Prosecution Insights
Last updated: April 19, 2026
Application No. 18/641,801

SYSTEMS AND METHODS OF GENERATING A RELATIONAL ATTRIBUTE NETWORK IN A STORAGE SYSTEM

Non-Final OA §103
Filed
Apr 22, 2024
Examiner
CURRAN, J MITCHELL
Art Unit
2169
Tech Center
2100 — Computer Architecture & Software
Assignee
DELL PRODUCTS, L.P.
OA Round
3 (Non-Final)
61%
Grant Probability
Moderate
3-4
OA Rounds
3y 6m
To Grant
96%
With Interview

Examiner Intelligence

Grants 61% of resolved cases
61%
Career Allow Rate
65 granted / 106 resolved
+6.3% vs TC avg
Strong +35% interview lift
Without
With
+34.8%
Interview Lift
resolved cases with interview
Typical timeline
3y 6m
Avg Prosecution
18 currently pending
Career history
124
Total Applications
across all art units

Statute-Specific Performance

§101
13.9%
-26.1% vs TC avg
§103
57.6%
+17.6% vs TC avg
§102
15.4%
-24.6% vs TC avg
§112
5.6%
-34.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 106 resolved cases

Office Action

§103
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 . Detailed Action This is an Office Action for application 18/641,801, filed on 12/08/2025. Claims 1, 11 and 20 are currently amended. Claims 1-20 are pending and examined below. Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/08/2025 has been entered. Response to Arguments Applicant's arguments filed 12/08/2025 have been fully considered but they are not persuasive. Applicant argues that newly amended claim 1 language generating a correlation network graph from the correlation matrix, the graph including a plurality of nodes corresponding to the plurality of attributes and a plurality of undirected edges, each undirected edge connecting a corresponding pair of the plurality of nodes and being weighted according to a correlation value between a corresponding pair of the plurality of attributes. is not taught by previously cited reference arts Mueller et al. (WO 2020/010350) and Chen et al. (US Pub 2025/0062953). Examiner agrees that Chen does not explicitly teach all the new language about the new features of the correlation network graph. However, previously uncited portions of Mueller (Fig. 3A; Par. [0126, 282]) teach illustrating the strength of the connection in a graph based on coefficients associated with the connection and can include undirected graphs. Therefore, argument is unpersuasive. 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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mueller et al. (WO 2020/010350) in view of Chen et al. (US Pub 2025/0062953). Regarding claim 1, Mueller teaches A computer-implemented method comprising: receiving telemetry data from one or more system components, the telemetry data including a plurality of values associated with a plurality of attributes; (Fig. 1a; Abs. Par. [000115] a number of stored time series data variables (i.e. telemetry data including values and attributes) are received by the user in the process of generating the correlation graph) storing the telemetry data in a data structure according to the plurality of attributes; (Fig. 1a; Abs., Par. [000115] a number of stored time series variables (i.e. telemetry data) are selected by the user in the process of generating the correlation graph) generating graph, the graph including a plurality of nodes corresponding to the plurality of attributes and a plurality of undirected edges, each undirected edge connecting a corresponding pair of the plurality of nodes and being weighted according to a correlation value between a corresponding pair of the plurality of attributes. (Fig. 3A; Par. [0126, 282] the strength of the connections between edges in the graph correspond to the size of the coefficient associated with connections between points in the graph) Mueller does not explicitly teach generating a correlation matrix from the data structure reflecting one or more relations between each of the plurality of attributes; and generating a correlation network graph from the correlation matrix, the graph including a plurality of nodes corresponding to the plurality of attributes. However, from the same field, Chen teaches generating a correlation matrix from the data structure reflecting one or more relations between each of the plurality of attributes; and (Fig. 3; Par. [0004, 58-9] correlation matrices are differences of correlations between batches of collected data metrics through determined statistics of the batches across timesteps (i.e. reflecting one or more relations between attributes)) generating a correlation network graph from the correlation matrix, the graph including a plurality of nodes corresponding to the plurality of attributes. (Fig. 3; Par. [0058-9] causal graph (i.e. correlation network graph) is generated from correlation matrices) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the cloud monitoring system of Chen into the telemetry data of Mueller. The motivation for this combination would have been to improve the performance, reliability, and speed of the cloud system as explained in Chen (Par. [0016]). Regarding claim 2, Mueller and Chen teach claim 1 as shown above, and Mueller further teaches The method of claim 1 wherein the one or more system components includes a first component and a second component, wherein generating the graph includes identifying a relationship between a first attribute of the first component and a second attribute of the first component