DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-27 are examined.
Claims 1-27 are rejected.
Claim Objections
Per claim 7, the claim language recites “executing the troubleshooting action as a computer implemented command to modify operation a computing device hosting the application;” however, there is a grammatical error present in this recitation and necessary amendment is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 24 recites “auto-heal mechanism” and claim 27 recites “auto-learning mechanism.” They are interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 24 and 27 are rejected under 35 U.S.C. 112(a).
Claim 24 recites "auto-heal mechanism” and claim 27 recites “auto-learning mechanism.” The limitations fulfill the 3-prong test for being interpreted as invoking 35 U.S.C. 112(f). See MPEP 2181. The limitation is a generic placeholder that is modified by functional language, and is not further modified in the claim by a definite structure or material for performing the claimed function. The specification does not provide for a particular structure that embodies this limitation. The claim therefore lacks written description support for this limitation.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 24 and 27 are rejected under 35 U.S.C. 112(b).
Claim 24 recites "auto-heal mechanism" and claim 27 recites “auto-learning mechanism” which invokes 35 U.S.C. 112(f) but lacks support in the specification to limit the interpretation of the term. (See the 35 U.S.C. 112(a) rejection above.) A limiting structure cannot be found to embody this limitation and guide its interpretation when examining the claim. The element is therefore indefinite.
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 1-23, 25-27 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Below is an evaluation using the 2019 Revised Patent Subject Matter Eligibility Guidance.
Per claim 1,
Step 1 is satisfied because method steps are processes.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include
extracting error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application;
This is akin to a user examining the error related information and mentally identifying relevant error related information from the logs.
parsing the error related information to identify a set of error messages related to the troubleshooting case;
This is akin to a user mentally synthesizing the aforementioned relevant error related information into a set of problem areas to examine.
converting the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases;
This is akin to a user mentally assigning a list of indexes to the set of problem areas identified earlier.
generating an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
This is akin to a user mentally making a judgment call regarding which problem areas they are examining is related to which historic case(s).
performing a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and
This is akin to a user mentally identifying the historic case(s) that are relevant to the identified case.
At step 2a prong 2, additional elements that integrate the judicial exception into a practical application are not recited. Recited details about
in response to the output corresponding to a historic troubleshooting case, implementing a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application.
is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception (See MPEP §§ 2106.04(d), 2106.05(g).).
At step 2b, the additional element does not amount to significantly more than the judicial exception because it is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to repeating a troubleshooting action that has been performed before, which is a well-understood, routine, and conventional activity per MPEP 2106.05(d)(II).
Per claims 2-4, 6, 9-10, the following additional claim limitations are recited however they do not integrate the judicial exception into a practical application because the various technical functions can either be done as part of the mental process or with the use of a pen and paper. No additional elements are recited for analysis under step 2a prong 2 or step 2b.
implementing the matching procedure as a set intersection function that identifies error mappings that have a maximum overlap of error tokens with error tokens of the error mapping for the troubleshooting case, wherein an error token corresponds to an index of an error message of the set of error messages.
This is akin to a user mentally analyzing the set of identified issues and determining which historic case(s) has the largest amount of overlap regarding the problem set.
implementing the matching procedure as a longest common subsequence function that identifies error mappings that have a longest common sequence of error tokens with error tokens of the error mapping for the troubleshooting case, wherein an error token corresponds to an index of an error message of the set of error messages.
This is akin to a user mentally analyzing the set of identified issues and determining which historic case(s) is the most relevant given the sequencing and the ordering of the noted issues.
implementing the matching procedure as an inverse document frequency function that assigns reduced weights to error tokens that occur more frequently than other error tokens within the global file, wherein an error token corresponds to an index of an error message of the set of error messages.
This is akin to a user mentally analyzing the set of identified issues and determining which historic case(s) is the most relevant given that you are applying weights given the rarity of specific mitigating factors.
escalating the service ticket for advance troubleshooting, wherein the service ticket is escalated to skip basic troubleshooting.
