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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action.
In response to Examiner's communication of 2/12/2026, Applicant responded on 6/11/2026. Amended claim 1, 3, 6, 11, 13, 16.
Claims 1-20 are pending in this application and have been examined.
Response to Amendment
Applicant's amendments to claims 1, 3, 6, 11, 13, 16 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 3, 6, 11, 13, 16 are not sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…The claims are directed to a technology-based system that includes a garment, sensor modules, and first and second processors coupled to the sensor modules. The first and second processors are recited as particular machines configured to implement specific algorithms based on multiple types of data provided by the sensor modules. The output from the processors is useful information relating to job tasks performed by subjects wearing the garments. This combination of functional components is not abstract... they now recite a plurality of sensor modules attached to the garment. Movement and timestamp data is received from each of the plurality of sensor modules. Features of each of a plurality of time windows are computed and applied to a pre-trained job task classifier to determine kinematic states. Time windows are aggregated by the kinematic states to provide total state output values characterizing a job task summary. These amended claims recite a technology-based system, including garments that can be worn by subjects, and related computer technology that efficiently generates highly accurate and valuable information relating to job tasks. The computationally-extensive algorithms used in connection with a plurality of sensors of the claimed systems are not capable of being practically performed in the human mind. They are therefore not directed to patent-ineligible mental processes. Nor do these claimed systems recite humans evaluating stress and fatigue, and are therefore not methods of organizing human activity. Moreover, even assuming arguendo that the claims recite an abstract idea, which the Applicant does not concede, any such abstract idea is incorporated into a practical application. For example, they recite substantially more than mere instructions to apply a judicial exception using a generic computer element. They also recite features in addition to any abstract ideas that are unconventional or otherwise more than what is well-understood, routine or conventional in the field….” The Examiner respectfully disagrees.
The claims indeed recite and direct to, … efficiently generates highly accurate and valuable information relating to job tasks…, which is a problem directed to, a mental process, organizing human activity, as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem. Additionally, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components, i.e. computer, garment, sensors, performing extra solution activities, gathering data and outputting data, and generally linked to a technical environment, i.e. computer, garment, sensors. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2 (apply it and general link).
Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive. However, Applicant’s remarks are moot in light of new grounds of rejections necessitated by Applicant’s amendments.
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 (similarly 11) recites, “A system comprising:
(A) …;
(B) … and configured to generate movement data related to the movement of the garment and timestamp data associated with the movement data;
(C) a …, wherein the … is configured to:
i) communicate with the …;
ii) receive the movement data and timestamp data from each of the …;
iii) store in a … session data including the movement data and the associated timestamp data from the …; and
iv) generate one or more session messages comprising the session data; and
(D) a …, wherein the … is configured to:
i. receive the one or more session messages from the ….;
ii. compile the one or more session messages in order of the timestamp data;
iii. determine a plurality of time windows comprised of a pre- determined and sequential amount of session messages;
iv. compute one or more features of each of the plurality of time windows, wherein each feature comprises at least a minimum value, a maximum value or a standard deviation;
v. apply the one or more features to at least one pre-trained job task classifier for determining at least two kinematic states for at least one of the time windows;
vi. determine a total session time;
vii. aggregate each of the time windows by each of the at least two kinematic states and compute for each of the at least two kinematic states a total state output value wherein the state output value is a function of said total session time; and
viii. output a job task summary comprised of at least two of the total state output values.”
Claim 11 additionally recites, “
…
v) apply the one or more features to a first pre-trained job state classifier for determining a first kinematic state for at least one of the time windows;
vi) apply the one or more features to a second pre-trained job state classifier for determining a second kinematic state for at least one of the time windows
…”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …generate movement data related to the movement of the garment and timestamp data associated with the movement data; (C) a …, wherein the … is configured to: i) communicate with the …; ii) receive the movement data and timestamp data from each of the …; iii) store in a … session data including the movement data and the associated timestamp data from the …; and iv) generate one or more session messages comprising the session data; and (D) a …, wherein the … is configured to: i. receive the one or more session messages from the ….; ii. compile the one or more session messages in order of the timestamp data; iii. determine a plurality of time windows comprised of a pre- determined and sequential amount of session messages; iv. compute one or more features of each of the plurality of time windows, wherein each feature comprises at least a minimum value, a maximum value or a standard deviation; v. apply the one or more features to at least one pre-trained job task classifier for determining at least two kinematic states for at least one of the time windows; vi. determine a total session time; vii. aggregate each of the time windows by each of the at least two kinematic states and compute for each of the at least two kinematic states a total state output value wherein the state output value is a function of said total session time; and viii. output a job task summary comprised of at least two of the total state output values… v) apply the one or more features to a first pre-trained job state classifier for determining a first kinematic state for at least one of the time windows; vi) apply the one or more features to a second pre-trained job state classifier for determining a second kinematic state for at least one of the time windows…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the above identified limitations, therefore, the claims recite a mental process.
Further, …generate movement data related to the movement of the garment and timestamp data associated with the movement data; (C) a …, wherein the … is configured to: i) communicate with the …; ii) receive the movement data and timestamp data from each of the …; iii) store in a … session data including the movement data and the associated timestamp data from the …; and iv) generate one or more session messages comprising the session data; and (D) a …, wherein the … is configured to: i. receive the one or more session messages from the ….; ii. compile the one or more session messages in order of the timestamp data; iii. determine a plurality of time windows comprised of a pre- determined and sequential amount of session messages; iv. compute one or more features of each of the plurality of time windows, wherein each feature comprises at least a minimum value, a maximum value or a standard deviation; v. apply the one or more features to at least one pre-trained job task classifier for determining at least two kinematic states for at least one of the time windows; vi. determine a total session time; vii. aggregate each of the time windows by each of the at least two kinematic states and compute for each of the at least two kinematic states a total state output value wherein the state output value is a function of said total session time; and viii. output a job task summary comprised of at least two of the total state output values… v) apply the one or more features to a first pre-trained job state classifier for determining a first kinematic state for at least one of the time windows; vi) apply the one or more features to a second pre-trained job state classifier for determining a second kinematic state for at least one of the time windows…, are humans evaluating stress and fatigue related to jobs and tasks performed by human workers, which are managing interactions and relationship between people, therefore the claims recite certain methods of organizing human activities.
Accordingly, the claims recite and directed to a mental process, certain methods of organizing human activities, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 11: system, a plurality of sensor modules, a garment, wherein the plurality of sensor modules are coupled to the garment, a first processor, wherein the first processor, memory module, second processor, wherein the second processor
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components.
Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “…receive…”, “…communicate…”, “…generate movement data…”, “…store…”, “…create…”, “…aggregate…”, “…output…”, “…generate…”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “…receive…”, “…generate movement data…”, “…store…”, “…create…”, “…aggregate…”, “…communicate…”, data output – “…communicate…”, “…output…”, “…generate…”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0053] Some of the general components utilized in this disclosure are widely known in the field of the disclosure, and their exact nature or type is not necessary for a person of ordinary skill in the art or science to understand the present disclosure; therefore, they will not be discussed in detail.
[0054] It is appreciated that components of networks, network transmission, the internet, databases, and software services are all well known in the art of the internet of things (loT) and thus their exact features are not needed for one skilled in the art to practice the present disclosure without undue experimentation, and thus will not be described in detail.
[0055] As used herein, the term "garment" is intended to indicate an article of clothing. While a shirt form factor is shown as the best mode of a garment, the present disclosure should not be limited to such a garment form factor. As used herein, a garment may be a jacket, pant, shirt, coverall, overall, glove, hat, and such.
[0056] As used herein, the term "processor" is intended to mean any electronic device which processes data based upon instructions. As used herein, the term "processor" may include memory modules, and any other common circuits necessary to execute instructions and create a desired output.
[0057] According to the present disclosure, Figure 6 shows a motion system 10 that is used to characterize one or more tasks of a job. At the highest level and as will be described later in detail, motion system 10 is comprised of a subject 60 generating kinematic movement data and timestamp data by wearing a motion shirt 20 having a plurality of sensors that communicate to a gateway 40.Gateway 40 transmits data and messages to a server system 50 that creates an output 100.Output 100 may provide a kinematic summary of tasks and states of a given job or create an optimized job and tasks for a given worker, also known as a job task summary. Output 100 may be a digital output 101, such as a web application or mobile application view, or a physical output 102, such as printed paper.
