DETAILED ACTION
This is a Non-Final Office action in response to the amendment filed 09/11/2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
Claims 1-15 are currently pending in the application and have been examined.
Response to Amendment
The amendment filed 09/11/2025 has been entered.
Response to Arguments
Claim Rejections 35 U.S.C. § 101:
Applicant submits on page 10 of the remarks that the amended claims do not recite a mental process. Examiner respectfully disagrees and notes that according to the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG), the October 2019 Updated Guidance and under the analysis of claims under step 2A of the Alice framework, if a claim limitation, under its broadest reasonable interpretation covers an observation or evaluation, then it falls under the “mental process" grouping of abstract ideas. Accordingly, the present claims are considered to be abstract ideas because they are directed to a mental process. Under the 2019 PEG, the “mental processes” grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Per the October 2019 Updated Guidance examples of claims that recite mental processes include: a claim directed to “collecting information, analyzing it, and displaying certain results of the collection and analysis” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind. Claims can recite a mental process even if they are claimed as being performed on a computer.
Applicant submits on page 12 of the remarks that the amended claims integrate the alleged abstract idea into a practical application. Examiner respectfully disagrees and notes that the present claims do not integrate the judicial exception into a practical application in a matter that imposes meaningful limit to the judicial exception.
Applicant submits on page 13 of the remarks that amended claims 1 and 15 add more than merely applying instructions to a generic computer. Examiner respectfully disagrees and notes that the present claims do not provide a combination of features or additional elements that amount to more than well-understood, routine conventional activities in the field. The claim limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Claim Rejections 35 U.S.C. § 103:
Applicant should submit an argument under the heading “Remarks” pointing out disagreements with the examiner’s contentions. Applicant must also discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them. It is not clear from Applicant’s arguments how the references do not disclose the claim limitations.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The following claim limitations are being interpreted under 112 (f): “input unit”, “processing unit”, “processing unit”, “output units”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
With respect to claims 1-15, the independent claims (claims 1 and 15) are directed, in part, to a system and a method for agenda generation. Step 1 – First pursuant to step 1 in the January 2019 Guidance, claims 1-14 are directed to a system which falls under the statutory category of a machine and claim 15 is directed to a method comprising a series of steps which falls under the statutory category of a process. However, these claim elements are considered to be abstract ideas because they are directed to a mental process which includes observations or evaluations.
As per Step 2A - Prong 1 of the subject matter eligibility analysis, the claims are directed, in part, to acquiring by at least one first sensor unit physiological data of a subject over an extended period of time prior to a current time point and providing the physiological data to a processing unit, wherein the physiological data is assigned to different times during the extended period of time, and wherein the physiological data comprises one or more of: heart rate, ventricular high rate, blood pressure, respiration rate, skin conductance, or step count; sleep data of the subject over the extended period of time and providing the sleep data to the processing unit, wherein the sleep data is assigned to different times during the extended period of time, and wherein the sleep data comprises one or more of: duration of sleep total daily sleep duration, onset of sleep, or quality of sleep;- receiving by an input unit, details of undertaken work data and undertaken leisure activity data of the subject over the extended period of time and providing the undertaken work data and undertaken leisure activity data to the processing unit, and wherein the undertaken work data and undertaken leisure activity data is assigned to different times during the extended period of time; details of planned work data and planned leisure activity data of the subject after the current time point and providing the planned work data and planned leisure activity data to the processing unit; first personal data and second personal data of the subject, and providing the first personal data and second personal data to the processing unit; implementing by the processing unit at least one trained machine learning algorithm and determining by the at least one trained machine learning algorithm, an energy score for the subject based on at least the physiological data, the sleep data of the subject, the first personal data, the second personal data, and the work- related data; at least one of: a sleep schedule comprising a planned onset and duration of sleep, a work schedule comprising scheduling of the planned work data, a leisure schedule comprising scheduling of the planned leisure activity data, and wherein the determining comprises analysis of the physiological data, the sleep data, the undertaken work data, the undertaken leisure activity data, the planned work data, the planned leisure activity data, and the energy score; and wherein the processing unit is further configured to dynamically adapt the at least one of the sleep schedule, the work schedule, or the leisure schedule in real time based on the computed energy score; and outputting by a plurality of output units at least one of the sleep schedule, the work schedules the leisure schedule, or the energy score. If a claim limitation, under its broadest reasonable interpretation covers an observation or evaluation, then it falls under the “mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