or second component. (Fig. 1A-B, 1D, Par. [000100] the path diagram view (1B) shows a visual analysis of both causation and correlation, including relationships between time series variables (i.e. telemetry attributes)) Regarding claim 3, Mueller and Chen teach claim 1 as shown above, and Mueller further teaches The method of claim 1 wherein generating the graph includes identifying an indirect relationship between a first attribute and a second attribute, wherein a first node of the plurality of nodes corresponding to the first attribute and a second node of the plurality of nodes corresponding to the second attribute are connected by at least two edges. (Fig. 11B; Par. [00042] in the given example, event c causes event e indirectly via chaining (i.e. two edges)) Regarding claim 4, Mueller and Chen teach claim 1 as shown above, and Mueller further teaches The method of claim 1 further comprising storing the graph in a database and providing a query interface configured to receive a user query relating to at least one of the plurality of attributes. (Par. [000221] users are able to save the discoveries of the analysis for later re-examination (i.e. user query for list of related components)) Regarding claim 5, Mueller and Chen teach claim 4 as shown above, and Mueller further teaches The method of claim 4 comprising generating a list of related components in response to the user query. (Par. [000221] users are able to save the discoveries of the analysis for later re-examination (i.e. user query for list of related components)) Regarding claim 6, Mueller and Chen teach claim 4 as shown above, and Mueller further teaches The method of claim 4 further comprising providing a response to the user query indicating the correlation between the at least two nodes. (Fig. 11B; Par. [00042] in the given example, event c causes event e indirectly through x via chaining (i.e. two nodes)) Regarding claim 7, Mueller and Chen teach claim 6 as shown above, and Mueller further teaches The method of claim 6 wherein the user query includes a prospective system change and the response includes a list of affected attributes related to the prospective system change. (Fig. 12B; Par. [000267-8] user inspects rows (i.e. a list) and chooses a column (i.e. prospective system change) to check its significance) Regarding claim 8, Mueller and Chen teach claim 1 as shown above, and Mueller further teaches The method of claim 1 wherein the one or more system components comprises a complex system. (Par. [00095] visual analytics is used in this system to better understand complex systems) Regarding claim(s) 9, Mueller and Chen teach claim 8 as shown above, and Chen further teaches The method of claim 8 wherein the complex system comprises a distributed storage system. (Par. [0090-1] monitoring agent (#325) can monitor the cloud system (i.e. distributed storage system)) Regarding claim 10, Mueller and Chen teach claim 1 as shown above, and Mueller further teaches The method of claim 1 wherein the telemetry data is further stored in the data structure according to an event time. (Fig. 1a; Abs. a number of stored time series (i.e. according to event time) data variables are received by the user in the process of generating the correlation graph) Regarding claim 11, while worded slightly differently, is rejected under the same rationale as claim 1. Mueller further teaches a memory; and at least one processor (Par. [000312] system includes memory and processors) Regarding claim 12, while worded slightly differently, is rejected under the same rationale as claim 2. Regarding claim 13, while worded slightly differently, is rejected under the same rationale as claim 3. Regarding claim 14, while worded slightly differently, is rejected under the same rationale as claim 4. Regarding claim 15, while worded slightly differently, is rejected under the same rationale as claim 5. Regarding claim 16, while worded slightly differently, is rejected under the same rationale as claim 6. Regarding claim 17, while worded slightly differently, is rejected under the same rationale as claim 7. Regarding claim 18, while worded slightly differently, is rejected under the same rationale as claim 8. Regarding claim 19, while worded slightly differently, is rejected under the same rationale as claim 9. Regarding claim 20, while worded slightly differently, is rejected under the same rationale as claim 11. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached on (571) 272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /J MITCHELL CURRAN/Examiner, Art Unit 2161 /SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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Prosecution Timeline

Apr 22, 2024
Application Filed
Mar 15, 2025
Non-Final Rejection — §103
May 16, 2025
Interview Requested
May 28, 2025
Applicant Interview (Telephonic)
May 28, 2025
Examiner Interview Summary
Jun 23, 2025
Response Filed
Oct 01, 2025
Final Rejection — §103
Dec 08, 2025
Response after Non-Final Action
Jan 05, 2026
Request for Continued Examination
Jan 22, 2026
Response after Non-Final Action
Mar 13, 2026
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
61%
Grant Probability
96%
With Interview (+34.8%)
3y 6m
Median Time to Grant
High
PTA Risk
Based on 106 resolved cases by this examiner. Grant probability derived from career allow rate.

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