[0058] Figure 1 shows a sensor module 30 having an outer plastic housing 31. Preferably housing 31 is waterproof. Figure 2 shows sensor module 30 with part of housing 31 removed as to show a battery 33, a printed circuit board 32, a sensor chip 34, and a sensor processor 35. Sensors, electronic components, and hardware are well known in the field of loT devices and thus the exact components or configuration of module 30 are not needed for one to understand and appreciate the present disclosure. Sensor 34 is preferably an inertial measurement unit (IMU) sensor that generates both movement and timestamp data and is comprised of at least one of an accelerometer 30A,agyroscope 30B, and a magnetometer 30C. Although any one or combination of sensor 30A, 30B, and 30C can be used within the spirit and scope of the present disclosure to understand body position and motion, the combination of 30A, 30B, and 30C provide the means of determining the absolute orientation of sensor module 30 in world space 3D coordinates. Conversion and data fusion of acceleration, gyroscope and magnetometer data into world space quaternions is well known in the art of motion capture and virtual reality. In addition to motion capture sensors, sensor module 30 may also contain an alternative sensor 30D that may be suitable for describing a given job, a state, or for a particular job location. 30D may be, but is not limited to, a GPS, flex sensor, bend sensor, strain sensor, heart rate sensor, tilt sensor, blood oxygen sensor, body temperature sensor, environmental sensor, or pressure sensor. Alternative sensor 30D may be any sensor that is common in the field of data capture or wearables and sensor chip 34 may be any combination of sensors into one or more packages or chips. Sensor chip 34 provides the means to create movement data that describes variables.
[0059] Sensor chip 34 communicates via traces on printed circuit board 32 to module processor 35. As is common in the art of electronics, module processor 35 contains firmware code that allows it to coordinate the components of sensor 30.Module processor 35 may perform functions, such as but not limited to, controlling the charging of battery 33, aggregating and store data from sensor chip 34, and sending and receiving information to gateway 40.Module processor 35 includes a memory module, which can be any type of common memory type, including but not limited to RAM, EPROM or flash memory types.
[0060] As shown in Figure 3, a plurality of sensor module 30 are placed on a garment 70 to create motion shirt 20.Garment 70 can be any type of common garment style, fabric or size, but for workforce applications it is preferably lightweight, long sleeved and snug to the body. According to the best mode of the present disclosure and for improved data accuracy, a zipper 71 is located slightly offset to the midplane of garment 70 as to not interfere with any optimal placements of sensor module 30 along the midplane. In addition to occupying midplane space, midline zippers are more likely to cause bunching of fabric. Also, according to the best mode of the present disclosure and shown in Figure 5 is that garment 70 has extended length sleeves which covers at least a portion of the wrist of subject 60. A thumb hole 73 enables the thumb of subject 70 to protrude through garment 70 and keep its sleeve and either sensor module 24 or 27 in an optimal position for data capture, but without overly restricting subject 70 from performing fine movements with his or her fingers. Thumb hole 73 provides the means of placing a motion sensor distal the wrist joint of subject 60 and to gather hand motion data.
[0081] While the wearable driven job assessment system herein described constitute preferred embodiments of the present disclosure, it is to be understand the present disclosure is not limited to these precise components, assemblies or methods, and that changes may be made therein without departing from the scope and spirt of the disclosure.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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.
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable by US Patent Publication to US20200297250A1 to Elhawary et al., (hereinafter referred to as “Elhawary”) in view of US Patent Publication to US20230214704A1 to SUKHTIPYAROGE. (hereinafter referred to as “SUKHTIPYAROGE”).
As per Claim 11, Elhawary teaches: A system comprising:
A) a plurality of sensor modules; (in at least [0086] a single primary wearable device 190 may be used and it may communicate with various sensors or transmitters on different parts of the user's body, as shown in FIG. 4A, in an environment in which the user is working, as shown in FIG. 4B, or on equipment the user is using. For example, a user may have a primary wearable device 190 that interacts with safety equipment worn by a user or with a humidity, temperature, or gas sensor located in a factory. [0278] FIG. 9 illustrates a method for calibrating wearable sensors 190. Such a method may be implemented using the sensor packaging 2000 shown in FIGS. 2B-2D in order to properly calibrate the sensors 190 prior to beginning the detection of particular activities.)
B) a garment, wherein the plurality of sensor modules are coupled to the garment and configured to generate movement data related to the movement of the garment and timestamp data associated with the movement data; (in at least [0086] a single primary wearable device 190 may be used and it may communicate with various sensors or transmitters on different parts of the user's body, as shown in FIG. 4A, in an environment in which the user is working, as shown in FIG. 4B, or on equipment the user is using. For example, a user may have a primary wearable device 190 that interacts with safety equipment worn by a user or with a humidity, temperature, or gas sensor located in a factory. [0151] FIG. 3B is a plot illustrating high risk postures over time for a worker, and FIG. 3C is a flowchart illustrating an alternative method for monitoring safety. While the basic method discussed may provide an alert, such as a vibration or device display indicating a high risk physical activity or movement any time such a movement was performed, such an approach may result in a large number of alerts to a worker. Such a large number of alerts may be ignored, or may irritate the worker. Accordingly, a separate metric, described herein and mentioned briefly above, may be used to evaluate accumulated risk over a period of time, and that metric may be used to determine whether a worker should be alerted for each individual high risk physical activity. This may take the form of a running gauge over a time window. [0207] the sensor device 190 may also be used to time stamp when a user comes into work, takes a break, or ends work by confirming the time period for which the user is within a work zone. Accordingly, a device trigger can be used to determine how many hours an employee has worked, and if they are allowed into the premises. Triggers can be based on a variety of activities, such as taking the device 190 out of its dock and returning it, putting the device 190 on and off of a belt, starting to walk after putting the device on, scanning an employee badge, or pressing a button or taking other action on the device 190 once a worker puts it on. [0214] FIG. 4A shows a system in which the wearable device 190, described generally as a wearable device 4010, may be implemented. As shown, a worker 4000 may be provided with a wearable device 4010. The wearable device 4010 typically includes multiple communication interfaces, as discussed above. This allows the wearable device 4010 to connect to servers 310 either directly or through a cloud computing interface 4020, in order to upload and receive information, as well as additional sensors 4030, 4040, 4050 on the worker's body which may communicate with the wearable device 4010 and may be provided to keep the worker productive and safe. These additional sensors 4030, 4040, 4050 may be applied directly to the worker's body, as in the case of sensor 4040, or may be integrated into safety equipment, such as sensor 4030, integrated into a harness, and 4050, integrated into a kneepad.)
C) a first processor, wherein the first processor is configured to: ([0214][0376])
i) communicate with the plurality of sensor modules; (in at least [0214] FIG. 4A shows a system in which the wearable device 190, described generally as a wearable device 4010, may be implemented. As shown, a worker 4000 may be provided with a wearable device 4010. The wearable device 4010 typically includes multiple communication interfaces, as discussed above. This allows the wearable device 4010 to connect to servers 310 either directly or through a cloud computing interface 4020, in order to upload and receive information, as well as additional sensors 4030, 4040, 4050 on the worker's body which may communicate with the wearable device 4010 and may be provided to keep the worker productive and safe. These additional sensors 4030, 4040, 4050 may be applied directly to the worker's body, as in the case of sensor 4040, or may be integrated into safety equipment, such as sensor 4030, integrated into a harness, and 4050, integrated into a kneepad.)
ii) receive the movement data and timestamp data from each of the plurality of sensor modules; (in at least [0207] the sensor device 190 may also be used to time stamp when a user comes into work, takes a break, or ends work by confirming the time period for which the user is within a work zone. Accordingly, a device trigger can be used to determine how many hours an employee has worked, and if they are allowed into the premises. Triggers can be based on a variety of activities, such as taking the device 190 out of its dock and returning it, putting the device 190 on and off of a belt, starting to walk after putting the device on, scanning an employee badge, or pressing a button or taking other action on the device 190 once a worker puts it on. [0285] FIG. 10A shows an acceleration profile corresponding to a user of the wearable device 4010 walking. FIG. 10B shows that acceleration profile modified by a known category of calibration error, where, for example, the wearable device 4010 is knocked out of alignment while the user is walking.)
iii) store in a memory module session data including the movement data and the associated timestamp data from the plurality of sensor modules; and (in at least [0155] The window over which the first physical activity is evaluated is typically closed at the conclusion of the activity. At that point, the signal segment corresponding to the first physical activity may be excerpted (3030) and used for further evaluation or storage. For example, it may be stored in a log of activity, [0177] the wearable device 190 can be used to evaluate the driving of vehicles such as trucks and forklifts, or the use of other industrial equipment. Based on the acceleration and deceleration profiles, worker driving can be evaluated and scored. The number of hard braking actions or extreme acceleration can be tracked. Speed of the vehicle can be estimated and if it surpasses a certain threshold, then action can be taken. In addition, if the vehicle is involved in an accident or an impact, it can also be detected and timestamped and data of the impact can be preserved for future investigation. [0326] The identification of an initiation time (at 9110) can take a variety of forms. In some embodiments, the identification is by identifying a signature in the first signal itself. In other embodiments, the worker may indicate that they are initiating a physical activity. For example, a worker may scan a code on a box prior to lifting or otherwise manipulating the box. Alternatively, the identification may be by a server in communication with the wearable device, and the first category of physical activity may be identified based on a schedule associated with the worker and stored on the server.)