As per Step 2A - Prong 2 of the subject matter eligibility analysis, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: sensor unit, processing unit, input unit, trained machine learning algorithm. These additional element in both steps are recited at a high-level of generality (i.e., as a generic device performing a generic computer function of receiving and storing data) such that these elements amount no more than mere instructions to apply the exception using a generic computer component. Examiner looks to Applicant’s specification in at least pages 19-20 to understand that the invention may be implemented in a generic environment that “In another exemplary embodiment, a computer program or computer program element is provided that is characterized by being configured to execute the method steps of the method according to one of the preceding embodiments, on an appropriate system. The computer program element might therefore be stored on a computer unit, which might also be part of an embodiment. This computing unit may be configured to perform or induce performing of the steps of the method described above. Moreover, it may be configured to operate the components of the above described apparatus and/or system. The computing unit can be configured to operate automatically and/or to execute the orders of a user. A computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method according to one of the preceding embodiments. This exemplary embodiment of the invention covers both, a computer program that right from the beginning uses the invention and computer program that by means of an update turns an existing program into a program that uses the invention. Further on, the computer program element might be able to provide all necessary steps to fulfill the procedure of an exemplary embodiment of the method as described above. According to a further exemplary embodiment of the present invention, a computer readable medium, such as a CD-ROM, USB stick or the like, is presented wherein the computer readable medium has a computer program element stored on it which computer program element is described by the preceding section. A computer program may be stored and/or distributed on a suitable medium, such as an optical storage medium or a solid state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the internet or other wired or wireless telecommunication systems. However, the computer program may also be presented over a network like the World Wide Web and can be downloaded into the working memory of a data processor from such a network. According to a further exemplary embodiment of the present invention, a medium for making a computer program element available for downloading is provided, which computer program element is arranged to perform a method according to one of the previously described embodiments of the invention.” Accordingly, these additional elements do not integrate the abstract idea into a practical application because they are mere instructions to implement the abstract idea on a computer.
As per Step 2B of the subject matter eligibility analysis, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are mere instructions to apply the abstract idea on a computer. When considered individually, these claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements and the invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense – i.e. a generic computer receives information from another generic computer, processes the information and then sends information back. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that amount to significantly more than the abstract idea itself. The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility. Regarding the use of sensors, although not considered general purpose computer elements, the examiner hereby takes official notice of the well-understood, routine and conventional nature of these additional element(s) and therefore these elements fail to add significantly more to the abstract idea recited in the claims. Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claims.
The dependent claims further refine the abstract idea. These claims do not provide a meaningful linking to the judicial exception. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as by describing the nature and content of the data that is received/sent. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not significantly more than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3, 5, 7, 9, 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Pub. No. 2021/0169417 (hereinafter; Burton) in view of US Pub. No. 2022/0199235 (hereinafter; Knicker), further in view of US Pub. No. 2008/0177836 (hereinafter; Bennett).
Regarding claims 1/15, Burton discloses:
An agenda generation system; method, comprising: at least one first sensor unit; an input unit; a processing unit; and a plurality of output units; (Burton [0009] discloses a sensor monitoring platform.) and wherein the physiological data comprises one or more of: heart rate, ventricular high rate, blood pressure, respiration rate, skin conductance, or step count; (Burton [0222] discloses physiological monitoring including heart rate.) (b) acquire sleep data of the subject over the extended period of time and provide the sleep data to the processing unit, wherein the sleep data is assigned to different times during the extended period of time, and wherein the sleep data comprises one or more of: duration of sleep; total daily sleep duration, onset of sleep, quality of sleep, (Burton discloses sleep quality [0205]; sleep duration [0205-0211]; sleep data views [3298], sleep patterns [0346].)
