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 Claims
This Final action is in reply to the arguments/amendments filed 10/30/2025.
Claims 1 and 9 have been amended.
Claims 2 and 4 have been canceled.
Claims 1, 3 and 6-15 are pending.
Response to Amendments/Arguments
With respect to the 35USC 101 rejection and claims 1-15, applicant argues, “Claims 1 and 9 introduce concrete, integrated, and tangible improvements to a complex technological process by coordinating a distributed network of smart belts, BLE beacons, and an AI server to address a specific buried-worker emergency scenario”; and that, “Claims 1 and 9 do not merely automate a known task-they describe a specific technical solution that improves the functionality of a computerized system. The claimed subject matter implements a non-traditional dual transmission path, in which smart belts intelligently select distinct BLE beacon sets to separately transmit alarm signals and biometric/environmental data based on real-time work environment conditions. This configuration enhances signal reliability and prioritization in obstructed environments”. Applicant subsequently states, “The presently claimed embodiment is not about a generalized concept like "helping workers," but rather a specific, integrated system of smart belts, BLE beacons, and an AI server that collectively solve a real-world technical problem’, and “they include significantly more. The amended details are not generic computer components-they are arranged in a novel way to yield tangible technical benefits… the smart belt does not merely transmit data; it dynamically selects beacon paths based on environmental conditions to optimize emergency signaling-a non-conventional process that improves network performance… They involve a complex, integrated system in which structural, signaling, and processing components work together to process multimodal sensor data, infer spatial distributions of gas and worker position, and compute divergence metrics to prioritize rescue scenarios… The claimed embodiments create a robust emergency response system that could not exist without this specific arrangement of hardware and functional logic”. Applicant’s arguments have been re-considered but are unpersuasive. Applicant’s specification emphasizes a method for utilizing an artificial intelligence system, including a plurality of smart belts and an artificial intelligence server that provides a worker safety control solution. The system includes detecting, by one of the plurality of smart belts, whether a worker is buried, generating, by the smart belt, biometric data and work environment data of the worker, generating, by the smart belt, an alarm signal (based on the biometric data and the work environment data); transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server, and generating a plurality of rescue scenarios by inferring the biometric data and the work environment data when the artificial intelligence server receives a plurality of alarm signals from the plurality of smart belts computing environment (¶5). Applicant’s disclosure further discusses, selecting, by the smart belt, first BLE beacons capable of transmitting a Bluetooth signal from among a plurality of BLE beacons based on work environment data of other workers, and transmitting an alarm signal together with the Bluetooth signal, to the artificial intelligence server through the first BLE beacons; and selecting, by the smart belt second BLE beacons different from the first BLE beacons capable of transmitting the Bluetooth signal from among the plurality of BLE beacons based on work environment data of the other workers, and transmitting biometric data and the work environment data together with the Bluetooth signal to the artificial intelligence server through the second BLE beacons after the alarm signal is transmitted (¶10). Examiner maintains that the claims are directed to the abstract idea for detecting whether a worker is buried, generating and collecting biometric and work environment data of the worker, transmitting an alarm and a plurality of rescue scenarios in a computing environment. The claims are considered an abstract idea because they pertain to certain methods of organizing human activity groupings of abstract ideas (i) mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (collecting work environment data, biometric data of a worker, generating a plurality of rescue scenarios based on the collected data and generating alarm signals); and (ii) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (detecting whether a worker is buried…collecting biometric and wok environment data, transmitting an alarm signal…generating a plurality of rescue scenarios). Claim 1 fails to operate the recited “artificial intelligence system”, “smart belts”, “artificial intelligence server” “BLE beacons”, “Bluetooth signal” “alarm signal” [claim 1]; “”data collection platform”, “a processor” [claim 9] (which are merely standard computer technology and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea recited above in a computing environment —see ¶5, ¶7-¶9, ¶14, ¶32, ¶34, ¶35, ¶37, ¶40, ¶43. Moreover, applicant’s “deep learning model” and “data collection platform” is/are merely being used as a processing tool for receiving data (signals) and based on rules-logic to further collect and process worker biometric and/or work environmental data and output a plurality of rescue scenarios for a worker which is fundamental to implementing the abstract idea and amounts to no more than mere instruction to apply the exception. Further, applicant’s BLE beacons are simply used for communicating and transmitting data (a Bluetooth signal based on work environment data of workers, and an alarm signal) and fails to integrate the abstract idea into a practical application because, there is no improvement to the signal or BLE beacon(s) itself; the additional element is merely used generically to transmit data via common computing components. Hence, the additional elements a “deep learning model”, “plurality of BLE beacons”, “data collection platform” are using old and well-known computing techniques implemented conventionally as a tool(s) to implement the judicial exception, and fails to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, Examiner maintains that the claims limitations are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101- see 2019 PEG and MPEP 2106.
As it relates to the 35USC 103 rejection, applicant states that, “the claims require that the smart belt: selects a first set of BLE beacons based on work environment data of other workers to transmit an alarm signal; selects a second, different set of BLE beacons-again based on work environment data of other workers-to transmit biometric and environmental data; and transmits the biometric and environmental data only after the alarm signal is transmitted. “This dual-path, sequenced transmission logic” is neither taught nor suggested by Martinez or Sobol. The citations to Martinez disclose wearable belts with sensors and data transmission, but do not describe beacon selection logic, let alone a bifurcated transmission path based on environmental context. The citations to Sobol discuss BLE beacon use and machine learning generally, but do not disclose dynamic beacon selection nor do they teach sequencing transmissions based on signal type or environmental data.”, applicant then states, “The Office Action does not identify any teaching in Sobol of selecting different beacon sets for different data types, nor does it explain how Sobol's generic BLE beacon architecture could be adapted to perform the claimed selection and sequencing logic. The cited art lacks any disclosure or suggestion of the claimed beacon selection mechanism, transmission ordering, or environmental-context routing” Applicant’s arguments have been considered but are unpersuasive and necessitated new grounds of rejection since the amendments change the steps of independent claim 1. Further, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “beacon selection logic …a bifurcated transmission path based on environmental context… do not disclose dynamic beacon selection nor do they teach sequencing transmissions based on signal type or environmental data”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
As noted in the Non-Final action, Martinez teaches a wearable belt including a variety of sensors for collecting information about the wearer’s environment and movements to identify activities performed including but not limited to slips, falls and unsafe lifting. The information is used to manage the productivity and safety of a workforce. Sobol discloses a machine learning model architecture/system executed by a wearable electronic device (i.e. belt) to perform various operations related to data input gathering, cleansing or related preprocessing as well as inference generating and related output providing of generated intelligence (predictive service, action plan, messages, alerts, and the like) for users. Sobol also discusses utilizing BLE beacons that allows for providing different warning or alert protocols and action plan relating to a context-based scenario and location-based awareness depending on where the wearable electronic device is in relation to the wearer’s surroundings. Applicant argues, “The citations to Sobol discuss BLE beacon use and machine learning generally, but do not disclose dynamic beacon selection nor do they teach sequencing transmissions based on signal type or environmental data.”, and that, “The Office Action does not identify any teaching in Sobol of selecting different beacon sets for different data types, nor does it explain how Sobol's generic BLE beacon architecture could be adapted to perform the claimed selection and sequencing logic. The cited art lacks any disclosure or suggestion of the claimed beacon selection mechanism, transmission ordering, or environmental-context routing”. Examiner respectfully disagrees and does not consider Sobol to be as limiting as applicant avers. See Sobol at least Fig 1, 3, 4-7, ¶10, ¶112, ¶123-¶126, ¶215, ¶216, ¶237, ¶327 where Sobol further discloses that the monitoring system also includes one or more BLE beacons, a base station and server whereby the wearable electronic device may be operated at least partially independent of other components in order to acquire location data from a GNSS and the BLE beacons. Sobol teaches that the wearable electronic device may acquire location data from BLE beacons for two-way communication to help avoid communication ambiguity between the wearable electronic devices and the forms of BLE beacons. Sobol further discloses that various events may be tracked including location change relative to a BLE beacon position that is closest to the wearable electronic device, different remote computing devices may interact with the application server and cloud and alerts from the wearable electronic device may be sequenced or prioritized based on the relative importance and that the system is able to be trained to identify patterns from the data the arises from ADL events. Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since it helps to avoid communication ambiguity between wearable electronic devices and BLE beacons whereby the differences among various types of beacons may be adjusted/configured/trained for a particular application (location determination/awareness, power, detection and the like) to interact with one or both of the application server and cloud and a person associated with their respective wearable electronic device (¶10, ¶112, ¶123-¶126, ¶215, ¶216, ¶327). Examiner has modified the rejection to further explain how the claim limitations are being interpreted based on applicant’s amendments and addressed each of applicant’s claims as noted below in this Final rejection.
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 1-3 and 6-15 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-3 and 6-15 are directed to a process (an act, or series of acts or steps) [claims 1-3 and 6-8], and a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices) [claims 9-15]. Thus, each of the claims fall within one of the four statutory categories.
Step 2A-Prong 1: Representative independent claim 1 recites in part, “detecting, by one of the plurality of smart belts, whether a worker is buried; generating, by the smart belt, biometric data and work environment data of the worker; generating, by the smart belt, an alarm signal based on the biometric data and the work environment data; transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server; and generating a plurality of rescue scenarios by inferring the biometric data and the work environment data when the artificial intelligence server receives a plurality of alarm signals from the plurality of smart belts; wherein the artificial intelligence system further includes a plurality of BLE beacons, and wherein the transmitting of the alarm signal to the artificial intelligence server includes: selecting, by the smart belt, first BLE beacons capable of transmitting a Bluetooth signal from among the plurality of BLE beacons based on work environment data of other workers; transmitting the alarm signal to the artificial intelligence server through the first BLE beacons using a Bluetooth signal, selecting, by the smart belt, second BLE beacons different from the first BLE beacons capable of transmitting the Bluetooth signal from among the plurality of BLE beacons based on work environment data of the other workers; transmitting the biometric data and the work environment data to the artificial intelligence server through the second BLE beacons using a Blue tooth signal after the alarm signal is transmitted
Claim 9 recites, “a data collection platform configured to collect work environment data and biometric data of a worker from a plurality of smart belts, wherein each of the plurality of smart belts is configured to transmit an alarm signal, the work environment data, and the biometric data to the data collection platform via a plurality of BLE beacons, a processor configured to generate a plurality of rescue scenarios based on the biometric data and the work environment data when alarm signals are received from the plurality of smart belts,wherein the alarm signals are generated based on the work environment data and the biometric data, wherein the work environment data includes information on temperature, humidity, noise, and gas of a work environment, and wherein the biometric data includes information on abdominal pressure, breathing rate, movement, and fall of the worker and wherein transmission via the plurality of BLE beacons includes, for each of the plurality of smart belts: selecting,, first BLE beacons capable of transmitting a Bluetooth signal from among the plurality of BLE beacons based on work environment data of other workers; transmitting the alarm signal to the artificial intelligence server through the first BLE beacons using a Bluetooth signal, selecting, second BLE beacons different from the first BLE beacons capable of transmitting the Bluetooth signal from among the plurality of BLE beacons based on work environment data of the other workers; transmitting the biometric data and the work environment data to the artificial intelligence server through the second BLE beacons using a Blue tooth signal after the alarm signal is transmitted.
