DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed 12-17-2025 has been entered.
Status of Claims
This action is a non-final rejection
Claims 1, 3-7, 9-20 are pending
Claims 2, 8 were cancelled
Claims 1, 4, 7, 17, 19, 20 were amended
Claims 1, 3-7, 9-20 are rejected under 35 USC § 101
Claims 1, 3-7, 9-20 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a domestic priority date of 5-13-2022
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-7, 9-20 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1, 3-7, 9-20, the claims recite an abstract idea of “performing contact tracing”.
Independent Claims 1, 7 and 15 are rejected under 35 U.S.C 101 based on the following analysis.
Regarding claim 1:
-Step 1 (Does the claim fall within a statutory category? YES): claim 1 recites a system of contact tracing.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
communicate .. to create a geo-fence within a designated area;
receive location information associated with a relative location of an asset based at least in part on information relating to the asset
communicate in the designated area, the information relating to regarding the relative location of the asset;
receive the information relating to a contact tracing event within the geo-fence within the designated area, to receive, a plurality of parameters associated with the information relating to the and received from the one or more sensors in the designated area space; and
determine a contact status of the asset based on the plurality of parameters, the plurality of parameters including a duration of time the
calculate the proximity score using one or more signals ......, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the of
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “performing contact tracing”. Alternatively, the claim belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people (including social activities, and following rules or instructions) as it recites: “performing contact tracing” (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 1 recites:
a plurality of aggregators configured to communicate in a meshed network
each of the plurality of aggregators comprising a processor and a transceiver, each of the plurality of aggregators being configured to receive location information
one or more sensors, each of the one or more sensors associated with the
one or more sensors configured to communicate, with the plurality of aggregators
a central computer in communication with the plurality of aggregators and configured to receive the information from at least one of the plurality of aggregators
the central computer configured to receive, from at least one of the plurality of aggregators, a plurality of parameters
wherein the central computer is configured to calculate the proximity score using one or more signals from the one or more sensors as captured by each aggregator.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 1 recites:
a plurality of aggregators configured to communicate in a meshed network
each of the plurality of aggregators comprising a processor and a transceiver, each of the plurality of aggregators being configured to receive location information
one or more sensors, each of the one or more sensors associated with the
one or more sensors configured to communicate, with the plurality of aggregators
a central computer in communication with the plurality of aggregators and configured to receive the information from at least one of the plurality of aggregators
the central computer configured to receive, from at least one of the plurality of aggregators, a plurality of parameters
wherein the central computer is configured to calculate the proximity score using one or more signals from the one or more sensors as captured by each aggregator;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)) Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Regarding claim 7:
-Step 1 (Does the claim fall within a statutory category? YES): claim 7 recites a method of contact tracing.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
transmitting a plurality of parameters relating to an asset, the asset including one or more tags positioned thereon for communicating in a designated area;
determining a localization of the asset in the designated area based on the plurality of parameters, determining a contact between the asset and one or more additional assets based on the determined localization of the asset and the one or more additional assets;
determining a contact status of the asset based on the plurality of parameters using an algorithm, the plurality of parameters including a duration of time the asset remains in the designated area and
determining a close contact between the asset and the one or more additional assets, a close contact defined as a contact between the asset and the one or more additional asset indicative of spreading disease;
wherein the proximity score is calculated using one or more signals..., the one or more signals being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the asset and a second numerical vector related to one or the one or more additional assets
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “performing contact tracing”. Alternatively, the claim belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people (including social activities, and following rules or instructions) as it recites: “performing contact tracing”. (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 7 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to an asset to a central computer;
determining a localization, by the central computer;
using an algorithm on the central computer;
proximity score is calculated using one or more signals from the one or more tags as captured by each aggregator.
