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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 August 4, 2026 has been entered.
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, 8-12, and 16-20 are rejected.
Claims 6, 7, 14, and 15 are objected to.
The following is a Non-Final Office Action in response to amendments and remarks filed August 4, 2026.
Response to Arguments
Regarding the 103 rejections, the rejections are withdrawn because the cited refences do not teach the newly amended limitations of the independent claims.
Regarding claim 8, Applicant asserts claim 8 is not mere duplication because claim 8 uses different inputs. Examiner respectfully does not find this assertion persuasive because Examiner does not find the use of additional fields is sufficiently dissimilar to the use fields recited in claim 1 to be more than mere duplication.
Regarding claims 3, 11, and 19, Applicant traverses the reliance on official notice. Examiner respectfully does not find this traversal adequate because the traversal does not state or explain why the noticed fact is not considered to be common knowledge or well-known in the art.
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”, further in view of Flinn, US Pub. No. 2021/0117814.
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, for display in a graphical user interface on a display system, the additional fields for display in an order determined using the weighted average (displays reports, e.g. ¶¶[0033], [0038]; see also ¶[0040] discussing sorting and weighting; ¶[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).
However the combination of Megiddo, Burhanuddin and Kavounis does teach but Flinn does teach:
generate, using one or more neural networks, for each of the additional fields predicted by the artificial intelligence system, a plurality of likelihood values associated with each respective additional field of the additional fields (uses neural networks to determines probabilities of semantic chains, ¶¶[0213]-[0214]; see also e.g., ¶¶[0042], [0207] and Fig. 1 and 14A discussing recommending content using combinations of semantic chains);
determine a weighted average based on the plurality of likelihood values generated for each of the additional fields (applies weights to chains, ¶[0191]; see also ¶¶[0189]-[0190] noting weights are probabilities; and ¶[0279] discussing various weightings);
and provide, for display in a graphical user interface on a display system, for display in an order determined using the weighted average (ranks recommendations, ¶¶[0139]-[0141]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the executive reports with predicted intent and hierarchical levels of KPIs of Megiddo, Burhanuddin and Kavounis with the fuzzy recommendations of Flinn 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, one of ordinary skill would have modified Megiddo, Burhanuddin and Kavounis to utilize fuzzy logic in situations where it is advantageous, e.g., for situations where the data is imprecise or noisy data effectively.
Regarding claim 4, the combination of Megiddo, Burhanuddin, Kavounis and Flinn 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, Kavounis and Flinn 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, 10 and 18 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, Kavounis and Flinn teaches all the limitations of claim 1 and Megiddo further teaches:
wherein the taxonomy comprises a number of HCM domains including a human resources domain (stored data includes employees, supervisors, etc., a client list, supplier list, or other list of business contacts, ¶[0041]).
However the combination of Megiddo, Burhanuddin, Kavounis and Flinn does not teach but Regier does teach:
a payroll domain (payroll, ¶[0457] and Fig. 8, ref. char. 82F)
a time domain (timekeeping Fig. 8, ref. char. 82F),
a benefits domain (representative of employees, Fig. 8, ref. char. 82F)
a performance management domain (termination, Fig. 8, ref. char. 82F)
a training & development domain (training, ¶[0386]),
and a talent acquisition domain (application process, Fig. 8, ref. char. 82F)
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, Burhanuddin, Kavounis and Flinn with the personnel information of Regier 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 based on user information , e.g. ¶[0041]. One of ordinary skill would have recognized these reports could be further by analyzing further data relevant to the business, e.g. the data listed in Regier.
Claims 10 and 18 recite similar limitations as claim 2 and accordingly are rejected for similar reasons as claim 2.
Claim(s) 3, 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Megiddo, Burhanuddin, Kavounis and Regier further in view of official notice.
Regarding claim 3, the combination of Megiddo, Burhanuddin, Kavounis and Regier 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, Kavounis and Regier 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 11 and 19 recite similar limitations as claim 3 and accordingly are rejected for similar reasons as claim 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
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 11-7.
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/BRENDAN S O'SHEA/Examiner, Art Unit 3626
