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
2. 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 1-19, 21
Claims 1-19, 21 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 a mental process using a computer as a tool 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/process, claims 8-14 describe a system, and claims 15-19, 21 describe a non-transitory computer-readable medium, therefore satisfying Step 1 of the analysis.
Step 2 Analysis for Claims 1-11
Step 2A – Prong 1: Is a Judicial Exception Recited?
Claim 1 recites receiving/collecting images/data depicting computer architecture comprising components, determining a mapping of metadata to the components based on historical descriptions using machine learning model, constructing a graph with the mapped metadata, determining failure points on the graph using a machine learning model and presenting the failure points. The limitations involve data collection, organizing, analysis, and presenting data. Such data observation and evaluation can be by a human and recites a mental process. 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 and off the shelf machine learning models (MLM’s) (i.e., use of a processor or a generic computing component). That is, nothing in the claim elements preclude the steps from practically being performed in the mind.
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.
Claims 2-4 merely recite the types of data being collected and evaluated.
Claim 5 recites “de-noising” the collected data. This is interpreted as merely reducing unneeded data from the collected and can be achieved via a human with a mental process.
Claim 6 recites using JSON formatting. Again, this is merely using a generic, off the shelf, computing component to use a computer as a tool for the mental process of mapping.
Claim 7 recites determining a remedial action and performing thereof. Without adequate detail, this can be interpreted as merely a mental process of correcting, deleting, adding, etc. data or simply alerting a user.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
Claims 1-7 recite MLM’s and a display. 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)).
The limitations also recite collecting data, organizing data, displaying data, and determining and performing a remedial action. These limitations describe insignificant extra-solution activity pertaining to mere data gathering, analyzing data, and generically applying 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 also recite the use of MLM’s to analyze the data. The limitations in the claims merely describe the use of machine learning without any specification of details pertaining to how the associated machine learning model is trained and/or how the actual machine learning is performed. The claims merely recited spatial analysis and structural analysis without details. Such details would include description of specific algorithms used in training the machine learning model. As currently written, the limitations in the amended claims describe certain types of data analysis performed on the data. The evaluations describe analyses 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. 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).
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, claims 1-7 contains additional elements which require evaluation as to whether they provide an inventive concept to the identified abstract idea. The MLM’s and display 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 data, data analysis, display of resultant data, and generically applying a resolution to an identified problem 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 1-7 also recite limitations regarding the use of machine learning and the training of a machine learning 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. 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 machine 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, 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 15-19, 21 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 15-19 21 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 medium storing instructions, that when executed by one or more processors cause performance of actions comprising”. The computer-readable medium and processors 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 processors to troubleshoot a performance problem recites intended use of the claimed limitations and does not represent “significantly more” than the identified judicial exception.
Claim Rejections - 35 USC § 102
3. 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.
4. 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.
5. Claim(s) 1-4, 6-11, 13-17, 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ezrielev et al. U.S. Patent 11,507,447.
As per claim 1, Ezrielev teaches a method comprising: receiving one or more images depicting a computer architecture comprising one or more components (column 45-56); determining, by a first machine learning model performing spatial analysis of the one or more images, a mapping of metadata to the one or more components (column 3, 45-56, wherein the analysis leads to nodes and edges of the collected data), wherein the first machine learning model is trained to output the mapping based on historical component descriptions (column 3, 59-64, wherein prior program logs are used for analysis); constructing, based on the mapping, an ordered graph indicating one or more relationships between the one or more components, wherein the ordered graph comprises the mapped metadata (column 5, lines 20-32); determining, by a second machine learning model performing structural analysis on the ordered graph, one or more failure points associated with the one or more components (column 5, lines 55-65); and presenting, using a display, a user interface depicting the one or more failure points (column 5, lines 62-65).
As per claim 2, Ezrielev teaches the method of claim 1, wherein the one or more components comprise one or more of: hardware components, or software components (column 3, lines 52-54).
As per claim 3, Ezrielev teaches the method of claim 1, further comprising training the first machine learning model based on the historical component descriptions, wherein the historical component descriptions comprise: domain-specific language associated with the computer architecture; and labeled images of diagram components (column 4, lines 23-29).
As per claim 4, Ezrielev teaches the method of claim 1, further comprising training the second machine learning model based on historical data associated with real-world failures (column 5, lines 3-15).
As per claim 6, Ezrielev teaches the method of claim 1, wherein the ordered graph is formatted according to JavaScript Object Notation (JSON) (column 5, lines 16-20).
