DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Applicant’s submission filed 10 December 2025 [hereinafter Response] has been entered, where:
Claims 1, 10, and 12 have been amended.
Claims 4 and 15 have been cancelled.
New claims 21 and 22 are presented for examination.
Claims 1-3, 5-14, and 16-22 are pending.
Claims 1-3, 5-14, and 16-22 are rejected.
Foreign priority is claimed to IN 202141046903, filed 14 October 2021. A certified copy of this paper has been filed 27 October 2022. Accordingly, receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
3. An information disclosure statement was submitted on 06 February 2026. The submissions comply with the provisions of 37 CFR 1.97. Accordingly, the Examiner considered the information disclosure statements.
Claim Rejections - 35 U.S.C. § 101
4. 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.
5. Claims 1-3, 5-14, and 16-22 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(a)]1 identifying, using at least one processor of a device for providing human wellness recommendation, a physical profile of each user present in an Internet of Things (IoT) environment,” “[(b)] monitoring, using UWB based sensors, a current activity of each user in the IoT environment and one or more locations associated with the current activity;” “[(d)] predicting a potential anomalous event by correlating the physical profile of each user with at least one of [(d.1)] the current activity of each user, [(d.2)] the one or more locations associated with the current activity, [(d.3)] a state of environment at the one or more locations associated, and [(d.4)] the operational state of the one or more IoT devices.” These activities of “[(a)] identifying,” “[(b)] monitoring,” and “[(d)] predicting,” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are mental processes, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a). The claim recites more details or specifics to the abstract idea of “[(b)] monitoring the current activity of each user”, where “[(b.1)] the monitoring performed without physical contact with the plurality of users, and [(b.2)] the monitoring performed [(b.2)] using multiple angular data received from UWB based sensors in a time-series manner, and accordingly, is merely more specific to the abstract idea. Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified abstract idea include “one or more IoT devices,” which is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not serve to integrate the abstract idea into a practical application. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a pre-processing, insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a post-processing insignificant extra-solution activity of producing a prediction result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
Also, the claim recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” which are pre-processing, insignificant extra-solution activities of data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes include “one or more IoT devices,” which is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not amount to significantly more than the abstract idea. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim also recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” are the well-understood, routine, and conventional activities of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claim 12 recites an electronic device, which is an article of manufacture, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(a)] identify a physical profile of each user present in an Internet of Things (IoT) environment,” “[(b)] monitor a current activity of each user in the IoT environment and one or more locations associated with the current activity;” “[(d)] predict a potential anomalous event by correlating the physical profile of each user with at least one of [(d.1)] the current activity of each user, [(d.2)] the one or more locations associated with the current activity, [(d.3)] a state of environment at the one or more locations associated, and [(d.4)] the operational state of the one or more IoT devices.” These activities of “identify,” “monitor,” and “predict,” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are mental processes, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a). The claim recites more details or specifics to the abstract idea of “[(b)] monitoring the current activity of each user”, where “[(b.1)] the monitoring performed without physical contact with the plurality of users, and [(b.2)] the monitoring performed [(b.2)] using multiple angular data received from UWB based sensors in a time-series manner, and accordingly, is merely more specific to the abstract idea. Thus, claim 12 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified abstract idea include “at least one processor,” “at least one memory communicatively coupled to the at least one processor, and configured to store processor-executable instructions, which on execution, cause,” and “one or more IoT devices,” which are generic computer components (processor, memory, IoT devices) used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not serve to integrate the abstract idea into a practical application. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a pre-processing, insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a post-processing insignificant extra-solution activity of producing a prediction result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
Also, the claim recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” which are pre-processing, insignificant extra-solution activities of data gathering, (MPEP § 2106.05(g)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 12 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes include “at least one processor,” “at least one memory communicatively coupled to the at least one processor, and configured to store processor-executable instructions, which on execution, cause,” and “one or more IoT devices,” which are generic computer components (processor, memory, IoT devices) used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not amount to significantly more than the abstract idea. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim also recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” are the well-understood, routine, and conventional activities of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claim 12 is subject-matter ineligible.
Claim 20 recites a non-transitory computer-readable storage medium, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101). Claim 20 recites “a computer program stored thereon that performs, when executed a processor, the method of claim 1.
However, under Step 2A Prong One, claim 1 recites the limitations of “[(a)] identifying a physical profile of each user present in an Internet of Things (IoT) environment,” “[(b)] monitoring a current activity of each user in the IoT environment and one or more locations associated with the current activity;” “[(d)] predicting a potential anomalous event by correlating the physical profile of each user with at least one of [(d.1)] the current activity of each user, [(d.2)] the one or more locations associated with the current activity, [(d.3)] a state of environment at the one or more locations associated, and [(d.4)] the operational state of the one or more IoT devices.” These activities of “identifying,” “monitoring,” and “predicting,” contain limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly are mental processes, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a). The claim recites more details or specifics to the abstract idea of “[(b)] monitoring the current activity of each user”, where “[(b.1)] the monitoring performed without physical contact with the plurality of users, and [(b.2)] the monitoring performed [(b.2)] using multiple angular data received from UWB based sensors in a time-series manner, and accordingly, is merely more specific to the abstract idea. Thus, claim 20 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified abstract idea include a “non-transitory computer-readable storage medium” “a processor,” and “one or more IoT devices,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not serve to integrate the abstract idea into a practical application. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a pre-processing, insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a post-processing insignificant extra-solution activity of producing a prediction result, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
The claim also recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” are the well-understood, routine, and conventional activities of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claim 20 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The claim includes include a “non-transitory computer-readable storage medium” “a processor,” and “one or more IoT devices,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. Also, the claim recites “an Internet of Things (IoT) environment,” which is generally linking the use of an abstract idea to a particular technological environment or field of use (such as an IoT environment), which does not amount to significantly more than the abstract idea. The claim recites the limitation of “[(c)] tracking an operational state of one or more IoT devices at the one or more locations within the IoT environment,” which is a well-understood, routine, and conventional activity of storing information in memory, (MPEP § 2106.05(d) sub II.iv), that does not amount to significantly more than the abstract idea. The claim also recites “[(e)] providing at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event,” which is a well-understood, routine, and conventional activity of transmitting data over a network, (MPEP § 2106.05(d) sub II.i), that does not amount to significantly more than the abstract idea.
The claim also recites additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of a “Recurrent Neural Network (RNN) technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than the abstract idea. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using Recurrent Neural Network (RNN) technique based classification,” are the well-understood, routine, and conventional activities of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claim 20 is subject-matter ineligible.
Claim 2 depends from claim 1. Claim 13 depends from claim 12. The claims recite more details or specifics to the abstract idea of “[(a)] identifying a physical profile,” where (claims 2 and 12: “the physical profile of each user is identified using [(a.1)] UWB based sensors and [(a.2)] a historic physical profile stored in a database”), and accordingly, are merely more specific to the abstract idea.
Under Step 2A Prong Two, the claims recite additional elements of “UWB based sensors,” and “database,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the limitation of “using [(a.1)] the UWB based sensors and [(a.2)] a historic physical profile stored in a database” is the pre-processing, insignificant extra-solution activity of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application. Under Step 2B, the claims recite additional elements of “UWB based sensors,” and “database,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the limitation of “using [(a.1)] the UWB based sensors and [(a.2)] a historic physical profile stored in a database” is the well-understood, routine, and conventional activity of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claims 2 and 13 are subject-matter ineligible.
