Notice of Pre-AIA or AIA Status
This Non-Final communication is in response to application no. 18/747,291 filed on 6/18/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 5-6, 12-13, and 19-20 are objected to because of the following informalities: In the limitations about automatically disapproving the changes it states: “indicating that that the second request is not allowed to be updated”. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a
judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly
more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-20 are within the four statutory (a process, machine, manufacture or composition of
matter.) Claims 1-7, 15-20 describe a machine and 8-14 describes a process.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
evaluate the first plurality of guidelines associated with the first request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a first plurality of knowledge areas based on the first plurality of guidelines, wherein: a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a first plurality of entry fields relating to the first knowledge area of the first plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a second plurality of entry fields relating to the second knowledge area of the first plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the first request comprising the first plurality of entry fields and the second plurality of entry fields; and (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
a machine learning algorithm configured to analyze and structure data in accordance with one or more machine learning models; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
receive a first order to generate a first request, wherein: the first order comprises a first plurality of guidelines associated with the first request; and the first request requests information associated with a communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with the one or more machine learning models; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the first request to a data aggregator configured to compile the information associated with the communication device type. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional elements “a machine learning algorithm…” and “in accordance…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “receive…” and “transmit…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, recites an additional abstract idea:
evaluate the second plurality of guidelines associated with the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
evaluate the plurality of changes to the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
in response to evaluating the plurality of changes, approve the plurality of changes to the second request based on one or more rules and policies indicating that the second request is allowed to be updated; (This is an abstract idea of a "Mental Process." The "approve" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The approval could be made manually by an individual.)
in response to approving the plurality of changes, determine a fifth knowledge area of the second plurality of knowledge areas referencing a fifth plurality of performance aspects of the communication device type; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fifth plurality of entry fields relating to the fifth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
update the second request to comprise the third plurality of entry fields, the fourth plurality of entry fields, and the fifth plurality of entry fields; and (This is an abstract idea of a "Mental Process." The "update" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The update could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with the machine learning algorithm; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to a reviewing entity; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
transmit the second request to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receive a second order…”, “transmit the second request to a reviewing entity”, “receive review feedback…” and “transmit the second request to the data aggregator” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “in accordance…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, recites an additional abstract idea:
evaluate the second plurality of guidelines associated with the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
evaluate the plurality of changes to the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
in response to evaluating the plurality of changes, disapprove the plurality of changes to the second request based on one or more rules and policies indicating that the second request is allowed to be updated; (This is an abstract idea of a "Mental Process." The "disapprove" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The disapproval could be made manually by an individual.)
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate a report indicating that the second request is not approved to proceed to the data aggregator; and (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with one or more machine learning models; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to a reviewing entity; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
transmit the report to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receive a second order…”, “transmit the second request to a reviewing entity”, “receive review feedback…” and “transmit the report to the data aggregator” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “in accordance…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 1, recites an additional abstract idea:
evaluate the second plurality of guidelines associated with the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
determine whether review feedback is received from the reviewing entity within a time period; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
in response to determining that the review feedback is absent from the reviewing entity within the time period, generate a report indicating that the second request is not approved to proceed to the data aggregator; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with one or more machine learning models; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to a reviewing entity; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
transmit the report to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receive a second order…”, “transmit the second request to a reviewing entity”, and “transmit the report to the data aggregator” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “in accordance…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, recites an additional abstract idea:
evaluate the second plurality of guidelines associated with the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
evaluate the plurality of changes to the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate a report indicating that the second request is not approved to proceed to the data aggregator; and (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with one or more machine learning models; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to a reviewing entity; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
In response to evaluating the plurality of changes, automatically disapprove the plurality of changes to the second request based on one or more rules and policies indicating that that the second request is not allowed to be updated; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the report to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receive a second order…”, “transmit the second request to a reviewing entity”, “receive review feedback…” and “transmit the report to the data aggregator” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “in accordance…” and “in response to evaluating…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, recites an additional abstract idea:
evaluate the second plurality of guidelines associated with the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
evaluate the plurality of changes to the second request (This is an abstract idea of a "Mental Process." The "evaluate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The evaluation could be made manually by an individual.)
