Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
IDS
The information disclosure statement (IDS) submitted on October 27, 2023 is being considered by the Examiner.
Drawing
The drawing filed on October 27, 2023 is accepted by the Examiner.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim rejection – 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. §102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. §102 (a)(2) as being anticipated by Sobol et al. (U.S. Patent No. 11,978,555, hereon Sobol).
In reference to claim 1: Sobol discloses a method (see Sobol, Abstract), comprising:
obtaining sensor data (see Sobol, Fig. 6, raw data or sensor data acquisition) characterizing at least one of an orientation and an acceleration of an edge instrument (see Sobol, column 43, lines 54-58) utilizing to perform a repetitive task by a user (see Sobol, column 93, table 3 and column 94, lines 21-27);
applying the sensor data to a processor-based machine learning model trained to identify one or more deviations from the expected sequence of actions associated with the repetitive task (time base data from bathroom visits) (see Sobol, column 109, lines 6-20), wherein the processor-based machine learning model is embedded in the edge instrument (see Sobol, column 111, lines 37-46, machine codes can be stored in the wearable electronics device); and
initiating at least one automated action in response to the processor-based machine learning model to identify one or more deviations from the expected sequence of actions (see Sobol, column 109, lines 6-20);
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (see Sobol, column 127, lines 39-59).
With regard to claim 2: Sobol further discloses that the processor-based machine learning model is compressed prior to being embedded in the edge instrument because the data are in packet format structure that are embedded within one form of IoT protocol stack, including the machine learning model (see Sobol, Figs. 3C through 3F).
With regard to claim 3: Sobol further discloses that the orientation of the edge instrument is determined using a gyroscope embedded in the edge instrument (see Sobol, column 44, lines 4-24).
With regard to claim 4: Sobol further discloses that the acceleration of the edge instrument is determined using an accelerometer embedded in the edge instrument (see Sobol, Figs. 2F and 11a, accelerometer data is obtained from the edge gadget).
With regard to claim 5: Sobol further discloses that the method comprising transforming the sensor data into at least one designated format prior to applying the sensor data to the processor-based machine learning model (see Sobol, column 3, lines 54-67).
With regard to claim 6: Sobol further discloses that the transforming the sensor data comprises one or more of compressing the sensor data and reducing noise in the sensor data (see Sobol, column 65, lines 33-51).
With regard to claim 7: Sobol further discloses that the at least one automated action comprises one or more of generating an alert and providing one or more remediation steps to address the one or more deviations from the expected sequence of actions (see Sobol, column 69, line 6-13).
With regard to claim 8: Sobol further discloses that the processor-based machine learning model is trained to identify the one or more deviations from the expected sequence of actions using training data obtained from one or more sensors embedded in an edge instrument during a performance of the repetitive task by at least one user (see Sobol, column 68, line 51 to column 69, line 22).
In reference to claim 9: Sobol discloses a non-transitory processor-readable storage medium having stored therein program code of one or more software programs (see Sobol, column 127, lines 39-59), wherein the program code when executed by at least one processing device causes the at least one processing device to perform the following steps:
obtaining sensor data (see Sobol, Fig. 6, raw data or sensor data acquisition) characterizing at least one of an orientation and an acceleration of an edge instrument (see Sobol, column 43, lines 54-58) utilized to perform a repetitive task by a user (see Sobol, column 93, table 3 and column 94, lines 21-27), wherein the sensor data is obtained from one or more sensors embedded in the edge instrument because the data are in packet format structure that are embedded within one form of IoT protocol stack, including the machine learning model (see Sobol, Figs. 3C through 3F) and wherein the repetitive task comprises a sequence of actions;
applying the sensor data to a processor-based machine learning model trained to identify one or more deviations from an expected sequence of actions associated with the repetitive task (time base data from bathroom visits) (see Sobol, column 109, lines 6-20), wherein the processor-based machine learning model is embedded in the edge instrument (see Sobol, column 111, lines 37-46, machine codes can be stored in the wearable electronics device); and
initiating at least one automated action in response to the processor-based machine learning model identifying the one or more deviations from the expected sequence of actions (see Sobol, column 109, lines 6-20).
.
With regard to claim 10: Sobol discloses the processor-based machine learning model is compressed prior to being embedded in the edge instrument because the data are in packet format structure that are embedded within one form of IoT protocol stack, including the machine learning model (see Sobol, Figs. 3C through 3F).
