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 .
Status of Claims
This action is a final rejection
Claims 1-5, 7, 9-22 are pending
Claims 6, 8 were cancelled
Claims 1, 14 was amended
Claims 1-5, 7, 9-22 are rejected under 35 USC § 101
Claims 1-5, 7, 9-22 are rejected under 35 USC § 112
Claims 1-5, 7, 9-22 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 12-10-2021
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6-6-2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 14 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Regarding claims 1, 14 and 15
Claims 1, 14 and 15 recite the limitation " wherein the prediction model comprises a machine learning model trained on the activity data associated with the worker and a ground truth label obtained from the stress assessment provided by the worker" in the fifth/last limitation of claims 1, 14 and 15. There is insufficient antecedent basis for this limitation in the claim since the previous limitation recites: “wherein the information about the activity of the worker comprises an indicator of the worker's stress state derived from activity data associated with the activity.
Since claims 2-5, 7, 9-13, 16-18 are dependent on claim 1 and claims 19-22 are dependent on claim 15 all claims 1-5, 7, 9-22 are all rejected under 35 U.S.C. 112(b) or 35 U.S.C 112 (pre AIA ).
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.
Claims 1-5, 7, 9-22 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-5, 7, 9-22 the claims recite an abstract idea of “predicting stress states”.
Independent claims 1, 14, 15 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): claims 1, 14, 15 recite respectively a computer implemented method, a non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement a method, and an apparatus for predicting stress states.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
receiving information about an activity of a worker associated with an organization;
predicting, using a prediction model, a stress state of the worker based on the information and a stress assessment provided by the worker; and
generating an indication of whether a process for implementation by the organization is to be modified in view of the predicted stress state of the worker,
wherein the information about the activity of the worker comprises an indicator of the worker's stress state derived from activity data associated with the activity, and
wherein the prediction model comprises a .. model trained on the activity data associated with the worker and a ground truth label obtained from the stress assessment provided by the worker
belonging to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “predicting stress states”. Alternatively, the selected abstract idea belongs to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting stress states” (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 1, 14, 15 recite:
machine learning model;
Claim 1 recites:
Computer;
Claim 14 recites:
A non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement a method.
Claim 15 recites:
processing circuitry communicatively coupled to an interface ;
a machine-readable medium storing instructions which, when executed by the processing circuitry.
Amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0121-0125) (refer to MPEP 2106.05(f)). (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two:
Claims 1, 14, 15 recite:
machine learning model;
Claim 1 recites:
Computer;
Claim 14 recites:
A non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement a method;
Claim 15 recites:
processing circuitry communicatively coupled to an interface ;
a machine-readable medium storing instructions which, when executed by the processing circuitry
Amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0121-0125) (refer to MPEP 2106.05(f)). (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible..
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of “predicting stress states”. The claim limitations include:
Claims 2, 16: further comprising receiving information about an asset associated with the organization for implementing at least part of the process, and wherein the indication of whether the process is to be modified comprises an indication of whether a change is to be made in terms of the asset used to implement at least part of the process.
Claims 3, 17: wherein the information about the asset comprises information about at least one of:
a role of the asset; and/or a
capability and/or availability of the asset for carrying out the activity.
Claims 4, 18: wherein the asset associated with the organization comprises at least one of:
the worker;
another worker associated with the organization; and/or
a resource associated with the organization.
Claims 5, 19: wherein the process comprises a workflow specifying a set of activities to be carried out by the organization as part of providing a service to a plurality of users of the service.
Claim 7: wherein the activity data comprises at least one of:
a timestamp associated with the activity;
a duration of the activity;
an idle time of the worker and/or another worker with the same role as the worker before and/or after the activity;
a number of parallel tasks of the worker and/or another worker with the same role as the worker for each activity carried out by the worker as part of the process;
a total number of activities related to the worker and/or another worker with the same role as the worker as part of the process;
a number of dependencies between the worker and other workers associated with the organization with the same or different roles to the worker;
a previous process and/or activity in which the worker and/or another worker was involved;
a type of the activity;
a type of the process;
a time of day associated with the activity;
a day of week associated with the activity;
a location associated with the activity; and/or
a classification of a patient being cared for as part of the process
Claims 9, 20: wherein the prediction model is configured to predict the stress state of the worker based on a role of the worker.
