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 .
Claim Objections
Claim 18 is objected to because of the following informalities: claim 18 recites "(AS) to AS similarity matrix" in line 3; however, this should be amended to read "AS-to-AS similarity matrix" to maintain consistent formatting. Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites "the set of application services" in lines 4-5 of the claim however there is no antecedent basis. The Examiner suggests amending this to recite "the plurality of application services."
Claim 20 recites "the one or more processors" however there is no antecedent basis.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Below is an evaluation using the 2019 Revised Patent Subject Matter Eligibility Guidance.
As per claim 1,
Step 1 Analysis: the claim is directed to a machine.
Step 2A Prong One Analysis:
The following limitations are the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III):
determine an AS-to-AS similarity matrix based on the incident data and the contextual data, wherein the AS-to-AS similarity matrix includes a similarity score … to measure a similarity …. This is akin to determining which applications are similar to each other.
determine an AS-to-AS affinity matrix based on the incident data and the contextual data, wherein the AS-to-AS affinity matrix includes an affinity score. This is akin to determining which applications are related to each other.
generate a set … based on the similarity matrix and the affinity matrix. This is akin to determining which applications are correlated to the first application based on its similarity and its contextual relationship with each other.
The above limitations are also considered the abstract ideas of a mathematical relationship.
The claim does not recite additional elements that integrate the judicial exception into a practical application. The limitation “to store incident data and contextual data related to an incident” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception.
Furthermore, the limitation “a computing device, a recommendation system, storage” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process.
Additionally, the following limitations are considered generally linking the use of the judicial exception to a particular technological environment or field of use:
associate with an application service (AS) including a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS, wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident
first application; second application; subset of applications; correlated applications
Step 2B Analysis: The claim does not recite additional elements that amount to significantly more than the judicial exception. The limitation “to store incident data and contextual data related to an incident” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. Furthermore, the additional element is directed to storing and retrieving information in memory, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Furthermore, the limitation “a computing device, a recommendation system, storage” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process.
Additionally, the following limitations are considered generally linking the use of the judicial exception to a particular technological environment or field of use:
associate with an application service (AS) including a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS, wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident
first application; second application; subset of applications; correlated applications
Per claims 2 and 3, the following limitations are considered mathematical relationships and therefore is considered an abstract idea:
Claim 2: wherein the affinity score associated with the first application and the second application measures a causality between the first application and the second application.
Claim 3: wherein the AS-to-AS affinity matrix is an asymmetric matrix.
Per claim 4, “wherein the application service is provided by one or more devices including a device coupled to the one or more processors of the computing device via a network” is considered an additional element that generally links the use of the judicial exception to a particular technological environment or field of use.
Per claim 5, “to generate the AS-to-AS similarity matrix based on cosine similarity of co- occurrence of the first application and the second application based on an incident-application relation database generated based on resolutions to historic incidents associated with the set of application services operated by the computing system” is considered a mathematical relationship and thus is an abstract idea.
Per claim 6, “the incident data further includes a text description for each application of the subset of applications” is a further refinement of the mental step described in the parent claim.
Per claim 7, “to generate the AS-to-AS similarity matrix based on cosine similarity of word vectors based on the text description for each application of the subset of applications of the incident data” is considered a mathematical relationship and thus is an abstract idea.
Per claim 8, “generate a recommendation table based on the similarity matrix and the affinity matrix, wherein a row of the recommendation table includes the first application and the set of correlated applications for the first application” is considered a mathematical relationship and thus is an abstract idea.
