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 .
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wicaksono (US Patent Application Publication No. 2022/0101220), and further in view of Govind (US Patent Application Publication No. 2012/0130765).
Regarding claim 1, Wicaksono teaches a method of constraint generation using large language models, the method comprising:
configuring, by a computing system, a first large language model of a knowledge base based on a first master prompt (Paragraphs 0043-0045, 0055-0056, 0059, 0062, 0069 configuring/ training NLP keywords/topics, and priority models with business information);
configuring, by the computing system, a second large language model of a workplan builder based on a second master prompt (Paragraphs 0046-0047, 0050, 0057-0058, 0060 training prioritization model with business plan to prioritize, and training optimization module with business constraints);
providing, by the computing system, a set of predefined constraint queries to the knowledge base; generating, by the computing system, a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model (Paragraphs 0062, 0066-0073 using models after training by providing incoming communications, and generating priority scores from urgency/importance, and also modified priority scores based on business constraints);
providing, by the computing system, the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder; generating, by the computing system, a set of second responses to the set of predefined constraint queries based on the second large language model (Paragraphs 0063, 0075 using SLA and other business criteria and identifying multiple selected candidate agents based on priority scores); and
determining, by the computing system, workplan constraints based on the set of second responses (Paragraphs 0060, 0064-0065, 0088 determining agent constraints for selected agents for workflow routing) (Paragraphs 0003, 0014-0093 for complete details).
Wicaksono does not explicitly teach orchestrator, but it would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention that Deferrable Workflow Module (Fig. 2) orchestrates the functionalities of various models.
However, in the similar field, Govind teaches configuring, by a computing system, a model of a knowledge base orchestrator (scheduling model) based on a first master prompt (Paragraph 0053); configuring, by the computing system, a second model of a workplan builder orchestrator (scheduling model) based on a second master prompt (Paragraph 0054); providing, by the computing system, a set of predefined constraint queries to the knowledge base orchestrator (scheduling model); and determining, by the computing system, workplan constraints based on the set of predefined constraint queries (Paragraphs 0056-0060) (Paragraphs 0011-0067 for details)..
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Wicaksono to include orchestrator for scheduling and queries as taught by Govind in order to enable a system that “solves the model using the objectives and constraints” (Govind, Paragraph 0057).
Regarding claim 2, Wicaksono teaches generating the knowledge base from documentation received from an end user (Paragraphs 0071-0073 using document to generate business related topics and urgency/priority knowledge).
Regarding claim 3, Wicaksono teaches generating the set of second responses comprises generating the set of second responses in a format defined by the second master prompt for the second large language model (Paragraphs 0062-0063 providing importance/urgency score in a format required by priority model).
Regarding claim 4, Wicaksono teaches the format comprises a JavaScript Object Notation (JSON) format (Paragraph 0015 JSON format as one of the formats).
Regarding claim 5, Wicaksono teaches an interface to store a workplan defined by the workplan constraints to a management system (Paragraphs 0049, 0063-0065 optimized workflow to router, Note: It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to use application programming interface (API) call communicating between modules from multiple techniques of message, direct procedure calls, remote procedure calls (RPC), and application programming interface (API) as was well known in the art.).
Regarding claim 6, Wicaksono teaches generating, by the computing system, an agent schedule based on the workplan constraints (Paragraphs 0049, 0063-0065, 0090).
Regarding claim 7, Wicaksono teaches the second master prompt requires responses to be one-word answers (Paragraphs 0056, high/ low urgency, 0069 high/ coarse level, travel/ non-travel).
Regarding claim 8, Wicaksono teaches a computing system for constraint generating using large language models, the system (Figs. 1-3) comprising:
at least one processor; and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the computing system (Paragraphs 0016-0017, 0023-0048) to:
configure a first large language model of a knowledge base based on a first master prompt (Paragraphs 0043-0045, 0055-0056, 0059, 0062, 0069 configuring/ training NLP keywords/topics, and priority models with business information);
configure a second large language model of a workplan builder based on a second master prompt (Paragraphs 0046-0047, 0050, 0057-0058, 0060 training prioritization model with business plan to prioritize, and training optimization module with business constraints);
provide a set of predefined constraint queries to the knowledge base; generate a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model (Paragraphs 0062, 0066-0073 using models after training by providing incoming communications, and generating priority scores from urgency/importance, and also modified priority scores based on business constraints);
provide the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder; generate a set of second responses to the set of predefined constraint queries based on the second large language model (Paragraphs 0063, 0075 using SLA and other business criteria and identifying multiple selected candidate agents based on priority scores); and
determine workplan constraints based on the set of second responses (Paragraphs 0060, 0064-0065, 0088 determining agent constraints for selected agents for workflow routing) (Paragraphs 0003, 0014-0093 for complete details).
Wicaksono does not explicitly teach orchestrator, but it would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention that Deferrable Workflow Module (Fig. 2) orchestrates the functionalities of various models.
However, in the similar field, Govind teaches configuring, by a computing system, a model of a knowledge base orchestrator (scheduling model) based on a first master prompt (Paragraph 0053); configuring, by the computing system, a second model of a workplan builder orchestrator (scheduling model) based on a second master prompt (Paragraph 0054); providing, by the computing system, a set of predefined constraint queries to the knowledge base orchestrator (scheduling model); and determining, by the computing system, workplan constraints based on the set of predefined constraint queries (Paragraphs 0056-0060) (Paragraphs 0011-0067 for details)..
