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 .
The following FINAL Office Action is in response to Applicant’s communication filed 09/18/2025 regarding Application 18/291,830.
Status of Claim(s)
Claim(s) 1-11, and 13-20 is/are currently pending and are rejected as follows.
Response to Arguments – 101 Rejection
Applicant’s arguments and amendments in regards to the previously applied 101 rejection have been fully considered but are not deemed persuasive.
Applicant argues that the claims as presented to not recite an abstract idea and should they recite an abstract idea, integrate it in such a way as to no be directed towards an abstract idea.
Examiner does not find the arguments persuasive as the Applicant’s claims recite an invention for the obtaining of user activity data which indicates usage statistics of a computing device by the user, where the activity data indicates an amount of time that the device is active by a screen being unlocked and a last action involving a computing device being determined to happen within a time frame, analyzing the activity data of the user to determine a personal score of the user, where the score relates to a stress level determined by values associated to an application, compare the personal score to the target score and collected scores of peers for a comparison results, based on the comparison determine a recommendation for the user and providing the recommendation for the user. These limitations recite acts that relate to Organizing Human Activity, specifically that of managing personal behavior or relationships or interactions between people. The claims were further analyzed to see if any additional elements recited were integrated in such a way as to render the invention as significantly more than the recited abstract idea. However, the additional elements recited, including but not limited to the recited non-transitory computer readable medium, a processor, and a computing device, were merely examples of adding “apply it” to the judicial exception and therefore was not enough to render the claims eligible under 101. The amendments presented further do not provide additional claims or limitations such as to render the claims eligible. Further elaboration regarding this determination is given in the amended 101 rejection below.
Applicant’s additional arguments are deemed persuasive and Examiner has withdrawn the determination of the claims being directed towards a Mental Process, and not being under one of the four statutory categories of invention.
Response to Arguments – 103 Rejection
Applicant’s arguments are rendered moot in view of the amended prior art rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-11, and 13-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is/are directed towards a judicial exception (i.e. law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1, 6, and 11 recite an invention for the obtaining of user usage data of an application on a computing device, determining a score for the users according to the usage data, comparing the score to a target score and/or a group of similar users' scores, providing a recommendation to a user, or modifying the schedule of the user according to a usage pattern determined from the usage statistics. These actions fall within a subject matter grouping which the courts have considered ineligible (Organizing Human Activity). These claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under Step 1 of the Alice/Mayo framework, it must be considered whether the claims are directed to one or more of the statutory classes. In the instant case, Claim(s) 1-5, and 16-18 are directed towards a product. Claim(s) 6-10 and 19-20 are directed towards a product. Claim(s) 11 and 13-15 are directed towards a product. Accordingly the claims fall within the four statutory category of invention (product) and will be further analyzed under 101.
Under Step 2A, Prong One, of the Alice/Mayo framework, it must be considered whether the claims are "directed to" an abstract idea. That is, whether the claims recite an abstract idea and fail to integrate the abstract idea into a particular application.
Independent claim 1 recites an invention for the obtaining activity data for usage statistics of a computing device by a user, analyzing the activity data to calculate a personal score for the user, comparing the personal score to a target score and score of peers, based on the comparison determine a recommendation for the user, and then provide the recommendation to the user, which recites the abstract ideas Organizing Human Activity in the following limitations:
obtain activity data of a user indicating usage statistics of a computing device by the user, wherein the activity data indicates an amount of time that the computing device is active as determined based on a screen of the computing device being unlocked and a last action involving the computing device being determined to have occurred within a programmed time frame
analyze the activity data of the user to determine a personal score of the user, wherein the personal score relates to a stress level determined by programmed stress values associated to an application
compare the personal score of the user to a target score and collected scores of peers of the user to determine a comparison result;
based on the comparison result, automatically determine a recommendation for the user; and
provide the recommendation to the user ...
Independent claim 6 recites an invention for the obtaining activity data for a group of users, determining a usage pattern and personal score for each member of the group of users, determining an average personal score of the group of users, comparing the individual scores to the average, and then generating and providing a recommendation based on the comparison which recites the abstract ideas of Organizing Human Activity in the following limitations:
determine a computing device is active, wherein the computing device is deemed active when a screen of the computing device is unlocked and a last action has been achieved
obtain activity data of a group of users;
perform K-means clustering to create a group of users from the plurality of users, wherein the K-means clustering creates the group of users based on interaction times with similar applications among the group of users
for each user of the group of users:
determine a usage pattern for the computer device based on the activity data; and
determine a personal score based on the usage pattern, wherein the personal score relates to a stress level determined by programmed stress values associated to an application
determine an average personal score for the group of users based on the personal score for each user;
compare the personal score of a user of the group of users to the average personal score to determine a comparison result; and
based on the comparison result, automatically determine and provide a recommendation to the user.
Independent claim 11 recites an invention for capturing data of a computing device including the usage statistics of a user using the device, analyzing the statistics to determine a usage pattern of the user, analyzing calendar data of the user, and based on the usage pattern of the user modifying the scheduling of an event in the calendar which recites the abstract ideas of Organizing Human Activity in the following limitations:
capture ... data of a computing device indicating usage statistics of a user using the computing device;
analyze the usage statistics to determine a usage pattern of the user, wherein...to determine the usage pattern of the user based on stress levels of the user determined according to stress values related to application usage by the usage indicated in the usage statistics
analyze calendar data of the user; and
based on the usage pattern of the user and the calendar data, automatically modify scheduling of an event included in the calendar data.
Dependent claim(s) 2-5, 7-10, and 13-20 merely further limit the abstract idea and are therefore subject to the same rationale provided above.
Under Step 2A, Prong Two, the claims recite the following additional elements:
Independent claims 1, 6, and 11 recite:
a non-transitory computer-readable medium storing instructions
a processor
a computing device
a scheduling application
These additional elements are mere instructions to implement the abstract idea (" Apply it") on a computer (See MPEP 2106.05(f)), or represent insignificant extra solution activity (See MPEP 2106.05(g)). These elements are recited with a high degree of generality, and the specification sets forth the general purpose nature of the technologies required to implement the invention (emphasis added).
Support for this determination can be found in Paragraph(s) [0012]-[0014], [0025], and [0043]-[0048] of Applicant's specification.
Under Step 2B, eligibility analysis evaluates whether the claims as a whole amounts to significantly more than the recited exception, i.e., whether any additional elements, or combinations of elements, adds an inventive concept (MPEP 2106.05). As explained with respect to Step 2A, Prong Two, there are several additional elements. The computer-readable medium storing instructions, processor, computing device, and scheduling application are at best, the equivalent of merely adding the words "apply it" to the abstract idea. Mere instructions to apply an abstract idea cannot provide an inventive concept (2106.05(f)). Additionally, the processor also represent insignificant extra solution activity (MPEP 2106.05(g)), specifically actions that would be deemed well-understood, routine, or conventional in the art, which does not provide an inventive concept (MPEP 2106.05(d)). Claims that amount to nothing more than an instruction to apply an abstract idea using a generic computer or are well-understood routine
Dependent claims 2-5, 7-10, and 13-20 do not recite any further additional elements and are thus rejected for the same reasons enumerated above.