Per claim 9, “to generate the row of the recommendation table, the recommendation system is configured to multiply a row of the AS-to- AS affinity matrix with the AS-to-AS similarity matrix” is considered an abstract idea of a mathematical calculation
Per claim 10, “generate the recommendation table, the recommendation system is further configured to: identify a plurality of pairwise application associations including an application association between a third application and a fourth application based on the incident-application relation database, wherein the application association between the third application and the fourth application exists when there is a co-occurrence of the third application and the fourth application occurring in a same incident data, or occurring in the same change record; and generate the recommendation table based on the plurality of pairwise application associations” is the abstract idea of a mathematical relationship. Additionally, “wherein the incident data and the contextual data are included in an incident-application relation database, and the contextual data further includes a change record to indicate that a plurality of applications are changed together within the computing system” 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 storing and retrieving information in memory, which the courts have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
Per claim 11, the claim is directed to a method. Furthermore, the following limitations are the abstract idea of a mathematical relationship:
determining an application service (AS) to AS similarity matrix; a similarity score
determining an AS-to-AS affinity matrix based on the incident data and the contextual data, wherein the AS-to-AS affinity matrix includes an affinity score associated
generating a recommendation table based on the similarity matrix and the affinity matrix
The following limitations are considered generally linking the use of the judicial exception to a particular technological environment or field of use:
first application; second application; correlated application; subset of applications
based on incident data and contextual data related to an incident associate with an AS including a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS, wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident
Per claim 12-17, they recite similar claim language as claims 2, 4, 5, 6, 7, and 9 respectively and thus are rejected for similar reasons as claims 2, 4, 5, 6, 7, and 9.
Per claim 18, the claim is directed to a machine. Furthermore, the following limitations are the abstract idea of a mathematical relationship:
determine an application service (AS) to AS similarity matrix based on incident data and contextual data …, and the AS-to-AS similarity matrix includes a similarity score … to measure a similarity;
determine an AS-to-AS affinity matrix based on the incident data and the contextual data, wherein the AS-to-AS affinity matrix includes an affinity score
generate a recommendation table based on the similarity matrix and the affinity matrix
The following limitations are considered generally linking the use of the judicial exception to a particular technological environment or field of use:
first application; second application; subset of applications; correlated applications
related to an incident associate with an AS a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS, wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident
The limitation “non-transitory computer readable medium; processor; instructions” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process.
Per claims 19-20, they recite similar claim language as claims 2 and 4 respectively and thus are rejected for similar reasons as claims 2 and 4.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Murthy et al (US 10860451 B1) from henceforth referred to as Murthy in view of Grechanik (US 20130086553 A1) from henceforth referred to as Grechanik.
Per claim 1, Murthy teaches
A computing device to operate a recommendation system, comprising:
storage configured to store incident data and contextual data related to an incident associate with an application service (AS)
(col 7, lines 13-18; data is stored in a database)
including a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS
(col 1, lines 16-30, distributed computing system offering large numbers of interconnected and interdependent computing modules (e.g., computing devices, network layers, software applications, databases, etc). Although the “subset of applications” is not explicitly stated, the prior art teaches the existence of a distributed computing system in which service is offered to the user through interconnected applications, and thus, when an error occurs, a series of events leading to errors in other computing modules needs to be analyzed. This teaches the system in which the subset of applications offer “a workflow” to the user, in which “a workflow” is interpreted as a defined method in which multiple computing components work together in order to offer an output),
wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident; and
(col 9, lines 48-56, alert is created when a specific error pattern is identified within the log. The alert will contain “nature of incident” which the Examiner interprets to include the root identifier to identify the application that caused the incident since the steps has already been taken to analyze possible resolutions)
the recommendation system operated by one or more processors
(col 13 lines 18-21, method steps can be performed by one or more processors)
coupled to the storage and configured to:
determine an AS-to-AS affinity matrix based on the incident data and the contextual data, wherein the AS-to-AS affinity matrix includes an affinity score associated with the first application and the second application; and
(col 9 line 67 – col 10 line 5, correlation matrix can be built in order to examine how each application is impacting the others.)
generate a set of correlated applications for the first application based on the …affinity matrix
(col 9 line 67 – col 10 line 5, correlation matrix can be built in order to examine how each application is impacting the others.)
Murthy fails to teach
determine an AS-to-AS similarity matrix based on the incident data and the contextual data, wherein the AS-to-AS similarity matrix includes a similarity score between a first application and a second application to measure a similarity between the first application and the second application selected from the subset of applications;
[generate a set of correlated applications for the first application based on the …] similarity matrix.
However, Grechanik teaches
determine an AS-to-AS similarity matrix based on the incident data and the contextual data, wherein the AS-to-AS similarity matrix includes a similarity score between a first application and a second application to measure a similarity between the first application and the second application selected from the subset of applications;
([0028] similarity matrix is created to represent a similarity score between two applications)
[generate a set of correlated applications for the first application based on the …] similarity matrix.