iv) generate one or more session messages comprising the session data; and (in at least [0093] The sensor devices may further incorporate LEDs, displays, or other methods for delivering feedback to the workers 110, 140, 172 wearing the sensors. For example, the device may utilize a display to display the risk metrics, or a goal, rank or other relevant information like battery and signal status. The display may be touch sensitive in order to provide a user interface by way of the display. Other information displayed can be error or warning messages when a worker is detected to not be wearing the device correctly, or in a variety of other scenarios discussed below in more detail. The device can also show information like number of steps taken by a worker, calories burned, active hours in the shift, current time and the time to next break etc. This information can be shown when a worker requests it, at regular intervals, or automatically when one of the methods described below are used to identify a relevant or hazardous situation. [0272] generate actionable visualizations by summarizing metrics recorded over the course of an evaluation period, or over an extended period of time, by providing charts indicating high risk times of days, weeks, or months, so that specific risks may be identified and addressed. The platform may further identify, for example, a percentage of high risk lifts or total number of high risk lifts performed in a specified period of time.)
D) a second processor, the second processor configured to: ([0214][0376])
i) receive the one or more session messages from the first processor; (in at least [0214] FIG. 4A shows a system in which the wearable device 190, described generally as a wearable device 4010, may be implemented. As shown, a worker 4000 may be provided with a wearable device 4010. The wearable device 4010 typically includes multiple communication interfaces, as discussed above. This allows the wearable device 4010 to connect to servers 310 either directly or through a cloud computing interface 4020, in order to upload and receive information, as well as additional sensors 4030, 4040, 4050 on the worker's body which may communicate with the wearable device 4010 and may be provided to keep the worker productive and safe. These additional sensors 4030, 4040, 4050 may be applied directly to the worker's body, as in the case of sensor 4040, or may be integrated into safety equipment, such as sensor 4030, integrated into a harness, and 4050, integrated into a kneepad. [0215] The multiple communication interfaces may provide an ability to communicate data in real time, including warnings and alerts, through the wearable device 4010, using long range communication methods like sub-GHz and cellular radio as well as short range communication methods, such as WiFi and Bluetooth. In some embodiments the wearable device 4010 may further provide a local wired port, such that sensors may be connected using, for example, a USB connection.)
ii) compile the one or more session messages in order of the timestamp data; (in at least [0154] may evaluate aggregate or accumulated risk, in the form of a cumulative risk metric, and risk associated with individual activities in combination. Such a combination allows for the leveraging of risk based insights. As shown in the flowchart, an entity implementing the method, such as a server 310 or a wearable device 190, may receive a signal generated from dynamic activity of the wearable device over time (3000). The method may then evaluate the signal (3010) and determine if a first physical activity was initiated (3015) by identifying an initiation time for the physical activity performed by the worker wearing the device 190. The method then evaluates the signals further and calculates measurements of the worker wearing the device 190 for the time period during the first physical activity from the first signal segment for a time period following the initiation time (3020). [0183] Each individual signal segment is then recorded in the log (at 3150) in order to generate a log of physical activity performed by the worker associated with the wearable device 190, such that the log defines the category of physical activity, the activity risk metric, and the time for each signal segment. The log may record additional data, such as the location of the physical activity. In some embodiments, the log may include a complete record of raw data recorded at the wearable device 190. In addition to raw data, the log may further include any calculated angles or metrics evaluated by the wearable device, as well as any kinematic variables, temperature, air pressure, and height measurements and changes at the time of the associated signal segment. [0272] generate actionable visualizations by summarizing metrics recorded over the course of an evaluation period, or over an extended period of time, by providing charts indicating high risk times of days, weeks, or months, so that specific risks may be identified and addressed. The platform may further identify, for example, a percentage of high risk lifts or total number of high risk lifts performed in a specified period of time.)
iii) determine a plurality of time windows comprised of a pre-determined and sequential amount of session messages; (in at least [0127] All of these statistics may be monitored over windows of data which may be calculated based on elements of the signal, such as those detected above in steps 420 and 430. [0149] evaluates individual activities, the server will continue to receive data from the sensor devices 190 a, b. Accordingly, the server may then store (530) a record of the first lift in a memory associated with the server and return to step 400 and continue monitoring the sensor data to determine if the worker is performing additional lifting activities. The server typically continues to monitor the data for additional lifting motions over the course of an evaluation period. In some embodiments, once multiple lifts have occurred, the method calculates (540) a frequency associated with the lifting motions identified and incorporates (550) that value into the risk models in order to monitor and evaluate risks associated with repetitive lifts. Such frequency data may be used in the NIOSH model described above, for example, to reduce the maximum recommended weight for a repeated lifting activity based on repetitive stresses and associated risks. [0150] After the conclusion (560) of an evaluation period during which lifting motions are evaluated, the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift. [0163] The cumulative risk metric may be for a sliding window of time immediately prior to the calculation of the activity risk metric (at 3040) for a particular physical activity. Accordingly, prior to generating the alert (at 3080), which may be, for example, haptic feedback, the method may determine if the user has been performing high risk physical activities over the most recent window. Such a window may be, for example, a half hour, or it may be longer, such as daily or weekly. Alternatively, the window of time may vary in length depending on the situation. [0164] The cumulative risk metric may be, for example, a risk frequency metric. Accordingly, the metric may be a measure of the frequency with which the activity risk metric was above the activity risk threshold during the sliding window of time for the cumulative risk metric. The frequency threshold may be, for example, a specified frequency goal or an average number of high-risk postures over a full day. Accordingly, while the flowchart in FIG. 3C shows the cumulative metric being maintained (at 3060) based on the generated risk metric (at 3040), it may instead draw from the comparison (at 3050) to consider the frequency of the activity risk metric demonstrating a high risk. Similarly, it may draw directly from the excerpted segment (at 3030) or the signal data (at 3020) to determine a cumulative metric based on variables different from the activity risk metric. [0166] the cumulative risk threshold used may change over the course of the day or across several days. Accordingly, the threshold may be lowered, and a worker may be more likely to receive an alert, during a time of day when the worker is fatigued. [0168] a worker can be alerted to high risk activities during a time when they are above the threshold. The device may alert the user, such as by haptic feedback, initially when the cumulative risk metric crosses the cumulative risk threshold. This would notify the worker when their unit has entered vibration mode. If the cumulative risk metric returns below the cumulative risk threshold, the device 190 would stop alerting the user for every high risk physical activity. [0272] generate actionable visualizations by summarizing metrics recorded over the course of an evaluation period, or over an extended period of time, by providing charts indicating high risk times of days, weeks, or months, so that specific risks may be identified and addressed. The platform may further identify, for example, a percentage of high risk lifts or total number of high risk lifts performed in a specified period of time.)
