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 07/28/2026 has been entered. Claims 1, 8 and 15 have been amended. Claims 1 – 20 are currently pending.
Response to Arguments
35 U.S.C. §103
3. Applicant's arguments, see Remarks pp. 13 -17, filed 07/28/2026, with
respect to the rejections of claims 1-20 under 35 U.S.C. §103 have been fully
considered and they are persuasive in part and non-persuasive in part
First, Applicant argues that the Scarpelli reference does not teach, “receiving data from a plurality of disparate data sources.”
Examiner respectfully disagrees and submits that within the broadest reasonable interpretation, disparate data sources are separate information systems, databases, or files that operate independently and do not naturally connect, communicate, or share compatible structures. Scarpelli teaches disparate sources of data such as human resource information system back end vendors 34 that provide employee records and data concerning compensation and benefits and internet service providers that provide sources of data as well.
Secondly, Applicant argues that the Dutta reference does not disclose the claimed language "extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions.” Neither the extraction limitation, or subsequent use of the extracted associations are disclosed in Dutta.
Examiner respectfully disagrees and submits that data mining does not happen in a vacuum. A POSIT will understand that a data mining process involves data cleaning, data integration, data selection/extraction, data transformation and data mining. Dutta teaches in paragraph [0033] a predictive model applied to the associated data in such a data mining process.
Third, Applicant argues that the amendments to the independent claims are not taught by the references on record
Examiner respectfully agrees.
Upon further consideration new grounds of rejection have been necessitated due
to Applicant's amendments and are made in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch and Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick
Claim Rejections – 35 U.S.C. §103
4. 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.
5. 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 1, 2, 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019 in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch and in further view of Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick
Regarding claim 1 Scarpelli teaches a non-transitory computer readable medium (a large permanent memory System [0018]) such as “non-transitory computer readable medium” storing instructions that, (software application [0018]) when executed by a processor, (a central processor [0018]) cause the processor (a central processor [0018]) to perform operations (enabled operations [0019]) comprising: receiving data from a plurality of disparate data sources, (The system software 31 may be operatively
Connected by buss or network link to human resource information system back end vendors 34 that provide employee records and data concerning compensation and benefits. It may also be connected to internet service providers 36 for access to data sources and storage. [0019]) the data (employee records and data [0019]) including a first (name, subject(s) taught, grade, tenure status, info Edit, and evaluations [0023]) and second plurality of variables; (unsatisfactory, basic, proficient, distinguished, or not applicable ratings. [0024]) wherein a first plurality of variables (name, subject(s) taught, grade, tenure status, info Edit, and evaluations [0023]) is associated with one entity (teacher [0023]) of a plurality of entities (staff list [0023]) in a position of a plurality of positions; (various administrative positions [0028]) wherein a second plurality of variables (unsatisfactory, basic, proficient, distinguished, or not applicable ratings. [0024]) is associated with a performance metric (teaching performance [0024]) of a plurality of performance metrics (rubrics [0028]) associated with the each of the plurality of entities; (staff list [0023]) wherein the plurality of disparate data sources (The system software 31 may be operatively Connected by buss or network link to human resource information system back end vendors 34 that provide employee records and data concerning compensation and benefits. It may also be connected to internet service providers 36 for access to data sources and storage. [0019]) includes at least one of: one or more input datasets, (a convenient database [0039]) one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring;
Scarpelli does not fully disclose generating a plurality of indexes, where the plurality of indexes comprises: a first index associated with the plurality of positions;
a second index associated with the plurality of entities; a third index associated with one or more characteristics associated with each of the plurality of entities;
and a fourth index associated with the plurality of performance metrics; and wherein, the received data includes more than one data type; aggregating the received data into a data store, and wherein, the received data includes more than one data type; aggregating the received data into a data store, wherein the received data is a uniform data type storing the plurality of indexes in a database; extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; generating, based on each of the plurality of entities, the associated performance metrics, and the extracted one or more associations, a flight index for each of the entities; wherein the flight index is a statistical measure of a likelihood that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; generating, based on each of the plurality of entities and the associated performance metrics, a performance index to each of the entities; comparing the flight index for each of the entities with the performance index of the same entity; and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher than a threshold flight probability; implementing, based on the identification, one or more changes to one or more policies of the organization; wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time.
