Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis is provided for the claims under the guidelines of MPEP 2106.
Regarding claim 1:
Step 1:
The claim recites “[a] system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to” the steps that follow. Thus the claim is to a machine, which is a statutory category of invention.
Step 2A prong 1:
The limitation “generate third data associated with the first application by analyzing the second data via natural language processing (NLP),” in its broadest reasonable interpretation, recites a mental process. No particular method of NLP analysis is described. A person could generate analysis data using NLP using judgment and evaluation.
The limitation (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the third data,” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine a threshold associated with an application using judgment.
The limitation (bold only) “determine, via the MLM and based on the fourth data, an updated first threshold associated with the second application,” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine an updated threshold associated with an application using judgment.
Thus, the claim recites an abstract idea.
Step 2A prong 2:
The further elements “receive first data associated with a first application,” “responsive to receiving the first data, retrieve second data associated with the first application,” and “responsive to transmitting the first threshold, receive fourth data associated with a second application” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further elements (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the third data” and (bold only) “and determine, via the MLM and based on the fourth data, an updated first threshold associated with the second application” recite the training and use of a machine learning model at a high level of generality. No particular model or method of training is described. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element “transmit the first threshold to a user” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
Thus, the additional elements merely recite the use of a computer as a tool to perform the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The further elements “receive first data associated with a first application,” “responsive to receiving the first data, retrieve second data associated with the first application,” and “responsive to transmitting the first threshold, receive fourth data associated with a second application” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The additional claim elements (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the third data” and (bold only) “and determine, via the MLM and based on the fourth data, an updated first threshold associated with the second application” recite mere instructions to apply the abstract idea.
The element “transmit the first threshold to a user” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 2:
For step 2A prong 1, claim 2 further limits claim 1 and the same elements in claim 2 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the first data corresponds to a failure of the first application” merely describes the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein the first data corresponds to a failure of the first application” merely describes the field of use to which the data applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 3:
For step 2A prong 1, claim 3 further limits claim 2 and the same elements in claim 3 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describes the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describes the field of use to which the data applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 4:
For step 2A prong 1, claim 4 further limits claim 2 and the same elements in claim 4 still recite an abstract idea. The further element “wherein the first threshold comprises a numerical value associated with resolving the failure” further limits the mental process identified in claim 1. but it remains a mental process. A person could determine a numerical value associated with resolving a failure using judgement. Thus the limitation adds to the abstract idea.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 5:
For step 2A prong 1, claim 5 further limits claim 1 and the same elements in claim 5 still recite an abstract idea.
The further element “wherein the first threshold comprises a threshold type and a threshold numerical value” further limits the mental process identified in claim 1. but it remains a mental process. A person could determine a type and a numerical value using judgement. Thus the limitation adds to the abstract idea.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 6:
For step 2A prong 1, claim 6 further limits claim 1 and the same elements in claim 6 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the fourth data comprises historical operational data” merely describes the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein the fourth data comprises historical operational data” merely describes the field of use to which the data applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 7:
For step 2A prong 1, claim 7 further limits claim 1 and the same elements in claim 7 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the instructions are further configured to cause the system to: receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the further element “wherein the instructions are further configured to cause the system to: receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 8:
For step 2A prong 1, claim 8 further limits claim 1 and the same elements in claim 8 still recite an abstract idea.
The further limitation “determine a first alert associated with the first application, the first alert based on the one or more attributes, wherein the first threshold corresponds to the first alert,” in its broadest reasonable interpretation, recites a mental process. No particular method of determination is described. A person could determine an alert using judgment. Thus the limitation adds to the abstract idea.
For step 2A prong 2, the further element “wherein the instructions are further configured to cause the system to: receive one or more attributes associated with the first application” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the further element “wherein the instructions are further configured to cause the system to: receive one or more attributes associated with the first application” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 9:
Step 1:
The claim recites “[a] system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to” the steps that follow. Thus the claim is to a machine, which is a statutory category of invention.
Step 2A prong 1:
The limitation (bold only) “determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application,” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine an updated threshold associated with an application using judgment.
The limitation “maintain the updated first threshold associated with the second application” in its broadest reasonable interpretation, recites a mental process. No particular maintenance is described. A person could maintain an updated threshold an updated threshold associated with an application using judgment.
Thus, the claim recites an abstract idea.
Step 2A prong 2:
The further elements “receive data associated with a second application” and “transmit a notification to the user, the notification requesting the user provide the first response based on the updated first threshold” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element “iteratively until a first response is received” recites the repetition of steps to induce a response, which is mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further elements (bold only) “determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application” recite the use of a machine learning model at a high level of generality. No particular model is described. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element “transmit a first threshold to a user, the first threshold associated with a first application” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
Thus, the additional elements merely recite the use of a computer as a tool to perform the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The further elements “receive data associated with a second application” and “transmit a notification to the user, the notification requesting the user provide the first response based on the updated first threshold” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element “iteratively until a first response is received” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The additional claim element (bold only) “determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application” recites mere instructions to apply the abstract idea.
