DETAILED ACTION
This office action is in response to the initial filing dated December 10, 2024.
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 .
Claim Status
Claims 1-12 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-6 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential elements, such omission amounting to a gap between the elements. See MPEP § 2172.01. The omitted elements are: the training dataset comprises human body data collected in a simulated working environment. Claim 2, from which claim 3 depends recites the training dataset comprises human body at a collected in a simulated working environment “and/or” an actual working environment. The broadest reasonable interpretation of claim 2 includes an alternative recitation such that the training dataset can be interpreted as only being dependent on an actual working environment. Given this interpretation, there is no simulated working environment used in the training dataset. Therefore, the details provided in claim 3 fail to further define the claimed subject matter unless the training dataset is explicitly claimed to comprise the data from the simulated environment.
Claims 4-6 are rejected as being dependent from a rejected base claim.
Claim 9 recites the limitation "the cognitive level, fatigue level and emotion level" in 4. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 7-10, and 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining on-duty status of personnel and performing on-duty status monitoring on the personnel based on the on-duty status determined for the personnel, which constitute a mental process. This judicial exception is not integrated into a practical application. Claim 1 lacks any recitation of structure corresponding to the claimed method. Independent claims 11 and 12 recite generic computer elements (processor, memory, and at least one application program in claim 11 and a non-transitory computer-readable storage medium in claim 12). Further, the claims recite extra-solution activity of collecting human body data and outputting prompt information. However, these elements are incidental to the primary process claimed. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only remaining claimed element is the status detection model which is obtained by pre-training a preset model. Therefore, the model is pre-trained and not trained as part of the claimed invention. The claimed invention merely uses the trained model as part of the determining which is considered part of the mental process abstract idea.
Dependent claims 2 and 7-10 are rejected as being dependent from a rejected base claim since they only add elements that would further be considered part of the abstract idea (data pre-processing, determining if a condition is met, and the use of physiological data)
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2 and 10-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Arthur et al. (Arthur; US PG Pub #2021/0233654).
As to claim 1, Arthur teaches a personnel on-duty status early-warning method (Paragraph [0102] teaches a mode of operation for a worker safety management system; Paragraph [0067] teaches generating warnings), comprising:
continuously collecting, in real time, human body data of personnel in an actual working environment (Paragraph [0102] teaches receiving raw physiological data from physiological sensors indicative of one or more physiological characteristic of a worker; Paragraphs [0033], [0068]-[0069], and [0077] teach streams of data from sensors associated with workers; Paragraph [0041] teaches performing predictive analytics to manage risks to workers while working in a physical environment);
determining on-duty status of the personnel corresponding to the human body data based on the collected human body data by using a status detection model (Paragraph [0103] teaches determining a safety risk score for a worker based at least in part on the physiological data for the worker and a risk profile associated with the worker; Paragraph [0033] teaches applying historical data and models to inbound streams to compute assertions, such as anomalies or predicted occurrences of safety events; Paragraph [0043] teaches determining a safety risk score based on one or more rules which may be generated by at least one model using machine learning; Paragraph [0045] teaches the score may be absolute, relative, or a classification), wherein the status detection model is obtained by pre-training a preset model based on a training dataset, and the training dataset comprises human body data and corresponding on-duty status annotation information (Paragraph [0071] teaches generating models using various learning styles; Paragraph [0073] teaches models are generated separately for each particular worker, population of workers, or environment based on physiological data; Paragraph [0096] teaches updating or re-training models based on additional physiological data to update risk factors or identify new risk factors; Paragraph [0045] teaches the safety score can be “Low risk”, “Medium risk”, “High risk”; Paragraph [0080] teaches assigning “high” based on the event stream including physiological data indicating an average heart rate; Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years” and that the type of worker is classified as “electrician”);
performing on-duty status monitoring on the personnel based on the on-duty status determined for the personnel (Paragraphs [0053] and [0099] teach predicting a safety event based on the risk score for the worker by comparing the risk score with a threshold); and
outputting early-warning prompt information in response to the on-duty status of the personnel during the on-duty status monitoring satisfying a preset early-warning condition (Paragraph [0005] teaches outputting an alert indicating that a risk score satisfies the threshold; Paragraph [0051] teaches outputting a notification indicative of the risk score for the worker in response to determining the safety risk score; Paragraph [0053] teaches outputting a notification in response to predicting that the worker will experience a safety event by determining that the risk score satisfies a threshold; Paragraph [0104] teaches outputting an indication of the safety risk score for the worker).