Claims 3 depends from claim 1. Claim 14 depends from claim 12. The claims recite more details or specifics to the abstract idea of “[(a)] identifying of the physical profile,” where (claims 3 and 14: performed [(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using a reinforcement learning technique involving feedback from a user of the IoT environment”), and accordingly, are merely more specific to the abstract idea), and accordingly, is merely more specific to the abstract idea.
Under Step 2A Prong Two, the claims recite additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Also, the claims recite additional element of “a reinforcement learning technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using a reinforcement learning technique involving feedback from a user of the IoT environment,” which is are pre-processing, insignificant extra-solution activities of data gathering, (MPEP § 2106.05(g)), that does not serve to integrate the abstract idea into a practical application.
Under Step 2B, the claims recite additional elements of “UWB based sensors,” which are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. Also, the claims recite additional element of “a reinforcement learning technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component that is used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to significantly more than an abstract idea. Also, the limitation of “[(a.1)] using multiple angular data received from UWB based sensors in a time-series manner and [(a.2)] using a reinforcement learning technique involving feedback from a user of the IoT environment,” which is using [(a.1)] the UWB based sensors and [(a.2)] a historic physical profile stored in a database” is the well-understood, routine, and conventional activity of storing and retrieving data in memory, (MPEP § 2106.05(d) sub II.iv) that does not provide significantly more than the abstract idea. Therefore, claims 3 and 14 are subject-matter ineligible.
Claim 5 depends from claim 1. Claim 16 depends from claim 12. The claims recite more details or specifics to the abstract idea of “[(b)] monitoring a current activity,” where (claims 5 and 16: “wherein the current activity includes [(b.1)] a physical activity and [(b.2)] is selected from a group comprising walking, moving, running, crawling, sitting, standing, jumping, bending, cleaning, or eating”), and accordingly, are merely more specific to the abstract idea. The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claims 5 and 16 are subject-matter ineligible.
Claim 6 depends directly or indirectly from claim 1. Claim 17 depends directly or indirectly from claim 12. The claims recite more details or specifics to the abstract idea of “[(a)] identifying a physical profile,” where (claims 6 and 17: “wherein the physical profile and the historic physical profile [(a.1)] both comprise information associated with at least one of user height, user body type, user shape, user age, user gender and stage type, user movement, user average speed of movement, or restricted user movement”), and accordingly, are merely more specific to the abstract idea. Therefore, claims 6 and 17 are subject-matter ineligible.
Claim 7 depends from claim 1. Claim 18 depends from claim 12. The claims recite more details or specifics to the abstract idea of [(d)] predicting a potential anomalous event by correlating,” where (claim 7 and 18: [(d.1)] wherein correlating . . . is performed using a supervised machine learning technique”), and accordingly, are merely more specific to the abstract idea.
Also, the claim recites “a supervised machine learning technique,” which is recited at a high-level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application under Step 2A Prong Two, and does not amount to significantly more under Step 2B. Therefore, claims 7 and 18 are subject-matter ineligible.
Claim 8 depends from claim 1. Claim 19 depends from claim 12. The claims provide more details or specifics to the additional element of “[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution,” where (claims 8 and 19: “[(e)] wherein the providing . . . is based on the predicted potential anomalous event and [(e.1)] similar historic event identified in the past at different location within the IoT environment and corresponding action performed by the one or more users to the similar historic event”), and accordingly, is merely more specific to the additional element. Therefore, claims 8 and 19 are subject-matter ineligible.
Claim 9 depends from claim 1. The claim recites more details or specifics of the additional element of “[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution,” which “[(e.1)] is indicated using the one or more IoT device present at the one or more locations within the IoT environment,” and accordingly, is merely more specific to the additional element. Therefore, claim 9 is subject-matter ineligible.
Claim 10 depends from claim 1. The claim recites more details or specifics of the additional element of “[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution,” which “[(e.1)] is indicated using the one or more IoT device present at the one or more locations within the IoT environment,” and accordingly, is merely more specific to the additional element. Therefore, claim 10 is subject-matter ineligible.
Claim 11 depends from claim 1. Under Step 2A Prong One, the claim recites the further limitation of “[(f)] determining an action performed by the one or more users on the one or more IoT devices at the one or more locations in the IoT environment to the at least one of wellness risk alert and wellness solution at the one or more locations,” and “[(g)] retraining the correlation between the physical profile of each user with at least one of the current activity of each user, the one or more locations associated with the current activity, the state of environment at the one or more locations associated, and the operational state of the one or more IoT devices.” The activity of “determining” and “retraining the correlation” contains limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly is a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. (MPEP § 2106.04(a). The abstract idea of these claims are not integrated into a practical application, (see MPEP § 2106.04(d)), nor do they amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), because the claims recite no more than the abstract idea. Therefore, claim 11 is subject-matter ineligible.
Claim 21 depends from claim 1. The claim recites more details or specifics of the additional element of “UWB sensors,” “wherein the UWB sensors are distributed in different rooms from each other,” and accordingly, is merely more specific to the additional element. Therefore, claim 21 is subject-matter ineligible.
Claim 22 depends from claim 1. The claim recites more details or specifics of the additional element of “at least one processor of a device,” “wherein the device for providing human wellness recommendation provides continuous tracking of a plurality of users in locations with an obstruction between a user and the UWB sensors,” and accordingly, is merely more specific to the additional element. Therefore, claim 22 is subject-matter ineligible.
Claim Rejections -35 U.S.C. § 103
6. 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.
7. 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.
8. 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.
9. Claims 1-3, 6, 7, 9, 10, 12-14, 17, 18, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200367807 to Lassoued et al. [hereinafter Lassoued ‘807] in view of US Published Application 20210096217 to Jadidian et al. [hereinafter Jadidian] and US Published Application 20230099622 to Fox et al. [hereinafter Fox].
