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 .
Response to Amendment
2. The Amendment filed on January 21st 2026 has been entered. Claims 19, 23 and 27 have been amended and claims 1 – 18, 22, 26 and 30 - 42 have been cancelled with claims 44 and 45 newly added. Claims 19 – 21, 23 – 25, 27 – 29 and 43 - 45 are currently pending.
Response to Arguments
35 U.S.C. §101
3. Applicant's arguments, see Remarks pp. 10 -19, filed January 21st 2026, with
respect to the rejections of claims 19 – 21, 23 – 25 and 27 - 29 under 35 U.S.C. §103 have been fully considered and they are persuasive.
Applicant argues that the amendments to the independent claims include using a training data set to affect transformations in that data utilizing machine learning and thus overcomes the statutory rejection
Examiner respectfully agrees. The transformations affected by the machine language algorithm include a practical solution and thus overcome the statutory rejection. The statutory rejection is withdrawn
35 U.S.C. §103
4. Applicant's arguments, see Remarks pp. 19 -22, filed January 21st 2026, with
respect to the rejections of claims 19 – 21, 23 – 25 and 27 - 29 under 35 U.S.C. §103 have been fully considered and they are persuasive.
Applicant argues that the amendments to the independent claims are not taught by art of record.
Examiner agrees in part and disagrees in part. Yildirmaz teaches inputting a training data into a machine language component but does not teach a graphical display of tools with selectable output formats
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of Carpenter et al., (United States Patent Publication Number 20190205811) hereinafter Carpenter
Claim Rejections – 35 U.S.C. §103
5. 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.
6. The factual inquiries set forth in Graham v John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied for establishing a background for determining obviousness
under 35 U.S.C. 103 are summarized as follows:
a. Determining the scope and contents of the prior art
b. Ascertaining the differences between the prior art and the claims at issue
c. Resolving the level of ordinary skill in the pertinent art
d. Considering objective evidence present in the application indicating
obviousness or nonobviousness
Claims 19 – 21, 23 – 25, 27 - 29 and 43 – 45 are rejected under 35 U.S.C. 103 as being unpatentable over Yildirmaz et al., (United States Patent Publication Number 20210004745) hereinafter Yildirmaz, in view of April et al. (United States Patent Publication Number 20110015958), hereinafter April and in further view of Carpenter et al., (United States Patent Publication Number 20190205811) hereinafter Carpenter
Regarding claim 19 Yildirmaz teaches a non-transitory computer readable medium storing instructions that, (Program code located in network data processing system 100 may be stored on a computer-recordable storage medium [0039]) when executed by at least one processor, (executed by a processor in processor unit 1204. [0094]) cause the at least one processor (processor in processor unit 1204. [0094]) to perform operations (perform the action or operation described. [0101]) comprising: receiving a training set of data, (training data and test data [0070]) the training set of data (training data and test data [0070]) including a plurality of training variables including at least one attrition datapoint; (voluntary employee turnover data associated with factors [0067])training a machine learning model using the plurality of training variables; ( Process 500 then performs iterative analysis on the training data by applying predictive algorithms to construct a predictive model (step 508). There are three main categories of machine learning: supervised, unsupervised, and reinforcement. Supervised machine learning comprises providing the machine with test data and the correct output value of the data … The algorithm, through trial and error, deciphers the patterns that exist between the input training data and the known output values to create a model that can reproduce the same underlying rules with new data [0071]) receiving data (gather data [0047]) EXAMPLE financial data [0050]; employment data [0051]; industry/sector data [0052]; training data and test data [0070] from a plurality of disparate data sources, (Information gathering 252 comprises internal 254 and external 256. Internal 254 is configured to gather data from internal databases 260. External 256 is configured to gather data from external databases 270 [0047]) the data (data [0047]) EXAMPLE financial data [0050]; employment data [0051]; industry/sector data [0052]; training data and test data [0070] including a plurality of variables, (Fig. 6, (600) independent variables, dependent variables [0067]) wherein each variable (each variable [0060]) of the plurality of variables (Fig. 6, (600) independent variables, dependent variables [0067]) is associated with (associated with [0055]) a data type and an entity (employee [0028]) of a plurality of entities (employees in a peer group [0056], managers [0057], employers [0083], firms [0084]) such as “plurality of entities” in a position; (supervisory jobs [0057]) such as “position” SEE ALSO good positions to apply for [0058] inputting the data into the trained machine learning model; (The data is divided by rows, with 70-80% used for training [0070]) distilling (constructing [0087]) such as “distilling” the data (data [0047]) EXAMPLE financial data [0050]; employment data [0051]; industry/sector data [0052]; training data and test data [0070] from the trained machine