DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 9, 2026, has been entered.
Claims 1, 13, and 19 are amended.
Claims 1-20 are pending.
Response to Amendments/Remarks
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the presently claimed process is tangible. See Remarks p. 16. The Examiner respectfully disagrees. Interacting with sensor data to create a schedule is not tangible; the process is purely logical and computational. The process does not involve any physical hardware components, other than generic sensors and computer components. The use of computers as tools to perform repetitive computational tasks is understood. No apparent improvement to the performance of a computer as a machine is recited in the claims.
The Applicant additionally submits that the claims recite an improvement in personality modeling. See Remarks p. 16. In response, the Examiner submits that the claims merely recite the idea of personality modeling – changing tone to improve persuasiveness in negotiations. The claimed process is human in nature – humans have been exhibiting this behavior throughout civilized history. Therefore, the claims attempt to manage human behavior. Any requirement of advanced computing power is not apparent in the claims.
The Applicant further contends the claims are subject matter eligible because the recited process is performed in real time. See Remarks p. 17. In response, the Examiner submits that the use of computers to perform real time calculations is understood.
The Applicant additionally contends that the claims recite significantly more than the recited abstract idea under step 2B of the Alice/Mayo test. The Examiner reiterates the response provided in the paragraphs, above. The sensors are generic computer components that do not provide a practical application or significantly more than the recited abstract idea. The voice modulator attempts to manage known human behavior, and no apparent improvement to voice modulator technology is recited. Lack of conventionality does not imply subject matter eligibility. The claims have been properly analyzed according to the guidance provided in MPEP §2106, and the claims have been determined to be ineligible. Additional elements outside the scope of the abstract idea have been evaluated, but they have been found to be generic computer hardware components. The claims are directed to an abstract idea without significantly more. The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further consideration of the prior art. Independent claims 1, 13, and 19 remain obvious over Cragun in view of Sarvana, Bahl, Zweig, and Stuttle. The Applicant traverses the rejection; pointing out what the prior art teaches, and what the claims recite. See Remarks pp. 20-49. In response, the Examiner points out that a statement which merely points out what a claim recites will not be considered an argument for separate patentability of the claim. See 37 CFR §41.37(c)(1)(iv).
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to adjusting attributes of tasks (as evidenced by exemplary independent claim 1; “adaptively adjusting . . . respective attributes of respective tasks”) and determining motivational personality attributes (as evidenced by exemplary independent claim 1 (“determining . . . a [ ] personality attribute . . . determined to enhance motivation and engagement for performance of the group of tasks”); abstract ideas. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “analyzing . . . task-related information;” “generating . . . a [ ] user state profile;” “adaptively adjusting . . . respective attributes of respective tasks of [ ] groups of tasks;” “presenting . . . task information to a device;” “determining . . . a [ ] personality attribute . . . determined to enhance motivation and engagement for performance of the group of tasks;” “receiving . . . feedback data . . . compris[ing] a negotiation indication;” “analyzing . . . the negotiation indication;” “adjust a characteristic of a voice;” “applying . . the [ ] personality attribute;” and “presenting . . . negotiation data. “ The steps are all steps for managing personal behavior related to the abstract ideas of adjusting attributes of tasks and determining motivational personality attributes that, when considered alone and in combination, are part of the abstract ideas of adjusting attributes of tasks and determining motivational personality attributes. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of adjusting attributes of tasks. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes determining an optimal task workflow based on task-related information; and providing motivational messaging to aid execution of the workflow.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (a system with a processor, sensors, and a virtual assistant device in independent claim 1; a system with a processor and memory; sensors; and a virtual assistant device and/or user equipment in independent claim 13; and a computer readable medium and a virtual assistant device in independent claim 19). See MPEP §2106.04(d)[I]. The claims do recite the use of machine learning, but the abstract ideas of adjusting attributes of tasks and determining motivational attributes are generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amounts to a technological environment that does not provide a practical application or significantly more than an abstract idea. See MPEP §2106.05(h). Other limitations recite a modulator component and a voice generator component, but those elements constitute software per se. Software per se is not patentable. See MPEP §2106.03. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (a system with a processor, sensors, and a virtual assistant device in independent claim 1; a system with a processor and memory; sensors; and a virtual assistant device and/or user equipment in independent claim 13; and a computer readable medium and a virtual assistant device in independent claim 19) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1, 2, 5, 7-9, 13, 15, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to Cragun et al. (hereinafter ‘CRAGUN’) in view of US 20180032944 A1 to Sarvana et al. (hereinafter ‘SARVANA’), US 20200117504 A1 to Bahl et al. (hereinafter ‘BAHL’), US 20220036251 A1 to Zweig et al. (hereinafter ‘ZWEIG’), and US 8856007 B1 to Stuttle et al. (hereinafter ‘STUTTLE’).
Claim 1 (Currently Amended)
CRAGUN discloses a method, comprising: analyzing, by a processing system comprising a processor (see abstract; evaluate scheduling information by one or more processors), task-related information relating to a group of tasks (see again abstract; generate goals and tasks based on scheduling information and activity information) associated with a user identity of a user (see abstract; a person’s daily goals), assessment information relating to assessing performance of an assigned task of the group of tasks (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals), and biometric information derived from real-time sensor data obtained from a group of sensors associated with the user identity (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals. See also col 4, ln 5-18; sensors), wherein the biometric information relates to health (see again col 3, ln 32-53; heart rate and blood pressure), diet (see col 5, ln 14-26 and col 6, ln 42-57; blood sugar and caloric consumption), and activity (see again col 5, ln 14-26; calories burned) associated with the user identity (see abstract and claim 1; a person).
generating, by the processing system, a dynamic user state profile (see abstract; evaluate information associated with a person) based on the biometric information (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals. See also col 4, ln 5-18; sensors), task-related information (see again abstract; generate goals and tasks based on scheduling information and activity information), and context data, (see col 3, ln 32-53; receiving data from local sensors, local devices, and/or external devices (e.g., smart home devices, biometric devices, point-of-sale systems, etc.).
CRAGUN does not specifically disclose, but SARVANA discloses, wherein the dynamic user state profile specifies a fatigue level (see ¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue), stress level (see again¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue).
CRAGUN further discloses performance capacity (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals).