As to claim 2, depending from the personnel on-duty status early-warning method according to claim 1, Arthur teaches wherein the training dataset comprises human body data collected in a simulated working environment, and corresponding on-duty status annotation information and on-duty information, and/or, the training dataset comprises historical human body data collected in an actual working environment and corresponding on-duty status annotation information and on-duty information (Paragraph [0018] teaches a personalized risk profile including historical physiological data; Paragraph [0042] teaches computing the safety risk score for a worker based on a risk profile including historical physiological data; Paragraph [0044] teaches historical physiological data when working within an environment; Paragraph [0049] teaches historical data at a time earlier than the current time, for example a previous workday); and
wherein the on-duty information comprises years of work and types of work (Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years” and that the type of worker is classified as “electrician”).
As to claim 10, depending from the personnel on-duty status early-warning method according to claim 1, Arthur teaches wherein the human body data comprises a variety of physiological data and/or electroencephalogram data (Paragraph [0018] teaches physiological data indicative of physiological conditions of the worker; Paragraph [0021] teaches one or more physiological sensors to detect one or more physiological characteristics).
As to claim 11, Arthur teaches a personnel on-duty status early-warning apparatus (Paragraphs [0007]-[0008] teach a computing device determining a safety risk score for a worker and outputting an indication of the score), comprising:
at least one processor;
a memory; and
at least one application program stored in the memory and executed by the at least one processor (Paragraph [0008] teaches the computing device includes memory and at least one processor where the memory includes instructions executed by the at least one processor; Paragraph [0056] teaches computing devices execute applications), wherein the at least one application program is configured to:
continuously collect, in real time, human body data of personnel in an actual working environment (Paragraph [0102] teaches receiving raw physiological data from physiological sensors indicative of one or more physiological characteristic of a worker; Paragraphs [0033], [0068]-[0069], and [0077] teach streams of data from sensors associated with workers; Paragraph [0041] teaches performing predictive analytics to manage risks to workers while working in a physical environment);
determine on-duty status of the personnel corresponding to the human body data based on the collected human body data by using a status detection model (Paragraph [0103] teaches determining a safety risk score for a worker based at least in part on the physiological data for the worker and a risk profile associated with the worker; Paragraph [0033] teaches applying historical data and models to inbound streams to compute assertions, such as anomalies or predicted occurrences of safety events; Paragraph [0043] teaches determining a safety risk score based on one or more rules which may be generated by at least one model using machine learning; Paragraph [0045] teaches the score may be absolute, relative, or a classification), wherein the status detection model is obtained by pre-training a preset model based on a training dataset, and the training dataset comprises human body data and corresponding on-duty status annotation information (Paragraph [0071] teaches generating models using various learning styles; Paragraph [0073] teaches models are generated separately for each particular worker, population of workers, or environment based on physiological data; Paragraph [0096] teaches updating or re-training models based on additional physiological data to update risk factors or identify new risk factors; Paragraph [0045] teaches the safety score can be “Low risk”, “Medium risk”, “High risk”; Paragraph [0080] teaches assigning “high” based on the event stream including physiological data indicating an average heart rate; Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years” and that the type of worker is classified as “electrician”);
perform on-duty status monitoring on the personnel based on the on-duty status determined for the personnel (Paragraphs [0053] and [0099] teach predicting a safety event based on the risk score for the worker by comparing the risk score with a threshold); and
output early-warning prompt information in response to the on-duty status of the personnel during the on-duty status monitoring satisfying a preset early-warning condition (Paragraph [0005] teaches outputting an alert indicating that a risk score satisfies the threshold; Paragraph [0051] teaches outputting a notification indicative of the risk score for the worker in response to determining the safety risk score; Paragraph [0053] teaches outputting a notification in response to predicting that the worker will experience a safety event by determining that the risk score satisfies a threshold; Paragraph [0104] teaches outputting an indication of the safety risk score for the worker).