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 (i.e., changing from AIA to pre-AIA ) 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) 1-5, 11, and 13-18 is/are currently rejected under 35 U.S.C. 103 as being unpatentable over Deodhar (US 2017/0116552 A1) in view of Delaney (US 2017/0100066 A1)
Claim(s) 1 –
Deodhar discloses the following limitations:
obtain activity data of a user indicating usage statistics of a computing device by the user, wherein the activity data indicates an amount of time that the computing device is active as determined based on a screen of the computing device being unlocked and a last action involving the computing device being determined to have occurred within a programmed time frame (Deodhar: Table 6; Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers ( even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications."; Paragraph 392, “In today's 24×7 work environment, there can be several variations from a single user and single CS theme. For example, a single user may work on different CS concurrently (home and work PCs, smartphones, tablets), multiple users may share the same CS, and several users may share a server possibly with a common login ID. The system envisaged by the present disclosure supports multi-user and multi-CS modes of operation. CS agents log each user's data on shared systems, provided each user logs in to the CS with one or more valid IDs in the user's record on the server. The typical IDs are the employee's sign-on ID (one or more, such as for the workgroup, company's network domain, and customer's network domain), employee identification number, phone extension, mobile number, email ID, and so on. If multiple users log into a shared CS using a common ID, the CS agent prompts for proper identification of the new user for correct allocation of the user's time utilization.”)
compare the personal score of the user to a target score and collected scores of peers of the user to determine a comparison result; (Deodhar: Paragraph 598, “V. user utilization for comparisons between the users within an organization:—The computation of the user utilization includes computation of a delivered capacity, an available capacity, a staffed capacity and capacity utilization. A pseudo-code for the computation of the user utilization, in accordance with an embodiment of the present disclosure, is now described.”; Paragraph 672, “for each goal that is set, inform the user about how the user's current trend compares with that of peers (average and Top 20%)”)
based on the comparison result, automatically determine a recommendation for the user; and (Deodhar: Paragraph 673, “identify the benefits of the proposed improvement to the user's work effectiveness index and the work-life balance index;”; Paragraph 811, “compute daily average distribution between top 20% of users and rest:— top 20 percentage of daily average work time=(Σ(daily average work time*workdays that week) over first 20 percentage of users in table)/(Σ(workdays that week) over first 20 percentage of users in table); mid 60 percentage of daily average work time=(Σ(daily average work time workdays that week) over 20-60 percentage of users in table)/(Σ(workdays that week) over 20-60 percentage of users in table); last 20 percentage of daily average work time=(Σ(daily average work time*workdays that week) over last 20% of users in table)/(Σ(workdays that week) over last 20% of users in table); (above list of users can also be grouped based on attributes of interest and their Work Patterns compared);”; Paragraph 1043, “In accordance with an embodiment of the present disclosure, a web user interface 430 is configured to facilitate views at each level of the organization hierarchy across Work pattern items. The web user interface 430 is further configured to selectively filter and drill down to generate and compare discrete effort data for any Work Pattern item across any business attribute. The Work Pattern items are selected from the group consisting of effort, habits, effort distribution across Purposes, Activities, applications and work units, work life balance index, capacity utilization, and work effectiveness index. The business attributes are selected from the group consisting of role, skills, salary, position, and location for the user, and from the group consisting of domain, vertical, cost and profit center, and priority for the organization sub-unit.”)
provide the recommendation to the user via a graphical interface (Deodhar: Paragraph 662, "recommend training, mentoring or moving to work more suited to the user's skills;"; Paragraph 663, "else, if the user is doing well on all the work parameters, then recommend to take up more challenging work and also, explore opportunities for improved work-life balance;"; Paragraph 833, "make recommendations to improve the sub-units performance."; Paragraph 898, “explore opportunities to encourage sub-unit staff to improve their work life balance; if unaccounted time in office is >1 hour, then recommend sub-unit users to review their data and reduce time spent in office; if work time on holidays is >0.5 hour, then recommend users to complete the work during work days; if work done at home on workdays marked as work from home is >0.5 hour, then recommend users to complete the work in office instead;”)
Deodhar does not explicitly disclose the following, however, in analogous art of workplace efficiency, Delaney discloses the following:
A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to execute an intelligent scheduling application to: (Delaney: Paragraph 20, “The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.”)
analyze the activity data of the user to determine a personal score of the user, wherein the personal score relates to a stress level determined by programmed stress values associated to an application (Delaney: Paragraph 16, “In embodiments, information identifying the breadth of tasks a user is performing may be used to count the number of context switches (e.g., transitions from one task to another). Further, stress scores or indicators may be assigned to each context switch based on the user's physical reactions that indicate the individual's stress level. For example, wearable biometrics devices may be used to determine the user's stress level, and a link can be established between the user's stress level and the context switch. Also, the type of context switch is determined based on user activity information. For example, the type of context switch identifies that the user transitioned from one task to another (e.g., from working on one project to working on another project, or working on one project followed by performing some other task, such as attending a meeting, taking a coffee break, etc.).”; Paragraph 18, “In embodiments, a report may be generated based on stress level information relating to context switches for a group for multiple users having certain commonalities (e.g., users with similar job titles, length of service, users working for the same department or manager, users having similar personality types, etc.). Thus, the report may identify average stress levels for users by category (job titles, department, personality types, etc.). Also, identifying an individual's stress level based on personality type may provide insight into the individuals’ behavior under different levels of stress.”; Paragraph 35, “The stress analysis server 210 may include one or more computing devices that implement the stress analysis component 46. The stress analysis server 210 may receive user activity and/or user information from the user activity and information server 215, and biometrics information for the user from the biometrics device 220. Based on the user activity information, the stress analysis server 210 may identify when the user experiences a context switch (e.g., transitions from one task to another), and the user's stress level associated with the context switch (e.g., based on the biometrics information received from the biometrics device 220). As described in greater detail below, the stress analysis server 210 may parse through the communication feeds identifying the user's activity to identify the tasks that a user is performing.”; Paragraph 36, “The user activity and information server 215 may include one or more computing devices that stores user activity information regarding a user. For example, the user activity and information server 215 may store information identifying a task that the user is currently performing (e.g., a work-related technical task, resolving a customer issue, attending a meeting, taking a meal break, etc.). In embodiments, the user activity and information server 215 may store a user's instant messages (e.g., from the user's social network account, personal/work instant messaging accounts, etc.), network usage activity (e.g., indicating networks or websites being accessed by the user), social media activity (e.g., public postings on the user's social media account), interactive media feeds (e.g., blog posts made by the user), e-mail messages, calendar events, telephone calling activity, location information, etc.) In embodiments, the user activity and information server 215 may store other information regarding the user, such as the user's job title, personality profile, etc. This information may be used to group stress level indicators from many users having certain commonalities (e.g., users with similar job titles, users working for the same department or manager, users having similar personality types, etc.).”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”)
Claim(s) 2 –
Deodhar in view of Delaney disclose the limitations of claim 1
Deodhar further discloses the following:
analyze calendar data of the user according to the personal score of the user to determine an analysis result; (Deodhar: Paragraph 41, "It is a further object of the present disclosure to provide a system that intelligently deduces and maps each online and offline time slot to the most appropriate Activity and Purpose from a hierarchy of possible Activities and Purposes assigned to the employee from a master list for the organization, based on applications and artifacts in case of online time slots, and for offline slots from information obtained from calendaring systems and various PDs (Presence Devices) and PD servers that indicate if the user was busy in meetings, calls, lab work, travel, remote visits, and so on."; Paragraph 79, "a comparator adapted to compare scheduled engagements, meetings, calls, lab work, travel time and remote visits of the user as obtained from the user's calendar on the CS agent and from local Presence Devices (PDs), with the duration of the offline time slots for determining the user's offline time utilization, wherein the local Presence Devices include smartphones with GPS that are connectable to or part of the CS agent"; Paragraph 312, "An operating System (OS) collector 302: The OS collector 302 runs in the background of the user's CS agent 300 and collects events related to the user's interaction with the CS and status of current active application window and artifacts related to the application, by interfacing with the CS's Operating System 304. It also picks up data from local calendaring applications and local PDs 302A interfacing with the CS, regarding time spent away from the CS on meetings, calls, travel and the like.")
