DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Final Office Action is responsive to Applicant's amendment filed on 20 August 2025. Applicant’s amendment on 20 August 2025 amended Claims 1, 28, and 30. Claims 40-43 is newly presented. Currently Claims 1-43 are pending and have been examined. The Examiner notes that the 101 rejection for claims has been maintained.
Response to Arguments
Applicant's arguments filed 20 August 2025 have been fully considered but they are not persuasive.
The Applicant argues on pages 13-14 that “improperly reject the alims for utilizing a mathematical process… the instant claims are similar to those in Thales Visionix inc. v. United States, in that the allegedly abstract processes used data form a set of physical sensors. The instant case, the claimed systems include a plurality of wearable devices having sensors that record motion data and communicate motion data to a monitoring system in response to the motion data and communicate motion data to monitoring system… not an abstract idea”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that upon review of the previous Office Action the Examiner did not identify the claims in the previous Office Action as a mathematical process, but as a mental process. The Examiner further notes that the Applicant’s reliance on Thales Visionix is misplaced because that case involved claims with specific technical solutions to problems rooted in sensor technology itself. In Thales Visionix, the Federal Circuit found eligible claims that used “a particular configuration of inertial sensors and a particular method of using the raw data from the inertial sensors” to solve the technical problem of tracking position without cumulative errors inherent in prior systems. The court emphasized the claims provided “a system and method that improve upon conventional approaches” to sensor tracking through a specific technical arraignment that reduced errors.”
The claims here lack any comparable technical specificity. The claims recite, generic motion sensors recording data, generic threshold comparison to identify events of interest, generic wireless communication of data windows, and generic analytics to quantify physicality. None of these elements represent particular sensor configurations, novel data processing methods, or specific technical solutions to sensor configurations, novel data processing methods, or specific technical solutions to sensor technology problems. The AI-SME Update makes clear that simply invoking sensors and data processing “at a high level of generality” without “any details about how” the system operates differently from conventional approaches results in claims that recite abstract ideas implemented using generic technology.
The claims describe what data is collected and how it is used for business decisions (assessing worker physicality and safety risk for workforce management); not how sensor technology or computer functionality is improved. Under MPEP 2106.04(a)(2), the claims recite: mental processes – observing motion data, evaluating whether events of interest occurred, assessing worker practically be performed in the human mind through observation, evaluation, and judgement about worker behavior.
The amended limitations regarding “trigger an alarm, flashing light or safety system” do not transform these abstract ideas into patent-eligible subject matter. Triggering alarms in response to detect conditions is routine output activity that has been well-understood in safety systems for decades. The 2025 memorandum confirms that such generic output operations represent “insignificant extra-solution activity” under MPEP 2016.05(g). The claims provide no technical details about how alarm triggering improves computer functionality, how the alarm system operates differently from conventional systems, or what specific technical implementation distinguishes this from any generic safety alert system. This precisely the type of “apply it” limitation that the guidance identifies as insufficient – adding conventional output actions (triggering alarms) to the application of abstract idea (detecting safety events through threshold comparison and analytics) without meaningful technical constraints.
Unlike Thales Visionix, which solved a technical sensor accuracy problem through specific sensor configurations and data processing methods, these claim use conventional sensor in conventional ways to gather data for business workforce management decisions. The monitoring of workers, assessment of their physical extraction, and triggering of alerts are business safety management activities automated through generic sensor technology – not improvements to sensor technology or computer functionality itself. The rejection is therefore maintained.
The Applicant argues on pages 14-15 that “the claimed wearable devices perform an improved method for communicating data to monitoring system that improve the functionality of the overall system/technology and thus integrate the wearable devices and monitoring system into a practical application and amounts to significantly more”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Applicant’s assertion that the claims recite an “improved method for communicating data” is contradicted by the claim language itself, which describes only conventional event-driven data transmission that has been standard practice in sensor networks and monitoring systems for decades. The claims recite: recording sensor data, identifying when data satisfies criteria (threshold comparison), in response, communicating a portion of data to a monitoring system. This describes routine conditional data transmission – a well-understood, conventional approach to reducing bandwidth usage in any network sensor system.
The 2025 memorandum requires distinguishing between claims that “improve computer capabilities or improve an existing technology” versus claims that “invoke computers or other machinery merely as a tool to perform an existing process.” The claims here fall squarely into the latter category. The specification’s discussion of “benefits” confirms this – stating that transmitting data only when events occur “reduces power usage.” These are expected results of transmitting less data, not technological improvements to how wireless communication, data processing , or sensor systems functions. Every conditional data transmission system since the inception of networked sensors achieves identical benefits by transmitting less data.
The AI-SME Update Example 47 Claim 2 directly addresses this type of argument, finding claims ineligible when they recite “receiving” and “outputting” data because these represent “mere data gathering and output recited at a high level of generality” that constitute “insignificant extra-solution activity” even when performed selectively. The Update explains such data communication remains “well-understood, routine, conventional activity” under MPEP 2016.05(d) because “receiving or transmitting data over a network” has been standard practice. The claims here simply add event-driven triggering to this conventional data transmission, which does not transform routine communication into a technological improvement.