CRAGUN does not specifically disclose, but SARVANA discloses, and recovery requirements associated with the user identity (see ¶[0009]; determining that the generated biometric score exceeds a threshold score associated with a first one of the assigned tasks, and in response to the determination that the generated biometric score exceeds the threshold score, modifying the determined schedule to replace the first assigned task with a break having a predetermined duration).
adaptively adjusting, by the processing system, respective attributes of respective tasks of the group of tasks based on: (i) a result of the analyzing of the task-related information (see ¶[0004]; apply one or more machine learning techniques and establish task scores that indicate a level of complexity and risk of injury for each task), the assessment information (see ¶[0011]; obtain data indicative of performance of the users during a completion of a task. The performance data may identify one of a work quality of a completion speed), and the biometric information (see abstract; obtain biometric data and determine a schedule for completing tasks); and (ii) the dynamic user state profile (see abstract; task and biometric information associated with a user), the adaptively adjusting resulting in respective adjusted attributes of the respective tasks (see abstract and ¶[0007]-[0011]; detect time-varying patterns using a machine learning algorithm. The time-varying patterns may include biometric scores during the time period. See also Fig. 2C; the risk score is a product of the biometric score and the task score), wherein the respective adjusted attributes comprise task prioritization, schedule adaptation, or resource allocation adjustments (see abstract and ¶[0109]-[0110] & [0120]; adaptively allocate resources. Implement processes based on the individual ability to access physical resources necessary to successfully complete the allocated task); and
presenting, by the processing system, task information to a virtual assistant device (see col 2, ln 62-col 3, ln 8; an application running on a mobile device assists users in achieving daily goals) associated with the user identity to facilitate performance of the group of tasks (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals).
CRAGUN does not specifically disclose, but STUTTLE discloses, wherein the virtual assistant device comprises a voice generator component and a modulator component (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN further discloses wherein the task information is determined based on the respective adjusted attributes (see abstract and ¶[0056]; determine a schedule for completing the tasks during the current workday. Optimize scheduling presentation data with a corresponding display unit, such as a touchscreen display).
CRAGUN does not specifically disclose, but BAHL discloses, and wherein the presenting of the task information further comprises presenting recommendation data that recommends a schedule for performance of the assigned task based on a performance metric associated with the assigned task (see ¶[0036]-[0037]; present recommendations from scheduler for review, modification, and approval. Collaborator is configured to provide feedback to optimize schedules. Job execution schedules include periodicity constraints, categorization data, and organization data).
CRAGUN does not specifically disclose, but ZWEIG discloses, determining, by the processing system, a dynamic personality attribute of a virtualized personality of the virtual assistant device based on: (i) the biometric information (see ¶[0016] and [0024]; sensor data related to a user); (ii) a context of an interaction between the virtual assistant device and the user identity (see ¶[0040] and [0055]; the current state); and (iii) feedback data indicative of motivational preferences of the user identity, wherein the dynamic personality attribute is adaptively determined to enhance motivation and engagement for performance of the group of tasks (see again ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.);
receiving, by the processing system and via the virtual assistant device, feedback data associated with the user identity, wherein the feedback data comprises a negotiation indication regarding the assigned task (see ¶[0055]; it is determined that the user rejects the recommendation).
analyzing, by the processing system, the negotiation indication and deriving a negotiation strategy by evaluating feedback data (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.), user sentiment analysis based on voice intonation (see ¶[0040]; for example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), task progress (see ¶[0047]; “We set a goal to do this exercise three times a week, you need one more exercise to achieve this goal”), and the dynamic user state profile (see abstract; analyze a historical dataset related to a user, and present a persuasive action).
The combination of CRAGUN and ZWEIG does not specifically disclose, but STUTTLE discloses, adjusting, by the processing system, the modulator component to adjust a characteristic of a voice produced by the voice generator component according to the dynamic personality attribute to obtain an adjusted voice characteristic (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN does not specifically disclose, but ZWEIG discloses, applying, by the processing system, the dynamic personality attribute to the virtual assistant device, wherein the dynamic personality attribute comprises the adjusted voice characteristic to achieve a resolution to a negotiation with the user with regard to the negotiation indication (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.); and
presenting, by the processing system, negotiation data to the virtual assistant device to facilitate negotiation with regard to the negotiation indication regarding the assigned task (see again ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional size, emphasizing benefits, etc. See also abstract; recommendation is presented by means of an input/output device), wherein the negotiation data reflects the negotiation strategy (see abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant.. For example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), predicted compliance likelihood (see again abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant), and motivational tone adjustments (see again abstract and ¶[0040]; the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses biometric data to detect time-varying patterns to optimize schedules during the workday. It would have been obvious to include the machine learning optimization as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). BAHL discloses scheduling and management of jobs that includes optimization based on periodicity constraints, categorization data, and organization data. It would have been obvious to include the constraints and data as taught by BAHL in the system executing the method of CRAGUN with the motivation to optimize a schedule for tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where a rejection of a recommendation causes the virtual assistant to consider historical data to determine an effective method of persuasion. It would have been obvious for one of ordinary skill in the art to consider an effective method of persuasion in response to a rejection as taught by ZWEIG in the system executing the method of CRAGUN with the motivation to schedule and recommend tasks; and encourage users to perform the tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where an effective manner of speaking is determined (see ¶[0040]). STUTTLE discloses synthesized voices that include characteristics for persuasiveness. It would have been obvious for one of ordinary skill in the art at the time of invention to include the characteristics as taught by STUTTLE in the system executing the method of CRAGUN with the motivation to communicate in an effective manner.
Claim 2 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN further discloses wherein the user identity is a first user identity (see abstract; a person),
CRAGUN does not explicitly disclose, but SARVANA discloses, wherein the assigned task is part of the group of tasks or is a previous task associated with the first user identity or a second user identity, and wherein the previous task is determined to be same as, or relevant with respect to, at least one task of the group of tasks, or wherein the previous task is determined to satisfy a defined task similarity criterion with respect to the at least one task (see ¶[0062]-[0064]; characterize user’s performance of tasks similar to the outstanding tasks).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that determines tasks of similar complexity to previously performed tasks. It would have been obvious to include the similarity determination as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
Claim 5 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN does not specifically disclose, but SARVANA discloses, further comprising: based on the analyzing and the adaptively adjusting, allocating, by the processing system, respective amounts of time for performance of the respective tasks or respective sub- tasks of the assigned task associated with the user identity (see ¶[0047], [0051], [0059] and [0075]; identify expected completion times associated with the outstanding tasks).