The element “transmit a first threshold to a user, the first threshold associated with a first application” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 10:
For step 2A prong 1, claim 10 further limits claim 9 and the same elements in claim 10 still recite an abstract idea.
The further limitation “generate third data associated with the first application by analyzing the second data via natural language processing (NLP),” in its broadest reasonable interpretation, recites a mental process. No particular method of NLP analysis is described. A person could generate analysis data using NLP using judgment and evaluation.
The further limitation (bold only) “and train the MLM to determine the first threshold associated with the first application based on the third data” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine a threshold associated with the first application using judgment.
Thus the limitations add to the abstract idea.
Step 2A prong 2:
The further elements “receive first data associated with the first application” and “responsive to receiving the first data, retrieve second data associated with the first application”recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element (bold only) “train the MLM to determine the first threshold associated with the first application based on the third data” recites the training and use of a machine learning model at a high level of generality. No particular model or method of training is described. The element thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
Step 2B:
The further elements “receive first data associated with the first application” and “responsive to receiving the first data, retrieve second data associated with the first application” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The additional element (bold only) “train the MLM to determine the first threshold associated with the first application based on the third data” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 11:
For step 2A prong 1, claim 11 further limits claim 10 and the same elements in claim 11 still recite an abstract idea.
For step 2A prong 2, the further elements “wherein: the first data corresponds to a failure of the first application; and the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describe the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein: the first data corresponds to a failure of the first application; and the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describe the field of use to which the data applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 12:
For step 2A prong 1, claim 12 further limits claim 9 and the same elements in claim 12 still recite an abstract idea.
The further element “wherein the first threshold comprises a threshold type and a threshold numerical value” further limits the mental process identified in claim 1. but it remains a mental process. A person could determine a type and a numerical value using judgement. Thus the limitation adds to the abstract idea.
For step 2A prong 2, and step 2B, no further elements remain to be considered. The claim as a whole does not amount to significantly more than the recited judicial exception and is ineligible under 35 U.S.C. 101.
Regarding claim 13:
For step 2A prong 1, claim 13 further limits claim 9 and the same elements in claim 13 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the notification comprises one or more of an email, a SMS message, an MMS message, a web push notification, a mobile push notification, a queue- based notification, or combinations thereof” recites mere data output, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the further element “wherein the notification comprises one or more of an email, a SMS message, an MMS message, a web push notification, a mobile push notification, a queue- based notification, or combinations thereof” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 14:
For step 2A prong 1, claim 14 further limits claim 9 and the same elements in claim 14 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the instructions are further configured to cause the system to: receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the further element “wherein the instructions are further configured to cause the system to: receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 15:
For step 2A prong 1, claim 15 further limits claim 9 and the same elements in claim 15 still recite an abstract idea.
For step 2A prong 2, the further limitation “wherein the first and second applications are of a similar type of application” merely limits the claim to particular fields of use, ones in which the two applications are similar, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein the first and second applications are of a similar type of application” merely describes the field of use to which the claim applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 16:
Step 1:
The claim recites “A system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to.” Thus the claim is to a machine, which is a statutory category of invention.
The limitation (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the first data,” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine a threshold associated with the first application using judgment.
The limitation (bold only) “determine, via the MLM and based on the second data, an updated first threshold associated with the first application,” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine an updated threshold associated with the first application using judgment
Thus, the claim recites an abstract idea.
Step 2A prong 2:
The further elements “receive first data associated with a first application” and “retrieve second data associated with the first application” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further elements “the first data corresponding to a failure of the first application” and “the second data corresponding to a time period prior to a start time of the failure” merely describe the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
The further elements (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the first data” and (bold only) “determine, via the MLM and based on the second data, an updated first threshold associated with the first application” recite the training and use of a machine learning model at a high level of generality. No particular model or method of training is described. The elements thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
Thus, the additional elements merely connect the abstract idea to a field of use, recite the use of a computer as a tool to perform the abstract idea or recite insignificant extra-solution activity. Taken alone, the additional elements do not integrate the abstract idea into a practical application. Considering the elements together as an ordered combination adds nothing that is not present from examining the elements individually. The elements, individually or together, do not describe an improvement in the functioning of technology.
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The further elements “receive first data associated with a first application” and “retrieve second data associated with the first application” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The further elements “the first data corresponding to a failure of the first application” and “the second data corresponding to a time period prior to a start time of the failure” merely describe the field of use to which the data applies, which does not provide an inventive concept.
