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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims fall within at least one of the four categories of patent eligible subject matter. However, the claimed invention is directed to performing collecting, and analyzing, identifying a solution, and generically applying said solution without significantly more.
The following is an analysis of the claims regarding subject matter eligibility in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG):
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-7 describe a method, claims 8-14 describe a system, and claims 15-20 describe a non-transitory computer-readable storage medium, therefore satisfying Step 1 of the analysis. If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
Step 2 Analysis for Claims 1-7
Step 2A – Prong 1: Is a Judicial Exception Recited?
Claim 1 recites receiving alarm data, determining if an RCA exists corresponding to the alarm, generating an RCA, by an AI model, if one does not exist, and generating and applying a resolution. The limitations describe processes that, under their broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components (i.e., use of an off-the-shelf AI model). That is, nothing in the claim elements preclude the steps from practically being performed in the mind. The method as claimed can be performed by a human and recites a mental process. An example of claims that recite mental processes cited in the October 2019 Update to the 2019 PEG includes “a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.”
The applicant has amended and has argued the new limitations of generating a resolution and applying it overcomes the rejection. The examiner contends that using the generic AI model to “generate” a resolution is merely using a computer as a tool to perform a mental process as there is no detail of generation of resolution other than using the AI model to correlate the collected data to predetermined, matching data, as recited in claim 2.
If a claim limitation, under its broadest reasonable interpretation, covers the practical performance of the limitation in the human mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, the claim recites an abstract idea.
Claim 2 recites various databases from which data is collected. The limitations describe processes that, under their broadest reasonable interpretation, covers performance of the limitations in the human mind but for the recitation of generic computer components (i.e., use of a database). That is, nothing in the claim elements preclude the steps from practically being performed in the mind. The method as claimed can be performed by a human and recites a mental process.
Claim 3 recites more data analysis and applying a generic solution to a generic problem.
Claim 4 recites a generic, high-level AI model that merely generates a new resolution without any detail on how this generation is performed. As such, the examiner interprets the model as a high-level computer component that is used as a tool for a mental process.
Claim 5 recites the same as claim along with basic mathematical concepts. If a claim limitation, under its broadest reasonable interpretation, describes the performance of mathematical calculations (even if a formula is not recited in the claim), then it falls within the “Mathematical Concepts” grouping of abstract ideas. See the 2019 Revised Patent Subject Matter Eligibility Guidance. Accordingly, claims 2-11 each recite an abstract idea.
Claim 6 recites data analysis and the mathematic concept of claim 5 along with updating the AI model. Without further detail on the process of updating, the examiner interprets this as merely adding data to the AI model, which is a mental process with a computer component as a tool.
Claim 7 recites more mere data analysis.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
Claim 1 recites a processor and an AI model. Even if the described methods are implemented on a computer, there is no indication that the combination of elements in the claim solves any particular technological problem other than merely taking advantage of the inherent advantages of using existing computer technology in its ordinary, off-the-shelf capacity to apply the identified judicial exceptions. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s). The processor cited in the claim is described at a high level of generality such that it represents no more than mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)). This limitation can also be viewed as nothing more than an attempt to generally link the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Claim 1 further recites receiving alarm data, determining if an RCA exists corresponding to the alarm, generating an RCA, by an AI model, if one does not exist, and identifying a resolution. These limitations describe insignificant extra-solution activity pertaining to mere data gathering, data analysis, and identifying a resolution to an identified problem, respectively, without providing any details regarding a specific problem being solved or specific remedial actions being taken. As such, these limitations do not integrate the abstract idea(s) into a practical application.
Claims 1-7 recite data collection, data analysis and the use of a AI model. The limitations in the claims merely describe the use of AI without any specification of details pertaining to how the AI model is trained and/or how the AI learning is performed. Such details would include description of specific algorithms used in training the AI model. As currently written, the limitations in the amended claims describe certain types of data and mathematical calculations and evaluations performed on the data. The mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. Stating in the amended dependent claims that the unsupervised learning comprises actions which describe a mental process and/or mathematical concepts is equivalent to merely specifying instructions to apply the judicial exception using unsupervised learning. See MPEP 2106.05(f). There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component is not a practical application of the abstract idea(s).
The applicant also argues the new claim language of “applying” is an improvement in the computer network technology. The examiner contends that the “applying the first resolution to the network to resolve the error” is merely an example of the act of mere instructions to apply an exception, as stated in the MPEP 2106.05(f). The examiner contends that without proper detail of what error is determined nor what the resolution entails, the claims fail to recite details of how a solution to a problem is accomplished.
Claims 2-7 describe further details regarding the data and/or statistical/mathematical calculations. These claims contain no additional elements which would integrate the abstract idea(s) into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the identified abstract idea(s).