CRAGUN does not specifically disclose, but STUTTLE discloses, wherein the adjusting of the modulator component further comprises adjusting any combination of a speed or a cadence of verbal words emitted by the voice generator component (see col 3, ln 15-28; if a portion of speech is meant to be persuasive, a low, fast male voice can be used, while a slower voice can be used to increase attention and information retention).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24) that monitors the speed with which the user completes the tasks (see col 2, ln 27-49). SARVANA discloses determining an expected completion time associate with outstanding tasks. It would have been obvious to include the expected completion time as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on the biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where an effective manner of speaking is determined (see ¶[0040]). STUTTLE discloses synthesized voices that include characteristics for persuasiveness. It would have been obvious for one of ordinary skill in the art at the time of invention to include the characteristics as taught by STUTTLE in the system executing the method of CRAGUN with the motivation to communicate in an effective manner.
Claim 7 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN further discloses wherein the task-related information is first task- related information (see again abstract; generate goals and tasks based on scheduling information and activity information), wherein the assessment information is first assessment information (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals), wherein the biometric information is first biometric information (see col 3, ln 32-53; receive data from biometric devices).
CRAGUN does not specifically disclose, but SARVANA discloses, wherein the respective attributes are respective first attributes (see ¶[0004[; apply one or more machine learning techniques and establish task scores that indicate a level of complexity and risk of injury for each task), wherein the result of the analyzing of the task-related information is a first result (see ¶[0004]; a machine learning technique that detects time-varying patterns in biometric data), and
wherein the method further comprises: subsequent to the adjusting of the respective first attributes of the respective tasks, receiving, by the processing system, feedback information associated with the user identity, second task-related information relating to the group of tasks associated with the user identity, second assessment information relating to assessing performance of the assigned task, or second biometric information relating to the health, the diet, or the activity associated with the user identity, wherein the feedback information relates to the group of tasks, or the health, the diet, or the activity associated with the user identity (see ¶[0011]; obtain data indicative of performance of the users during a completion of a task. The performance data may identify one of a work quality of a completion speed. See again ¶[0004] apply machine learning techniques that establish task scores that indicate a level of complexity and risk of injury for each task);
analyzing, by the processing system, the feedback information, the second task-related information, the second assessment information, or the second biometric information (see again ¶[0004]; apply one or more machine learning techniques);
adaptively adjusting, by the processing system, a second attribute or a portion of the respective first attributes of the respective tasks based on a second result of the analyzing of the feedback information, the second task-related information, the second assessment information, or the second biometric information (see ¶[0004[; apply one or more machine learning techniques and establish task scores that indicate a level of complexity and risk of injury for each task); and
to facilitate adjusting the performance of a portion of the group of tasks, presenting, by the processing system, updated task information to the virtual assistant device, wherein the updated task information relates to, and is determined based on, the adaptively adjusting of the second attribute or the portion of the respective first attributes of the respective tasks (see abstract and ¶[0056]; determine a schedule for completing the tasks during the current workday. Optimize scheduling presentation data with a corresponding display unit, such as a touchscreen display).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses biometric data to detect time-varying patterns to optimize schedules during the workday. It would have been obvious to include the machine learning optimization as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
Claim 8 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN additionally discloses further comprising: monitoring, by the processing system, a health metric associated with the user identity (see col 5, ln 14-26; biometric devices include any suitable devices for detecting information such as hear rate, blood pressure, etc.);
based on the monitoring, receiving, by the processing system, a portion of the biometric information that relates to the health metric (see again col 5, ln 14-26; biometric devices include any suitable devices for detecting information such as hear rate, blood pressure, etc.): and
CRAGUN does not specifically disclose, but SARVANA discloses, determining, by the system, the health metric associated with the user identity based on the analyzing of the portion of the biometric information, wherein the adaptive adjusting comprises adaptively adjusting the respective attributes of the respective tasks based on the result of the analyzing of the task-related information, the assessment information, and the biometric information comprising the portion of the biometric information that relates to the health metric (see abstract and ¶[0007]-[0011]; detect time-varying patterns using a machine learning algorithm. The time-varying patterns may include biometric scores during the time period. See also Fig. 2C; the risk score is a product of the biometric score and the task score).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses biometric data to detect time-varying patterns to optimize schedules during the workday. It would have been obvious to include the machine learning optimization as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
Claim 9 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN additionally discloses wherein the biometric information is first biometric information, wherein the respective attributes are respective first attributes (see again col 5, ln 14-26; biometric devices include any suitable devices for detecting information such as hear rate, blood pressure, etc.).
CRAGUN does not specifically disclose, but SARVANA discloses, and wherein the method further comprises: detecting. by the processing system, a fatigue level associated with the user identity based on analyzing second biometric information relating to the health. the diet, or the activity associated with the user identity (see ¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue):
in response to determining that the fatigue level exceeds a defined threshold fatigue level that indicates fatigue associated with the user identity is to be reduced. determining, by the processing system, a recovery time period associated with the user identity that enables a reduction of the fatigue associated with the user identity (see ¶[0009]; determining that the generated biometric score exceeds a threshold score associated with a first one of the assigned tasks, and in response to the determination that the generated biometric score exceeds the threshold score, modifying the determined schedule to replace the first assigned task with a break having a predetermined duration).
subsequent to the adjusting of the respective first attributes of the respective tasks, adaptively adjusting, by the processing system, a second attribute or a portion of the respective first attributes of the respective tasks associated with the user identity to accommodate the recovery time period, and wherein, to accommodate the recovery time period, none of the respective tasks that remain to be performed are scheduled to be performed during the recovery time period (see again ¶[0009]; determining that the generated biometric score exceeds a threshold score associated with a first one of the assigned tasks, and in response to the determination that the generated biometric score exceeds the threshold score, modifying the determined schedule to replace the first assigned task with a break having a predetermined duration).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data and determine a fatigue level for scheduling a break of predetermined duration. It would have been obvious for one of ordinary skill in the art at the time of invention to include the break based on fatigue level as taught by SARVANA in the system executing the method of CRAGUN with the motivation to determine an optimal schedule.