and recommend modification of the calendar data of the user according to the analysis result. (Deodhar: Paragraph 97, "suggest areas of improvements for the user,"; Paragraph 655, "if meeting or call or any other non-core activity time is high, then set a goal for lower time on the non-core activity"; Paragraph 891,
"check if time on meeting and communication is high, and set goals for lower time on meeting and communication activities;")
Claim(s) 3 –
Deodhar in view of Delaney disclose the limitations of claims 1-2
Deodhar does not explicitly disclose the following, however, in analogous art of workplace efficiency, Delaney discloses the following:
wherein the instructions cause the processor to automatically modify the calendar data of the user according to the analysis result without intervention by the user. (Delaney: Paragraph 22, “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”)
Claim(s) 4 –
Deodhar in view of Delaney disclose the limitations of claim 1
Deodhar further discloses the following:
wherein the instructions cause the processor to determine the personal score according to an amount of high stress time of the user as determined according to the usage statistics. (Deodhar: Paragraph 96, "provide a feedback to the user on highlights related to work effort, work output and the work life balance index,"; Paragraph 527, "A pseudo-code depicting the tagging operation, for each day (as a workday, holiday, vacation), performed by the user Work Pattern analyser 332, in accordance with an embodiment of the present disclosure, is now described. Once the user Work Pattern analyser 332 detects that yesterday is over, then it determines whether the day was a work day, weekend day, public holiday, or vacation, and whether it was work from office or home. If the information about the user's weekend days is not available, then intelligent inferencing based on Work Patterns is used to determine the weekend days. It may be that the user may not have a fixed weekend, as for example for support staff and independent contractors, in which case a 'variable work week' flag is introduced. Vacations and holidays too can be inferred based on the user's Work Patterns if that information is unavailable. In another embodiment, the user Work Pattern analyser 332 may employ a fuzzy logic to determine user vacations, weekends and holidays, shift timing, work from home and office and other locations, and unaccounted time in office."; Paragraph 638, "work time-too high if> 10 hours, high if 8-10 hours, good if between 6.5 to 8 hours, low if 5-6.5 hours, and too low if <5 hours;"; Paragraph 1060, "Senior executive management can get precise insights into effort spent on revenue earning work versus other tasks. Capacity utilization reports can be used to optimize staffing. Stress and burnout can be reduced by identifying teams and projects where there is sustained overutilization. Teams displaying low capacity utilization can increase their effort, leading to better quality results and on-time delivery."; Paragraph 598, "V. user utilization for comparisons between the users within an organization:-The computation of the user utilization includes computation of a delivered capacity, an available capacity, a staffed capacity and capacity utilization. A pseudo-code for the computation of the user utilization, in accordance with an embodiment of the present disclosure, is now described."; Paragraph 960, "set threshold of delivered capacity as percentage of available capacity to T % (T % is based on what the organization considers to be the optimal capacity utilization, the guideline being that at least 20% of the organization (users or sub-units at a particular level) should have capacity utilization above T % ); Paragraph 961, “for each relevant parameter type,"; Paragraph 1054, "The systems and methods of present disclosure support extensive analytics. The organization effort aggregation and analytics engine 414 derives a per-employee daily average of Work Pattern. This is a powerful metric that facilitates meaningful and direct comparisons between any two or more organization sub-units of any type, including individual employees. Various trends and reports are available to compare the average daily productive time across various Purposes, Activities, applications, artifacts, online and offline time distribution, work focus, breaks taken, capacity utilization and so on. The reports and trends are available on daily, weekly, monthly or cumulative basis over a specified time range, or during the project or organization lifecycle phases. The differences in the trends between the Top 20%, Middle 60% and Last 20% of organization sub-units can also be viewed, thereby encouraging others to emulate the performance of the Top 20%.") Examiner interprets the utilizations metrics of Deodhar to be equivalent to stress both under broadest reasonable interpretation by one of ordinary skill in the art, and in view of Applicant's specification.
Claim(s) 5 –
Deodhar in view of Delaney disclose the limitations of claim 1
Deodhar further discloses the following:
wherein the instructions cause the processor to determine the personal score according to an average stress of the user for a period of time as determined according to the usage statistics. (Deodhar: Paragraph 96, "provide a feedback to the user on highlights related to work effort, work output and the work life balance index,"; Paragraph 527, "A pseudo-code depicting the tagging operation, for each day (as a workday, holiday, vacation), performed by the user Work Pattern analyser 332, in accordance with an embodiment of the present disclosure, is now described. Once the user Work Pattern analyser 332 detects that yesterday is over, then it determines whether the day was a work day, weekend day, public holiday, or vacation, and whether it was work from office or home. If the information about the user's weekend days is not available, then intelligent inferencing based on Work Patterns is used to determine the weekend days. It may be that the user may not have a fixed weekend, as for example for support staff and independent contractors, in which case a 'variable work week' flag is introduced. Vacations and holidays too can be inferred based on the user's Work Patterns if that information is unavailable. In another embodiment, the user Work Pattern analyser 332 may employ a fuzzy logic to determine user vacations, weekends and holidays, shift timing, work from home and office and other locations, and unaccounted time in office."; Paragraph 638, "work time-too high if > 10 hours, high if 8-10 hours, good if between 6.5 to 8 hours, low if 5-6.5 hours, and too low if <5 hours;"; Paragraph 1060, "Senior executive management can get precise insights into effort spent on revenue earning work versus other tasks. Capacity utilization reports can be used to optimize staffing. Stress and burnout can be reduced by identifying teams and projects where there is sustained over-utilization. Teams displaying low capacity utilization can increase their effort, leading to better quality results and on-time delivery."; Paragraph 598, "V. user utilization for comparisons between the users within an organization:-The computation of the user utilization includes computation of a delivered capacity, an available capacity, a staffed capacity and capacity utilization. A pseudo-code for the computation of the user utilization, in accordance with an embodiment of the present disclosure, is now described."; Paragraph 960, "set threshold of delivered capacity as percentage of available capacity to T % (T % is based on what the organization considers to be the optimal capacity utilization, the guideline being that at least 20% of the organization (users or sub-units at a particular level) should have capacity utilization above T % )"; Paragraph: 961," for each relevant parameter type,"; Paragraph 1054, "The systems and methods of present disclosure support extensive analytics. The organization effort aggregation and analytics engine 414 derives a per-employee daily average of Work Pattern. This is a powerful metric that facilitates meaningful and direct comparisons between any two or more organization sub-units of any type, including individual employees. Various trends and reports are available to compare the average daily productive time across various Purposes, Activities, applications, artifacts, online and offline time distribution, work focus, breaks taken, capacity utilization and so on. The reports and trends are available on daily, weekly, monthly or cumulative basis over a specified time range, or during the project or organization lifecycle phases. The differences in the trends between the Top 20%, Middle 60% and Last 20% of organization sub-units can also be viewed, thereby encouraging others to emulate the performance of the Top 20%.") Examiner interprets the utilizations metrics of Deodhar to be equivalent to stress both under broadest reasonable interpretation by one of ordinary skill in the art, and in view of Applicant's specification.