The underlined limitations above demonstrate representative independent claim 1 is directed toward the abstract idea for detecting whether a worker is buried, generating and collecting biometric and work environment data of the worker, transmitting an alarm and a plurality of rescue scenarios in a computing environment.
Applicant’s specification emphasizes a method for operating an artificial intelligence system that provides a worker safety control solution and includes a plurality of smart belts and an artificial intelligence server, which includes detecting, by one of the plurality of smart belts, whether a worker is buried, generating, by the smart belt, biometric data and work environment data of the worker, generating, by the smart belt, an alarm signal based on the biometric data and the work environment data, transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server, and generating a plurality of rescue scenarios by inferring the biometric data and the work environment data when the artificial intelligence server receives a plurality of alarm signals from the plurality of smart belts computing environment (¶5).
Representative Claim 1 is considered an abstract idea because the steps for, ““detecting, by one of the plurality of smart belts, whether a worker is buried; generating, by the smart belt, biometric data and work environment data of the worker; generating, by the smart belt, an alarm signal based on the biometric data and the work environment data; transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server; and generating a plurality of rescue scenarios by inferring the biometric data and the work environment data when the artificial intelligence server receives a plurality of alarm signals from the plurality of smart belts; wherein the artificial intelligence system further includes a plurality of BLE beacons, and wherein the transmitting of the alarm signal to the artificial intelligence server includes: selecting, by the smart belt, first BLE beacons capable of transmitting a Bluetooth signal from among the plurality of BLE beacons based on work environment data of other workers; transmitting the alarm signal to the artificial intelligence server through the first BLE beacons using a Bluetooth signal, selecting, by the smart belt, second BLE beacons different from the first BLE beacons capable of transmitting the Bluetooth signal from among the plurality of BLE beacons based on work environment data of the other workers; transmitting the biometric data and the work environment data to the artificial intelligence server through the second BLE beacons using a Blue tooth signal after the alarm signal is transmitted, pertains to certain methods of organizing human activity groupings of abstract ideas (i) mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and (ii) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). The focus of claim 1 as a whole is directed to mental processes since the steps for “detecting,…whether a worker is buried; generating …biometric data and work environment data of the worker; generating…an alarm signal based on the biometric data and the work environment data; transmitting …the alarm signal, the biometric data, and the work environment data …generating a plurality of rescue scenarios by inferring the biometric data and the work environment data … selecting…first BLE beacons …transmitting the alarm signal; selecting …second BLE beacons different from the first BLE beacons…transmitting the biometric data and the work environment data …after the alarm signal is transmitted, are directed to concepts performed in the human mind or by a human being with a pen and paper. With the exception of generic computing components, the limitations are merely utilizing computing components as a tool to perform the process. There is nothing in the claims themselves that foreclose them from being practically performed by a human being with a pen and paper since a human being can observe, evaluate, judge and/or provide an opinion whether a worker is buried, collecting and generating biometric data, work environment data, and a plurality of rescue scenarios based on the collected data and transmitting an alarm. Hence, the claim limitations are directed to the mental processes grouping of abstract ideas. The steps are also directed to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (following rules and/or instructions for collecting data, detecting whether a worker is buried… transmitting an alarm signal…generating a plurality of rescue scenarios). Hence, the claim limitations are directed to certain methods of organizing human activity grouping of abstract ideas--see MPEP 2106.04(II).
Independent claim 9 recites substantially similar limitations as independent claim 1, therefore, it is also abstract based on the same rationale as independent claim 1.
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “artificial intelligence system”, “smart belts”, “artificial intelligence server” “alarm signal” “plurality of BLE beacons” [claim 1]; “data collection platform”, “a processor” [claim 9], merely provide an abstract-idea based solution using data gathering and analysis and merely provide instructions for organizing human activity, and implementing the abstract idea recited above utilizing the “artificial intelligence system”, “smart belts”, “artificial intelligence server” “alarm signal” “plurality of BLE beacons” [claim 1]; “data collection platform”, “a processor” [claim 9], as tools to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h).
Independent claim 1 fails to operate the recited “artificial intelligence system”, “smart belts”, “artificial intelligence server” “alarm signal” “plurality of BLE beacons” [claim 1]; “data collection platform”, “a processor” [claim 9] (which are merely standard computer technology and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea recited above in a computing environment —see MPEP 2106.05(a). Further applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). Applicant’s limitations as recited above do nothing more than supplement the abstract idea using generic processing and networking components performing generic computer functions (detecting, collecting, transmitting, generating, selecting) such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a).
Dependent claims 2,3, 6-8 and 10-15 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2,3, 6-8 and 10-15 recite additional data gathering and processing steps. For example dependent claim recites in part, “…wherein the artificial intelligence server includes…”; claim 3 recites in part, “wherein the work environment data includes information on”, claim 6 recites in part, “wherein the generating of the target rescue scenario by inferring the information on the gas includes:”; claim 7 recites in part, “wherein the biometric data includes information on”; claim 8 recites in part, “ wherein the target rescue scenario is a rescue scenario first generated by the processor among the plurality of rescue scenarios based on”; claim 10 recites in part, “wherein, when a first alarm signal among the alarm signals is received”, claim 11 recites in part, “wherein the processor sequentially generates the plurality of rescue scenarios based on”, claim 12 recites in part, “wherein the processor receives one of a rescue signal or a distress signal from each of the plurality of smart belts”, claim 13 recites in part, “wherein the inferring of the information on the gas includes: “, claim 14 recites in part, “wherein the generating of the target rescue scenario further includes”, claim 15 recites in part, “wherein the target rescue scenario is a rescue scenario first generated by the processor”, which are still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components.
The additional elements in the dependent claims “deep learning model”, amounts to no more than applying the judicial exception using generic computing components, linking the use of the judicial exception to a computing environment. In this case, the “deep learning model” is generically used to further process and store data and fails to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Hence is nonetheless directed towards fundamentally the same abstract idea as their respective independent claim since they fail to impose any meaningful limits on practicing the abstract idea. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to 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 above, with respect to integration of the abstract idea into a practical application, the additional elements “artificial intelligence system”, “smart belts”, “artificial intelligence server” “alarm signal” “plurality of BLE beacons” [claim 1]; “data collection platform”, “a processor” [claim 9], amount to no more than mere instructions to apply the exception using a generic computer component - see ¶5: “detecting, by one of the plurality of smart belts, whether a worker is buried, generating, by the smart belt, biometric data and work environment data of the worker, generating, by the smart belt, an alarm signal based on the biometric data and the work environment data, transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server”; ¶7: ““the artificial intelligence server may include a processor including a deep learning model for inferring the biometric data and the work environment data, and the generating of the plurality of rescue scenarios may include inferring first work environment data and first biometric data, when the processor receives a first alarm signal from among the plurality of alarm signals”; ¶9: “the artificial intelligence system may further include a plurality of BLE beacons …first BLE beacons capable of transmitting a Bluetooth signal from among the plurality of BLE beacons based on work environment data of other workers, and transmitting the alarm signal together with the Bluetooth signal to the artificial intelligence server”; ¶14: “an artificial intelligence server which provides a worker safety control solution, which includes a data collection platform that collects work environment data and biometric data of a worker from a plurality of smart belts”; ¶32: “the artificial intelligence system 100 includes an artificial intelligence server 110 and a smart belt 120”; ¶32: “The artificial intelligence server 110 may include a data collection platform 111 and a processor 112”; ¶34: “The data collection platform 111 may collect data from external devices or external systems. The data collection platform 111 may include a memory (not illustrated) for temporarily storing received data. However, without being limited thereto, the data collection platform 111 may include any means including a storage space capable of collecting and storing data. The data collection platform 111 may collect data from external devices or external systems. The data collection platform 111 may include a memory (not illustrated) for temporarily storing received data. However, without being limited thereto, the data collection platform 111 may include any means including a storage space capable of collecting and storing data”; ¶35: “The processor 112 may drive the operating system and applications of the artificial intelligence system 100 by loading data from the data collection platform 111 and performing a data processing operation”; ¶40: “The smart belt 120 may include a multi-modal sensor 121, an alarm device 122, a control logic 123, a communication network 124, an SOS button 125, and a low-frequency sound generator 126”; ¶43: “The control logic 123 may control all components of the smart belt 120 and overall operations of the smart belt 120. For example, the control logic 123 may transmit a first control signal to the alarm device 122 such that the alarm device 122 may generate an alarm signal based on the biometric data and the work environment data”). Further, the “plurality of BLE beacons” are used for communicating and transmitting data (a Bluetooth signal based on work environment data of workers, and an alarm signal) and fails to integrate the abstract idea into a practical application because, there is no improvement to the signal or BLE beacon(s) itself; the additional element is merely used generically to transmit data via common computing components Examiner notes that the “artificial intelligence system”, “smart belts”, “artificial intelligence server” “alarm signal” “plurality of BLE beacons” [claim 1]; “data collection platform”, “a processor” [claim 9], recited in the claim limitations are generic computing components used as a tool (communication between an electronic device (smart belt), server, data collection platform, processor) using well-understood, routine, conventional activities previously known to the industry to apply the abstract idea for detecting whether a worker is buried, generating and collecting biometric and work environment data of the worker, transmitting an alarm and a plurality of rescue scenarios which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea).
The additional elements in the dependent claims including applicant’s “deep learning model” also amount to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. In this case, the ““deep learning model” is also generically used to further process received data/information via common computing components (see applicant’s specification ¶7: “the artificial intelligence server may include a processor including a deep learning model for inferring the biometric data and the work environment data”; ¶37: “The deep learning model 112a may output a target rescue scenario when receiving specific biometric data and work environment data”). Hence the deep learning model is merely being used as a processing tool for receiving data (signals) and based on rules-logic to further process worker biometric and/or work environmental data and output a plurality of rescue scenarios for a worker which is fundamental to implementing the abstract idea and amounts to no more than mere instruction to apply the exception using generic computer components and fails to integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for detecting whether a worker is buried, generating and collecting biometric and work environment data of the worker, transmitting an alarm and a plurality of rescue scenarios in a computing environment. Hence, claims 1-3 and 6-15 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101. See 2019 PEG and MPEP 2106.