This is akin to a user deciding that a specific problem set requires further analysis.
pre-processing the error related information, prior to parsing the error related information, to remove timestamps and user specific information from the error related information.
This is akin to a user mentally dismissing or physically crossing out information such as timestamps and user specific information.
pre-processing, utilizing word embeddings, the error related information, prior to parsing the error related information, to deduplicate error messages to remove duplicate error messages to create a set of deduplicated error messages, wherein parsing the error related information comprises parsing the deduplicated error messages.
This is akin to a user examining the error related information and skimming over or mentally dismissing any duplicate materials when identifying the set of problem areas.
Per claim 5, 7, and 8, additional elements are recited; however, they do not integrate the judicial exception into a practical application nor do they consider to be significantly more. In claim 5, the additional element “generating a service ticket for the troubleshooting case,” as well as the additional element from claim 8 “display troubleshooting instructions” is an insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)). Claim 7 recites “executing the troubleshooting action as a computer implemented command to modify operation a computing device hosting the application.” Using BRI, this can include modifying the display in order to showcase the troubleshooting action which is again an insignificant extra-solution activity. Furthermore, these outputting of data is considered well-understood, routine, and conventional as expressly stated in MPEP 2106.05(d)(II).
Per claim 11,
Step 1 is satisfied because a machine is being claimed.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include
extract error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application;
This is akin to a user examining the error related information and mentally identifying relevant error related information from the logs.
parse the error related information to identify a set of error messages related to the troubleshooting case;
This is akin to a user mentally synthesizing the aforementioned relevant error related information into a set of problem areas to examine.
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases;
This is akin to a user mentally assigning a list of indexes to the set of problem areas identified earlier.
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
This is akin to a user mentally making a judgment call regarding which problem areas they are examining is related to which historic case(s).
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output;
This is akin to a user mentally identifying the historic case(s) that are relevant to the identified case.
At step 2a prong 2, additional elements that integrate the judicial exception into a practical application are not recited. Recited details about
and in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application
is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception (See MPEP §§ 2106.04(d), 2106.05(g).). Furthermore, the additional element such as “computing device,” “a memory,” “a processor,” generally link the use of the judicial exception to a particular technological environment or field of use (MPEP §§ 2106.04(d), 2106.05(h))
At step 2b, the additional element does not amount to significantly more than the judicial exception because it is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to repeating a troubleshooting action that has been performed before, which is a well-understood, routine, and conventional activity per MPEP 2106.05(d)(II). Additionally, the additional element such as “computing device,” “a memory,” “a processor,” generally link the use of the judicial exception to a particular technological environment or field of use (MPEP §§ 2106.04(d), 2106.05(h))
Per claims 12-16, additional claim limitations are recited; however, they do not integrate the judicial exception into a practical application nor do they consider to be significantly more. Claim 12 recites utilizing “weights” however that can be done mentally. Claim 13 recites “utilizing the global file to represent unique error messages observed for an application problem,” but that can be done with an aid of a pen and paper. Claim 14 recites the use of “vector-based similarity scoring function” and calculating the similarity scores and “[selecting] an existing error message within the global file that has a highest similarity score” and “[assigning] an index of the existing error message” but that can be done mentally or with an aid of pen and paper. Claim 15 recites “covert a list of error vectors into the list of indexes corresponding to the global file,” but that can be done with an aid of pen and paper. Claim 16 recites “create the global file consisting of unique error messages based upon errors extracted from [various sources]” but that can be done with an aid of pen and paper.
Per claim 17,
Step 1 is satisfied because a machine is being claimed.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include
extract error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application;
This is akin to a user examining the error related information and mentally identifying relevant error related information from the logs.
parse the error related information to identify a set of error messages related to the troubleshooting case;
This is akin to a user mentally synthesizing the aforementioned relevant error related information into a set of problem areas to examine.
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases;
This is akin to a user mentally assigning a list of indexes to the set of problem areas identified earlier.