The claims provide no specific technical details that would demonstrate an actual improvement to communication technology: no novel wireless protocols, no particular data compression methods, no specific network architectures, no technical constraints on how the “improved” communication differs from any conventional event-trigger sensor data transmission system. The 2025 memorandum emphasizes that claims must demonstrate “a particular solution to a problem or a particular way to achieve a desire outcome” rather than “merely claiming the idea of a solution.” The claims here recite only the idea of selective data transmission (transmit when events occur) without any particular technical means of achieving this that would represent an advancement in wireless communication, sensor technology, or computer networking.
Furthermore, even accepting arguendo that reducing data transmission improves system efficiency, the problem being solved is a business monitoring problem, not a computer technology problem. The system’s purpose is assessing worker physicality and safety risk for workforce management decisions – a business objective. Using less bandwidth to accomplish business monitoring more efficiently represents business process optimization through conventional technology, not technological improvement. The specification confirms this, describing the invention’s benefits as facilitating “assessment of safety risks faced by workers” and helping “better assess risk posed to workers” – these are business workforce management benefits, not technological advancements.
The amended alarm-triggering limitation does not change this analysis. As previously explained, triggering alarms based on detected conditions is viewed as a routine output activity. The claims provide no details about how this alarm system improves upon conventional alert systems beyond applying the abstract idea of safety event detection (threshold comparison and analytics) to trigger conventional alarm outputs. Under the 2025 memorandum, “even when considered in combination, the additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept.”
The Applicant argues on pages 15-16 that “the amended independent claim 1 to specify that the system is configured to perform real world activity… in response to determining that a safety event has occurred. This triggering real world activity is similar to aspects of patent eligible claims of Example 46 of the USPTO Appendix 1 to the October 2019 Update… similar to Example 46, in instant claim 1, the determination of whether a safety event has occurred from information obtained by the allegedly abstract analysis/data – processing is used to trigger corrective action such that the monitoring system is able to control an alarm, flashing light, or other safety system in an area where the safety event occurred so as to integrate the allegedly abstract analysis/data-processing into a practical application. The 101 rejections of claims 1-15 are believed to be moot”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the Applicant’s reliance on Example 46 is fundamentally misplaced because Example 46 is viewed to involve claims with specific technical implementation for controlling manufacturing equipment, whereas the claims here recite only generic alarm triggering without any technical specificity that would distinguish this from conventional safety alert systems.
Example 46 found eligible claims reciting “a particular arrangement of elements (transceivers, sensors, and controller configured in a particular way)” that enabled “automatic, real-time monitoring of moving machine parts and active adjustment to ensure the machine operates within tolerance.” The Example emphasized: the controller receives information from sensors and transceivers about sensor locations, uses this information along with positional data of the machine, determines whether the machine is operating within tolerance, the claims provided specific structural and functional relationships between elements. The Example concluded this constituted “a particular solution” that “amounts to significantly more” because it provided technical details about how the system-controlled manufacturing processes.
The amended limitations merely recites “trigger an alarm, flashing light or safety system in an area where the safety event occurred” within providing: any particular arrangement of system components, any specific configuration of how the monitoring system interfaces with or controls alarm/light/safety systems, any technical details about signal transmission, control protocols, or activation mechanism, any particular method of determining which alarm/light/safety system to trigger or how triggering occurs, any structural or functional relationships between monitoring system components an alarm systems, or any indication of improvements to alarm technology or how these systems operate differently from conventional implementations.
Under MPEP 2016.05(d), triggering alarms in response to detected safety conditions has been standard practice in industrial systems for over a century. From fire alarm systems detecting smoke, to machinery emergency stops detecting operator proximity, to gas leak detectors triggering ventilation systems – automated alarm triggering based on sensor-detected hazardous conditions is quintessentially conventional. The claims provide no details demonstrating any particular technical advancement in how alarm triggering occurs beyond the conventional approach of: detect condition causes a trigger alert.
The AI-SME Update confirms that under MPEP 2106.05(g), “insignificant extra-solution activity” includes “outputting a result… without other meaningful limitations.” Triggering an alarm is simply outputting the result of the abstract idea of the safety analytics – the natural consequences of detecting a safety event. The Update explains sch output remains insignificant extra-solution activity unless the claims provide “something significantly more than the abstract idea itself,” which requires more than “merely adding conventional computer implementation” to abstract concepts.
Example 46 involved controlling manufacturing equipment operation to ensure machines operated within tolerance – a technical manufacturing control problem. The claims here involve assessing work safety risk and alerting personnel – a business workforce management problem. The specification confirms this, stating objectives of “assesses risk posed to workers,” “better inform and address workplace injuries,” and “facilitate early intervention when safety risks are detected.” These are business objectives. The fact that conventional alarms are triggered as output does not transform business safety management into a technical improvement.
Under the 2025 memorandum, the claims fails all three factors: they recite only the idea of a solution (trigger alarms when safety events are detected) rather than a particular technical solution with implementation details; sensors, computers and alarms are invoked as tools to perform the existing business process of workplace safety monitoring rather than to improve computer, sensor, or alarm technologies; and the application is general (encompass any system triggering alarms based on safety analytics) rather than particular with meaningful technical constraints.