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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1,3 and 6-15 are rejected under 35 U.S.C. 103 as being unpatentable over Martinez et al., US Patent Application Publication No US 2020/0174517 A1 in view of Sobol et al., US Patent Application Publication No US 2021/0319894 A1.
With respect to claims 1 and 9,
Martinez discloses,
detecting, by one of the plurality of smart belts, whether a worker is buried (Abstract: “a belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements… data is processed to identify activities performed by the wearer such as walking, driving, and working at heights. In some examples, events such as aggressive driving events, slips and falls, and unsafe lifting are detected”; ¶55: “a wearable belt that, when worn by a subject, collects data regarding a subject (such as a person) and the subject's environment… the sensors include sensors that monitor environmental conditions such as temperature, humidity, altitude, sound exposure, and geographic location. In another example, the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken”; ¶61 “Activities detected may include driving activities, working from height activities, lifting activities, walking jogging and running activities, traversing stairs, sleeping, tripping, falling, bending, twisting, pushing, and idle activities”;¶66 “The server 108 receives information from the belt 102 and, in some examples, from one or more additional belts”; Fig 6, ¶94: “each belt in the set of belts 602 connects to a Web server 608. The Web server 608 is connected to an application server 610 which maintains a log database 612 and a database of sensor data 614”; ¶104: “the data server processes the sensor data from each belt to identify activities performed by the wearer of each belt. The activities may include activities such as driving, working from heights, and other activities …the activity information associated with each belt is used to synthesize 912 a set of measures that are attributable to the wearer. The measures may include things such as amount of time spent idle”) Applicant’s disclosure discusses at ¶85: “the smart belt 120 may detect whether the worker is buried due to the collapse of the building based on the sensing data of the multi-modal sensor 121. The sensing data may include information about temperature, humidity, and brightness of the buried environment.; ¶87: “the multi-modal sensor 121 may obtain the worker's biometric data and work environment data. The worker's work environment data may include information about temperature, humidity, noise, and gas in the work environment, and the worker's biometric data may include information about the worker's abdominal pressure, breathing rate, movement, and fall”. Martinez discloses a wearable belt that, when worn by a subject, collects data regarding a subject and the subject's environment via sensors, including detecting various activities of a user including an amount of time spent idle. Examiner interprets activity information associated with a belt measuring the amount of time spent idle and/or idle activities of a worker as teaching applicant’s “detecting… whether a worker is buried”.
a data collection platform configured to collect work environment data and biometric data of a worker from a plurality of smart belts (Abstract: “A belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements”; ¶56: “the belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones)... the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”;¶83: “the sensors on the belt strap may include temperature sensors, humidity sensors, moisture sensors, microphones, audible speakers, accelerometers, altimeters, atmospheric sensors, chemical contamination sensors, radiation sensors, and environmental sensors”)
generating, by the smart belt, biometric data and work environment data of the worker (Abstract: “A belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements”; ¶56: “the belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones)... the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”;¶83: “the sensors on the belt strap may include temperature sensors, humidity sensors, moisture sensors, microphones, audible speakers, accelerometers, altimeters, atmospheric sensors, chemical contamination sensors, radiation sensors, and environmental sensors”)
generating, by the smart belt, an alarm signal based on the biometric data and the work environment data (¶111: “the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor”; ¶112: “temperature and humidity monitoring may be used to detect and alert users of abnormal changes”; ¶113: “Working from height, even as low as knee height, may be considered a risk in some environments, and the sensors are able to determine if such a risk exists”; ¶178: “the belt provides a customizable alert system. Based on the safety concerns of each industry, there is a need to focus on some specific activities like extreme bending, twisting, or driving. The belt incorporates the provision to set the user alert functionality based on requirement. In some examples, the alert occurs in the form of a buzz. If an employee is doing an activity which is not considered safe as per the specific industry rules, the buzzer will be triggered, conveying to the employees that they need to alter the method of performing a certain activity”; claims 6-14)
a data collection platform configured to collect work environment data and biometric data of a worker from a plurality of smart belts (¶55: “cause the belt to collect data from a collection of sensors on the belt. In some examples, the sensors include sensors that monitor environmental conditions such as temperature, humidity, altitude, sound exposure, and geographic location. In another example, the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken… in a business environment, a manager is able to use the system to acquire information regarding the productivity and safety of both individual workers and the workforce as a whole; ¶56: “the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”; ¶57: “the belt may connect to remote environmental sensors in the vicinity of the subject via an infrared, radiofrequency, Bluetooth, or Wi-Fi connection”; Fig 4, ¶88: “the belt records metadata associated with the piece of data such as environmental conditions at the time the data was read, a resolution and scale for the data, a name or an identifier associated with the sensor, or other metadata”; ¶94: “In an embodiment, each belt in the set of belts 602 connects to a Web server 608. The Web server 608 is connected to an application server 610 which maintains a log database 612 and a database of sensor data 614”; Fig 13, ¶118: “data analysis models for activity classification are based at least in part on a collection of distributed sensors on an electronic device, such as a belt …a collection of distributed sensors is used to classify workplace events such as slips, trips, falls, collisions, walking, running, lifting weights, drilling, painting, pushing, pulling heavy objects and so on. When a belt containing the sensors is worn by a user, the sensors, which may include sensors such as an accelerometer, gyroscope, magnetometer, pressure sensor, and altimeter, start capturing sensor data 1316. The sensor data 1316 is collected by the sensor data acquisition component 1314. In an embodiment, the sensor data is transferred to the sensor data store 1306 via the data orchestration component 1312. In some embodiments, the sensor data store 1306 is a storage service accessible via an online service”)
wherein each of the plurality of smart belts is configured to transmit an alarm signal, the work environment data, and the biometric data to the data collection platform via a plurality of BLE beacons (¶111: “the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor”; ¶112: “temperature and humidity monitoring may be used to detect and alert users of abnormal changes”; ¶113: “Working from height, even as low as knee height, may be considered a risk in some environments, and the sensors are able to determine if such a risk exists”; ¶178: “the belt provides a customizable alert system. Based on the safety concerns of each industry, there is a need to focus on some specific activities like extreme bending, twisting, or driving. The belt incorporates the provision to set the user alert functionality based on requirement. In some examples, the alert occurs in the form of a buzz. If an employee is doing an activity which is not considered safe as per the specific industry rules, the buzzer will be triggered, conveying to the employees that they need to alter the method of performing a certain activity”; claims 6-14)
a processor configured to generate a plurality of rescue scenarios based on the biometric data and the work environment data when alarm signals are received from the plurality of smart belts (Abstract: “A belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements. A communication interface on the belt allows sensor data collected by the belt to be transferred to a storage server”; ¶56: “Various embodiments of the belt may include different configurations of sensors. In one embodiment, the belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones). In another embodiment, the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”; ¶60: “the belt device includes a processor and memory containing executable instructions that, as a result of being executed by the processor, transform the data or process the data into new data which is stored in a writable memory on the belt device. The new data or transform data may then be transmitted to a remote server for additional processing”; ¶65: “The server 108 receives information from the belt 102 and, in some examples, from one or more additional belts”; ¶68: “The server 108 may provide event notifications to the administrator 112. Notifications may be provided for events such as unsafe driving events, fall events, excessive idle time events, injury events, or medical emergency events. In one example, the administrator 112 is able to define a bounded geographical area, and events are provided when the subject 104 enters or exits the bounded geographical area”; Fig 4, ¶88: “the belt records metadata associated with the piece of data such as environmental conditions at the time the data was read, a resolution and scale for the data, a name or an identifier associated with the sensor, or other metadata”; ¶111: “In some embodiments, pressure sensors are used to act as strain sensors… the information on strain helps determine the risk of injury the wearer is exposed to. To address this limitation, pressures at multiple points on the belt (which is generally to be proportional to strain) is used to model risk of back injury… In some examples, the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor)”; ¶112: “Temperature and humidity sensors continuously monitor the temperature and humidity where the employee is working. The temperature and humidity readings will be correlated with productivity and safety to see if temperature or humidity is a dependent factor. In some examples, temperature and humidity monitoring may be used to detect and alert users of abnormal changes in battery temperature and, if necessary, turn off the battery”; Fig 13)
wherein the alarm signals are generated based on the work environment data and the biometric data, wherein the work environment data includes information on temperature, humidity, noise, and gas of a work environment, and wherein the biometric data includes information on abdominal pressure, breathing rate, movement, and fall of the worker (¶55: “the sensors include sensors that monitor environmental conditions such as temperature, humidity, altitude, sound exposure, and geographic location. In another example, the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken”; ¶56: “belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones). In another embodiment, the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”; ¶110: “the belt uses FSRs to obtain strain data around the waist of a person. FSRs can sense the amount of pressure applied on their surface, which is proportional to the amount of strain at that location”; ¶111: “there are a number of ways that the accuracy of sensor data collected is improved even if the belt is not being properly worn. In some examples, the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor)”; ¶113: “Altimeter and motion sensors data will be used to approximately determine when, how long, and at what height a person is working, when he/she is working from heights, be it indoors or outdoors… ultrasonic ranging sensors or a radar altimeter may be used to determine height above the ground level. Working from height, even as low as knee height, may be considered a risk in some environments, and the sensors are able to determine if such a risk exists”; ¶118: “data analysis models for activity classification are based at least in part on a collection of distributed sensors on an electronic device, such as a belt …a collection of distributed sensors is used to classify workplace events such as slips, trips, falls, collisions, walking, running, lifting weights, drilling, painting, pushing, pulling heavy objects and so on. When a belt containing the sensors is worn by a user, the sensors, which may include sensors such as an accelerometer, gyroscope, magnetometer, pressure sensor, and altimeter, start capturing sensor data 1316. The sensor data 1316 is collected by the sensor data acquisition component 1314”)
Martinez discloses all of the above limitations, Martinez does not distinctly describe the following limitations, but Sobol however as shown discloses,
transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server (¶2: “a wearable electronic device and corresponding system for monitoring one or more of location, environmental, activity and physiological (LEAP) data of a wearer of the device, and more particularly to a wearable electronic device that communicates such data through a wireless cellular low power wide area network (LPWAN) in order to allow such data to be used to identify one or more of location, health, safety or other indicia pertaining to the wearer of the device”; ¶10: “The set of machine codes includes a machine code to receive location data from the wireless communication module, a machine code to receive at least one of environmental data, activity data and physiological data from at least one of a plurality of sensors that are signally cooperative with the wearable electronic device, a machine code to perform at least a portion of a machine learning model based on at least one of the received LEAP data such that the machine learning model produces an output based on such data and a machine code to transmit at least one of the received LEAP data and output of the machine learning model through the wireless communication module as the low power wide area network signal using at least a cellular-based wireless protocol”; ¶72: “the receipt and transmission of location data is understood to be selective insofar as an incoming signal (in the case of received location data) from a remote source is detectable by the wearable electronic device, or when an outgoing signal (in the case of transmitted location data) from the wearable electronic device is detectable by a remote cellular tower, server, base station or related receiver”; ¶164: “the wearable electronic device 100 may be configured to have TinyML or edge functionality. In such case, it can operate to perform ML and other artificial intelligence (AI)-like functions for IoT-related applications and may include at least some attributes of the aforementioned SOC, ASIC, FPGA and GPU chipsets. Such a configuration helps facilitate deep learning-level machine learning while still preserving autonomous, on-board intelligence-generating capability”; ¶170: “processor or processors 173A may be programmed to perform machine learning functions, such as through a trained artificial neural network to determine, among other things, whether a patient associated with a particular wearable electronic device 100 is at risk of developing an infection or other adverse health condition”; ¶203: “the wearable electronic device 100 may include a screen formed in the top plate 130 such that the screen is capable of displaying information collected by wearable electronic device 100, including any alerts generated”; ¶206: “Referring with particularity to FIG. 2I, compared to the stand-alone, wristband-based platform with the rigid structural housing 110 of FIGS. 2A through 2H, FIG. 2I depicts a flexible platform 190 that can be worn, imbedded in or affixed to the patient's skin, clothing or accessories such as belts, shoes, hats, eyeglasses or the like”; ¶217: “The inclusion of machine learning functionality by the wearable electronic device 100—especially when at least some of such functionality is performed in a TinyML, edge-like or fog-like manner via its on-band capability—further may be used to increase the efficiencies associated with operating a nursing home, hospital, assisted living or related patient healthcare facility, as well as the operation of industrial-based or other enterprises such as those mentioned elsewhere in the present disclosure”;)
Applicant’s disclosure teaches an artificial intelligence server comprising a deep learning model and a data collection platform ¶33-¶36. Examiner interprets at least the machine learning functions, (i.e. through a trained artificial neural network (including deep-learning level machine learning) to determine, risk(s) associated with a particular wearable electronic device, whereby at least the machine code receiving at least one of environmental data, activity data and physiological data from at least one of a plurality of sensors that are signally cooperative with the wearable electronic device, may include a screen capable of displaying/outputting/transmitting information (LEAP data) collected by wearable electronic device (i.e. belt) including outputting/transmitting any alerts generated as taught by Sobol as teaching applicant’s limitation, “transmitting, by the smart belt, the alarm signal, the biometric data, and the work environment data to the artificial intelligence server”.