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator (This is an abstract idea of a "Mental Process." The "determine" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
generate a report indicating that the second request is not approved to proceed to the data aggregator; and (This is an abstract idea of a "Mental Process." The "generate" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in accordance with one or more machine learning models; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to a reviewing entity; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
In response to evaluating the plurality of changes, automatically disapprove the plurality of changes to the second request based on historical data associated with the communication device type indicating that that the second request is not allowed to be updated; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the report to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “receive a second order…”, “transmit the second request to a reviewing entity”, “receive review feedback…” and “transmit the report to the data aggregator” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional elements “in accordance…” and “in response to evaluating…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
train the machine learning algorithm to dynamically associate the first plurality of entry fields and the second plurality of entry fields in the first request to the first plurality of guidelines; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
after using the first request to train the machine learning algorithm, receive a second order to generate a second request, wherein: the second order comprises the first plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the first plurality of guidelines associated with the second request in accordance with the one or more machine learning models; and in response to evaluating the first plurality of guidelines, automatically generate the second request comprising the first plurality of entry fields and the second plurality of entry fields; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
transmit the second request to the data aggregator. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements “train…” and “execute the machine learning algorithm…” are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “receive…” and “transmit…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 7 is ineligible.
With respect to claim 8:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 1. Therefore, claim 8 is ineligible.
With respect to claim 9:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 9. Therefore, claim 9 is ineligible.
With respect to claim 10:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 3. Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 4. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 5. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 6. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 7. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 1. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 9. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 3. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 4. Therefore, claim 18 is ineligible.
With respect to claim 19:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 5. Therefore, claim 19 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 6. Therefore, claim 20 is ineligible.
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, 7-8, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable by Tommasi (US 2020/0335183 A1) in view of Indurkar (US 10,798,574 B1).
Regarding claim 1, Tommasi teaches:
An apparatus, comprising: a memory operable to store: a machine learning algorithm configured to analyze and structure data in accordance with one or more machine learning models; and ([0050] “The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.” And [0019] “A machine learning mechanism may use the heterogeneous historical input and/or feedback information to build customized questionnaire from dynamically generated customized question provided by the dialog system based on each respective response/answer to a previous question/query.”)
a processor communicatively coupled to the memory and configured to: ([0050] “The components of computer system/server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16.”)
receive a first order to generate a first request, wherein: the first order comprises a first plurality of guidelines associated with the first request; and the first request requests information associated with a communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the first order to generate the first request, execute the machine learning algorithm to: ([0019] “A machine learning mechanism may use the heterogeneous historical input and/or feedback information to build customized questionnaire from dynamically generated customized question provided by the dialog system based on each respective response/answer to a previous question/query.”)
evaluate the first plurality of guidelines associated with the first request in accordance with the one or more machine learning models; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the first plurality of guidelines associated with the first request, determine a first plurality of knowledge areas based on the first plurality of guidelines, wherein: a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” Tommasi teaches knowledge areas but not related to performance aspects of a communication device)
determine a first plurality of entry fields relating to the first knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a second plurality of entry fields relating to the second knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the first request comprising the first plurality of entry fields and the second plurality of entry fields; and ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Tommasi does not teach:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type;
transmit the first request to a data aggregator configured to compile the information associated with the communication device type.
However, Indurkar does:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
transmit the first request to a data aggregator configured to compile the information associated with the communication device type. (Col 10 lines 32-42 “For example, an original equipment manufacturer (OEM) may provide an event via an API to the certification application 126 in response to generation of a sales manifest. The event from the OEM may identify the mobile communication device 102 by mobile equipment identifier (MEID), equipment serial number, or another identifier. The event may also include a subscriber identity module (SIM) identity. The certification application 126 may store the event from the OEM as associated with the mobile communication device 102 in the data store 128.”)
Tommasi and Indurkar are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar. One would be motivated to do this better understanding of the devices during certification.
Regarding claim 7, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
train the machine learning algorithm to dynamically associate the first plurality of entry fields and the second plurality of entry fields in the first request to the first plurality of guidelines; ([0026] “Improve knowledge and learn with each iteration and interaction through machine learning processes;”)
after using the first request to train the machine learning algorithm, receive a second order to generate a second request, wherein: the second order comprises the first plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the first plurality of guidelines associated with the second request in accordance with the one or more machine learning models; and ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the first plurality of guidelines, automatically generate the second request comprising the first plurality of entry fields and the second plurality of entry fields; and ([0064] “one or more existing/previous questions and questionnaires and responses/answers to various queries and additional feedback. Using the collected input data, the cognitive system may generate/build questions and/or select questions from previous interactions with similar clients (e.g., patients, etc.) and/or identify/determine one or more relevant questions to include in questionnaire.”)