Claims 10, 21: modifying the process based on the generated indication, wherein the modifying is based on an objective, wherein the objective comprises at least one of:
reducing the stress state of the worker and/or at least one other worker associated with the organization;
reducing a cost of implementing the process;
reducing a duration of implementing the process; and/or
increasing throughput of the process
Claims 11, 22: wherein the process is modified based on a number of the objectives to be taken into account, and wherein the modification is configured to vary the process in order to meet a condition by varying at least one of:
wherein the process is modified based on a number of the objectives to be taken into account, and wherein the modification is configured to vary the process in order to meet a condition by varying at least one of:
an order of a set of activities to be implemented as part of the process depending on predefined data specifying an allowed order;
a choice of which worker of the worker and/or another worker associated with the organization is to be assigned to which of a set of activities associated with the process depending on predefined data specifying a capability and/or availability of the worker and/or the other worker;
an assignment of a resource associated with the organization to the process;
a proposed duration of an activity associated with the process where there is an opportunity to vary the duration by accelerating or omitting an unnecessary task associated with the activity; and/or
a scheduling of a set of users of a service provided by the organization
Claim 12: wherein modifying the process comprises causing .. to send a notification .. associated with the organization, wherein the notification is configured to cause .. to facilitate a change to the process based on the generated indication, wherein .. at least one of:
associated with the worker and configured to provide an instruction for the worker based on the notification;
associated with another worker and configured to provide an instruction for the other worker based on the notification;
associated with a manager of the process for managing a set of assets associated with the organization and configured to cause an asset of the set of assets to implement the change based on the notification by sending an instruction to another computing device associated with the asset of the set of assets in order to implement the change; and/or
associated with a resource of the organization and configured to control an operation of the resource based on the notification
Claim 13: comprising receiving input data, .. associated with an admin of the organization, indicative of a prioritization of the objective to be taken into account when generating the indication of whether the process for implementation by the organization is to be modified in view of the predicted stress state of the worker, wherein the objective to be taken into account comprises at least one of:
the stress state of the worker and/or another worker associated with the organization;
the cost of implementing the process;
the duration of the process; and/or
the throughput of the process
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 12:
workflow engine;
computing device;
Claim 13: receiving input data, entered via an electronic interface;
Claim 21: processing circuitry;
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 12:
workflow engine;
computing device;
Claim 13: electronic interface;
Claim 21: processing circuitry;
Claim Rejections - 35 USC § 103
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
non-obviousness.
Claims 1-5, 7, 9-22 are rejected under 35 U.S.C. 103 as being un-patentable by Skiba et.al (US 20160180277 A1) hereinafter “Skiba”; in view of Joswick et.al. (WO 2022212052 A1) hereinafter “Joswick”
Regarding claims 1, 14, 15 Skiba teaches:
A computer-implemented method, comprising: (See at least [0057] via: “…FIG. 3 illustrates work assignment system 300 ... In one embodiment, work assignment system 300 comprises portions of communication system 100 associated with routing work items in a contact center, such as to agent 202 for processing thereby. In one embodiment server 216 is in communication with work assignment mechanism 116 having work assignment engine 112. Work assignment engine 112 and/or routing engine 132 routes work items to a number of agents based upon the availability agent particulars, particular skills of the agent, particular needs of the work item, channel (e.g., text, voice-call, video-call, email, etc.), or other means for matching work items to agents for processing. Routing engine 112 manages the work use of agents such as agent 202 to manage workload, pacing, wait queue, etc ..”; in addition see at least [0048] via: “…the work assignment mechanism 116 comprises a work assignment engine 120 which enables the work assignment mechanism 116 to make intelligent routing decisions for work items. In some embodiments, the work assignment engine 120 is configured to administer and make work assignment decisions in a queueless contact center,..”)
A non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement the method of claim 1.; processing circuitry communicatively coupled to an interface and a machine-readable medium storing instructions which, when executed by the processing circuitry, cause the processing circuitry to: (See at least [0072] via: “… the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor (GPU or CPU) or logic circuits programmed with the instructions to perform the methods (FPGA). These machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software…”; in addition see at least [0075] via: “… embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium such as storage medium. A processor(s) may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
receiving information about an activity of a worker associated with an organization; (See at least [0005] via: “…Inputs for which a system is operable to detect, collect, and analyze burnout indicators include, but are not limited to: …”; in addition see at least [0006] via: “…Agent speech off-call; in addition see at least [0007] via: “…Agent speech during supervisor discussion and/or supervised/consultative transfer; in addition see at least [0008] via: “… Speech characterization (voice based emotion detection, speech analysis, keywords) ; in addition see at least [0009] via: “… Video characterization (gesture, posture, facial expression); in addition see at least [0010] via: “…Questionnaire (stated state); in addition see at least [0011] via: “… Ranking; in addition see at least [0012] via: “…Overtime acceptance and efficiency; in addition see at least [0013] via: “…and/or Accuracy of typing and typing rate…”).