learning model ( applying predictive algorithms to construct a predictive model (step 508). There are three main categories of machine learning: supervised, unsupervised, and reinforcement [0071]) into a plurality of indexes (index values [0075]) to convert (converted [0075]) the data (data [0047]) EXAMPLE financial data [0050]; employment data [0051]; industry/sector data [0052]; training data and test data [0070] into the plurality of indexes (index values [0075]) to be usable by a single data structure, (common index framework [0075]) retrieving (gathering [0047]) a set of data elements (growth potential index and voluntary turnover rate [0079]) from the plurality of indexes, (index values [0075]) wherein the set of data elements(growth potential index and voluntary turnover rate [0079]) includes information (distribution of index values across all firms in the sample [0084]) associated with (associated with [0055]) the plurality of entities; (employees in a peer group [0056], managers [0057], employers [0083]) such as “plurality of entities” assigning an attrition index (The predicted voluntary employee turnover is then converted into a percentage of observed employee turnover to form an index (step 516). The index is calculated by dividing the observed value by the predicted value and then multiplying by 100 [0083])such as “attrition index” to each of the information (distribution of index values across all firms in the sample [0084]) included in the set of data elements; (growth potential index and voluntary turnover rate [0079]) and predicting, (predict [0048]) using the attrition index, (The predicted voluntary employee turnover is then converted into a percentage of observed employee turnover to form an index (step 516). The index is calculated by dividing the observed value by the predicted value and then multiplying by 100 [0083])such as “attrition index” attrition (employee turnover [0051]) such as “attrition” for each entity (employee [0055]) of the plurality of entities, (employees in a peer group [0056], managers [0057], employers [0083]) such as “plurality of entities” wherein the attrition (employee turnover [0051]) such as “attrition” is a binary event. (voluntary employee turnover; employee growth opportunity [0067]) such as “binary event”
Yildirmaz does not fully disclose by: sending for display on a graphical user interface, using data visualization tools, the predicted attrition; transforming the predicted attrition via the graphical user interface based on a first transformation and a second transformation; sending for display on the graphical user interface the transformed predicted attrition with multi-format user-selectable output format options; and presenting a selected option of the multi-format user selectable output format options for data access and analysis, assigning a binary value to each variable of the plurality of variables, wherein each variable is a categorical value, a numerical value, or an ordinal value; generating, using the binary value of each variable, an index for each data type and each entity of the plurality of entities; and storing each index in a database;
April teaches by: assigning a binary value to each variable (Fig. 12, (1250) Gender, “M” “F” ; Dependents, “Y” “N” and Personality Type, “Contrib” “Vision” [0024], [0097]) of the plurality of variables, (Fig. 12, (1250) Gender, Dependents, and Personality Type [0024], [0097]) such as “plurality of variables” wherein each variable is a categorical value, (Fig. 12, (1250) Gender [0024], [0097]) such as “categorical value” a numerical value, (Fig. 12, (1250) Dependents [0024], [0097]) such as “numerical value” or an ordinal value; (Fig. 12, (1250) Personality Type [0024], [0097]) such as “ordinal value” generating, (determining [0055], [0120]) such as “generating” using the binary value of each variable, (Fig. 12, (1250) Gender, “M” “F” ; Dependents, “Y” “N” and Personality Type, “Contrib” “Vision” [0024], [0097]) an index (Fig. 5, (550) movement probability [0079]) such as “index” for each data type and each entity (employee [0078]) of the plurality of entities; (employees [0078]) and storing (storing [0126]) each index (Fig. 5, (550) movement probability [0079]) such as “index” in a database; (data store [0048])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yildirmaz to incorporate the teachings of April whereby assigning a binary value to each variable of the plurality of variables, wherein each variable is a categorical value, a numerical value, or an ordinal value; generating, using the binary value of each variable, an index for each data type and each entity of the plurality of entities; and storing each index in a database. By doing so the impact of turnover and movement may be modeled, and the tradeoff between readiness (the ability of an organization to staff its labor requirements in a timely manner) and cost may be assessed. April [0005]