Claim 13 (Currently Amended)
CRAGUN discloses a system, comprising: a processing system comprising a processor; and a memory that stores executable instructions that, when executed by the processing system (see abstract and col 3, ln 54-col 4, ln 4 & Fig. 2; evaluate scheduling information by one or more processors. Mobile device includes a processor and memory), facilitate performance of operations, comprising:
receiving sensor information from a group of sensors associated with a user identity to obtain sensor data (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals. See also col 4, ln 5-18; sensors):
analyzing task-related data relating to a group of tasks (see again abstract; generate goals and tasks based on scheduling information and activity information) associated with a user identity of a user (see abstract; a person’s daily goals), assessment data relating to assessing performance of an assigned task of the group of tasks (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals), and biometric data derived from real-time sensor data (see col 3, ln 32-53; receive data from biometric devices) relating to health (see again col 3, ln 32-53; heart rate and blood pressure), diet (see col 5, ln 14-26 and col 6, ln 42-57; blood sugar and caloric consumption), and activity (see again col 5, ln 14-26; calories burned) associated with the user identity (see abstract and claim 1; a person).
generating a dynamic user state profile (see abstract; evaluate information associated with a person) based on the biometric information (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals. See also col 4, ln 5-18; sensors), task-related information (see again abstract; generate goals and tasks based on scheduling information and activity information), and context data (see col 3, ln 32-53; receiving data from local sensors, local devices, and/or external devices (e.g., smart home devices, biometric devices, point-of-sale systems, etc.).
CRAGUN does not specifically disclose, but SARVANA discloses, wherein the dynamic user state profile specifies a fatigue level (see ¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue), stress level (see again¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue).
CRAGUN further discloses, performance capacity (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals).
CRAGUN does not specifically disclose, but SARVANA discloses and recovery requirements associated with the user identity (see ¶[0009]; determining that the generated biometric score exceeds a threshold score associated with a first one of the assigned tasks, and in response to the determination that the generated biometric score exceeds the threshold score, modifying the determined schedule to replace the first assigned task with a break having a predetermined duration).
adaptively modifying respective elements associated with respective tasks of the group of tasks based on: (i) a result of the analyzing of the task-related data (see ¶[0004]; apply one or more machine learning techniques and establish task scores that indicate a level of complexity and risk of injury for each task), the assessment data (see ¶[0011]; obtain data indicative of performance of the users during a completion of a task. The performance data may identify one of a work quality of a completion speed), and the biometric data (see abstract; obtain biometric data and determine a schedule for completing tasks); and (ii) the dynamic user state profile (see abstract; task and biometric information associated with a user), and
communicating task data to a virtual assistant device of or associated with user equipment (see col 2, ln 62-col 3, ln 8; an application running on a mobile device assists users in achieving daily goals) associated with the user identity to facilitate performance of the group of tasks (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals).
CRAGUN does not specifically disclose, but STUTTLE discloses, wherein the virtual assistant device comprises a voice generator component and a modulator component (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN further discloses wherein the task data relates to, and is determined based on, the adaptively modifying of the respective elements associated with the respective tasks of the group of tasks (see abstract and ¶[0056]; determine a schedule for completing the tasks during the current workday. Optimize scheduling presentation data with a corresponding display unit, such as a touchscreen display),
CRAGUN does not specifically disclose, but BAHL discloses, and wherein the communication of the task data further comprises communicating recommendation data that recommends a schedule for performance of the assigned task based on a performance metric associated with the assigned task (see ¶[0036]-[0037]; present recommendations from scheduler for review, modification, and approval. Collaborator is configured to provide feedback to optimize schedules. Job execution schedules include periodicity constraints, categorization data, and organization data).
CRAGUN does not specifically disclose, but ZWEIG discloses, determining a dynamic personality attribute of a virtualized personality of the virtual assistant device based on: (i) the sensor data (see ¶[0016] and [0024]; sensor data related to a user); (ii) a context of an interaction between the virtual assistant device and the user identity (see ¶[0040] and [0055]; the current state), and (iii) feedback data indicative of motivational preferences of the user identity, wherein the dynamic personality attribute is determined to enhance motivation and engagement for performance of the assigned task (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.);
receiving, via the virtual assistant device, feedback data associated with the user identity, wherein the feedback data comprises a negotiation indication regarding the assigned task (see ¶[0055]; it is determined that the user rejects the recommendation);
analyzing the negotiation indication and deriving a negotiation strategy by evaluating feedback data (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.), user sentiment analysis based on voice intonation (see ¶[0040]; for example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), task progress (see ¶[0047]; “We set a goal to do this exercise three times a week, you need one more exercise to achieve this goal”), and the dynamic user state profile (see abstract; analyze a historical dataset related to a user, and present a persuasive action).
The combination of CRAGUN and ZWEIG does not specifically disclose, but STUTTLE discloses, adjusting the modulator component to adjust a characteristic of a voice produced by the voice generator component according to the dynamic personality attribute to obtain an adjusted voice characteristic (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN does not specifically disclose, but ZWEIG discloses, applying the dynamic personality attribute to the virtual assistant device, wherein the dynamic personality attribute comprises the adjusted voice characteristic to achieve a resolution to a negotiation with the user with regard to the negotiation indication (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.); and
communicating negotiation data to the virtual assistant device to facilitate negotiation with regard to the negotiation indication regarding the assigned task (see again ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional size, emphasizing benefits, etc. See also abstract; recommendation is presented by means of an input/output device), wherein the negotiation data reflects the negotiation strategy (see abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant.. For example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), predicted compliance likelihood (see again abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant), and motivational tone adjustments (see again abstract and ¶[0040]; the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses biometric data to detect time-varying patterns to optimize schedules during the workday. It would have been obvious to include the machine learning optimization as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). BAHL discloses scheduling and management of jobs that includes optimization based on periodicity constraints, categorization data, and organization data. It would have been obvious to include the constraints and data as taught by BAHL in the system executing the method of CRAGUN with the motivation to optimize a schedule for tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where a rejection of a recommendation causes the virtual assistant to consider historical data to determine an effective method of persuasion. It would have been obvious for one of ordinary skill in the art to consider an effective method of persuasion in response to a rejection as taught by ZWEIG in the system executing the method of CRAGUN with the motivation to schedule and recommend tasks; and encourage users to perform the tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where an effective manner of speaking is determined (see ¶[0040]). STUTTLE discloses synthesized voices that include characteristics for persuasiveness. It would have been obvious for one of ordinary skill in the art at the time of invention to include the characteristics as taught by STUTTLE in the system executing the method of CRAGUN with the motivation to communicate in an effective manner.
Claim 15 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the system as set forth in claim 13.