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
This is akin to a user mentally making a judgment call regarding which problem areas they are examining is related to which historic case(s).
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and
This is akin to a user mentally identifying the historic case(s) that are relevant to the identified case.
At step 2a prong 2, additional elements that integrate the judicial exception into a practical application are not recited. Recited details about
in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application.
is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception (See MPEP §§ 2106.04(d), 2106.05(g).). Furthermore, the additional element such as “non-transitory machine readable medium,” generally link the use of the judicial exception to a particular technological environment or field of use (MPEP §§ 2106.04(d), 2106.05(h))
At step 2b, the additional element does not amount to significantly more than the judicial exception because it is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore, the additional element is directed to repeating a troubleshooting action that has been performed before, which is a well-understood, routine, and conventional activity per MPEP 2106.05(d)(II). Additionally, the additional element such as “non-transitory machine readable medium,” generally link the use of the judicial exception to a particular technological environment or field of use (MPEP §§ 2106.04(d), 2106.05(h))
Per claims 18-23, 25-27, additional elements are recited; however, they do not integrate the judicial exception into a practical application nor do they consider to be significantly more.
Claim 18 recites “[iterating] through pre-processed documents of error messages using word embeddings and similarity threshold” but that can be done mentally. The additional claim language of “add one or more error messages of the pre-processed documents to the global file based upon the one or more error messages” is simply an insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)) and can furthermore be considered well-understood, routine, and conventional as expressly stated in MPEP 2106.05(d)(II).
Claim 19 recites “[utilizing] the matching procedure to map incoming problem errors with resolved service tickets based upon error patterns” but that can be done mentally. The additional claim language of “[utilizing] troubleshooting actions to resolve incoming troubleshooting cases” is simply an insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)) and can furthermore be considered well-understood, routine, and conventional as expressly stated in MPEP 2106.05(d)(II).
Claim 20 recites “[generating] a summary describing or linking to historic troubleshooting cases and suggested knowledge base articles for resolving the troubleshooting case for the application” and “[displaying] the troubleshooting action as the summary to a user associated with the application” but they are simply an insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)) and can furthermore be considered well-understood, routine, and conventional as expressly stated in MPEP 2106.05(d)(II).
Claim 21 recites “[detecting] that the error mapping is an unmapped error based upon the error mapping not exceeding a similarity threshold with respect to the error mappings of the historic troubleshooting case;” however this can be done mentally.
Claim 22 recites “[utilizing] the error mappings to detect at least one of a duplicate knowledge base article or a defective service ticket” and “[removing] the least one of the duplicate knowledge base article or the defective service ticket,” however this can be done with an aid of a pen and paper.
Claim 23 recites “[identifying] an error message occurring within logs above a threshold” and depending on which type of the error, either “[implementing] an action” or “[removing] the error message.” The removal can be done with an aid of a pen and paper, and the implementation is considered an insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)) and can furthermore be considered well-understood, routine, and conventional as expressly stated in MPEP 2106.05(d)(II).
Claim 25 recites “[identifying] and [masking] sensitive data within the secure logs” but this can be done with an aid of pen and paper.
Claim 26 recites “[executing] a quality of assurance test” but this can be done mentally.
Claim 27 recites “auto-learning mechanism” but this can be done mentally or with an aid of pen and paper depending on the implementation method.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 5-7, 11-15, 17-19, 21, 24, 26-27 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal et al (US 20240330090 A1) from henceforth referred to as Mandal in view of Dande et al (US 20250315337 A1) from henceforth referred to as Dande.