Regarding claims 1, 12, and 20, Lassoued ‘807 teaches [a]method for providing human wellness recommendation based on Ultra-Wideband (UWB) based human activity detection (Lassoued ‘807 ¶ 0003 teaches “a method for intelligent monitoring of a health state of a user while engaged in use of a computing device [(that is, a method for providing human wellness recommendation )]) of claim 1, [a]n electronic device for providing human wellness recommendation based on Ultra-Wideband (UWB) based human activity detection (Lassoued ‘807 ¶ 0081 teaches a “computing environment 402 may also include the computer system 12, as depicted in FIG. 1 [(that is, “computer system 12” is an electronic device for providing human wellness recommendation)]”) of claim 12, and [a] non-transitory computer-readable storage medium, having a computer program stored thereon that performs, when executed by a processor, the method of claim 1 (Lassoued ‘807 ¶ 0112 teaches “a method executed as instructions on a machine, where the instructions are included on at least one computer readable medium or one non-transitory machine-readable storage medium [(that is, a non-transitory computer readable storage medium)]”; Lassoued ‘807 ¶ 0125 teaches “computer readable program instructions may be provided to a processor of a general-purpose computer [(that is, having a computer program stored thereon that performs, when executed by a processor, the method of claim 1)]”) of claim 20, comprising:
[(a)] identifying, using at least one processor of a device for providing human wellness recommendation (Lassoued ‘807, Abstract, teaches “intelligent monitoring of a health state of a user by a processor. . . . One or more mitigating actions may be identified and recommended [(that is, using at least one processor of a device for providing human wellness recommendation)]”), a physical profile of each user present in an Internet of Things (IoT) environment (Lassoued ‘807 ¶ 0074 & Fig. 4, teaches “intelligent monitoring of a health state of a user while engaged in operation of a computing device in a computing environment [Examiner annotations in dashed-line text boxes]:
PNG
media_image1.png
858
800
media_image1.png
Greyscale
Lassoued ‘807 ¶ 0013 teaches “[a]n IoT network may include one or more IoT devices or ‘smart devices,’ which are physical objects such as appliances with computing devices embedded therein [(that is, an Internet of Things (IoT) environment)]”; Lassoued ‘807 ¶ 0015 teaches “[a] health state of a user may be learned according to user behavior, physical conditions of the user, activities relating to computing activities (e.g., writing reports, working on a particular project, writing emails, claiming expenses, browsing the internet, watching a movie on a connected device such as tablet or smart TV, etc.) of the user, or a combination thereof [(that is, identifying a physical profile of each user)]”; Lassoued ‘807 ¶ 0025 teaches “health state may include at least one or more medical conditions, a well-being (e.g., subjective well-being "SWB", emotional well-being, mental well-being, physical well-being, or an overall wellbeing) of the user, an emotional state of the user, biometric data, behavior patterns, a health profile of the user, or a combination thereof”);
[(b)] monitoring . . . a current activity of each user of a plurality of users in the IoT environment (Lassoued ‘807 ¶ 0015 teaches “[a] health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to be implemented by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device”; Lassoued ‘807 ¶ 0030 teaches “sensors may include biometric sensors, wearable sensors, computers, handheld devices (e.g., Global Positioning System “GPS” device or step counters), smart phones, and/or other sensor based devices [(that is, monitoring . . . a current activity of each user of a plurality of users in the IoT environment)]”) and one or more locations associated with the current activity (Lassoued ‘807 ¶ 0023 teaches “contextual data may include an environment such as, for example, a place where a user's activity is taking place (e.g., business, vehicle, hospital, gym, outdoors, etc.) [(that is, one or more locations associated with the current activity)]”),
[(b.1)] the monitoring performed without physical contact with the plurality of users (Lassoued ‘807 ¶ 0030 teaches “sensors may include biometric sensors, wearable sensors, computers, handheld devices (e.g., Global Positioning System ‘GPS’ device or step counters), smart phones, and/or other sensor based devices; Lassoued ‘807 ¶ 0031 teaches “the “health state” of a particular user may depend greatly upon contextual factors, such as a correlation or relationship between the health state and [activities of daily living/context of daily living (ADLs/CDLs)] of the user [(that is, “ADLs/CDLs” is monitoring performed without physical contact)], and other contextual factors such as defined by a user or learned via artificial intelligence; also, Lassoued ‘807 ¶ 0067 teaches a “[d]evice layer 55 as shown includes sensor 52, actuator 53, ‘learning’ thermostat 56 with integrated processing, sensor, and networking electronics, camera 57, controllable household outlet/receptacle 58, and controllable electrical switch 59 as shown. Other possible devices may include, but are not limited to various additional sensor devices, networking devices, electronics devices (such as a remote-control device), additional actuator devices, so called “smart” appliances such as a refrigerator or washer/dryer, and a wide variety of other possible interconnected objects [(that is, “GPS,” “smart phones,” “networking electronics,” “cameras,” etc., also provide monitoring performed without physical contact with the plurality of users)]”;
[Examiner notes that the plain meaning of “monitoring performed without physical contact” is the use of sensors that do not require direct physical contact with the object being measured; accordingly, a broadest reasonable interpretation of the term “monitoring performed without physical contact” covers the teachings of Lassoued ‘807 including monitoring of “activities of daily living / context of daily living,” which is not inconsistent with the Applicant’s disclosure. (MPEP § 2111)]), and
* * *
[(c)] tracking an operational state (Lassoued ‘807 ¶ 0019 teaches “the present invention may receive/us as input [(that is, tracking)] . . . 2) context data . . . . In one aspect, the activity data may include, for example, heart rate and stress levels, fatigue, productivity measured as a function of typing speed, computer activity, etc. [(that is, “productivity measured as a function of typing speed, computer activity, etc.” is tracking an operational state)])”; Lassoued ‘807 ¶ 0020 teaches “[i]n step 1, the present invention may learn from the input data, one or more machine learning models of correlations between the user's health state and activities of the user in the context of external conditions (context data). In step 2, the present invention may monitor the user's activities, one or more parameters of the health state of the user, and/or external conditions. In step 3, the present invention may predict/project the user's health state depending on a current and planned activities, current health state data, and contextual information”) of one or more IoT devices at the one or more locations within the IoT environment (Lassoued ‘807 ¶ 0066 & Fig. 3 teaches “the various devices inclusive of the device layer 55 may incorporate a network of entities collectively known as the “internet of things” (IoT) [Examiner annotations in dashed-line text boxes]:
PNG
media_image2.png
648
600
media_image2.png
Greyscale
Lassoued ‘807 ¶ 0067 teaches “Device layer 55 as shown includes sensor 52, actuator 53, “learning” thermostat 56 with integrated processing, sensor, and networking electronics, camera 57, controllable household outlet/receptacle 58, and controllable electrical switch 59 as shown. Other possible devices may include, but are not limited to various additional sensor devices, networking devices, electronics devices (such as a remote-control device), additional actuator devices, so called “smart” appliances such as a refrigerator or washer/dryer, and a wide variety of other possible interconnected objects”);
[(d)] predicting a potential anomalous event by correlating (Lassoued ‘807 ¶ 0020 teaches “[i]n step 1, the present invention may learn from the input data, one or more machine learning models of correlations between the user's health state and activities of the user in the context of external conditions (context data) [(that is, predicting . . . by correlating)]. . . . In step 4, the present invention may, when extended periods of time and/or high peaks of stress are observed or predicted [(that is, “extended periods of time and/or high peaks of stress” is a potential anomalous event)], plan/execute (task swapping or rescheduling) that optimizes, balances, and/or mitigates negative effects of the user's health state”) the physical profile of each user with at least one of
[(d.1)] the current activity of each user (Lassoued ‘807 ¶ 0020 teaches “the present invention may predict/project the user's health state depending on a current and planned activities [(that is, current activity of each user)], current health state data [(that is, the physical profile of each user)], and contextual information”),
[(d.2)] the one or more locations associated with the current activity (Lassoued ‘807 ¶ 0023 teaches “contextual data may include an environment such as, for example, a place where a user's activity is taking place (e.g., business, vehicle, hospital, gym, outdoors, etc.)”),
[(d.3)] a state of environment at the one or more locations associated (Lassoued ‘807 ¶ 0023 teaches that “’Context’ may refer to any information about external conditions related to the environment where the user is performing the activity (e.g., physical/virtual location data, time of the day, temperature, humidity, season, etc.) [(that is, a state of environment at the one or more locations associated)]”), and
[(d.4)] the operational state of the one or more IoT devices (Lassoued ‘807 ¶ 0019 teaches “the present invention may receive/us as input [(that is, tracking)] . . . 2) context data . . . . In one aspect, the activity data may include, for example, heart rate and stress levels, fatigue, productivity measured as a function of typing speed, computer activity, etc. [(that is, “productivity measured as a function of typing speed, computer activity, etc.” is the operational state of the one or more IoT devices )]”); and
[(e)] providing, by the at least one processor, at least one of wellness risk alert and wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event (Lassoued ‘807 ¶ 0016 teaches to “manage, adjust, and/or recommend one or more corrective/mitigating actions of the user during a defined time period (e.g., during the day) such as, for example, by attempting to maintain one of the parameters associated with the health state (e.g., stress level) below and/or above a defined threshold and/or avoiding peeks or long periods of stress [(that is, wellness solution to at least one user identified in the IoT environment based on the predicted potential anomalous event)]”).