The additional claim elements (bold only) “train a machine learning model (MLM) to determine a first threshold associated with the first application based on the first data” and (bold only) “determine, via the MLM and based on the second data, an updated first threshold associated with the first application” recite mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 17:
For step 2A prong 1, claim 17 further limits claim 16 and the same elements in claim 17 still recite an abstract idea. The further limitation (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine a threshold associated with an application using judgment. Thus the limitation adds to the abstract idea.
Step 2A prong 2:
The further elements “transmit the updated first threshold to a user” and “responsive to transmitting the updated first threshold, receive third data associated with a second application” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” recites the use of a machine learning model at a high level of generality. No particular model or method of training is described. The element thus merely recite the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The further elements “transmit the updated first threshold to a user” and “responsive to transmitting the updated first threshold, receive third data associated with a second application” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The additional element (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 18:
For step 2A prong 1, claim 18 further limits claim 16 and the same elements in claim 18 still recite an abstract idea.
The further limitation “generate fourth data associated with the first application by analyzing the third data via natural language processing (NLP),” in its broadest reasonable interpretation, recites a mental process. No particular method of NLP analysis is described. A person could generate analysis data using NLP using judgment and evaluation.
The further limitation “wherein determining the first threshold is further based on the fourth data” limits the data used in the mental process described in claim 16 but it remains a mental process.
Thus the limitations add to the abstract idea.
For step 2A prong 2, the further limitation “responsive to receiving the first data, retrieve third data associated with the first application” recites mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
For step 2B, the claim as a whole does not amount to significantly more than the recited judicial exception. The further limitation “responsive to receiving the first data, retrieve third data associated with the first application” recites mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 19:
For step 2A prong 1, claim 19 further limits claim 18 and the same elements in claim 19 still recite an abstract idea.
For step 2A prong 2, the further element “wherein the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describes the field of use to which the data applies, which does not integrate the abstract idea into a practical application.
For step 2B, the further element “wherein the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both” merely describes the field of use to which the data applies, which does not provide an inventive concept.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Regarding claim 20:
For step 2A prong 1, claim 20 further limits claim 16 and the same elements in claim 20 still recite an abstract idea.
The further limitation (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” in its broadest reasonable interpretation, recites a mental process. No particular determination method is described. A person could determine a threshold associated with an application using judgment.
The limitation “maintain the second threshold associated with the second application” in its broadest reasonable interpretation, recites a mental process. No particular maintenance is described. A person could maintain an updated threshold an updated threshold associated with an application using judgment.
Thus the limitations add to the abstract idea.
Step 2A prong 2:
The further element “transmit the updated first threshold to a user” recites mere data transmission, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further elements “receive third data associated with a second application” and “transmit a notification to the user, the notification requesting the user provide the first response based on the second threshold” recite mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
The further element (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” recites the use of a machine learning model at a high level of generality. No particular model is described. The element thus merely recites the use of a computer as a tool to perform the abstract idea, and are equivalent to adding the words “apply it” or the equivalent to the judicial exception (MPEP 2106.05(f)).
The further element “iteratively until a first response is received” recites the repetition of steps to induce a response, which is mere data gathering, which is insignificant extra-solution activity (MPEP 2106.05(g)).
Step 2B:
The claim as a whole does not amount to significantly more than the recited judicial exception.
The further element “transmit the updated first threshold to a user” recites mere data transmission, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The elements “receive third data associated with a second application,” “transmit a notification to the user, the notification requesting the user provide the first response based on the second threshold,” and “iteratively until a first response is received” recite mere data gathering, which is recognized as well-understood, routine, and conventional activity in the art (see MPEP § 2106.05(d)(II)(i)).
The further element (bold only) “determine, via the MLM and based on the third data, a second threshold associated with the second application” recites mere instructions to apply the abstract idea.
Even when considered in combination, the additional elements connect the abstract idea to a field of use, represent mere instructions to apply the abstract idea to a computer or represent insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 16 rejected under 35 U.S.C. 102(a) (2) as being anticipated by Rane et al., US Pre-Grant Publication No. 2024/0303529 (hereafter Rane) .
Rane teaches:
“A system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to”: Rane, paragraph 0008, “In another particular aspect, a system [system ] for machine learning-based application management includes a memory and one or more processors [one or more processors] communicatively coupled to the memory [a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system].”
“receive first data associated with a first application, the first data corresponding to a failure of the first application”: Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [receive first data associated with a first application, wherein the first data corresponds to a failure of the first application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
“train a machine learning model (MLM) to determine a first threshold associated with the first application based on the first data”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine [train a machine learning model (MLM) … based on the first data] 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof”; Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [to determine a first threshold associated with the first application], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“retrieve second data associated with the first application, the second data corresponding to a time period prior to a start time of the failure”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery [retrieve second data associated with the first application, the second data corresponding to a time period prior to a start time of the failure] and investigation data, other information, or a combination thereof.”