Claim(s) 11 –
Deodhar discloses the following limitations:
capture, via the intelligent scheduling application, processor data of the computing device indicating usage statistics of a user using the computing device; (Deodhar: Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers ( even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications.")
analyze calendar data of the user; and (Deodhar: Paragraph 248, "At step 116 the method includes comparing scheduled engagements, meetings, calls, lab work, travel time and remote visits of the user as obtained from the user's calendar on the CS and from local Presence Devices (PDs), wherein the local Presence Devices include smartphones with GPS, that are connectable to or a part of the CS agent, with the duration of the offline time slots for determining the user's offline time utilization."; Paragraph 312, "An operating System (OS) collector 302: The OS collector 302 runs in the background of the user's CS agent 300 and collects events related to the user's interaction with the CS and status of current active application window and artifacts related to the application, by interfacing with the CS's Operating System 304. It also picks up data from local calendaring applications and local PDs 302A interfacing with the CS, regarding time spent away from the CS on meetings, calls, travel and the like."; Paragraph 312, "A time tracker 306: The time tracker 306 receives the collected data from the OS Collector 302 and aggregates the data chronologically into time slots pertaining to online time on applications and artifacts on the Computing System of the user
(322B) CS and offline time on scheduled meetings, calls and travel as obtained from local calendaring applications and PDs.")
Deodhar does not explicitly disclose the following, however, in analogous art of workplace efficiency, Delaney discloses the following:
A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to execute an intelligent scheduling application to: (Delaney: Paragraph 20, “The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.”)
analyze the usage statistics to determine a usage pattern of the user, wherein...to determine the usage pattern of the user based on stress levels of the user determined according to stress values related to application usage by the usage indicated in the usage statistics (Delaney: Paragraph 47, “At step 415, the user's tasks are stored in a context switching stress table. For example, based on identifying the user's tasks at step 410, the stress analysis server 210 may store the tasks in a context switching stress table. In embodiments, the stress analysis server 210 may monitor the user's activity to monitor the user's tasks. The stress analysis server 210 may store information identifying a transition from one task to another in the context switching stress table. As further described in FIG. 7A, each entry in the context switching stress table may identify a task from which the user transitioned to a subsequent task.”; Paragraph 53, “At step 435, an aggregate stress indicator value is determined. For example, the stress analysis server 210 may determine an aggregate stress indicator value for the user based on multiple stress indicator values associated with multiple context switches. In embodiments, the stress analysis server 210 may receive a request from to determine an aggregate stress indicator for the user over the course of a particular time period (e.g., a particular day, week, month, etc.). The aggregate stress indicator value may factor in the individual stress indicator values from the context switches over the course of the particular time.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
based on the usage pattern of the user and the calendar data, automatically modify scheduling of an event included in the calendar data. (Delaney: Paragraph 22, “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”)
Claim(s) 13 –
Deodhar in view of Delaney disclose the limitations of claim 11
Deodhar further discloses the following:
wherein the instructions cause the processor to provide the user with a notification that a second event included in the calendar data is recommended for modification. (Deodhar: Paragraph 97, "suggest areas of improvements for the user,"; Paragraph 655, "if meeting or call or any other non-core activity time is high, then set a goal for lower time on the non-core activity"; Paragraph 891, "check if time on meeting and communication is high, and set goals for lower time on meeting and communication activities;")
Claim(s) 14 –
Deodhar in view of Delaney disclose the limitations of claim 11
Deodhar further discloses the following:
wherein the instructions cause the processor to provide the user with a notification that a period of time indicated in the calendar data is recommended for allocation as a break period. (Deodhar: Paragraph 322, "The user can view minute by minute details of the captured and mapped time for past few days, and higher level analysis such as trends and reports of time utilization on work across Purposes and Activities, Work Patterns such as work focus through uninterrupted time on important activities, distractions, breaks taken and work units completed. The user can edit Activity-Purpose mappings, and utilize the trends to ensure adequate and right quality of effort, benchmark current performance against goals, improve productivity and optimize work-life balance."; Paragraph 504, "if online work time>90 minutes, then suggest the user to take a short break via the local user interface 322;")
Claim(s) 15 –
Deodhar in view of Comerford disclose the limitations of claim 11
Deodhar does not explicitly disclose the following, however, in analogous art of workplace efficiency, Delaney discloses the following:
analyze calendar data of other users; and (Delaney: Paragraph 15, “The present invention generally relates to monitoring stress levels in individuals, and more particularly, to determining the stress impact on an individual when switching tasks or multitasking. In accordance with aspects of the present invention, information identifying the number and types of task switches (e.g., transitioning from performing one task to performing another task) is used to determine a stress indicator or score for a user. This stress indicator may assist the user to identify how his or her stress level is impacted when transitioning from one type of task to another, and to better plan tasks throughout the day in order to optimize or reduce stress levels. Also, management personnel may use the stress indicators from a group of users or employees to proactively reduce or manage the stress of their employees by re-arranging tasks. A reduction in stress levels, advantageously, may lead to a more productive workforce.”; Paragraph 17, “In embodiments, user activity information is used to determine when a context switch occurs, and the type of context switch. For example, user activity information, such as the user's social media postings, instant messages, e-mail messages, calendar events, phone records, document editing activity, user location, and/or other information identifying the user's activity may be used to identify when the user has transitioned from performing one task to performing a different task. The user's stress level during the transition may be measured using biometrics devices, and information linking the user's stress level during the transition can be provided to the user and/or to other interested personnel (e.g., workplace managers, family members, etc.). In embodiments, a report may be generated identifying a list of context switches or transitions experienced by the user over a period of time (e.g., a day, a week, etc.). Further, the report may identify the types of context switch, or the activities to and from which the user transitioned. The report may identify the stress level at each context switch, and may further identify an aggregate stress level for the user during the period of time. Based on information in the report, a user can identify ways to reduce his or her overall stress level. As an illustrative example, if the report identifies that a user's stress level is lower when transitioning from Task A to Task B than it is when transitioning from Task A to Task C, the user can modify his or her future schedule to replace transitions from Task A to Task C with transitions from Task A to Task B.”; Paragraph 41, “At step 405, user activity information is received. For example, the stress analysis server 210 may receive user activity information for a particular user from the user activity and information server 215. In embodiments, the user activity information may include communications feeds associated with the users, such as the user's social media postings, instant messages, e-mail messages, calendar events, etc. Further, the user activity information may include phone records/usage activity, document editing activity, network usage activity, user location, and/or other information identifying the user's activity. In embodiments, the user may manually update a diary, timecard, or record identifying the user's activity, although in a preferred embodiment, the user's activity is identified with minimal to no manual entries of activity by the user. Additional details regarding the receipt of user activity information by the stress analysis server 210 is described in greater detail below with respect to FIG. 5.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