Per claim 1, Mandal teaches
A method, comprising:
extracting error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application (Fig. 7, step 702, retrieve a monitoring service alert, wherein the monitoring service alert comprises a text string, including user generated context text)
parsing the error related information to identify a set of error messages related to the troubleshooting case (Fig. 7, step 704, programmatically parse the text string of the monitoring service alert to segregate the monitoring service alert into an alert message problem component and an alert auxiliary details component; Fig. 7, step 706, feature extraction)
Mandal fails to explicitly teach
converting the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases
generating an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
performing a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and
in response to the output corresponding to a historic troubleshooting case, implementing a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application
However, Dande teaches
converting the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases (Fig. 4, step 404, checksum engine generates a unique cryptographic hash signature for the received error, effectively converting the error data into a distinct checksum for efficient indexing and retrieval)
generating an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes; (Fig. 4, step 406, matching engine compares the generated checksum against a database of stored checksums to identify if the error pattern is known)
performing a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and (Fig. 4, step 408, machine learning algorithms utilized to analyze the checksum and recognize error patterns, enhancing the precision of matching and identification of potential solutions from historical data).
in response to the output corresponding to a historic troubleshooting case, implementing a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application (Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action).
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal with that of Dande because by utilizing cryptographic hash signatures, the system will be able to better “recognize patterns and respond with pre-determined corrective actions.” This will facilitate “quicker resolution of individual errors,” but also allow the system to become more proactive. The incorporation of the rules and the matching engine can help the system learn from each incident and adapt appropriately and more effectively over time (Dande, [0005]).
Per claim 5, Mandal in view of Dande teaches
The method of claim 1, comprising:
in response to the output not corresponding to a historic troubleshooting case, determining that the troubleshooting case for the application relates to an error without a currently defined solution; and generating a service ticket for the troubleshooting case (Dande, [0087] if a match is not found in the checksum database, indicating an unfamiliar error pattern, the system may flag the error for further analysis or escalate it to the support team)
Per claim 6, Mandal in view of Dande teaches
The method of claim 5, comprising:
escalating the service ticket for advance troubleshooting, wherein the service ticket is escalated to skip basic troubleshooting (Dande, [0087] if a match is not found in the checksum database, indicating an unfamiliar error pattern, the system may flag the error for further analysis or escalate it to the support team)
Per claim 7, Mandal in view of Dande teaches
The method of claim 1, comprising:
Executing the troubleshooting action as a computer implemented command to modify operation a computing device hosting the application (Dande, [0076] an example of a corrective measure is restarting a service).
Per claim 11, Mandal teaches
A computing device (Fig. 2, 108), comprising:
a memory comprising machine executable code (Fig. 2, data storage media 206); and
a processor coupled to the memory, the processor configured to execute the machine executable code to cause the machine to (FIG 2, Processor 202):
extract error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application; (Fig. 7, step 702, retrieve a monitoring service alert, wherein the monitoring service alert comprises a text string, including user generated context text)
parse the error related information to identify a set of error messages related to the troubleshooting case; (Fig. 7, step 704, programmatically parse the text string of the monitoring service alert to segregate the monitoring service alert into an alert message problem component and an alert auxiliary details component; Fig. 7, step 706, feature extraction)
Mandal fails to explicitly teach
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases;
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and
in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application.
However, Dande teaches
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases; (Fig. 4, step 404, checksum engine generates a unique cryptographic hash signature for the received error, effectively converting the error data into a distinct checksum for efficient indexing and retrieval)
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes; (Fig. 4, step 406, matching engine compares the generated checksum against a database of stored checksums to identify if the error pattern is known)
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and (Fig. 4, step 408, machine learning algorithms utilized to analyze the checksum and recognize error patterns, enhancing the precision of matching and identification of potential solutions from historical data).
in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application. (Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action).
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal with that of Dande because by utilizing cryptographic hash signatures, the system will be able to better “recognize patterns and respond with pre-determined corrective actions.” This will facilitate “quicker resolution of individual errors,” but also allow the system to become more proactive. The incorporation of the rules and the matching engine can help the system learn from each incident and adapt appropriately and more effectively over time (Dande, [0005]).