Dutta referencing Porschia teaches generating a plurality of indexes, (collect and analyze attributes [0033]) such as “plurality of indexes” where the plurality of indexes (collect and analyze attributes [0033]) such as “plurality of indexes” comprises: a first index associated with the plurality of positions; (attribute related to job responsibilities [0033]) such as “a first index” SEE ALSO Fig 4 “job title” a second index associated with the plurality of entities; (ABS., plurality of workers) (attributes related to all workers [0025]/company employees [0033]) such as “a second index associated with the plurality of entities” a third index associated with one or more characteristics associated with each of the plurality of entities; (attributes related to an employee’s background [0033]) such as “a third index” and a fourth index associated with the plurality of performance metrics; (attributes related to an employee’s past performance [0033]) such as “a fourth index” storing the plurality of indexes in a database; (database system 30 may store employee data, such as job responsibilities, past performance, compensation, and any other relevant attributes [0031]) extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; (Predictive analytic module 16 applies a data mining model to those attributes. The data mining model analyzes the attributes as they relate to all employees and identifies a pattern between the attributes and the future performance of the employees [0033]) generating, based on each of the plurality of entities, (all workers [0025]/company employees [0033]) the associated performance metrics, (employee’s past performance [0033]) and the extracted one or more associations, (identified pattern between the attributes and the future performance of the employees [0033]) a flight index (attrition for a worker [0025]) such as “flight index” for each of the entities; (a predicted attrition section 410 that shows the likelihood of an employee leaving. Predicted attrition section 410 includes an "Individual" column which shows the predicted attrition for each of the individual workers as high, medium or low. [0057]) SEE Fig 5B “attrition for a team or group of workers” [0059] wherein the flight index(attrition for a worker [0025]) such as “flight index” is a statistical measure of a likelihood (probability of attrition [0059]) that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; (likelihood that an employee leaves a company [0074]) SEE ALSO Page 1, Porschia Parker “accepting a role that is better than opportunity than your current position” generating, based on each of the plurality of entities (all workers [0025]/company employees [0033])and the associated performance metrics, (employee’s past performance [0033])a performance index to each of the entities; (ABS., predict the future characteristic of a worker. In one
example, the future characteristic is the future performance of the employee) (Fig. 2 (220) predict future performance [0042]) comparing the flight index for each of the entities with the performance index of the same entity; (Fig. 5A illustrates an example of a predicted worker performance and attrition user interface 500 that shows the
predicted performance and attrition for each worker in a team or group. Predicted worker performance and attrition user interface 500 includes a chart view 510 which graphically represents the predicted attrition and predicted performance
for each worker in an XY chart. The y-axis of chart view 510 shows the predicted attrition, while the x-axis shows the predicted performance. Predicted worker performance and attrition user interface 500 also includes a table view 510 that
lists each of the workers, their average predicted attrition level, and their average predicted performance level in a table format. [0058]) and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher (Predicted worker performance and attrition user interface 500 allows managers to easily identify those employees or teams that are predicted high performers and are also predicted to be at a high risk of loss. [0058]) (Table view 620 lists the individual or team name, actual performance rating for the individual or team, the probability of attrition, the predicted attrition reason, the predicted performance level [0060]) than a threshold flight probability; (predicted attrition rated as “medium” [0057]) such as “threshold flight probability” implementing, based on the identification, one or more changes to one or more policies of the organization; (As a result,
managers are able to take necessary steps to retain those employees or groups before they make a decision to leave [0058]) wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; (the organization's human resource system is able to predict, forewarn and help managers to take corrective actions to improve organizational stability. [0027]) SEE ALSO Page 1, Porschia Parker “accepting a role that is better than opportunity than your current position” and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time (Fig. 11FIG. 11 illustrates an example of a probability of attrition graph 1100 according to one embodiment. According to this embodiment, the manager can view the worker's predicted attrition and performance both before and after the proposed action. For example, each time the manager changes an attribute, such as a grade change or salary change, the probability of attrition graph 1100 is updated to show the effect of the change. Similar to FIG. 5, the y-axis of the probability of attrition graph 1100 shows the predicted attrition, while the x-axis shows the predicted performance [0070])
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 Scarpelli to incorporate the teachings of Dutta referencing Porschia whereby generating a plurality of indexes, where the plurality of indexes comprises: a first index associated with the plurality of positions; a second index associated with the plurality of entities; a third index associated with one or more characteristics associated with each of the plurality of entities; and a fourth index associated with the plurality of performance metrics; storing the plurality of indexes in a database; extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; generating, based on each of the plurality of entities, the associated performance metrics, and the extracted one or more associations, a flight index for each of the entities; wherein the flight index is a statistical measure of a likelihood that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; generating, based on each of the plurality of entities and the associated performance metrics, a performance index to each of the entities; comparing the flight index for each of the entities with the performance index of the same entity; and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher than a threshold flight probability; implementing, based on the identification, one or more changes to one or more policies of the organization; wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time. By doing so a pattern can be identified between the attributes and a future characteristic of the workers, and using the identified pattern to predict the future characteristic of a worker. Dutta [0006]