With regard to claim 11: Sobol further discloses that the orientation of the edge instrument is determined using a gyroscope embedded in the edge instrument (see Sobol, column 44, lines 4-24).
With regard to claim 12: Sobol further discloses that the acceleration of the edge instrument is determined using an accelerometer embedded in the edge instrument (see Sobol, Figs. 2F and 11a, accelerometer data is obtained from the edge gadget).
With regard to claim 13: Sobol further discloses that the non-transitory process-readable medium comprising one or more of compressing the sensor data and reducing noise in the sensor data (see Sobol, column 65, lines 33-51).
With regard to claim 14: Sobol further discloses that the at least one automated action comprises one or more of generating an alert and providing one or more remediation steps to address the one or more deviations from the expected sequence of actions (see Sobol, column 69, line 6-13).
In reference to claim 15: Sobol discloses an apparatus (see Sobol, Fig. 1) comprising:
at least one processing device (see Sobol, Fig. 2F, CPU, 173A) comprising a processor coupled to a memory (173B); the at least one processing device being configured to implement the following steps:
obtaining sensor data (see Sobol, Fig. 6, raw data or sensor data acquisition) characterizing at least one of an orientation and an acceleration of an edge instrument (see Sobol, column 43, lines 54-58) utilizing to perform a repetitive task by a user (see Sobol, column 93, table 3 and column 94, lines 21-27), wherein the sensor data is obtained from one or more sensors embedded in the edge instrument because the data are in packet format structure that are embedded within one form of IoT protocol stack, including the machine learning model (see Sobol, Figs. 3C through 3F) and wherein the repetitive task comprises a sequence of actions;
applying the sensor data to a processor-based machine learning model trained to identify one or more deviations from an expected sequence of actions associated with the repetitive task (time base data from bathroom visits) (see Sobol, column 109, lines 6-20), wherein the processor-based machine learning model is embedded in the edge instrument (see Sobol, column 111, lines 37-46, machine codes can be stored in the wearable electronics device); and
initiating at least one automated action in response to the processor-based machine learning model identifying the one or more deviations from the expected sequence of actions (see Sobol, column 109, lines 6-20).
With regard to claim 16: Sobol further discloses that the processor-based machine learning model is compressed prior to being embedded in the edge instrument because the data are in packet format structure that are embedded within one form of IoT protocol stack, including the machine learning model (see Sobol, Figs. 3C through 3F).
With regard to claim 17: Sobol further discloses that the orientation of the edge instrument is determined using a gyroscope embedded in the edge instrument (see Sobol, column 44, lines 4-24).
With regard to claim 18: Sobol further discloses that the acceleration of the edge instrument is determined using an accelerometer embedded in the edge instrument (see Sobol, Figs. 2F and 11a, accelerometer data is obtained from the edge gadget).
With regard to claim 19: Sobol further discloses that the apparatus comprising one or more of compressing the sensor data and reducing noise in the sensor data (see Sobol, column 65, lines 33-51).
With regard to claim 20: Sobol further discloses that the at least one automated action comprises one or more of generating an alert and providing one or more remediation steps to address the one or more deviations from the expected sequence of actions (see Sobol, column 69, line 6-13).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Moustafa et al. (U.S. PAP 2022/0126864) discloses an autonomous vehicle system. Sensor data is received from a plurality of sensors, where the plurality of sensors includes a first set of sensors and a second set of sensors, and at least a portion of the plurality of sensors are coupled to a vehicle. Control of the vehicle is automated based on at least a portion of the sensor data generated by the first set of sensors. Passenger attributes of one or more passengers within the autonomous vehicles are determined from sensor data generated by the second set of sensors. Attributes of the vehicle are modified based on the passenger attributes and the sensor data generated by the first set of sensors.
Zhou et al. (U.S. Patent No. 8,862,393) discloses systems, methods and applications utilizing the convergence of any combination of the following three technologies: wireless positioning or localization technology, wireless communications technology and sensor technology. In particular, certain embodiments of the present invention relate to a remote device that includes a sensor for determining or measuring a desired parameter, a receiver for receiving position data from the Global Positioning System (GPS) satellite system, a processor for determining whether or not alert conditions are present and a wireless transceiver for transmitting the measured parameter data and the position data to a central station, such as an application service provider (ASP)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIAS DESTA whose telephone number is (571)272-2214. The examiner can normally be reached M-F: 8:30 to 5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew M Schechter can be reached at 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ELIAS DESTA/
Primary Examiner, Art Unit 2857
Prosecution Insights