As to claim 12, Arthur teaches a non-transitory computer-readable storage medium on which a computer program is stored, wherein when the computer program is executed in a computer (Paragraph [0138] teaches a computer-readable medium comprising instructions executed in a processor to perform a method; Paragraph [0008] teaches a memory including instructions executed by at least one processor), the computer is instructed to execute:
continuously collecting, in real time, human body data of personnel in an actual working environment (Paragraph [0102] teaches receiving raw physiological data from physiological sensors indicative of one or more physiological characteristic of a worker; Paragraphs [0033], [0068]-[0069], and [0077] teach streams of data from sensors associated with workers; Paragraph [0041] teaches performing predictive analytics to manage risks to workers while working in a physical environment);
determining on-duty status of the personnel corresponding to the human body data based on the collected human body data by using a status detection model (Paragraph [0103] teaches determining a safety risk score for a worker based at least in part on the physiological data for the worker and a risk profile associated with the worker; Paragraph [0033] teaches applying historical data and models to inbound streams to compute assertions, such as anomalies or predicted occurrences of safety events; Paragraph [0043] teaches determining a safety risk score based on one or more rules which may be generated by at least one model using machine learning; Paragraph [0045] teaches the score may be absolute, relative, or a classification), wherein the status detection model is obtained by pre-training a preset model based on a training dataset, and the training dataset comprises human body data and corresponding on-duty status annotation information (Paragraph [0071] teaches generating models using various learning styles; Paragraph [0073] teaches models are generated separately for each particular worker, population of workers, or environment based on physiological data; Paragraph [0096] teaches updating or re-training models based on additional physiological data to update risk factors or identify new risk factors; Paragraph [0045] teaches the safety score can be “Low risk”, “Medium risk”, “High risk”; Paragraph [0080] teaches assigning “high” based on the event stream including physiological data indicating an average heart rate; Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years” and that the type of worker is classified as “electrician”);
performing on-duty status monitoring on the personnel based on the on-duty status determined for the personnel (Paragraphs [0053] and [0099] teach predicting a safety event based on the risk score for the worker by comparing the risk score with a threshold); and
outputting early-warning prompt information in response to the on-duty status of the personnel during the on-duty status monitoring satisfying a preset early-warning condition (Paragraph [0005] teaches outputting an alert indicating that a risk score satisfies the threshold; Paragraph [0051] teaches outputting a notification indicative of the risk score for the worker in response to determining the safety risk score; Paragraph [0053] teaches outputting a notification in response to predicting that the worker will experience a safety event by determining that the risk score satisfies a threshold; Paragraph [0104] teaches outputting an indication of the safety risk score for the worker).
Claim Rejections - 35 USC § 103
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.
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.
Claims 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Arthur et al. (Arthur; US PG Pub #2021/0233654) as applied to claim 2 above, and further in view of Aimone et al. (Aimone; US PG Pub #2016/0077547).
As to claim 3, depending from the personnel on-duty status early-warning method according to claim 2, Arthur does not explicitly teach the method further comprising:
determining work tasks, interactive elements, control methods and feedback mechanisms;
building the simulated working environment based on the work tasks, the interactive elements, the control methods and the feedback mechanisms;
generating task instructions and sending the task instructions to a terminal device of tested personnel to prompt the tested personnel to execute the work tasks corresponding to the task instructions in the simulated working environment;
obtaining human body data corresponding to the tested personnel when executing the work tasks and corresponding on-duty status annotation information and on-duty information;
generating the training dataset based on the human body data corresponding to the tested personnel and corresponding on-duty status annotation information and on-duty information; and
training the preset model with the training dataset to obtain the status detection model.