([0029] similarity matrix is used in order to find existing applications that matches the specified pattern)
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 Murthy with that of Grechanik because by analyzing the similarities of the application based off of their semantic and contextual layer, the system can offer a more accurate results to the users (Grechanik, [0003]).
As per claim 2, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the affinity score associated with the first application and the second application measures a causality between the first application and the second application.
(Murthy, col 9 line 67 – col 10 line 5, correlation matrix is built using information such as predecessor and successor application for the type of ticket, which teaches causality)
As per claim 3, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the AS-to-AS affinity matrix is an asymmetric matrix. (although Murthy does not go into the details behind “the correlation matrix”
(Murthy, col 3 line 50-col 4 line 7), Grechanik offers a similar concept of using the API call between each application in order to create a matrix called TDM to represent association between the applications [042] In TDM, each row corresponds to a unique package API call and each column corresponds to a unique application found in the Application Archive; although not explicitly stated, because the number of columns does not necessarily correspond to the number of rows, it is not a symmetric matrix)
As per claim 4, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the application service is provided by one or more devices including a device coupled to the one or more processors of the computing device via a network.
(Murthy, col 1 lines 16-20, teaches a distributed computing systems; col 13 line 22-26, teaches a number of customer-facing devices; col 14 line 20-24, teaches various distributing mechanisms)
As per claim 5, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the recommendation system is further configured to generate the AS-to-AS similarity matrix based on cosine similarity of co- occurrence of the first application and the second application based on an incident-application relation database generated based on resolutions to historic incidents associated with the set of application services operated by the computing system.
(Grechanik, [0025] teaches that the co-occurrence of application is included in the analysis and [0051] teaches that one can conduct such analysis of documentation by evaluating the cosine between word vectors in order to find word similarities. It is the examiner’s interpretation that co-occurrence can be evaluated from its input data stream, such as the contextual data).
As per claim 6, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the incident data further includes a text description for each application of the subset of applications.
(Grechanik, [0052] documentation content of the application can be used as part of the evaluation process; [0027] metadata of the application can be extracted for evaluation)
As per claim 7, Murthy in view of Grechanik teaches
The computing device of claim 6, wherein the recommendation system is further configured to generate the AS-to-AS similarity matrix based on cosine similarity of word vectors based on the text description for each application of the subset of applications of the incident data.
(Grechanik, [0051] cosine between word vectors can be used in order to find word similarities between documents; [0052] for instance, this technique can be used to determine similarity between two vectors)
As per claim 8, Murthy in view of Grechanik teaches
The computing device of claim 1, wherein the recommendation system is further configured to generate a recommendation table based on the similarity matrix (Grechanik, [0029] similarity matrix is used in order to find existing applications that matches the specified pattern) and the affinity matrix (Murthy, col 9 line 67 – col 10 line 5, correlation matrix can be built in order to examine how each application is impacting the others.)
Murthy in view of Grechanik fails to explicitly teach
wherein a row of the recommendation table includes the first application and the set of correlated applications for the first application.
Although Murthy in view of Grechanik fails to explicitly teach wherein a row of the recommendation table includes the first application and the set of correlated applications for the first application, Grechanik does teach the UI in which the user can click through different applications and a list of similar applications is returned ([0031]). Since having separate lists versus having the data in one table with each row representing the context of the list is functionally similar and is interchangeable and thus offers a predictable result of delivering the output to the user in an easily understood manner, it is obvious to a person of ordinary skill in the art to combine the teachings of Murthy with that of Grechanik in order to teach this claim language.
As per claim 9, Murthy in view of Grechanik teaches
The computing device of claim 8, wherein to generate the row of the recommendation table, the recommendation system is configured to multiply a row of the AS-to- AS affinity matrix with the AS-to-AS similarity matrix.