Kirsch teaches and wherein, the received data (received calendar information [0069]) includes more than one data type; (in one or more of a CalDAV format, an MS Exchange format, a Google calendar format, or another standard calendar format, or may use a format native to a [0069])aggregating the received data (The mobile device 120 then aggregates 506 the calendar data to identify and remove duplicate entries. [0069]) into a data store, (calendar database [0062], [0081]) wherein the received data (received calendar information [0069]) is a uniform data type (This step may also include processing the data into a uniform data type and container size [0069])
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 Scarpelli in view of Dutta to incorporate the teachings of Kirsch and wherein, the received data includes more than one data type; aggregating the received data into a data store, wherein the received data is a uniform data type. By doing so the mobile device aggregates the calendar data from the two different calendar sources into the vehicle format, and processes the calendar data to remove duplicate entries. Furthermore, the mobile device may time scale the received calendar data and/or apply filters to the calendar data to remove data types not suitable for use within the vehicle. Kirsch [0008]
Bhowmick teaches and wherein the generated indexes (Fig. 4, (406) index i, [0052], [0053], [0055], [0060] and Fig. 14A (1406) index j [0086], [0087]) are intermediary data points (vectors [0088]) created based on the aggregation of the received data; (As shown in FIG. 14B, a server-side process 1410 can aggregate client-side vectors and from client-side process 1400. Server-side process 1410 can receive input including a set of n vectors and indices {(v10 j 1), ... , (vn, jn)}, a privacy parameter E, and a set of k hash functions H={h1 , h2 , ... hk} that each map [p] to [ml, as shown at block 1411. [0081])
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 Scarpelli in view of Dutta and Kirsch to incorporate the teachings of Bhowmick wherein the generated indexes are intermediary data points created based on the aggregation of the received data. By doing so the learning server can learn the most prominent sequences via aggregate data provided by a variety of client devices. Bhowmick [0081]
Regarding claim 2 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the non-transitory computer readable medium of claim 1,
Scarpelli as modified does not fully disclose the extracting the one or more associations further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover.
Dutta referencing Porschia teaches the extracting the one or more associations (identified pattern between the attributes [0033])further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, (Fig. 11 the manager can view the worker’s predicted performance [0070]) team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover (for example by predictive analytic module 16, to identify patterns that resulted in a voluntary termination. Thus, in this case, the data mining tool looks for past cases where the worker was terminated and the termination was of a voluntary nature [0040])
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia wherein the extracting the one or more associations further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover. By doing so The data mining tool can then analyze these cases to find patterns between employee attributes and attrition. Dutta [0040]
Regarding claim 5 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the non-transitory computer readable medium of claim 1,
Scarpelli as modified does not fully disclose the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; providing the graphical user interface for display on a user device; receiving, from the graphical user interface via the user device, the one or more input datasets; and generating the third index based on the one or more input datasets.
Dutta teaches the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; ( 13a and 13b Predictive model user interface 1300 shows the factors contributing to attrition in a bar graph 1310. Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073]) SEE ALSO Figs. 6, 9, 10 providing the graphical user interface for display on a user device; (The prediction may be provided to the user via a graphical user interface, such as a table or graph. [0042]) receiving, from the graphical user interface via the user device, the one or more input datasets; (Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073]) and generating the third index (employee’s background [0033]) such as “a third index” based on the one or more input datasets(Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073])
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; providing the graphical user interface for display on a user device; receiving, from the graphical user interface via the user device, the one or more input datasets; and generating the third index based on the one or more input datasets. By doing so user interface 501 that shows the predicted performance and attrition for a team or group of workers. Dutta [0059]
Regarding claim 7 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the non-transitory computer readable medium of claim 1,
Scarpelli as modified does not fully disclose wherein the one or more characteristics of the third index includes one or more of the following: commute time, years in an associated position in an organization, time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status.
Dutta referencing Porschia teaches wherein the one or more characteristics of the third index (employee’s background [0033]) such as “a third index” includes one or more of the following: commute time, years in an associated position in an organization, (TABLE 1 length of service [0038], [0066]) time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status.