modify scheduling of the event included in the calendar data according to the calendar data of the other users. (Delaney: Paragraph 15, “The present invention generally relates to monitoring stress levels in individuals, and more particularly, to determining the stress impact on an individual when switching tasks or multitasking. In accordance with aspects of the present invention, information identifying the number and types of task switches (e.g., transitioning from performing one task to performing another task) is used to determine a stress indicator or score for a user. This stress indicator may assist the user to identify how his or her stress level is impacted when transitioning from one type of task to another, and to better plan tasks throughout the day in order to optimize or reduce stress levels. Also, management personnel may use the stress indicators from a group of users or employees to proactively reduce or manage the stress of their employees by re-arranging tasks. A reduction in stress levels, advantageously, may lead to a more productive workforce.”; Paragraph 17, “In embodiments, user activity information is used to determine when a context switch occurs, and the type of context switch. For example, user activity information, such as the user's social media postings, instant messages, e-mail messages, calendar events, phone records, document editing activity, user location, and/or other information identifying the user's activity may be used to identify when the user has transitioned from performing one task to performing a different task. The user's stress level during the transition may be measured using biometrics devices, and information linking the user's stress level during the transition can be provided to the user and/or to other interested personnel (e.g., workplace managers, family members, etc.). In embodiments, a report may be generated identifying a list of context switches or transitions experienced by the user over a period of time (e.g., a day, a week, etc.). Further, the report may identify the types of context switch, or the activities to and from which the user transitioned. The report may identify the stress level at each context switch, and may further identify an aggregate stress level for the user during the period of time. Based on information in the report, a user can identify ways to reduce his or her overall stress level. As an illustrative example, if the report identifies that a user's stress level is lower when transitioning from Task A to Task B than it is when transitioning from Task A to Task C, the user can modify his or her future schedule to replace transitions from Task A to Task C with transitions from Task A to Task B.”; Paragraph 41, “At step 405, user activity information is received. For example, the stress analysis server 210 may receive user activity information for a particular user from the user activity and information server 215. In embodiments, the user activity information may include communications feeds associated with the users, such as the user's social media postings, instant messages, e-mail messages, calendar events, etc. Further, the user activity information may include phone records/usage activity, document editing activity, network usage activity, user location, and/or other information identifying the user's activity. In embodiments, the user may manually update a diary, timecard, or record identifying the user's activity, although in a preferred embodiment, the user's activity is identified with minimal to no manual entries of activity by the user. Additional details regarding the receipt of user activity information by the stress analysis server 210 is described in greater detail below with respect to FIG. 5.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”)
Claim(s) 16 –
Deodhar in view of Delaney disclose the limitations of claim 1
Deodhar further discloses the following:
wherein the activity data includes processor data of the computing device (Deodhar: Paragraph 62, “One more object of the present disclosure is to provide a system that protects the user privacy by not allowing any visibility into user's personal time details, optionally providing the user with a user private time selector to disable employee's time tracking for specified duration, optionally blocking access to work related details such as applications and artifacts, and optionally reducing the resolution of user's work data to daily, weekly, or monthly averages instead of real-time information to make it seem less intrusive.”; Paragraph 223, “select frequency: default is real-time, but can be changed to daily, weekly or monthly average of Work Patterns to make it less intrusive;”; Paragraph 232, “The present disclosure discloses a variety of methods and systems to meet the needs of employee privacy, organization culture, and the different privacy laws of countries where the organization may operate. This includes not allowing access to individual personal time details, a local user interface that enables the user to confirm this, and providing individual work data visibility to the organization only to the extent appropriate, including the option of voluntary sharing of work trends by employees.”)
Claim(s) 17 –
Deodhar in view of Delaney disclose the limitations of claims 1 and 16
Deodhar further discloses the following:
wherein the processor data includes tasks that are configured to be executed by the processor and an amount of processing capability dedicated to a particular task for a given amount of time (Deodhar: Paragraph 63, “A further object of the present disclosure is to provide administrative capabilities to the organization to limit individual level work data visibility only to a few selected staff members, and disabling individual work data view for senior staff (above a certain designation).”; Paragraph 189, “collector to measure and improve the exact work effort at individual level throughout the day by:”; Paragraph 442, “if the user has no other CS agent, then move to next step of checking for the user PD data, else for each other CS agent effort map copy in the server effort map database, in order of priority set by the server according to the functional capability of the CS agent;”; Paragraph 548, “A pseudo-code to find out the user's home and workplace (office) location using an alternate method, in accordance with an embodiment of the present disclosure, is now described. The user Work Pattern analyser 332 finds out the user's home and office locations using different methods based on the capability of the CS agent. This is done in the first week of usage, and repeated thereafter if it is detected that the user's home or office has changed, as explained below:”)
Claim(s) 18 –
Deodhar in view of Delaney disclose the limitations of claim 1
Deodhar further discloses the following:
wherein the last action is at least one of: an input received from the user, an audio output provided by the user, or a video output provided by the user (Deodhar: Paragraph 247, “At step 114 the method includes marking the user's offline time slots by determining each period of inactivity time during which no movement of physical input devices is detected for more than a predetermined period of time, wherein the physical input devices are selected from the group consisting of keyboard, keypad, touchpad and mouse.”; Paragraph 751, “The local user interface 322 provides a lot of detailed information about high level work trends, with the ability to drill down to minute by minute accounting of time spent on personal and work related activities. This is typically available for the past 7-30 days. Trends displayed on the local user interface 322 include first Activity and last Activity time (online or offline), first online and last online time, total time in between, online and offline time, and breakup on work and personal. Work Time trends and reports across Purposes, Activities, Applications and artifacts are available for each day, or on weekly basis.”)
Claim(s) 6-10, and 19-20 is/are currently rejected under 35 U.S.C. 103 as being unpatentable over Deodhar (US 2017/0116552 A1) in view of Delaney (US 2017/0100066 A1) and Schaeppi (US 2022/0342791 A1)
Claim(s) 6 –
Deodhar discloses the following limitations:
determine a computing device is active, wherein the computing device is deemed active when a screen of the computing device is unlocked and a last action has been achieved (Deodhar: Table 6; Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers ( even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications."; Paragraph 392, “In today's 24×7 work environment, there can be several variations from a single user and single CS theme. For example, a single user may work on different CS concurrently (home and work PCs, smartphones, tablets), multiple users may share the same CS, and several users may share a server possibly with a common login ID. The system envisaged by the present disclosure supports multi-user and multi-CS modes of operation. CS agents log each user's data on shared systems, provided each user logs in to the CS with one or more valid IDs in the user's record on the server. The typical IDs are the employee's sign-on ID (one or more, such as for the workgroup, company's network domain, and customer's network domain), employee identification number, phone extension, mobile number, email ID, and so on. If multiple users log into a shared CS using a common ID, the CS agent prompts for proper identification of the new user for correct allocation of the user's time utilization.”)
obtain activity data of a group of users; (Deodhar: Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers (even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications."; Paragraph 201, "automatically collect the organization hierarchy (grouping of individual employees into teams, projects, divisions in one or more hierarchies based, for example, on functions, services lines, and locations) from the organization's existing application data stores;"; Paragraph 187, "There was felt a need for a system that could deliver actionable and objective metrics that can help optimize enterprise effort in every aspect of the business. The system should also provide required protection for individual privacy, and restrict visibility of work effort as per the requirements of the organization and privacy laws of the countries it operates in. The senior management of an organization should have access to a global platform where they can compare their own organization's productivity and work effort in relation to with other peer organizations.")
for each of a group of users:
determine a usage pattern based on the activity data; (Deodhar: Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers ( even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications."; Paragraph 201, "automatically collect the organization hierarchy
(grouping of individual employees into teams, projects, divisions in one or more hierarchies based, for example, on functions, services lines, and locations) from the organization's existing application data stores;"; Paragraph 187, "There was felt a need for a system that could deliver actionable and objective metrics that can help optimize enterprise effort in every aspect of the business. The system should also provide required protection for individual privacy, and restrict visibility of work effort as per the requirements of the organization and privacy laws of the countries it operates in. The senior management of an organization should have access to a global platform where they can compare their own organization's productivity and work effort in relation to with other peer organizations.")