CRAGUN does not specifically disclose, but SARVANA discloses, wherein the operations further comprise: based on the analyzing, determining a group of sub-tasks of the assigned task, a sequence of performance of the sub-tasks of the group of sub-tasks, or respective amounts of time to allocate for the performance of the sub-tasks (see ¶[0047], [0051], [0059] and [0075]; identify expected completion times associated with the outstanding tasks).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24) that monitors the speed with which the user completes the tasks (see col 2, ln 27-49). SARVANA discloses determining an expected completion time associate with outstanding tasks. It would have been obvious to include the expected completion time as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on the biometric patterns of users.
Claim 19 (Currently Amended)
CRAGUN discloses a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system comprising a processor (see abstract and col 3, ln 54-col 4, ln 4 & Fig. 2; evaluate scheduling information by one or more processors. Mobile device includes a processor and memory), facilitate performance of operations, comprising: evaluating task-related information relating to a group of tasks (see again abstract; generate goals and tasks based on scheduling information and activity information) associated with a user identity of a user (see abstract; a person’s daily goals), assessment information relating to assessing performance of an assigned task (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals), and biometric information derived from real-time sensor data (see col 3, ln 32-53; receive data from biometric devices relating to health (see again col 3, ln 32-53; heart rate and blood pressure), diet (see col 5, ln 14-26 and col 6, ln 42-57; blood sugar and caloric consumption), and activity associated with the user identity (see abstract and claim 1; a person).
generating a dynamic user state profile (see abstract; evaluate information associated with a person) based on the biometric information (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals. See also col 4, ln 5-18; sensors), task-related information (see again abstract; generate goals and tasks based on scheduling information and activity information), and context data (see col 3, ln 32-53; receiving data from local sensors, local devices, and/or external devices (e.g., smart home devices, biometric devices, point-of-sale systems, etc.).
CRAGUN does not specifically disclose, but SARVANA discloses, wherein the dynamic user state profile specifies a fatigue level (see ¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue), stress level (see again¶[0004]; correlate these time-varying patterns to expected levels of stress and fatigue).
CRAGUN further discloses, performance capacity (see again abstract; manage a person’s daily goals based on the person’s recent performance in completing certain of the daily goals).
CRAGUN does not specifically disclose, but SARVANA discloses and recovery requirements associated with the user identity (see ¶[0009]; determining that the generated biometric score exceeds a threshold score associated with a first one of the assigned tasks, and in response to the determination that the generated biometric score exceeds the threshold score, modifying the determined schedule to replace the first assigned task with a break having a predetermined duration).
adaptively altering respective properties associated with respective tasks of the group of tasks based on: (i) a result of the evaluating of the task-related information (see ¶[0004[; apply one or more machine learning techniques and establish task scores that indicate a level of complexity and risk of injury for each task), the assessment information (see ¶[0011]; obtain data indicative of performance of the users during a completion of a task. The performance data may identify one of a work quality of a completion speed), and the biometric information (see abstract; obtain biometric data and determine a schedule for completing tasks); and (ii) the dynamic user state profile (see abstract; task and biometric information associated with a user), the adaptively altering resulting in respective altered properties associated with the respective tasks respective property of (see abstract; task scores indicate a level of complexity for tasks. Examiner Note: executing the method taught by SARVANA results in calculation of a time-varying risk score for tasks, which alters properties associated with the tasks):
communicating task information to a virtual assistant device of or associated with a device (see col 2, ln 62-col 3, ln 8; an application running on a mobile device assists users in achieving daily goals) associated with the user identity to facilitate performance of the group of tasks (see col 3, ln 32-53; biometric feedback indicating heart rate and bold pressure used to modify a user’s goals).
CRAGUN does not specifically disclose, but STUTTLE discloses, wherein the virtual assistant device comprises a voice generator component and a modulator component (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN further discloses, wherein the task information is determined based on the respective altered properties (see abstract and ¶[0056]; determine a schedule for completing the tasks during the current workday. Optimize scheduling presentation data with a corresponding display unit, such as a touchscreen display).
CRAGUN does not specifically disclose, but BAHL discloses, and wherein the communication of the task data further comprises communicating recommendation data that recommends a schedule for performance of the assigned task based on a performance metric associated with the assigned task (see ¶[0036]-[0037]; present recommendations from scheduler for review, modification, and approval. Collaborator is configured to provide feedback to optimize schedules. Job execution schedules include periodicity constraints, categorization data, and organization data).
CRAGUN does not specifically disclose, but ZWEIG discloses, determining a dynamic personality attribute of a virtualized personality of the virtual assistant device based on: (i) the biometric information (see ¶[0016] and [0024]; sensor data related to a user) or a context of an interaction between the virtual assistant device and a user associated with the user identity (see ¶[0040] and [0055]; the current state); and (ii) feedback data indicative of motivational preferences of the user identity, wherein the personality attribute is determined to enhance motivation and engagement for performance of the group of tasks (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.).
receiving, via the virtual assistant device, feedback data associated with the user identity, wherein the feedback data comprises a negotiation indication regarding the assigned task (see ¶[0055]; it is determined that the user rejects the recommendation).
analyzing the negotiation indication and deriving a negotiation strategy by evaluating feedback data (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.), user sentiment analysis based on voice intonation (see ¶[0040]; for example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), task progress (see ¶[0047]; “We set a goal to do this exercise three times a week, you need one more exercise to achieve this goal”), and the dynamic user state profile (see abstract; analyze a historical dataset related to a user, and present a persuasive action).
The combination of CRAGUN and ZWEIG does not specifically disclose, but STUTTLE discloses, adjusting the modulator component to adjust a characteristic of a voice produced by the voice generator component according to the personality attribute to obtain an adjusted voice characteristic (see abstract and col 3, ln 29-39; generate synthesized utterances. A synthetic voice with vocal characteristics. See also col 7, ln 16-31; gender characteristics, tone volume, and overtone volume).