Though Lassoued ‘807 teaches that a health state of a user may be learned while engaged in one or more activities associated with a computing device through IoT sensors, and that a machine learning module, implemented with an artificial neural network, learns a health state of a user, Lassoued ‘807, however, does not explicitly teach –
* * *
[(b) monitoring . . . a current activity] . . . , [(b.2)] the monitoring performed using multiple angular data received from UWB based sensors in a time-series manner and [(b.3)] using Recurrent Neural Network (RNN) technique based classification;
* * *
But Jadidian teaches “monitoring, using UWB based sensors, a current activity of each user,” (Jadidian ¶ 0022 & Fig. 1), and further teaches –
* * *
[(b) monitoring . . . a current activity] . . . [(b.2)] the monitoring performed using multiple angular data received from UWB based sensors in a time-series manner (Jadidian ¶ 0022 & Fig. 1, teaches “illustrating an example of an electronic device performing radar measurements [Examiner annotations in dashed-line text boxes]:”
PNG
media_image3.png
725
600
media_image3.png
Greyscale
Jadidian ¶ 0050 teaches the “electronic device 110 may determine a location of an object 118 (such as an individual, furniture, a wall or boundary, etc.) in an environment 100 (such as a room) that includes electronic device 110. Note that the location may include a range 120 or distance to object 118 and/or an angular position 122 of object 118 [(that is, using multiple angular data received from UWB based sensor)]”; also, Jadidian ¶ 0053 teaches “the transmitted wireless signals 116 include pulsed radar signals and/or continuous-wave radar signals [(that is, “pulsed” or “continuous-wave” are received from UWB based sensors in a time-series manner)]. For example, the pulsed radar signals may offer low power consumption (with a range resolution of, e.g., 5 cm, less than 5 cm, 5-10 cm, etc.), and the continuous-wave radar signals may provide rich Doppler measurements (with a range resolution of, e.g., a fraction of a centimeter)”; with respect to multiple sensors, Jadidian ¶ 0068 teaches “FIG. 1 as an example, in alternative embodiments, different numbers and/or types of electronic devices may be present. For example, some embodiments may include more or fewer electronic devices. As another example, in other embodiments, different electronic devices can be transmitting and/or receiving packets or frames. In some embodiments, different electronic devices may be transmitting and/or receiving radar signals [(that is, “different numbers and/or types of electronic devices” provides for multiple angular data)]”) . . . .
Lassoued ‘807 and Jadidian are from the same or similar field of endeavor. Lassoued ‘807 teaches measuring a health state parameter by an IoT device/sensor. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify Lassoued ‘807 pertaining to IoT sensors with the UWB devices of Jadidian.
The motivation to do so is because the “the measurement techniques may allow accurate, low-cost determination of the location of an object in an environment, such as an individual. Moreover, the measurement techniques may be used to identify the individual, a vital sign(s) and/or a medical condition(s) of the individual, and/or one or more parameters or properties of the object or the environment.” (Jadidian ¶ 0097).
Though Lassoued ‘807 and Jadidian teach that a health state of a user may be learned while engaged in one or more activities associated with a computing device through UWB based IoT sensors, and that a machine learning module, implemented with an artificial neural network, learns a health state of a user, the combination of Lassoued ‘807 and Jadidian, however, does not explicitly teach –
[(b)] monitoring . . . the current activity] . . . and [(b.3)] using Recurrent Neural Network (RNN) technique based classification.
But Fox teaches -
[(b) monitoring . . . the current activity] . . . and [(b.3)] using Recurrent Neural Network (RNN) technique based classification (Fox ¶ 0138 teaches “the respiratory therapy system 120 can use a classifier for determining the sleep status of the user [(that is, “sleep status” is monitoring the current activity)]. The classifier can be derived from any one or more of . . . a recurrent neural network [(that is, using Recurrent Neural Network (RNN) technique based classification )]”).
Lassoued ‘807, Jadidian and Fox are from the same or similar field of endeavor. Lassoued ‘807 teaches measuring a health state parameter by an IoT device/sensor. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device. Fox teaches a sleep staging classifier based on one or more of supervised machine learning, deep learning, a convolutional neural network, or a recurrent neural network.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Lassoued ‘807 and Jadidian pertaining to IoT sensors using UWB communication protocols with the recurrent neural network machine learning of Fox.
The motivation to do so is because a need exists “to develop improved devices, systems, and methods for inferring states and stages of a respiratory therapy user's sleep in order to more accurately assess the user's condition and the efficacy of the applied therapy, which can improve sleep architecture by treating sleep-disordered breathing.” (Fox ¶ 0013).
Examiner notes that the Applicant’s preamble does not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
Regarding claims 2 and 13, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 1 and 12, respectively, as described above in detail.
Lassoued ‘807 teaches -
[(a)] wherein the physical profile of each user is identified using . . . [(a.2)] a historic physical profile stored in a database (Lassoued ‘807 ¶ 0078 teaches “[t]he features and/or parameters database 404 may also include user profiles (e.g., a collection of user profiles with various information relating to the health state of each user); Lassoued ‘807 ¶ 0083 teaches “[t]he user profile component 410 may include data (e.g., historical data and/or real-time data) relating to a health state of a user ( e.g., the well-being of the user) [(that is, identified using . . . a historic physical profile stored in a database)], [activities of daily living (ADLs)], [context of daily living (CDLs)], behavioral patterns and characteristics, biometric data, medical history data, contextual data, feedback information, and data associated with the knowledge domain/ontology”).
Though Lassoued ‘807 teaches that a health state of a user may be learned while engaged in one or more activities associated with a computing device through IoT sensors, Lassoued ‘807, however, does not explicitly teach -
[(a)] wherein the physical profile of each user is identified using [(a.1)] UWB based sensors . . . .
But Jadidian teaches -
[(a)] wherein the physical profile of each user is identified using [(a.1)] UWB based sensors (Jadidian ¶ 0043 teaches an “electronic device [110] performs the radar measurements using radar signals in one or more bands of frequencies. . . . Notably, the wireless signals may be compatible include or may use UWB or 'pulse radio', and/or may be compatible with an IEEE 802.15 standard (such as IEEE 802.15.4)”; Jadidian ¶ 0050 teaches an “electronic device 110 (such as . . . a wearable device, e.g., a smartwatch, a wireless speaker; an IoT device [(that is, UWB-based sensors)], a smart appliance, a set-top box, a security device, or another type of electronic device) may include one or more radar transmitters 112 and N separate radar receivers (Rx) 114 that are co-located in electronic device 110) . . . .