iv) compute one or more features of each of the plurality of time windows, wherein each feature comprises at least a minimum value, a maximum value or a standard deviation; (in at least [0132] ergonomic risk models may be implemented as well, and may require extracting different values from the data. For example, if implementing the risk model developed by Marras et al using his Lumbar Motion Monitor, the data extracted from the signals may be: Average twisting velocity of the torso during the lift activity, computed in a way similar to the calculation of the asymmetry angle discussed above, except using angular velocity. Maximum moment on the lower back, which is computed by multiplying the maximum horizontal distance between the load and the worker's trunk and the weight of the object lifted. Maximum sagittal flexion of the torso, which is determined by extracting the offset bending angle of the lower back relative to a vertical axis (usually gravity). Maximum lateral velocity of the torso, which may be determined from the accelerometer gyroscope in the back sensor. Frequency of lifts specified in lifts per minute, which can be obtained from the frequency of lift detection. [0138] the risk models specified may be used to calculate a maximum recommended lifting weight based on a workers lifting technique. This is done by using the variables extracted from the signals in a risk model. For example, the NIOSH risk model may be used to calculate a recommended weight limit. Further, the model may be used to calculate a lifting index identifying a risk associated with any particular lifting action or task. Further, while the model is discussed in terms of lifts, such a model or a similar model may be used to evaluate other activities as well in order to determine a risk level for such activities. The model used may then provide numerical results, or those results may be classified in terms of low, medium, and high risk lifts. Similarly, underlying values for variables may be implemented directly in the models, or they may be mapped on to low, medium, or high values. [0142] determine a maximum recommended weight for any given lift (480). Where the risk model used supports a determination for a single point in time, the risk model may be implemented immediately following the detection of an initiation of a lifting activity at step 420. In such an embodiment, the information from the moment of time detected is immediately extracted and processed. [0167] the cumulative risk metric may be based on kinematic variables including at least one of back bending angle and cumulative trunk loading. For example, the cumulative risk metric may be based on a time integration of the kinematic variables over time. Accordingly, in such an embodiment, the metric is based on an integration of the back bending angle over time and an integration of the trunk loading window over time. The cumulative risk metric may be based on variables different than those on which the activity risk metric is based, or it may be based on the same set of variables. Accordingly, the activity risk metric may be based on a first physical model while the cumulative risk metric may be based on a different physical model. [0168] a worker can be alerted to high risk activities during a time when they are above the threshold. The device may alert the user, such as by haptic feedback, initially when the cumulative risk metric crosses the cumulative risk threshold. This would notify the worker when their unit has entered vibration mode. If the cumulative risk metric returns below the cumulative risk threshold, the device 190 would stop alerting the user for every high risk physical activity. [0169] workers would get more buzzes at the times of day when their risky activity was at its highest (regardless of whether or not it is at start of shift or end of shift), and would ideally get fewer buzzes in situations like bathroom breaks, lunch times, etc. It would also give the safest workers the opportunity to not get buzzes at all during a given day even if they still do 5-10 high risk postures (HRP) here or there. [0170] additional cumulative risk metrics may be maintained by the method or system described. Accordingly, a worker may be monitored for cumulative risk over the window discussed above, as well as a complete day and/or week. Other cumulative variables may include cumulative trunk loading, position and timing variables, and integrated kinematic variables, such as daily integrated back sagittal angle or velocity. [0172] in addition to lifts, a variety of other physical activities and events are detectable. These other physical activities or events may include actual risk indications, as well as behaviors indicative of potential risk. For example, jerks, or sudden motion put extra strain on a worker's body, and such jerks may be identified by identifying spikes in the acceleration data. The amplitude of the spikes along with their distribution over time can be used to determine the intensity of and frequency of a jerk. If such jerks are overly frequent, or overly aggressive, a safety manager may be alerted to the activity.)
v) apply the one or more features to a first … job state … for determining a first kinematic state for at least one of the time windows; (in at least [0138] the risk models specified may be used to calculate a maximum recommended lifting weight based on a workers lifting technique. This is done by using the variables extracted from the signals in a risk model. For example, the NIOSH risk model may be used to calculate a recommended weight limit. Further, the model may be used to calculate a lifting index identifying a risk associated with any particular lifting action or task. Further, while the model is discussed in terms of lifts, such a model or a similar model may be used to evaluate other activities as well in order to determine a risk level for such activities. The model used may then provide numerical results, or those results may be classified in terms of low, medium, and high risk lifts. Similarly, underlying values for variables may be implemented directly in the models, or they may be mapped on to low, medium, or high values. [0139] Using the NIOSH risk model as an example, a recommended weight limit for a single lift may be calculated by simply determining each of the values discussed above, determining an appropriate multiplier used in the model (typically determined from a table associated with the model, or by calculating an appropriate ratio) and multiplying the relevant multipliers. Accordingly, the recommended weight limit may be determined from the equation RWL=LC*HM*VM*DM*AM*FM where LC is a constant multiplier for the formula, typically 51 lbs., and HM, VM, DM, AM, and FM are the multipliers associated with the calculated values of H, V, D, A, and F respectively. In some embodiments, an additional multiplier may be used to incorporate the duration of lifting tasks. While the NIOSH risk model is described, other risk models may be implemented as well. Further, by dividing an actual weight lifted by the recommended weight limit generated by the NIOSH model, a lifting index may be generated providing an evaluation of the risk associated with a specified lifting activity. [0141] While NIOSH and Marras models are described, other risk models may be utilized as well, such as Liberty Mutual® tables, RUBA, RULA, and others. For example, the signals from the sensor 190 may be used to estimate the compression at a specified vertebrae of the spine using a biomechanical model. That compression may then be compared to a maximum limit, such as the 770 lbs. prescribed by OSHA, in order to classify a lift as potentially high risk. [0150] After the conclusion (560) of an evaluation period during which lifting motions are evaluated, the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift. [0306] As shown in FIG. 13, various categorized risks associate with a worker, including risks based on the sensor data generated and evaluated using the methods described, may be used to predict injury, absenteeism, and other risk. Over time, the model may then be informed of actual injuries, etc., which may then refine the model. [0170] a worker may be monitored for cumulative risk over the window discussed above, as well as a complete day and/or week. Other cumulative variables may include cumulative trunk loading, position and timing variables, and integrated kinematic variables, such as daily integrated back sagittal angle or velocity. [0172] in addition to lifts, a variety of other physical activities and events are detectable. These other physical activities or events may include actual risk indications, as well as behaviors indicative of potential risk. For example, jerks, or sudden motion put extra strain on a worker's body, and such jerks may be identified by identifying spikes in the acceleration data. The amplitude of the spikes along with their distribution over time can be used to determine the intensity of and frequency of a jerk. If such jerks are overly frequent, or overly aggressive, a safety manager may be alerted to the activity. [0173] jumps can be detected and impact on the health of knees can be inferred. For example, a distribution worker might jump off of a truck with or without package in hands. The impact of jump on the knees can be detected by looking at spikes in the acceleration in the vertical direction and correlated with sudden changes in detected height. The magnitude of the acceleration and height when the jump occurs can give an indication of the intensity of the impact. This can be reported, and feedback provided to reduce the number of jumps that occur, or in order to reduce the intensity of the jumps. These jumps can often lead to impact and cumulative injuries on knees, ankles and hips. [0175] the wearable device 190 could be used to measure when someone is climbing up a ladder, and the amount of time spent on each part of the ladder, or if a user is using an elevator, by monitoring a change in the user's elevation. The rate of change of height can help determine the action being performed. [0176] slips which do not lead to falls can also be detected, and considered as near misses. In addition, these episodes can also be used to log information about incidents, such as the time, and to which worker they happened to. [0177] the wearable device 190 can be used to evaluate the driving of vehicles such as trucks and forklifts, or the use of other industrial equipment. Based on the acceleration and deceleration profiles, worker driving can be evaluated and scored. The number of hard braking actions or extreme acceleration can be tracked. Speed of the vehicle can be estimated and if it surpasses a certain threshold, then action can be taken. In addition, if the vehicle is involved in an accident or an impact, it can also be detected and timestamped and data of the impact can be preserved for future investigation. [0178] an activity log for a user over time can help determine if they are exposed to gradual risks. The platform described may detect a number and frequency of repetitions doing a specific task, a number of steps taken, and an amount of motion, vigor of motion, time spent seated or standing or moving. These various factors can help determine the amount of fatigue and necessary resting period to help recover. As discussed below, the system may further incorporate environmental factors, such as localized humidity, in order to refine a model for the amount of fatigue a user might be experiencing. Such additional detail may be from environmental sensors, or from additional sensors incorporated into the wearable device 190 to measure, for example dehydration. Alternatively additional sensors may be worn elsewhere on the body, as discussed in relation to FIG. 4A below. The activity log may also be used to evaluate user productivity or evaluate other results of physical activities, such as accidents, [0179] an activity log may be used to determine a worker's schedule and shift scheduled activities to create optimal rest/work intervals. The system may use worker data to determine the effectiveness of their recovery periods, and thereby suggest schedule changes. The system may integrate with necessary systems at the facility to ensure that recovery schedules do not compromise business demands. Further, the system may incorporate worker preferences and company preferences in order to improve work/life balance. [0183] Each individual signal segment is then