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia wherein the one or more characteristics of the third index includes one or more of the following: commute time, years in an associated position in an organization, time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status. By doing so the user might still be interested to see whether changing such an attribute will have a positive or negative effect on the worker. Dutta [0066]
Claims 3, and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019, in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch in view of Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick and in further view of French et al., (United States Patent Publication Number 2017/0039508) hereinafter French
Regarding claim 3 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the non-transitory computer readable medium of claim 1,
Scarpelli as modified does not fully disclose the operations further comprising: creating a distribution of flight probability for each of the plurality of positions, wherein the distribution uses the flight index for each entity of the plurality of entities; and generating, using the distribution, a quantity of identified entities having a flight index higher than a threshold flight probability in each of the plurality of positions over a duration of time; and extracting, based on the generation and the third index, a characteristic flight probability metric.
Dutta referencing Porschia teaches a quantity of identified entities having a flight index higher than a threshold flight probability (Fig. 5a individuals depicted having high attrition above the medium attrition [0058]) and extracting, based on the generation and the third index, (employee’s background [0033]) a characteristic flight probability metric (the average probability of attrition [0059])
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 Scarpelli to incorporate the teachings of Dutta wherein a quantity of identified entities having a flight index higher than a threshold flight probability and extracting, based on the generation and the third index, a characteristic flight probability metric. By doing so a predicted attrition of the worker can be attained. Dutta [0059]
French teaches the operations (operations [0071]) further comprising: creating a distribution of flight probability for each of the plurality of positions, (employees with the job title by an attrition rate at the store for employees with the job title [0023]) wherein the distribution (attrition rate [0023]) uses the flight index (employee attrition [0019]) for each entity of the plurality of entities; (Workforce, as used herein, refers to persons engaged in or available for work at a store. Workforce may also be referred to as employees, staff, personnel, workers, labor force, human resources, manpower, and the like. [0020]) and generating, (generating [0016]) using the distribution, (attrition rate [0023]) in each of the plurality of positions (job titles of employees [0020]) over a duration of time; (duration of time for which the employee is hired (e.g., temporary, permanent). [0020])
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 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick to incorporate the teachings of French the operations further comprising: creating a distribution of flight probability for each of the plurality of positions, wherein the distribution uses the flight index for each entity of the plurality of entities; and generating, using the distribution, in each of the plurality of positions over a duration of time. By doing so job positions are fulfilled with appropriate candidates .French [0019]
Regarding claim 4 Scarpelli in view of Dutta referencing Porschia, Kirsch, Bhowmick and French teaches the non-transitory computer readable medium of claim 3,
Scarpelli as modified does not fully disclose the operations further comprising generating a visualization of the distribution.
Dutta teaches the operations further comprising generating a visualization of the distribution (Figs. 5a and 5b user interface depicting the attrition distribution. the y-axis of the probability of attrition graph 1100 shows the predicted attrition [0058] – [0059], [0070])
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 Scarpelli in view of Kirsch, Bhowmick and French to incorporate the teachings of Dutta referencing Porschia wherein the operations further comprising generating a visualization of the distribution. By doing so predictive analytic dashboard user interface 1000 can generate a list of the actions the manager specified during the what-if analysis, or it will allow the manager to initiate an action. Dutta [0069]
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019 in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch in view of Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick and in further view of Williams (United States Patent Publication Number 2015/0242793) hereinafter Williams
Regarding claim 6 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the non-transitory computer readable medium of claim 5,
Scarpelli as modified does not fully disclose the operations further comprising: receiving, from communications between each of the plurality of entities, the one or more input second datasets; and generating the third index based on the one or more second input datasets.