Unlike Example 46’s specific technical implementation for manufacturing control, the claims here recite high-level functional results (trigger alarms) of applying abstract ideas (threshold comparison, analytics, safety assessment) using generic technology (sensors, wireless communication, conventional alarms) without technical specifics. This represents precisely what MPEP 2106.05(g) identifies as insufficient: “ data gathering… combined with an instruction to apply or use an exception does not add significantly more.” The triggering of conventional alarms based on abstract safety analytics, described generically without technical implementation details, fails to integrate the exception into a practical application and fails to amount to significantly more. The 101 rejection is therefore maintained.
The Applicant argues on page 16 that “the cited references have not been shown to teach all aspects of the instant claim… in the cited references have not shown to teach aspects related to wearable devices that communicate recording motion data to monitoring system in response to the motion data satisfying a set of criteria… the completion of the analysis is interpreted as satisfying a set of criteria that results are communicated by the server not by the wearable devices as alleged”.
The Examiner respectfully disagrees.
In response to the arguments in the Examiner notes that the upon review of the claims is not clear which aspect of the claim requires that “results are communicated by the server not by the wearable devices as alleged.” With respect to the previous claims which stated in response to identifying an instance when the recorded motion data satisfies the predetermined set of criteria, communicating a portion of the recorded motion data to the monitoring system the Examiner pointed to col. 5, line (49) – col. 6, line (27) of Kiran which is viewed to teach based on monitored tasks send alerts and results to supervisors which for clarification it was noted that for the same independent claim that col. 9, lines (10-27) of Kiran teaches performing analytics based on wearable devices. The argument does not make it clear how this cited portion does not teach the claimed limitation and it is therefore viewed that the existing prior art teaches these previously claimed limitations.
If the argument is directed to the amendment to the claims the Examiner notes that it is moot in view of the new grounds of rejection necessitated by amendment. The rejection is therefore maintained.
The Applicant's remaining arguments filed 20 August 2025 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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 1-43 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-43 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-43 is/are directed to the abstract idea of monitoring the movement of workers utilizing wearable sensors and assessing worker risk. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-43) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes (from the January 2019 §101 Examination Guidelines), claim(s) (1-15, 16-27, 28-29, and 30-43) is/are directed to a system and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental process. Specifically, the independent claims 1, 16, 28, and 30 recite a mental process: as drafted, the claim recites the limitation of assessing worker risk which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a device, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the device language, the claim encompasses the user manually analyzing collected worker data and analyzing it to assess worker risk. The mere nominal recitation of a device does not take the claim limitation out of the mental processes grouping. It has been established by ongoing guidance that claims that contain a generic processor are still viewed as mental process when they contain limitations that can practically be performed in the human mind, however this is different for instance when the human mind is not equipped to perform the claim limitations (network monitoring, data encryption for communication, and rendering images). Therefore, these limitations are viewed a mental process. Additionally, with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Note to the Applicant per the 2019 October Guidance: The 2019 PEG sets forth a test that distills the relevant case law to aid in examination, and does not attempt to articulate each and every decision. As further explained in the 2019 PEG, the Office has shifted its approach from the case-comparison approach in determining whether a claim recites an abstract idea and instead uses enumerated groupings of abstract ideas. The enumerated groupings are firmly rooted in Supreme Court precedent as well as Federal Circuit decisions interpreting that precedent. By grouping the abstract ideas, the 2019 PEG shifts examiners’ focus from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. In sum, the 2019 PEG synthesizes the holdings of various court decisions to facilitate examination.
Step 2A Prong 2
Specifically the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally that data recording, identifying, monitoring, determining, and communicating steps required to use the correlation do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the performing analytics step. The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (monitoring the movement of workers utilizing wearable sensors and assessing worker risk). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): record motions, identifying instances, monitoring systems, determining if a safety event has occurred, and communicating performs the comparing step. The recording, identifying, monitoring, determining, and communicating steps are recited at a high level of generality (i.e., as a general means of managing data for use in the performing analytics step), and amounts to mere data management, which is a form of insignificant extra-solution activity. The device that performs the performing analytics step is also recited at a high level of generality, and merely automates the performing analytics step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the device).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, which in this case it is not clear that the specification sets forth an improvement in technology, the claim must not reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the October 2019 Guidance: Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-43 recite(s) recording motion, identifying instances, monitoring is configured, determining a safety event, communicating recorded motion, and perform analytics which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for recording, identifying, monitoring, determining, communicating, and performing analytics which is the abstract idea steps of valuing an idea (monitoring the movement of workers utilizing wearable sensors and assessing worker risk) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. monitoring the movement of workers utilizing wearable sensors and assessing worker risk). Using a computer to recording, identifying, monitoring, determining, communicating, and performing analytics the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The monitoring the movement of workers utilizing wearable sensors and assessing worker risk would clearly be to a mental activity that a company would go through in order to decide how assess workers’ risk. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to assess worker risk:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 2-5, 8-15, 17-21, 25-27, and 32-38 recite limitations which further limit the claimed analysis of data.