transmit the second request to the data aggregator. ([0064] “One or more queries/questions may be issued/asked to one or more clients (e.g., doctor, patient, domain expert) to fill in any missing, uncertain, and/or incomplete information relating to the client and/or client responses (e.g., to validate the explanations and augment the knowledge base/domain knowledge if the knowledge base/domain knowledge is insufficient to generate a complete and/or complete explanation with a certain confidence). The intelligent system may use the additional knowledge to improve the knowledge base(s), either by adding new questions and/or by updating existent questionnaires (e.g., existent questions or relations in the knowledge base and/or in the clinical data) if the existent relations were (partially) outdated or invalid.”)
Regarding claim 8, Tommasi teaches:
A method, comprising: ([0003] “In one embodiment, by way of example only, a method for implementing intelligent generation of questions or questionnaires, again by a processor, is provided”)
receiving a first order to generate a first request, wherein: the first order comprises a first plurality of guidelines associated with the first request; and the first request requests information associated with a communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the first order to generate the first request, execute the machine learning algorithm to: ([0019] “A machine learning mechanism may use the heterogeneous historical input and/or feedback information to build customized questionnaire from dynamically generated customized question provided by the dialog system based on each respective response/answer to a previous question/query.”)
evaluating the first plurality of guidelines associated with the first request in accordance with the one or more machine learning models; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the first plurality of guidelines associated with the first request, determining a first plurality of knowledge areas based on the first plurality of guidelines, wherein: a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” Tommasi teaches knowledge areas but not related to performance aspects of a communication device)
determining a first plurality of entry fields relating to the first knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determining a second plurality of entry fields relating to the second knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generating the first request comprising the first plurality of entry fields and the second plurality of entry fields; and ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Tommasi does not teach:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type;
transmitting the first request to a data aggregator configured to compile the information associated with the communication device type.
However, Indurkar does:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
transmitting the first request to a data aggregator configured to compile the information associated with the communication device type. (Col 10 lines 32-42 “For example, an original equipment manufacturer (OEM) may provide an event via an API to the certification application 126 in response to generation of a sales manifest. The event from the OEM may identify the mobile communication device 102 by mobile equipment identifier (MEID), equipment serial number, or another identifier. The event may also include a subscriber identity module (SIM) identity. The certification application 126 may store the event from the OEM as associated with the mobile communication device 102 in the data store 128.”)
Tommasi and Indurkar are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar. One would be motivated to do this better understanding of the devices during certification.
Regarding claim 14, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Regarding claim 15, Tommasi teaches:
A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to: ([0083] “The functionality 500 may be implemented as a method executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine-readable storage medium”)
receive a first order to generate a first request, wherein: the first order comprises a first plurality of guidelines associated with the first request; and the first request requests information associated with a communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the first order to generate the first request, execute the machine learning algorithm to: ([0019] “A machine learning mechanism may use the heterogeneous historical input and/or feedback information to build customized questionnaire from dynamically generated customized question provided by the dialog system based on each respective response/answer to a previous question/query.”)
evaluate the first plurality of guidelines associated with the first request in accordance with the one or more machine learning models; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the first plurality of guidelines associated with the first request, determine a first plurality of knowledge areas based on the first plurality of guidelines, wherein: a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” Tommasi teaches knowledge areas but not related to performance aspects of a communication device)
determine a first plurality of entry fields relating to the first knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a second plurality of entry fields relating to the second knowledge area of the first plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the first request comprising the first plurality of entry fields and the second plurality of entry fields; and ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Tommasi does not teach:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type;
transmit the first request to a data aggregator configured to compile the information associated with the communication device type.
However, Indurkar does:
a first knowledge area of the first plurality of knowledge areas referencing a first plurality of performance aspects of the communication device type; and a second knowledge area of the first plurality of knowledge areas referencing a second plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
transmit the first request to a data aggregator configured to compile the information associated with the communication device type. (Col 10 lines 32-42 “For example, an original equipment manufacturer (OEM) may provide an event via an API to the certification application 126 in response to generation of a sales manifest. The event from the OEM may identify the mobile communication device 102 by mobile equipment identifier (MEID), equipment serial number, or another identifier. The event may also include a subscriber identity module (SIM) identity. The certification application 126 may store the event from the OEM as associated with the mobile communication device 102 in the data store 128.”)