(Grechanik, [0056] the matrices may be combined by a matrix operator into the similarity matrix where an interpolation weight for each similarity matrix. Although multiplication may not have been explicitly stated, since there is a finite number of matrix operators, it is obvious to a person of ordinary skill in the art to have used multiplication as a way of combination)
As per claim 10, Murthy in view of Grechanik teaches
The computing device of claim 8, wherein the incident data and the contextual data are included in an incident-application relation database
(Murthy, col 7, lines 13-18; data is stored in a database),
and the contextual data further includes a change record to indicate that a plurality of applications are changed together within the computing system, and
(Murthy, col 7 line 61 - col 8 line 5, teaches that date and time stamp are “important guidepost” in uncovering the cause of the issue and thus, the data and timestamp info is extracted from the ticket and the associated log entries are found. This teaches the claim language because the “change record” refers to the log information and whether or not they are “changed together” could be deciphered from their associated timestamps)
to generate the recommendation table ([0031] the information is presented to the user. As described earlier, although explicit use of “table” is not taught, the prior art does teach an obvious substitution by teaching the use of separate lists for each applications), the recommendation system is further configured to:
identify a plurality of pairwise application associations including an application association between a third application and a fourth application
(Grechanik, [0031] based off of contextual data extracted, such as from the application metadata and the api call mapping to one another, pairwise application association is made between each pair of applications)
based on the incident-application relation database
(Murthy, col 7, lines 13-18; data is stored in a database),
wherein the application association between the third application and the fourth application exists when there is a co-occurrence of the third application and the fourth application occurring in a same incident data, or occurring in the same change record
(Murthy, col 7 line 61 – col 8 line 5, teaches that date and time stamp from the logs are used as “important guidepost” in uncovering the cause of the issue and thus, the data and timestamp info is extracted from associated logs. It is the examiner’s interpretation that co-occurrence within a log is captured through this passage; and
generate the recommendation table based on the plurality of pairwise application associations ([0031] the information is presented to the user. As described earlier, although explicit use of “table” is not taught, the prior art does teach an obvious substitution by teaching the use of separate lists for each application. Furthermore, since the resulting list is based off of data from the pairwise application association, it is the Examiner’s interpretation that the final list of correlated application teaches this claim language unless the claim language is further modified to be narrower in scope)
As per claim 11, Murthy teaches
A method performed by a recommendation system, comprising:
determining an application service (AS) to AS similarity matrix based on incident data and contextual data
(FIG. 1, database logs; network logs; application logs 108)
related to an incident associate with an AS
(col 9, lines 48-56, an alert is created when a specific error pattern is identified in the log)
including a subset of applications selected from a set of applications, wherein the set of applications support a plurality of application services including the AS operating within a computing system including the computing device, and the subset of applications of the AS form a workflow to provide a service for the AS
(col 1, lines 16-30, distributed computing system offering large numbers of interconnected and interdependent computing modules (e.g., computing devices, network layers, software applications, databases, etc). Although the “subset of applications” is not explicitly stated, the prior art teaches the existence of a distributed computing system in which service is offered to the user through interconnected applications, and thus, when an error occurs, a series of events leading to errors in other computing modules needs to be analyzed. This teaches the system in which the subset of applications offer “a workflow” to the user, in which “a workflow” is interpreted as a defined method in which multiple computing components work together in order to offer an output),
wherein the incident data includes at least a root identifier to identify a root cause application of the workflow that causes other applications of the workflow to generate the incident, and
(col 9, lines 48-56, alert is created when a specific error pattern is identified within the log. The alert will contain “nature of incident” which the Examiner interprets to include the root identifier to identify the application that caused the incident since the steps has already been taken to analyze possible resolutions)
determining an AS-to-AS affinity matrix based on the incident data and the contextual data, wherein the AS-to-AS affinity matrix includes an affinity score associated with the first application and the second application; and
(col 9 line 67 – col 10 line 5, correlation matrix can be built in order to examine how each application is impacting the others.)
generating a recommendation table based on the similarity matrix and the affinity matrix, wherein a row of the recommendation table includes the first application and a set of correlated applications for the first application.
Murthy fails to explicitly teach
the AS-to-AS similarity matrix includes a similarity score between a first application and a second application to measure a similarity between the first application and the second application selected from the subset of applications;
generating a recommendation table based on the similarity matrix and the affinity matrix, wherein a row of the recommendation table includes the first application and a set of correlated applications for the first application.