Claims 24, 29, 31, and 41-43 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Kiran which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 6, 7, 22, 23, 39, and 40 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the assessing of worker risk. This is not a technical or technological problem but is rather in the realm of worker ergonomic working conditions and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
With respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the claims that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication could include a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-6, 8-13-22, and 24-43 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kiran et al. (U.S. Patent 12,109,015 B1) (hereafter Kiran) in view of BUCCHIERI et al. (U.S. Patent Publication 2024/0087444 A1) (hereafter Bucchieri).
Referring to Claim 1, Kiran teaches a system for evaluating worker safety, said system comprising:
a plurality of wearable devices (see; Fig. 9 of Kiran teaches a plurality of wearable devices distributed to multiple workers).
a monitoring system communicatively connected to the plurality of wearable devices (see; Fig. 9 of Kiran teaches a plurality of wearable devices distributed to multiple workers, that are connected to the system by Bluetooth and wireless connections, Fig. 6 that can be attached to multiple parts of the body for utilization to measure different characteristics).
wherein each of the plurality of wearable devices is configured to be worn by a respective one of a plurality of workers during a work shift (see; col. 6, lines (1-27) of Kiran teaches wearables are utilized at work during their shifts, fig. 9 including multiple people).
wherein each of the plurality of wearable devices includes one or more sensors (see; col. 5, lines (49-67) of Kiran teaches wearable devices that include sensors to capture movement data).
wherein the one or more sensors includes a motion sensor (see; col. 2, lines (1-23) of Kiran teaches monitoring relative motion of a worker).
wherein each of the plurality of wearable devices is configured to: record motion data from the motion sensor (see; col. 2, lines (1-23) of Kiran teaches monitoring relative motion of a worker).
identify instances when the recorded motion data satisfies a predetermined set of criteria (see; col. 10, line 5, col. (49) – line 6, col. (27) of Kiran teaches motions are scored for multiple purposes including safety and productivity (i.e. predetermined criteria – scored items, based on col. 2, lines (1-23) monitoring relative motion of a worker).
in response to identifying an instance when the recorded motion data satisfies the predetermined set of criteria, communicating a portion of the recorded motion data to the monitoring system (see; col. 5, line (49) – col. 6, line (27) of Kiran teaches based on monitored tasks send alerts and results to supervisors).
wherein the monitoring system is configured to perform analytics on the portion of the recorded motion data received from the plurality of wearable devices (see; col. 9, lines (10-27) of Kiran teaches performing analytics based on wearable devices).
Kiran does not explicitly disclose the following limitations, however,
Bucchieri teaches wherein the analytics performed by monitoring system is configure to determine if a safety event has occurred (see; Abstract of Bucchieri teaches a predictive system for safety in the workplace, par. [0029] which controls the safety of the workforce in real time (i.e. monitoring) based on collected data and risk profiles, par. [0091] utilizing a risk calculation module) and
wherein in response to determining a safety event has occurred, the monitoring system is configured to trigger an alarm, flashing lights, or other safety system in an area where the safety event occurred, thereby alerting others as to the event and in an attempt to prevent further injury or damage (see; par. [0119] of Bucchieri teaches the triggering of an alert from the risk module (i.e. safety system) sent to identify high risk of accident (i.e. prevent further injury)).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose wherein the analytics performed by monitoring system is configure to determine if a safety event has occurred, and wherein in response to determining a safety event has occurred, the monitoring system is configured to trigger an alarm, flashing lights, or other safety system in an area where the safety event occurred, thereby alerting others as to the event and in an attempt to prevent further injury or damage.
Bucchieri discloses wherein the analytics performed by monitoring system is configure to determine if a safety event has occurred, and wherein in response to determining a safety event has occurred, the monitoring system is configured to trigger an alarm, flashing lights, or other safety system in an area where the safety event occurred, thereby alerting others as to the event and in an attempt to prevent further injury or damage.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Kiran wherein the analytics performed by monitoring system is configure to determine if a safety event has occurred, and wherein in response to determining a safety event has occurred, the monitoring system is configured to trigger an alarm, flashing lights, or other safety system in an area where the safety event occurred, thereby alerting others as to the event and in an attempt to prevent further injury or damage as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to quantify physicality exhibited by each of the plurality of workers during the work shift (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements (i.e. quantify)).
Referring to Claim 3, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
wherein the analytics performed by the monitoring system is configured to for at least one of the plurality of workers, to quantify physicality exhibited by the worker during the work shift based on the motion data (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements of the relative motion of workers (i.e. quantify)).
Referring to Claim 4, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by the monitoring system is configured to for at least one of the plurality of workers, to quantify the number of instances in which the motion data recorded by the plurality of wearable devices satisfied the predetermined set of criteria (see; col. 5, line (49) – col. 6, line (37) of Kiran teaches monitoring workers and specifically monitoring repetitive motion ad provide a score (i.e. determined criteria)).
Referring to Claim 5, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to for at least one of the plurality of workers, to quantify physicality exhibited by the worker during the work shift based on the motion data, the number of instances in which the motion data recorded by the plurality of wearable devices satisfied the predetermined set of criteria, and the length of the work shift of the worker (see; col. 10, lines (9-41) of Kiran teaches monitoring a worker to determine a scoring (i.e. quantify) of performance and safety of workers throughout the shift with the scores (i.e. predetermined criteria)).