CRAGUN does not specifically disclose, but ZWEIG discloses, applying the personality attribute to the virtual assistant device, wherein the personality attribute comprises the adjusted voice characteristic to achieve a resolution to a negotiation with the user with regard to the negotiation indication (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.); and
communicating negotiation data to the virtual assistant device to facilitate negotiation with regard to the negotiation indication regarding the assigned task (see again ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional size, emphasizing benefits, etc. See also abstract; recommendation is presented by means of an input/output device) wherein the negotiation data reflects the negotiation strategy (see abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant.. For example, the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way), predicted compliance likelihood (see again abstract and ¶[0040]; The persuasive action is a convincing method having the highest probability to cause the user to accept the recommendation of the digital assistant), and motivational tone adjustments (see again abstract and ¶[0040]; the extracted historical dataset may indicate that the user reacts in a positive manner when the digital assistant 120 voice speaks in a cynical way).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses biometric data to detect time-varying patterns to optimize schedules during the workday. It would have been obvious to include the machine learning optimization as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). BAHL discloses scheduling and management of jobs that includes optimization based on periodicity constraints, categorization data, and organization data. It would have been obvious to include the constraints and data as taught by BAHL in the system executing the method of CRAGUN with the motivation to optimize a schedule for tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where a rejection of a recommendation causes the virtual assistant to consider historical data to determine an effective method of persuasion. It would have been obvious for one of ordinary skill in the art to consider an effective method of persuasion in response to a rejection as taught by ZWEIG in the system executing the method of CRAGUN with the motivation to schedule and recommend tasks; and encourage users to perform the tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where an effective manner of speaking is determined (see ¶[0040]). STUTTLE discloses synthesized voices that include characteristics for persuasiveness. It would have been obvious for one of ordinary skill in the art at the time of invention to include the characteristics as taught by STUTTLE in the system executing the method of CRAGUN with the motivation to communicate in an effective manner.
Claim 20 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the non-transitory machine-readable medium as set forth in claim 19.
CRAGUN further discloses wherein the group of tasks comprises the assigned task (see abstract; tasks and group of the tasks).
CRAGUN does not specifically disclose, but SARVANA discloses, and wherein the adaptively altering comprises adaptively altering an order, a sequence, or a schedule of the performance of the respective tasks, adaptively altering an amount of time allocated to perform the assigned task, adaptively altering a priority level associated with the assigned task, adaptively altering a determination regarding an amount of progress that has been made towards completion of the assigned task, adaptively altering instructions relating to performance of the assigned task, adaptively altering a reminder, notification, or motivation message relating to the task [sic], adaptively altering calendar information relating to a task in an electronic calendar, or adaptively altering a reward that is to be presented in connection with completion of the assigned task or the group of tasks (see ¶[0047], [0051], [0059] and [0075]; identify expected completion times associated with the outstanding tasks).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24) that monitors the speed with which the user completes the tasks (see col 2, ln 27-49). SARVANA discloses determining an expected completion time associate with outstanding tasks. It would have been obvious to include the expected completion time as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on the biometric patterns of users.
Claim(s) 3 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., and US 8856007 B1 to STUTTLE et al. as applied to claim 1 above, and further in view of US 20160171633 A1 to DeWalt et al. (hereinafter ‘DEWALT’).
Claim 3 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN further discloses wherein the group of tasks comprises the assigned task (see abstract; tasks and group of the tasks).
CRAGUN does not explicitly disclose, but SARVANA discloses, wherein the analyzing comprises performing an artificial intelligence-based analysis on the task-related information, the assessment information, the biometric information, or feedback information associated with the user identity and relating to the group of tasks (see ¶[0004]; apply one or more machine learning techniques to the biometric data to detect patterns and establish task scores relating to complexity and risk).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but DEWALT discloses, wherein the method further comprises: based on the artificial intelligence-based analysis, learning, by the processing system, a level of expertise associated with the user identity with respect to performance of the assigned task, wherein the adaptively adjusting comprises adaptively adjusting an attribute of the assigned task based on the level of expertise (see ¶[0055] and [0070]; differences in the skill level or ability can be analyzed using sensor data. An algorithm for labeling sensor data points with task tags includes using a machine learning algorithm. Differences in ability can increase the efficiency with a worker can complete a specific task).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. It would have been obvious to include the machine learning as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. DEWALT discloses analyzing sensor data to determine differences in level of skill. It would have been obvious to determine differences in level of skill as taught by DEWALT in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
Claim 6 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN further discloses wherein the group of tasks comprises the assigned task (see abstract; tasks and group of the tasks).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not explicitly disclose, but DEWALT discloses,, and wherein the adaptively adjusting comprises adaptively adjusting an order, a sequence, or a schedule of the performance of the respective tasks, adaptively adjusting an amount of time allocated to perform the assigned task, adaptively adjusting a priority level associated with the assigned task, adaptively adjusting a determination regarding an amount of progress that has been made towards completion of the assigned task, adaptively adjusting instructions that indicate how the assigned task is to be performed, adaptively adjusting a reminder, notification, or motivation message relating to the assigned task, adaptively adjusting calendar information relating to the assigned task in an electronic calendar, or adaptively adjusting a reward that is to be presented in connection with completion of the assigned task or the group of tasks (see ¶[0046]; the data can be analyzed to identify sequences of projects and interactions between the one or more work teams that maximize productivity).
CRAGUN does not specifically disclose, but STUTTLE discloses, wherein the adjusting of the modulator component further comprises adjusting any combination of an inflection of the voice, a tone of the voice, or verbal words (see col 5, ln 43-52; , a parametric representation of emphatic reading could be approximated using an emphasis level raising pitch and duration mid-syllable. For example, one voice using a number of different emphasis levels can generate the entire speech, with each emphasis level involving one or more prosody changes, such as the afore-mentioned rise in mid-syllabic pitch and duration).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. DEWALT discloses analyzing sensor data to determine sequences of projects. It would have been obvious to determine sequences of projects as taught by DEWALT in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where an effective manner of speaking is determined (see ¶[0040]). STUTTLE discloses synthesized voices that include characteristics for persuasiveness. It would have been obvious for one of ordinary skill in the art at the time of invention to include the characteristics as taught by STUTTLE in the system executing the method of CRAGUN with the motivation to communicate in an effective manner.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., and US 8856007 B1 to STUTTLE et al. as applied to claim 1 above, and further in view of US 20180330302 A1 to Peterson et al (hereinafter ‘PETERSON’).