Regarding claims 3 and 14, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 1 and 12, respectively, as described above in detail.
Lassoued ‘807 teaches -
wherein the identifying of the physical profile of each user present in the IoT environment is performed . . . [(a.2)] using a reinforcement learning technique involving feedback from a user of the IoT environment (Lassoued ‘807 ¶ 0092 teaches “the machine learning module 406 may cognitively learn a health state of a user while operating the UE 420. The estimation/predictive modeling (or machine learning modeling), as described herein, may be performed using a wide variety of methods or combinations of methods, such as . . . reinforcement learning and so forth”; Lassoued ‘807 ¶ 0103 teaches “[t]he well-being controller 512 may also receive feedback on each recommendations from the user either explicitly (e.g., rating/ranking of recommendations) or implicitly (e.g., user ignoring recommendations) to learn improved future recommendations [(that is, using a reinforcement learning technique involving feedback from a user of the IoT environment)]”).
Though Lassoued ‘807 teaches that a health state of a user may be learned while engaged in one or more activities associated with a computing device through IoT sensors, Lassoued ‘807, however, does not explicitly teach -
wherein the identifying of the physical profile of each user present in the IoT environment is performed [(a.1)] using multiple angular data received from UWB based sensors in a time-series manner . . . .
But Jadidian teaches –
wherein the identifying of the physical profile of each user present in the IoT environment is performed [(a.1)] using multiple angular data received from UWB based sensors in a time-series manner (Jadidian ¶ 0022 & Fig. 1, teaches “illustrating an example of an electronic device performing radar measurements [Examiner annotations in dashed-line text boxes]:”
PNG
media_image3.png
725
600
media_image3.png
Greyscale
Jadidian ¶ 0050 teaches the “electronic device 110 may determine a location of an object 118 (such as an individual, furniture, a wall or boundary, etc.) in an environment 100 (such as a room) that includes electronic device 110. Note that the location may include a range 120 or distance to object 118 and/or an angular position 122 of object 118 [(that is, using multiple angular data received from UWB based sensor)]”; also, Jadidian ¶ 0053 teaches “the transmitted wireless signals 116 include pulsed radar signals and/or continuous-wave radar signals [(that is, “pulsed” or “continuous-wave” are received from UWB based sensors in a time-series manner)]. For example, the pulsed radar signals may offer low power consumption (with a range resolution of, e.g., 5 cm, less than 5 cm, 5-10 cm, etc.), and the continuous-wave radar signals may provide rich Doppler measurements (with a range resolution of, e.g., a fraction of a centimeter)”; with respect to multiple sensors, Jadidian ¶ 0068 teaches “FIG. 1 as an example, in alternative embodiments, different numbers and/or types of electronic devices may be present. For example, some embodiments may include more or fewer electronic devices. As another example, in other embodiments, different electronic devices can be transmitting and/or receiving packets or frames. In some embodiments, different electronic devices may be transmitting and/or receiving radar signals [(that is, “different numbers and/or types of electronic devices” provides for multiple angular data)]”) . . . .
Regarding claims 6 and 17, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 2 and 13, respectively, as described above in detail.
Lassoued ‘807 teaches -
[(a)] wherein the physical profile and the historic physical profile both comprise (Lassoued ‘807¶ 0083 teaches “[t]he user profile component 410 may include data (e.g., historical data and/or real-time data) relating to a health state of a user ( e.g., the well-being of the user), ADLs, CDLs, behavioral patterns and characteristics, biometric data, medial history data, contextual data, feedback information, and data associated with the knowledge domain/ontology [(that is, the “real-time data” is physical profile and “historical data” is the historic physical profile)]”) information associated with at least one of user height, user body type, user shape, user age, user gender and stage type, user movement, user average speed of movement, or restricted user movement (Lassoued ‘807 ¶ 0022 teaches “‘User health conditions’ may be parameters that may indicate a presence/absence, type, and/or degree of a chronic or acute disease (e.g., diabetes, epilepsy, angina, etc.), symptom (e.g., headache, faint, etc.), history of medications/prescriptions, age [(that is, information associated with . . . user age)], etc. . . . The user health state may include behavioral parameters that may be parameters characterizing the behavior of a user such as, for example, falling asleep, slow reaction time [(that is, information associated with . . . user average speed of movement)], posture [(that is, information associated with . . . restricted user movement)], etc.”).
Regarding claims 7 and 18, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 1 and 12, respectively, as described above in detail.
Lassoued ‘807 teaches -
[(d.1)] wherein correlating the physical profile of each user with the at least one of the current activity of each user, the one or more locations associated with the current activity, the state of environment at the one or more locations associated, and the operational state of the one or more IoT devices is performed using a supervised machine learning technique (Lassoued ‘807 ¶ 0092 teaches “the machine learning module 406 may cognitively learn a health state of a user while operating the UE 420. The estimation/predictive modeling (or machine learning modeling), as described herein, may be performed using a wide variety of methods or combinations of methods, such as supervised learning [(that is, correlating . . . is performed using a supervised machine learning technique)]”; Lassoued ‘807 ¶ 0105 teaches “learning module 518 may generate one or more learned models 514, which may be retrieved by the well-being controller 512 for recommending mitigating activities (e.g., rescheduling or swapping activities of the user 502 while interacting with computer 530). In one aspect, the learned models 514 may include, for example, a correlation between the activity data and user's health state while taking into account context information. The learned models 514 may include models that support a prediction the user's health state depending on the user's current activity and previous activities, current well-being data, and context information”).
Regarding claim 9, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claim 1 as described above in detail.
Lassoued ‘807 teaches -
[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution to the at least one user identified in the IoT environment (Lassoued ‘807 teaches “a prediction component 408 for cognitively learning a health state of a user and recommending personalized advice, suggestions, or notifications of a user profile to minimize one or more possible negative impacts upon the health state of the user and/or to avoid adverse impacts on the user's health state, by one or more IoT devices 420 associated with the IoT device component 416 in the recommendation system 430 [(that is, the providing of the at least one of wellness risk alert and wellness solution to the at least one user identified in the IoT environment )]) [(e.1)] is performed using a classification technique (Lassoued ‘807 ¶ 0092 teaches the “estimation/predictive modeling . . . may be performed using a wide variety of methods or combinations of methods, such as . . . naive bays classifier, . . . ensembles of classifiers, . . . ordinal classification, . . . statistical classification, linear classifiers, . . . quadratic classifiers [(that is, performed using a classification technique)]”).
Regarding claim 10, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claim 1 as described above in detail.