Referring to Claim 6, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds a predetermined threshold magnitude of acceleration stored in a memory (see; col. 10, line (9-41) of Kiran teaches monitoring motion of a worker is scored and can identify speeds that may be fast and can potentially hurt a joint and utilizes thresholds for analysis, col. 13, lines (4-18) where speed and acceleration are monitored).
Referring to Claim 8, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to derive one or more data metrics from the motion data received from the plurality of wearable devices (see; col. 22, lines (1-32) of Kiran teaches deriving metric data from sensors, Fig. 9 where multiple users are being monitored and all the data analyzed to compare users).
wherein the analytics performed by monitoring system is configured to rank the plurality of workers using at least one of the one or more data metrics (see; col. 23, lines (13-25) of Kiran teaches data is collected from workers from wearable devices, the data can be used to compare and identify the one that is the most effective at tasks (i.e. rank)).
Referring to Claim 9, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to quantify physicality exhibited by each of the plurality of workers during the work shift (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements (i.e. quantify)).
wherein the analytics performed by monitoring system is configured to rank the plurality of workers by the physicality of the workers (see; col. 23, lines (13-25) of Kiran teaches data is collected from workers from wearable devices, the data can be used to compare and identify the one that is the most effective at tasks (i.e. rank)).
Referring to Claim 10, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to identify correlations in the portions of motion data received from the plurality of wearable devices that are indicative of events of interest (see; par. 25, lines (13-27) of Kiran teaches real time monitoring from wearable devices that determine unsafe motions performed (i.e. events of interest)).
Referring to Claim 11, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to quantify physicality exhibited by each of the plurality of workers during the work shift (see; par. 10, lines (9-41) of Kiran teaches monitored motion is scored (i.e. analytic) and can identify the performance of a user related to performance and safety while working).
wherein the analytics performed by monitoring system is configured to rank the plurality of workers by the physicality of the workers and identify a subset of the plurality of workers having the highest physicality (see; col. 23, lines (13-25) of Kiran teaches data is collected from workers from wearable devices, the data can be used to compare and identify the one that is the most effective at tasks (i.e. rank), par. 10, lines (9-41) utilizing monitored motion is scored (i.e. analytic) and can identify the performance of a user related to performance and safety while working).
wherein the analytics performed by monitoring system is configured to use data of the subset of the plurality of workers to train one or more classifiers to identify one or more data metrics are correlated with events of interest (see; col. 12, lines (15-46) of Kiran data transformation and filters provide the information which can be used to improve the workers ergonomic performance (i.e. events of interests)).
Referring to Claim 12, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of:
the analytics performed by monitoring system is configured to quantify physicality exhibited by each of the plurality of workers during the work shift (see; col. 10, lines (9-41) of Kiran teaches monitoring a worker to determine a scoring (i.e. quantify) of performance and safety of workers throughout the shift with the scores (i.e. predetermined criteria)).
wherein the analytics performed by monitoring system is configured to rank the plurality of workers by the physicality of the workers and identify a subset of the plurality of workers having the highest physicality (see; col. 23, lines (13-25) of Kiran teaches data is collected from workers from wearable devices, the data can be used to compare and identify the one that is the most effective at tasks (i.e. rank), par. 10, lines (9-41) utilizing monitored motion is scored (i.e. analytic) and can identify the performance of a user related to performance and safety while working).
wherein the analytics performed by monitoring system is configured to use data of the subset of the plurality of workers to train one or more classifiers to identify motions correlated with the events of interest (see; col. 5, line (62) – col. 6, line (27) of Kiran teaches training and optimizing the ergonomics to enhance the productivity will provide protection for the worker, col. 12, lines (15-46) where the data transformation and filters provide the information which can be used to improve the workers ergonomic performance (i.e. events of interests)).
Referring to Claim 13, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
wherein the analytics performed by monitoring system is configured to train one or more classifiers to identify the events of interest from a second sensor of the one or more sensors (see; Fig. 9 of Kiran teaches multiple locations to measure different movements of different parts of the body, col. 5, line (62) – col. 6, line (27) where the data is used to train and optimize the worker, col. 12, lines (15-46) which will improve the workers ergonomic performance to prevent risk (i.e. event of interest)).
Referring to Claim 14, see discussion of claim 1 above, while Kiran teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Bucchieri teaches the monitoring system is configured to perform analytics on the motion data received from the plurality of wearable devices to identify accidents, trips, or falls that occur during the work shift (see; Abstract of Bucchieri teaches a wearable device that is used to monitor occupational accidents which is used minimize occupational risk (i.e. trip or fall) based on sensed motion data).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose the monitoring system is configured to perform analytics on the motion data received from the plurality of wearable devices to identify accidents, trips, or falls that occur during the work shift.
Bucchieri discloses the monitoring system is configured to perform analytics on the motion data received from the plurality of wearable devices to identify accidents, trips, or falls that occur during the work shift.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Kiran the monitoring system is configured to perform analytics on the motion data received from the plurality of wearable devices to identify accidents, trips, or falls that occur during the work shift as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 15, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
wherein the monitoring system is configured to perform analytics on the motion data received from the plurality of wearable devices to identify repetitive motions of the plurality of workers (see; col. 5, line (49) – col. 6, line (37) of Kiran teaches monitoring workers and specifically monitoring repetitive motion ad provide a score (i.e. determined criteria)).