generating a plurality rescue scenarios by inferring the biometric data and the work environment data when the artificial intelligence server receives a plurality of alarm signals from the plurality of smart belts (Abstract: “The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device”; ¶22: “an embodiment of the first aspect may include configuring the machine learning model to determine a health condition of the individual to include… an inference module for indicating a likelihood that the individual suffers from agitation based on a trained machine learning model that is formed by data contained within at least one of the reference database storing module, preprocessing module and feature extracting module”; ¶36: “executing a clinical intervention to reduce the likelihood that the individual is suffering from at least one change in the health condition”;configuring at least one of a network base station, a network server and an application server to receive at least one component or portion of LEAP data that corresponds to the individual and that has been transmitted by the wearable electronic device over a cellular-based LPWAN signal, performing at least one security operation upon the received data and transmitting the received data that has been subjected to the at least one security operation to at least one of the wearable electronic device and a remote computing device”; ¶164: “the wearable electronic device 100 may be configured to have TinyML or edge functionality. In such case, it can operate to perform ML and other artificial intelligence (AI)-like functions for IoT-related applications and may include at least some attributes of the aforementioned SOC, ASIC, FPGA and GPU chipsets. Such a configuration helps facilitate deep learning-level machine learning while still preserving autonomous, on-board intelligence-generating capability”; ¶170: “processor or processors 173A may be programmed to perform machine learning functions, such as through a trained artificial neural network to determine, among other things, whether a patient associated with a particular wearable electronic device 100 is at risk of developing an infection or other adverse health condition”; ¶207: “Electronically, the logic device 173 and the corresponding portion of the machine code may be configured to take context-based scenarios into consideration… location-based awareness may include instructing the wearer to take a particular course of action such as a direction to walk, going back to bed, staying put or the like. The alert or warning protocols may including sending audio or visual messages to the wearer”; ¶216: “with the gyroscopes, accelerometers, impact sensors and other forms of sensors 121, fall detection alerts are also conveyed automatically and in real-time”; ¶217: “The inclusion of machine learning functionality by the wearable electronic device 100—especially when at least some of such functionality is performed in a TinyML, edge-like or fog-like manner via its on-band capability—further may be used to increase the efficiencies associated with operating a nursing home, hospital, assisted living or related patient healthcare facility, as well as the operation of industrial-based or other enterprises such as those mentioned elsewhere in the present disclosure”; ¶220: “provides a method of performing analysis of the data collected by the wearable electronic device 100 in order to automate the building of data-driven clinical decision-making models with limited human intervention”; ¶366: “one or more signals associated with the LEAP data are detected by one or more of the sensors 121 and the first and second wireless communication sub-modules 175A, 175B. These and additional data—as well as the inferring of one or more criteria associated with a particular health condition—may be used to provide CDS that in turn may correlate to an action plan or related therapy recommendation”)
wherein the artificial intelligence system further includes: a plurality of BLE beacons (Fig 1, ¶112: “the system 1 includes one or more of a wearable electronic device 100, one or more BLE beacons 200, a base station (also referred to as an access point, base transceiver station, radio base station or cell, typically including in 3GPP parlance one or more of a Node B, evolved NodeB, eNodeB, eNB (in LTE), or gNodeB, gNB) 300 to perform direct communication with the wearable electronic device 100, as well as a server 400 the last of which may function as at least a portion of a carrier-grade data network that includes an IoT service capability, in which case the server may be considered to have an IoT service capability server/application server (SCS/AS) functionality. In another form, the wearable electronic device 100 may be operated at least partially independent of other components within the system 1 in order to acquire—among other types of data—location data from a GNSS 10 and the BLE beacons 200”)
wherein the transmitting of the alarm signal to the artificial intelligence server includes: selecting, by the smart belt, first BLE beacons capable of transmitting a Bluetooth signal from among the plurality of BLE beacons based on work environment data of other workers; and transmitting the alarm signal together with the Bluetooth signal, to the artificial intelligence server through the first BLE beacons (¶10: “machine codes includes a machine code to receive location data from the wireless communication module, a machine code to receive at least one of environmental data, activity data and physiological data from at least one of a plurality of sensors that are signally cooperative with the wearable electronic device, a machine code to perform at least a portion of a machine learning model based on at least one of the received LEAP data such that the machine learning model produces an output based on such data and a machine code to transmit at least one of the received LEAP data and output of the machine learning model through the wireless communication module as the low power wide area network signal using at least a cellular-based wireless protocol”; Fig 1, ¶112: “an overview of the patient monitoring system 1 architecture is shown. In one form, the system 1 includes one or more of a wearable electronic device 100, one or more BLE beacons 200, a base station (also referred to as an access point, base transceiver station, radio base station or cell, typically including in 3GPP parlance one or more of a Node B, evolved NodeB, eNodeB, eNB (in LTE), or gNodeB, gNB) 300 to perform direct communication with the wearable electronic device 100, as well as a server 400 the last of which may function as at least a portion of a carrier-grade data network that includes an IoT service capability, in which case the server may be considered to have an IoT service capability server/application server (SCS/AS) functionality. In another form, the wearable electronic device 100 may be operated at least partially independent of other components within the system 1 in order to acquire—among other types of data—location data from a GNSS 10 and the BLE beacons 200”; ¶123 “transmit-only functionality of the room beacons 200A and the elopement beacons 200B in their communication with the one or more of the various wearable electronic devices 100 promotes lower-cost and less complicated installation and maintenance than in situations where two-way communication between the wearable electronic devices 100 and the BLE beacons 200 may be present. Such an arrangement also helps to avoid communication ambiguity between the wearable electronic devices 100 and these two forms of the BLE beacons 200”; ¶125: “When the dedicated external device application recognizes the wearable electronic device 100 as a nearby BLE-enabled device, the application links the device to an action or piece of content (such as that which may be stored in the cloud 500) and displays it to a local or suitably-connected remote user. In one form, an online-based approach lets a user manage, configure and update BLE beacons 200 and their profiles”; ¶126: “one or more room beacons 200A each send out their respective UUIDs at regular intervals (such as about ten times every second, although depending on the settings, such frequency can be increased or decreased). In addition to using the UUID or related identifier as a way to acquire relative location between the BLE beacon 200 and the wearable electronic device 100, when a dedicated application that has been set up on the wearable electronic device 100 as well as other remote computing devices 900 of FIGS. 4 and 5 recognizes the UUID, it links the location-based information from the RSSI signal to the SCS/AS 400, cloud 500 or the like that in turn can be sent (such as over the internet) for display to a caregiver, family member or other interested party that has a suitably-equipped application on their own remote computing device 900”; ¶215: “various events may be tracked, including (1) location change relative to a BLE beacon 200 position that is closest to the wearable electronic device 100… the various different remote computing devices 900 may be made to interact with one or both of the application server 420 and cloud 500 in order to be notified of events or to check on a person associated with the wearable electronic device 100”; ¶216: the transmission of alerts from the wearable electronic device 100 may be sequenced or prioritized based on the relative importance of the type of caregiver C to the individual that is sending the alert, by proximity of the various caregivers C to the wearable electronic device 100, or by some other approach“; ¶327: “if more fine-grained detection is needed, there may be multiple such BLE beacons 200 arranged in various places within the bathroom BR in order to improve the spatio-temporal nature of the data being collected… when used in a machine learning mode of operation, the system 1 of the present disclosure is able to learn to extract features, as well as to be trained to identify patterns from the data that arises from ADL events in an experiential and ad hoc way”)
selecting, by the smart belt, second BLE beacons different from the first BLE beacons capable of transmitting the Bluetooth signal from among the plurality of BLE beacons based on work environment data of the other workers (¶10: “machine codes includes a machine code to receive location data from the wireless communication module, a machine code to receive at least one of environmental data, activity data and physiological data from at least one of a plurality of sensors that are signally cooperative with the wearable electronic device, a machine code to perform at least a portion of a machine learning model based on at least one of the received LEAP data such that the machine learning model produces an output based on such data and a machine code to transmit at least one of the received LEAP data and output of the machine learning model through the wireless communication module as the low power wide area network signal using at least a cellular-based wireless protocol”; ¶123: “transmit-only functionality of the room beacons 200A and the elopement beacons 200B in their communication with the one or more of the various wearable electronic devices 100 promotes lower-cost and less complicated installation and maintenance than in situations where two-way communication between the wearable electronic devices 100 and the BLE beacons 200 may be present. Such an arrangement also helps to avoid communication ambiguity between the wearable electronic devices 100 and these two forms of the BLE beacons 200”; FIGS. 2A, 2H, ¶124: “should an individual press a nurse call button 131 ….located on the wearable electronic device 100, the corresponding panic, distress or related request for assistance is sent from the wearable electronic device 100, through the base station 300 and one or more of the SCS/AS 400 and cloud 500 and to patient-monitoring equipment such as the remote computing device 900 in order to then have a message sent (for example, over a wireless internet, WiFi, cellular, wired telephone or other suitable connection) to the various nurse ID beacons 200C so that one that is in closest proximity or readily-available to lend assistance may do so. functional difference among the various types of beacons 200A, 200B and 200C may be in their transmit power, identifier or the like. It will be appreciated that these and other similarities and differences may be adjusted in order to configure the BLE beacon 200 for a particular application, and that all such variants are deemed to be within the scope of the present disclosure. For example, the room beacon 200A may be arranged to cover a relatively large area such as a room, and as such may transmit at a power sufficient to cover the entirety of such room as part of an RSSI or other distance-based finding approach”; ¶215: “various events may be tracked, including (1) location change relative to a BLE beacon 200 position that is closest to the wearable electronic device 100… the various different remote computing devices 900 may be made to interact with one or both of the application server 420 and cloud 500 in order to be notified of events or to check on a person associated with the wearable electronic device 100”)