Per claim 12, Mandal in view of Dande teaches
The computing device of claim 11, wherein the machine executable code causes the machine to:
utilize the matching procedure to assign weights to error tokens represented as the error mappings. (Dande, [0077] scoring system to rate potential matches based on their similarity to existing known errors)
Per claim 13, Mandal in view of Dande teaches
The computing device of claim 11, wherein the machine executable code causes the machine to: utilize the global file to represent unique error messages observed for an application program (Dande, [0006] database of stored checksums representing known error patterns and its corrective action)
Per claim 14, Mandal in view of Dande teaches
The computing device of claim 11, wherein the machine executable code causes the machine to:
utilize a vector-based similarity scoring function to calculate similarity scores for pre-processed error messages in comparison with existing error messages within the global file;
select an existing error message within the global file that has a highest similarity score with respect to a pre-processed error message; and
assign an index of the existing error message to the pre-processed error message.
(Dande, [0077] In cases where the checksum partially matches multiple known errors, the matching engine might use a scoring system to rate the potential matches based on their similarity. The system could choose the highest scoring match to apply the associated solution)
Per claim 15, Mandal in view of Dande teaches
The computing device of claim 11, wherein the machine executable code causes the machine to:
Process a plurality of error messages from the logs to convert a list of error vectors (Mandal, Fig. 7, feature extractions) into the list of indexes corresponding to the global file (Dande, [0077] checksum of an error is mapped to known errors using the matching engine)
Per claim 17, Mandal teaches
A non-transitory machine readable medium (Fig. 2, 206 data storage media) comprising instructions for performing a method, which when executed by a machine, causes the machine to:
extract error related information from logs associated with an application, wherein the error related information corresponds to a troubleshooting case for the application; (Fig. 7, step 702, retrieve a monitoring service alert, wherein the monitoring service alert comprises a text string, including user generated context text)
parse the error related information to identify a set of error messages related to the troubleshooting case; (Fig. 7, step 704, programmatically parse the text string of the monitoring service alert to segregate the monitoring service alert into an alert message problem component and an alert auxiliary details component; Fig. 7, step 706, feature extraction)
Mandal fails to explicitly teach
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases;
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes;
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and
in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application.
However, Dande teaches
convert the set of error messages into a list of indexes corresponding to a global file created from errors of historic troubleshooting cases; (Fig. 4, step 404, checksum engine generates a unique cryptographic hash signature for the received error, effectively converting the error data into a distinct checksum for efficient indexing and retrieval)
generate an error mapping for the troubleshooting case using the global file, wherein the error mapping is populated with the list of indexes; (Fig. 4, step 406, matching engine compares the generated checksum against a database of stored checksums to identify if the error pattern is known)
perform a matching procedure to compare the error mapping of the troubleshooting case to error mappings created for the historic troubleshooting cases using the global file to generate an output; and (Fig. 4, step 408, machine learning algorithms utilized to analyze the checksum and recognize error patterns, enhancing the precision of matching and identification of potential solutions from historical data).
in response to the output corresponding to a historic troubleshooting case, implement a troubleshooting action associated with the historic troubleshooting case to address the troubleshooting case for the application. (Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action).
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal with that of Dande because by utilizing cryptographic hash signatures, the system will be able to better “recognize patterns and respond with pre-determined corrective actions.” This will facilitate “quicker resolution of individual errors,” but also allow the system to become more proactive. The incorporation of the rules and the matching engine can help the system learn from each incident and adapt appropriately and more effectively over time (Dande, [0005]).
Per claim 18, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
iterate through pre-processed documents of error messages using word embeddings and similarity thresholds to determine whether the error messages are already represented by the global file; and (Dande, [0077] uses similarity in order to score potential matches)
add one or more error messages of the pre-processed documents to the global file based upon the one or more error messages not being represented by the global file. (Dande, [0087] if a match is not found in the checksum database, indicating an unfamiliar error pattern, the system may flag the error. The unmatched checksum and associated error data are logged for future reference)
Per claim 19, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
utilize the matching procedure to map incoming problem errors with resolved service tickets based upon error patterns; and (Dande, Fig. 4, step 406, matching engine compares the generated checksum against a database of stored checksums to identify if the error pattern is known)
utilize troubleshooting actions to resolve incoming troubleshooting cases. (Dande, Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action)
Per claim 21, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
detect that the error mapping is an unmapped error based upon the error mapping not exceeding a similarity threshold with respect to the error mappings of the historic troubleshooting cases. (Dande, [0077] uses similarity in order to score potential matches; [0087] if match is not found, it is considered an unfamiliar error pattern)
Per claim 24, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to: implement, through an auto-heal mechanism incorporated into the application, the troubleshooting action. (Dande, Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action).