Referring to Claim 16, Kiran in view of Bucchieri teaches a system for assessing safety risk of a worker. Claim 16 recites the same or similar limitations as those addressed above in claim 1, Claim 16 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 17, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the sensor data received by the monitoring system includes motion data sampled from a motion sensor of the one or more sensors (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements of the relative motion of workers (i.e. quantify)).
wherein the monitoring system is configured to quantify physicality exhibited by the worker based on the motion data (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements (i.e. quantify motion data)).
Referring to Claim 18, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the sensor data includes motion data sampled from a motion sensor of the one or more sensors (see; col. 5, lines (49-67) of Kiran teaches monitoring sensor data that includes motion data the wearable device).
wherein the monitoring system is configured to quantify physicality exhibited by the worker based on the motion data and the number of instances that the sensor data satisfies the predetermined set of criteria in the work shift (see; col. 5, line (49) – col. 6, line (37) of Kiran teaches monitoring workers and specifically monitoring repetitive motion ad provide a score (i.e. determined criteria)).
Referring to Claim 19, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the sensor data includes motion data sampled from a motion sensor of the one or more sensors (see; col. 5, lines (49-67) of Kiran teaches monitoring sensor data that includes motion data the wearable device).
wherein the monitoring system is configured to quantify physicality exhibited by the worker based on the motion data and the number of instances that the sensor data satisfies the predetermined set of criteria in the work shift, and the length of the work shift (see; col. 10, lines (9-41) of Kiran teaches monitoring a worker to determine a scoring (i.e. quantify) of performance and safety of workers throughout the shift with the scores (i.e. predetermined criteria)).
Referring to Claim 20, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the sensor data includes motion data sampled from a motion sensor of the one or more sensors (see; col. 5, lines (49-67) of Kiran teaches monitoring sensor data that includes motion data the wearable device).
wherein the monitoring system is configured to quantify physicality exhibited by the worker based on the motion data and the number of instances that the buffered samples of sensor data satisfies the predetermined set of criteria in the work shift, and the amount that the length of the work shift exceeds 8.5 hours (see; col. 16, lines (31-45) of Kiran teaches a full 8+ hours of a full shift measured event every 5 minutes throughout the intervals (i.e. buffer), throughout the shift).
Referring to Claim 21, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the sensor data received from the wearable device to derive one or more data metrics correlated with high-risk events (see; col. 10, line (9-41) of Kiran teaches monitoring motion of a worker is scored and can identify speeds that may be fast and can potentially hurt a joint and utilizes thresholds for analysis, col. 13, lines (4-18) where speed and acceleration are monitored).
Referring to Claim 22, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the sensor data includes motion data, wherein the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds a predetermined threshold magnitude of acceleration stored in a memory (see; col. 10, line (9-41) of Kiran teaches monitoring motion of a worker is scored and can identify speeds that may be fast and can potentially hurt a joint and utilizes thresholds for analysis, col. 13, lines (4-18) where speed and acceleration are monitored).
Referring to Claim 24, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
a plurality of wearable devices including the wearable device (see; Fig. 6 and Fig. 9 of Kiran teaches a plurality of wearable devices that can be attached to multiple locations on the body).
wherein the plurality of wearable devices configured to be worn by a plurality of workers during the work shift (see; Fig. 6 and Fig. 9 of Kiran teaches a plurality of wearable devices that can be attached to multiple locations on the body).
wherein the monitoring system is configured to receive sensor data from the plurality of wearable devices and quantify physicality exhibited by each of the plurality of workers during the work shift (see; Fig. 6 and Fig. 9 of Kiran teaches a plurality of wearable devices that can be attached to multiple locations on the body).
wherein the monitoring system is configured to rank the plurality of workers according to the quantified physicality of the workers (see; col. 10, lines (42-61) of Kiran teaches ranking productivity of workers from, col. 9, lines (10-27) based on monitored the wearable monitors).
Referring to Claim 25, see discussion of claim 16 above, while Kiran teaches the method above Claim 25 recites the same or similar limitations as those addressed above in claim 14, Claim 25 is therefore rejected for the same or similar limitations as set forth above in claim 14.
Referring to Claim 26, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
wherein the monitoring system is configured to perform analytics on the sensor data received from the wearable device to identify repetitive motions of the worker (see; col. 5, line (49) – col. 6, line (37) of Kiran teaches monitoring workers in order to score their movement (i.e. analytics) based on collected sensor data from wearable devices).
Referring to Claim 27, see discussion of claim 16 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the sensor data received from the wearable device to identify high risk events (see; col. 10, line (9-41) of Kiran teaches monitoring motion of a worker is scored and can identify speeds that may be fast and can potentially hurt a joint and utilizes thresholds for analysis, col. 13, lines (4-18) where speed and acceleration are monitored).