“and determine, via the MLM and based on the second data, an updated first threshold associated with the first application”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated ]. […] Similarly, models described with reference to server 102 may also be implemented, in whole or in part, or may otherwise access, one or more ML models. To illustrate, the failure engine 120 [failure engine produces thresholds associated with applications, engine is retrained, hence producing new thresholds upon retraining], the recovery model 122, or both, may include or correspond to one or more NNs, one or more SVMs, one or more decision trees, one or more random forests, one or more regression models, one or more BNs, one or more DBNs, one or more NB models, one or more Gaussian processes, one or more HMMs, one or more regression models, or the like”; Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof”; Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application][based on the updated first threshold], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–8 and 17–19 rejected under 35 U.S.C. 103 over Rane in view of Kinney et al., US Pre-Grant Publication No. 2010/0023201 (hereafter Kinney).
Regarding claim 1:
Rane teaches:
“A system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to”: Rane, paragraph 0008, “In another particular aspect, a system [system ] for machine learning-based application management includes a memory and one or more processors [one or more processors] communicatively coupled to the memory [a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system].”
“receive first data associated with a first application”: Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [receive first data associated with a first application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
(bold only) “responsive to receiving the first data, retrieve second data associated with the first application”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [retrieve second data associated with the first application], other information, or a combination thereof.”
“generate third data associated with the first application by analyzing the second data via natural language processing (NLP)”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations [generate third data associated with the first application by analyzing the second data via natural language processing (NLP)] on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof.”
“train a machine learning model (MLM) to determine a first threshold associated with the first application based on the third data”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine [train a machine learning model (MLM) … based on the third data] 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof”; Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [to determine a first threshold associated with the first application], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“transmit the first threshold to a user”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores [transmit the first threshold to a user] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
(bold only) “responsive to transmitting the first threshold, receive fourth data associated with a second application; determine, via the MLM and based on the fourth data, an updated first threshold associated with the second application”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated using data from multiple applications, including fourth data associated with a second application]. […] Similarly, models described with reference to server 102 may also be implemented, in whole or in part, or may otherwise access, one or more ML models. To illustrate, the failure engine 120 [failure engine produces thresholds associated with applications, engine is retrained using data from multiple applications, hence producing new thresholds upon retraining, therefore determine, via the MLM and based on the fourth data, an updated first threshold associated with the second application], the recovery model 122, or both, may include or correspond to one or more NNs, one or more SVMs, one or more decision trees, one or more random forests, one or more regression models, one or more BNs, one or more DBNs, one or more NB models, one or more Gaussian processes, one or more HMMs, one or more regression models, or the like.”
Rane does not explicitly teach:
(bold only) “responsive to receiving the first data, retrieve second data associated with the first application”
(bold only) “responsive to transmitting the first threshold, receive fourth data associated with a second application”
Kinney teaches:
(bold only) “responsive to receiving the first data, retrieve second data associated with the first application”: Kinney, paragraph 0088, “In response to detecting the event [responsive to receiving the first data], the event is compared to a policy (operation 706). The policy is used to provide a capability to automatically determine whether additional vehicle data is needed without user intervention. Further, the use of the policy also helps ensure that the requested data is consistent for a particular type of event. As a result, an analysis of similar events from different vehicles may be analyzed with each other or compared to each other. A determination is then made as to whether additional vehicle data is needed [retrieve second data] (operation 708).”
(bold only) “responsive to transmitting the first threshold, receive fourth data associated with a second application”: Kinney, paragraph 0089, “The process then sends a request to the vehicle for data Identified using the policy (operation 712) [responsive to transmitting the first threshold]. The requested data is then received [receive fourth data] (operation 714). This received data is stored (operation 716) with the process terminating thereafter.”
Kinney and Rane are analogous arts as they are both related to data acquisition for the processing of anomalous events. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the responsive data retrieval of Kinney with the teachings of Rane to arrive at the present invention, in order to improve the quality data used in the event analysis, as stated in Kinney, paragraph 0096, “The different advantageous embodiments provide a capability to select additional data in addition to predefined data that may be sent by an aircraft during its mission.”
Regarding claim 2:
Rane as modified by Kinney teaches “[t]he system of claim 1.”
Rane further teaches “wherein the first data corresponds to a failure of the first application”: Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [wherein the first data corresponds to a failure of the first application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
Regarding claim 3:
Rane as modified by Kinney teaches “[t]he system of claim 2.”
Rane further teaches “wherein the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [the second data corresponds to one or more investigations associated with the failure], other information, or a combination thereof.”
