Prosecution Insights
Last updated: July 17, 2026
Application No. 18/437,006

REPORTING TAXONOMY

Final Rejection §103
Filed
Feb 08, 2024
Priority
Dec 01, 2020 — continuation of 11/900,328
Examiner
O'SHEA, BRENDAN S
Art Unit
3626
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
ADP Inc.
OA Round
4 (Final)
31%
Grant Probability
At Risk
5-6
OA Rounds
7m
Est. Remaining
69%
With Interview

Examiner Intelligence

Grants only 31% of cases
31%
Career Allowance Rate
57 granted / 185 resolved
-21.2% vs TC avg
Strong +39% interview lift
Without
With
+38.6%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
33 currently pending
Career history
241
Total Applications
across all art units

Statute-Specific Performance

§101
5.7%
-34.3% vs TC avg
§103
87.6%
+47.6% vs TC avg
§102
5.1%
-34.9% vs TC avg
§112
1.6%
-38.4% vs TC avg
Black line = Tech Center average estimate • Based on career data from 185 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 . Status of the Claims Claims 1-4, 6-12, and 14-20 are all the claims pending in the application. Claims 1, 9, and 17 are amended. Claims 1-4, 6-12, and 14-20 are rejected. The following is a Final Office Action in response to amendments and remarks filed Feb. 3, 2026. Response to Arguments Regarding the 103 rejections, the rejections are maintained for the following reasons. Applicant asserts the cited references do not teach existing reports sorted based on intents because Kavounis teaches relates to performance metrics which are not analogous to reports in the present claims. Examiner respectfully does not find this assertion persuasive because performance metrics are reporting performances and thus, under the broadest reasonable interpretation, are within the scope of reports. Further, Examiner notes one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Examiner finds Megiddo teaches many of the new limitations in ¶¶[0030]-[0031]. Please see below for the complete analysis of the newly amended claim limitations. Second, Applicant asserts the cited references do not teach a comparison of data fields to existing data fields because Burhanuddin does not teach the claimed comparison. Examiner respectfully does not find this assertion persuasive because one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Both the Megiddo and Burhanuddin references were used to teach this concept. Further, Examiner notes Burhanuddin teaches determining what data a user would like to view based on data in previously viewed report, ¶[0074] and thus teaches a comparison of data fields to existing data field. Third, Applicant asserts the cited references do not teach predicting additional fields from data in existing reports. Examiner respectfully does not find this assertion persuasive because Megiddo explicitly teaches the customized reports are emphasizing variations in data from previous reports, ¶¶[0060]-[0061]. In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art. Claim Objections Claims 6, 7, 14, and 15 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. 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 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, 4, 8, 9, 12, 16, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Megiddo et al, US Pub. No. 2009/0106059, herein referred to as "Megiddo" in view of Burhanuddin et al, US Pub. No. 2018/0330248, herein referred to as "Burhanuddin", further in view of Kavounis, US Pub. No. 2002/0116213, herein referred to as “Kavounis”. Regarding claim 1 Megiddo teaches: a computer system comprising one or more processors, coupled with memory, to (e.g. ¶¶[0022]-[0023], [0082]-[0083] and Fig. 11): identify a subset of data fields for inclusion in a new report (receive set of results, ¶[0039]; see also ¶[0031] discussing data in set of results); train one or more machine learning models of an artificial intelligence system of the computer system, using a taxonomy comprising a hierarchical classification scheme including existing reports (uses various machine learning techniques including neural network methodologies, ¶¶[0065], [0066], [0071], that is trained from previous collected data, ¶[0063] and system collects prior reports, ¶[0030], and stored data includes employees, supervisors, etc., a client list, supplier list, or other list of business contacts, ¶[0041]) wherein the intents of the existing reports used to sort the existing reports into the HCM categories are determined from respective sets of data fields included in the existing reports (prior reports are segregated based suitable qualification of data pertinent an employee, manager, executive, member of a board of directors, etc., ¶[0030]; see also ¶[0031] discussing using tags to identify pertinent subjects segregated in the report store) determine, using the artificial intelligence system in the computer system, the new report based on a comparison of the subset of data fields to the existing reports to the respective sets of data fields included in, wherein the intent for the new report includes an HCM category of the HCM categories (generates report containing information pertinent to the particular user based on a comparison to prior information, ¶[0074]; see also ¶[0028] noting executive summary is intended to provide a quick overview of information most likely of interest; see also e.g., ¶), predict, using the artificial intelligence system based on the intent determined by the artificial intelligence system and from the respective sets of data fields included in the existing reports, additional fields for the new report (generates report containing information pertinent to the particular user, ¶[0074]; see also e.g., ¶¶[0060]-[0061] noting report is customized to the user to emphasize changes from prior data); and provide the set of additional fields for display in a graphical user interface on a display system (displays reports, e.g. ¶¶[0033], [0038]; see also ¶[0088] discussing output devices like displays). However Megiddo does not explicitly teach but Burhanuddin does teach: to determine, using an artificial intelligence system in the computer system, an intent for the new report (predicts user intent using machine learning, e.g. ¶¶[0056], [0094] to generate list of recommended reports, e.g. ¶[0101]); based on a comparison of the subset of data fields to the existing reports (intent is determined based on comparisons to past behavior, where intent is the likelihood that the user would like to view particular data (e.g., a data chart) based on past data, e.g., ¶¶[0069], [0074]) Further, it would have been obvious before the effective filing date of the claimed invention, to