transmitting the biometric data and the work environment data together with the Bluetooth signal to the artificial intelligence server through the second BLE beacons after the alarm signal is transmitted (¶10: “machine codes includes a machine code to receive location data from the wireless communication module, a machine code to receive at least one of environmental data, activity data and physiological data from at least one of a plurality of sensors that are signally cooperative with the wearable electronic device, a machine code to perform at least a portion of a machine learning model based on at least one of the received LEAP data such that the machine learning model produces an output based on such data and a machine code to transmit at least one of the received LEAP data and output of the machine learning model through the wireless communication module as the low power wide area network signal using at least a cellular-based wireless protocol”; ¶124: “should an individual press a nurse call button 131 (also referred to as a help button, and that will be discussed in more detail in conjunction with FIGS. 2A and 2H) that is located on the wearable electronic device 100, the corresponding panic, distress or related request for assistance is sent from the wearable electronic device 100, through the base station 300 and one or more of the SCS/AS 400 and cloud 500 and to patient-monitoring equipment such as the remote computing device 900 in order to then have a message sent (for example, over a wireless internet, WiFi, cellular, wired telephone or other suitable connection) to the various nurse ID beacons 200C so that one that is in closest proximity or readily-available to lend assistance may do so. Another functional difference among the various types of beacons 200A, 200B and 200C may be in their transmit power, identifier or the like. It will be appreciated that these and other similarities and differences may be adjusted in order to configure the BLE beacon 200 for a particular application, and that all such variants are deemed to be within the scope of the present disclosure. For example, the room beacon 200A may be arranged to cover a relatively large area such as a room, and as such may transmit at a power sufficient to cover the entirety of such room as part of an RSSI or other distance-based finding approach. In this type of operation, the room beacon 200A sends an identifier such as a universally unique identifier (UUID) or similar data package, so that the wearable electronic device 100 may measure RSSI and use this measurement to assist with location determination of the relative position between them”; ¶216: the transmission of alerts from the wearable electronic device 100 may be sequenced or prioritized based on the relative importance of the type of caregiver C to the individual that is sending the alert, by proximity of the various caregivers C to the wearable electronic device 100, or by some other approach“; ¶327: “if more fine-grained detection is needed, there may be multiple such BLE beacons 200 arranged in various places within the bathroom BR in order to improve the spatio-temporal nature of the data being collected… when used in a machine learning mode of operation, the system 1 of the present disclosure is able to learn to extract features, as well as to be trained to identify patterns from the data that arises from ADL events in an experiential and ad hoc way”)
Martinez teaches a wearable belt including a variety of sensors for collecting information about the wearer’s environment and movements to identify activities performed including but not limited to slips, falls and unsafe lifting. The information is used to manage the productivity and safety of a workforce.
Sobol discloses a machine learning model architecture/system executed by a wearable electronic device (i.e. belt) to perform various operations related to data input gathering, cleansing or related preprocessing as well as inference generating and related output providing of generated intelligence (predictive service, action plan, messages, alerts, and the like) for users. Sobol further discloses that the monitoring system also includes one or more BLE beacons, a base station and server whereby the wearable electronic device may be operated at least partially independent of other components in order to acquire location data from a GNSS and the BLE beacons. Sobol teaches that the monitoring system also includes one or more BLE beacons, a base station and server whereby the wearable electronic device may be operated at least partially independent of other components in order to acquire location data from a GNSS and the BLE beacons. Sobol further teaches that the wearable electronic device may acquire location data from BLE beacons for two-way communication to help avoid communication ambiguity between the wearable electronic devices and the forms of BLE beacons. Sobol discusses that various events may be tracked including location change relative to a BLE beacon position that is closest to the wearable electronic device, different remote computing devices may interact with the application server and cloud and alerts from the wearable electronic device may be sequenced or prioritized based on the relative importance and that the system is able to be trained to identify patterns from the data the arises from ADL events. Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since it helps to avoid communication ambiguity between wearable electronic devices and BLE beacons whereby the differences among various types of beacons may be adjusted/configured/trained for a particular application (location determination/awareness, power, detection and the like) to interact with one or both of the application server and cloud and a person associated with their respective wearable electronic device (¶10, ¶112, ¶123-¶126, ¶215, ¶216, ¶327).
With respect to claims 2 and 10,
Martinez and Sobol disclose all of the above limitations, Sobol further discloses,
wherein the artificial intelligence server includes a processor including a deep learning model for inferring the biometric data and the work environment data (¶2: “a wearable electronic device and corresponding system for monitoring one or more of location, environmental, activity and physiological (LEAP) data of a wearer of the device”; ¶22: “an embodiment of the first aspect may include configuring the machine learning model to determine a health condition of the individual to include… an inference module for indicating a likelihood that the individual suffers from agitation based on a trained machine learning model that is formed by data contained within at least one of the reference database storing module, preprocessing module and feature extracting module”; ¶70: “execute an inference such that the machine learning model provides a predictive analytical output using a portion of acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model”; ¶110: “it will be appreciated that the wearable electronic device and its associated support or peripheral systems and components may be used for other applications as well, such as being worn by an individual (including a nurse, firefighter, police officer, soldier, construction site worker, athlete, factory/manufacturing worker, oil, gas or coal worker or the like) to provide accurate safety-determination or location-determination in potentially dangerous, architecturally-hardened (such as a jail with thick concrete walls) or otherwise hard-to-track locations, as well as to assess employee productivity, safety compliance and related metrics that may be of interest to an employer or operator of a facility, site, dwelling, hospital, retail environment, hospitality or other enterprise where personnel monitoring may be beneficial… at least a portion of a machine learning model may be included on the wearable electronic device 100 as a way to correlate the acquired data to an output corresponding to the health, safety, efficiency, productivity or other performance metric of the individual. Within the present context, issues related to the safety or health of a person associated with the wearable electronic device 100 are those that can be identified and acted upon by the wearable electronic device 100… the wearable electronic device 100 may include a gas sensor such that if a wearer on a construction site encounters a harmful gaseous environment on the site, upon detection of the presence of the gas by the sensor, the wearable electronic device 100 reacts to inform the wearer, as well as to send corresponding data to a remote operator”; ¶164: “the wearable electronic device 100 may be configured to have TinyML or edge functionality. In such case, it can operate to perform ML and other artificial intelligence (AI)-like functions for IoT-related applications and may include at least some attributes of the aforementioned SOC, ASIC, FPGA and GPU chipsets. Such a configuration helps facilitate deep learning-level machine learning while still preserving autonomous, on-board intelligence-generating capability”; ¶169: “the wearable electronic device 100 may be used in conjunction with a model (such as a machine learning model as discussed in more detail later) to compute predicted outcomes derived from the data being acquired”; ¶170: “processor or processors 173A may be programmed to perform machine learning functions, such as through a trained artificial neural network to determine, among other things, whether a patient associated with a particular wearable electronic device 100 is at risk of developing an infection or other adverse health condition”)
wherein the generating of the plurality of rescue scenarios includes inferring first work environment data and first biometric data, when the processor receives a first alarm signal from among the plurality of alarm signals wherein, when a first alarm signal among the alarm signals is received, the processor infers first biometric data and first work environment data included in the first alarm signal (Abstract: “The device may also include sensors to collect one or more of environmental, activity and physiological data. The device may transmit some or all of its acquired data to the system to provide a predictive model to correlate changes in the acquired data to corresponding health, safety or related changes to a wearer of the device. In one form, the predictive health care protocol uses a machine learning model at least some of which may be performed on the device”; ¶60: “configuring at least one of a network base station, a network server and an application server to receive at least one component or portion of LEAP data that corresponds to the individual and that has been transmitted by the wearable electronic device over a cellular-based LPWAN signal, performing at least one security operation upon the received data and transmitting the received data that has been subjected to the at least one security operation to at least one of the wearable electronic device and a remote computing device”;¶162: “FIGS. 2F and 2G in conjunction with FIG. 1, the electronic components that make up the power, processing, communication and sensing functions of the wearable electronic device 100 are shown in a disassembled (exploded) view in FIG. 2F and an as-assembled view in FIG. 2G… each of the various wireless communication sub-modules 175A, 175B and 175C may be described according to which of the various signals they are configured to operate on… names or descriptors may also be applied to these various wireless communication sub-modules 175A, 175B and 175C in specific situations where the data being received or sent pertains to one or more of the other forms of the LEAP data discussed herein, and such names will be apparent from the context”; “¶203: “the wearable electronic device 100 may include a screen formed in the top plate 130 such that the screen is capable of displaying information collected by wearable electronic device 100, including any alerts generated”; ¶207: “the context-based scenario may include some measure of location-based awareness such that different warning or alert protocols may be enacted depending on where the wearable electronic device 100 is in relation to the wearer's surroundings. An example of such location-based awareness may include instructing the wearer to take a particular course of action such as a direction to walk, going back to bed, staying put or the like. The alert or warning protocols may including sending audio or visual messages to the wearer through the previously-discussed display 173F or speaker 173G of the wearable electronic device 100”; ¶216: “with the gyroscopes, accelerometers, impact sensors and other forms of sensors 121, fall detection alerts are also conveyed automatically and in real-time”; ¶366: “one or more signals associated with the LEAP data are detected by one or more of the sensors 121 and the first and second wireless communication sub-modules 175A, 175B. These and additional data—as well as the inferring of one or more criteria associated with a particular health condition—may be used to provide CDS that in turn may correlate to an action plan or related therapy recommendation”)
Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since it allows for facilitating deep learning-level machine learning via a predictive model to correlate changes related to a wearer of the electronic device whereby inferring of one or more criteria associated with a particular individual and the respective wearable electronic device may be used to correlate to an action plan or related recommendation (Abstract, ¶22, ¶60, ¶162, ¶203, ¶207, ¶216, ¶366).
