Prosecution Insights
Last updated: April 19, 2026
Application No. 18/634,183

PREDICTING RESOURCE-RELATED FAILURES USING MULTI-DIMENSIONAL-BASED MACHINE LEARNING TECHNIQUES

Non-Final OA §102
Filed
Apr 12, 2024
Examiner
LIN, KATHERINE Y
Art Unit
2113
Tech Center
2100 — Computer Architecture & Software
Assignee
DELL PRODUCTS, L.P.
OA Round
1 (Non-Final)
91%
Grant Probability
Favorable
1-2
OA Rounds
2y 5m
To Grant
98%
With Interview

Examiner Intelligence

Grants 91% — above average
91%
Career Allow Rate
320 granted / 351 resolved
+36.2% vs TC avg
Moderate +7% lift
Without
With
+7.1%
Interview Lift
resolved cases with interview
Typical timeline
2y 5m
Avg Prosecution
31 currently pending
Career history
382
Total Applications
across all art units

Statute-Specific Performance

§101
23.4%
-16.6% vs TC avg
§103
36.8%
-3.2% vs TC avg
§102
22.1%
-17.9% vs TC avg
§112
6.3%
-33.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 351 resolved cases

Office Action

§102
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 § 102 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, 7-8, 10-12, 16-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhan et al. (US11610679). Zhan discloses: 1. A computer-implemented method comprising: obtaining data pertaining to at least one resource-related activity involving at least one resource and one or more users; (col 1, ln 55-56) predicting one or more failures associated with the at least one resource-related activity by processing at least a portion of the obtained data using one or more machine learning techniques; (col 2, ln 30-35) predicting one or more reasons attributed to at least one of the one or more predicted failures by processing the at least a portion of the obtained data using the one or more machine learning techniques; and (col 2, ln 30-35) performing one or more automated actions based at least in part on at least a portion of the one or more predicted failures and at least a portion of the one or more predicted reasons; (col 7, ln 60-65) wherein the method is performed by at least one processing device comprising a processor coupled to a memory. (col 3, ln 6) 2. The computer-implemented method of claim 1, wherein predicting one or more failures associated with the at least one resource-related activity comprises processing at least a portion of the obtained data using at least one multi-output neural network model. (fig 4: multi-task learning) 3. The computer-implemented method of claim 2, wherein predicting one or more reasons attributed to at least one of the one or more predicted failures comprises processing the at least a portion of the obtained data using the at least one multi-output neural network model. (fig 4: multi-task learning; col 19, 20-30) 7. The computer-implemented method of claim 1, wherein the at least one resource- related activity is ongoing, and wherein performing one or more automated actions comprises automatically initiating one or more course correction activities directed at avoid the at least a portion of the one or more predicted failures and related to the at least a portion of the one or more predicted reasons. (col 7, ln 60-65) 8. The computer-implemented method of claim 1, wherein performing one or more automated actions comprises automatically training at least a portion of the one or more machine learning techniques using feedback related to one or more of the at least a portion of the one or more predicted failures and the at least a portion of the one or more predicted reasons. (col 17, ln 40-50) Claim(s) 10-12 is/are rejected as being the medium implemented by the method of claim(s) 1-3, and is/are rejected on the same grounds. Claim(s) 16-18 is/are rejected as being the apparatus implemented by the method of claim(s) 1-3, and is/are rejected on the same grounds. Allowable Subject Matter Claim(s) 4-6, 9, 13-15, 19-20 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE LIN whose telephone number is (571)431-0706. The examiner can normally be reached Monday-Friday; 8 a.m. - 5 p.m. EST. 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, 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. 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. /KATHERINE LIN/Primary Examiner, Art Unit 2113
Read full office action

Prosecution Timeline

Apr 12, 2024
Application Filed
Mar 07, 2026
Non-Final Rejection — §102 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12596953
QUANTUM ERROR CORRECTION USING NEURAL NETWORKS
2y 5m to grant Granted Apr 07, 2026
Patent 12591476
EMPTY PAGE DETECTION
2y 5m to grant Granted Mar 31, 2026
Patent 12585556
ACTIVE COMPONENT DRIVEN COMPUTATIONAL SERVER RELIABILITY AND FAILURE PREVENTION SYSTEM
2y 5m to grant Granted Mar 24, 2026
Patent 12585530
SINGLE SIGNAL DEBUG PORT
2y 5m to grant Granted Mar 24, 2026
Patent 12585560
REFINING PARAMETER SETTINGS FOR COPY SERVICES
2y 5m to grant Granted Mar 24, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

AI Strategy Recommendation

Get an AI-powered prosecution strategy using examiner precedents, rejection analysis, and claim mapping.
Powered by AI — typically takes 5-10 seconds

Prosecution Projections

1-2
Expected OA Rounds
91%
Grant Probability
98%
With Interview (+7.1%)
2y 5m
Median Time to Grant
Low
PTA Risk
Based on 351 resolved cases by this examiner. Grant probability derived from career allow rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month