Carpenter teaches sending for display (displayed [0021]) on a graphical user interface, (the dashboard [0013]) using data visualization tools, (manipulative interactive charts [0024]) the predicted attrition; (employee turnover [0024]) transforming the predicted attrition (employee turnover [0024]) via the graphical user interface (the dashboard [0013]) based on a first transformation (turnover analysis on those employees eligible for and/or participating in the employer's well-being program [0026]) and a second transformation; ( as well as turnover analysis in relation to employees' well-being program participation. [0026])sending for display (displayed [0021]) on the graphical user interface(the dashboard [0013]) the transformed predicted attrition (Data relating to the employee's level of activity, in tum, can be based on the number of points and levels the user achieved in their program (both metrics may not be standardized across organizations) and employee activity scores (a standardized score showing the extent of employee participation). The Dashboard can then use this data to predict the probability of turnover for an employee, with a return value between zero and one. When this data is aggregated by the Dashboard, the Dashboard can then report the likelihood of turnover, for example, for business units. [0026]) with multi-format user-selectable output format options; (Fig. 2, dashboard including pie-chart, linear graph and bar graph [0005]) and presenting a selected option (Fig. 2 anyone of Turnover rates by Limeade Registration status, Turnover and registration rates by demographic and Turnover rates by Challenge Participation [0005]) of the multi-format user selectable output format options(Fig. 2, dashboard including pie-chart, linear graph and bar graph [0005]) for data access and analysis ( analyzing the results of these models, the AUROC proved to be an accurate way to measure the performance of the models. [0031])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yildirmaz in view of April to incorporate the teachings of Carpenter wherein sending for display on a graphical user interface, using data visualization tools, the predicted attrition; transforming the predicted attrition via the graphical user interface based on a first transformation and a second transformation; sending for display on the graphical user interface the transformed predicted attrition with multi-format user-selectable output format options; and presenting a selected option of the multi-format user selectable output format options for data access and analysis. By doing so with the Dashboard as a tool, companies can focus on employee turnover because the cost of replacing an employee can be devastating to budget and morale-and in many cases, turnover is avoidable. Carpenter [0023]
Claims 23 and 27 correspond to claim 19 and are rejected accordingly
Regarding claim 20 Yildirmaz in view of April and Carpenter teaches the non-transitory computer readable medium of the claim 19,
Yildirmaz as modified further teaches the operations (action or operation described. [0101]) further comprising: creating a distribution of attrition (Figs. 9 10 are negative relationship between a growth potential index and voluntary turnover rate at both an industry level and a firm level [0024], [0025], [0079]) for the position, (supervisory jobs [0057]) such as “position” wherein the distribution (Figs. 9 10 are negative relationship between a growth potential index and voluntary turnover rate at both an industry level and a firm level [0024], [0025], [0079]) uses the attrition (employee turnover [0051]) such as “attrition” of each entity (employee [0055]) of the plurality of entities, (employees in a peer group [0056], managers [0057], employers [0083]) such as “plurality of entities”; and generating, (generate [0070]) using the distribution, (Fig. 11 the growth potential index as a distribution of index values across all firms in the sample. [0084]) in the position (supervisory jobs [0057]) such as “position” over a duration of time (the employee turnover rankings are compared to the actual observed employee turnover over a subsequent time period ( e.g., month, quarter, year, etc.) [0086])
Claims 24 and 28 correspond to claim 20 and are rejected accordingly
Regarding claim 21 Yildirmaz in view of April and Carpenter teaches the non-transitory computer readable medium of the claim 20,
Yildirmaz as modified further teaches the operations (action or operation described. [0101]) further comprising generating (generate [0070]) a visualization of the distribution (Figs. 9 “Standardized Growth Potential Index, Industry Level” [0024], [0079] Fig. 10, “Standardized Growth Potential Index, All Firms, Equal Weights” [0025], [0079] and Fig. 11, “Distribution of Firms by Grown Potential Index” [0026], [0084])
Claims 25 and 29 correspond to claim 21 and are rejected accordingly
Regarding claim 43 Yildirmaz in view of April and Carpenter teaches the non-transitory computer readable medium of the claim 19,
Yildirmaz as modified further does not fully disclose wherein the multi-format user-selectable output format options include graphs, videos, images, or plain text
Carpenter teaches wherein the multi-format user-selectable output format options include graphs, videos, images, or plain text(Fig. 2, dashboard including pie-chart, linear graph and bar graph [0005])
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yildirmaz in view of April to incorporate the teachings of Carpenter wherein the multi-format user-selectable output format options include graphs, videos, images, or plain text. By doing so employers can review and manipulate interactive charts to understand population turnover trends in, for example, departments, locations, countries, and other categories, by slicing the data by various demographic tags. Carpenter [0024]
Claims 44 and 45 correspond to claim 43 and are rejected accordingly
Conclusion
7. 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 extension fee 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 date of this
final action.
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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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