predicting, using a prediction model, a stress state of the worker based on the information and a stress assessment provided by the worker; (See at least [0016] via: “…a system using the attributes derived from agent inputs, such as those described above, and the historical results of agent turnover, performance evaluations, and supervisor ratings, the system can build and maintain a model correlating the features and indicators to predict current and future agent fatigue and potential turnover..”; in addition see at least [0017] via: “…The model created may be continuously updated based on the monitoring of agent performance and interactions with customers to provide a machine-learning system. While aspects of the model are generic in nature (e.g., behavior common to all or nearly all agents) there are aspects that may be specific to a single agent, a class of agents, a level of experience, etc. Using such a model, the system can be used to automatically alter the routing of work items to specific agents and notify supervisors of agents entering fatigue thresholds or agents predicted to become a turnover risk. A supervisor may be presented such information via a dashboard updated by the system. In one embodiment, the supervisors may adjust and/or approve any desired routing rules, staff schedules, etc. Results may also be integrated into any additional tools and applications to provide a comprehensive view (e.g., used in workforce planning, hiring, and scheduling)..”)
generating an indication of whether a process for implementation by the organization is to be modified in view of the predicted stress state of the worker, (See at least [0014] via: “…provide positive incentives so that agents do not “game” the system. The system can use the predictive data to lighten the load of those agents who might otherwise take overtime when they shouldn't and/or provide agents with easier, more interesting, or otherwise more desirable work items. The system may change an agent's utilization and routing, including using the agents smartly as escalation agents or specialists that fulfill a specific need and provide additional challenges to head off boredom, fatigue, or burnout. In addition to notifications provided to automatic systems, alerts may be provided to supervisors as set by an administrator when indicators and thresholds are hit…”; in addition see at least [0015] via: “…Alternately, if an agent is not responding to burnout countermeasures or an agent's actions provide indisputable evidence of burnout, embodiments disclosed herein, may automatically assign the agent more burnout-associated or difficult work, and thereby reduce the stress load on other agents, such as to reduce the burnout potential of the other agents. Scheduling systems may be modified to minimize the opportunity for the agent to socialize with other agents, such as by automatically adjusting breaks and/or work schedule. As a result of reducing the interactions between a burnt-out agent and his or her colleagues, the negative sentiment associated with the burnt-out agent has fewer opportunities to “infect” other agents…”)
wherein the information about the activity of the worker comprises an indicator of the worker's stress state derived from activity data associated with the activity, (See at least [0052] via: “…agent 202 interacts with the input devices in a manner that may reveal mental states associated with burnout, such as frustration, lack of customer/coworker empathy, anger, indifference, fatigue, inability to concentrate, etc. Audio input device, microphone 204, may be associated with a voice stress analysis component operable to determine a level of stress associated with the speech of agent 202. Camera 206 may capture additional visual indicators of stress associated with burnout, for example, weaving of the hands, I motions, facial expressions, etc. Camera 206 may be operable to detect infrared images which may show heat patters of the agent's head and face, which may further indicate the agent is experiencing burnout…”; in addition see at least [0056] via: “…classification of agents, that includes agent 202, may then be the subject of mitigation activities. For example, agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame. Agent 202 is identified as suffering from burnout, server 216 may determine that all agents associated with tax-related work items should receive some burnout mitigation activities which may be the same for all agents in the group or different for at least two agents. As a result, agent 202 may receive two non-tax related work items per shift, designed to provide variety and a break from tax-related work items, and all other agents processing tax-related work items, receive one non-tax related work item per shift…”; in addition see at least [0058] via: “… Server 216, upon determining agent 202 is facing a burnout condition signals work assignment mechanism 116, work assignment engine 112, and/or routing engine 132 to alleviate stresses and/or increase variety or number of more interesting work items routed to agent 202. For example, agent 202 may have a particular interest in learning a new language and, following the signal of server 216 to work assignment mechanism 116 work assignment engine 112, or running engine 132 additional work items having that particular language associated there with our routed towards agent 202. In another example agent 202 may not like a particular subject matter and have fewer such calls routed to agent 202. In yet another example, agent 202 is particularly fond of a particular geographic region and, in response to a signal from server 216, receives more work items associated with customers from that particular geographic region…”) .