combine the executive reports of Megiddo with the predicted intent of Burhanuddin because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, Megiddo teaches generating customized reports for a user, e.g. Abstract. One of ordinary skill would have recognized these reports could be further personalized by determining the intent of the user, e.g. as taught by Burhanuddin. However the combination of Megiddo and Burhanuddin does not teach but Kavounis does teach: including existing reports sorted into Human Capital Management (HCM) categories based on intents of the existing reports (KPIs are grouped into hierarchical levels that generally align to the user's network hierarchy, ¶[0084] and Fig. 3; see also ¶¶[0085]-[0087] discussing examples). Further, it would have been obvious before the effective filing date of the claimed invention, to combine the executive reports with predicted intent of Megiddo and Burhanuddin with the hierarchical levels of KPIs of Kavounis because Kavounis explicitly teaches the hierarchical levels of KPIs are useful because different users in different levels of an organization look at the performance in different ways and typically want to analyze the data differently, ¶[0084]; see also ¶¶[0085]-[0087] discussing examples; and MPEP 2143.I(G). Regarding claim 4, the combination of Megiddo, Burhanuddin and Kavounis teaches all the limitations of claim 1 and Megiddo further teaches: wherein in identifying the subset of data fields, the computer system is further configured: to receive a selection of the subset of data fields in a user input generated by at least one of a human machine interface or the artificial intelligence system (set of data is filtered based on user specified information, ¶[0076]; see also ¶[0074] noting report emphasizes changes in user-specified information), wherein the subset of data fields is selected from data fields of human resources information generated in providing human resource services (filtering is based on user contact list, ¶[0076]; see also ¶[0041] employees, supervisors, etc., a client list, supplier list, or other list of business contacts). Regarding claim 8, the combination of Megiddo, Burhanuddin and Kavounis teaches all the limitations of claim 1 and Kavounis further teaches: wherein the report manager is further configured: in response to receiving a selection of a field from the additional fields (updates the customized data to reflect a user's modifications, ¶[0062]). However the combination of Megiddo and Burhanuddin does not explicitly teach: to redetermine, using the artificial intelligence system, the intent of the new report based on the subset and the field selected; to determine, using the artificial intelligence system, second additional fields based on the redetermined intent of the new report; and to display the second set of additional fields in the graphical user interface. Nevertheless, it would have been obvious at the time of filing to redetermine the intent and determine a second set of additional data fields because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, claim 8 essentially recites repeating the steps of claim 1 based on user input and Examiner finds no evidence repeating the steps of claim 1 would produce new and unexpected results. Claims 9, 12, and 16 recite similar limitations as claims 1, 4, and 8 and accordingly are rejected for similar reasons as claims 1, 4, and 8. Claims 17 and 20 recite similar limitations as claims 1 and 4 and further recite computer-readable storage media and program code. Megiddo teaches these limitations in ¶¶[0022]-[0023]. Accordingly claims 17 and 20 are rejected for similar reasons as claims 1 and 4. Claim(s) 2, 3, 10, 11, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Megiddo, Burhanuddin and Kavounis further in view of Regier et al, US Pub. No. 2019/0392541 herein referred to as "Regier". Regarding claim 2, the combination of Megiddo, Burhanuddin and Kavounis teaches all the limitations of claim 1 and Megiddo further teaches: wherein the HCM categories are a part of at least one of a number of HCM domains including a payroll domain, a human resources domain, a time domain, a benefits domain, a performance management domain, a training and development domain, or a talent acquisition domain (stored data includes employees, supervisors, etc., a client list, supplier list, or other list of business contacts, ¶[0041]). Regarding claim 3, the combination of Megiddo, Burhanuddin and Kavounis teaches all the limitations of claim 2 and does not explicitly teach: wherein each of the number of HCM domains comprises one or more of the HCM categories related to a corresponding HCM domain; wherein the payroll domain comprises the HCM domains including compensation, liens and garnishments, information, direct deposit, and taxes; wherein the human resources domain comprises the HCM domains including organization structure, employee, lifecycle transactions, job profile, policies, grievances, authorization, and cost center; wherein the time domain comprises the HCM domains including scheduling, attendance, and balance; wherein the benefits domain comprises the HCM domains including plans, costs, retirement, enrollments, dependents, and insurance; wherein the performance management domain comprises the HCM domains including reviews, succession planning, goals, recognition, and rewards; wherein the training and development domain comprises the HCM domains including education, training, and compliance; and wherein the talent acquisition domain comprises the HCM domains including requisitions, job postings, applications, talent profile, and referrals. Nevertheless the limitations of claim 3 are obvious in light of Megiddo, Burhanuddin and Kavounis because Examiner has taken official notice that a taxonomy including all the data listed claim 3 is common knowledge, see MPEP 2144.03. That is, Examiner has taken official notice that human resource administration systems are known in the art and organize the claimed data in manners sufficiently similar the recitations of claim 3 such that claim 3 is obvious. Claims 10, 11, 18, and 19 recite similar limitations as claims 2 and 3 and accordingly are rejected for similar reasons as claims 2 and 3. Allowable Subject Matter Claims 6, 7, 14, and 15 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 Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6. 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, Nathan Uber can be reached at (571) 270-3923. 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. /BRENDAN S O'SHEA/Examiner, Art Unit 3626
Read full office action