However, Grechanik teaches
the AS-to-AS similarity matrix includes a similarity score between a first application and a second application to measure a similarity between the first application and the second application selected from the subset of applications;
([0028] similarity matrix is created to represent a similarity score between two applications)
generating a recommendation table based on the similarity matrix ([0028] similarity matrix is created to represent a similarity score between two applications) and the affinity matrix (col 9 line 67 – col 10 line 5, correlation matrix can be built in order to examine how each application is impacting the others.), wherein a row of the recommendation table includes the first application and a set of correlated applications for the first application ([0031] the information is presented to the user. As described earlier, although explicit use of “table” is not taught, the prior art does teach an obvious substitution by teaching the use of separate lists for each applications).
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 Murthy with that of Grechanik because by analyzing the similarities of the application based off of their semantic and contextual layer, the system can offer a more accurate results to the users (Grechanik, [0003]).
As per claim 12, Murthy in view of Grechanik teaches
The method of claim 10, wherein the affinity score associated with the first application and the second application measures a causality between the first application and the second application.
(Murthy, col 9 line 67 – col 10 line 5, correlation matrix is built using information such as predecessor and successor application for the type of ticket, which teaches causality)
As per claim 13, Murthy in view of Grechanik teaches
The method of claim 10, wherein the application service is provided by one or more devices including a device coupled to the one or more processors via a network.
(Murthy, col 1 lines 16-20, teaches a distributed computing systems; col 13 line 22-26, teaches a number of customer-facing devices; col 14 line 20-24, teaches various distributing mechanisms)
As per claim 14, Murthy in view of Grechanik teaches
The method of claim 10, further comprising: generating the AS-to-AS similarity matrix based on cosine similarity of co-occurrence of the first application and the second application based on an incident-application relation database generated based on resolutions to historic incidents associated with the plurality of application services.
(Grechanik, [0025] teaches that the co-occurrence of application is included in the analysis and [0051] teaches that one can conduct such analysis of documentation by evaluating the cosine between word vectors in order to find word similarities. It is the examiner’s interpretation that co-occurrence can be evaluated from its input data stream, such as the contextual data).
As per claim 15, Murthy in view of Grechanik teaches
The method of claim 10, wherein the incident data further includes a text description for each application of the subset of applications.
(Grechanik, [0052] documentation content of the application can be used as part of the evaluation process; [0027] metadata of the application can be extracted for evaluation)
As per claim 16, Murthy in view of Grechanik teaches
The method of claim 15, further comprising: generating the AS-to-AS similarity matrix based on cosine similarity of word vectors based on the text description for each application of the subset of applications of the incident data.
(Grechanik, [0051] cosine between word vectors can be used in order to find word similarities between documents; [0052] for instance, this technique can be used to determine similarity between two vectors)
As per claim 17, Murthy in view of Grechanik teaches
The method of claim 10, wherein the generating the row of the recommendation table comprises multiplying a row of the AS-to-AS affinity matrix with the AS-to-AS similarity matrix.
(Grechanik, [0056] the matrices may be combined by matrix operator into the similarity matrix where an interpolation weight for each similarity matrix. Although multiplication may not have been explicitly stated, since there is a finite number of matrix operators, it is obvious to a person of ordinary skill in the art to have used multiplication as a way of combination)
As per claim 18, Murthy in view of Grechanik teaches similar claim limitation as claim 11 and is rejected for similar reasons. It additionally recites a non-transitory computer readable medium including instructions for causing a processor to perform operations (col 13 lines 10-20, machine-readable storage device for execution or to control the operation of a data processing apparatus).
As per claim 19 and 20, the claim recites similar claim limitation as claim 12 and 13 respectively and thus are rejected for similar reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190163549 A1 teaches a sensor data that utilizes affinity matrix to see if a specific fault signature is similar enough to the historical data.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAYO LISA RUSIN whose telephone number is (703)756-1679. The examiner can normally be reached Monday-Friday 8:30 - 5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.R./Examiner, Art Unit 2114
/ASHISH THOMAS/Supervisory Patent Examiner, Art Unit 2114