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 7 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to an asset to a central computer;
determining a localization, by the central computer;
using an algorithm on the central computer;
proximity score is calculated using one or more signals from the one or more tags as captured by each aggregator;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)) Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Regarding claim 15:
-Step 1 (Does the claim fall within a statutory category? YES): claim 15 recites a method of contact tracing.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
transmitting a plurality of parameters relating to one or more assets, each of the one or more assets including one or more tags positioned thereon for communicating in a designated area, the one or more assets including at least a first asset and a second asset;
determining, a proximity score related to a proximity between the first asset and the second asset based on the plurality of parameters from the plurality of parameters;
determining, a duration score related to a duration of time the first asset and the second asset are in the designated area based on the plurality of parameters from the plurality of parameters;
determining a contact status of the first asset and the second asset based on the proximity score and the duration score using an algorithm, the contact status being a normalized combination of aggregated duration and proximity score of the asset;
the algorithm comparing the proximity score to a threshold proximity value stored and comparing the duration score to a threshold duration value stored;
belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “performing contact tracing”. Alternatively, the claim belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people (including social activities, and following rules or instructions) as it recites: “performing contact tracing”. (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claim 15 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to one or more assets to a central computer;
determining, by a central computer, a proximity score;
determining, by a central computer, a duration score;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)). Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two,
Claim 15 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to one or more assets to a central computer;
determining, by a central computer, a proximity score;
determining, by a central computer, a duration score;
Amounting to additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. Support for this can be found in the specification, paragraphs (0087-0136). (refer to MPEP 2106.05(f)) Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recite additional limitations that further define the abstract idea of “performing contact tracing”. These claim limitations include:
Claims 3 and 9: wherein, compare the proximity score with a stored proximity threshold value to determine if the asset and the additional asset are in close proximity indicative of spreading disease;
Claims 4, 10 and 20: wherein, calculate a contact duration score using timestamps of all the signals received in the designated area;
Claims 5 and 11: wherein, compare the contact duration score with a stored duration threshold value to determine if the asset and the additional asset are in contact long enough to be indicative of spreading disease;
Claims 6, 13 : create a contact database of contacts between the asset and the one or more additional assets for automatic contact tracing;
Claim 12: further comprising generating an alert if the contact is determined to be a close contact indicative of spreading disease;
Claim 14: further comprising creating a geo-fence within the designated area defining the designated area:
Claim 16: wherein the contact status is determined to be a close contact when the proximity score exceeds the threshold proximity value and the duration score exceeds the threshold duration value, indicating a contact that is indicative of spreading disease;
Claim 17: creates a contact database of first asset and the second asset
Claim 18: further comprising creating a geo-fence within the designated area defining the designated area, capable of communicating;
Claim 19: wherein, calculate a proximity score using one or more signals, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the first the second asset
Claim 20: further comprising calculating a contact duration score using timestamps of the one or more
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claims 3 and 9:
central computer:
Claims 4:
central computer is configured to calculate a contact duration score;
signals received by the plurality of aggregators;
Claims 5 and 11:
central computer;
Claims 6, 13 and 17:
central computer creates a contact database;
Claims 10 and 20:
central computer is configured to calculate a contact duration score;
signals received by the plurality of aggregators
Claim 14:
plurality of aggregators;
central computer;
network.
Claim 18:
plurality of aggregators;
central computer;
network.
Claim 19:
calculating a proximity score using one or more signals from the tags as captured by each aggregator;
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite additional elements that are recited at a high-level of generality such that it amounts to no more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claims 3 and 9:
central computer:
Claims 4:
central computer is configured to calculate a contact duration score;
signals received by the plurality of aggregators;
Claims 5 and 11:
central computer;
Claims 6, 13 and 17:
central computer creates a contact database;
Claims 10 and 20:
central computer is configured to calculate a contact duration score;
signals received by the plurality of aggregators
Claim 14:
plurality of aggregators;
central computer;
network.
Claim 18:
plurality of aggregators;
central computer;
network.
Claim 19:
calculating a proximity score using one or more signals from the tags as captured by each aggregator;
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
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
non-obviousness.
Claims 1, 3, 6-7, 9, 12-14 are rejected under 35 U.S.C. 103 as being un-patentable by Merjanian et.al. (US 20220285036 A1) hereinafter “Merjanian” in view of Mesirow et.al (US 20210313074 A1) hereinafter “Mesirow”.
Regarding claim 1 Merjanian teaches:
a plurality of aggregators (multiple… beacons 230A, 230B) configured to communicate in a meshed network (mesh network) to create a geo-fence (geofence) within a designated area, each of the plurality of aggregators comprising a processor (computing devices) and a transceiver each of the plurality of aggregators being configured to receive location information associated with a relative location of an asset based at least in part on information relating to the asset (receive, .. and/or transmit); (See at least [0021] via: “… The disclosed systems and methods may track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto). Both methods may be applied to presently disclosed technology at preexisting beacons and/or newly installed beacons at known or recordable locations of interest…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented. Specifically, FIG. 2 provides an exemplary real-world implementation of the system 100 conceptually illustrated in FIG. 1. The environment 200 may include multiple nodes or computing devices (e.g., a server/cloud services 240, … beacons 230A, 230B, 230C, 230E, 230E) and/or other components… Connections between the components may form a mesh network, with individual components operating as a node within the mesh network. A node within the mesh network may refer to a connection point that can receive, generate, store, and/or transmit information along one or more network routes… The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210. … The location coordinates of the stationary nodes may be determined with a high level of specificity… and the coordinates (or other location information) of such nodes may be stored in a memory location within the system 200 (e.g., a memory at servers 240 hosting an EME 140, for example). As such, the exact location of the stationary nodes may be known to the system and used as the basis for highly reliable location determinations made concerning other elements of the system such as mobile nodes (e.g., via trilateration, triangulation, signal strength derivations, signal direction information, etc.). ..”; ;in addition see at least [claim 7] via: “… generating a set of virtual boundaries based in part on the known location of the at least one stationary node, the at least one stationary node comprising a plurality of proximity beacons..”)