Claim 12 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but PETERSON discloses, further comprising: receiving, by the processing system, work environment information relating to a work environment associated with the group of tasks or the user identity, wherein the analyzing comprises analyzing the task-related information, the assessment information, the biometric information, and the work environment information (see ¶[0017] & [0027] and Fig. 1; biometric indicator baseline trends for monitoring an employee under controlled environment and exercise conditions. Patterns of high performance and low performance may be associated with biometric indicators 401, indicators of production 402, and time-of-day data, time-of-year data, location data, and specific task data. Specific task data may be a type of work assignment the employee performed.).
wherein the adaptively adjusting comprises adaptively adjusting the respective attributes of the respective tasks based on the result of the analyzing of the task- related information, the assessment information, the biometric information, and the work environment information (see ¶[0024]; bathroom visits can be logged. Historical data predicts increased visits associated with an employee profile in a monthly pattern. Automatically reassign the employee to an area close to a bathroom for a work assignment to increase work efficiency).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). PETERSON discloses optimizing worker efficiency by using biometric data and work environment data to determine an optimal schedule. It would have been obvious to optimize schedules using work environment data and biometric data as taught by PETERSON in the system executing the method of CRAGUN with the motivation to increase worker efficiency (see PETERSON ¶[0024]),
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., .and US 8856007 B1 to STUTTLE et al. as applied to claim 13 above, and further in view of US 20090125359 A1 to Knapic et al. (hereinafter ‘KNAPIC’).
Claim 14 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the system as set forth in claim 13.
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but KNAPIC discloses, wherein the group of tasks comprises the assigned task, and wherein the adaptively modifying further comprises: adaptively re-arranging a first order of performance of remaining tasks of the group of tasks to generate a second order of performance of the remaining tasks (see ¶[0070]; optimize task sequence and assignment based on feedback from resource task updates and project plan changes); and
adaptively modifying respective scheduling of the performance of the remaining tasks (see again ¶[0070]; optimize task sequence and assignment based on feedback from resource task updates and project plan changes).
The remaining limitations of :adaptively modifying a priority level associated with the assigned task;
adaptively modifying an amount of time allocated to perform the assigned task;
adaptively modifying instructions relating to performance of the assigned task;
adaptively modifying a determination relating to an amount of progress that has been made towards completion of the assigned task or the group of tasks;
adaptively modifying a reminder, notification, or motivation message associated with the assigned task;
adaptively modifying calendar data in an electronic calendar, wherein the calendar data is associated with the assigned task; or
adaptively modifying reward data relating to a reward that is able to be presented in connection with completion of the assigned task or the group of tasks are claimed in the alternative. Therefore, the prior art, cited above (KNAPIC), meets the claim limitations.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). KNAPIC discloses management of project tasks that includes updating task sequencing for the optimal utilization of resources and time. It would have been obvious to include the updating of sequencing as taught by KNAPIC in the system executing the method of CRAGUN with the motivation to optimize task scheduling.
Claim(s) 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., and ., and US 8856007 B1 to STUTTLE et al. as applied to claim 13 above, and further in view of US 20240004723 A1 to Jiang et al. (hereinafter ‘JIANG’).
Claim 16 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the system as set forth in claim 13.
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but JIANG discloses, wherein the operations further comprise: determining a sequence of sub-tasks of the assigned task and instructions for performing the sequence of the sub-tasks that satisfy a defined task performance efficiency criterion based on analysis of feedback data and the defined task performance efficiency criterion (see ¶[[0110]; determine a re-distributed task set with a time duration below a threshold. See also ¶[0079]; use a task for data history analysis),
wherein the feedback data relates to previous performance of a previous assigned task that is determined to be same as or relevant with respect to the assigned task or is determined to satisfy a defined task similarity criterion with respect to the assigned task (see ¶[0058]; a target task may be selected based on similarity analysis among the plurality of task sets),
wherein the feedback data is received from devices associated with user identities (see ¶[0027]; active user accounts).
CRAGUN further discloses wherein the communicating of the task data comprises communicating instruction data relating to the instructions for performing the sequence of the sub- tasks to the user equipment to facilitate performance of the assigned task in accordance with the instructions (see abstract and ¶[0056]; determine a schedule for completing the tasks during the current workday. Optimize scheduling presentation data with a corresponding display unit, such as a touchscreen display).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). JIANG discloses workflow optimization and redistribution that includes determining task similarity to select tasks in a workflow. It would have been obvious to use the task similarity as taught by JIANG in the system executing the method of CRAGUN with the motivation to optimize a task schedule.
Claim 17 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the system as set forth in claim 13.
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but JIANG discloses, wherein the operations further comprise: determining whether to modify the schedule to obtain a modification to the schedule based on a determination regarding whether implementation of a modification to the schedule is able to satisfy the performance metric (see ¶[[0110]; determine a re-distributed task set with a time duration below a threshold. See also ¶[0079]; use a task for data history analysis) ,
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). JIANG discloses workflow optimization and redistribution that includes determining task similarity to select tasks in a workflow. It would have been obvious to use the task similarity as taught by JIANG in the system executing the method of CRAGUN with the motivation to optimize a task schedule.
Claim 18 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, STUTTLE, and JIANG discloses the system as set forth in claim 17.
CRAGUN does not specifically disclose, but ZWEIG discloses, wherein the feedback data further comprises a request to modify the schedule (see ¶[0052]; rejection of a recommendation. See also ¶[0038]-[0039]; make recommendations to listen to music or take medications at times), wherein the personality attribute is determined to achieve a resolution to a negotiation with the user with regard to the request to modify the schedule, and wherein the operations further comprise see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.);
communicating, via the virtual assistant device, the negotiation data to the user to facilitate negotiation with the user with regard to the request to modify the schedule to facilitate the determining of whether to modify the schedule (see ¶[0040] and [0055]; a customized persuasive action is applied to present the recommendation to the user based on analysis of the historical dataset, the current state, and the feedback data. Extract a historical dataset that is indicative of an effective persuasive method using a rational explanation, the user’s emotional state, emphasizing benefits, etc.)
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). Suggestions and encouragement to perform the tasks may be provided. See col 3, ln 21-31. ZWEIG discloses presenting recommendations using a virtual assistant for users to perform activities, where a rejection of a recommendation causes the virtual assistant to consider historical data to determine an effective method of persuasion. It would have been obvious for one of ordinary skill in the art to consider an effective method of persuasion in response to a rejection as taught by ZWEIG in the system executing the method of CRAGUN with the motivation to schedule and recommend tasks; and encourage users to perform the tasks.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., US 8856007 B1 to STUTTLE et al. and US 20160171633 A1 to DEWALT et al. as applied to claims 1 and 3 above, and further in view of US 20190394289 A1 to Lehrian et al. (hereinafter ‘LEHRIAN’).
Claim 4 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, STUTTLE, and DEWALT discloses the method as set forth in claim 3.