With respect to claims 3 and 11,
Martinez and Sobol disclose all of the above limitations, Sobol further discloses,
wherein, when a first alarm signal among the alarm signals is received, the processor infers first biometric data and first work environment data included in the first alarm signal, wherein the processor sequentially generates the plurality of rescue scenarios based on the biometric data and the work environment data (¶205: “the wearable electronic device 100 may also be equipped with notifiers configured to provide the patient with any alert generated by the logic device 173. These notifiers can be in the form of any technology that would catch the attention of the patient or caregiver to bring attention to the fact that he or she is aware that the processor 173A has received information that may correspond to a change in status of the patient, as well as other alerts, such as whether the patient could be in danger. Some exemplary notifiers include vibration (i.e., haptic) motors, LED lights and an audio speaker such a speaker 173G. Thus, in one form, the wearable electronic device 100 is to be able to play spoken voice, music, or sound that may be used to help comfort an elderly wearer of the device, particularly a wearer who may be suffering from ADRD. This feature can also be used to produce an audible alarm to a remote speaker such as WiFi speaker 800 when a wearer of the device is passing a specific choke point, such as walking out a door in a manner similar to RFID functionality. Thus, when a patient with the wearable electronic device 100 is close to a specific beacon such as one or more of the previously-discussed elopement beacons 200B, the wearable electronic device 100 sends an event signal over the internet to the cloud 500 to play an audio file on the WiFi speaker 800 or, if needed, directly to the on-device speaker 173G. Likewise with images that may be depicted on display 173F, such files may be customized with the image or sounds of a person or persons familiar to the patient, which in turn may have a soothing, calming effect, as well as heighten the cognitive state of the patient. Relatedly, the wearable electronic device 100 may be configured to allow for caretakers, family members and other responders to communicate directly—and in real-time—with the patient through the display 173F, audio speaker 173G, audio microphone or the like. In one form, such a message may also be played through one or more of the WiFi speakers 800 that may be placed in a convenient location in the patient's home, apartment or related dwelling”)
wherein, when information on the gas among the work environment data is received, the processor generates a target rescue scenario by inferring the information on the gas (¶110: “it will be appreciated that the wearable electronic device and its associated support or peripheral systems and components may be used for other applications as well, such as being worn by an individual (including a nurse, firefighter, police officer, soldier, construction site worker, athlete, factory/manufacturing worker, oil, gas or coal worker or the like) to provide accurate safety-determination or location-determination in potentially dangerous, architecturally-hardened (such as a jail with thick concrete walls) or otherwise hard-to-track locations, as well as to assess employee productivity, safety compliance and related metrics that may be of interest to an employer or operator of a facility, site, dwelling, hospital, retail environment, hospitality or other enterprise where personnel monitoring may be beneficial… when one or more forms of LEAP data are received by the wearable electronic device 100 for or about an individual that is associated with a particular site, dwelling, hospital, healthcare facility or other of the aforementioned enterprises, it is with the understanding that such associated individual may include an individual who is actually wearing the wearable electronic device 100 such that the acquired data is for the wearer's benefit, as well as for associated individuals that are using acquired data from the device being worn by another in order to perform an analysis, assessment or make a decision based on such acquired data … being able to identify a sense of location (that is to say, the right place at the right time), or whether the person has fallen, or whether the person may be at risk of adverse health conditions based on patterns sensed by the wearable electronic device 100 are some types of health and safety concerns that may be related to the health or safety of the person. In an even more particular example, the wearable electronic device 100 may include a gas sensor such that if a wearer on a construction site encounters a harmful gaseous environment on the site, upon detection of the presence of the gas by the sensor, the wearable electronic device 100 reacts to inform the wearer, as well as to send corresponding data to a remote operator”; ¶144: “messages that are generated by a particular event (such as a wearer being in one or more of an unauthorized location or during an unexpected time of day) may be sent to the wearable electronic device 100 in order to alert the wearer to take an alternate course of action; in one form, the message may be visual …or audio (such as through one or both of a standalone WiFi speaker 800 or the speaker 173G included in the wearable electronic device 100)”; ¶178: “activity sensors 121B that are used to collect activity data may include accelerometers, gyroscopes, magnetometers or the like, while the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, natural gas, poison gas, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like… numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event; ¶207: “the context-based scenario may include some measure of location-based awareness such that different warning or alert protocols may be enacted depending on where the wearable electronic device 100 is in relation to the wearer's surroundings. An example of such location-based awareness may include instructing the wearer to take a particular course of action such as a direction to walk, going back to bed, staying put or the like. The alert or warning protocols may including sending audio or visual messages to the wearer through the previously-discussed display 173F or speaker 173G of the wearable electronic device 100”; Fig 6, ¶237: “various machine learning approaches may in one form follow an ordered sequence of operations performed on the LEAP data acquired by the wearable electronic device 100 …this ordered sequence may be used to perform an action plan so that it can provide guidance on changes in medication dosages, changes in dietary or activity protocols, changes in occupational or physical therapy plans or the like. Moreover, because such diagnosis is based on the acquired LEAP data that is specific to a particular individual, such diagnosis and the ensuing action plan could qualify as personalized medicine and related individualized-profile clinical decision-making. The first three steps 1100, 1200, 1300 form the core of data management, while the last two steps 1400, 1500 make up learning, inference or related analytics to acquire intelligence from the initial voluminous data set. As such, it will be appreciated that the first three steps 1100, 1200, 1300 may be performed in conjunction with or independently of the latter steps 1400, 1500 and that all such variants or combinations may form part of a machine learning-based analysis”)
Sobol discloses a machine learning workflow – see Fig 6, comprising an ordered sequence of operations performed on the LEAP data acquired by the wearable electronic device. Sobol further discloses that a trained machine learning model may also use an inference step which utilizes acquired real-time LEP data specific to a particular individual in order to draw inferences and action plan from the acquired data of a person. Hence, providing a personalized action plan specific to a particular individual whereby additional steps in the workflow (ordered sequence) include learning, inference or related analytics to acquire intelligence form the initial data set such that steps may be performed in conjunction or independent of the machine learning-based analysis/workflow. Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since it allows for providing different warning or alert protocols and action plan relating to a context-based scenario and location-based awareness depending on where the wearable electronic device is in relation to the wearer’s surroundings (Fig. 6, ¶110, ¶144, ¶178, ¶207, ¶237).
With respect to claims 6 and 13,
Martinez and Sobol disclose all of the above limitations, Sobol further discloses,
wherein the generating of the target rescue scenario by inferring the information on the gas includes: recognizing a gas leakage amount based on the information on the gas in the work environment; (¶18: “an embodiment of the first aspect may include at least one sensor that detects environmental data, where such sensor includes an ambient temperature sensor, an ambient pressure sensor, an ambient humidity sensor, a carbon monoxide sensor, a carbon dioxide sensor, a natural gas sensor, a poison gas sensor, a smoke detector, an ambient light sensor, a motion sensor and a microphone”; ¶110: “such associated individual may include an individual who is actually wearing the wearable electronic device 100 such that the acquired data is for the wearer's benefit, as well as for associated individuals that are using acquired data from the device being worn by another in order to perform an analysis, assessment or make a decision based on such acquired data… at least a portion of a machine learning model may be included on the wearable electronic device 100 as a way to correlate the acquired data to an output corresponding to the health, safety, efficiency, productivity or other performance metric of the individual. Within the present context, issues related to the safety or health of a person associated with the wearable electronic device 100 are those that can be identified and acted upon by the wearable electronic device 100… being able to identify a sense of location (that is to say, the right place at the right time), or whether the person has fallen, or whether the person may be at risk of adverse health conditions based on patterns sensed by the wearable electronic device 100 are some types of health and safety concerns that may be related to the health or safety of the person. In an even more particular example, the wearable electronic device 100 may include a gas sensor such that if a wearer on a construction site encounters a harmful gaseous environment on the site, upon detection of the presence of the gas by the sensor, the wearable electronic device 100 reacts to inform the wearer, as well as to send corresponding data to a remote operator”; ¶178: “activity sensors 121B that are used to collect activity data may include accelerometers, gyroscopes, magnetometers or the like, while the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, natural gas, poison gas, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like. Additional ones of sensors 121D used for other functions, such as cameras, microphones, wear-detection sensors or the like may also be included”)
predicting the gas leakage amount in the work environment; and performing a first calculation on a difference between the recognition result and the prediction result (¶70: “train the machine learning model using training data selected from the extracted at least one feature vector and (d) execute an inference such that the machine learning model provides a predictive analytical output using a portion of acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model”; ¶154: “other forms of the LEAP data may be used in conjunction with the physiological data in order to help infer whether an individual that is associated with the wearable electronic device 100 is at risk of developing an adverse health condition based on a quantifiable mathematical interaction of the defining attributes of such things with one or both of the event data that is collected by the wearable electronic device 100 and baseline data that may be either taken from the wearable electronic device 100 or a lookup table or other local or remote source of such data”; ¶174: “providing the ability to perform more real-time calculations on the acquired LEAP data”; ¶178: “activity sensors 121B that are used to collect activity data may include …environmental sensors 121A used to collect environmental data may include those configured to acquire …carbon monoxide, carbon dioxide, natural gas, poison gas, smoke or the like, and the physiological sensors 121C used to collect physiological data may include those configured to acquire heart rate, breathing rate, glucose, blood pressure, cardiac activity, temperature, oxygen saturation, smells (such as total volatile organic compounds (TVOC)) or the like…numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event”; ¶245: “the wearable electronic device 100 may analyze activity-related data (such as wearer movement) and make a determination to further process or to forward reports related to abnormally low movement of the wearer. This in turn can be compared to other data (such as baseline data associated with the wearer) to determine if an alert or further processing may be needed”; ¶249: “the feature vectors (which may occupy a corresponding feature space) are subjected to a scalar multiplication process in order to construct a weighted predictor function. Moreover, feature construction may be achieved by adding features to those feature vectors that have been previously generated, where operators used to perform such construction may include arithmetic operators (specifically, addition, subtraction, multiplication and division), equality conditions (specifically, equal or not equal) and array operators (specifically, maximums, minimums and averages) among others. In one form, the analytics associated with these feature vectors may be performed in order to ascertain classification-based results (for example, whether the sensed parameter or attribute is less than, equal to or greater than a threshold that may itself be based on a known relative baseline, absolute baseline or other measure of interest), or to perform a regression in order to determine whether the sensed parameter or its attribute can be correlated to the likelihood of an event outcome”) Examiner interprets at least the analytics performed by the feature vectors to determine the likelihood of an event outcome, and (mathematically) comparing/correlating individual activity-related data to baseline data associated with the wearer of the wearable electronic device as taught by Sobol as teaching applicant’s first calculation on a difference between the recognition result (activity-related data) and the prediction result (baseline data).