one or more sensors (mobile nodes), each of the one or more sensors associated with the asset (Each mobile node 120A and 120B … is associated with a user of that mobile node), the one or more sensors configured to communicate, with the plurality of aggregators in the designated area, (beacons), the information relating to the asset (provide location information.. of mobile nodes) regarding the relative location of the asset to each of the plurality of aggregators; (See at least [0024] via: “…The system may include stationary nodes and mobile nodes. Stationary nodes may be fixed in a specific location that is known to the system (e.g., beacons, gateways, or other computing devices in a known/stored location), while mobile nodes may move about from one location to another (e.g., mobile phones of users that move about). The stationary nodes in one embodiment may comprise beacons, gateways, or other nodes that, alone or together with other nodes, provide location information (e.g., a location or a micro-location) of mobile nodes that comprise smartphones. A location may include a GPS coordinate or other coordinate designating geographic location. A micro-location may include more specific details about a location, including a room, floor, or other specific details. The stationary nodes (e.g., beacons, gateways) may unidirectionally or bidirectionally communicate with mobile nodes…”; in addition see at least [0027] via: “…FIG. 1 is a block diagram illustrating an example epidemic response system in accordance with one or more embodiments of the disclosed technology. As shown, epidemic response system 100 (hereafter, system 100) may include one or more mobile nodes 120A-120B, one or more stationary nodes 130A-130C, and one or more administration nodes 150 in communication with an epidemic management entity 140 (hereinafter “EME 140”). Each mobile node 120A and 120B (e.g., smartphone) is associated with a user of that mobile node..”; in addition see at least [0038] via: “…Distances between mobile nodes can be determined by respective locations of the mobile nodes, which may be determined in whole or in part by the mobile nodes' position/proximity relative to a stationary node..”; in addition see at least [0041] via: “… A user location component 308 in accordance with one or more embodiments of the present disclosure may be configured to obtain location data of one or more mobile nodes 120A-120B (e.g., exposed nodes 122 and unexposed nodes 124). In some embodiments such data may be obtained via location resources (e.g., location circuitry) local to such nodes, and may be provided to system 100 over network 160. User location data is indicative of the geospatial location of one or more of mobile nodes 120A-120B associated with or connected to system 100 (collectively, “units”). In some embodiments, the location of a mobile node may be determined with high degree of accuracy using triangulation and/or trilateration based on distances to one or more multiple stationary nodes..”)
a central computer (emergency management entity (“EME”) 140 ) in communication with the plurality of aggregators (communication between one or more of: mobile nodes 120A-120B, stationary nodes 130A-130C, EME 140) and configured to receive the information from at least one of the plurality of aggregators relating to a contact tracing (track contacts … between individuals) event (emerging crisis ..e.g., a health crisis) within the geo-fence (geofence) within the designated area, the central computer configured to receive, from at least one of the plurality of aggregators, a plurality of parameters associated with the information relating to the asset and received from the one or more sensors (received data may be analyzed to identify events, patterns, and/or trends of users associated with mobile nodes ) in the designated area space; (See at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components. These components and their associated functionality may be embodied in non-transitory computer readable medium including instructions implemented by a processor of a mobile node (e.g., a downloaded epidemic “app”), a remote server, cloud-based services and/or a combination thereof. As a non-limiting example, processing components for contact trace engine 142, population monitor 144, and asset track engine 146 may be implemented entirely in a cloud-based platform such as Amazon Web Services (“AWS”) or similar platform. In this example, AWS may host one or more of the dedicated functions attributed to the EME 140 illustrated in FIG. 1. For example, mobile nodes 120A and 120B may be configured to transmit health, location, or other relevant data over BLE and/or a network 160 to the EME 140 hosted on AWS. The data received from the mobile nodes 120A and 120B may be used, directly or indirectly, as the input for components 142, 144, and 146. EME 140, with one or more of its components, analyzes the received data to identify events, patterns or trends concerning the data, e.g., the spread of an infectious disease. For example, the received data may be analyzed to identify events, patterns, and/or trends of users associated with mobile nodes 120A and 120B that may have been exposed to an infectious agent. As shown, the communication between one or more of: mobile nodes 120A-120B, stationary nodes 130A-130C, EME 140, and administration nodes components 150, may occur, directly or indirectly, over any one or more communications links (e.g., wired or wireless connections) including via one or more networks 160 (e.g., cellular network, Bluetooth® network, ZigBee® network, Wi-Fi® network, etc.), inclusive of the hardware and software required to establish such a communications link (e.g., communications interfaces such as cellular chipsets, Bluetooth® modules, ZigBee® modules, Wi-Fi® modules, etc.)…”; in addition see at least [0032] via: “…Referring still to FIG. 1, mobile nodes 120A-120B may run an application such as a mobile epidemic management application (hereafter “Epidemic App”) operable to communicate or cause a communication, directly or indirectly, with one or more other elements within system 100. In some embodiments, Epidemic App is hosted on EME 140 as a virtual resource accessible to mobile nodes 120A-120B. Epidemic App may facilitate access to one or more resources by, for, from, or on behalf of the mobile nodes 120A-120B, stationary nodes 130A-130C, EME 140,..”; in addition see at least [0021] via: “…enable an administrator to effectively address an outstanding or emerging crisis (e.g., a health crisis), for example, as applied to a pandemic that is spread by infectious agents such as COVID-19. The disclosed systems and methods may track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto)..”)