determine an average personal score for the group of users based on the personal score for each user; (Deodhar: Paragraph 156, "productivity improvements are achieved through employee self-awareness by tracking user's own Work Patterns as provided on the local Computing System agent and by comparing against the goals set by the managers and the organization;"; Paragraph 158, "user's profile is defined and comparisons are made with peers having a similar profile and who voluntarily but anonymously shared their respective effort data, wherein the user's profile is selected from the group consisting of role, seniority, location and skills."; Paragraph 187, "There was felt a need for a system that could deliver actionable and objective metrics that can help optimize enterprise effort in every aspect of the business. The system should also provide required protection for individual privacy, and restrict visibility of work effort as per the requirements of the organization and privacy laws of the countries it operates in. The senior management of an organization should have access to a global platform where they can compare their own organization's productivity and work effort in relation to with other peer organizations.") Paragraph 648, "work output parameters (output.volume, output.schedule variance,output.effort variance) if available, are best viewed as independent parameters, the independent parameters improves as the user time effectiveness score improves;"; Paragraph 1043, "in accordance with an embodiment of the present disclosure, a web user interface 430 is configured to facilitate views at each level of the organization hierarchy across Work pattern items. The web user interface 430 is further configured to selectively filter and drill down to generate and compare discrete effort data for any Work Pattern item across any business attribute. The Work Pattern items are selected from the group consisting of effort, habits, effort distribution across Purposes, Activities, applications and work units, work life balance index, capacity utilization, and work effectiveness index. The business attributes are selected from the group consisting of role, skills, salary, position, and location for the user, and from the group consisting of domain, vertical, cost and profit center, and priority for the organization sub-unit.")
compare the personal score of a user of the group of users to the average personal score to determine a comparison result; and (Deodhar: Paragraph 156, "productivity improvements are achieved through employee self-awareness by tracking user's own Work Patterns as provided on the local Computing System agent and by comparing against the goals set by the managers and the organization;"; Paragraph 158, "user's profile is defined and comparisons are made with peers having a similar profile and who voluntarily but anonymously shared their respective effort data, wherein the user's profile is selected from the group consisting of role, seniority, location and skills."; Paragraph 187, "There was felt a need for a system that could deliver actionable and objective metrics that can help optimize enterprise effort in every aspect of the business. The system should also provide required protection for individual privacy, and restrict visibility of work effort as per the requirements of the organization and privacy laws of the countries it operates in. The senior management of an organization should have access to a global platform where they can compare their own organization's productivity and work effort in relation to with other peer organizations."; Paragraph 648, "work output parameters (output.volume, output.schedule variance,output.effort variance) if available, are best viewed as independent parameters, the independent parameters improves as the user time effectiveness score improves;"; Paragraph 1043, "in accordance with an embodiment of the present disclosure, a web user interface 430 is configured to facilitate views at each level of the organization hierarchy across Work pattern items. The web user interface 430 is further configured to selectively filter and drill down to generate and compare discrete effort data for any Work Pattern item across any business attribute. The Work Pattern items are selected from the group consisting of effort, habits, effort distribution across Purposes, Activities, applications and work units, work life balance index, capacity utilization, and work effectiveness index. The business attributes are selected from the group consisting of role, skills, salary, position, and location for the user, and from the group consisting of domain, vertical, cost and profit center, and priority for the organization sub-unit.")
based on the comparison result, automatically determine and provide a recommendation to the user. (Deodhar: Paragraph 662, "recommend training, mentoring or moving to work more suited to the user's skills;"; Paragraph 663, "else, if the user is doing well on all the work parameters, then recommend to take up more challenging work and also, explore opportunities for improved work-life balance;"; Paragraph 833, "make recommendations to improve the sub-units performance.")
Deodhar does not explicitly disclose the following, however, in analogous art of scheduling efficiency, Delaney discloses the following:
A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to execute an intelligent scheduling application to: (Delaney: Paragraph 20, “The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.”)
Determine a personal score based on the usage pattern wherein the personal score relates to a stress level determined by programmed stress values associated to an application (Delaney: Paragraph 16, “In embodiments, information identifying the breadth of tasks a user is performing may be used to count the number of context switches (e.g., transitions from one task to another). Further, stress scores or indicators may be assigned to each context switch based on the user's physical reactions that indicate the individual's stress level. For example, wearable biometrics devices may be used to determine the user's stress level, and a link can be established between the user's stress level and the context switch. Also, the type of context switch is determined based on user activity information. For example, the type of context switch identifies that the user transitioned from one task to another (e.g., from working on one project to working on another project, or working on one project followed by performing some other task, such as attending a meeting, taking a coffee break, etc.).”; Paragraph 18, “In embodiments, a report may be generated based on stress level information relating to context switches for a group for multiple users having certain commonalities (e.g., users with similar job titles, length of service, users working for the same department or manager, users having similar personality types, etc.). Thus, the report may identify average stress levels for users by category (job titles, department, personality types, etc.). Also, identifying an individual's stress level based on personality type may provide insight into the individuals'behavior under different levels of stress.”; Paragraph 35, “The stress analysis server 210 may include one or more computing devices that implement the stress analysis component 46. The stress analysis server 210 may receive user activity and/or user information from the user activity and information server 215, and biometrics information for the user from the biometrics device 220. Based on the user activity information, the stress analysis server 210 may identify when the user experiences a context switch (e.g., transitions from one task to another), and the user's stress level associated with the context switch (e.g., based on the biometrics information received from the biometrics device 220). As described in greater detail below, the stress analysis server 210 may parse through the communication feeds identifying the user's activity to identify the tasks that a user is performing.”; Paragraph 36, “The user activity and information server 215 may include one or more computing devices that stores user activity information regarding a user. For example, the user activity and information server 215 may store information identifying a task that the user is currently performing (e.g., a work-related technical task, resolving a customer issue, attending a meeting, taking a meal break, etc.). In embodiments, the user activity and information server 215 may store a user's instant messages (e.g., from the user's social network account, personal/work instant messaging accounts, etc.), network usage activity (e.g., indicating networks or websites being accessed by the user), social media activity (e.g., public postings on the user's social media account), interactive media feeds (e.g., blog posts made by the user), e-mail messages, calendar events, telephone calling activity, location information, etc.) In embodiments, the user activity and information server 215 may store other information regarding the user, such as the user's job title, personality profile, etc. This information may be used to group stress level indicators from many users having certain commonalities (e.g., users with similar job titles, users working for the same department or manager, users having similar personality types, etc.).”)
Deodhar in view of Delaney do not explicitly disclose the use of K-means clustering, however, an analogous art of stress management, Schaeppi discloses the following:
perform K-means clustering to create a group of users from the plurality of users, wherein the K-means clustering creates the group of users based on interaction times with similar applications among the group of users (Schaeppi: Paragraph 56, “Group component 112 may be configured to identify clusters of users that have similar sets of the psychological parameter values. The identification of clusters may be based on the sets of psychological parameter values for the individual users and/or other information. The identifying of the clusters of the users may include, by way of non-limiting example, latent class analysis, hierarchical clustering, k-means clustering, mean-shifting clustering, machine learning, dimensionality reduction, principle component analysis, supervised learning, and/or other grouping techniques. The users may be classified into clusters of users with similar sets of psychological parameter values. That is, given the similar sets of psychological parameter values, the users in a given cluster may interact with digital application environments that include the same applications or similar application types, and/or use the same applications or the applications of the similar application type and the digital application environments in a similar manner. The clusters may include a first cluster, a second cluster, and/or other clusters. The first cluster may include the first user on the basis of the first set of psychological parameter values. The second cluster may include other users that have other similar sets of psychological parameter values. The second cluster may include the second user on the basis of the second set of psychological parameter values.”; Paragraph 57, “In some implementations, group component 112 may be configured to identify the clusters of the users based on the behavioral information, the sets of psychological parameter values, and/or other information. In some implementations, the users may be included in multiple clusters or the cluster the individual users are included in may change based on the behavioral information. In some implementations, the determined behavioral information may be stored to electronic storage 128 in association with the users.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. Schaeppi discloses a method for measuring stress in relation to application use. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”). It would further be obvious to combine the methods of Deodhar in view of Delaney with the teachings of Schaeppi in order to improve the digital applications and environments of employees as disclosed by Schaeppi (Schaeppi: Paragraph 4, “As such, the psychological attributes indicated by the psychological assessment may addressed by adjusting with what and how the users interact and use their digital application environments.”)