Williams teaches receiving, from communications between each of the plurality of entities, (the commenting actions by user 2 and user 3 generates connections between them and the originator of the blog, user 1. [0096]) the one or more input second datasets; (aforementioned datasets [0071]) and generating the third index (employee's age, employee Grade … etc. [0038]) such as “third index” based on the one or more second input datasets. (aforementioned datasets [0071])
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 Scarpelli in view of Dutta referencing Porschia, Kirch and Bhowmick to incorporate the teachings of Williams the operations further comprising: receiving, from communications between each of the plurality of entities, the one or more input second datasets; and generating the third index based on the one or more second input datasets. By doing so the relational data shown here is generated by arranging an action (here blog post creation or commenting) and providing user
identification and other pertinent information. The relational data is then transformed into a graph where each employee is a node, and the actions between the employees/users are used to connect the nodes. Williams [0096]
Claims 8, 9, 12, 14 – 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019 in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch and Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick
Regarding claim 8 Scarpelli teaches a method (ABS., method) (Figs. 1 – 31 method [0014]) comprising: receiving data from a plurality of disparate data sources, (The system software 31 may be operatively Connected by buss or network link to human resource information system back end vendors 34 that provide employee records and data concerning compensation and benefits. It may also be connected to internet service providers 36 for access to data sources and storage. [0019]) the data (employee records and data [0019]) including a first (name, subject(s) taught, grade, tenure status, info Edit, and evaluations [0023]) and second plurality of variables; (unsatisfactory, basic, proficient, distinguished, or not applicable ratings. [0024]) wherein a first plurality of variables (name, subject(s) taught, grade, tenure status, info Edit, and evaluations [0023]) is associated with one entity (teacher [0023]) of a plurality of entities (staff list [0023]) in a position of a plurality of positions; (various administrative positions [0028]) wherein a second plurality of variables (unsatisfactory, basic, proficient, distinguished, or not applicable ratings. [0024]) is associated with a performance metric (teaching performance [0024]) of a plurality of performance metrics (rubrics [0028]) associated with the each of the plurality of entities; (staff list [0023]) wherein the plurality of disparate data sources (The system software 31 may be operatively Connected by buss or network link to human resource information system back end vendors 34 that provide employee records and data concerning compensation and benefits. It may also be connected to internet service providers 36 for access to data sources and storage. [0019]) includes at least one of: one or more input datasets, (a convenient database [0039]) one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring;
Scarpelli does not fully disclose generating a plurality of indexes, where the plurality of indexes comprises: a first index associated with the plurality of positions;
a second index associated with the plurality of entities; a third index associated with one or more characteristics associated with each of the plurality of entities;
and a fourth index associated with the plurality of performance metrics; and wherein the generated indexes are intermediary data points created based on the aggregation of the received data storing the plurality of indexes in a database; extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; generating, based on each of the plurality of entities, the associated performance metrics, and the extracted one or more associations, a flight index for each of the entities; wherein the flight index is a statistical measure of a likelihood that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; generating, based on each of the plurality of entities and the associated performance metrics, a performance index to each of the entities; and wherein, the received data includes more than one data type; aggregating the received data into a data store, wherein the received data is a uniform data type; and wherein the generated indexes are intermediary data points created based on the aggregation of the received data; comparing the flight index for each of the entities with the performance index of the same entity; and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher than a threshold flight probability; implementing, based on the identification, one or more changes to one or more policies of the organization; wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time.
Dutta teaches generating a plurality of indexes, (collect and analyze attributes [0033]) such as “plurality of indexes” where the plurality of indexes (collect and analyze attributes [0033]) such as “plurality of indexes” comprises: a first index associated with the plurality of positions; (attribute related to job responsibilities [0033]) such as “a first index” SEE ALSO Fig 4 “job title” a second index associated with the plurality of entities; (ABS., plurality of workers) (attributes related to all workers [0025]/company employees [0033]) such as “a second index associated with the plurality of entities” a third index associated with one or more characteristics associated with each of the plurality of entities; (attributes related to an employee’s background [0033]) such as “a third index” and a fourth index associated with the plurality of performance metrics; (attributes related to an employee’s past performance [0033]) such as “a fourth index” storing the plurality of indexes in a database; (database system 30 may store employee data, such as job responsibilities, past performance, compensation, and any other relevant attributes [0031]) extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; (Predictive analytic module 16 applies a data mining model to those attributes. The data mining model analyzes the attributes as they relate to all employees and identifies a pattern between the attributes and the future performance of the employees [0033]) generating, based on each of the plurality of entities, (all workers [0025]/company employees [0033]) the associated performance metrics, (employee’s past performance [0033]) a flight index (attrition for a worker [0025]) such as “flight index” for each of the entities; (a predicted attrition section 410 that shows the likelihood of an employee leaving. Predicted attrition section 410 includes an "Individual" column which shows the predicted attrition for each of the individual workers as high, medium or low. [0057]) SEE Fig 5B “attrition for a team or group of workers” [0059] generating, based on each of the plurality of entities (all workers [0025]/company employees [0033])and the associated performance metrics, (employee’s past performance [0033])a performance index to each of the entities; (ABS., predict the future characteristic of a worker. In one