determine a contact status of the asset based on the plurality of parameters, the plurality of parameters including a duration of time (duration of an event) the asset remained in the designated area and distance between one asset and one or more additional assets (Distances between mobile nodes) in the designated area (The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary). (See at least [0018] via: “…The present disclosure addresses the deficiencies of conventional systems, and provides multi-layer contact tracing and population density monitoring that aids organizations in: (1) identifying locations of infected individuals and exposed individuals (including real-time or near real-time location data); (2) identifying path information showing such individual's trail to arriving at their present location; (3) identifying locations of persons, equipment, assets, or other resources that may be exposed or have been exposed to an infectious agent; (4) establishing communications channels between administrators of organizations and infected persons and/or exposed persons; (5) automatically notifying administrators and individuals when pre-set population density thresholds are exceeded; and (6) various other features that enable more timely and effective tracking of infectious diseases during a health crisis…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented… One or more of the mobile phones shown within environment 200 may operate as mobile nodes of the environment 200 and may be tracked to determine if they have entered, within, or left a designated area or zone of interest. The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210…”; in addition see at least [0038] via: “…FIG. 2 shows several distance measures (e.g., X1, X2, X3, X4) that may be determined and/or collected by the system 100 to accurately identify exposed nodes 122 and evaluate when a “contact” or other traceable event has occurred between two mobile nodes. As discussed above, reference identifiers beginning with 220 depict user devices and reference identifiers beginning with 230 depict beacons. The following distances are described in reference to these categorizations and begin with (1) X1: the distance between user device 220C and user device 220D; (2) X2: the distance between user device 220B and beacon 230E; (3) X3 the distance between user device 220A and beacon 230A; (4) the distance between user device 220E and user device 220F. Distances between mobile nodes can be determined by respective locations of the mobile nodes, which may be determined in whole or in part by the mobile nodes' position/proximity relative to a stationary node, and the derived distances can be used as thresholds for a 2-layer proximity analysis (which may be critical when applied in connection with infectious distances). In embodiments, if a node is in within a close contact threshold with respect to another node, a record of the close contact is collected. The record may be associated with a user identifier (e.g., tied to a user ID of the node) stored in the EME 240. Close contact thresholds may be preset, modifiable, or dynamically adaptable based on external conditions, and can be based on the characteristics of the pathogen of interest. For example, a close contact may be defined for COVID-19 purposes as occurring when any individual who was within 6 feet of an infected person for at least 15 minutes starting from 2 days before illness onset (or prior to positive pathogen collection for asymptomatic individuals) until the time the individual is isolated. Thus, a contact threshold between mobile nodes can be 6 feet and continuous for 15 minutes. In another example, a close contact may be defined for a different disease as occurring at a first distance/time threshold if the temperature falls within a first range (e.g., a temperature range at which the pathogen can live longer) and a second distance/time threshold if the temperature falls within a second range (e.g., a temperature range at which the pathogen cannot survive for as long), and the close contact threshold may dynamically adapt based on the temperature in the relevant area (as may be measured by the mobile nodes in the zone of interest, as may be measured by the stationary nodes in the zone of interest, or as may be obtained from an external resource such as the Weather Channel® database from IBM®). Other static and/or dynamic thresholds may be used and based on distance between nodes for any purpose and based on any factor that may be relevant to the contact or tracing analysis (e.g., exposure time to the infectious agent or pathogen of concern, duration of an event, etc.)…”; in addition see at least [0055] via: “…suppose that an implementation of system 100 considers two different factors in determining priority. Suppose, in this example, that Factor1 represents a score based on person proximity contact tracing deriving the number of instances the mobile node (i.e., node under consideration) contacts or comes within a predefined distance of other nodes or nodes in a designated time period and Factor2 represents a score based on area proximity contact tracing deriving the number of instances a node under consideration enters populated areas (e.g., as specified by geofences) in a designated time period. Such scores and/or scoring criteria may be preset or otherwise predefined and stored within system 100. Further factors may include attributes that define an infectious agent such as infectivity, pathogenicity, virulence, toxicity, invasiveness, antigenicity, mode of transmission, morbidity, mortality, etc. These attributes can be used in combination with the proximity scores of Factor1 and Factor2 (e.g., data from person proximity component 312 and area proximity component 310) to determine the impact a particular infectious disease can have on public health and thus increase the accuracy of the calculated priority score…”)
However Merjanian is silent the following limitation that is taught by Mesirow:
the contact status being a normalized combination of aggregated duration and proximity score of the asset, wherein the central computer is configured to calculate a proximity score using one or more signals from the one or more sensors as captured by each aggregator, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the asset and a second numerical vector related to one or the one of more additional assets. (See at least [0028] via: “…generating the quantification of risk comprises: generating a vector comprising a plurality of vector components, wherein each of the plurality of vector components corresponds is computed based on comparing the signal data for to a respective predefined component threshold; calculating a weighted sum of the vector components; comparing the weighted sum of the vector components to a predefined threshold to determine a risk category...”; in addition see at least [0055] via: “…Once signal data for the target user has been retrieved, the system may use the signal data to determine which other users (e.g., which other user devices) have been proximate to the target user during the target time period. For each such proximate user, the system may calculate a quantification and/or characterization of risk (e.g., a quantification of exposure risk, contamination risk, infection risk, and/or disease risk), such as a numerical risk score (e.g., a “proximity score”), a risk classification (e.g., high risk, medium risk, low risk), and/or a proximity classification (e.g., high proximity, medium proximity, low proximity). In some embodiments, a quantification and/or characterization of risk (e.g., exposure risk or disease risk) may include and/or be provided as a quantification and/or characterization of time in proximity and/or closeness of physical proximity, such as a proximity score. The quantification and/or characterization of risk and/or proximity score may be calculated in accordance with one or more predefined algorithms and/or using one or more machine learning algorithms. In some embodiments, the calculation may be based on a number of times a user was proximate to an exposed user, a time at which a user was proximate to an exposed user, a length of time over which a user was proximate to an exposed user, and/or a closeness of physical proximity (e.g., closeness of physical distance, for example calculated and/or inferred based on signal strength) between a user and an exposed user and/or between devices with which the user and the exposed user are associated. In some embodiments, a quantified and/or characterized risk may be determined with respect to a specific disease and/or pathogen, and the system may be configured to calculate different risks (e.g., exposure risks, contamination risks, infection risks, disease risks) for a single user for different diseases and/or pathogens…:; in addition see at least [0056] via: “…the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user. In some embodiments, the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users. For example, duration of contact may be estimated based on observed overlapping time between two mobile electronic devices and may be incorporated with frequency of overlap. Physical proximity (e.g., physical distance) may be estimated based on signal strength (e.g., RSSI) of BLE signals transmitted by nearby mobile electronic devices. If a second user's mobile electronic device observes a similar set of ambient WiFi and/or Bluetooth signals as an infected user's mobile electronic device for an overlapped time, or the second user's mobile electronic device detects the infected user's BLE signal, the system may determine that the second user has been in contact with the infected user. In some embodiments, a second user may be determined to be at a higher risk (e.g., higher proximity score) if the second user's mobile electronic device detected similar and/or overlapping signals with an infected user with a higher signal strength and/or for a longer period of time..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the calculation of a proximity score. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
Regarding claim 7 Merjanian teaches:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to an asset to a central computer, the asset including one or more tags positioned thereon for communicating with the plurality of aggregators in a designated area; (See at least [0021] via: “… track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto). Both methods may be applied to presently disclosed technology at preexisting beacons and/or newly installed beacons at known or recordable locations of interest…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented. Specifically, FIG. 2 provides an exemplary real-world implementation of the system 100 conceptually illustrated in FIG. 1. The environment 200 may include multiple nodes or computing devices (e.g., a server/cloud services 240, user devices 220A, 220B, 220C, 220D, 220E, 220F, 220G, 220H, assets or equipment 230F, beacons 230A, 230B, 230C, 230E, 230E) and/or other components. In one embodiment, user devices 220A-220H may be mobile computing devices such as, for example, smartphones (as shown in FIGS. 1 and 2), tablets, netbooks, laptop computers, or any other mobile node able to communicate over a wired or wireless network. Any two or more components of the environment 200 may be communicatively connected to each other. Connections between the components may form a mesh network, with individual components operating as a node within the mesh network. A node within the mesh network may refer to a connection point that can receive, generate, store, and/or transmit information along one or more network routes. One or more of the mobile phones shown within environment 200 may operate as mobile nodes of the environment 200 and may be tracked to determine if they have entered, within, or left a designated area or zone of interest. The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210. Alternatively, a single beacon, such as beacon 230E in FIG. 2, may be used to dynamically generate the geofence 210 as a radius around the point location of beacon 230E. The area of the geofence 210 may be based on beacons already disposed on the premises of an organization, beacons placed in position for ad hoc events or meetings, and/or a combination thereof. The location coordinates of the stationary nodes may be determined with a high level of specificity (with a smaller margin of error than a mobile phone may be able to produce with its native GPS module), and the coordinates (or other location information) of such nodes may be stored in a memory location within the system 200 (e.g., a memory at servers 240 hosting an EME 140, for example). As such, the exact location of the stationary nodes may be known to the system and used as the basis for highly reliable location determinations made concerning other elements of the system such as mobile nodes (e.g., via trilateration, triangulation, signal strength derivations, signal direction information, etc.). ..”; in addition see at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components. These components and their associated functionality may be embodied in non-transitory computer readable medium including instructions implemented by a processor of a mobile node (e.g., a downloaded epidemic “app”), a remote server, cloud-based services and/or a combination thereof. As a non-limiting example, processing components for contact trace engine 142, population monitor 144, and asset track engine 146 may be implemented entirely in a cloud-based platform such as Amazon Web Services (“AWS”) or similar platform. In this example, AWS may host one or more of the