Lassoued ‘807 teaches -
[(e)] wherein the at least one of wellness risk alert and wellness solution [(e)] is indicated using one or more IoT device, of the one or more IoT devices, present at the one or more locations within the IoT environment (Lassoued ‘807 ¶ 0075 teaches “a recommendation system 430, and a user equipment (“UE”) 420, such as a desktop computer, laptop computer, tablet, smart phone, and/or another electronic device that may have one or more processors and memory [(that is, “user equipment” is the one or more IoT device)]. The UE 420, the recommendation system 430, and the computing environment 402 may each be associated with and/or in communication with each other, by one or more communication methods, such as a computing network [(that is, the at least one of wellness risk alert and wellness solution [(e.1)] is indicated using the one or more IoT device present at the one or more locations within the IoT environment)]”).
Regarding claim 21, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claim 1, as described above in detail.
Lassoued ‘807 teaches -
wherein the UWB sensors are distributed in different rooms from each other (Lassoued ‘807 ¶ 0018 teaches “[t]he contextual data may include an environment such as, for example, a place where a user's activity is taking place (e.g., business, vehicle, hospital, gym, outdoors, etc. [(that is, different rooms from each other)]). ‘Context’ may refer to any information about external conditions related to the environment where the user is performing the activity (e.g., physical/virtual location data, time of the day, temperature, humidity, season, etc.) [(that is, sensors are distributed in different rooms from each other )]”).
Jadidian teaches UWB sensors for object location, further that “electronic device 110 may determine a location of an object 118 (such as an individual, furniture, a wall or boundary, etc.) in an environment 100 (such as a room) that includes electronic device 110. Note that the location may include a range 120 or distance to object 118 and/or an angular position 122 of object 118.” (Jadidian ¶ 0050).
10. Claims 5, 8, 16, and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200367807 to Lassoued et al. [hereinafter Lassoued ‘807] in view of US Published Application 20210096217 to Jadidian et al. [hereinafter Jadidian], US Published Application 20230099622 to Fox et al. [hereinafter Fox], and US Published Application 20200168335 to Lassoued et al. [hereinafter Lassoued ‘335].
Regarding claims 5 and 16, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 1 and 12, respectively, as described above in detail.
Though Lassoued ‘807, Jadidian, and Fox teaches monitoring of current activities in an IoT network, the combination of Lassoued ‘807, Jadidian, and Fox, however, does not explicitly teach –
[(a)] wherein the current activity includes [(a.1)] a physical activity and [(a.2)] is selected from a group comprising walking, moving, running, crawling, sitting, standing, jumping, bending, cleaning, or eating.
But Lassoued ‘335 teaches -
[(a)] wherein the current activity includes [(a.1)] a physical activity and [(a.2)] is selected from a group comprising walking, moving, running, crawling, sitting, standing, jumping, bending, cleaning, or eating (Lassoued ‘335 ¶ 0019 teaches “‘user activity’ may refer to physical activities of a user [(that is, a physical activity)], such as driving [(that is, “driving” inherently includes sitting)], exercising [(that is, “exercising” inherently includes activities of moving, running, jumping, bending)], travelling, walking, sleeping, etc.”; also, Lassoued ‘335 ¶ 0031 teaches leaning contextual factors through physical activity, where “[e]ach learned health state may be saved as part of a user profile and/or retained in a knowledge domain. For example, the cognitive learning may learn preferred ADLs for particular priorities (e.g., brush teeth before leaving to work [(that is, cleaning)]), preferences (dining at a particular restaurant [(that is, “dining” is inherently eating)]), or even time periods (e.g., walking to work on warm, sunny days while taking a cab to work on rainy days).”).
Lassoued ‘807, Jadidian, Fox, and Lassoued ‘335 are from the same or similar field of endeavor. Lassoued ‘807 teaches intelligent monitoring of a health state of a user that may be learned while engaged in one or more activities associated with a computing device. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device. Fox teaches a sleep staging classifier based on one or more of supervised machine learning, deep learning, a convolutional neural network, or a recurrent neural network. Lassoued ‘335 teaches an intelligent health recommendation service where a health state of a user may be learned according to user behavior, physical conditions of the user, activities of daily living (ADL) and associated context of daily living (CDL) of the user, or a combination thereof.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Lassoued ‘807, Jadidian, and Fox pertaining to IoT sensors using UWB communication protocols using a recurrent neural network machine with the physical activities of Lassoued ‘335.
The motivation to do so is to “to improve performance in many aspects of life such as daily activities, physical, emotional, mental activities, environmental conditions, and other functions, and also to contribute to the regulation of the various physiological systems of the organism ( e.g., person) such as, the immune system.” (Lassoued ‘335 ¶ 0026).
Regarding claims 8 and 19, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claims 1 and 12, respectively, as described above in detail.
Though Lassoued ‘807, Jadidian, and Fox teaches monitoring of current activities in an IoT network, the combination of Lassoued ‘807, Jadidian, and Fox, however, does not explicitly teach –
[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution to the at least one user identified in the IoT environment is based on
the predicted potential anomalous event and
similar historic event identified in the past at different location within the IoT environment and
corresponding action performed by the one or more users to the similar historic event.
But Lassoued ‘335 teaches -
[(e)] wherein the providing of the at least one of wellness risk alert and wellness solution to the at least one user identified in the IoT environment is based on
the predicted potential anomalous event (Lassoued ‘335 ¶ 0029 teaches a “stream of feedback data may be processed and the real-time flux of information enables the generation of knowledge or knowledge domain/ontology and enables the learning a health state of a user and generating personalized advice (e.g., suggestions, warnings, alerts, or recommendations) relating to the learned health state for adjusting one or more ADLs, CDLs, or other activities and environments that may negatively impact the person's well-being or state of health [(that is, the “may negatively impact” is the predicted potential anomalous event)], using cloud computing and/or edge computing technology”) and
similar historic event identified in the past (Lassoued ‘335 ¶ 0032 teaches an “ontology may include, but is not limited to, the knowledge domain or data repository of a collection of material, information, content and/or other resources related to a particular subject or subjects. For example, the ontology may include, data relating to a user's health state. The ontology may have defined ADLs, CDLs, and a user profile (e.g., calendar information, historical data relating to medical conditions of the user, emotional/physical/mental condition of the user, preferences, priorities, biomedical data, psychophysical parameters of the user, medical history, emotional data, skills set, and the like [(that is, a “collection” is similar historic event identified in the past)]”) at different location within the IoT environment (Lassoued ¶ 0013 teaches “[t]he present invention may be omnipresent (e.g., at home, in a car, outdoors, etc.) [(that is, “omnipresent” is different locations within the IoT environment)], and may activate the appropriate health applications, plugins and devices depending on the user activity and location”) and
corresponding action performed by the one or more users to the similar historic event (Lassoued ‘335 ¶ 0023 teaches “the intelligent, personalized health recommendation service (e.g., a health assistant) may provide a monitoring and mitigation phase [(that is, a “mitigation phase” is a corresponding action performed by the one or more users)]”; Lassoued ‘335 ¶ 0027 teaches a “[context of daily living (CDL)] may also include one or more dimensions such as, for example, time, location, environment conditions, weather conditions, traffic conditions, and the like. A knowledge domain may provide one or more correlations or relationships between a person's health state and the ADLs and CDLs [(that is, the “correlations” is corresponding action performed by the one or more users to the similar historic event)]”).
