the one or more digital forms.
Pretorius teaches insert signatures into the one or more digital forms (the template may include one or more custom components, such as fields for inserting signatures, paragraph 0022,0030)
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: insert signatures into the one or more digital forms. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 7, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: generate a shipping label.
Pretorius teaches generate a shipping label (“After the immigration application is finalized, it may be stored on the system for file retention, retrieval, or electronic submission. Alternately, or additionally, the immigration application may be printed for physical submission or transmitted to a client machine.” Paragraph 0034, One skilled in the art will recognize that physical submission would require generating a shipping label).
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 the system of Balzer to comprise wherein the subset of task bots, when executed by the one or more processors, causes the one or more processors to: generate a shipping label. One would have been motivated to make such a combination for “assisting in the management of immigration data and the preparation and submission of immigration applications” Pretorius [0003].
As to dependent claim 8, Balzer teaches the system of claim 1, Balzer further teaches wherein the orchestrator bot, when executed by the one or more processors, causes the one or more processors to:
monitor an execution status of the subset of task bots (“the orchestrator 118 may further analyze the service response to improve accuracy,” paragraph 0032).
As to dependent claim 9, Balzer teaches the system of claim 1, Balzer does not appear to expressly teach wherein the memory further stores one or more quality control bots,
wherein the orchestrator bot, when executed by the one or more processors, causes the one or more processors to:
execute the one or more quality control bots; and
wherein the one or more quality control bots, when executed by the one or more processors, causes the one or more processors to:
examine the immigration petition for potential errors based on a predefined set of validation rules, and
responsive to finding potential errors, output a notification regarding the potential errors.
Pretorius teaches examine the immigration petition for potential errors based on a predefined set of validation rules, and responsive to finding potential errors (“According to various embodiments, finalizing the immigration application may involve submitting the application for review by one or more reviewers. Such reviewers may include, but are not limited to: an automated review process, … As part of the review process, one or more errors or omissions may be identified and corrected.” Paragraph 0034), and
output a notification regarding the potential errors (“For example, errors may be corrected via business logic. As another example, errors may be corrected by prompting a user to supply missing or erroneous information” paragraph 0034).
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 the system of Balzer to comprise wherein the orchestrator bot, when executed by the one or more processors, causes the one or more processors to: execute the one or more quality control bots; and wherein the one or more quality control bots, when executed by the one or more processors, causes the one or more processors to: examine the immigration petition for potential errors based on a predefined set of validation rules, and responsive to finding potential errors, output a notification regarding the potential errors. One would have been motivated to make such a combination to reduce the time and cost associated with manual review.
As to dependent claim 10, Balzer teaches the system of claim 9, Balzer does not appear to expressly teach wherein the one or more quality control bots, when executed by the one or more processors, further causes the one or more processors to:
extract, using a machine learning model trained to perform optical character recognition, text data from one or more scanned images in the petitioner data; and
compare the text data to data in the immigration petition.
Pretorius teaches extract, using a machine learning model trained to perform optical character recognition, text data from one or more scanned images in the petitioner data; and compare the text data to data in the immigration petition (“According to various embodiments, finalizing the immigration application may involve submitting the application for review by one or more reviewers. Such reviewers may include, but are not limited to: an automated review process, … As part of the review process, one or more errors or omissions may be identified and corrected.” Paragraph 0034. Examiner notes that automated document review uses AI, machine learning and natural language processing (NLP) to analyze, and extract key information from large volumes of text, and do comparison).
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 the system of Balzer to comprise wherein the one or more quality control bots, when executed by the one or more processors, further causes the one or more processors to: extract, using a machine learning model trained to perform optical character recognition, text data from one or more scanned images in the petitioner data; and compare the text data to data in the immigration petition. One would have been motivated to make such a combination to reduce the time and cost associated with manual review.
Claims 11-20 are substantially the same as claims 1-10 and are therefore rejected under the same rational as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Voicu et al. US 20250026022 A1 teaches system and methods of providing alternative robotic form-filling activities.
Bakshi et al. US 20240378078 A1 teaches arrangement of bot hubs to a bot orchestrator on a virtual bot host server to cause the bot orchestrator to instantiate the bots to form the determined arrangement of bot hubs and to process tasks from the first workflow using the at least one bot.
Vemulapalli et al. US 20230409304 A1 Orchestration For Robotic Process Automation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHELET SHIBEROU whose telephone number is (571)270-7493. The examiner can normally be reached Monday-Friday 9:00 AM-5:00 PM Eastern Time.
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/MAHELET SHIBEROU/Primary Examiner, Art Unit 2171