wherein the prediction model comprises a machine learning model trained on the activity data associated with the worker [and a ground truth label obtained from the stress assessment provided by the worker]. (See at least [0004] via: “…Embodiments disclosed herein solve the above and other problems by providing, in part, automatic systems and means for detecting, and responding to, agent burnout. Detecting inputs and analysis may utilize voice characterization, typing rate, rankings, automatic questionnaires and/or other means to provide predictors of burnout in advance of an agent quitting…”; in addition see at least [0016] via: “… a system using the attributes derived from agent inputs, such as those described above, and the historical results of agent turnover, performance evaluations, and supervisor ratings, the system can build and maintain a model correlating the features and indicators to predict current and future agent fatigue and potential turnover…”; in addition see at least [0017] via: “…The model created may be continuously updated based on the monitoring of agent performance and interactions with customers to provide a machine-learning system…”)
However Skiba is silent regarding a prediction model comprising a machine learning model trained on a ground truth label obtained from the stress assessment provided by the worker as taught by Joswick
a prediction model comprising a machine learning model trained on a ground truth label obtained from the stress assessment provided by the worker (See at least [0074] via: “…stress level may be determined using statistical or machine learning-based classification techniques. For example, determining that the user has a stress level includes using a machine learning model trained using ground truth data that includes self-assessments in which users labelled portions of experiences with stress level labels. For example, to determine the ground truth data that includes self- assessments, a group of subjects, while watching a cooking instructional video, could be prompted at different time intervals (e.g., every 30 seconds) Alternatively, or additionally, the ground truth data that includes self-assessments while watching a video includes different examples stress events. For example, the different stress events could be displayed and transitioned between each stress event to each subject in an XR environment while a user is wearing an HMD. The “stress events” may include high stress event such as walking on a virtual plank on a high rise (e.g., physical stress event), having the user take a math test (e.g., cognitive stress event), or simulate a user having to make a presentation to a group of people (e.g., social stress event). Additionally, lower stress events may also be included for recording low stress levels, either in between the high stress events, or shown separately (e.g., a mediation video with calming sounds/music). After each “stress event”, each subject could be prompted at or after a particular stress event in the video content to enter his or her stress level…”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Skiba to incorporate the teachings of Joswick. Those in the art would have recognized that Skiba’s teaching regarding building models based on a machine -learning system for detecting, and responding to, agent burnout using attributes derived from agent inputs, historical results of agent turnover, performance evaluations, and supervisor ratings, could be modified to include Joswick’s teaching determining user stress levels using a machine learning model trained using ground truth data that includes self-assessments in which users labelled portions of experiences with stress level labels. The combination of Skiba and Joswick is useful in providing a more solid and rugged machine learning model to detect stress levels in workers that uses historical results of agent turnover, performance evaluations, and supervisor ratings in addition to the labelled portions of experiences of workers with stress level labels
Regarding claims 2 & 16 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1 & 1, 10, 13 respectively. Skiba also teaches:
further comprising receiving information about an asset associated with the organization for implementing at least part of the process, and wherein the indication of whether the process is to be modified comprises an indication of whether a change is to be made in terms of the asset used to implement at least part of the process. (See at least [0056] via: “…a classification of agents may be identified as indicating burnout without necessarily having indications from each agent within the class. The classification of agents, that includes agent 202, may then be the subject of mitigation activities. For example, agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame. Agent 202 is identified as suffering from burnout, server 216 may determine that all agents associated with tax-related work items should receive some burnout mitigation activities which may be the same for all agents in the group or different for at least two agents. As a result, agent 202 may receive two non-tax related work items per shift, designed to provide variety and a break from tax-related work items, and all other agents processing tax-related work items, receive one non-tax related work item per shift..” )
Regarding claims 3 & 17 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1, 2 & 1, 10, 13, 16 respectively. Skiba also teaches:
wherein the information about the asset comprises information about at least one of:
a role of the asset; (See at least [0056] via: “…agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame…”; in addition see at least [0069] via: “… mitigation options 614 provide various mitigation options to server 216 to execute to mitigate the burnout of agent 202. Mitigation options 614 may include, for example, changing a routing of work items by work assignment engine 120, changing work hours associated with agent 202, utilizing agent 202 for other tasks (e.g., escalation, review, supervisory, etc.), or other actions as may be appropriate…”) and/or
a capability and/or availability of the asset for carrying out the activity.
Regarding claims 4 & 18 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1, 2 & 1, 10, 13, 16 respectively. Skaiba also teaches:
wherein the asset associated with the organization comprises at least one of:
the worker (See at least [0051] via: “…agent 202 is a human agent resource 112…”; in addition see at least [0056] via: “…agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame…”) ;
another worker associated with the organization; and/or
a resource associated with the organization.
Regarding claims 5 & 19 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1 & 15 respectively. Skiba also teaches:
wherein the process comprises a workflow specifying a set of activities to be carried out by the organization as part of providing a service to a plurality of users of the service. (See at least [0056] via: “…agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame...”)