Lassoued ‘807, Jadidian, Fox, and Lassoued ‘335 are from the same or similar field of endeavor. Lassoued ‘807 teaches intelligent monitoring of a health state of a user that may be learned while engaged in one or more activities associated with a computing device. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device. Fox teaches a sleep staging classifier based on one or more of supervised machine learning, deep learning, a convolutional neural network, or a recurrent neural network. Lassoued ‘335 teaches an intelligent health recommendation service where a health state of a user may be learned according to user behavior, physical conditions of the user, activities of daily living (ADL) and associated context of daily living (CDL) of the user, or a combination thereof.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Lassoued ‘807, Jadidian, and Fox pertaining to IoT sensors using UWB communication protocols using a recurrent neural network machine with the physical activities of Lassoued ‘335.
11. Claim 11 is rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200367807 to Lassoued et al. [hereinafter Lassoued ‘807] in view of US Published Application 20210096217 to Jadidian et al. [hereinafter Jadidian], US Published Application 20230099622 to Fox et al. [hereinafter Fox], and US Published Application 20210057101 to Desa et al. [hereinafter Desa].
Regarding claim 11, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claim 1 as described above in detail.
Lassoued ‘807 teaches -
further comprising:
[(f)] determining an action performed by the one or more users on the one or more IoT devices at the one or more locations in the IoT environment to the at least one of wellness risk alert and wellness solution at the one or more locations (Lassoued ‘807 ¶ 0088 teaches “[a] feedback component 414 may use a variety of feedback information relating to the recommendation system 430 and feedback information pertaining to the user may be stored and maintained in the feedback component 414 and used by the machine learning module 406, the features and/or parameters 404, or both. The feedback component 414 may collect a variety of feedback information for the user relating to the health state and the recommended mitigating suggestions. For example, the feedback component 414 may collect data gathered from the user (e.g., cognitive interaction and reasoning), the UE 420, the IoT device component 416 or other source for learning the health state of a user, contextual information, or combination thereof; Lassoued ‘807 ¶ 0103 teaches “[t]he well-being controller 512 may also receive feedback on each recommendations from the user either explicitly (e.g., rating/ranking of recommendations) or implicitly (e.g., user ignoring recommendations) to learn improved future recommendations [(that is, the “feedback information” provides for determining an action performed by the one or more users on the one or more IoT devices at the one or more locations in the IoT environment to the at least one of wellness risk alert and wellness solution at the one or more locations)]”); and
* * *
Though Lassoued ‘807, Jadidian, and Fox teaches a cognitive learning process using artificial intelligence may learn each of the actions, decisions, ADLs, CDLs, behavior patterns of a user, a medical profile (which may include data relating to medical care or medical conditions), or other activities, the combination of Lassoued ‘807, Jadidian, and Fox, however, does not explicitly teach –
further comprising:
* * *
and [(g)] retraining the correlation between the physical profile of each user with at least one of the current activity of each user, the one or more locations associated with the current activity, the state of environment at the one or more locations associated, and the operational state of the one or more IoT devices
But Desa teaches -
further comprising:
* * *
and [(g)] retraining the correlation between the physical profile of each user with at least one of the current activity of each user, the one or more locations associated with the current activity, the state of environment at the one or more locations associated, and the operational state of the one or more IoT devices (Desa ¶0051 teaches “[n]eural network (specifically Long Short-Term Networks) and deep learning models are continually updated with time series inputs of labeled activities, mobility measures, discovered activities sequences, and vital signs [(that is, “continually updated” is retraining the correlation)]. These models identify significant changes in these inputs over time, allowing the recognition of anomalous activity on the part of the monitored individual”; Desa ¶ 0050 teaches “[b]ased on health conditions [(that is, the physical profile of each user)], these algorithms are adjusted dynamically; for example, the models may be “tuned” or further trained. . . . Support vector machine, logistic regressions, random forest, sequential pattern mining and convolutional neural network methods are applied within a set of model metrics that is depended on individual health conditions”; Desa ¶ 0010 teaches “receive at least one measurement from at least one sensor; and determine the activity of the patient based on: (i) training data from a population of patients and/or training data from a home of the single monitored patient, and (ii) the received at least one measurement from the at least one sensor; wherein the activity includes at least one of: a common daily activity; an activity of daily living (ADL) [(that is, retraining the correlation between the physical profile of each user with at least one of the current activity of each user)]”).
Lassoued ‘807, Jadidian, Fox and Desa are from the same or similar field of endeavor. Lassoued ‘807 teaches intelligent monitoring of a health state of a user that may be learned while engaged in one or more activities associated with a computing device. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device. Fox teaches a sleep staging classifier based on one or more of supervised machine learning, deep learning, a convolutional neural network, or a recurrent neural network. Desa teaches in-home monitoring and an early health crisis alarm system for elderly individuals and patients with chronic diseases.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Lassoued ‘807, Jadidian, and Fox pertaining to intelligent health monitoring with the patient health model retraining and/or updating of Desa.
The motivation to do so is for “an in-home patient monitoring system that operates without the use of microphones, cameras, or other data gathering with the potential to allow direct identification of home occupants.” (Desa ¶ 0013).
12. Claims 1-3, 6, 7, 9, 10, 12-14, 17, 18, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200367807 to Lassoued et al. [hereinafter Lassoued ‘807] in view of US Published Application 20210096217 to Jadidian et al. [hereinafter Jadidian], US Published Application 20230099622 to Fox et al. [hereinafter Fox], and US Published Application 20200228943 to Martin et al. [hereinafter Martin].
Regarding claim 22, the combination of Lassoued ‘807, Jadidian, and Fox teaches all of the limitations of claim 1, as described above in detail.
Though Lassoued ‘807, Jadidian, and Fox teach an IoT environment for intelligent monitoring of a health state of a user, the combination of Lassoued ‘807, Jadidian, and Fox, however, does not explicitly teach -
wherein the device for providing human wellness recommendation provides continuous tracking of a plurality of users in locations with an obstruction between a user and the UWB sensors.
But Martin teaches -
wherein the device for providing human wellness recommendation provides continuous tracking of a plurality of users in locations with an obstruction between a user and the UWB sensors (Martin, Fig. 1, teaches a UWB indoor positioning system [Examiner annotations in dashed-line text-boxes]:
PNG
media_image4.png
953
1271
media_image4.png
Greyscale
Martin ¶ 0043 teaches “a UWB indoor positioning system 100 includes UWB beacons 102 [(that is, UWB sensors , also referred to as anchors, installed across an indoor space 120, for example but not limited to across a ceiling 122 [(that is, an obstruction between a user and the UWB sensor)] in the indoor space 120. . . . The UWB indoor positioning system 100 further includes at least one UWB tag 104 [(that is, the device provides continuous tracking)], also referred to as a ranger, configured to wirelessly communicate with the UWB beacons 102 such that a computing device 111 executing a control software 112, such as a compute module located/executed/hosted in the cloud 108 and in communication with the UWB beacons 102, may determine/track the location of the UWB tag 104 and/or enable or disable a building environment device and/or a building security device based on such communication [(that is, ”the device for providing human wellness recommendation provides continuous tracking of a plurality of users in locations with an obstruction between a user and the UWB sensors)]”).