(c) acquire work-related data during a latest work period prior to the current time point, and provide the work-related data to the processing unit; wherein the input unit is configured to (a) receive details of undertaken work data and undertaken leisure activity data of the subject over the extended period of time and provide the undertaken work data and undertaken leisure activity data to the processing unit, and wherein the undertaken work data and undertaken leisure activity data is assigned to different times during the extended period of time; (b) receive details of planned work data and planned leisure activity data of the subject after the current time point and provide the planned work data and planned leisure activity data to the processing unit; (Burton discloses input details. See at least [0364-0365] Circadian Algorithm Input: Information or Information Derivations Based on Sleep, Fitness or Other Health Applications, Devices and/or Systems [0365] information or derivation from any combination of information such as (but not limited to) a clock or mobile phone or other software applications or systems containing information relating to subject/patient's activity, sports, work, sleep, wake, alarm clock, schedules or routines or timing data.)
(c) receive first personal data and second personal data of the subject, and provide the first personal data and second personal data to the processing unit: wherein the processing unit is configured to implement one or more trained machine learning algorithms to: (Burton discloses the use of machine learning. See at least [2749-2754].)
Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose an energy score. However, Bennett discloses the following limitations:
(a) determine an energy score for the subject based on at least the physiological data, the sleep data of the subject, the first personal data, the second personal data, and the work-related data; (Bennett discloses an energy score, See at least [0188]; [0227].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the system for managing health programs of Bennett in order to compare results based on numerical scores (Bennett abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
(b) determine -at least one of: a sleep schedule comprising a planned onset and duration of sleep, a work schedule comprising scheduling of the planned work data or determine a leisure schedule comprising scheduling of the planned leisure activity data, and wherein the determination comprises analysis of: the physiological data, the sleep data, the undertaken work data, the undertaken leisure activity data, the planned work data, the planned leisure activity data, and the energy score; (Burton [0038] discloses one or more “clock” determinations (including circadian clock (CC) factors (as well as any of or any combination of a subject/individual's travel, work, social, relation, schedule (i.e. clock, calendar, itinerary, agenda, requirements for any of or any combination of (but not limited to) sleep/wake, work, social activities, leisure, relaxation, sports or exercise activities; whereby “clock” refers to daily or weekly schedules according to an individual's social schedule (i.e. “social clock) or work schedule (i.e. work clock) or travel agenda/schedule (travel clock) etc.))
wherein the processing unit is further configured to dynamically adapt the at least one of: the sleep schedule, the work schedule, or the leisure schedule in real time based on the computed energy score; and
and wherein the plurality of output units are configured to output at least one of: the sleep schedule, the work schedule, the leisure schedule, or the energy score. (Burton discloses outputs, see at least [0368-0370] whereby outputs of said context analysis model (not limited to) in one embodiment example of a model only can include any of or any combination of (but not limited to): phase shift between inbuilt circadian clock rhythms and local environment time-zone properties or related subject/patient schedules or required routines and/or time cycles.)
Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about assigning data to different times. However, Knicker discloses the following limitations:
wherein the at least one first sensor unit is configured to (a) acquire physiological data of a subject over an extended period of time prior to a current time point and provide the physiological data to the processing unit, (Knicker [0005] discloses a multi-sensor health monitoring platform. The method comprises applying a machine learning model to predict patient needs and patient activity trends based on physiological features and activity features of the patient.) wherein the physiological data is assigned to different times during the extended period of time, (Knicker [0026] discloses The multi-sensor platform uses data from sensors, time stamped and tagged.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the multisensory platform of Knicker in order to predict patient needs and activity trends (Knicker abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 2, Burton discloses:
System according to claim 1, the system comprising: at least one second sensor unit; (Burton [0009] discloses a sensor monitoring platform with multiple sensors.)