Claim(s) 7 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claim 1
Deodhar further discloses the following:
wherein the activity data indicates an amount of time spent by the user interacting via the processor with applications. (Deodhar: Paragraph 9, "The term 'Activity' in this specification relates to the nature of work on which time is spent by an employee towards achieving the assigned objectives. The list of activities is determined by the organization based on its business. For instance, Activity can include online ones like design, programming, testing, documentation, communication, and offline ones such as meetings, calls, lab work, travel, and visits."; Paragraph 55, "One more object of the present disclosure is to provide a system that creates an n-dimensional effort data cube and includes an analytics engine to provide for generation of custom reports by defining the parameters to be viewed and compared against, filters for selecting a subset, in which the parameters comprise any and every data item sourced, including online and offline time, applications, Activities, Purposes, artifacts, organization sub-units, organization attributes, along with ability for statistical analysis based on totals, averages, maximum and minimum values, standard deviations and others."; Paragraph 38, "A related object of the present disclosure is to provide a system that automatically tracks the exact time spent by the employee on one or more personal CS, any CS shared with other users through a common login, and remote servers (even if the servers do not belong to the organization), by determining the user's time on the currently active application and associated artifacts such as files, folders, websites and other artifacts related to the applications.")
Claim(s) 8 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claims 6-7
Deodhar further discloses the following:
wherein the instructions cause the processor to determine the usage pattern as an amount of stress attributable to each of the applications. (Deodhar: Paragraph 96, "provide a feedback to the user on highlights related to work effort, work output and the work life balance index,"; Paragraph 150, "In accordance with the present disclosure, the organization effort aggregation and analytics engine is further configured to deduce a best working pattern, and top performers at individual and organization sub-unit level, the organization effort aggregation and analytics engine further configured to determine unusual Work Patterns and the recent positive and negative deviations in the Work Patterns for an organization sub-unit, the organization effort aggregation and analytics engine further configured to generate a report including specific actions that can be undertaken to improve the efforts of the users."; Paragraph 207, "compute the per-employee Daily Average Work Pattern for any specified sub-unit and duration of interest, for which it becomes necessary to infer and account for the various complexities such as employees working on multiple CS, in more than one project, employees with different roles, shift timings, variable work weeks, holidays and vacations, work done while on holidays and vacation days, geographically distributed teams with different work weeks and holidays, variable nature of work in different organization sub-units, complex organization hierarchies including matrix structures etc." Paragraph 527, "A pseudo-code depicting the tagging operation, for each day (as a workday, holiday, vacation), performed by the user Work Pattern analyser 332, in accordance with an embodiment of the present disclosure, is now described. Once the user Work Pattern analyser 332 detects that yesterday is over, then it determines whether the day was a work day, weekend day, public holiday, or vacation, and whether it was work from office or home. If the information about the user's weekend days is not available, then intelligent inferencing based on Work Patterns is used to determine the weekend days. It may be that the user may not have a fixed weekend, as for example for support staff and independent contractors, in which case a 'variable work week' flag is introduced. Vacations and holidays too can be inferred based on the user's Work Patterns if that information is unavailable. In another embodiment, the user Work Pattern analyser 332 may employ a fuzzy logic to determine user vacations, weekends and holidays, shift timing, work from home and office and other locations, and unaccounted time in office."; Paragraph 638, "work time-too high if > 10 hours, high if 8-10 hours, good if between 6.5 to 8 hours, low if 5-6.5 hours, and too low if <5 hours;"; Paragraph 1060, "Senior executive management can get precise insights into effort spent on revenue earning work versus other tasks. Capacity utilization reports can be used to optimize staffing. Stress and burnout can be reduced by identifying teams and projects where there is sustained over-utilization. Teams displaying low capacity utilization can increase their effort, leading to better quality results and on-time delivery."; Paragraph 598, "V. user utilization for comparisons between the users within an organization:-The computation of the user utilization includes computation of a delivered capacity, an available capacity, a staffed capacity and capacity utilization. A pseudo-code for the computation of the user utilization, in accordance with an embodiment of the present disclosure, is now described."; Paragraph 960, "set threshold of delivered capacity as percentage of available capacity to T % (T % is based on what the organization considers to be the optimal capacity utilization, the guideline being that at least 20% of the organization (users or sub-units at a particular level) should have capacity utilization above T % ); [0961] for each relevant parameter type,"; Paragraph 1054, "The systems and methods of present disclosure support extensive analytics. The organization effort aggregation and analytics engine 414 derives a per-employee daily average of Work Pattern. This is a powerful metric that facilitates meaningful and direct comparisons between any two or more organization sub-units of any type, including individual employees. Various trends and reports are available to compare the average daily productive time across various Purposes, Activities, applications, artifacts, online and offline time distribution, work focus, breaks taken, capacity utilization and so on. The reports and trends are available on daily, weekly, monthly or cumulative basis over a specified time range, or during the project or organization lifecycle phases. The differences in the trends between the Top 20%, Middle 60% and Last 20% of organization sub-units can also be viewed, thereby encouraging others to emulate the performance of the Top 20%.") Examiner interprets the utilizations metrics of Deodhar to be equivalent to stress both under broadest reasonable interpretation by one of ordinary skill in the art, and in view of Applicant's specification.