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
In the instant case, as detailed in the analysis for Step 2A-Prong 2, claim 1 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The processor and AI model recited in the claim describe a generic computer processor and/or computer components at a high level and do not represent “significantly more” than the judicial exception.
The limitations pertaining to gathering of object information, data analysis, and generically identifying a resolution to an error describe insignificant extra-solution activity and are written at a high level in a generic manner without providing any details regarding a specific problem being solved or specific remedial actions being taken. Therefore, these limitations recite no additional elements that would amount to significantly more than the abstract ideas defined in the claim.
Claims 2-7 recite limitations regarding the use of an AI model. As discussed above in the Step 2A - Prong 2 analysis regarding integration of the abstract idea into a practical application, the limitations, as currently written, describe mathematical calculations and evaluations describe mathematical concepts that can be performed by a human (i.e., as a mental process and/or by using pen/paper) and are therefore directed to the identified judicial exception. There is no indication that the combination of elements solves a technological problem other than merely taking advantage of the inherent advantages of using existing artificial intelligence technology (i.e., machine learning) in its ordinary, off-the-shelf capacity to apply the identified judicial exception. Simply implementing the abstract idea(s) on a general purpose processor or other generic computer component, or utilizing generic artificial intelligence technology to apply the identified judicial exception, does not describe an inventive concept.
Step 2 Analysis for Claims 8-14
Claims 8-14 contain limitations for a system which are similar to the limitations for the methods specified in claims 1-7, respectively. As such, the analysis under Step 2A – Prong 1, Step 2A – Prong 2, and Step 2B for claims 8-14 is similar to that presented above for claims 1-7.
In light of the above, the limitations in claims 8-14 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract ideas(s). Claims 8-14 are therefore not patent eligible.
Step 2 Analysis for Claims 15-20
Claims 15-20 contain limitations for a non-transitory computer-readable medium which are similar to the limitations for the methods specified in claims 1-7, respectively. As such, the analysis under Step 2A – Prong 1 and Step 2A – Prong 2 for claims 15-20 is similar to that presented above for claims 1-7.
Step 2B: Do the Claims Provide an Inventive Concept?
When evaluating whether the claims provide an inventive concept, the presence of any additional elements in the claims need to be considered to determine whether they add “significantly more” than the judicial exception.
Claim 15 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea.
Claim 15 recites the additional elements of a “non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least processor to...” The computer-readable storage medium and processor cited in the claim describe generic computer components at a high level and do not represent “significantly more” than the identified judicial exception. The enabling of the processor to execute the steps of claim 1 recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, 8, 10-12, 15, 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Garimella et al. U.S. Patent Application Publication US2013/0227103A1.
As per claim 1, Garimella teaches a method of network auto-healing performed by at least one processor, the method comprising: receiving an indication that an alarm corresponding to an error in a network is triggered (¶ 0020); determining whether an existing root cause analysis (RCA) corresponds to the error (¶ 0031); based on determining that an existing RCA does not correspond to the error (¶ 0031), generating, by a knowledge-based artificial intelligence (Al) model, a first RCA for resolving the error (¶ 0035, 0031); and generating, by the knowledge-based AI model, a first resolution to the error based on the first RCA; and applying the first resolution to the network to resolve the error (¶ 0035).
As per claim 3, Garimella teaches the method of claim 1, further comprising, based on determining that the existing RCA does correspond to the error: identifying an existing resolution to the error based on the existing RCA; and applying the existing resolution to the network to resolve the error (¶ 0041).
As per claim 4, Garimella teaches the method of claim 3, further comprising, based on the existing resolution not resolving the error, generating, by the knowledge-based Al model and using at least information regarding a failure of the existing resolution, a new resolution for resolving the error (¶ 0039, 0035).
As per claim 5, Garimella teaches the method of claim 1, further comprising: determining whether a number of loops for the knowledge-based Al model exceeds a predetermined loop threshold; and based on the number of loops not exceeding the predetermined loop threshold: generating the first resolution; and applying the first resolution to the network to resolve the error (¶ 0031, 0041, 0035 wherein the examiner interprets the “loop” threshold to be 1 and the model inquires the data to see if an existing resolution exists and if so, it applies it and, if not, the model is prompted to generate a new resolution).
As per claim 8, Garimella teaches a system for network auto-healing, the system comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to: receive an indication that an alarm corresponding to an error in a network is triggered; determine whether an existing root cause analysis (RCA) corresponds to the error; based on determining that an existing RCA does not correspond to the error, generate, by a knowledge-based artificial intelligence (Al) model, a first RCA for resolving the error; and generate, by the knowledge- based AI model, a first resolution to the error based on the first RCA; and apply the first resolution to the network to resolve the error (¶ 0020, 0031, 0035, see claim 1).