Tommasi and Indurkar are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar. One would be motivated to do this better understanding of the devices during certification.
Claims 2-3, 5-6, 9-10, 12-13, 16-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable by Tommasi in view of Indurkar and Moss (US 2008/0059520 A1).
Regarding claim 2, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the second plurality of guidelines associated with the second request in accordance with the machine learning algorithm; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the second plurality of guidelines associated with the second request, determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” )
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
determine a fifth plurality of entry fields relating to the fifth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
Indurkar further teaches:
a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
in response to approving the plurality of changes, determine a fifth knowledge area of the second plurality of knowledge areas referencing a fifth plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
Neither Tommasi nor Indurkar teach the rest of the limitations. However, Moss does:
transmit the second request to a reviewing entity; ([0029] “Once created, validation questionnaires 208 are electronically distributed, typically through network 204, to one or more predetermined questionnaire reviewers 310. Questionnaire reviewers may include, but are not limited to, policy makers or enforcers chosen from candidate reviewers 222.”)
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to receiving the review feedback from the reviewing entity, execute the machine learning algorithm to: evaluate the plurality of changes to the second request in accordance with the machine learning algorithm; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to evaluating the plurality of changes, approve the plurality of changes to the second request based on one or more rules and policies indicating that the second request is allowed to be updated; ([0030] “In an embodiment of the invention, the policy exists and policy validator 206 is implemented to verify its validity. If the policy is found to lack validity, it can be revised, revalidated, re-approved, and released for implementation. In another embodiment of the invention, the policy is proposed and policy validator 206 is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
update the second request to comprise the third plurality of entry fields, the fourth plurality of entry fields, and the fifth plurality of entry fields; and ([0030] “In an embodiment of the invention, the policy exists and policy validator 206 is implemented to verify its validity. If the policy is found to lack validity, it can be revised, revalidated, re-approved, and released for implementation. In another embodiment of the invention, the policy is proposed and policy validator 206 is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
transmit the second request to the data aggregator. ([0030] “In another embodiment of the invention, the policy is proposed and policy validator 206 is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
Tommasi, Indurkar and Moss are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss. One would be motivated to do this to create better policies (Moss [0004]).
Regarding claim 3, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the second plurality of guidelines associated with the second request in accordance with the machine learning algorithm; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the second plurality of guidelines associated with the second request, determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” )
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Indurkar further teaches:
a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
Neither Tommasi nor Indurkar teach the rest of the limitations. However, Moss does:
transmit the second request to a reviewing entity; ([0029] “Once created, validation questionnaires 208 are electronically distributed, typically through network 204, to one or more predetermined questionnaire reviewers 310. Questionnaire reviewers may include, but are not limited to, policy makers or enforcers chosen from candidate reviewers 222.”)
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to receiving the review feedback from the reviewing entity, execute the machine learning algorithm to: evaluate the plurality of changes to the second request in accordance with the machine learning algorithm; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to evaluating the plurality of changes, disapprove the plurality of changes to the second request based on one or more rules and policies; ([0030] “In an embodiment of the invention, the policy exists and policy validator 206 is implemented to verify its validity. If the policy is found to lack validity, it can be revised, revalidated, re-approved, and released for implementation.”)
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator; generate a report indicating that the second request is not approved to proceed to the data aggregator; and transmit the report to the data aggregator. ([0006] “If the policy is found to lack validity, it can be revised, revalidated, and released for implementation. In another embodiment of the invention, the policy is proposed and the policy validator is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
Tommasi, Indurkar and Moss are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss. One would be motivated to do this to create better policies (Moss [0004]).
Regarding claim 5, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the second plurality of guidelines associated with the second request in accordance with the machine learning algorithm; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the second plurality of guidelines associated with the second request, determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” )
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Indurkar further teaches:
a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
Neither Tommasi nor Indurkar teach the rest of the limitations. However, Moss does:
transmit the second request to a reviewing entity; ([0029] “Once created, validation questionnaires 208 are electronically distributed, typically through network 204, to one or more predetermined questionnaire reviewers 310. Questionnaire reviewers may include, but are not limited to, policy makers or enforcers chosen from candidate reviewers 222.”)