Referring to Claim 28, Kiran in view of Bucchieri teaches a rating scale to be used in an evaluation form. Claim 28 recites the same or similar limitations as those addressed above in claim 1, Claim 28 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exceptions:
wherein the wearable device identifies instances when the motion data exceeds a predetermined threshold (see; Fig. 16 and col. 13, line (55) – col. 14, line (14) of Kiran teaches a wearable device provides real time motion data, that can provide col. 10, line (9-41) monitoring motion of a worker is scored and can identify speeds that may be fast and can potentially hurt a joint and utilizes thresholds for analysis (i.e. predetermined threshold)).
the monitoring system is configured to receive the motion data from the wearable device and quantify physicality exhibited by the worker based on the motion data (see; col. 5, lines (40-43) of Kiran teaches monitoring and characterizing physical performance of workers, col. 21, lines (28-48) during shifts, col. 2, line (1-23) performing analytics and scoring the movements of the relative motion of workers (i.e. quantify)).
Referring to Claim 29, see discussion of claim 28 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is further configured to rank the physicality of the worker with physicality of other workers (see; col. 23, lines (13-25) of Kiran teaches data is collected from workers from wearable devices, the data can be used to compare and identify the one that is the most effective at tasks (i.e. rank)).
Referring to Claim 30, Kiran in view of Bucchieri teaches a rating scale to be used in an evaluation form. Claim 30 recites the same or similar limitations as those addressed above in claim 1, Claim 30 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exceptions:
wherein the wearable device receives higher density sensor data from the one or more sensors (see; Fig. 18, 1802 of Kiran teaches raw data from a wearable containing sensors (i.e. high density)).
wherein the wearable device is configured to derive lower density sensor data from the higher density sensor data (see; Fig. 18, 1814 and 1816 of Kiran teaches filtering and sorting of raw data to structured output (i.e. low density)).
wherein the wearable device is configured to communicate the lower density sensor data to the monitoring system (see; col. 27, lines (11-23) of Kiran teaches wearable data is communicated to a display to provide activity scores (i.e. low density)).
Referring to Claim 31, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the wearable device is further configured to, in response to identifying an instance when the higher density sensor data satisfies a predetermined set of criteria indicative of an event of interest, communicating a window of the higher density sensor data to the monitoring system (see; col. 10, lines (9-61) of Kiran teaches based on raw data that is viewed a too fast (i.e. event) the data is scored and reporting it based on threshold points).
Referring to Claim 32, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the wearable device is configured to derive the lower density sensor data from the higher density sensor data by averaging the higher density sensor data for a period of time (see; col. 11, line (64) – col. 12, line (14) of Kiran teaches the wearable data (i.e. high density) that takes the data that is cleaned and transformed (i.e. low density) take average data to provide summarized scores).
Referring to Claim 33, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
wherein the wearable device is configured to derive the lower density sensor data from the higher density sensor data by selecting a subset of samples of the higher density sensor data (see; col. 10, lines (9-61) of Kiran teaches taking sensor data used to determine scores and provides an example of using specific data points to provide scoring for actors for specific task).
Referring to Claim 34, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the lower density sensor data received from the wearable device to quantify physicality exhibited by the worker (see; col. 10, lines (9-61) of Kiran teaches providing scoring and ranking movement recorded by the wearable device).
Referring to Claim 35, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the lower density sensor data received from the wearable device to classify activity of the worker (see; col. 26, lines (5-38) of Kiran teaches processing the performance data to develop a set of scores of each subject (i.e. classify activities)).
Referring to Claim 36, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the lower density sensor data received from the wearable device to classify motions of the worker (see; col. 26, lines (5-38) of Kiran teaches processing the performance data to develop a set of scores of each subject (i.e. classify activities)).
Referring to Claim 37, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
determine position of the wearable device on the worker (see; Fig. 6 and col. 19, lines (32-60) of Kiran teaches monitoring the sensor of workers and identifying the positions of the limbs).
perform analytics on the lower density sensor data received from the wearable device to classify motions of the worker based in part on the determined position of the wearable device on the worker (see; col. 26, lines (5-38) of Kiran teaches processing the performance data to develop a set of scores of each subject (i.e. classify activities)).
Referring to Claim 38, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
determine orientation of the wearable device (see; Fig. 6 and Fig. 7 of Kiran teaches monitoring a worker with wearable devices and col. 19, lines (32-60) monitoring the sensor of workers and identifying the positions of the limbs (i.e. orientation)).
perform analytics on the lower density sensor data received from the wearable device to classify motions of the worker based in part on the determined orientation of the wearable device (see; col. 19, lines (32-60) of Kiran teaches monitoring the sensor of workers and identifying the positions of the limbs (i.e. orientation), col. 10, lines (9-61) providing scoring and ranking movement recorded by the wearable device).
Referring to Claim 39, see discussion of claim 30 above, while Kiran in view of Bucchieri teaches the system above, Kiran further discloses a system having the limitations of,
the monitoring system is configured to perform analytics on the lower density sensor data received from the wearable device to classify motions and/or activity of the worker from a set of including repetitive lifting, standing, jumping, walking, running, ascending and/or descending stairs, ascending and/or descending ladders, twisting, bending, throwing, egress from a defined area, improper form of motion, improper posture, and lack of motion (see; col. 10, lines (9-41) of Kiran teaches monitoring the motion and activity of a worker, col. 5, line (49) – col. 6, line (37) monitoring workers and specifically monitoring repetitive motion ad provide a score (i.e. determined criteria, col. 10, lines (9-41) monitoring a worker to determine a scoring (i.e. quantify) of performance and safety of workers throughout the shift with the scores (i.e. predetermined criteria), col. 12, lines (15-46) events of interest such as moving too fast and hurting your joints).