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, STUTTLE, and DEWALT does not specifically disclose, but LEHRIAN discloses, wherein the performing of the artificial intelligence- based analysis comprises performing the artificial intelligence-based analysis on information relating to respective levels of expertise associated with respective user identities with respect to the assigned task and respective performance metrics associated with performance of the assigned task or a similar task associated with the respective user identities, wherein the similar task is determined to satisfy a defined task similarity criterion with regard to the assigned task (see ¶[0025] and [0031]; determine that the user has initiated past tasks that are similar to taking out the trash. Use any suitable method for determining that tasks are similar. A similarity score over a threshold value may indicate that tasks are associated with a similar or same activity. Analyze deterministic activity data using machine learning techniques).
CRAGUN does not specifically disclose, but DEWALT discloses, wherein the method further comprises: based on the artificial intelligence-based analysis, learning, by the processing system, respective differences between the level of expertise associated with the user identity and the respective levels of expertise associated with the respective user identities with respect to the assigned task (see ¶[0055] and [0070]; differences in the skill level or ability can be analyzed using sensor data. An algorithm for labeling sensor data points with task tags includes using a machine learning algorithm. Differences in ability can increase the efficiency with a worker can complete a specific task).
wherein the adaptively adjusting comprises adaptively adjusting the attribute of the assigned task based on the respective differences between the level of expertise and the respective levels of expertise, and based on the respective performance metrics (see ¶]055-[0057]; differences in skill level or ability can be analyzed. Determine efficiencies for workers completing tasks dependencies. Determine an optimal number of workers assigned to a task).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that determines tasks of similar complexity to previously performed tasks. It would have been obvious to include the similarity determination as taught by SARVANA in the system executing the method of CRAGUN with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that determines tasks of similar complexity to previously performed tasks. LEHRIAN discloses techniques for proactive reminders that determines task similarity using machine learning and applying a threshold to a similarity score. It would have been obvious to include the similarity score and threshold as taught by LEHRIAN in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that determines tasks of similar complexity to previously performed tasks. DEWALT discloses analyzing sensor data to determine differences in level of skill. It would have been obvious to determine differences in level of skill as taught by DEWALT in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., and US 8856007 B1 to STUTTLE et al. as applied to claim 1 above, and further in view of US 20100094899 A1 to Yiu et al (hereinafter ‘YIU’) and US 20120123835 A1 to Chu (hereinafter ‘CHU’).
Claim 10 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN further discloses wherein the respective tasks comprise the assigned task (see abstract; tasks and group of the tasks).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but YIU discloses, and wherein the method further comprises: in response to determining that the assigned task associated with the user identity has been completed, determining, by the processing system, reward information relating to a reward to present to an account or an interface associated with the user identity based on a task type of the assigned task that was completed, an amount of time utilized to complete the assigned task, or a priority level associated with the assigned task (see ¶[0026]) payment may be based on an hourly rate).
The combination of CRAGUN, SARVANA, BAHL, and ZWEIG does not explicitly disclose, but CHU discloses, presenting, by the processing system, the reward information to the account or the interface associated with the user identity (see abstract; award points for a completed task to an individual. See also ¶[0017]; a user interface and rewards choice facility).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). YIU discloses a problem solving network where payment is provided for tasks based on an hourly rate. It would have been obvious to include payment based on an hourly rate as taught by YIU in the system executing the method of CRAGUN with the motivation to provide an incentive for completing tasks.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). CHU discloses awarding individuals for tasks performed. It would have been obvious to include the awards as taught by CHU in the system executing the method of CRAGUN with the motivation to provide an incentive for completing tasks.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 8930290 B2 to CRAGUN et al. in view of US 20180032944 A1 to SARVANA et al., US 20200117504 A1 to BAHL et al., US 20220036251 A1 to ZWEIG et al., and US 8856007 B1 to STUTTLE et al. as applied to claim 1 above, and further in view of US 20160063192 A1 to Johnson et al. (hereinafter ‘JOHNSON’) and US 20160171633 A1 to DEWALT et al.
Claim 11 (Previously Presented)
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE discloses the method as set forth in claim 1.
CRAGUN additionally discloses further comprising: receiving, by the processing system, the sensor information from a group of sensors associated with the user identity (see col 2, ln 26-49; receive input from sensors or external devices), wherein the biometric information comprises the sensor information (see col 3, ln 32-53 and Fig. 1; receive data from local sensors and biometric devices).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but JOHNSON discloses, receiving, by the system, priority information, relating to respective priority levels associated with the respective tasks, from the virtual assistant device associated with the user identity or a data source device, wherein the task-related information or the assessment information comprises the priority information (see ¶[0147]; if concurrent tasks have the same priority, logic is called to determine which task has priority).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, and STUTTLE does not specifically disclose, but DEWALT discloses, wherein the adaptively adjusting comprises adaptively adjusting the respective attributes of the respective tasks based on the result of the analyzing of the sensor information (see ¶[0055] and [0070]; differences in the skill level or ability can be analyzed using sensor data. An algorithm for labeling sensor data points with task tags includes using a machine learning algorithm. Differences in ability can increase the efficiency with a worker can complete a specific task).
The combination of CRAGUN, SARVANA, BAHL, ZWEIG, STUTTLE, and DEWALT does not specifically disclose, but JOHNSON discloses, and the priority information (see ¶[0147]-[0148] and [0193] optimize task resource assignments and priorities. Monte Carlo determines a system structure from historical assumptions and discrete events).
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. DEWALT discloses analyzing sensor data to determine differences in level of skill. It would have been obvious to determine differences in level of skill as taught by DEWALT in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. JOHNSON discloses optimizing state transitions sets for schedule risk management that includes optimizing task resource assignments based on priorities. It would have been obvious to include the priorities as taught by JOHNSON in the system executing the method of CRAGUN with the motivation to optimize task scheduling.
CRAGUN discloses an adaptive cognitive support system that uses biometric data to manage a person’s daily goals, discern performance patterns, and determine optimal tasks (see col 7, ln 5-24). SARVANA discloses a machine learning technique for biometric-based resource allocation that uses machine learning to analyze biometric data. DEWALT discloses analyzing sensor data to determine differences in level of skill. It would have been obvious to determine differences in level of skill as taught by DEWALT in the system executing the method of CRAGUN and SARVANA with the motivation to optimize a schedule based on biometric patterns of users.
Conclusion
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624