As per claim 10, Garimella teaches the system of claim 8, wherein the at least one processor is further configured to, based on determining that the existing RCA does correspond to the error: identify an existing resolution to the error based on the existing RCA; and apply the existing resolution to the network to resolve the error (¶ 0041).
As per claim 11, Garimella teaches the system of claim 10, wherein the at least one processor is further configured to, based on the existing resolution not resolving the error, generate, by the knowledge-based Al model and using at least information regarding a failure of the existing resolution, a new resolution for resolving the error (¶ 0039, 0035).
As per claim 12, Garimella teaches the system of claim 8, wherein the at least one processor is further configured to: determine whether a number of loops for the knowledge-based Al model exceeds a predetermined loop threshold; and based on the number of loops not exceeding the predetermined loop threshold: generate the first resolution; and apply the first resolution to the network to resolve the error (0031, 0041, see claim 5).
As per claim 15, Garimella teaches a non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: receive an indication that an alarm corresponding to an error in a network is triggered; determine whether an existing root cause analysis (RCA) corresponds to the error; based on determining that an existing RCA does not correspond to the error, generate, by a knowledge-based artificial intelligence (Al) model, a first RCA for resolving the error; and generate, by the knowledge-based AI model, a first resolution to the error based on the first RCA; and apply the first resolution to the network to resolve the error (¶ 0020, 0031, 0035, see claim 1).
As per claim 17, Garimella teaches the storage medium of claim 15, wherein the instructions, when executed, further cause the at least one processor to, based on determining that the existing RCA does correspond to the error: identify an existing resolution to the error based on the existing RCA; and apply the existing resolution to the network to resolve the error (¶ 0041).
As per claim 18, Garimella teaches the storage medium of claim 17, wherein the instructions, when executed, further cause the at least one processor to, based on the existing resolution not resolving the error, generate, by the knowledge-based Al model and using at least information regarding a failure of the existing resolution, a new resolution for resolving the error (¶ 0039, 0035).
As per claim 19, Garimella teaches the storage medium of claim 15, wherein the instructions, when executed, further cause the at least one processor to: determine whether a number of loops for the knowledge-based Al model exceeds a predetermined loop threshold; and based on the number of loops not exceeding the predetermined loop threshold: generate the first resolution; and apply the first resolution to the network to resolve the error (¶ 0031, 0041, see claim 5).
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.
Claim(s) 2, 7, 9, 14, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garimella in view of Bowman-Amuah U.S. Patent 3,324,647.
As per claim 2, Garrimella teaches the method of claim 1, wherein the knowledge-based Al model is configured to generate the first RCA by retrieving information (¶ 0031, 0035). Bowman-Amuah teaches the information being from at least one of a troubleshooting methods of procedure (T-MOP) database, a resolution method of procedure (R-MOP) database, and a change request (CR) database (column 2, line 125 – column 126, line 4; column 125, 60-65). It would have been obvious to one of ordinary skill in the art to use the process of Bowman-Amuah in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Bowman-Amuah in the process of Garimella because using the process of Bowman-Amuah would yield the predictable result of providing data to a training model to update and provide the correlation of network incidents to the proper resolution.
As per claim 7, Garrimella teaches the method of claim 1. Bowman-Amuah teaches it further comprising determining whether the error corresponds to a change request (CR); and based on determining that the error corresponds to the CR, refraining from applying the first resolution to the network (column 64, lines 1-7, wherein the changes to a specific component are not run separately, but resolution is refrained from execution until grouped).
As per claim 9, Garrimella teaches the system of claim 8, wherein the knowledge-based Al model is configured to generate the first RCA by retrieving information (¶ 0031, 0035). Bowman-Amuah teaches it from at least one of a troubleshooting systems of procedure (T-MOP) database, a resolution system of procedure (R-MOP) database, and a change request (CR) database (column 2, line 125 – column 126, line 4; column 125, 60-65). It would have been obvious to one of ordinary skill in the art to use the process of Bowman-Amuah in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Bowman-Amuah in the process of Garimella because using the process of Bowman-Amuah would yield the predictable result of providing data to a training model to update and provide the correlation of network incidents to the proper resolution.
As per claim 14, Garrimella teaches the system of claim 8. Bowman-Amuah teaches wherein the at least one processor is further configured to determine whether the error corresponds to a change request (CR); and based on determining that the error corresponds to the CR, refrain from applying the first resolution to the network (column 64, lines 1-7, wherein the changes to a specific component are not run separately, but resolution is refrained from execution until grouped).
As per claim 16, Garrimella teaches the storage medium of claim 15, wherein the knowledge-based Al model is configured to generate the first RCA by retrieving information (¶ 0031, 0035). Bowman-Amuah teaches the information is from at least one of a troubleshooting systems of procedure (T-MOP) database, a resolution system of procedure (R- MOP) database, and a change request (CR) database (column 2, line 125 – column 126, line 4; column 125, 60-65). It would have been obvious to one of ordinary skill in the art to use the process of Bowman-Amuah in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Bowman-Amuah in the process of Garimella because using the process of Bowman-Amuah would yield the predictable result of providing data to a training model to update and provide the correlation of network incidents to the proper resolution.