Regarding claim 4:
Rane as modified by Kinney teaches “[t]he system of claim 2.”
Rane further teaches “wherein the first threshold comprises a numerical value associated with resolving the failure”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores [first threshold] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [comprises a numerical value associated with resolving the failure], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Regarding claim 5:
Rane as modified by Kinney teaches “[t]he system of claim 1.”
Rand further teaches “wherein the first threshold comprises a threshold type and a threshold numerical value”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores [a threshold numerical value] 174 may represent confidence values that a corresponding predicted application failure [a threshold type] is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Regarding claim 6:
Rane as modified by Kinney teaches “[t]he system of claim 1.”
Rane further teaches “wherein the fourth data comprises historical operational data”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [the fourth data comprises historical operational data], other information, or a combination thereof.”
Regarding claim 7:
Rane as modified by Kinney teaches “[t]he system of claim 1.”
Rane further teaches “receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request”: Rane, paragraphs 0035–0036, “The text used to rain [sic] the failure engine 120 may be generated based on user input [receive a request from the user], based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data, other information, or a combination thereof. In some such implementations, the failure engine 120 may also be configured to output failure scores [transmitting the first threshold to the user is responsive to receiving the request] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Regarding claim 8:
Rane as modified by Kinney teaches “[t]he system of claim 1.”
Rane further teaches “receive one or more attributes associated with the first application; and determine a first alert associated with the first application, the first alert based on the one or more attributes, wherein the first threshold corresponds to the first alert”: Rane, paragraph 0038, “The model repository 150 may provide the log data 161 as input to the anomaly detection models 152 to cause the anomaly detection models 152 to generate anomaly data 180 that indicates one or more detected anomalies corresponding to one or more applications. The server 102 may provide the anomaly data 180, and optionally the log data 161, as input data to the failure engine 120 to cause the failure engine 120 to output the indicators 172 ( e.g., one or more indicators of applications that are predicted to fail), the reasons 176 (e.g., one or more reasons for predicted application failures), the failure scores 174 (e.g., one or more failure scores corresponding to reasons for failure associated with the predicted application failures, one or more failure scores corresponding to the predicted application failures, or a combination thereof), or a combination thereof.”
Regarding claim 17:
Rane teaches “[t]he system of claim 16.”
Rane further teaches :
“transmit the updated first threshold to a user”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores [transmit the updated first threshold to a user] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
(bold only) “responsive to transmitting the updated first threshold, receive third data associated with a second application” and “determine, via the MLM and based on the third data, a second threshold associated with the second application”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated with data from multiple applications, hence, receive third data associated with a second application and determine, via the MLM and based on the third data] […] Similarly, models described with reference to server 102 may also be implemented, in whole or in part, or may otherwise access, one or more ML models. To illustrate, the failure engine 120 [failure engine produces thresholds associated with applications, engine is retrained using data from multiple applications, hence producing new thresholds upon retraining, therefore determine … a second threshold associated with the second application], the recovery model 122, or both, may include or correspond to one or more NNs, one or more SVMs, one or more decision trees, one or more random forests, one or more regression models, one or more BNs, one or more DBNs, one or more NB models, one or more Gaussian processes, one or more HMMs, one or more regression models, or the like.”
Rane does not explicitly teach (bold only) “responsive to transmitting the updated first threshold, receive third data associated with a second application.”
Kinney teaches (bold only) “responsive to transmitting the updated first threshold, receive third data associated with a second application”: Kinney, paragraph 0089, “The process then sends a request to the vehicle for data Identified using the policy (operation 712) [responsive to transmitting the updated first threshold]. The requested data is then received [receive third data] (operation 714). This received data is stored (operation 716) with the process terminating thereafter.”
Kinney and Rane are analogous arts as they are both related to data acquisition for the processing of anomalous events. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the responsive data retrieval of Kinney with the teachings of Rane to arrive at the present invention, in order to improve the quality data used in the event analysis, as stated in Kinney, paragraph 0096, “The different advantageous embodiments provide a capability to select additional data in addition to predefined data that may be sent by an aircraft during its mission.”
Regarding claim 18:
Rane teaches “[t]he system of claim 16.”
Rane further teaches :
(bold only) “responsive to receiving the first data, retrieve third data associated with the first application”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [retrieve third data associated with the first application], other information, or a combination thereof.”
“generate fourth data associated with the first application by analyzing the third data via natural language processing (NLP)”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations [generate fourth data associated with the first application by analyzing the third data via natural language processing (NLP)] on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof.”
“wherein determining the first threshold is further based on the fourth data”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 [wherein determining the first threshold is further based on the fourth data] may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof.”
Rane does not explicitly teach (bold only) “responsive to receiving the first data, retrieve third data associated with the first application.”