and wherein the further physiological data comprises one or more of: heart rate, ventricular high rate, blood pressure, respiration rate, skin conductance, step count; (Burton [0222] discloses physiological monitoring including heart rate.) wherein the at least one second sensor unit is configured to acquire further sleep data of the one or more further subjects over the extended period of time and provide the further sleep data to the processing unit, wherein the further sleep data is assigned to different times during the extended period of time, and wherein the further sleep data comprises one or more of: duration of sleep; total daily sleep duration, onset of sleep, quality of sleep, (Burton discloses sleep quality [0205]; sleep duration [0205-0211]; sleep data views [3298], sleep patterns [0346].) wherein the input unit is configured to receive details of undertaken school/work data and further undertaken leisure activity data of the one or more further subjects over the extended period of time and provide the undertaken school/work data and further undertaken leisure activity data to the processing unit, and wherein the undertaken school/work data and further undertaken leisure activity data is assigned to different times during the extended period of time; wherein the input unit is configured to receive details of planned school/work data and further planned leisure activity data of the one or more further subjects after the current time point and provide the planned school/work data and further planned leisure activity data to the processing unit; (Burton discloses input details. See at least [0364-0365] Circadian Algorithm Input: Information or Information Derivations Based on Sleep, Fitness or Other Health Applications, Devices and/or Systems [0365] information or derivation from any combination of information such as (but not limited to) a clock or mobile phone or other software applications or systems containing information relating to subject/patient's activity, sports, work, sleep, wake, alarm clock, schedules or routines or timing data.) wherein the determination by the at least one trained machine learning algorithm of the sleep schedule or work schedule or leisure schedule comprises analysis of: the further physiological data, the further sleep data, the undertaken school/work data, the further undertaken leisure activity data, the planned school/work data, and the further planned leisure activity data. (Burton [0038] discloses one or more “clock” determinations (including circadian clock (CC) factors (as well as any of or any combination of a subject/individual's travel, work, social, relation, schedule (i.e. clock, calendar, itinerary, agenda, requirements for any of or any combination of (but not limited to) sleep/wake, work, social activities, leisure, relaxation, sports or exercise activities; whereby “clock” refers to daily or weekly schedules according to an individual's social schedule (i.e. “social clock) or work schedule (i.e. work clock) or travel agenda/schedule (travel clock) etc.)
Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about assigning data to different times. However, Knicker discloses the following limitations:
wherein the at least one second sensor unit is configured to acquire further physiological data of one or more further subjects over the extended period of time prior and provide the further physiological data to the processing unit, (Knicker [0005] discloses a multi-sensor health monitoring platform. The method comprises applying a machine learning model to predict patient needs and patient activity trends based on physiological features and activity features of the patient.) wherein the further physiological data is assigned to different times during the extended period of time, (Knicker [0026] discloses The multi-sensor platform uses data from sensors, time stamped and tagged.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the multisensory platform of Knicker in order to predict patient needs and activity trends (Knicker abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 3, Burton discloses:
System according to claim 2, wherein the determination of the work schedule comprises an identification of a conflict between a work item of the planned work data of the subject and a school/work item of the planned school/work data of the one or more further subjects. (Burton discloses conflicting clock cycles and conflicting clock time-cycle requirements; See at least [0423-0424]; [0469].)
Regarding claim 5, Burton discloses:
System according to claim 2, wherein the determination of the work schedule comprises an identification of a conflict between a work item of the planned work data of the subject and a leisure activity item of the planned leisure activity data of the one or more further subjects. (Burton discloses conflicting clock cycles and conflicting clock time-cycle requirements; See at least [0423-0424]; [0469].)
Regarding claim 7, Burton discloses:
System according to claim 2, wherein the determination of the leisure schedule comprises an identification of a conflict between a leisure activity item of the planned leisure activity data of the subject and a school/work item of the planned school/work data of the one or more further subjects. (Burton discloses conflicting clock cycles and conflicting clock time-cycle requirements; See at least [0423-0424]; [0469].)
Regarding claim 9, Burton discloses:
System according to claim 2, wherein the determination of the leisure schedule comprises an identification of a conflict between a leisure activity item of the planned leisure activity data of the subject and a leisure activity item of the planned leisure activity data of the one or more further subjects. (Burton discloses conflicting clock cycles and conflicting clock time-cycle requirements; See at least [0423-0424]; [0469].)