Referring to Claim 40, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Bucchieri teaches the motion sensor is a three-axis accelerometer (see; par. [0064] of Bucchieri teaches a motion sensor that measures three-axis accelerometer).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose the motion sensor is a three-axis accelerometer.
Bucchieri discloses the motion sensor is a three-axis accelerometer.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Kiran the motion sensor is a three-axis accelerometer as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 41, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Bucchieri teaches the recorded motion data is not communicated to the monitoring system in absence of the recorded motion data satisfying the predetermined set of criteria (see; par. [0025] of Bucchieri teaches the identification of negative events, par. [0108] based on risk triggers will result in action taken, par. [0105]-[0107] based on the risk profile provide a communication).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose the recorded motion data is not communicated to the monitoring system in absence of the recorded motion data satisfying the predetermined set of criteria.
Bucchieri discloses the recorded motion data is not communicated to the monitoring system in absence of the recorded motion data satisfying the predetermined set of criteria.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Kiran the recorded motion data is not communicated to the monitoring system in absence of the recorded motion data satisfying the predetermined set of criteria as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 42, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Bucchieri teaches in further response to the analytics performed by monitoring system, the monitoring system is configured to perform one or more actions (see; par. [0031]-[0037] of Bucchieri teaches a predictive system that provides analytics based on monitored sensor data and identify vital values of the user to mitigate risk, par. [0119] tehn send an alert (i.e. action)).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose in further response to the analytics performed by monitoring system, the monitoring system is configured to perform one or more actions.
Bucchieri discloses in further response to the analytics performed by monitoring system, the monitoring system is configured to perform one or more actions.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Kiran in further response to the analytics performed by monitoring system, the monitoring system is configured to perform one or more actions as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 43, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Bucchieri teaches in further response to the analytics performed by monitoring system, the monitoring system is configured to automatically send an electronic message to one or more persons in response to receiving the portion of the recorded motion data (see; par. [0091]-[0094] of Bucchieri teaches a risk calculation through the analysis, par. [0119] based on determined risk profile can send an alert, par. [0031]-[0037] that has predicted a risk to the workforce).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Bucchieri teaches predictive system and method for safety in the workplace and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose in further response to the analytics performed by monitoring system, the monitoring system is configured to automatically send an electronic message to one or more persons in response to receiving the portion of the recorded motion data.
Bucchieri discloses in further response to the analytics performed by monitoring system, the monitoring system is configured to automatically send an electronic message to one or more persons in response to receiving the portion of the recorded motion data.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Kiran in further response to the analytics performed by monitoring system, the monitoring system is configured to automatically send an electronic message to one or more persons in response to receiving the portion of the recorded motion data as taught by Bucchieri since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Bucchieri teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Claim 7 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kiran et al. (U.S. Patent 12,109,015 B1) (hereafter Kiran) in view of BUCCHIERI et al. (U.S. Patent Publication 2024/0087444 A1) (hereafter Bucchieri) in further view of Hawkins, III et al. (U.S. Patent Publication 2015/0099945 A1) (hereafter Hawkins).
Referring to Claim 7, see discussion of claim 1 above, while Kiran in view of Bucchieri teaches the system above, Kiran in view of Bucchieri does not explicitly disclose a system having the limitations of, however,
Hawkins teaches the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs (see; par. [0035] of Hawkins teaches a wearable device attached to the torso of a user and can identify acceleration of 2Gs).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hawkins teaches activity monitoring computing device and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs.
Hawkins discloses the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Kiran the the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs as taught by Hawkins since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Hawkins teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 23, see discussion of claim 16 above, while Kiran teaches the system above, Kiran does not explicitly disclose a system having the limitations of, however,
Hawkins teaches the sensor data includes motion data, wherein the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs (see; par. [0035] of Hawkins teaches a wearable device attached to the torso of a user and can identify acceleration of 2Gs).
The Examiner notes that Kiran teaches similar to the instant application teaches monitoring performance of a physical activity. Specifically, Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hawkins teaches activity monitoring computing device and as it is comparable in certain respects to Kiran which monitoring performance of a physical activity as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Kiran discloses the use of wearable sensors that can detect relative motion of a worker and can communicate the data to determine performance scoring and identify repetitive motion issues. However, Kiran fails to disclose the sensor data includes motion data, wherein the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs.
Hawkins discloses the sensor data includes motion data, wherein the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of the sensor data includes motion data, wherein the predetermined set of criteria is satisfied when the motion data indicates a magnitude of acceleration that exceeds approximately 2Gs as taught by Hawkins since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Kiran, and Hawkins teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Elhawary et al. (U.S. Patent 10,772,538 B1) discloses a system and method for monitoring safety and productivity of physical tasks.
Frederick et al. (U.S. Patent Publication 2022/0147889 A1) discloses device, system and method for assessing worker risk.
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/SSS/
Patent Examiner, Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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