Kinney teaches (bold only) “responsive to receiving the first data, retrieve third data associated with the first application”: (bold only) “responsive to receiving the first data, retrieve third data associated with the first application”: Kinney, paragraph 0088, “In response to detecting the event [responsive to receiving the first data], the event is compared to a policy (operation 706). The policy is used to provide a capability to automatically determine whether additional vehicle data is needed without user intervention. Further, the use of the policy also helps ensure that the requested data is consistent for a particular type of event. As a result, an analysis of similar events from different vehicles may be analyzed with each other or compared to each other. A determination is then made as to whether additional vehicle data is needed [retrieve third data] (operation 708).”
Kinney and Rane are analogous arts as they are both related to data acquisition for the processing of anomalous events. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the responsive data retrieval of Kinney with the teachings of Rane to arrive at the present invention, in order to improve the quality data used in the event analysis, as stated in Kinney, paragraph 0096, “The different advantageous embodiments provide a capability to select additional data in addition to predefined data that may be sent by an aircraft during its mission.”
Regarding claim 19:
Rane as modified by Kinney teaches “[t]he system of claim 18.”
Rane further teaches “wherein the third data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [the third data corresponds to one or more investigations associated with the failure], other information, or a combination thereof.”
Claims 9, 12–15, and 20 rejected under 35 U.S.C. 103 over Rane in view of Chen et al., US Pre-Grant Publication No. 2017/0177623 (hereafter Chen).
Regarding claim 9:
Rane teaches:
“[a] system comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to”: Rane, paragraph 0008, “In another particular aspect, a system [system ] for machine learning-based application management includes a memory and one or more processors [one or more processors] communicatively coupled to the memory [a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system].”
“transmit a first threshold to a user, the first threshold associated with a first application”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores [transmit the first threshold to a user] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [the first threshold associated with a first application], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“receive data associated with a second application”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated using data from multiple applications]”; Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [receive data associated with a second application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
“determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application” and “and transmit a notification to the user, the notification requesting the user provide the first response based on the updated first threshold”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated ]. […] Similarly, models described with reference to server 102 may also be implemented, in whole or in part, or may otherwise access, one or more ML models. To illustrate, the failure engine 120 [failure engine produces thresholds associated with applications, engine is retrained, hence producing new thresholds upon retraining], the recovery model 122, or both, may include or correspond to one or more NNs, one or more SVMs, one or more decision trees, one or more random forests, one or more regression models, one or more BNs, one or more DBNs, one or more NB models, one or more Gaussian processes, one or more HMMs, one or more regression models, or the like”; Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof”; Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [determine, via a machine learning model (MLM) and based on the data, an updated first threshold associated with the second application][based on the updated first threshold], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“maintain the updated first threshold associated with the second application”: Rane, paragraph 0031, “For example, a first group of anomaly detection models that correspond to applications having a highly cyclic component for a particular KPI may benefit from additional training ( e.g., updating) according to a frequency of a corresponding cycle, while a second group of anomaly detection models that correspond to applications that are very active during a particular season ( e.g., a repeating time period) may benefit from being updated frequently during the particular season but not during other seasons. In such an example, based on the training frequencies 116 representing this information, the sequences 166 may include scheduled training for the first group of anomaly detection models at time periods selected based on the frequency of the cycle and scheduled training for the second group of anomaly detection models that correspond to the particular season [maintain the updated first threshold associated with the second application].”
Rane does not explicitly teach:
“iteratively until a first response is received”
(bold only) “and transmit a notification to the user, the notification requesting the user provide the first response based on the updated first threshold”
Chen teaches “iteratively until a first response is received” and “transmit a notification to the user, the notification requesting the user provide the first response based on the updated first threshold”: Chen, paragraph 0048, “If the user does not approve the caption (435-NO) a request for additional reference data may be sent to the user at 450. The request may be displayed to the user on a UI, or may be sent to the user by an email, SMS message, instant message, or any other transmission mechanism that may be apparent to a person of ordinary skill in the art [transmit a notification to the user, the notification requesting the user provide the first response]. After the request is transmitted, the process 400 may return to 430 to generate a new (e.g., second) caption that may be using any additional reference data received from the user in response to the request and the new caption generated may again optionally be submitted to a user for approval prior to posting at 435 and repeated until the user approves of the caption as discussed below [iteratively until a first response is received]. In this manner, multiple captions may be generated in series.”
Chen and Rane are analogous arts as the iterative requests of Chen are reasonably pertinent to the alerts of Rane. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the iterative response requests of Chen with the teachings of Rane to arrive at the present invention, in order to ensure a response is given by users to alerts, as stated in Chen, paragraph 0048, “After the request is transmitted, the process 400 may return to 430 to generate a new (e.g., second) caption that may be using any additional reference data received from the user in response to the request and the new caption generated may again optionally be submitted to a user for approval prior to posting at 435 and repeated until the user approves of the caption as discussed below.”