recorded in the log (at 3150) in order to generate a log of physical activity performed by the worker associated with the wearable device 190, such that the log defines the category of physical activity, the activity risk metric, and the time for each signal segment. The log may record additional data, such as the location of the physical activity. In some embodiments, the log may include a complete record of raw data recorded at the wearable device 190. In addition to raw data, the log may further include any calculated angles or metrics evaluated by the wearable device, as well as any kinematic variables, temperature, air pressure, and height measurements and changes at the time of the associated signal segment. [0307] FIG. 14 is a flowchart illustrating a method for adjusting insurance based on worker safety. Typically, insurance policies for workers compensation are based on industry expectations for losses for a particular category of worker, historical losses for a client, and several other factors. Accordingly, risk metrics tied to particular workers or groups of workers would be valuable in more precisely assigning insurance risk in the industry. [0308] The method provided initially associates a risk score (9000) with each worker of a group of workers. While the method is described with respect to one worker of the group of workers, each worker in the group would typically be provided (9005) with a wearable device 190 for use in the method, and the method would modify the risk score associated with each worker. Typically, the risk score is a predictive metric for predicting whether an individual or a group has a high probability of incurring an injury. [0309] a first signal is received (9010) from a wearable device 4010 worn by the first worker and generated by dynamic activity of the wearable device over time. An initiation time for a first physical activity of a first category of physical activity performed by the first worker is then identified (9020) in the first signal, and a first signal segment is defined (9030) in the first signal, the first signal segment corresponding to the time period in which the first physical activity is performed. In some embodiments, the first signal segment is extracted from the first signal, while in others, the first signal segment is used directly as part of the first signal. As discussed above, the time window for the first signal segment may be defined based on an expected end time for the first physical activity, or it may be determined based on details in the first signal. [0326] The identification of an initiation time (at 9110) can take a variety of forms. In some embodiments, the identification is by identifying a signature in the first signal itself. In other embodiments, the worker may indicate that they are initiating a physical activity. For example, a worker may scan a code on a box prior to lifting or otherwise manipulating the box. Alternatively, the identification may be by a server in communication with the wearable device, and the first category of physical activity may be identified based on a schedule associated with the worker and stored on the server. [0328] After a physical activity is performed by the worker, a payment is generated (9140) and provided to the worker based on a calculated rate associated with the first category of physical activity. The calculated rate is based on both the category of physical activity performed and the risk score associated with worker for that particular category of physical activity. As noted above, the risk score is specific to the category of physical activity. Accordingly, when a user performs a lift, for example, they may identify the initiation of the lift by scanning a box to be lifted and then perform the lift. After performing the lift, the worker will be assigned a payment calculated based on the fact that the activity was a lift and the workers personal risk score associated with lifting. [0331] After the activity risk metric is generated (at 9150), the metric may be used to modify the risk score (at 9160) associated with the worker and the first category of physical activity. In this way, the risk score may be adjusted to reflect the risk associated with the particular worker performing the particular type of task. If the activity risk metric reflects a low risk performance of the physical activity by the worker, the risk score may be modified such that a larger payment is generated (at 9140) for future activities. Similarly, if the activity risk metric reflects a high risk performance of the physical activity, the risk score may be modified such that future payments are reduced. [0334] each worker has a plurality of risk scores, each associated with different categories of physical activities, the modification of the risk score associated with one category would not affect the risk score of a different category. Accordingly, if a worker has good posture for lifts, for example, they may get increased payments for such lifts regardless of their performance of other activities, such as driving)
vi) apply the one or more features to a second … job state … for determining a second kinematic state for at least one of the time windows; (in at least [0138] the risk models specified may be used to calculate a maximum recommended lifting weight based on a workers lifting technique. This is done by using the variables extracted from the signals in a risk model. For example, the NIOSH risk model may be used to calculate a recommended weight limit. Further, the model may be used to calculate a lifting index identifying a risk associated with any particular lifting action or task. Further, while the model is discussed in terms of lifts, such a model or a similar model may be used to evaluate other activities as well in order to determine a risk level for such activities. The model used may then provide numerical results, or those results may be classified in terms of low, medium, and high risk lifts. Similarly, underlying values for variables may be implemented directly in the models, or they may be mapped on to low, medium, or high values. [0139] Using the NIOSH risk model as an example, a recommended weight limit for a single lift may be calculated by simply determining each of the values discussed above, determining an appropriate multiplier used in the model (typically determined from a table associated with the model, or by calculating an appropriate ratio) and multiplying the relevant multipliers. Accordingly, the recommended weight limit may be determined from the equation RWL=LC*HM*VM*DM*AM*FM where LC is a constant multiplier for the formula, typically 51 lbs., and HM, VM, DM, AM, and FM are the multipliers associated with the calculated values of H, V, D, A, and F respectively. In some embodiments, an additional multiplier may be used to incorporate the duration of lifting tasks. While the NIOSH risk model is described, other risk models may be implemented as well. Further, by dividing an actual weight lifted by the recommended weight limit generated by the NIOSH model, a lifting index may be generated providing an evaluation of the risk associated with a specified lifting activity. [0141] While NIOSH and Marras models are described, other risk models may be utilized as well, such as Liberty Mutual® tables, RUBA, RULA, and others. For example, the signals from the sensor 190 may be used to estimate the compression at a specified vertebrae of the spine using a biomechanical model. That compression may then be compared to a maximum limit, such as the 770 lbs. prescribed by OSHA, in order to classify a lift as potentially high risk. [0150] After the conclusion (560) of an evaluation period during which lifting motions are evaluated, the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift. [0170] a worker may be monitored for cumulative risk over the window discussed above, as well as a complete day and/or week. Other cumulative variables may include cumulative trunk loading, position and timing variables, and integrated kinematic variables, such as daily integrated back sagittal angle or velocity. [0172] in addition to lifts, a variety of other physical activities and events are detectable. These other physical activities or events may include actual risk indications, as well as behaviors indicative of potential risk. For example, jerks, or sudden motion put extra strain on a worker's body, and such jerks may be identified by identifying spikes in the acceleration data. The amplitude of the spikes along with their distribution over time can be used to determine the intensity of and frequency of a jerk. If such jerks are overly frequent, or overly aggressive, a safety manager may be alerted to the activity. [0173] jumps can be detected and impact on the health of knees can be inferred. For example, a distribution worker might jump off of a truck with or without package in hands. The impact of jump on the knees can be detected by looking at spikes in the acceleration in the vertical direction and correlated with sudden changes in detected height. The magnitude of the acceleration and height when the jump occurs can give an indication of the intensity of the impact. This can be reported, and feedback provided to reduce the number of jumps that occur, or in order to reduce the intensity of the jumps. These jumps can often lead to impact and cumulative injuries on knees, ankles and hips [0175] the wearable device 190 could be used to measure when someone is climbing up a ladder, and the amount of time spent on each part of the ladder, or if a user is using an elevator, by monitoring a change in the user's elevation. The rate of change of height can help determine the action being performed. [0176] slips which do not lead to falls can also be detected, and considered as near misses. In addition, these episodes can also be used to log information about incidents, such as the time, and to which worker they happened to. [0177] the wearable device 190 can be used to evaluate the driving of vehicles such as trucks and forklifts, or the use of other industrial equipment. Based on the acceleration and deceleration profiles, worker driving can be evaluated and scored. The number of hard braking actions or extreme acceleration can be tracked. Speed of the vehicle can be estimated and if it surpasses a certain threshold, then action can be taken. In addition, if the vehicle is involved in an accident or an impact, it can also be detected and timestamped and data of the impact can be preserved for future investigation. [0178] an activity log for a user over time can help determine if they are exposed to gradual risks. The platform described may detect a number and frequency of repetitions doing a specific task, a number of steps taken, and an amount of motion, vigor of motion, time spent seated or standing or moving. These various factors can help determine the amount of fatigue and necessary resting period to help recover. As discussed below, the system may further incorporate environmental factors, such as localized humidity, in order to refine a model for the amount of fatigue a user might be experiencing. Such additional detail may be from environmental sensors, or from additional sensors incorporated into the wearable device 190 to measure, for example dehydration. Alternatively additional sensors may be worn elsewhere on the body, as discussed in relation to FIG. 4A below. The activity log may also be used to evaluate user productivity or evaluate other results of physical activities, such as accidents [0179] an activity log may be used to determine a worker's schedule and shift scheduled activities to create optimal rest/work intervals. The system may use worker data to determine the effectiveness of their recovery periods, and thereby suggest schedule changes. The system may integrate with necessary systems at the facility to ensure that recovery schedules do not compromise business demands. Further, the system may incorporate worker preferences and company preferences in order to improve work/life balance. [0183] Each individual signal segment is then recorded in the log (at 3150) in order to generate a log of physical activity performed by the worker associated with the wearable device 190, such that the log defines the category of physical activity, the activity risk metric, and the time for each signal segment. The log may record additional data, such as the location of the physical activity. In some embodiments, the log may include a complete record