Prosecution Timeline

Show 10 earlier events
Jul 01, 2025
Response after Non-Final Action
Jul 15, 2025
Request for Continued Examination
Jul 21, 2025
Response after Non-Final Action
Nov 05, 2025
Non-Final Rejection mailed — §103
Jan 27, 2026
Applicant Interview (Telephonic)
Jan 27, 2026
Examiner Interview Summary
Feb 03, 2026
Response Filed
Jun 03, 2026
Final Rejection mailed — §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12632871
METHOD AND SYSTEM FOR IMPROVING CUSTOMER EXPERIENCE BASED ON A DEVICE CONTEXT-DRIVEN RECOMMENDATION
3y 5m to grant Granted May 19, 2026
Patent 12541807
Machine Learning System and Method for Contextual Decision-Making in Watchlist Screening and Monitoring
1y 9m to grant Granted Feb 03, 2026
Patent 12505496
SYSTEM FOR INTERACTION REGARDING REAL ESTATE SALES
2y 9m to grant Granted Dec 23, 2025
Patent 12417438
A System for Workforce Talent Discovery, Tracking and Development
5y 0m to grant Granted Sep 16, 2025
Patent 12373794
METHOD AND SYSTEM FOR RESUME DATA EXTRACTION
3y 8m to grant Granted Jul 29, 2025
Study what changed to get past this examiner. Based on 5 most recent grants.

Strategy Recommendation AI-generated — please review before filing

Get a prosecution strategy drawn from examiner precedents, rejection analysis, and claim mapping.
Typically takes 5-10 seconds — AI-generated, attorney review required before filing

Prosecution Projections

5-6
Expected OA Rounds
31%
Grant Probability
69%
With Interview (+38.6%)
3y 1m (~7m remaining)
Median Time to Grant
High
PTA Risk
Based on 185 resolved cases by this examiner. Grant probability derived from career allowance 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