example, the future characteristic is the future performance of the employee) (Fig. 2 (220) predict future performance [0042]) wherein the flight index(attrition for a worker [0025]) such as “flight index” is a statistical measure of a likelihood (probability of attrition [0059]) that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; (likelihood that an employee leaves a company [0074]) SEE ALSO Page 1, Porschia Parker “accepting a role that is better than opportunity than your current position” comparing the flight index for each of the entities with the performance index of the same entity; (Fig. 5A illustrates an example of a predicted worker performance and attrition user interface 500 that shows the predicted performance and attrition for each worker in a team or group. Predicted worker performance and attrition user interface 500 includes a chart view 510 which graphically represents the predicted attrition and predicted performance for each worker in an XY chart. The y-axis of chart view 510 shows the predicted attrition, while the x-axis shows the predicted performance. Predicted worker performance and attrition user interface 500 also includes a table view 510 that lists each of the workers, their average predicted attrition level, and their average predicted performance level in a table format. [0058]) and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher (Predicted worker performance and attrition user interface 500 allows managers to easily identify those employees or teams that are predicted high performers and are also predicted to be at a high risk of loss. [0058]) (Table view 620 lists the individual or team name, actual performance rating for the individual or team, the probability of attrition, the predicted attrition reason, the predicted performance level [0060]) than a threshold flight probability; (predicted attrition rated as “medium” [0057]) such as “threshold flight probability” implementing, based on the identification, one or more changes to one or more policies of the organization; (As a result, managers are able to take necessary steps to retain those employees or groups before they make a decision to leave [0058]) wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; (the organization's human resource system is able to predict, forewarn and help managers to take corrective actions to improve organizational stability. [0027]) and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time (Fig. 11FIG. 11 illustrates an example of a probability of attrition graph 1100 according to one embodiment. According to this embodiment, the manager can view the worker's predicted attrition and performance both before and after the proposed action. For example, each time the manager changes an attribute, such as a grade change or salary change, the probability of attrition graph 1100 is updated to show the effect of the change. Similar to FIG. 5, the y-axis of the probability of attrition graph 1100 shows the predicted attrition, while the x-axis shows the predicted performance [0070])
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 Scarpelli to incorporate the teachings of Dutta referencing Porschia whereby generating a plurality of indexes, where the plurality of indexes comprises: a first index associated with the plurality of positions; a second index associated with the plurality of entities; a third index associated with one or more characteristics associated with each of the plurality of entities; and a fourth index associated with the plurality of performance metrics; storing the plurality of indexes in a database; extracting one or more associations from the plurality of indexes, wherein each of the one or more associations includes one or more probabilistic distributions based on a relationship between each of the plurality of performance metrics and each of the plurality of positions; generating, based on each of the plurality of entities, the associated performance metrics, and the extracted one or more associations, a flight index for each of the entities; wherein the flight index is a statistical measure of a likelihood that the associated entity will change from the associated entity's current position within an organization to one or more new positions outside of the organization; generating, based on each of the plurality of entities and the associated performance metrics, a performance index to each of the entities; comparing the flight index for each of the entities with the performance index of the same entity; and identifying, based on the comparison, a flight probability of one or more chosen entities of the plurality of entities being higher than a threshold flight probability; implementing, based on the identification, one or more changes to one or more policies of the organization; wherein the one or more changes to one or more policies are configured to reduce the statistical likelihood that the one or more chosen entities will change from a current positions of the one or more chosen entities current position within the organization to one or more new positions outside of the organization; and monitoring, based on the implementation, the flight index of the one or more chosen entities over a period of time. By doing so a pattern can be identified between the attributes and a future characteristic of the workers, and using the identified pattern to predict the future characteristic of a worker. Dutta [0006]
Kirsch teaches and wherein, the received data (received calendar information [0069]) includes more than one data type; (in one or more of a CalDAV format, an MS Exchange format, a Google calendar format, or another standard calendar format, or may use a format native to a [0069])aggregating the received data (The mobile device 120 then aggregates 506 the calendar data to identify and remove duplicate entries. [0069]) into a data store, (calendar database [0062], [0081]) wherein the received data (received calendar information [0069]) is a uniform data type (This step may also include processing the data into a uniform data type and container size [0069])
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 Scarpelli in view of Dutta to incorporate the teachings of Kirsch and wherein, the received data includes more than one data type; aggregating the received data into a data store, wherein the received data is a uniform data type. By doing so the mobile device aggregates the calendar data from the two different calendar sources into the vehicle format, and processes the calendar data to remove duplicate entries. Furthermore, the mobile device may time scale the received calendar data and/or apply filters to the calendar data to remove data types not suitable for use within the vehicle. Kirsch [0008]
Bhowmick teaches and wherein the generated indexes (Fig. 4, (406) index i, [0052], [0053], [0055], [0060] and Fig. 14A (1406) index j [0086], [0087]) are intermediary data points (vectors [0088]) created based on the aggregation of the received data; (As shown in FIG. 14B, a server-side process 1410 can aggregate client-side vectors and from client-side process 1400. Server-side process 1410 can receive input including a set of n vectors and indices {(v10 j 1), ... , (vn, jn)}, a privacy parameter E, and a set of k hash functions H={h1 , h2 , ... hk} that each map [p] to [ml, as shown at block 1411. [0081])
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 Scarpelli in view of Dutta and Kirsch to incorporate the teachings of Bhowmick wherein the generated indexes are intermediary data points created based on the aggregation of the received data. By doing so the learning server can learn the most prominent sequences via aggregate data provided by a variety of client devices. Bhowmick [0081]
Claim 15 corresponds to claim 8 and is rejected accordingly
Regarding claim 9 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the method of claim 8,
Scarpelli as modified does not fully disclose the extracting the one or more associations further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover.