of raw data recorded at the wearable device 190. In addition to raw data, the log may further include any calculated angles or metrics evaluated by the wearable device, as well as any kinematic variables, temperature, air pressure, and height measurements and changes at the time of the associated signal segment. [0306] As shown in FIG. 13, various categorized risks associate with a worker, including risks based on the sensor data generated and evaluated using the methods described, may be used to predict injury, absenteeism, and other risk. Over time, the model may then be informed of actual injuries, etc., which may then refine the model. [0307] FIG. 14 is a flowchart illustrating a method for adjusting insurance based on worker safety. Typically, insurance policies for workers compensation are based on industry expectations for losses for a particular category of worker, historical losses for a client, and several other factors. Accordingly, risk metrics tied to particular workers or groups of workers would be valuable in more precisely assigning insurance risk in the industry. [0308] The method provided initially associates a risk score (9000) with each worker of a group of workers. While the method is described with respect to one worker of the group of workers, each worker in the group would typically be provided (9005) with a wearable device 190 for use in the method, and the method would modify the risk score associated with each worker. Typically, the risk score is a predictive metric for predicting whether an individual or a group has a high probability of incurring an injury. [0309] a first signal is received (9010) from a wearable device 4010 worn by the first worker and generated by dynamic activity of the wearable device over time. An initiation time for a first physical activity of a first category of physical activity performed by the first worker is then identified (9020) in the first signal, and a first signal segment is defined (9030) in the first signal, the first signal segment corresponding to the time period in which the first physical activity is performed. In some embodiments, the first signal segment is extracted from the first signal, while in others, the first signal segment is used directly as part of the first signal. As discussed above, the time window for the first signal segment may be defined based on an expected end time for the first physical activity, or it may be determined based on details in the first signal. [0326] The identification of an initiation time (at 9110) can take a variety of forms. In some embodiments, the identification is by identifying a signature in the first signal itself. In other embodiments, the worker may indicate that they are initiating a physical activity. For example, a worker may scan a code on a box prior to lifting or otherwise manipulating the box. Alternatively, the identification may be by a server in communication with the wearable device, and the first category of physical activity may be identified based on a schedule associated with the worker and stored on the server. [0328] After a physical activity is performed by the worker, a payment is generated (9140) and provided to the worker based on a calculated rate associated with the first category of physical activity. The calculated rate is based on both the category of physical activity performed and the risk score associated with worker for that particular category of physical activity. As noted above, the risk score is specific to the category of physical activity. Accordingly, when a user performs a lift, for example, they may identify the initiation of the lift by scanning a box to be lifted and then perform the lift. After performing the lift, the worker will be assigned a payment calculated based on the fact that the activity was a lift and the workers personal risk score associated with lifting. [0331] After the activity risk metric is generated (at 9150), the metric may be used to modify the risk score (at 9160) associated with the worker and the first category of physical activity. In this way, the risk score may be adjusted to reflect the risk associated with the particular worker performing the particular type of task. If the activity risk metric reflects a low risk performance of the physical activity by the worker, the risk score may be modified such that a larger payment is generated (at 9140) for future activities. Similarly, if the activity risk metric reflects a high risk performance of the physical activity, the risk score may be modified such that future payments are reduced. [0334] each worker has a plurality of risk scores, each associated with different categories of physical activities, the modification of the risk score associated with one category would not affect the risk score of a different category. Accordingly, if a worker has good posture for lifts, for example, they may get increased payments for such lifts regardless of their performance of other activities, such as driving.)
vii) determine a total session time; (in at least [0150] After the conclusion (560) of an evaluation period during which lifting motions are evaluated, the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift. [0151] FIG. 3B is a plot illustrating high risk postures over time for a worker, and FIG. 3C is a flowchart illustrating an alternative method for monitoring safety. While the basic method discussed may provide an alert, such as a vibration or device display indicating a high risk physical activity or movement any time such a movement was performed, such an approach may result in a large number of alerts to a worker. Such a large number of alerts may be ignored, or may irritate the worker. Accordingly, a separate metric, described herein and mentioned briefly above, may be used to evaluate accumulated risk over a period of time, and that metric may be used to determine whether a worker should be alerted for each individual high risk physical activity. This may take the form of a running gauge over a time window. [0163] The cumulative risk metric may be for a sliding window of time immediately prior to the calculation of the activity risk metric (at 3040) for a particular physical activity. Accordingly, prior to generating the alert (at 3080), which may be, for example, haptic feedback, the method may determine if the user has been performing high risk physical activities over the most recent window. Such a window may be, for example, a half hour, or it may be longer, such as daily or weekly. Alternatively, the window of time may vary in length depending on the situation. [0164] The cumulative risk metric may be, for example, a risk frequency metric. Accordingly, the metric may be a measure of the frequency with which the activity risk metric was above the activity risk threshold during the sliding window of time for the cumulative risk metric. The frequency threshold may be, for example, a specified frequency goal or an average number of high-risk postures over a full day. [0179] an activity log may be used to determine a worker's schedule and shift scheduled activities to create optimal rest/work intervals. The system may use worker data to determine the effectiveness of their recovery periods, and thereby suggest schedule changes. The system may integrate with necessary systems at the facility to ensure that recovery schedules do not compromise business demands. Further, the system may incorporate worker preferences and company preferences in order to improve work/life balance.)
viii) aggregate each of the time windows by each of the at least two kinematic states and compute for each of the at least two kinematic states a total state output value wherein the state output value is a function of said total session time; and (in at least [0138] the risk models specified may be used to calculate a maximum recommended lifting weight based on a workers lifting technique. This is done by using the variables extracted from the signals in a risk model. For example, the NIOSH risk model may be used to calculate a recommended weight limit. Further, the model may be used to calculate a lifting index identifying a risk associated with any particular lifting action or task. Further, while the model is discussed in terms of lifts, such a model or a similar model may be used to evaluate other activities as well in order to determine a risk level for such activities. The model used may then provide numerical results, or those results may be classified in terms of low, medium, and high risk lifts. Similarly, underlying values for variables may be implemented directly in the models, or they may be mapped on to low, medium, or high values. [0139] Using the NIOSH risk model as an example, a recommended weight limit for a single lift may be calculated by simply determining each of the values discussed above, determining an appropriate multiplier used in the model (typically determined from a table associated with the model, or by calculating an appropriate ratio) and multiplying the relevant multipliers. Accordingly, the recommended weight limit may be determined from the equation RWL=LC*HM*VM*DM*AM*FM where LC is a constant multiplier for the formula, typically 51 lbs., and HM, VM, DM, AM, and FM are the multipliers associated with the calculated values of H, V, D, A, and F respectively. In some embodiments, an additional multiplier may be used to incorporate the duration of lifting tasks. While the NIOSH risk model is described, other risk models may be implemented as well. Further, by dividing an actual weight lifted by the recommended weight limit generated by the NIOSH model, a lifting index may be generated providing an evaluation of the risk associated with a specified lifting activity. [0141] While NIOSH and Marras models are described, other risk models may be utilized as well, such as Liberty Mutual® tables, RUBA, RULA, and others. For example, the signals from the sensor 190 may be used to estimate the compression at a specified vertebrae of the spine using a biomechanical model. That compression may then be compared to a maximum limit, such as the 770 lbs. prescribed by OSHA, in order to classify a lift as potentially high risk. [0150] After the conclusion (560) of an evaluation period during which lifting motions are evaluated, the risk models may be used to evaluate (570) aggregate risk over the time period. In some embodiments a worker's shift may be divided into blocks of time, such as half hour blocks, for use as evaluation periods. In some embodiments, the evaluation period is instead the entirety of the worker's shift. [0151] FIG. 3B is a plot illustrating high risk postures over time for a worker, and FIG. 3C is a flowchart illustrating an alternative method for monitoring safety. While the basic method discussed may provide an alert, such as a vibration or device display indicating a high risk physical activity or movement any time such a movement was performed, such an approach may result in a large number of alerts to a worker. Such a large number of alerts may be ignored, or may irritate the worker. Accordingly, a separate metric, described herein and mentioned briefly above, may be used to evaluate accumulated risk over a period of time, and that metric may be used to determine whether a worker should be alerted for each individual high risk physical activity. This may take the form of a running gauge over a time window. [0171] the cumulative risk metric may be used to trigger a modification of a design of a job assigned to the wearer. For example, if the cumulative risk metric is above the cumulative risk threshold for a specified amount of time, the design of the job for a specific worker may be modified to require the wearer to utilize assist equipment. [0183] Each individual signal segment is then recorded in the log (at 3150) in order to generate a log of physical activity performed by the worker associated with the wearable device 190, such that the log defines the category of physical activity, the activity risk metric, and the time for each signal segment. The log may record additional data, such as the location of the physical activity. In some embodiments, the log may include a complete record of raw data recorded at the wearable device 190. In addition to raw data, the log may further include any calculated angles or metrics evaluated by the wearable device, as well as any kinematic variables, temperature, air pressure, and height measurements and changes at the time of the associated signal segment.)