dedicated functions attributed to the EME 140 illustrated in FIG. 1. For example, mobile nodes 120A and 120B may be configured to transmit health, location, or other relevant data over BLE and/or a network 160 to the EME 140 hosted on AWS. The data received from the mobile nodes 120A and 120B may be used, directly or indirectly, as the input for components 142, 144, and 146. EME 140, with one or more of its components, analyzes the received data to identify events, patterns or trends concerning the data, e.g., the spread of an infectious disease. For example, the received data may be analyzed to identify events, patterns, and/or trends of users associated with mobile nodes 120A and 120B that may have been exposed to an infectious agent. As shown, the communication between one or more of: mobile nodes 120A-120B, stationary nodes 130A-130C, EME 140, and administration nodes components 150, may occur, directly or indirectly, over any one or more communications links (e.g., wired or wireless connections) including via one or more networks 160 (e.g., cellular network, Bluetooth® network, ZigBee® network, Wi-Fi® network, etc.), inclusive of the hardware and software required to establish such a communications link (e.g., communications interfaces such as cellular chipsets, Bluetooth® modules, ZigBee® modules, Wi-Fi® modules, etc.)…”)
determining a localization, by the central computer, of the asset in the designated area based on the plurality of parameters, determining a contact between the asset and one or more additional assets based on the determined localization of the asset and the one or more additional assets; (See at least [0024] via: “…The system may include stationary nodes and mobile nodes. Stationary nodes may be fixed in a specific location that is known to the system (e.g., beacons, gateways, or other computing devices in a known/stored location), while mobile nodes may move about from one location to another (e.g., mobile phones of users that move about). The stationary nodes in one embodiment may comprise beacons, gateways, or other nodes that, alone or together with other nodes, provide location information (e.g., a location or a micro-location) of mobile nodes that comprise smartphones. A location may include a GPS coordinate or other coordinate designating geographic location. A micro-location may include more specific details about a location, including a room, floor, or other specific details. The stationary nodes (e.g., beacons, gateways) may unidirectionally or bidirectionally communicate with mobile nodes (e.g., user devices) and securely communicate with one or more servers or cloud-computing platforms. In one embodiment, the nodes comprising stationary nodes and mobile nodes may bidirectionally communicate with Amazon Web Services (“AWS”) or similar platform over Bluetooth® Low Energy (“BLE”) or other communication protocol and/or a network..”; in addition see at least [0038] via: “…FIG. 2 shows several distance measures (e.g., X1, X2, X3, X4) that may be determined and/or collected by the system 100 to accurately identify exposed nodes 122 and evaluate when a “contact” or other traceable event has occurred between two mobile nodes. As discussed above, reference identifiers beginning with 220 depict user devices and reference identifiers beginning with 230 depict beacons. The following distances are described in reference to these categorizations and begin with (1) X1: the distance between user device 220C and user device 220D; (2) X2: the distance between user device 220B and beacon 230E; (3) X3 the distance between user device 220A and beacon 230A; (4) the distance between user device 220E and user device 220F. Distances between mobile nodes can be determined by respective locations of the mobile nodes, which may be determined in whole or in part by the mobile nodes' position/proximity relative to a stationary node, and the derived distances can be used as thresholds for a 2-layer proximity analysis (which may be critical when applied in connection with infectious distances). In embodiments, if a node is in within a close contact threshold with respect to another node, a record of the close contact is collected. The record may be associated with a user identifier (e.g., tied to a user ID of the node) stored in the EME 240. Close contact thresholds may be preset, modifiable, or dynamically adaptable based on external conditions, and can be based on the characteristics of the pathogen of interest. For example, a close contact may be defined for COVID-19 purposes as occurring when any individual who was within 6 feet of an infected person for at least 15 minutes starting from 2 days before illness onset (or prior to positive pathogen collection for asymptomatic individuals) until the time the individual is isolated. Thus, a contact threshold between mobile nodes can be 6 feet and continuous for 15 minutes. In another example, a close contact may be defined for a different disease as occurring at a first distance/time threshold if the temperature falls within a first range (e.g., a temperature range at which the pathogen can live longer) and a second distance/time threshold if the temperature falls within a second range (e.g., a temperature range at which the pathogen cannot survive for as long), and the close contact threshold may dynamically adapt based on the temperature in the relevant area (as may be measured by the mobile nodes in the zone of interest, as may be measured by the stationary nodes in the zone of interest, or as may be obtained from an external resource such as the Weather Channel® database from IBM®). Other static and/or dynamic thresholds may be used and based on distance between nodes for any purpose and based on any factor that may be relevant to the contact or tracing analysis (e.g., exposure time to the infectious agent or pathogen of concern, duration of an event, etc.)…”)