Regarding claim 7 Skiba and Joswick teach the invention as claimed and detailed with respect to claim 1. Skiba also teaches:
wherein the activity data comprises at least one of:
a timestamp associated with the activity;
a duration of the activity;
an idle time of the worker and/or another worker with the same role as the worker before and/or after the activity;
a number of parallel tasks of the worker and/or another worker with the same role as the worker for each activity carried out by the worker as part of the process;
a total number of activities related to the worker and/or another worker with the same role as the worker as part of the process;
a number of dependencies between the worker and other workers associated with the organization with the same or different roles to the worker;
a previous process and/or activity in which the worker and/or another worker was involved;
a type of the activity (See at least [0056] via: “…agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame…”);
a type of the process;
a time of day associated with the activity;
a day of week associated with the activity;
a location associated with the activity; and/or
a classification of a patient being cared for as part of the process.
Regarding claims 9 & 20 Skiba and Joswick teach the invention as claimed and detailed with respect to claim 1 & 15 respectively. Skiba also teaches:
wherein the prediction model is configured to predict the stress state of the worker based on a role of the worker. (See at least [0004] via: “…Embodiments disclosed herein solve the above and other problems by providing, in part, automatic systems and means for detecting, and responding to, agent burnout. Detecting inputs and analysis may utilize voice characterization, typing rate, rankings, automatic questionnaires and/or other means to provide predictors of burnout in advance of an agent quitting…”; in addition see at least [0016] via: “… a system using the attributes derived from agent inputs, such as those described above, and the historical results of agent turnover, performance evaluations, and supervisor ratings, the system can build and maintain a model correlating the features and indicators to predict current and future agent fatigue and potential turnover…”; in addition see at least [0017] via: “…The model created may be continuously updated based on the monitoring of agent performance and interactions with customers to provide a machine-learning system…”)
Regarding claims 10 & 21 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1 & 15 respectively. Skiba also teaches:
modifying the process based on the generated indication, wherein the modifying is based on an objective, wherein the objective comprises at least one of:
reducing the stress state of the worker and/or at least one other worker associated with the organization; (See at least [0005] via: “…Inputs for which a system is operable to detect, collect, and analyze burnout indicators..”; in addition see at least [0014] via: “…The system can use the predictive data to lighten the load of those agents who might otherwise take overtime when they shouldn't and/or provide agents with easier, more interesting, or otherwise more desirable work items. The system may change an agent's utilization and routing, including using the agents smartly as escalation agents or specialists that fulfill a specific need and provide additional challenges to head off boredom, fatigue, or burnout. In addition to notifications provided to automatic systems, alerts may be provided to supervisors as set by an administrator when indicators and thresholds are hit…”; in addition see at least [0015] via: “…Alternately, if an agent is not responding to burnout countermeasures or an agent's actions provide indisputable evidence of burnout, embodiments disclosed herein, may automatically assign the agent more burnout-associated or difficult work, and thereby reduce the stress load on other agents, such as to reduce the burnout potential of the other agents. Scheduling systems may be modified to minimize the opportunity for the agent to socialize with other agents, such as by automatically adjusting breaks and/or work schedule. As a result of reducing the interactions between a burnt-out agent and his or her colleagues, the negative sentiment associated with the burnt-out agent has fewer opportunities to “infect” other agents…”; in addition see at least [0015] via: “… )
reducing a cost of implementing the process;
reducing a duration of implementing the process; and/or
increasing throughput of the process
Regarding claims 11 & 22 Skiba and Joswick, teach the invention as claimed and detailed with respect to claims 1, 10 & 15, 21 respectively. Skiba also teaches:
wherein the process is modified based on a number of the objectives to be taken into account, and wherein the modification is configured to vary the process in order to meet a condition by varying at least one of:
an order of a set of activities to be implemented as part of the process depending on predefined data specifying an allowed order;
a choice of which worker of the worker and/or another worker associated with the organization is to be assigned to which of a set of activities associated with the process depending on predefined data specifying a capability and/or availability of the worker and/or the other worker; (See at least [0056] via: “…a classification of agents may be identified as indicating burnout without necessarily having indications from each agent within the class. The classification of agents, that includes agent 202, may then be the subject of mitigation activities. For example, agent 202 and related agents may be handling tax-related work items during tax preparation season or other high-stress, high-activity time frame. Agent 202 is identified as suffering from burnout, server 216 may determine that all agents associated with tax-related work items should receive some burnout mitigation activities which may be the same for all agents in the group or different for at least two agents. As a result, agent 202 may receive two non-tax related work items per shift, designed to provide variety and a break from tax-related work items, and all other agents processing tax-related work items, receive one non-tax related work item per shift..”; in addition see at least [0014] via: “…The system can use the predictive data to lighten the load of those agents who might otherwise take overtime when they shouldn't and/or provide agents with easier, more interesting, or otherwise more desirable work items. The system may change an agent's utilization and routing, including using the agents smartly as escalation agents or specialists that fulfill a specific need and provide additional challenges to head off boredom, fatigue, or burnout. In addition to notifications provided to automatic systems, alerts may be provided to supervisors as set by an administrator when indicators and thresholds are hit…” )
an assignment of a resource associated with the organization to the process;
a proposed duration of an activity associated with the process where there is an opportunity to vary the duration by accelerating or omitting an unnecessary task associated with the activity; and/or
a scheduling of a set of users of a service provided by the organization.