Claim(s) 6, 13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Garimella in view of Madhava Rao et al. U.S Patent Application Publication US2019/0130310A1.
As per claim 6, Garimella teaches the method of claim 1, further comprising: determining whether a number of loops for the knowledge-based Al model in generating RCAs exceeds a predetermined loop threshold; and based on the number of loops exceeding the predetermined loop threshold: identifying at least one abnormality with the knowledge-based Al model(¶ 0031, 0041, wherein the examiner interprets the “loop” threshold to be 1 and the model inquires the data to see if an existing resolution exists and if so, it applies it and, if not, the model is prompted to generate a new resolution, and wherein the abnormality is the missing resolution). Rao teaches updating the knowledge-based Al model based on the identified abnormality (¶ 0025-0028). It would have been obvious to one of ordinary skill in the art to use the process of Rao in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Rao in the process of Garimella because updating the database of Rao would yield the predictable result of appending new solutions to the AI model for future network errors.
As per claim 13, Garimella teaches the system of claim 8, wherein the at least one processor is further configured to: determine whether a number of loops for the knowledge-based Al model in generating RCAs exceeds a predetermined loop threshold; and based on the number of loops exceeding the predetermined loop threshold: identifying at least one abnormality with the knowledge-based Al model(¶ 0031, 0041, wherein the examiner interprets the “loop” threshold to be 1 and the model inquires the data to see if an existing resolution exists and if so, it applies it and, if not, the model is prompted to generate a new resolution, and wherein the abnormality is the missing resolution). Rao teaches updating the knowledge-based Al model based on the identified abnormality (¶ 0025-0028). It would have been obvious to one of ordinary skill in the art to use the process of Rao in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Rao in the process of Garimella because updating the database of Rao would yield the predictable result of appending new solutions to the AI model for future network errors.
As per claim 20, Garimella teaches the storage medium of claim 15, wherein the instructions, when executed, further cause the at least one processor to: determine whether a number of loops for the knowledge-based Al model in generating RCAs exceeds a predetermined loop threshold; and based on the number of loops exceeding the predetermined loop threshold: identifying at least one abnormality with the knowledge-based Al model(¶ 0031, 0041, wherein the examiner interprets the “loop” threshold to be 1 and the model inquires the data to see if an existing resolution exists and if so, it applies it and, if not, the model is prompted to generate a new resolution, and wherein the abnormality is the missing resolution). Rao teaches updating the knowledge-based Al model based on the identified abnormality (¶ 0025-0028). It would have been obvious to one of ordinary skill in the art to use the process of Rao in the process of Garimella. One of ordinary skill in the art would have been motivated to use the process of Rao in the process of Garimella because updating the database of Rao would yield the predictable result of appending new solutions to the AI model for future network errors.
Response to Arguments
Applicant's arguments filed 10/8/25 have been fully considered but they are not persuasive.
The applicant has amended and has argued the new limitations of generating a resolution and applying it overcomes the rejection. The examiner respectfully disagrees. As stated in the rejection, the examiner contends that using the generic AI model to “generate” a resolution is merely using a computer as a tool to perform a mental process as there is no detail of generation of resolution other than using the AI model to correlate the collected data to predetermined, matching data, as recited in claim 2.
The applicant argues that the claims are similar to the case of SRI that was deemed as not being able to be performed in the human mind. The examiner contends that SRI recited network monitors that analyzed network packets and the present invention merely receives alarms that an error has occurred in the network and analyzes the collected data to see if it matches prior alarms or if it is new.
The applicant also argues the new claim language is an improvement in the computer network technology. The examiner contends that the “applying the first resolution to the network to resolve the error” is merely an example of the act of mere instructions to apply an exception, as stated in the MPEP 2106.05(f). The examiner contends that without proper detail of what error is determined nor what the resolution entails, the claims fail to recite details of how a solution to a problem is accomplished.
The applicant has also argued the new limitations overcome the cited art. The examiner respectfully disagrees. As the applicant has stated, Garmelli does teach wherein the AI model “aids” the knowledge-based to identify and generate resolutions to the detected alarms. Unlike the applicant, the examiner interprets this paragraph as the AI is a tool that the larger engine uses as a component for the described process. That is, if a knowledge based model uses AI to process the recited steps, it can be reasonably interpreted as a knowledge based AI model.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER S MCCARTHY whose telephone number is (571)272-3651. The examiner can normally be reached Monday-Friday 8:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bryce Bonzo can be reached at (571)272-3655. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER S MCCARTHY/Primary Examiner, Art Unit 2113