In the field of bio-signal processing methods (Paragraph [0044]), Aimone teaches determining work tasks (Paragraph [0063] teaches guiding through an exercise or routine with an objective; Paragraph [0030] teaches a sequence of VR events as part of a training program), interactive elements, control methods and feedback mechanisms (Paragraphs [0028]-[0030] teach an interactive VR environment where the user interacts in that world using input data, content including VR events are displayed, and feedback is provided to the user);
building the simulated working environment based on the work tasks, the interactive elements, the control methods and the feedback mechanisms (Paragraph [0102] teaches building a VR environment);
generating task instructions and sending the task instructions to a terminal device of tested personnel to prompt the tested personnel to execute the work tasks corresponding to the task instructions in the simulated working environment (Paragraph [0054] teaches an other computing device or server that provides the wearable device with content to create the VR environment; Paragraph [0063] teaches the wearable device is used to guide one or more users through an exercise or routine);
obtaining human body data corresponding to the tested personnel when executing the work tasks (Paragraph [0028] teaches receiving bio-signal data from the bio-signal sensor during the VR event);
generating the training dataset based on the human body data corresponding to the tested personnel; and
training the preset model with the training dataset to obtain the status detection model (Paragraph [0078] teaches initializing a prediction model for a new user with no previous history based on aggregate of other users’ data; Paragraph [0079] teaches using previous VR sessions to create a new prediction model). It would have been obvious to one of ordinary skill in the art to modify the model of Arthur with the simulated training of Aimone to train a preset model with a training dataset from a simulated environment including corresponding on-duty status annotation information and on-duty information because this provides a rich set of information to increase the accuracy of the prediction model of user state (Paragraph [0088]).
As to claim 5, depending from the personnel on-duty status early-warning method according to claim 3, Arthur teaches wherein the obtaining human body data corresponding to the tested personnel when executing work tasks and corresponding on-duty status annotation information and on-duty information, comprises:
during the process of the tested personnel executing work tasks, obtaining the human body data corresponding to the tested personnel, a working duration of the tested personnel (Paragraph [0048] teaches a period of time for generating physiological data of a worker; Paragraph [0068] teaches a subset of physiological data associated with a particular time period; Paragraph [0069] teaches an acquisition timestamp of data; Paragraph [0075] teaches physiological data indicating how much a worker has sweat over a period of time; Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years”), and limb movement data of the tested personnel (Paragraph [0072] teaches determining a level of movement such as a number of steps);
inputting the human body data corresponding to the tested personnel into the preset model to generate on-duty status of the tested personnel (Paragraph [0103] teaches determining a safety risk score for a worker based at least in part on the physiological data for the worker and a risk profile associated with the worker; Paragraph [0033] teaches applying historical data and models to inbound streams to compute assertions, such as anomalies or predicted occurrences of safety events; Paragraph [0043] teaches determining a safety risk score based on one or more rules which may be generated by at least one model using machine learning; Paragraph [0045] teaches the score may be absolute, relative, or a classification);
determining whether the on-duty status is an abnormal state (Paragraphs [0024] and [0036] teach detecting abnormal worker behavior), whether the working duration reaches a preset duration, and/or whether limb movements of the tested personnel are qualified based on the limb movement data (Paragraphs [0072] and [0104] teach determining whether high level of movement relates to an increased risk); and
generating the on-duty status annotation information and on-duty information of the tested personnel based on the determination result (Paragraph [0045] teaches the safety score can be “Low risk”, “Medium risk”, “High risk”; Paragraph [0080] teaches assigning “high” based on the event stream including physiological data indicating an average heart rate; Paragraph [0081] teaches determining work experience for a worker is classified as “more than 10 years” and that the type of worker is classified as “electrician”).
However, Arthur does not explicitly teach the work tasks of a simulated environment, generating simulated on-duty status of the tested personnel, and determining whether the simulated on-duty status is an abnormal state.
In the field of bio-signal processing methods (Paragraph [0044]), Aimone teaches the work tasks of a simulated environment (Paragraph [0063] teaches guiding through an exercise or routine with an objective; Paragraph [0030] teaches a sequence of VR events as part of a training program) and generate simulated on-duty status of the tested personnel (Paragraph [0028] teaches receiving bio-signal data from the bio-signal sensor during the VR event). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Arthur with the simulated data of Aimone to determine whether the simulated on-duty status is an abnormal state because this provides a rich set of information to increase the accuracy of the prediction model of user state (Paragraph [0088]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Arthur et al. (Arthur; US PG Pub #2021/0233654) in view of Aimone et al. (Aimone; US PG Pub #2016/0077547) as applied to claim 5 above, and further in view of Lee et al. (Lee; US PG Pub #2019/0258944).