determining a contact status of the asset based on the plurality of parameters using an algorithm on the central computer, the plurality of parameters including a duration of time the asset remains in the designated area and a distance between the asset and one or more additional assets in the designated area; (See at least [0018] via: “…The present disclosure addresses the deficiencies of conventional systems, and provides multi-layer contact tracing and population density monitoring that aids organizations in: (1) identifying locations of infected individuals and exposed individuals (including real-time or near real-time location data); (2) identifying path information showing such individual's trail to arriving at their present location; (3) identifying locations of persons, equipment, assets, or other resources that may be exposed or have been exposed to an infectious agent; (4) establishing communications channels between administrators of organizations and infected persons and/or exposed persons; (5) automatically notifying administrators and individuals when pre-set population density thresholds are exceeded; and (6) various other features that enable more timely and effective tracking of infectious diseases during a health crisis…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented… One or more of the mobile phones shown within environment 200 may operate as mobile nodes of the environment 200 and may be tracked to determine if they have entered, within, or left a designated area or zone of interest. The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210…”; in addition see at least [0038] via: “…FIG. 2 shows several distance measures (e.g., X1, X2, X3, X4) that may be determined and/or collected by the system 100 to accurately identify exposed nodes 122 and evaluate when a “contact” or other traceable event has occurred between two mobile nodes. As discussed above, reference identifiers beginning with 220 depict user devices and reference identifiers beginning with 230 depict beacons. The following distances are described in reference to these categorizations and begin with (1) X1: the distance between user device 220C and user device 220D; (2) X2: the distance between user device 220B and beacon 230E; (3) X3 the distance between user device 220A and beacon 230A; (4) the distance between user device 220E and user device 220F. Distances between mobile nodes can be determined by respective locations of the mobile nodes, which may be determined in whole or in part by the mobile nodes' position/proximity relative to a stationary node, and the derived distances can be used as thresholds for a 2-layer proximity analysis (which may be critical when applied in connection with infectious distances). In embodiments, if a node is in within a close contact threshold with respect to another node, a record of the close contact is collected. The record may be associated with a user identifier (e.g., tied to a user ID of the node) stored in the EME 240. Close contact thresholds may be preset, modifiable, or dynamically adaptable based on external conditions, and can be based on the characteristics of the pathogen of interest. For example, a close contact may be defined for COVID-19 purposes as occurring when any individual who was within 6 feet of an infected person for at least 15 minutes starting from 2 days before illness onset (or prior to positive pathogen collection for asymptomatic individuals) until the time the individual is isolated. Thus, a contact threshold between mobile nodes can be 6 feet and continuous for 15 minutes. In another example, a close contact may be defined for a different disease as occurring at a first distance/time threshold if the temperature falls within a first range (e.g., a temperature range at which the pathogen can live longer) and a second distance/time threshold if the temperature falls within a second range (e.g., a temperature range at which the pathogen cannot survive for as long), and the close contact threshold may dynamically adapt based on the temperature in the relevant area (as may be measured by the mobile nodes in the zone of interest, as may be measured by the stationary nodes in the zone of interest, or as may be obtained from an external resource such as the Weather Channel® database from IBM®). Other static and/or dynamic thresholds may be used and based on distance between nodes for any purpose and based on any factor that may be relevant to the contact or tracing analysis (e.g., exposure time to the infectious agent or pathogen of concern, duration of an event, etc.)…”; in addition see at least [0055] via: “…suppose that an implementation of system 100 considers two different factors in determining priority. Suppose, in this example, that Factor1 represents a score based on person proximity contact tracing deriving the number of instances the mobile node (i.e., node under consideration) contacts or comes within a predefined distance of other nodes or nodes in a designated time period and Factor2 represents a score based on area proximity contact tracing deriving the number of instances a node under consideration enters populated areas (e.g., as specified by geofences) in a designated time period. Such scores and/or scoring criteria may be preset or otherwise predefined and stored within system 100. Further factors may include attributes that define an infectious agent such as infectivity, pathogenicity, virulence, toxicity, invasiveness, antigenicity, mode of transmission, morbidity, mortality, etc. These attributes can be used in combination with the proximity scores of Factor1 and Factor2 (e.g., data from person proximity component 312 and area proximity component 310) to determine the impact a particular infectious disease can have on public health and thus increase the accuracy of the calculated priority score…”; in addition see at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components. These components and their associated functionality may be embodied in non-transitory computer readable medium including instructions implemented by a processor of a mobile node (e.g., a downloaded epidemic “app”), a remote server, cloud-based services and/or a combination thereof. As a non-limiting example, processing components for contact trace engine 142, population monitor 144, and asset track engine 146 may be implemented entirely in a cloud-based platform such as Amazon Web Services (“AWS”) or similar platform. In this example, AWS may host one or more of the dedicated functions attributed to the EME 140 illustrated in FIG. 1. For example, mobile nodes 120A and 120B may be configured to transmit health, location, or other relevant data over BLE and/or a network 160 to the EME 140 hosted on AWS. The data received from the mobile nodes 120A and 120B may be used, directly or indirectly, as the input for components 142, 144, and 146. EME 140, with one or more of its components, analyzes the received data to identify events, patterns or trends concerning the data, e.g., the spread of an infectious disease…”; in addition see at least [0043] via: “…Location data may indicate geospatial location of a user associated with the unit, including longitude and latitude coordinates, degrees/minutes/seconds location parameters, altitude above sea level, altitude above ground level, etc. User location component 308 may be utilized to identify geospatial location of a user. User location component 308 may comprise one or more circuits, modules, or chips local to the units themselves. For example, location component 308 may include a GPS sensor, an altimeter, a pressure sensor (e.g., a barometer), and the like. In some embodiments user location component 308 may further comprise hardware and software operating on EME 140 and communicatively coupled with location sensors of one or more units..”)