Per claim 26, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
execute a quality of assurance test for the application to identify and auto-triage the troubleshooting case as a known issue. (Dande, [0065] data goes through pre-processing engine which ensures the quality of the data which aids in the "auto-triage" of the troubleshooting case that occurs in Figure 4 step 408 and 410 wherein upon successful pattern recognition to a known issue, automated execution scripts that correspond to the identified error pattern will run)
Per claim 27, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
execute an auto-learning mechanism on a periodic basis to learn new error mappings identified while processing incoming troubleshooting cases. (Dande, [0077] the ability of matching engine to suggest potential solutions is an iterative and adaptive process. It is continually refined as the trained ML model processes new errors and outcomes)
Claim 8, 16, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of Qureshi et al (US 7490073 B1) from henceforth referred to as Qureshi.
Per claim 8, Mandal in view of Dande fails to teach
The method of claim 1, comprising:
Executing the troubleshooting action to display troubleshooting instructions to a user associated with the application
However, Qureshi teaches
The method of claim 1, comprising:
Executing the troubleshooting action to display troubleshooting instructions to a user associated with the application (col 76, rows 6-26, user interface to display detected features, problems, recommended corrective actions, etc.)
It is obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the teachings of Mandal in view of Dande with that of Qureshi because by alerting administrators about relevant events and assisting them in their troubleshooting process, it results in “increased deployment uptime, decreased software management costs, reduced need for (potentially erroneous) human intervention, [etc.]” (Qureshi, col 5, rows 4-10).
Per claim 16, Mandal in view of Dande fails to explicitly teach
the computing device of claim 11, wherein the machine executable code causes the machine to:
create the global file consisting of unique error messages based upon errors extracted from logs of past service tickets, notes sections of the past service tickets, and knowledge base articles.
However, Qureshi teaches
the computing device of claim 11, wherein the machine executable code causes the machine to:
create the global file consisting of unique error messages based upon errors extracted from logs of past service tickets, notes sections of the past service tickets, and knowledge base articles. ((Col 6, Row 60-Col 7, Row 34) encoded knowledge can comprise, without limitation, known problem states or “problems” associated with the deployed application. …[…] Knowledge can also be encoded from other sources, such as documented errors associated with the managed application 10, best practices that specify recommended configurations of the managed application 10, existing or customized diagnostic tools, and feedback data collected over time from deployment sites (i.e., knowledge learned from the deployments 10 themselves)).
It is obvious a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the teachings of Mandal in view of Dande with that of Qureshi in order to teach the creation of the global file using the aforementioned resources because the use of expanded set of data types for extraction have numerous advantages such as “increased deployment uptime, decreased software management costs, reduced need for human intervention, [etc.]” (Qureshi, col 5, lines 4-9).
Per claim 20, Mandal in view of Dande fails to teach
The non-transitory machine-readable medium of claim 17, wherein the instructions cause the machine to:
generate a summary describing or linking to historic troubleshooting cases and suggested knowledge base articles for resolving the troubleshooting case for the application; and
display the troubleshooting action as the summary to a user associated with the application
However, Qureshi teaches
The non-transitory machine-readable medium of claim 17, wherein the instructions cause the machine to:
generate a summary describing or linking to historic troubleshooting cases and suggested knowledge base articles for resolving the troubleshooting case for the application; and
display the troubleshooting action as the summary to a user associated with the application (col 76, rows 6-26, user interface to display detected features, problems, recommended corrective actions, etc.)