As per claim 7, Ezrielev teaches the method of claim 1, further comprising: determining, based on the one or more failure points, one or more remedial actions for the computer architecture; and performing, based on detecting a failure of a subset of the one or more components, the one or more remedial actions (column 5, lines 55-64).
As per claim 8, Ezrielev teaches system comprising: a computing device; a first machine learning model; and a second machine learning model; wherein the computing device is configured to: receive one or more images depicting a computer architecture comprising one or more components; receive, from the first machine learning model, a mapping of metadata to the one or more components; construct, based on the mapping, an ordered graph indicating one or more relationships between the one or more components, wherein the ordered graph comprises the metadata; receive, from the second machine learning model, one or more failure points associated with the computer architecture; and present, using a display, a user interface depicting the one or more failure points; wherein the first machine learning model is configured to determine the mapping by performing spatial analysis of the one or more images; and wherein the second machine learning model is configured to determine the one or more failure points by performing structural analysis on the ordered graph (column 3, lines 45-56; column 5, lines 20-32, 55-65, see claim 1).
As per claim 9, Ezrielev teaches the system of claim 8, wherein the one or more components comprise one or more of: hardware components, or software components (column 3, liens 52-54).
As per claim 10, Ezrielev teaches the system of claim 8, wherein the first machine learning model is trained based on: domain-specific language associated with the computer architecture; and labeled images of diagram components (column 4, lines 23-29).
As per claim 11, Ezrielev teaches the system of claim 8, wherein the second machine learning model is trained based on historical data associated with real-world failures (column 5, lines 3-15).
As per claim 13, Ezrielev teaches the system of claim 8, wherein the ordered graph is formatted according to JavaScript Object Notation (JSON) (column 5, lines 16-20).
As per claim 14, Ezrielev teaches the system of claim 8, wherein the computing device is further configured to: determine, based on the one or more failure points, one or more remedial actions for the computer architecture; and perform, based on detecting a failure of a subset of the one or more components, the one or more remedial actions (column 5, lines 55-64).
As per claim 15, Ezrielev teaches a non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, cause performance of actions comprising: receiving one or more images depicting a computer architecture comprising one or more components; determining, by a first machine learning model performing spatial analysis of the one or more images, a mapping of metadata to the one or more components; constructing, based on the mapping, an ordered graph indicating one or more relationships between the one or more components, wherein the ordered graph comprises the metadata; determining, by a second machine learning model performing structural analysis on the ordered graph, one or more failure points associated with the one or more components (column 3, lines 45-56; column 5, lines 20-32, 55-65, see claim 1); determining, based on the one or more failure points, one or more remedial actions for the computer architecture; and performing, based on detecting a failure of a subset of the one or more components, the one or more remedial actions (column 5, lines 55-64).
As per claim 16, Ezrielev teaches the non-transitory computer-readable medium storing computer instructions of claim 15, wherein the one or more components comprise one or more of: hardware components, or software components (column 3, lines 52-54).
As per claim 17, Ezrielev teaches the non-transitory computer-readable medium storing computer instructions of claim 15, when executed by the one or more processors, further cause performance of actions comprising: training the first machine learning model based on one or more of: domain-specific language associated with the computer architecture, or labeled images of diagram components (column 4, lines 23-29); and training the second machine learning model based on historical data associated with real-world failures (column 5, lines 3-15).
As per claim 19, Ezrielev teaches the non-transitory computer-readable medium storing computer instructions of claim 15, wherein the ordered graph is formatted according to JavaScript Object Notation (JSON) (column 5, lines 16-20).
Claim Rejections - 35 USC § 103
6. 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.
7. 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.
8. Claim(s) 5, 12, 18, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ezrielev in view of Kholodkav et al. U.S. Patent Application Publication US2025/0147754.
As per claim 5, Ezrielev teaches the method of claim 1. Kholodkav teaches it further comprising de-noising the one or more images prior to the determining the mapping (¶ 0070, 0026). It would have been obvious to one of ordinary skill in the art to use the process of Kholodkov in the process of Ezrielev. One of ordinary skill in the art would have been motivated to use the process of Kholodkov in the process of Ezrielev because using the process of Kholodkov would yield the predictable result of suppressing unneeded data to better analyze pertinent data for better results.