Claim(s) 9 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claims 6-8
Deodhar further discloses the following:
wherein the instructions cause the processor to determine the personal score as a function of the amount of stress attributable to each of the applications. (Deodhar: Paragraph 96, "provide a feedback to the user on highlights related to work effort, work output and the work life balance index,"; Paragraph 527, "A pseudo-code depicting the tagging operation, for each day (as a workday, holiday, vacation), performed by the user Work Pattern analyser 332, in accordance with an embodiment of the present disclosure, is now described. Once the user Work Pattern analyser 332 detects that yesterday is over, then it determines whether the day was a work day, weekend day, public holiday, or vacation, and whether it was work from office or home. If the information about the user's weekend days is not available, then intelligent inferencing based on Work Patterns is used to determine the weekend days. It may be that the user may not have a fixed weekend, as for example for support staff and independent contractors, in which case a 'variable work week' flag is introduced. Vacations and holidays too can be inferred based on the user's Work Patterns if that information is unavailable. In another embodiment, the user Work Pattern analyser 332 may employ a fuzzy logic to determine user vacations, weekends and holidays, shift timing, work from home and office and other locations, and unaccounted time in office."; Paragraph 638, "work time-too high if > 10 hours, high if 8-10 hours, good if between 6.5 to 8 hours, low if 5-6.5 hours, and too low if <5 hours;"; Paragraph 1060, "Senior executive management can get precise insights into effort spent on revenue earning work versus other tasks. Capacity utilization reports can be used to optimize staffing. Stress and burnout can be reduced by identifying teams and projects where there is sustained over-utilization. Teams displaying low capacity utilization can increase their effort, leading to better quality results and on-time delivery."; Paragraph 598, "V. user utilization for comparisons between the users within an organization:-The computation of the user utilization includes computation of a delivered capacity, an available capacity, a staffed capacity and capacity utilization. A pseudo-code for the computation of the user utilization, in accordance with an embodiment of the present disclosure, is now described."; Paragraph 960, "set threshold of delivered capacity as percentage of available capacity to T % (T % is based on what the organization considers to be the optimal capacity utilization, the guideline being that at least 20% of the organization (users or sub-units at a particular level) should have capacity utilization above T % ) ; [0961] for each relevant parameter type,"; Paragraph 1054, "The systems and methods of present disclosure support extensive analytics. The organization effort aggregation and analytics engine 414 derives a per-employee daily average of Work Pattern. This is a powerful metric that facilitates meaningful and direct comparisons between any two or more organization sub-units of any type, including individual employees. Various trends and reports are available to compare the average daily productive time across various Purposes, Activities, applications, artifacts, online and offline time distribution, work focus, breaks taken, capacity utilization and so on. The reports and trends are available on daily, weekly, monthly or cumulative basis over a specified time range, or during the project or organization lifecycle phases. The differences in the trends between the Top 20%, Middle 60% and Last 20% of organization sub-units can also be viewed, thereby encouraging others to emulate the performance of the Top 20%.") Examiner interprets the utilizations metrics of Deodhar to be equivalent to stress both under broadest reasonable interpretation by one of ordinary skill in the art, and in view of Applicant's specification.
Claim(s) 10 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claim 6
Deodhar does not explicitly disclose the following, however, in analogous art of scheduling efficiency, Delaney teaches the following:
wherein the instructions cause the processor to automatically modify the calendar data of the user according to the analysis result without intervention by the user. (Delaney: Paragraph 22, “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. Schaeppi discloses a method for measuring stress in relation to application use. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”).
Claim(s) 19 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claim 6
Deodhar does not explicitly disclose the following, however, in analogous art of scheduling efficiency, Delaney discloses the following:
wherein the personal score of the user is high stress minutes per data for the user and the average personal score is an average of high stress minutes per day for a group of users (Delaney: Paragraph 52, “In embodiments, the context switching stress table may be populated over time to identify the user's stress levels for different context switches. In embodiments, information in the context switching stress table may be used to later predict the user's stress levels based on their context switches even when biometrics data is not available (e.g., since the context switching stress table was originally populated when biometrics data was available).”; Paragraph 53, “At step 435, an aggregate stress indicator value is determined. For example, the stress analysis server 210 may determine an aggregate stress indicator value for the user based on multiple stress indicator values associated with multiple context switches. In embodiments, the stress analysis server 210 may receive a request from to determine an aggregate stress indicator for the user over the course of a particular time period (e.g., a particular day, week, month, etc.). The aggregate stress indicator value may factor in the individual stress indicator values from the context switches over the course of the particular time.”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”; Paragraph 71, “Referring to FIG. 7B, context switching stress table 750 for the particular user may be populated with stress indicator values (e.g., stress scores) when stress analysis server 210 receives biometrics data. The stress analysis server 210 may populate the context switching stress table 750 with the stress indicator values based on the timestamps of the stress indicator values and the timestamps of the context switches. In embodiments, the context switching stress table 750 may identify the user's average stress score (e.g., the aggregate stress indicator value) based on the individual stress scores for each context switch. In embodiments, the average stress score may be based on individual stress scores for different time periods (e.g., an average stress score for a particular day, week, month, etc.).”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. Schaeppi discloses a method for measuring stress in relation to application use. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”).
Claim(s) 20 –
Deodhar in view of Delaney and Schaeppi disclose the limitations of claim 6
Deodhar does not explicitly disclose the following, however, in analogous art of scheduling efficiency, Delaney discloses the following:
wherein the comparison result indicates that the user is less of more stress than the group of users (Delaney: Paragraph 15, “The present invention generally relates to monitoring stress levels in individuals, and more particularly, to determining the stress impact on an individual when switching tasks or multitasking. In accordance with aspects of the present invention, information identifying the number and types of task switches (e.g., transitioning from performing one task to performing another task) is used to determine a stress indicator or score for a user. This stress indicator may assist the user to identify how his or her stress level is impacted when transitioning from one type of task to another, and to better plan tasks throughout the day in order to optimize or reduce stress levels. Also, management personnel may use the stress indicators from a group of users or employees to proactively reduce or manage the stress of their employees by re-arranging tasks. A reduction in stress levels, advantageously, may lead to a more productive workforce.”; Paragraph 18, “In embodiments, a report may be generated based on stress level information relating to context switches for a group for multiple users having certain commonalities (e.g., users with similar job titles, length of service, users working for the same department or manager, users having similar personality types, etc.). Thus, the report may identify average stress levels for users by category (job titles, department, personality types, etc.). Also, identifying an individual's stress level based on personality type may provide insight into the individuals'behavior under different levels of stress.”; Paragraph 36, “The user activity and information server 215 may include one or more computing devices that stores user activity information regarding a user. For example, the user activity and information server 215 may store information identifying a task that the user is currently performing (e.g., a work-related technical task, resolving a customer issue, attending a meeting, taking a meal break, etc.). In embodiments, the user activity and information server 215 may store a user's instant messages (e.g., from the user's social network account, personal/work instant messaging accounts, etc.), network usage activity (e.g., indicating networks or websites being accessed by the user), social media activity (e.g., public postings on the user's social media account), interactive media feeds (e.g., blog posts made by the user), e-mail messages, calendar events, telephone calling activity, location information, etc.) In embodiments, the user activity and information server 215 may store other information regarding the user, such as the user's job title, personality profile, etc. This information may be used to group stress level indicators from many users having certain commonalities (e.g., users with similar job titles, users working for the same department or manager, users having similar personality types, etc.).”; Paragraph 55, “In embodiments, the stress analysis server 210 may store context switching stress tables for multiple different users. Accordingly, the stress analysis server 210 may generate a report identifying the stress indicator values for multiple different users. In embodiments, the stress analysis server 210 may generate a report that identifies the stress indicator values of users meeting certain criteria or having particular commonalities (e.g., users of similar personality types, job titles, or users who work in the same department, have the same manager, etc.). In embodiments, data in the report may be sorted in ascending or descending order of stress indicator values (e.g., to identify individuals having the highest stress indicator values, and/or context switches that have the highest stress values). Based on context switches that have the highest stress values, adjustments can be made to a user's schedule to minimize those context switches that have the highest stress values. Also, adjustments may be made to the schedules of those individuals who have commonalities and whose stress indicator values are relatively higher. For example, the schedule of individuals of a particular job title may be adjusted based on information indicating that these individuals experience higher levels of stress than others.”)
Deodhar discloses a method of using interaction metrics to determine the efficiency of an organization based on the behavior analysis of individuals. Delaney discloses an invention for measuring an determining stress levels for individuals and groups within an organization. Schaeppi discloses a method for measuring stress in relation to application use. At the time of Applicant’s filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Deodhar with the teachings of Delaney in order to improve the productivity and efficiency of the employees of an organization as disclosed by Delaney (Delaney: Paragraph 72, “Based on this information, a user can identify ways to reduce his or her overall stress level, and employers can better manage their employees tasks to reduce overall company stress and improve productivity.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624