Dutta referencing Porschia teaches the extracting the one or more associations (identified pattern between the attributes [0033])further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, (Fig. 11 the manager can view the worker’s predicted performance [0070]) team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover (for example by predictive analytic module 16, to identify patterns that resulted in a voluntary termination. Thus, in this case, the data mining tool looks for past cases where the worker was terminated and the termination was of a voluntary nature [0040])
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia wherein the extracting the one or more associations further comprises: identifying one or more indicia related to the at least one of: one or more input datasets, one or more second input datasets, individual performance monitoring, team monitoring, and market monitoring; and wherein the one or more indicia are associated historical conditions related to employee turnover. By doing so The data mining tool can then analyze these cases to find patterns between employee attributes and attrition. Dutta [0040]
Claim 16 corresponds to claim 9 and is rejected accordingly
Regarding claim 12 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the method of claim 8,
Scarpelli as modified does not fully disclose the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; providing the graphical user interface for display on a user device; receiving, from the graphical user interface via the user device, the one or more input datasets; and generating the third index based on the one or more input datasets.
Dutta referencing Porschia teaches the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; ( 13a and 13b Predictive model user interface 1300 shows the factors contributing to attrition in a bar graph 1310. Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073]) SEE ALSO Figs. 6, 9, 10 providing the graphical user interface for display on a user device; (The prediction may be provided to the user via a graphical user interface, such as a table or graph. [0042]) receiving, from the graphical user interface via the user device, the one or more input datasets; (Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073]) and generating the third index (employee’s background [0033]) such as “a third index” based on the one or more input datasets(Bar graph 1310 lists the contributing factors on the x-axis and the number of workers affected on the y-axis [0073])
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia the operations further comprising: generating a graphical user interface containing information entry fields for receiving user input regarding input datasets; providing the graphical user interface for display on a user device; receiving, from the graphical user interface via the user device, the one or more input datasets; and generating the third index based on the one or more input datasets. By doing so user interface 501 that shows the predicted performance and attrition for a team or group of workers. Dutta [0059]
Claim 19 corresponds to claim 12 and is rejected accordingly
Regarding claim 14 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the method of claim 10,
Scarpelli as modified does not fully disclose wherein the one or more characteristics of the third index includes one or more of the following: commute time, years in an associated position in an organization, time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status.
Dutta referencing Porschia teaches wherein the one or more characteristics of the third index (employee’s background [0033]) such as “a third index” includes one or more of the following: commute time, years in an associated position in an organization, (TABLE 1 length of service [0038], [0066]) time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status.
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia wherein the one or more characteristics of the third index includes one or more of the following: commute time, years in an associated position in an organization, time spent at team building events, messages sent within the organization, age, gender, race, sexual orientation, and marital status. By doing so the user might still be interested to see whether changing such an attribute will have a positive or negative effect on the worker. Dutta [0066]
Claims 10, 11, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019 in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch in view of Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick and in further view of French et al., (United States Patent Publication Number 2017/0039508) hereinafter French
Regarding claim 10 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick teaches the method of claim 9,
Scarpelli as modified does not fully disclose the operations further comprising: creating a distribution of flight probability for each of the plurality of positions, wherein the distribution uses the flight index for each entity of the plurality of entities; and generating, using the distribution, a quantity of identified entities having a flight index higher than a threshold flight probability in each of the plurality of positions over a duration of time; and extracting, based on the generation and the third index, a characteristic flight probability metric.