ix) output a job task summary comprised of at least two of the total state output values. (in at least [0151] FIG. 3B is a plot illustrating high risk postures over time for a worker, and FIG. 3C is a flowchart illustrating an alternative method for monitoring safety. While the basic method discussed may provide an alert, such as a vibration or device display indicating a high risk physical activity or movement any time such a movement was performed, such an approach may result in a large number of alerts to a worker. Such a large number of alerts may be ignored, or may irritate the worker. Accordingly, a separate metric, described herein and mentioned briefly above, may be used to evaluate accumulated risk over a period of time, and that metric may be used to determine whether a worker should be alerted for each individual high risk physical activity. This may take the form of a running gauge over a time window. [0272] a platform implementing the method may generate actionable visualizations by summarizing metrics recorded over the course of an evaluation period, or over an extended period of time, by providing charts indicating high risk times of days, weeks, or months, so that specific risks may be identified and addressed. The platform may further identify, for example, a percentage of high risk lifts or total number of high risk lifts performed in a specified period of time. [0273] Such an evaluation may be done in real-time by providing such feedback during a work shift. Alternatively, or in addition, the platform may provide (750) an end of day evaluation. Such an evaluation may, for example, demonstrate worsening posture over the course of the day indicating fatigue. In such a scenario, the platform may provide a recommendation (760) such as a scheduling change or a reorganization of tasks. For example, the platform may recommend lifting heavier objects earlier in a shift. [0274] Such an evaluation may be done in real-time by providing such feedback during a work shift. Alternatively, or in addition, the platform may provide (750) an end of day evaluation. Such an evaluation may, for example, demonstrate worsening posture over the course of the day indicating fatigue. In such a scenario, the platform may provide a recommendation (760) such as a scheduling change or a reorganization of tasks. For example, the platform may recommend lifting heavier objects earlier in a shift. [0275] Metrics relating to productivity of individual workers may be further developed, and productivity based metrics may be utilized to evaluate relationships between fatigue and productivity. Accordingly, the platform may provide estimates of return on investment for individual pieces of equipment that may both reduce injury risk and increase productivity. In some cases, a reduction in injury risk may lower productivity, while a requirement for a worker increasing productivity may increase the risk for that particular worker. [0330] An activity risk metric is then generated (9150) from a risk model based on the measurements of the wearer for the time period during the physical activity, the activity risk metric being indicative of a risk level of the execution of the physical activity by the wearer. [0331] After the activity risk metric is generated (at 9150), the metric may be used to modify the risk score (at 9160) associated with the worker and the first category of physical activity. In this way, the risk score may be adjusted to reflect the risk associated with the particular worker performing the particular type of task. If the activity risk metric reflects a low risk performance of the physical activity by the worker, the risk score may be modified such that a larger payment is generated (at 9140) for future activities. Similarly, if the activity risk metric reflects a high risk performance of the physical activity, the risk score may be modified such that future payments are reduced.)
Although implied, Elhawary does not expressly disclose the following limitations, which however, are taught by SUKHTIPYAROGE,
…pre-trained job state classifier… (in at least [0041] the motion sensors 105 transmit or otherwise provide motion data 110 to a machine learning system 120. The motion data 110 may generally be indicative of the orientation of one or more parts (e.g., the hands or arms) of a user at various points in time, movement of these parts (which may include the direction of movement and/or the acceleration or speed of the movement) [0045] the motion sensor 105 may only begin recording motion data 110 upon being triggered (e.g., by the user selecting a button or otherwise initiating recording). When the user has completed the action, they can similarly turn off the recording. In one such embodiment, the machine learning system 120 may evaluate only this window of relevant motion data 110 for the action. In a related embodiment, the motion sensor 105 may continuously record motion data 110, but only transmit it when the user indicates to do so. For example, upon completing an action, the user may cause the motion sensor 105 to transmit some portion of the previously-collected data to the machine learning system 120. This may include, for example, transmitting a defined window of data (e.g., the last ten minutes), transmitting a user-indicated window [0140] the machine learning system generates a predicted action using the data. In embodiments, the machine learning system may generate the predicted action by using one or more trained classifier models to classify the input motion data based on the action that the user was performing when the motion data was captured. As discussed above, these models may have been trained (by the machine learning system or by some other system(s) or component(s)) based on labeled motion data to generally identify and classify a variety of user actions, such as assistive or caregiving actions [0175] the machine learning system can use this indication as a label for the original motion data that was used to train the model(s). Subsequently, the machine learning system can use this newly-labeled data to refine the model(s), as discussed above. Although the illustrated example depicts refining the model individually for each (corrected) record for conceptual clarity, in some embodiments, the machine learning system can store the newly-labeled records for subsequent use in refining the models (e.g., during a refinement or fine-tuning phase).)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Elhawary as taught by SUKHTIPYAROGE, with a reasonable expectation of success if arriving at the claimed invention. One of ordinary skill in the art would have been motivated to make this modification to the teachings of Elhawary with the motivation of, …Improved systems and techniques to automatically classify and record such tasks…improved machine learning models for classifying motion data based on the underling action(s) being performed…by training and using the machine learning model(s) to classify motion data, the system may be able to automatically record or chart user actions in order to significantly reduce manual effort, increase the accuracy of the records, and improve the overall system through creation of better data and improved results for the patients and users. Further, any downstream systems or models that rely on this event data can be significantly improved. For example, downstream machine learning systems that make predictions or inferences based on the performed actions (e.g., to track user status) will be greatly improved by more accurate and voluminous action records….the system can retrieve motion data recorded at the time of the action being performed, and automatically label the motion data based on the action that was performed. This can allow the system to automatically label a vast amount of training data, enabling improved and more efficient training of the machine learning model(s)., as recited in SUKHTIPYAROGE.
As per Claim 12, Elhawary teaches: The system of claim 11 wherein each of the sensor modules is configured to
determine at least one of acceleration data, gyroscopic data, tilt data, location (GPS) data, environmental data, heart rate data, body temperature data, blood pressure data, quaternion, stretch, flex, or a combination thereof. (in at least [0089] FIG. 2A, each wearable device 190 may include a sensor array 200 including a 3-axis accelerometer 210, a 3-axis gyroscope 220, a 3-axis magnetometer 230, a temperature sensor 240, and an altitude sensor 250, such as a barometric pressure sensor. Each sensor device 190 may further include a communication module 260 which may include multiple communication interfaces. For example, each sensor device 190 may have a short range communication interface 270 for enabling communications between a first sensor device 190 a and a second sensor device 190 b worn by a single user. The short range communication interface 270 may further be used to receive signals from additional sensors or devices on the user's body, such as safety equipment, or from sensors or other transmitters in the user's immediate environment. The wearable device 190 may further contain a longer range communication interface 280 for connecting, for example, to a Wi-Fi or cellular network. Each wearable device 190 may further include a computation module 285, including a processor 290 and a memory 300. [0201] In addition to the identification of particular activities based on extraction of orientation and acceleration data from the sensor device 190, additional context may be provided for the data by sensors or communication protocols used to calculate a user's location within a facility or a distance from another object. For example, in an indoor facility, beacons may be distributed, and Wifi or Bluetooth signal strength or triangulation may be used to estimate the position of the invented device inside the facility. Similarly, in an outdoor environment, GPS can be used to estimate location. [0213] Productivity metrics may be developed and utilized based on frequency of detection of certain activities. Such data may be analyzed to search for relationships with other detected metrics. For example, productivity can be correlated to changes in dehydration as measured by sweat sensors, such as in the system discussed below in reference to FIG. 4B, or to times of the day, weather, or ambient or body temperature.)
As per Claim 13, Elhawary teaches: The system of claim 11,
wherein the session data comprises a body location for each of the plurality of sensors on the garment. (in at least [0083] Each of the workers 110, 140, 172 would typically be wearing at least one sensor device, and in some embodiments, two sensor devices, 190 a, b for recording movement. Typically, where two sensors are provided, the sensors used may be a wrist sensor device 190 a, ideally located on the wrist or forearm of the dominant hand, and a back sensor device 190 b, ideally located approximately at the height of the L1 and L2 vertebrae, but other sensor device types may be implemented as well. The wrist sensor may incorporated into a wrist device, such as a bracelet or a wristwatch, and the back device may be incorporated into a chest strap, weight belt or back brace, for example. Where only one sensor device 190 is provided, it is typically applied to a worker 110, 140, 172 on or near the worker's hip. However, the device 190 may be applied elsewhere and the necessary dimensions and measurements may be extrapolated from data recorded from the sensor device 190. The sensor device 190 may take a variety of forms, and is referred to herein as any of a sensor, a device, or a sensor device.)