Regarding claim 12:
Rane as modified by Chen teaches “[t]he system of claim 9.”
Rand further teaches “wherein the first threshold comprises a threshold type and a threshold numerical value”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores [a threshold numerical value] 174 may represent confidence values that a corresponding predicted application failure [a threshold type] is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Regarding claim 13:
Rane as modified by Chen teaches “[t]he system of claim 9.”
Chen further teaches “wherein the notification comprises one or more of an email, a SMS message, an MMS message, a web push notification, a mobile push notification, a queue- based notification, or combinations thereof”: Chen, paragraph 0048, “If the user does not approve the caption (435-NO) a request for additional reference data may be sent to the user at 450. The request may be displayed to the user on a UI, or may be sent to the user by an email, SMS message, instant message, or any other transmission mechanism that may be apparent to a person of ordinary skill in the art [wherein the notification comprises one or more of an email, a SMS message, an MMS message, a web push notification, a mobile push notification, a queue- based notification, or combinations thereof]. After the request is transmitted, the process 400 may return to 430 to generate a new (e.g., second) caption that may be using any additional reference data received from the user in response to the request and the new caption generated may again optionally be submitted to a user for approval prior to posting at 435 and repeated until the user approves of the caption as discussed below. In this manner, multiple captions may be generated in series.”
Chen and Rane are combinable for the rationale given under claim 9.
Regarding claim 14:
Rane as modified by Chen teaches “[t]he system of claim 9.”
Rane further teaches “receive a request from the user, wherein transmitting the first threshold to the user is responsive to receiving the request”: Rane, paragraphs 0035–0036, “The text used to rain [sic] the failure engine 120 may be generated based on user input [receive a request from the user], based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data, other information, or a combination thereof. In some such implementations, the failure engine 120 may also be configured to output failure scores [transmitting the first threshold to the user is responsive to receiving the request] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Regarding claim 15:
Rane as modified by Chen teaches “[t]he system of claim 9.”
Rane further teaches “wherein the first and second applications are of a similar type of application”: Rane, paragraph 0005, “For example, in response to receiving a detected anomaly output by an anomaly detection model that corresponds to a first application, the failure engine may output predicted failures for a second application and a third application that are related to the first application in the application dependency graph.”
Regarding claim 20:
Rane teaches “[t]he system of claim 16.”
Rane further teaches:
transmit the updated first threshold to a user”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores [transmit the updated first threshold to a user] 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur, confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“receive third data associated with a second application”: Rane, paragraph 0025, “The model repository 150 may be configured to generate, train, execute, update, and/or store one or more machine learning (ML) models for use in performing one or more of the application management operations described herein. For example, the model repository 150 may manage anomaly detection models 152 that are configured to detect anomalies with corresponding applications (e.g., the first application 133, the second application 136, and the Nth application 139), as further described herein [hence, models are updated using data from multiple applications]”; Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [receive third data associated with a second application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
“determine, via the MLM and based on the third data, a second threshold associated with the second application” and (bold only) “transmit a notification to the user, the notification requesting the user provide the first response based on the second threshold”: Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [determine, via the MLM and based on the third data, a second threshold associated with the second application][based on the second threshold], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
“maintain the second threshold associated with the second application”: Rane, paragraph 0031, “For example, a first group of anomaly detection models that correspond to applications having a highly cyclic component for a particular KPI may benefit from additional training ( e.g., updating) according to a frequency of a corresponding cycle, while a second group of anomaly detection models that correspond to applications that are very active during a particular season ( e.g., a repeating time period) may benefit from being updated frequently during the particular season but not during other seasons. In such an example, based on the training frequencies 116 representing this information, the sequences 166 may include scheduled training for the first group of anomaly detection models at time periods selected based on the frequency of the cycle and scheduled training for the second group of anomaly detection models that correspond to the particular season [maintain the second threshold associated with the second application].”
Rane does not explicitly teach:
“iteratively until a first response is received”
(bold only) “transmit a notification to the user, the notification requesting the user provide the first response based on the second threshold”
Chen teaches “iteratively until a first response is received” and “transmit a notification to the user, the notification requesting the user provide the first response based on the second threshold”: Chen, paragraph 0048, “If the user does not approve the caption (435-NO) a request for additional reference data may be sent to the user at 450. The request may be displayed to the user on a UI, or may be sent to the user by an email, SMS message, instant message, or any other transmission mechanism that may be apparent to a person of ordinary skill in the art [transmit a notification to the user, the notification requesting the user provide the first response]. After the request is transmitted, the process 400 may return to 430 to generate a new (e.g., second) caption that may be using any additional reference data received from the user in response to the request and the new caption generated may again optionally be submitted to a user for approval prior to posting at 435 and repeated until the user approves of the caption as discussed below [iteratively until a first response is received]. In this manner, multiple captions may be generated in series.”