It is obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the teachings of Mandal in view of Dande with that of Qureshi because by alerting administrators about relevant events and assisting them in their troubleshooting process, it results in “increased deployment uptime, decreased software management costs, reduced need for (potentially erroneous) human intervention, [etc.]” (Qureshi, col 5, rows 4-10).
Claim 9 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of Akula et al (“Redact sensitive data from your logs on-prem by using Observability Pipelines,” Data Dog, April 18, 2024) from henceforth referred to as Akula.
Per claim 9, Mandal in view of Dande fails to explicitly teach
The method of claim 1, comprising: pre-processing the error related information, prior to parsing the error related information, to remove timestamps and user specific information from the error related information.
However, Akula teaches
The method of claim 1, comprising: pre-processing the error related information, prior to parsing the error related information, to remove timestamps and user specific information from the error related information. (page 1, teaches that sensitive data should be redacted from logs)
It is obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the teachings of Akula with that of Mandal in view of Dande in order to teach the removal of sensitive data because the collection of sensitive data may be subject to specific laws that will “require heightened obligations pertaining to data protection” (Akula, page 1).
Per claim 25, Mandal in view of Dande fails to explicitly teach
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to: in response to detecting that the logs are maintained as secure logs with restricted access, identify and mask sensitive data within the secure logs.
However, Akula teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to: in response to detecting that the logs are maintained as secure logs with restricted access, identify and mask sensitive data within the secure logs. (Akula, page 1, teaches that sensitive data should be redacted from logs)
It is obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to combine the teachings of Akula with that of Mandal in view of Dande in order to teach the removal of sensitive data because the collection of sensitive data may be subject to specific laws that will “require heightened obligations pertaining to data protection” (Akula, page 1).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of “Data deduplication,” Wikipedia article, updated on 13 March 2024, from henceforth referred to as Wiki-NPL.
Per claim 10, Mandal in view of Dande fails to explicitly teach
The method of claim 1, comprising: pre-processing, utilizing word embeddings, the error related information, prior to parsing the error related information, to deduplicate error messages to remove duplicate error messages to create a set of deduplicated error messages, wherein parsing the error related information comprises parsing the deduplicated error messages.
However, Wiki-NPL teach
The method of claim 1, comprising: pre-processing, utilizing word embeddings, the error related information, prior to parsing the error related information, to deduplicate error messages to remove duplicate error messages to create a set of deduplicated error messages, wherein parsing the error related information comprises parsing the deduplicated error messages. (page 1, 4th paragraph, in order to take entire files or large section of files that are identical and replace them with a shared copy)
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal in view of Dande with that of Wiki-NPL in order to teach deduplication of the error messages because the deduplication process reduces the amount of storage needed (Wiki-NPL, page 2).
Claim 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of Iguazio (“What is the Classification Threshold in Machine Learning?” Iguazio, captured by Wayback Machine on April 15th, 2024) from henceforth referred to as Iguazio.
As per claim 22, Mandal in view of Dande fails to explicitly teach
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
utilize the error mappings to detect at least one of a duplicate knowledge base article or a defective service ticket; and
remove the least one of the duplicate knowledge base article or the defective service ticket.
However, Iguazio teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
utilize the error mappings to detect at least one of a duplicate knowledge base article or a defective service ticket; and
remove the least one of the duplicate knowledge base article or the defective service ticket.
(page 3, teaches using threshold as a way to classify information. One classification may be "spam" which would usually resolve in a discard of said message)
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal in view of Dande with that of Iguazio since Mandal in view of Dande already mentions the use of pre-processing in order to standardize or make the size of the data manageable in order to better effectively process the data and Iguazio simply offers more explicit details behind what is happening within the classification method (MPEP 2143 - (A) Combining prior art elements according to known methods to yield predictable results).