Regarding claim 12 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1, 10. Skiba also teaches:
wherein modifying the process comprises causing a workflow engine to send a notification to a computing device associated with the organization, wherein the notification is configured to cause the computing device to facilitate a change to the process based on the generated indication, wherein the computing device is at least one of:
associated with the worker and configured to provide an instruction for the worker based on the notification; (See at least [0057] via: “…FIG. 3 illustrates work assignment system 300 ... In one embodiment, work assignment system 300 comprises portions of communication system 100 associated with routing work items in a contact center, such as to agent 202 for processing thereby. In one embodiment server 216 is in communication with work assignment mechanism 116 having work assignment engine 112. Work assignment engine 112 and/or routing engine 132 routes work items to a number of agents based upon the availability agent particulars, particular skills of the agent, particular needs of the work item, channel (e.g., text, voice-call, video-call, email, etc.), or other means for matching work items to agents for processing. Routing engine 112 manages the work use of agents such as agent 202 to manage workload, pacing, wait queue, etc ..”; in addition see at least [0052] via: “…agent 202 interacts with the input devices in a manner that may reveal mental states associated with burnout..”; in addition see at least [0055] via: “… One server 216 determines agent 202 is a candidate for burnout, that is, identified as suffering from burnout or is showing indicators associated with burnout, a response action to mitigate the burnout of agent 202 is selected and deployed by server 216. Server 216 may perform mitigation activities designed to mitigate the burnout potential of agent 202, alternatively, if agent 202 is part of a group of agents (e.g., agents addressing similar work items, etc.) additional mitigation activities may be applied to all agents within the group. As an example, server 202 may cause routing engine 132 to alter the work items sent to agent 202 and/or routing certain work items to other agent 214. Additionally, server 216 may notify supervisor 212 of the mitigating action and/or the burnout potential of agent 202. Additionally, server 216 may select an action to mitigate the burnout of agent 202 and, prior to execution, seek permission from supervisor 212 regarding mitigation activities..”; in addition see at least [0058] via: “… Server 216, upon determining agent 202 is facing a burnout condition signals work assignment mechanism 116, work assignment engine 112, and/or routing engine 132 to alleviate stresses and/or increase variety or number of more interesting work items routed to agent 202.…”; in addition see at least [0048] via: “…the work assignment mechanism 116 comprises a work assignment engine 120 which enables the work assignment mechanism 116 to make intelligent routing decisions for work items. In some embodiments, the work assignment engine 120 is configured to administer and make work assignment decisions in a queueless contact center,..”; in addition see at least [0070] via: “…mitigation selection 616 selects a mitigation option from mitigation option 614. Mitigation selection 616 may further consider burnout event history 618, as well as burnout quantification 612 as inputs to select the one or more mitigating activities. Mitigation selection 616 executes and causes server 612 to notify work assignment engine 120 and/or other systems of the contact center in accord with the mitigation activity. …”)
associated with another worker and configured to provide an instruction for the other worker based on the notification;
associated with a manager of the process for managing a set of assets associated with the organization and configured to cause an asset of the set of assets to implement the change based on the notification by sending an instruction to another computing device associated with the asset of the set of assets in order to implement the change; and/or
associated with a resource of the organization and configured to control an operation of the resource based on the notification.