As per claim 12, Ezrielev teaches the system of claim 8, wherein the computing device is further configured to: wherein the first machine learning model is configured to perform the determining the mapping by performing the spatial analysis of the one or more images (column 3, lines 45-56). Kholodkov teaches to de-noise the one or more images; and prior to receiving the mapping, send the de-noised one or more images to the first machine learning model (¶ 0070, 0026). It would have been obvious to one of ordinary skill in the art to use the process of Kholodkov in the process of Ezrielev. One of ordinary skill in the art would have been motivated to use the process of Kholodkov in the process of Ezrielev because using the process of Kholodkov would yield the predictable result of suppressing unneeded data to better analyze pertinent data for better results.
As per claim 18, Ezrielev teaches the non-transitory computer-readable medium storing computer instructions of claim 15. Kholodkov teaches when executed by the one or more processors, further cause performance of actions comprising de-noising the one or more images prior to the determining the mapping (¶ 0070, 0026). It would have been obvious to one of ordinary skill in the art to use the process of Kholodkov in the process of Ezrielev. One of ordinary skill in the art would have been motivated to use the process of Kholodkov in the process of Ezrielev because using the process of Kholodkov would yield the predictable result of suppressing unneeded data to better analyze pertinent data for better results.
As per claim 21, Ezrielev teaches the non-transitory computer-readable medium storing computer instructions of claim 15, wherein the first machine learning model is configured to perform the determining the mapping by performing the spatial analysis of the one or more images (column 3, lines 45-56). Kholodkov teaches the de-noising the one or more images; and prior to determining the mapping, sending the de-noised one or more images to the first machine learning model (¶ 0070, 0026). It would have been obvious to one of ordinary skill in the art to use the process of Kholodkov in the process of Ezrielev. One of ordinary skill in the art would have been motivated to use the process of Kholodkov in the process of Ezrielev because using the process of Kholodkov would yield the predictable result of suppressing unneeded data to better analyze pertinent data for better results.
Response to Arguments
9. Applicant's arguments filed 12/23/25 have been fully considered but they are not persuasive.
With respect to the USC 101 rejection, the applicant argues the current language does not constitute a mental process as the operations involve complex computational analysis. The examiner respectfully disagrees. The applicant argues the trained machine learning model that performs the claimed limitations cannot be a mental process. The examiner refers the applicant to the MPEP 2106. 04(a)(2) III C, wherein a claim can still recite a mental process even if the process requires a computer. The limitations in the claims merely describe the use of machine learning model without any specification of details pertaining to how the associated machine learning model is trained and/or how the actual machine learning is performed. Such details would include description of specific algorithms used in training the machine learning model. As it is written, the claims are directed to the use of off the shelf machine learning models in its ordinary, off-the-shelf capacity to apply the identified judicial exception. This is further interpreted as such by the applicant’s own statement that the claimed models use the off the shelf availability of a Bayesian network machine learning (ML) model. The examiner merely interprets the claims as collecting data, using a ML model to analyze the data and output to another ML model and display the results. This process can be performed by a human with the aid of a generic computing component (ML) as a tool. This argument is also applicable to the Step 2B argument presented by the applicant.
The applicant also argues the claims are directed to a practical application. The examiner respectfully disagrees. The applicant states the claims are directed to concrete technical solutions including spatial analysis of images, mapping of metadata, an ordered graph of components, and analysis of the graph to determine failure points. The examiner interprets these limitations as mere data analysis to provide an outcome that could be used for solutions, but no solution is performed with the analyzed data. The applicant further states the claims describe an improvement to computer reliability and self-healing capabilities. The examiner contends the claims provide a process that could lead to improved computer reliability if the process was applied, but it is not. The claims are directed to an idea of a solution without an actual solution, much less a self-healing solution, as is not claimed.
The applicant is urged to use the claimed process to actual remediate the failure points rather than just displaying for future application.
The applicant also argues that the cited art, Ezrielev, does not teach the claimed limitations. The examiner respectfully disagrees. The applicant states in the Remarks, page 14, that Ezrielev does not teach ‘“one or more images” that “comprise architecture diagrams depicting hardware components, software components, and connections therebetween”’. The examiner contends that this limitation is not in the current claim language. The independent claims only recite “one or more components” and claim 2 recites the components as either software or hardware, but not both and not any interconnections. However, Ezrielev does teach, column 3, lines 52-54, the depiction of software (programs) and their interconnections on a graph.
The applicant also argues the cited art of Kholodkov does not teach denoising the images prior to mapping. The examiner respectfully disagrees. The examiner has cited paragraph 0070 in the prior action. While the paragraph does list the components that perform the denoising, the actual denoising process can best be seen in paragraph 0026.
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