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to receiving the review feedback from the reviewing entity, execute the machine learning algorithm to: evaluate the plurality of changes to the second request in accordance with the machine learning algorithm; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to evaluating the plurality of changes, automatically disapprove the plurality of changes to the second request based on one or more rules and policies indicating that the second request is not allowed to be updated; ([0030] “In an embodiment of the invention, the policy exists and policy validator 206 is implemented to verify its validity. If the policy is found to lack validity, it can be revised, revalidated, re-approved, and released for implementation.”)
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator; generate a report indicating that the second request is not approved to proceed to the data aggregator; and transmit the report to the data aggregator. ([0006] “If the policy is found to lack validity, it can be revised, revalidated, and released for implementation. In another embodiment of the invention, the policy is proposed and the policy validator is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
Tommasi, Indurkar and Moss are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss. One would be motivated to do this to create better policies (Moss [0004]).
Regarding claim 6, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the second plurality of guidelines associated with the second request in accordance with the machine learning algorithm; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the second plurality of guidelines associated with the second request, determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” )
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
generate the second request comprising the third plurality of entry fields and the fourth plurality of entry fields; ([0071] “The questionnaire composer 414 may assemble a series/flow of questions (questionnaire) needed to obtain detailed, relevant, and/or meaningful information from the client 402 in a human-consumable format. The series/flow of questions may be modified based on the client's 402 answers to a previous question and/or series of questions. The questionnaire composer 414 may use the output of the question generator 416 and the similarity analysis 418 components to build, generate, create, the series/flow of questions that maximizes the probability of collecting an intended/desired information from the client 402 (e.g., patient, business/marking entity/customer).”)
Indurkar further teaches:
a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
Neither Tommasi nor Indurkar teach the rest of the limitations. However, Moss does:
transmit the second request to a reviewing entity; ([0029] “Once created, validation questionnaires 208 are electronically distributed, typically through network 204, to one or more predetermined questionnaire reviewers 310. Questionnaire reviewers may include, but are not limited to, policy makers or enforcers chosen from candidate reviewers 222.”)
receive review feedback from the reviewing entity, the review feedback comprising a plurality of changes to the second request; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to receiving the review feedback from the reviewing entity, execute the machine learning algorithm to: evaluate the plurality of changes to the second request in accordance with the machine learning algorithm; ([0030] “Responses from questionnaire reviewers 210 to validation questionnaires 208 are aggregated, analyzed and scored by policy validator 206 to determine the validity of the policy.”)
in response to evaluating the plurality of changes, automatically disapprove the plurality of changes to the second request based on historical data associated with the communication device type indicating that the second request is not allowed to be updated; ([0030] “In an embodiment of the invention, the policy exists and policy validator 206 is implemented to verify its validity. If the policy is found to lack validity, it can be revised, revalidated, re-approved, and released for implementation.” And Tommasi teaches using historical data in forming the questions/reviewing them [0067] “That is, the interaction history 426 may refer to historical interactions with domain experts and/or clients (e.g., patients) using previously generated questionnaires such as, for example, by actively learning if the questions are relevant for a given context based on collected feedback.”))
in response to disapproving the plurality of changes, determine that the second request is not approved to proceed to the data aggregator; generate a report indicating that the second request is not approved to proceed to the data aggregator; and transmit the report to the data aggregator. ([0006] “If the policy is found to lack validity, it can be revised, revalidated, and released for implementation. In another embodiment of the invention, the policy is proposed and the policy validator is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
Tommasi, Indurkar and Moss are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss. One would be motivated to do this to create better policies (Moss [0004]).
Regarding claim 9, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 10, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 12, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 13, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 16, Tommasi in view of Indurkar teaches claim 15 as outlined above. The claim recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 17, Tommasi in view of Indurkar teaches claim 15 as outlined above. The claim recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 19, Tommasi in view of Indurkar teaches claim 15 as outlined above. The claim recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 20, Tommasi in view of Indurkar teaches claim 15 as outlined above. The claim recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable by Tommasi in view of Indurkar, Moss and VanLoo (US 2021/0383408 A1).