Regarding claim 13 Skiba and Joswick teach the invention as claimed and detailed with respect to claims 1, 10. Skiba also teaches:
comprising receiving input data, entered via an electronic interface associated with an admin of the organization, indicative of a prioritization of the objective to be taken into account when generating the indication of whether the process for implementation by the organization is to be modified in view of the predicted stress state of the worker, wherein the objective to be taken into account comprises at least one of:
the stress state of the worker and/or another worker associated with the organization; (See at least [0005] via: “…Inputs for which a system is operable to detect, collect, and analyze burnout indicators..”; in addition see at least [0014] via: “…The system can use the predictive data to lighten the load of those agents who might otherwise take overtime when they shouldn't and/or provide agents with easier, more interesting, or otherwise more desirable work items. The system may change an agent's utilization and routing, including using the agents smartly as escalation agents or specialists that fulfill a specific need and provide additional challenges to head off boredom, fatigue, or burnout. In addition to notifications provided to automatic systems, alerts may be provided to supervisors as set by an administrator when indicators and thresholds are hit…”; in addition see at least [0015] via: “…Alternately, if an agent is not responding to burnout countermeasures or an agent's actions provide indisputable evidence of burnout, embodiments disclosed herein, may automatically assign the agent more burnout-associated or difficult work, and thereby reduce the stress load on other agents, such as to reduce the burnout potential of the other agents. Scheduling systems may be modified to minimize the opportunity for the agent to socialize with other agents, such as by automatically adjusting breaks and/or work schedule. As a result of reducing the interactions between a burnt-out agent and his or her colleagues, the negative sentiment associated with the burnt-out agent has fewer opportunities to “infect” other agents…”)
the cost of implementing the process;
the duration of the process; and/or
the throughput of the process
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Reiner (US 20130006064 A1) - METHOD AND APPARATUS FOR REAL-TIME MEASUREMENT AND ANALYSIS OF OCCUPATIONAL STRESS AND FATIGUE AND PERFORMANCE OUTCOME PREDICTIONS - teaches: a method and apparatus to objectively measure stress and fatigue using measurement tools, record stress and fatigue related data in a standardized database, create automated prompts and alerts based upon pre-defined stress and fatigue thresholds (which are derived based upon individual end-user and task performance), provide a number of interventions (which can be preferentially selected by the individual end-user), create data-driven best practice guidelines though meta-analysis of the database, and provide an objective tool for comparative technology assessment.
JANSSEN (US 20160300024 A1) - PREDICTION OF CRITICAL WORK LOAD IN RADIATION THERAPY WORKFLOW – teaches: A method and related system for supporting task scheduling. Completion times of tasks of a process are predicted based on historical data held in a database (HIS). Based on the predictions, a workload measure for a resource associated with performing the task is computed. Also, there is established whether the predicted completion times will result in overshooting predefined due-dates as held in a rules database (DB-REG). The work load measure and/or the overshoot is indicted as graphical indicators in a graphical user interface (GUI). The workload measure and/or the overshoot indicators are computed and displayed in real-time.
Reiner (US 20130311190 A1) - METHOD AND APPARATUS OF SPEECH ANALYSIS FOR REAL-TIME MEASUREMENT OF STRESS, FATIGUE, AND UNCERTAINTY - teaches: speech analysis to provide real-time measurement of end-user stress, fatigue, and uncertainty in decision-making. The present invention monitors "technology-induced" stressors by increasing the inherent functionality of individual monitoring technologies, so as to perform multiple applications in a single setting. In addition to the continued use of speech recognition technology for computerized report transcription, the present invention simultaneously measures and analyzes occupational stress and fatigue in real-time, specific to the unique profile of each individual end-user and context of the task being performed. The derived user-specific stress/fatigue analytics may be used in the creation of a number of workflow and quality enhancing deliverables, including customizable intervention strategies for stress/fatigue reduction, creation of automated workflow templates, and targeted quality assurance and peer review
Response to Arguments
Applicant's arguments filed 11-17-2025, have been fully considered but not found
persuasive.
Applicant amended independent claim 14, as posted in the above analysis with additions underlined and deletions as ..
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 14, 15 since the claims recite respectively a computer implemented method, a non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement a method, and an apparatus for predicting stress states.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “predicting stress states”. Alternatively the claim belongs to certain methods of organizing human activity under managing personal relationships or interactions between people as it recites “predicting stress states”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that the invention does not belong to the grouping of mental processes under concepts performed in the human mind as it recites “predicting stress states”. Neither does it belong to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “predicting stress states”. Regarding the mental process the Applicant argues that the invention cannot be practically performed by the human mind. Furthermore the Applicant argues that there does not seem to be any “human activity” regarding organizing human activity under managing personal behavior or relationships or interactions between people.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Examiner explains the method used to select the abstract idea, which is to strip the additional elements from the claims. As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of amended limitation of claims 1, 14, 15:
Grouping of claims 1, 14, 15:
receiving information about an activity of a worker associated with an organization;
predicting, using a prediction model, a stress state of the worker based on the information and a stress assessment provided by the worker; and
generating an indication of whether a process for implementation by the organization is to be modified in view of the predicted stress state of the worker;
wherein the information about the activity of the worker comprises an indicator of the worker's stress state derived from activity data associated with the activity; and
wherein the prediction model comprises a machine learning model trained on the activity data associated with the worker and a ground truth label obtained from the stress assessment provided by the worker.
The selected abstract idea (boldened limitations) of claims 1, 14, 15 can be implemented by pencil and paper and thus belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites “detection of health related behaviors related to eating”. Alternatively, the selected abstract idea belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “detection of health related behaviors related to eating”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
Step 2A Prong TWO
The Applicant argues that even if the invention belongs to an abstract idea the claimed subject matter is directed to a practical application based on the amendments. Specifically the Applicant argues that the claims represent an improvement as the specification describes a problem in a technology or technical field which would have been understood by one of ordinary skill in the art as an improvement over that problem. Accordingly the Applicant requests withdrawal of the 101 rejection.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Applicant refers to a colloquial interpretation of a practical application. What is required instead is a demonstration of improvement to the functioning of a computer, or to any other technology or technical field that the invention has not recited. All of the additional elements include physical components that are generic whose function amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea.