wherein the inferring of the information on the gas includes: recognizing a gas leakage amount based on the information on the gas in the work environment (¶110: “the wearable electronic device 100 may include a gas sensor such that if a wearer on a construction site encounters a harmful gaseous environment on the site, upon detection of the presence of the gas by the sensor, the wearable electronic device 100 reacts to inform the wearer, as well as to send corresponding data to a remote operator”; ¶178: “activity sensors 121B that are used to collect activity data may include accelerometers, gyroscopes, magnetometers or the like, while the environmental sensors 121A used to collect environmental data may include those configured to acquire temperature, ambient pressure, humidity, carbon monoxide, carbon dioxide, natural gas, poison gas, smoke or the like”; claim 9: “wherein at least one sensor that detects environmental data is selected from the group consisting of an ambient temperature sensor, an ambient pressure sensor, an ambient humidity sensor, a carbon monoxide sensor, a carbon dioxide sensor, a natural gas sensor, a poison gas sensor, a smoke detector”; ¶248: “An instance is an example or observation of the data being collected, and may be further defined with an attribute (or input attribute) that is a specific numerical value of that particular instance, while a label is the output, target or answer that the machine learning algorithm is attempting to solve, the feature is a numerical value that corresponds to an input or input variable in the form of the sensed parameters, whereas a feature vector is a multidimensional representation (that is to say, vector, array or tensor) of the various features that are used to represent the object, phenomenon or thing that is being measured by the sensors 121 or other data-gathering components of the wearable electronic device 100”; ¶249: “the feature vectors (which may occupy a corresponding feature space) are subjected to a scalar multiplication process in order to construct a weighted predictor function. Moreover, feature construction may be achieved by adding features to those feature vectors that have been previously generated, where operators used to perform such construction may include arithmetic operators (specifically, addition, subtraction, multiplication and division), equality conditions (specifically, equal or not equal) and array operators (specifically, maximums, minimums and averages) among others. In one form, the analytics associated with these feature vectors may be performed in order to ascertain classification-based results (for example, whether the sensed parameter or attribute is less than, equal to or greater than a threshold that may itself be based on a known relative baseline, absolute baseline or other measure of interest), or to perform a regression in order to determine whether the sensed parameter or its attribute can be correlated to the likelihood of an event outcome”)
Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since allows for determining whether a sensed parameter or its attribute can be correlated to the likelihood of an event outcome via analytics associated with feature vectors and/or by comparing activity data to baseline data of the individual of the wearable electronic device to infer whether the wearer is at risk of developing an adverse health condition or if an alert may be needed (Fig. 6, ¶110, ¶154, ¶178, ¶245, ¶248-¶250)
With respect to claim 7,
Martinez and Sobol disclose all of the above limitations, Martinez further discloses,
wherein the biometric data includes information on abdominal pressure, breathing rate, movement, and fall of the worker (¶55: “the sensors include sensors that monitor environmental conditions such as temperature, humidity, altitude, sound exposure, and geographic location. In another example, the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken”; ¶56: “belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones). In another embodiment, the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”; ¶110: “the belt uses FSRs to obtain strain data around the waist of a person. FSRs can sense the amount of pressure applied on their surface, which is proportional to the amount of strain at that location”)
wherein the generating of the target rescue scenario further includes inferring information on the movement of the worker, and wherein the inferring of the information on the movement includes: recognizing a position of the worker based on the information on the movement (Abstract: “A belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements; Fig 11, ¶14: “FIG. 11 illustrates an example of a breadcrumb report that shows the movement of the subject”; ¶55: “the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken. In an embodiment, the belt includes a wireless interface, such as a Wi-Fi or cellular interface, for transmitting the data to a remote server. In various embodiments, the remote server records the environmental conditions and other sensor data and determines a set of activities performed by the subject. Using an administrative console or other interface, an administrator is able to monitor the activity of a plurality of subjects”; ¶61: “the system performs activity detection as part of processing the data collected by the belt device. Activities detected may include driving activities, working from height activities, lifting activities, walking jogging and running activities, traversing stairs, sleeping, tripping, falling, bending, twisting, pushing, and idle activities. Information about these activities may be used to improve the safety, productivity, and effectiveness of a work environment. Insights from this analysis may also be used to study activity patterns of an individual or a collection of individuals differentiated based on occupation, location, health condition, and other factors”; Fig 10, ¶107: “the computer system collects activity data from a belt worn by a subject… the computer system correlates each step activity with a direction to determine a distance moved in a particular direction. At block 1008, the computer system generates a breadcrumb report by mapping the distance and direction of travel of each successive step from a known start location. At block 1010, the system determines if GPS data is available, and if GPS data is available, the position predicted by the step activity is corrected to improve the accuracy of the report”; Fig 11, ¶108: “A breadcrumb report is the map of the movement of the user either outdoors or indoors… the system uses step counting along with direction measurement (using a magnetometer or other direction sensor) to generate an indoor breadcrumb report. When available, a GPS signal may be used to augment and/or correct the breadcrumb report”; ¶109: “In some examples, the pressure sensor is a force sensitive resistor (“FSR”). In some embodiments, the pressure sensor 1206 may be mounted directly to the belt strap 1202. In some embodiments, the pressure sensor is a switch that is mounted on the inside of the belt which is depressed when the belt is under sufficient tension. In some examples, the pressure sensor protrudes into the interior of the belt to maintain contact with the wearer even if the belt is loose”; ¶110: “An advantage of using an FSR is that strain at multiple locations around the back can be measured, which can then be combined with information from other sensors to provide insights on the classification and prediction of various activities, especially the amount of bending, strain on the person's back, and method of bending”; ¶111: “pressures at multiple points on the belt (which is generally to be proportional to strain) is used to model risk of back injury… the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor)”; ¶112: “temperature and humidity monitoring may be used to detect and alert users of abnormal changes in battery temperature and, if necessary, turn off the battery”; ¶173: “predictive modeling assesses the probability of accidents associated with a specific job, specific employee, time, and location. This information may be used to assign jobs for improved safety and productivity”; ¶178: “the belt provides a customizable alert system. Based on the safety concerns of each industry, there is a need to focus on some specific activities like extreme bending, twisting, or driving. The belt incorporates the provision to set the user alert functionality based on requirement” The supervisors in an organization can choose which factors to set as alert triggers”)
Sobol further discloses,
predicting the position of the worker; and performing a second calculation on a difference between the recognition result and the prediction result ((¶70: “train the machine learning model using training data selected from the extracted at least one feature vector and (d) execute an inference such that the machine learning model provides a predictive analytical output using a portion of acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model”; ¶154: “other forms of the LEAP data may be used in conjunction with the physiological data in order to help infer whether an individual that is associated with the wearable electronic device 100 is at risk of developing an adverse health condition based on a quantifiable mathematical interaction of the defining attributes of such things with one or both of the event data that is collected by the wearable electronic device 100 and baseline data that may be either taken from the wearable electronic device 100 or a lookup table or other local or remote source of such data”; ¶174: “providing the ability to perform more real-time calculations on the acquired LEAP data”; ¶178: “numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event”; ¶245: “the wearable electronic device 100 may analyze activity-related data (such as wearer movement) and make a determination to further process or to forward reports related to abnormally low movement of the wearer. This in turn can be compared to other data (such as baseline data associated with the wearer) to determine if an alert or further processing may be needed”; ¶249: “the feature vectors (which may occupy a corresponding feature space) are subjected to a scalar multiplication process in order to construct a weighted predictor function. Moreover, feature construction may be achieved by adding features to those feature vectors that have been previously generated …the analytics associated with these feature vectors may be performed in order to ascertain classification-based results (for example, whether the sensed parameter or attribute is less than, equal to or greater than a threshold that may itself be based on a known relative baseline, absolute baseline or other measure of interest), or to perform a regression in order to determine whether the sensed parameter or its attribute can be correlated to the likelihood of an event outcome”) Examiner interprets at least the predictive output based on acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model; using other forms of LEAP data with physiological data to infer whether an individual associated with a wearable electronic device is a risk based on quantifiable attributes of the event(s) collected by the wearable electronic device and the baseline data from the wearable electronic device, lookup table or other remote source of data as taught by Sobol as teaching applicant’s second calculation (predictive output) on a difference between the recognition result (acquired LEAP) and the prediction result (LEAP data used to train the machine learning model).
Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since allows for constructing feature vectors in order to construct a weighted predictor function in order to ascertain classification-based results for determining whether a sensed parameter or its attribute can be correlated to the likelihood of an event outcome (¶70, ¶154, ¶174, ¶178, ¶245, ¶249).