As per claim 23, Mandal in view of Dande teaches
The non-transitory machine readable medium of claim 17, wherein the instructions cause the machine to:
identify an error message occurring within logs above a threshold (Dande, Fig. 4, step 408, machine learning algorithms utilized to analyze the checksum and recognize error patterns, enhancing the precision of matching and identification of potential solutions from historical data).
in response to the error message corresponding to a first type of error, implement an action to address an error associated with the error message; and (Dande, Fig. 4, step 410, upon successful pattern recognition, the system triggers predefined automated execution scripts that correspond to the identified error pattern for immediate corrective action)
Mandal in view of Dande fails to explicitly teach
in response to the error message corresponding to a second type of error, remove the error message from the logs.
However, Iguazio teaches
in response to the error message corresponding to a second type of error, remove the error message from the logs. (Iguazio, page 3, teaches using threshold as a way to classify information. One classification may be "spam" which would usually resolve in a discard of said message)
It is obvious to a person of ordinary skill in the art to combine the teachings of Mandal in view of Dande with that of Iguazio since Mandal in view of Dande already mentions the use of pre-processing in order to standardize or make the size of the data manageable in order to better effectively process the data and Iguazio simply offers more explicit details behind what is happening within the classification method (MPEP 2143 - (A) Combining prior art elements according to known methods to yield predictable results).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of “Python set intersection() method,” w3 schools, September 5th, 2018 from henceforth referred to as Set-Intersection NPL.
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Per claim 2, Mandal in view of Dande fails to teach
The method of claim 1, comprising:
implementing the matching procedure as a set intersection function that identifies error mappings that have a maximum overlap of error tokens with error tokens of the error mapping for the troubleshooting case, wherein an error token corresponds to an index of an error message of the set of error messages
However, Set-Intersection NPL teaches
The use of the set intersection method as a way to determine similarity between two or more sets (page 1)
It is obvious to a person of ordinary skill in the art to take the teachings of Set-Intersection NPL and combine it with the teachings of Mandal in view of Dande because using set intersection function will help judge the similarity between two or more sets (Set-Intersection NPL, page 1).
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of “Longest Common Subsequence,” Enjoy Algorithms, August 26th, 2021 from henceforth referred to as LCS-NPL.
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Per claim 3, Mandal in view of Dande fails to teach
The method of claim 1, comprising:
implementing the matching procedure as a longest common subsequence function that identifies error mappings that have a longest common sequence of error tokens with error tokens of the error mapping for the troubleshooting case, wherein an error token corresponds to an index of an error message of the set of error messages.
However, LCS-NPL teaches
Longest common subsequence can be used to find the longest sequence within the two sequence while maintaining the order (pages 1 and 16).
It is obvious to a person of ordinary skill in the art to take the teachings of Set-Intersection NPL and combine it with the teachings of Mandal in view of Dande because using set intersection function will help judge which set will provide the longest common subsequence of the error set whilst maintaining the relative order (page 1).
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Mandal in view of Dande in view of “tf-idf,” Wikipedia, 15 March 2024 from henceforth referred to as IDF-NPL.
Per claim 4, Mandal in view of Dande fails to teach
The method of claim 1, comprising:
implementing the matching procedure as an inverse document frequency function that assigns reduced weights to error tokens that occur more frequently than other error tokens within the global file, wherein an error token corresponds to an index of an error message of the set of error messages.
IDF-NPL teaches
implementing the matching procedure as an inverse document frequency function that assigns reduced weights to error tokens that occur more frequently than other error tokens within the global file, wherein an error token corresponds to an index of an error message of the set of error messages.
(the use of measuring the importance of the words by applying weights based on its frequency of occurrence (page 1)).
It is obvious to a person of ordinary skill in the art to take the teachings of Set-Intersection NPL and combine it with the teachings of Mandal in view of Dande because tf-idf weighting scheme are often used in order to rank a document’s relevancy given a user query (page 1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jesse, Martin ("What is a support ticket?" ZenDesk, June 6th, 2024) provides teaches the generation of support ticket and how it fits into the overall support structure.
Additional prior art illustrating the inventive concept can be found in PTO-892.
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/K.L.R./ Examiner, Art Unit 2114
/JOSEPH R KUDIRKA/ Primary Patent Examiner, Art Unit 2114