The Examiner restates that claim does not integrate the abstract idea into a practical application. Claims 1, 14, 15 recite additional elements that do not impose a meaningful limit on the abstract idea:
Claims 1, 14, 15 recite:
machine learning model;
Claim 1 recites:
Computer;
Claim 14 recites:
A non-transitory machine-readable medium storing instructions which, when executed by processing circuitry, cause the processing circuitry to implement a method;
Claim 15 recites:
processing circuitry communicatively coupled to an interface ;
a machine-readable medium storing instructions which, when executed by the processing circuitry
The additional elements as recited above for claims 1, 14, 15 amounts to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions. As a result, the Examiner restates the rejection of the invention under 35 USC §101.
Step 2B
Similar to the analysis under Step 2A Prong Two, the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). The use of generic computer components, in combination, do not perform functions that are not merely generic, and non-conventional even if the generic computer operations on a generic computing device is used to implement the abstract idea. Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
In order evaluate whether the claim recites additional elements that amount to an inventive concept what could be shown is:
Adding a specific limitation (unconventional other than what is well-understood, routine, conventional (WURC) activity in the field - see MPEP 2106.05(d)
The Applicant has not demonstrated the above listed condition.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The Applicant argues that the machine learning model taught by Joswick, alone or in combination with the prediction disclosed by Skiba, fails to disclose the claimed machine learning model.
Specifically, the claimed machine learning model is trained with both: (i) the activity data associated with the worker; and (ii) a ground truth label obtained from the stress assessment provided by the worker.
Notably, the claim requires that the ground truth label - which is used to train the model – is obtained from "the stress assessment provided by the worker," which is the same worker for which the trained model will be used to predict a stress state for.
In contrast, there is no teaching or suggestion in Joswick, alone or in combination with the prediction disclosed by Skiba, of a machine learning model that is trained using a ground truth label obtained from a stress assessment provided by the worker. Although Joswick discloses "a machine learning model trained using ground truth data that includes self-assessments in which users labelled portions of experiences with stress level labels," these self-assessments in which users labelled portions of experiences with stress level labels are NOT provided by the same users for which the trained model will be utilized. There is absolutely no teaching or suggestion to use a machine learning model that is trained on a ground truth extracted from a stress assessment provided by the worker for which the model will be utilized. Indeed, paragraph [0074] discloses a generic method for training a machine learning model, which is in stark contrast to the specific, novel, and non-obvious method for training the claimed machine learning model..
The Examiner disagrees with the Applicant’s arguments since they are not persuasive. The Applicant argues that the following limitations refer to a specific worker:
“predicting, using a prediction model, a stress state of the worker based on the information and a stress assessment provided by the worker”
“wherein the prediction model comprises a machine learning model trained on the activity data associated with the worker and a ground truth label obtained from the stress assessment provided by the worker”
Nevertheless the claim language interpreted under the broadest reasonable interpretation may refer to workers in general which is how the Examiner interpreted the claim. The first limitation of claim 1: "receiving information about an activity of a worker associated with an organization" can refer to specific workers or to workers in general. If the information is related to a particular worker's activities, it is specific to that individual. However, if the information encompasses multiple workers or is about the overall activities of workers within an organization, it may be considered general. Thus, the context of the claim is essential to determine its specificity and based on the claim language this specificity has not been clearly defined. In addition, based on the specification paragraph [0002] the invention relates to workers in plural rather than to a specific worker . “Workload is considered to be a source of stress and burnout for workers of an organization such as a hospital department. For example, workers such as clinicians associated with certain hospital departments may be subject to high workload owing to the nature of their work. By way of example, workers in radiology departments may experience significant amounts of stress given the clinical relevance of their work. An outcome of the work may be improved if stress could be eased, where such stress has been detected in the organization. An example way to determine worker stress situations and its root causes may involve engaging a specialist stress assessment service. Questionnaires and interviews may be used by such a stress assessment service to analyze stress and its roots. As part of this service, an analysis of the current stress situation in an organization may be carried out, followed by implementation of a change to how workers are managed, and followed by analysis of the outcome of implementing the change. The analyses may be carried out manually by specially trained consultants”
The Examiner thus restates that Skiba in combination with Joswick successfully teaches the above cited limitations that the Applicant refers to.
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1-5, 7, 9-22 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101 in addition to maintaining the rejection under 35 USC §103. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
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 PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684