As per Claim 14, Elhawary teaches: The system of claim 11, wherein the second processor is further configured to
filter session messages. (in at least [0104] identifying the initiation of a lift may comprise only filtering of data to reduce noise and cancel any drift. Typically, filtering is applied, such as a band pass filter, to ensure that more resource intensive processing is applied only once a lifting activity is detected within the more minimally processed data. For example, drift in height sensor data and gyroscope data may be filtered to reduce noise prior to identifying a lifting activity, and then the filtered data may be utilized to detect the initiation of a lifting task with a reduced number of false positives. )
As per Claim 15, Elhawary teaches: The system of claim 11,
wherein the job task summary quantifies the kinematic requirements of a job. (in at least [0167] the cumulative risk metric may be based on kinematic variables including at least one of back bending angle and cumulative trunk loading. For example, the cumulative risk metric may be based on a time integration of the kinematic variables over time. Accordingly, in such an embodiment, the metric is based on an integration of the back bending angle over time and an integration of the trunk loading window over time. The cumulative risk metric may be based on variables different than those on which the activity risk metric is based, or it may be based on the same set of variables. Accordingly, the activity risk metric may be based on a first physical model while the cumulative risk metric may be based on a different physical model. [0170] additional cumulative risk metrics may be maintained by the method or system described. Accordingly, a worker may be monitored for cumulative risk over the window discussed above, as well as a complete day and/or week. Other cumulative variables may include cumulative trunk loading, position and timing variables, and integrated kinematic variables, such as daily integrated back sagittal angle or velocity. [0275] productivity of individual workers may be further developed, and productivity based metrics may be utilized to evaluate relationships between fatigue and productivity. Accordingly, the platform may provide estimates of return on investment for individual pieces of equipment that may both reduce injury risk and increase productivity. In some cases, a reduction in injury risk may lower productivity, while a requirement for a worker increasing productivity may increase the risk for that particular worker. The platform described may determine an appropriate balance of increasing a worker's productivity while maintaining the risk metric below a specified threshold.)
As per Claim 16, Elhawary teaches: The system of claim 11,
wherein the system comprises two or more garments, each garment comprising a plurality of sensor modules, and the first processor is configured to store a session identifier that is different for each of two or more subjects. (in at least [0235] FIG. 4C shows an additional system in which the wearable device 190 may be implemented. As shown, the worker 4000 may similarly be provided with a wearable device 4010 which includes multiple communication interfaces, as in the system shown in FIG. 4B. However, in such an implementation, the short range communication interface 4100 provided may be used by a first worker 4000 a to communicate with additional workers 4000 b-e in order to bypass long range communication, or to pass a signal to different workers 4000 c within range of long range communication 4110 when the first worker 4000 a is out of such range. [0259] workers can receive individualized messages at their own devices 4010. This may be based on worker biomechanical data, such as specific advice to focus on reducing twists, for example. Messages may further advise taking breaks as a response to fatigue metrics. Messages can further relate to worker task assignments related to certain stations, machines, or locations. For example, “station number XY12” would allow a worker to confirm which station they are assigned to. Similarly, a machine or task may be assigned to a worker. The worker may then push a button or tap the wearable device to indicate task completion. [0364] Where a worker uses their badge to associate a wearable device with themselves, the ID must be a human readable or machine readable passive or active ID. The ID can be included in a workers employee access badge, phone, wallet, clothing, or the like. In such an embodiment, a worker association with a wearable device could be recorded at a server and in an appropriate database. This may be by way of the wearable device itself, or it may be by way of a kiosk associated with a docking location capable of reading the ID on the device as well as the employee ID. Data integrity in a large workplace is important, since data can be associated with any one of hundreds or thousands of workers.)
As per Claim 17, Elhawary teaches: The system of claim 16, wherein the second processor is configured to
compile the one or more session messages in order of the timestamp data and by the session identifier. (in at least [0151] FIG. 3B is a plot illustrating high risk postures over time for a worker, and FIG. 3C is a flowchart illustrating an alternative method for monitoring safety. While the basic method discussed may provide an alert, such as a vibration or device display indicating a high risk physical activity or movement any time such a movement was performed, such an approach may result in a large number of alerts to a worker. Such a large number of alerts may be ignored, or may irritate the worker. Accordingly, a separate metric, described herein and mentioned briefly above, may be used to evaluate accumulated risk over a period of time, and that metric may be used to determine whether a worker should be alerted for each individual high risk physical activity. This may take the form of a running gauge over a time window. [0154] evaluate aggregate or accumulated risk, in the form of a cumulative risk metric, and risk associated with individual activities in combination. Such a combination allows for the leveraging of risk based insights. As shown in the flowchart, an entity implementing the method, such as a server 310 or a wearable device 190, may receive a signal generated from dynamic activity of the wearable device over time (3000). The method may then evaluate the signal (3010) and determine if a first physical activity was initiated (3015) by identifying an initiation time for the physical activity performed by the worker wearing the device 190. The method then evaluates the signals further and calculates measurements of the worker wearing the device 190 for the time period during the first physical activity from the first signal segment for a time period following the initiation time (3020). [0207] the sensor device 190 may also be used to time stamp when a user comes into work, takes a break, or ends work by confirming the time period for which the user is within a work zone. Accordingly, a device trigger can be used to determine how many hours an employee has worked, and if they are allowed into the premises. Triggers can be based on a variety of activities, such as taking the device 190 out of its dock and returning it, putting the device 190 on and off of a belt, starting to walk after putting the device on, scanning an employee badge, or pressing a button or taking other action on the device 190 once a worker puts it on.)
As per Claim 18, Elhawary teaches: The system of claim 11,
wherein the first and second processors are the same. (in at least [0235] FIG. 4C shows an additional system in which the wearable device 190 may be implemented. As shown, the worker 4000 may similarly be provided with a wearable device 4010 which includes multiple communication interfaces, as in the system shown in FIG. 4B. However, in such an implementation, the short range communication interface 4100 provided may be used by a first worker 4000 a to communicate with additional workers 4000 b-e in order to bypass long range communication, or to pass a signal to different workers 4000 c within range of long range communication 4110 when the first worker 4000 a is out of such range.)
As per Claim 19, Elhawary teaches: The system of claim 11, wherein the first or second processor
calculates a joint angle of a subject. (in at least [0102] The server then evaluates (420) both signals to determine if any portion of the signal represents the initiation of a lifting activity. If a lifting activity is identified in the data, the server then further evaluates (430) both signals to identify an end point of the lifting activity. In some embodiments, this detection of an initiation of a lifting activity and an end point of the lifting activity is by way of a rules based approach directly using variables obtained from the sensor data, or based on variables detectable after only minimal signal processing. This rules based approach may include, for example, measuring the back angle with respect to the gravity plane and determining when it passes a threshold. This type of threshold may be static or variable, depending on other elements of the lift. Arm elevation angles may further be used to detect lifts above the shoulder [0167] the cumulative risk metric may be based on kinematic variables including at least one of back bending angle and cumulative trunk loading. For example, the cumulative risk metric may be based on a time integration of the kinematic variables over time. Accordingly, in such an embodiment, the metric is based on an integration of the back bending angle over time and an integration of the trunk loading window over time. The cumulative risk metric may be based on variables different than those on which the activity risk metric is based, or it may be based on the same set of variables. Accordingly, the activity risk metric may be based on a first physical model while the cumulative risk metric may be based on a different physical model.)
As per Claim 20, Elhawary teaches: The system of claim 11,
wherein job task summary further comprises a classification of stress or fatigue. (in at least [0149] While the method evaluates individual activities, the server will continue to receive data from the sensor devices 190 a, b. Accordingly, the server may then store (530) a record of the first lift in a memory associated with the server and return to step 400 and continue monitoring the sensor data to determine if the worker is performing additional lifting activities. The server typically continues to monitor the data for additional lifting motions over the course of an evaluation period. In some embodiments, once multiple lifts have occurred, the method calculates (540) a frequency associated with the lifting motions identified and incorporates (550) that value into the risk models in order to monitor and evaluate risks associated with repetitive lifts. Such frequency data may be used in the NIOSH model described above, for example, to reduce the maximum recommended weight for a repeated lifting activity based on repetitive stresses and associated risks. [0165] the cumulative risk metric may be a measure of rest periods between instances of the activity risk metric being above the activity risk threshold. This may allow the cumulative risk metric to consider cumulative rest time. Alternatively, the cumulative risk metric may be a measure of the overall number of physical activities performed during the sliding window of time, such that the system may determine whether a worker is likely to be fatigued. Being fatigued from a large number of physical activities may result in a worker being more susceptible to the risk associated with high risk individual physical activities.)
As per Claim 1-10, substantially recite the subject matter of Claim 11-20 and are rejected based on the same reasoning and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PO HAN MAX LEE whose telephone number is (571)272-3821. The examiner can normally be reached on Mon-Thurs 8:00 am - 7:00 pm.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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