With respect to claims 8 and 15,
Martinez and Sobol disclose all of the above limitations, Martinez further discloses,
wherein the target rescue scenario is a rescue scenario first generated by the processor among the plurality of rescue scenarios based on the first calculation result and the second calculation result (Fig 10, ¶107: “the computer system collects activity data from a belt worn by a subject… the computer system correlates each step activity with a direction to determine a distance moved in a particular direction. At block 1008, the computer system generates a breadcrumb report by mapping the distance and direction of travel of each successive step from a known start location. At block 1010, the system determines if GPS data is available, and if GPS data is available, the position predicted by the step activity is corrected to improve the accuracy of the report”; Fig 11, ¶108: “A breadcrumb report is the map of the movement of the user either outdoors or indoors… the system uses step counting along with direction measurement (using a magnetometer or other direction sensor) to generate an indoor breadcrumb report. When available, a GPS signal may be used to augment and/or correct the breadcrumb report”; ¶110: “An advantage of using an FSR is that strain at multiple locations around the back can be measured, which can then be combined with information from other sensors to provide insights on the classification and prediction of various activities, especially the amount of bending, strain on the person's back, and method of bending”; ¶111: “pressures at multiple points on the belt (which is generally to be proportional to strain) is used to model risk of back injury… the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor). In another example, a foamy material placed under the pressure sensors (see the figure below), ensures that the pressure sensor is able to generate some signal even when the belt is somewhat loose (from bending or improper wearing)
With respect to claim 12,
Martinez and Sobol disclose all of the above limitations, Martinez further discloses,
wherein the processor receives one of a rescue signal or a distress signal from each of the plurality of smart belts before generating the plurality of rescue scenarios after receiving the alarm signals, and wherein the one signal is determined by each of the plurality of smart belts based on the biometric data of the worker (Abstract: “A belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements. A communication interface on the belt allows sensor data collected by the belt to be transferred to a storage server”; ¶56: “Various embodiments of the belt may include different configurations of sensors. In one embodiment, the belt includes a number of environmental sensors such as light sensors, temperature sensors (thermometers), atmospheric pressure sensors (absolute or relative air pressure sensors), humidity sensors, global-positioning sensors (such as Global Positioning System (“GPS”) or Global Navigation Satellite System (“GLONASS”) sensors), air quality sensors (particulate sensors or chemical sensors), moisture sensors, radiation sensors, acceleration sensors (accelerometers), shock and vibration sensors (Piezo electric sensors), orientation sensors (flux magnetometers or gravity sensors), and sound sensors (microphones). In another embodiment, the belt includes a number of sensors designed to measure characteristics of the subject such as body temperature sensors, strain gauges connected to various portions of the belt, clothing of the subject, or the subject itself, heart rate monitors, motion sensors, blood pressure sensors, or biometric sensors”; ¶60: “the belt device includes a processor and memory containing executable instructions that, as a result of being executed by the processor, transform the data or process the data into new data which is stored in a writable memory on the belt device. The new data or transform data may then be transmitted to a remote server for additional processing”; ¶65: “The server 108 receives information from the belt 102 and, in some examples, from one or more additional belts”; ¶68: “The server 108 may provide event notifications to the administrator 112. Notifications may be provided for events such as unsafe driving events, fall events, excessive idle time events, injury events, or medical emergency events. In one example, the administrator 112 is able to define a bounded geographical area, and events are provided when the subject 104 enters or exits the bounded geographical area”; Fig 4, ¶88: “the belt records metadata associated with the piece of data such as environmental conditions at the time the data was read, a resolution and scale for the data, a name or an identifier associated with the sensor, or other metadata”; ¶111: “In some embodiments, pressure sensors are used to act as strain sensors… the information on strain helps determine the risk of injury the wearer is exposed to. To address this limitation, pressures at multiple points on the belt (which is generally to be proportional to strain) is used to model risk of back injury… In some examples, the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor”; ¶112: “Temperature and humidity sensors continuously monitor the temperature and humidity where the employee is working. The temperature and humidity readings will be correlated with productivity and safety to see if temperature or humidity is a dependent factor. In some examples, temperature and humidity monitoring may be used to detect and alert users of abnormal changes in battery temperature and, if necessary, turn off the battery”; ¶178: “the belt provides a customizable alert system. Based on the safety concerns of each industry, there is a need to focus on some specific activities like extreme bending, twisting, or driving. The belt incorporates the provision to set the user alert functionality based on requirement” The supervisors in an organization can choose which factors to set as alert triggers”)
With respect to claim 14,
Martinez and Sobol disclose all of the above limitations, Martinez further discloses,
wherein the generating of the target rescue scenario further includes inferring information on the movement of the worker, and wherein the inferring of the information on the movement includes: recognizing a position of the worker based on the information on the movement; (Abstract: “a belt wearable by a human subject includes a variety of sensors that collect information about the wearer, the wearer's environment, and the wearer's movements… data is processed to identify activities performed by the wearer such as walking, driving, and working at heights. In some examples, events such as aggressive driving events, slips and falls, and unsafe lifting are detected”; ¶55: “a wearable belt that, when worn by a subject, collects data regarding a subject (such as a person) and the subject's environment… the sensors include sensors that monitor environmental conditions such as temperature, humidity, altitude, sound exposure, and geographic location. In another example, the system includes sensors that monitor the subject and record motion, acceleration, body temperature, heart rate, and steps taken”; ¶61 “Activities detected may include driving activities, working from height activities, lifting activities, walking jogging and running activities, traversing stairs, sleeping, tripping, falling, bending, twisting, pushing, and idle activities”;¶66 “The server 108 receives information from the belt 102 and, in some examples, from one or more additional belts”; Fig 6, ¶94: “each belt in the set of belts 602 connects to a Web server 608. The Web server 608 is connected to an application server 610 which maintains a log database 612 and a database of sensor data 614”; ¶104: “the data server processes the sensor data from each belt to identify activities performed by the wearer of each belt. The activities may include activities such as driving, working from heights, and other activities …the activity information associated with each belt is used to synthesize 912 a set of measures that are attributable to the wearer. The measures may include things such as amount of time spent idle”; ¶110: “strain at multiple locations around the back can be measured, which can then be combined with information from other sensors to provide insights on the classification and prediction of various activities, especially the amount of bending, strain on the person's back, and method of bending”; ¶111: “pressures at multiple points on the belt (which is generally to be proportional to strain) is used to model risk of back injury… the wearer is alerted using an LED signal or buzzer that the belt is not aligned correctly or not worn tight enough (based at least in part on information provided by the pressure sensor)”; ¶112: “temperature and humidity monitoring may be used to detect and alert users of abnormal changes in battery temperature and, if necessary, turn off the battery”; ¶173: “predictive modeling assesses the probability of accidents associated with a specific job, specific employee, time, and location. This information may be used to assign jobs for improved safety and productivity”; ¶178: “the belt provides a customizable alert system. Based on the safety concerns of each industry, there is a need to focus on some specific activities like extreme bending, twisting, or driving. The belt incorporates the provision to set the user alert functionality based on requirement” The supervisors in an organization can choose which factors to set as alert triggers”)
Sobol further discloses,
predicting the position of the worker; and performing a second calculation on a difference between the recognition result and the prediction result (¶70: “train the machine learning model using training data selected from the extracted at least one feature vector and (d) execute an inference such that the machine learning model provides a predictive analytical output using a portion of acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model”; ¶154: “other forms of the LEAP data may be used in conjunction with the physiological data in order to help infer whether an individual that is associated with the wearable electronic device 100 is at risk of developing an adverse health condition based on a quantifiable mathematical interaction of the defining attributes of such things with one or both of the event data that is collected by the wearable electronic device 100 and baseline data that may be either taken from the wearable electronic device 100 or a lookup table or other local or remote source of such data”; ¶174: “providing the ability to perform more real-time calculations on the acquired LEAP data”; ¶178: “numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event”; ¶245: “the wearable electronic device 100 may analyze activity-related data (such as wearer movement) and make a determination to further process or to forward reports related to abnormally low movement of the wearer. This in turn can be compared to other data (such as baseline data associated with the wearer) to determine if an alert or further processing may be needed”; ¶249: “the feature vectors (which may occupy a corresponding feature space) are subjected to a scalar multiplication process in order to construct a weighted predictor function. Moreover, feature construction may be achieved by adding features to those feature vectors that have been previously generated …the analytics associated with these feature vectors may be performed in order to ascertain classification-based results (for example, whether the sensed parameter or attribute is less than, equal to or greater than a threshold that may itself be based on a known relative baseline, absolute baseline or other measure of interest), or to perform a regression in order to determine whether the sensed parameter or its attribute can be correlated to the likelihood of an event outcome”) Examiner interprets at least the predictive output based on acquired LEAP data that is not the same as a portion of the LEAP data that was used to train the machine learning model; using other forms of LEAP data with physiological data to infer whether an individual associated with a wearable electronic device is a risk based on quantifiable attributes of the event(s) collected by the wearable electronic device and the baseline data from the wearable electronic device, lookup table or other remote source of data as taught by Sobol as teaching applicant’s second calculation (predictive output) on a difference between the recognition result (acquired LEAP) and the prediction result (LEAP data used to train the machine learning model).
Martinez and Sobol are directed to the same field of endeavor since they are related to monitoring and detecting user biometric and environmental data via a wearable device in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to combine the method/system for collecting data regarding an individual and their environment via sensors on a wearable belt of Martinez with the machine learning/artificial intelligence functionality of a wearable electronic device as taught by Sobol since allows for constructing feature vectors in order to construct a weighted predictor function in order to ascertain classification-based results for determining whether a sensed parameter or its attribute can be correlated to the likelihood of an event outcome (¶70, ¶154, ¶174, ¶178, ¶245, ¶249).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Martinez et al., US Patent Application Publication No US 2020/0174517 A1, “Wearable Electronic Belt Device”, relating to method/system that includes a wearable belt that, when worn by a subject, collects data regarding a subject (such as a person) and the subject's environment to acquire information regarding the productivity and safety of both individual workers and the workforce as a whole.
Faizan et al., US Patent Application Publication No US 2023/0069173 A1, “Smart Rescue System”, relating to a method/system including a mobile device and server-controlled system for rescue to be done by ambulances, and which is coordinated with an interactive application accessible by authorized people.
Smith et al., US Patent Application Publication No US 2013/0297551A1, “System and Method for Providing Intelligent Location Information” relating to a method/system to predictive intelligence for mobile devices via an application that leverages the predictive intelligence generated in the system that can be used to display information alerts (e.g., weather, traffic, parking, restaurant, and other information), provide contextually appropriate interactions at an appropriate time, trigger application actions, intelligently complete transactions based on location and travel routes, trigger physical interactions (e.g., turn on/off lights, a vehicle, heater/air-conditioning system, etc.), and/or perform other actions in response to and in anticipation of a user's location and the context of that location.
Lee et al., (KR102455031B1), “Wearable Device for Workers, Worker Safety Management System, and Method Therefor”, relating to a wearable device for a worker for managing the safety of an operator working on a track side or in a track, a worker safety management system, and a method thereof in which one or more wearable devices having a communication module and a display unit and configured in a form wearable by a worker and an IoT server device communicating with wearable devices interlock with each other to manage the safety of a worker.
Park et al., (KR20130021058A), “Smart Monitoring Management System”, relating to transmitting various information about a situation of a site in real time using a smart ware and to smoothly control a work on site according to a command of a center; whereby a smart ware wirelessly transmits photographed and detected site information by photographing the peripheral situation of a worker or by detecting with a specific sensor.
Sobol et al., US Patent Application Publication No US 2021/0319894 A1, "Wearable Electronic Device and System Using Low-power Cellular Telecommunication Protocols” related to a wearable electronic device that communicates such data through a wireless cellular low power wide area network (LPWAN) in order to allow such data to be used to identify one or more of location, health, safety or other indicia pertaining to the wearer of the device.
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.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Kimberly L. Evans whose telephone number is 571.270.3929. The Examiner can normally be reached on Monday-Friday, 9:30am-5:00pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached at 571.272.6782.
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/KIMBERLY L EVANS/Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626