As to claim 6, depending from the personnel on-duty status early-warning method according to claim 5, Arthur does not explicitly teach wherein the on-duty status annotation information at least comprises fatigue data and/or emotion data, and the generating the on-duty status annotation information of the tested personnel, comprises:
sending a fatigue assessment scale and/or an emotion scale to the terminal device of the tested personnel through a subjective questionnaire; and
receiving fatigue data of the fatigue assessment scale and/or emotion data of the emotion scale corresponding to the tested personnel.
In the field of worker sensing methods, Lee teaches wherein the on-duty status annotation information at least comprises fatigue data and/or emotion data (Paragraphs [0070]-[0075] teach predicting a worker’s focus state including anxiety), and the generating the on-duty status annotation information of the tested personnel, comprises:
sending a fatigue assessment scale and/or an emotion scale to the terminal device of the tested personnel through a subjective questionnaire; and
receiving fatigue data of the fatigue assessment scale and/or emotion data of the emotion scale corresponding to the tested personnel (Paragraph [0077] teaches the worker reports their focus state periodically by the system issuing prompts to the worker to report their focus state via a smartphone, smartwatch, or the worker’s computer). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Arthur with the emotional assessment of Lee because a personalized model improves performance (Paragraph [0077]).
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Arthur et al. (Arthur; US PG Pub #2021/0233654) as applied to claim 1 above, and further in view of Lee et al. (Lee; US PG Pub #2019/0258944).
As to claim 7, depending from the personnel on-duty status early-warning method according to claim 1, Arthur does not explicitly teach wherein the determining on-duty status of the personnel corresponding to the human body data based on the obtained human body data by using a status detection model, comprises:
performing data pre-processing and feature extraction on the human body data;
inputting the extracted features into the status detection model to determine fatigue data and emotion data corresponding to the human body data, wherein the on-duty status annotation information in the training dataset corresponding to the status detection model comprises fatigue data and emotion data; and
inputting the fatigue data and emotion data into a preset classification model to obtain the on-duty status of the personnel corresponding to the human body data, wherein the on-duty status comprises a cognitive load level, a fatigue level and an emotion level.
In the field of worker sensing methods, Lee teaches wherein the determining on-duty status of the personnel corresponding to the human body data based on the obtained human body data by using a status detection model, comprises:
performing data pre-processing (Paragraphs [0065]-[0067] teaches cleaning up raw physiological data) and feature extraction on the human body data (Paragraph [0068] teaches features computed based on the cleaned data);
inputting the extracted features into the status detection model to determine fatigue data and emotion data corresponding to the human body data, wherein the on-duty status annotation information in the training dataset corresponding to the status detection model comprises fatigue data and emotion data (Paragraph [0070] teaches inputting the features into a machine learning model to predict a focus state; Paragraphs [0071]-[0075] teach an anxiety state vs. non-anxiety state and an idle state vs. non-idle state; Paragraph [0097] teaches that idle periods are rest periods which are indicative of fatigue); and
inputting the fatigue data and emotion data into a preset classification model to obtain the on-duty status of the personnel corresponding to the human body data, wherein the on-duty status comprises a cognitive load level, fatigue level, and an emotion level (Paragraphs [0071]-[0075] teach a flow state vs non-flow state, bored vs non-bored state, anxiety state vs. non-anxiety state, and an idle state vs. non-idle state; Paragraph [0002] teaches flow as the worker’s mind is fully immersed and actively engaged in a task; Paragraph [0048] teaches determining patterns of worker’s focus state; Paragraph [0084] teaches aggregating small window size to represent a larger window, such as classifying “increasing frustration” based on flow and anxiety). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Arthur with the processing of Lee because this helps optimize productivity and work quality (Paragraph [0001]).