Regarding claim 4, Tommasi in view of Indurkar teaches claim 1 as outlined above. Tommasi further teaches:
receive a second order to generate a second request, wherein: the second order comprises a second plurality of guidelines associated with the second request; and the second request requests additional information associated with the communication device type; ([0014] “One or more customized queries may be generated for according to a client profile, similar profiles of alternative clients, one or more historical interactions with the alternative clients, one or more goals defined by a domain expert, a domain knowledge, historical queries, or a combination thereof using a machine learning operation.”)
in response to receiving the second order to generate the second request, execute the machine learning algorithm to: evaluate the second plurality of guidelines associated with the second request in accordance with the machine learning algorithm; ([0067] “The automatic questionnaire generator 410 may be in association with a domain knowledge 422, one or more client profiles 424, an interaction history 426 (e.g., history interaction between a defined client and other clients), goals 428 (e.g., health state/wellness goals), and/or initial/bootstrap questions & questionnaires (e.g., existing questions and/or questionnaires). “)
in response to evaluating the second plurality of guidelines associated with the second request, determine a second plurality of knowledge areas based on the second plurality of guidelines, wherein: ([0075] “In one aspect, domain knowledge 422 may include structured data, such as, for example, knowledge graphs, various models (e.g., list of drugs, treatments and/or side effects, etc.), unstructured data such as, for example, books and scientific literature, etc. In one aspect, the domain knowledge 422 may be an ontology of concepts representing a domain of knowledge. A thesaurus or ontology may be used as the domain knowledge 422. In one aspect, the term “domain” is a term intended to have its ordinary meaning. In addition, the term “domain” may include an area of expertise for a system or a collection of material, information, content and/or other resources related to a particular subject or subjects. A domain can refer to information related to any particular subject matter or a combination of selected subjects.” )
determine a third plurality of entry fields relating to the third knowledge area of the second plurality of knowledge areas; and ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
determine a fourth plurality of entry fields relating to the fourth knowledge area of the second plurality of knowledge areas; ([0072] “In one aspect, the question generator 416 may provide arguments (e.g., positive evidence and/or negative evidence) to provide positive or negative support/reasons for the customized questions. The arguments may provide the base data (e.g., raw material/information) to build, generate, create, and/or re-generate the customized questions.”)
Indurkar further teaches:
a third knowledge area of the second plurality of knowledge areas referencing a third plurality of performance aspects of the communication device type; and (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
a fourth knowledge area of the second plurality of knowledge areas referencing a fourth plurality of performance aspects of the communication device type; (Col 10 lines 15-21 “The certification application 126 may additionally or alternatively receive the mobile device performance information associated with the mobile communication device 102 from the device client 108 and store the mobile device performance information as associated with the mobile communication device 102 in the data store 128.”)
Neither Tommasi nor Indurkar teach the rest of the limitations. However, Moss does teach some of them:
transmit the second request to a reviewing entity; ([0029] “Once created, validation questionnaires 208 are electronically distributed, typically through network 204, to one or more predetermined questionnaire reviewers 310. Questionnaire reviewers may include, but are not limited to, policy makers or enforcers chosen from candidate reviewers 222.”)
generate a report indicating that the second request is not approved to proceed to the data aggregator; and transmit the report to the data aggregator. ([0006] “If the policy is found to lack validity, it can be revised, revalidated, and released for implementation. In another embodiment of the invention, the policy is proposed and the policy validator is implemented to predetermine its validity such that it can be revised as necessary prior to being approved and released for implementation.”)
Tommasi, Indurkar and Moss are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss. One would be motivated to do this to create better policies (Moss [0004]).
VanLoo teaches the rest of the limitations:
determine whether review feedback is received from the reviewing entity within a time period; in response to determining that the review feedback is absent from the reviewing entity within the time period, generate a report indicating that the second request is not approved to proceed to the data aggregator; and ([0058] “In some example embodiments, when the fraud prevention server 130 does not receive an indication from the medical consumer as to whether the authorization request was initiated by the medical consumer or not, a default action is taken after a predetermined period of time. The default action may be to allow the authorization request to proceed normally or to reject the authorization request.”)
Tommasi, Indurkar, Moss and VanLoo are considered analogous art to the claimed invention because they are in the same field of endeavor being machine learning/certification framework. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the questionnaire generation of Tommasi with the device performance knowledge base information of Indurkar with the human reviewer of Moss with the auto rejection after a certain amount of time of VanLoo. One would be motivated to do this to have a backup plan in the case where it is taking too long.
Regarding claim 11, Tommasi in view of Indurkar teaches claim 8 as outlined above. The claim recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 18, Tommasi in view of Indurkar teaches claim 15 as outlined above. The claim recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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