Chen and Rane are analogous arts as the iterative requests of Chen are reasonably pertinent to the alerts of Rane. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the iterative response requests of Chen with the teachings of Rane to arrive at the present invention, in order to ensure a response is given by users to alerts, as stated in Chen, paragraph 0048, “After the request is transmitted, the process 400 may return to 430 to generate a new (e.g., second) caption that may be using any additional reference data received from the user in response to the request and the new caption generated may again optionally be submitted to a user for approval prior to posting at 435 and repeated until the user approves of the caption as discussed below.”
Claims 10–11 rejected under 35 U.S.C. 103 over Rane as modified by Chen in view of Kinney.
Regarding claim 10:
Rane as modified by Chen teaches “[t]he system of claim 9.”
Rane further teaches:
“receive first data associated with a first application”: Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [receive first data associated with a first application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
(bold only) “responsive to receiving the first data, retrieve second data associated with the first application”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [retrieve second data associated with the first application], other information, or a combination thereof.”
“generate third data associated with the first application by analyzing the second data via natural language processing (NLP)”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations [generate third data associated with the first application by analyzing the second data via natural language processing (NLP)] on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof.”
“train the machine learning model (MLM) to determine a first threshold associated with the first application based on the third data”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine [train a machine learning model (MLM) … based on the third data] 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118, historical recovery and investigation data, other information, or a combination thereof”; Rane, paragraph 0036, “In some such implementations, the failure engine 120 may also be configured to output failure scores 174 associated with the reasons 176, the indicators 172, or both. The failure scores 174 may represent confidence values that a corresponding predicted application failure is likely to occur [to determine a first threshold associated with the first application], confidence values that a corresponding reason correctly explains a related application failure prediction, or both.”
Rane as modified by Chen does not explicitly teach (bold only) “responsive to receiving the first data, retrieve second data associated with the first application.”
Kinney teaches (bold only) “responsive to receiving the first data, retrieve second data associated with the first application”: Kinney, paragraph 0088, “In response to detecting the event [responsive to receiving the first data], the event is compared to a policy (operation 706). The policy is used to provide a capability to automatically determine whether additional vehicle data is needed without user intervention. Further, the use of the policy also helps ensure that the requested data is consistent for a particular type of event. As a result, an analysis of similar events from different vehicles may be analyzed with each other or compared to each other. A determination is then made as to whether additional vehicle data is needed [retrieve second data] (operation 708).”
Kinney and Rane are analogous arts as they are both related to data acquisition for the processing of anomalous events. It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to have combined the responsive data retrieval of Kinney with the teachings of Rane to arrive at the present invention, in order to improve the quality data used in the event analysis, as stated in Kinney, paragraph 0096, “The different advantageous embodiments provide a capability to select additional data in addition to predefined data that may be sent by an aircraft during its mission.”
Regarding claim 11:
Rane as modified by Chen and Kinney teaches “[t]he system of claim 10.”
Rane further teaches:
“the first data corresponds to a failure of the first application”: Rane, paragraph 0035, “In some implementations, the server 102 may further train the failure engine 120 to output reasoning associated with the predicted application failures. To illustrate, the server 102 may train the failure engine 120 based on the application dependency graph 118 and text data derived from the application dependency graph 118 to configure the failure model to output reasons 176 that correspond to the indicators 172 of the predicted application failures. For example, if the second application 136 is predicted to fail based on receipt of an anomaly associated with the first application 133 [the first data corresponds to a failure of the first application], the reasons 176 may include text that describes that the success rate of the second application 136 (e.g., a KPI) is dependent on the first application 133.”
“the second data corresponds to one or more investigations associated with the failure, one or more attempts to resolve the failure, or both”: Rane, paragraph 0035, “The text used to rain [sic] the failure engine 120 may be generated based on user input, based on performance of one or more natural language processing (NLP) operations on the application dependency graph 118 , historical recovery and investigation data [the second data corresponds to one or more investigations associated with the failure], other information, or a combination thereof.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Toal et al., US Pre-Grant Publication No. 2020/0233775, discloses methods for the dynamic alteration of thresholds related to failures in software applications.
Cella et al., US Pre-Grant Publication No. 2019/0025813, discloses a haptic user interface that, in the event of an emergency in a user’s location, signals the user repeatedly until the user makes an acceptable response (e.g., in paragraph 0794).
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/VAS/Examiner, Art Unit 2129
/ADAM C STANDKE/Primary Examiner, Art Unit 2129
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