As to claim 8, depending from the personnel on-duty status early-warning method according to claim 1, Arthur does not explicitly teach wherein the determining on-duty status of the personnel corresponding to the human body data based on the obtained human body data, comprises:
performing data pre-processing and feature extraction on the human body data; and
inputting the extracted features into the status detection model to determine the on-duty status of the personnel corresponding to the human body data, wherein the on-duty status annotation information in the training dataset corresponding to the status detection model comprises cognitive load level annotation information, fatigue level annotation information and emotion level annotation information.
In the field of worker sensing methods, Lee teaches wherein the determining on-duty status of the personnel corresponding to the human body data based on the obtained human body data, comprises:
performing data pre-processing (Paragraphs [0065]-[0067] teaches cleaning up raw physiological data) and feature extraction on the human body data (Paragraph [0068] teaches features computed based on the cleaned data); and
inputting the extracted features into the status detection model to determine the on-duty status of the personnel corresponding to the human body data (Paragraph [0070] teaches inputting the features into a machine learning model to predict a focus state), wherein the on-duty status annotation information in the training dataset corresponding to the status detection model comprises cognitive load level annotation information, fatigue level annotation information and emotion level annotation information (Paragraphs [0081] and [0083] teach labeled feature vectors are passed into a training routine to return a class with the highest probability as a prediction; Paragraphs [0071]-[0075] teach a flow state vs non-flow state, bored vs non-bored state, anxiety state vs. non-anxiety state, and an idle state vs. non-idle state; Paragraph [0002] teaches flow as the worker’s mind is fully immersed and actively engaged in a task; Paragraph [0097] teaches that idle periods are rest periods which are indicative of fatigue). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the annotation of Arthur with the processing of Lee because this helps optimize productivity and work quality (Paragraph [0001]).
As to claim 9, depending from the personnel on-duty status early-warning method according to claim 8, Arthur teaches wherein the performing on-duty status monitoring on the personnel based on the on-duty status determined for the personnel, comprises:
determining whether a levels exceed respective level thresholds (Paragraphs [0005], [0053], and [0099] teach determining a safety risk score satisfies a threshold score by being greater than the threshold; Paragraph [0041] teaches determining a heart rate exceeds a threshold heart rate; Paragraph [0091] teaches a temperature increases above a threshold temperature; Paragraph [0095] teaches comparing separate category risk scores to threshold scores); and
determining that the on-duty status of the personnel does not meet the preset early-warning condition in response to all of the levels do not exceed respective level thresholds, or
determining that the on-duty status of the personnel meets the preset early-warning condition in response to any one of the levels exceeds respective level threshold (Paragraph [0095] teaches determining a worker is not appropriate for a job based on a single category exceeding a threshold). However, Arthur does not explicitly teach the levels are cognitive load level, fatigue level and emotion level.
In the field of worker sensing methods, Lee teaches the levels are cognitive load level (Paragraphs [0070]-[0071] teach a flow state vs non-flow state; Paragraph [0002] teaches flow as the worker’s mind is fully immersed and actively engaged in a task), fatigue level (Paragraphs [0070] and [0074] teach an idle state vs. non-idle state; Paragraph [0097] teaches that idle periods are rest periods which are indicative of fatigue) and emotion level (Paragraphs [0070] and [0072] teach an anxiety state vs. non-anxiety state). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the levels of Arthur with the processing categories of Lee such that the levels would be cognitive load level, fatigue level, and emotion level because tracking these factors helps optimize productivity and work quality (Paragraph [0001]).
Allowable Subject Matter
Claim 4 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mohammadrezazadeh et al. (US PG Pub #2019/0227626) teaches generating a model by correlating performance metrics obtained during task performance in a simulation environment (Paragraph [0015]).
Rajput et al. (US PG Pub #2023/0395235) teach tracking cognitive load (Paragraphs [0126]-[0128]) and the use of work data including role and years (Paragraph [0081]).
Boivin et al. (US PG Pub #2023/0040562) teach determining levels of fatigue of a user (Paragraphs [0040]-[0041]) and prompt the user to take a break (Paragraph [0039]).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RYAN W SHERWIN whose telephone number is (571)270-7269. The examiner can normally be reached M-F, 9:00-5:00 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached at 571.270.1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RYAN W SHERWIN/ Primary Examiner, Art Unit 2688
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