Dutta referencing Porschia teaches a quantity of identified entities having a flight index higher than a threshold flight probability (Fig. 5a individuals depicted having high attrition above the medium attrition [0058]) and extracting, based on the generation and the third index, (employee’s background [0033]) a characteristic flight probability metric (the average probability of attrition [0059])
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 Scarpelli in view of Kirsch and Bhowmick to incorporate the teachings of Dutta referencing Porschia wherein a quantity of identified entities having a flight index higher than a threshold flight probability and extracting, based on the generation and the third index, a characteristic flight probability metric. By doing so a predicted attrition of the worker can be attained. Dutta [0059]
French teaches the operations (operations [0071]) further comprising: creating a distribution of flight probability for each of the plurality of positions, (employees with the job title by an attrition rate at the store for employees with the job title [0023]) wherein the distribution (attrition rate [0023]) uses the flight index (employee attrition [0019]) for each entity of the plurality of entities; (Workforce, as used herein, refers to persons engaged in or available for work at a store. Workforce may also be referred to as employees, staff, personnel, workers, labor force, human resources, manpower, and the like. [0020]) and generating, (generating [0016]) using the distribution, (attrition rate [0023]) in each of the plurality of positions (job titles of employees [0020]) over a duration of time; (duration of time for which the employee is hired (e.g., temporary, permanent). [0020])
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 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick to incorporate the teachings of French the operations further comprising: creating a distribution of flight probability for each of the plurality of positions, wherein the distribution uses the flight index for each entity of the plurality of entities; and generating, using the distribution, in each of the plurality of positions over a duration of time. By doing so job positions are fulfilled with appropriate candidates .French [0019]
Claim 17 corresponds to claim 10 and is rejected accordingly
Regarding claim 11 Scarpelli in view of Dutta referencing Porschia, Kirsch, Bhowmick and French teaches the method of claim 10,
Scarpelli as modified does not fully disclose the operations further comprising generating a visualization of the distribution.
Dutta referencing Porschia teaches the operations further comprising generating a visualization of the distribution (Figs. 5a and 5b user interface depicting the attrition distribution. the y-axis of the probability of attrition graph 1100 shows the predicted attrition [0058] – [0059], [0070])
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 Scarpelli in view of Kirsch, Bhowmick and French to incorporate the teachings of Dutta referencing Porschia wherein the operations further comprising generating a visualization of the distribution. By doing so predictive analytic dashboard user interface 1000 can generate a list of the actions the manager specified
during the what-if analysis, or it will allow the manager to initiate an action. Dutta [0069]
Claim 18 corresponds to claim 11 and is rejected accordingly
Claims 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Scarpelli et al. (United States Patent Publication Number 20060173731), hereinafter Scarpelli in view of Dutta et al. (United States Patent Publication Number 2011/0307303 ), hereinafter referred to as Dutta referencing Porschia Parker, “How to Gracefully Leave a Job for Another Opportunity”, BioSpace, October 7th 2019 in view of David M. Kirsch (United States Patent Number 2012/0197523) hereinafter Kirsch in view of Bhowmick et al., (United States Patent Number 2019/0370334) hereinafter Bhowmick and in further view of Williams (United States Patent Publication Number2015/0242793) hereinafter Williams
Regarding claim 13 Scarpelli in view of Dutta referencing Porschia, Kirsh and Bhowmick teaches the method of claim 12,
Scarpelli as modified does not fully disclose the operations further comprising: receiving, from communications between each of the plurality of entities, the one or more input second datasets; and generating the third index based on the one or more second input datasets.
Williams teaches receiving, from communications between each of the plurality of entities, (the commenting actions by user 2 and user 3 generates connections between them and the originator of the blog, user 1. [0096]) the one or more input second datasets; (aforementioned datasets [0071]) and generating the third index (employee's age, employee Grade … etc. [0038]) such as “third index” based on the one or more second input datasets. (aforementioned datasets [0071])
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 Scarpelli in view of Dutta referencing Porschia, Kirsch and Bhowmick to incorporate the teachings of Williams the operations further comprising: receiving, from communications between each of the plurality of entities, the one or more input second datasets; and generating the third index based on the one or more second input datasets. By doing so the relational data shown here is generated by arranging an action (here blog post creation or commenting) and providing user identification and other pertinent information. The relational data is then transformed into a graph where each employee is a node, and the actions between the employees/users are used to connect the nodes. Williams [0096]
Claim 20 corresponds to claim 13 and is rejected accordingly
Conclusion
6. 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).
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/KWEKU WILLIAM HALM/Examiner, Art Unit 2166
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