Lassoued ‘807, Jadidian, Fox and Martin are from the same or similar field of endeavor. Lassoued ‘807 teaches intelligent monitoring of a health state of a user that may be learned while engaged in one or more activities associated with a computing device. Jadidian teaches enabling the use of radar measurements of health-related monitoring and applications by an electronic device. Fox teaches a sleep staging classifier based on one or more of supervised machine learning, deep learning, a convolutional neural network, or a recurrent neural network. Martin teaches ultra-wideband (UWB) positioning that determines movement or location information of the UWB tag.
Thus, it would have been obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant’s invention to modify the combination of Lassoued ‘807, Jadidian, and Fox pertaining to intelligent health monitoring with the continuous tracking via USB sensors of Martin.
The motivation to do so is because “improvements are desired in control systems using UWB positioning.” (Martin ¶ 0006).
Response to Arguments
13. Examiner has fully considered Applicant’s arguments, and responds below accordingly.
Section 101
14. Applicant submits that “we amended to recite "the monitoring of the current activity of each user in the IoT environment is performed by the at least one processor using multiple angular data received from the UWB based sensors in a time-series manner and using Recurrent Neural Network (RNN) technique based classification." The specification describes at least three improvements in the claims.” (Response at p. 7).
“First, we note that paragraphs [00133] and [00134] describe some of the efficiency improvements of the present application as claimed.” (Response at pp. 7-8 (citing Specification ¶ 0133)).
“Second, the RNN technique improves the accuracy of the classification. See specification at paragraphs [0041]-[0043] ("During the training phase, the reinforcement learning technique is provided with feedback from a user to improve the accuracy for the physical profile of each user in the IoT environment.").” (Response at p. 8).
“Third, as noted in paragraph [00132] of the application as originally filed, the use of a physical profile and UWB instead of sensitive biometric data such as facial recognition improves the accuracy and the privacy of the users and reduces the security risks of the device.” (Response at p. 8 (citing Specification ¶ 0132)).
Applicant submits that “[t]he present claims improve the functioning of the devices by using less processor intensive methods (UWB instead of image recognition), they improve accuracy of the device by using UWB which can improve tracking in environments with obstruction, and the claims improve security by using less sensitive tracking data (a physical profile instead of sensitive biometric data such as facial recognition). Finally, the claims use a specific algorithm "the monitoring of the current activity of each user in the IoT environment is performed by the at least one processor using multiple angular data received from the UWB based sensors in a time-series manner and using Recurrent Neural Network (RNN) technique based classification." As recently held in Ex Parte Harris, No. 2025-000962 (P.T.A.B. Oct. 23, 2025), technical improvements such as an improvement to security and limitations which integrate the judicial exception into a practical application are patent eligible. See Ex Parte Harris, No. 2025-000962 (P.T.A.B. Oct. 23, 2025) at pp. 11-12.” (Response at p. 9).
Examiner’s Response:
Examiner respectfully submits that the generic computer components of the claims are used in the ordinary and expected manner, and accordingly, do not serve to integrate the abstract idea into a practical application.
For Step 2A Prong Two, the rejection identify any additional elements recited in the claim beyond the identified judicial exception; and evaluates the integration of the judicial exception into a practical application by explaining that the claim as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application using the considerations set forth in MPEP §§ 2106.04(d), 2106.05(a)- (c) and (e)- (h). Examiners should give weight to all of the claimed additional elements in Prong Two, even if those elements represent well-understood, routine, conventional activity.
For example, the claims recite “one or more IoT devices,” “UWB sensors,” a “RNN technique,” which are used to implement the abstract idea into a practical application, that does not serve to integrate the abstract idea into a practical application. (MPEP § 2106.05(f)).
With regard to the improvements, an abstract idea made more efficient remains an abstract idea. Also, the characteristics of classification accuracy is inherent to an RNN technique, while UWB sensors may be secure, however, such features are not tethered to the instant claims.
Therefore, the pending claims are subject-matter ineligible.
Sections 102 & 103
15. Applicant submits that “Applicants submit that the cited references fail to disclose or render obvious the presently claimed combination of features recited in the independent claims.” (Response at p. 10).
Applicant submits that “[w]e believe that Lassoued' 807 requires physical contact with a user to monitor health and that modifying Lassoued' 807 to remove physical contact would render Lassoued' 807 unsatisfactory for its intended purpose.” (Response at p. 12).
Also, Applicant submits that “Jadidian does not disclose or suggest ‘the monitoring of the current activity of each user in the IoT environment is performed by the at least one processor using multiple angular data received from the UWB based sensors in a time-series manner and using Recurrent Neural Network (RNN) technique based classification’.” (Response at p. 13).
Examiner’s Response:
Examiner respectfully disagrees, because Lassoued ‘807 teaches monitoring “without physical contact,” as set out above in detail. For example, GPS sensors are capable of monitoring without physical contact. Also, with regard to Jadidian, the feature of using UWB sensors for user location and tracking is provided, while Fox is relied upon for the use of a recurrent neural network, which inherently is used for time-series based data.
However, the claims do not set out the nature of the sensor data, simply that “monitoring is performed.”
Also, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Where a rejection of a claim is based on two or more references, a reply that is limited to what a subset of the applied references teaches or fails to teach, or that fails to address the combined teaching of the applied references may be considered to be an argument that attacks the reference(s) individually, as is the case here with the cited prior art of Lassoued ‘807, Jadidian, and Fox.
Moreover, the rejections hereinabove clearly sets forth which claim limitations are taught by each of the prior art references, and the reason why it would be obvious to a person having ordinary skill in the art as of the effective filing date of the Applicant's invention to combine their teachings, and Applicant has not explained why the cited prior art references cannot be combined in the manner set forth in the rejection.
Conclusion
16. 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.
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(US Published Application 20180279946 to Nachman et al.) teaches identifying environmental factors and user context that affect sleep quality . Embodiments use information about the static sleep environment, as well as dynamic environmental factors , such as sound , light , movement , correlated with user context, such as physical and emotional state , as well , as recent behavior to classify sleep data . The correlated and classified sleep data may be used to provide change recommendations, where implementing the recommended change is believed to improve the user ' s sleep quality .
(US Published Application 20230140151 to Rezai et al.) teaches a wellness-relevant parameter representing the user is monitored at a portable device over a deFofined period to produce a time series for the wellness-relevant parameter. A first set and a second set of either cognitive assessment data or psychosocial assessment data are obtained for the user at respective first and second times in the defined period. A value is assigned to the user via a predictive model according to the time series for the wellness-relevant parameter, the first set of either cognitive assessment data or psychosocial assessment data, and the second set of either cognitive assessment data or psychosocial assessment data.
(Klavestad et al., “Monitoring Activities of Daily Living using UWB Radar Technology: A Contactless Approach,” MDPI (2020)) teaches the significance of using non-wearable UWB sensors for developing non-intrusive, unobtrusive, and privacy-preserving monitoring of elderly ADLs. A controlled experiment was setup, implementing multiple non-wearable sensors in a smart home Lab setting. A total of nine (n = 9) participants were involved in conducting predefined scenarios of ADLs- cooking, eating, resting, sleeping and mobility. We employed the UWB sensing prototype and conventional implementation technologies, and the sensed data of both systems were stored, analysed and their performances were compared.
18. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
1 Reference markers are applied to the claim limitations for the limited purpose of evaluation of the claims herein.
Prosecution Insights