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Wicaksono to include orchestrator for scheduling and queries as taught by Govind in order to enable a system that “solves the model using the objectives and constraints” (Govind, Paragraph 0057).
Regarding claim 9, Wicaksono teaches to generate the knowledge base from documentation received from an end user (Paragraphs 0071-0073 using document to generate business related topics and urgency/priority knowledge).
Regarding claim 10, Wicaksono teaches to generate the set of second responses comprises to generate the set of second responses in a format defined by the second master prompt for the second large language model (Paragraphs 0062-0063 providing importance/urgency score in a format required by priority model).
Regarding claim 11, Wicaksono teaches the format comprises a JavaScript Object Notation (JSON) format (Paragraph 0015 JSON format as one of the formats).
Regarding claim 12, Wicaksono teaches an interface to store a workplan defined by the workplan constraints to a management system (Paragraphs 0049, 0063-0065 optimized workflow to router, Note: It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to use application programming interface (API) call communicating between modules from multiple techniques of message, direct procedure calls, remote procedure calls (RPC), and application programming interface (API) as was well known in the art.).
Regarding claim 13, Wicaksono teaches to generate an agent schedule based on the workplan constraints (Paragraphs 0049, 0063-0065, 0090).
Regarding claim 14, Wicaksono teaches the second master prompt requires responses to be one-word answers (Paragraphs 0056, high/ low urgency, 0069 high/ coarse level, travel/ non-travel).
Regarding claim 15, Wicaksono teaches one or more non-transitory machine-readable storage media comprising a plurality of instructions stored thereon that, in response to execution by a computing system, causes the computing system (Paragraphs 0016-0017, 0023-0048) to:
configure a first large language model of a knowledge base based on a first master prompt (Paragraphs 0043-0045, 0055-0056, 0059, 0062, 0069 configuring/ training NLP keywords/topics, and priority models with business information);
configure a second large language model of a workplan builder based on a second master prompt (Paragraphs 0046-0047, 0050, 0057-0058, 0060 training prioritization model with business plan to prioritize, and training optimization module with business constraints);
provide a set of predefined constraint queries to the knowledge base; generate a set of first responses to the set of predefined constraint queries based on a knowledge base and the first large language model (Paragraphs 0062, 0066-0073 using models after training by providing incoming communications, and generating priority scores from urgency/importance, and also modified priority scores based on business constraints);
provide the set of predefined constraint queries and the respective first responses to the set of predefined constraint queries to the workplan builder; generate a set of second responses to the set of predefined constraint queries based on the second large language model (Paragraphs 0063, 0075 using SLA and other business criteria and identifying multiple selected candidate agents based on priority scores); and
determine workplan constraints based on the set of second responses (Paragraphs 0060, 0064-0065, 0088 determining agent constraints for selected agents for workflow routing) (Paragraphs 0003, 0014-0093 for complete details).
Wicaksono does not explicitly teach orchestrator, but it would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention that Deferrable Workflow Module (Fig. 2) orchestrates the functionalities of various models.
However, in the similar field, Govind teaches configuring, by a computing system, a model of a knowledge base orchestrator (scheduling model) based on a first master prompt (Paragraph 0053); configuring, by the computing system, a second model of a workplan builder orchestrator (scheduling model) based on a second master prompt (Paragraph 0054); providing, by the computing system, a set of predefined constraint queries to the knowledge base orchestrator (scheduling model); and determining, by the computing system, workplan constraints based on the set of predefined constraint queries (Paragraphs 0056-0060) (Paragraphs 0011-0067 for details)..
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Wicaksono to include orchestrator for scheduling and queries as taught by Govind in order to enable a system that “solves the model using the objectives and constraints” (Govind, Paragraph 0057).
Regarding claim 16, Wicaksono teaches to generate the knowledge base from documentation received from an end user (Paragraphs 0071-0073 using document to generate business related topics and urgency/priority knowledge).
Regarding claim 17, Wicaksono teaches to generate the set of second responses comprises to generate the set of second responses in a format defined by the second master prompt for the second large language model (Paragraphs 0062-0063 providing importance/urgency score in a format required by priority model).
Regarding claim 18, Wicaksono teaches the format comprises a JavaScript Object Notation (JSON) format (Paragraph 0015 JSON format as one of the formats).
Regarding claim 19, Wicaksono teaches an interface to store a workplan defined by the workplan constraints to a management system (Paragraphs 0049, 0063-0065 optimized workflow to router, Note: It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to use application programming interface (API) call communicating between modules from multiple techniques of message, direct procedure calls, remote procedure calls (RPC), and application programming interface (API) as was well known in the art.).
Regarding claim 20, Wicaksono teaches the second master prompt requires responses to be one-word answers (Paragraphs 0056, high/ low urgency, 0069 high/ coarse level, travel/ non-travel).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEMANT PATEL whose telephone number is (571)272-8620. The examiner can normally be reached M-F 8:00 AM - 4:30 PM EST.
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, Carolyn Edwards can be reached at 571-270-7136. 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.
HEMANT PATEL
Primary Examiner
Art Unit 2694
/HEMANT S PATEL/ Primary Examiner, Art Unit 2694