Regarding claim 11, Burton discloses:
System according to claim 2, wherein the determination of the leisure schedule comprises an identification of a specific leisure activity item that does not occur often enough. (Burton discloses events occurrences in at least [3270].)
Regarding claim 12, Burton discloses:
System according to claim 11, wherein the determination of the leisure schedule comprises a scheduling of one or more time for undertaking of the special leisure activity item comprising utilization of the planned work activity data of the subject, the planned leisure activity data of the subject, the planned school/work data of the one or more further subjects, and the planned leisure activity data of the one or more further subjects. (Burton discloses events occurrences in at least [3270].)
Regarding claim 13, Burton discloses:
System according to claim 1, wherein the first personal data comprises one or more of: age, weight, gender, height; (See Burton at least [2055]; [2368]; [4014].)
wherein the second personal data comprises one or more of: perceived energy level of the subject, perceived fatigue level of the subject, perceived mood of the subject; (Burton [1052] discloses monitoring information such as fatigue; [0422] discloses mood.)
and wherein the work related data comprises one or more of: personal smartphone usage data, work phone usage data. (Burton discloses gathering data from a phone system; See at least [0147].)
Regarding claim 14, Burton discloses:
System according to claim 13, wherein the processing unit is further configured to continuously train the at least one machine learning algorithm based on the received sensor data, the input data, the previously determined energy scores, and previously generated schedules. (Burton discloses the use of machine learning. See at least [2749-2754].)
Claim(s) 4, 6, 8, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burton in view of Knicker, in view of Bennett, further in view of US Pub. No. 2016/0147968 (hereinafter; Coney).
Regarding claim 4, Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about rescheduling activities. However, Coney discloses the following limitations:
System according to claim 3, wherein determination of the work schedule comprises a proposed re-scheduling of the work item or a proposed re-scheduling of the school/work item comprising utilization of the undertaken work data of the subject and the undertaken school/work data of the one or more further subjects. (Coney discloses rescheduling in at least [0092].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the programming system of Coney in order to help users perform health events in accordance to a health schedule (Coney abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 6, Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about rescheduling activities. However, Coney discloses the following limitations:
System according to claim 5, wherein determination of the work schedule comprises a proposed re-scheduling of the work item or a proposed re-scheduling of the leisure activity item comprising utilization of the undertaken work data of the subject and the undertaken leisure activity data of the one or more further subject. (Coney discloses rescheduling in at least [0092].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the programming system of Coney in order to help users perform health events in accordance to a health schedule (Coney abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 8, Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about rescheduling activities. However, Coney discloses the following limitations:
System according to claim 7, wherein determination of the leisure schedule comprises a proposed re-scheduling of the leisure activity item or a proposed re-scheduling of the school/work item comprising utilization of the undertaken leisure activity data of the subject and the undertaken school/work data of the one or more further subjects. (Coney discloses rescheduling in at least [0092].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the programming system of Coney in order to help users perform health events in accordance to a health schedule (Coney abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Regarding claim 10, Although Burton discloses sensors for acquiring physiological data and using machine learning to analyze the information, Burton does not specifically disclose details about rescheduling activities. However, Coney discloses the following limitations:
System according to claim 9, wherein determination of the leisure schedule comprises a proposed re-scheduling of the leisure activity item of the subject or a proposed re-scheduling of the leisure activity item of the one or more further subjects comprising utilization of the undertaken leisure activity data of the subject and the undertaken leisure activity data of the one or more further subjects. (Coney discloses rescheduling in at least [0092].)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the monitoring system of Burton with the programming system of Coney in order to help users perform health events in accordance to a health schedule (Coney abstract) because the references are analogous since they both fall within Applicant's field of endeavor and are reasonably pertinent to the problem with which Applicant is concerned.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCIS Z SANTIAGO-MERCED whose telephone number is (571)270-5562. The examiner can normally be reached M-F 7am-4:30pm EST.
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/FRANCIS Z. SANTIAGO MERCED/Examiner, Art Unit 3625