determining a close contact between the asset and the one or more additional assets, a close contact defined as a contact between the asset and the one or more additional asset indicative of spreading disease. see at least [0038] via: “…FIG. 2 shows several distance measures (e.g., X1, X2, X3, X4) that may be determined and/or collected by the system 100 to accurately identify exposed nodes 122 and evaluate when a “contact” or other traceable event has occurred between two mobile nodes. As discussed above, reference identifiers beginning with 220 depict user devices and reference identifiers beginning with 230 depict beacons. The following distances are described in reference to these categorizations and begin with (1) X1: the distance between user device 220C and user device 220D; (2) X2: the distance between user device 220B and beacon 230E; (3) X3 the distance between user device 220A and beacon 230A; (4) the distance between user device 220E and user device 220F. Distances between mobile nodes can be determined by respective locations of the mobile nodes, which may be determined in whole or in part by the mobile nodes' position/proximity relative to a stationary node, and the derived distances can be used as thresholds for a 2-layer proximity analysis (which may be critical when applied in connection with infectious distances). In embodiments, if a node is in within a close contact threshold with respect to another node, a record of the close contact is collected. The record may be associated with a user identifier (e.g., tied to a user ID of the node) stored in the EME 240. Close contact thresholds may be preset, modifiable, or dynamically adaptable based on external conditions, and can be based on the characteristics of the pathogen of interest. For example, a close contact may be defined for COVID-19 purposes as occurring when any individual who was within 6 feet of an infected person for at least 15 minutes starting from 2 days before illness onset (or prior to positive pathogen collection for asymptomatic individuals) until the time the individual is isolated. Thus, a contact threshold between mobile nodes can be 6 feet and continuous for 15 minutes. In another example, a close contact may be defined for a different disease as occurring at a first distance/time threshold if the temperature falls within a first range (e.g., a temperature range at which the pathogen can live longer) and a second distance/time threshold if the temperature falls within a second range (e.g., a temperature range at which the pathogen cannot survive for as long), and the close contact threshold may dynamically adapt based on the temperature in the relevant area (as may be measured by the mobile nodes in the zone of interest, as may be measured by the stationary nodes in the zone of interest, or as may be obtained from an external resource such as the Weather Channel® database from IBM®). Other static and/or dynamic thresholds may be used and based on distance between nodes for any purpose and based on any factor that may be relevant to the contact or tracing analysis (e.g., exposure time to the infectious agent or pathogen of concern, duration of an event, etc.)…”)
However Merjanian is silent the following limitation that is taught by Mesirow:
the contact status being a normalized combination of aggregated duration and proximity score of the asset, wherein the proximity score is calculated using one or more signals from the one or more tags as captured by each aggregator, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the asset and a second numerical vector related to one or the one or more additional assets. (See at least [0028] via: “…generating the quantification of risk comprises: generating a vector comprising a plurality of vector components, wherein each of the plurality of vector components corresponds is computed based on comparing the signal data for to a respective predefined component threshold; calculating a weighted sum of the vector components; comparing the weighted sum of the vector components to a predefined threshold to determine a risk category...”; in addition see at least [0055] via: “…Once signal data for the target user has been retrieved, the system may use the signal data to determine which other users (e.g., which other user devices) have been proximate to the target user during the target time period. For each such proximate user, the system may calculate a quantification and/or characterization of risk (e.g., a quantification of exposure risk, contamination risk, infection risk, and/or disease risk), such as a numerical risk score (e.g., a “proximity score”), a risk classification (e.g., high risk, medium risk, low risk), and/or a proximity classification (e.g., high proximity, medium proximity, low proximity). In some embodiments, a quantification and/or characterization of risk (e.g., exposure risk or disease risk) may include and/or be provided as a quantification and/or characterization of time in proximity and/or closeness of physical proximity, such as a proximity score. The quantification and/or characterization of risk and/or proximity score may be calculated in accordance with one or more predefined algorithms and/or using one or more machine learning algorithms. In some embodiments, the calculation may be based on a number of times a user was proximate to an exposed user, a time at which a user was proximate to an exposed user, a length of time over which a user was proximate to an exposed user, and/or a closeness of physical proximity (e.g., closeness of physical distance, for example calculated and/or inferred based on signal strength) between a user and an exposed user and/or between devices with which the user and the exposed user are associated. In some embodiments, a quantified and/or characterized risk may be determined with respect to a specific disease and/or pathogen, and the system may be configured to calculate different risks (e.g., exposure risks, contamination risks, infection risks, disease risks) for a single user for different diseases and/or pathogens…:; in addition see at least [0056] via: “…the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user. In some embodiments, the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users. For example, duration of contact may be estimated based on observed overlapping time between two mobile electronic devices and may be incorporated with frequency of overlap. Physical proximity (e.g., physical distance) may be estimated based on signal strength (e.g., RSSI) of BLE signals transmitted by nearby mobile electronic devices. If a second user's mobile electronic device observes a similar set of ambient WiFi and/or Bluetooth signals as an infected user's mobile electronic device for an overlapped time, or the second user's mobile electronic device detects the infected user's BLE signal, the system may determine that the second user has been in contact with the infected user. In some embodiments, a second user may be determined to be at a higher risk (e.g., higher proximity score) if the second user's mobile electronic device detected similar and/or overlapping signals with an infected user with a higher signal strength and/or for a longer period of time..”)
Regarding claims 3 and 9 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claims 1 and 7 respectively Merjanian is silent the following claim that is taught by Mesirow:
wherein the central computer is configured to compare the proximity score with a stored proximity threshold value to determine if the asset and the additional asset are in close proximity indicative of spreading disease. (See at least [0057] via: “…proximity characterizations and/or risk characterizations may classify one or more users/devices into “high proximity score,” “medium proximity score,” and “low proximity score” categories as follows. A high proximity score may be assigned when a second user's mobile electronic device scanned similar ambient WiFi/Bluetooth signals as those signals scanned by a target user's mobile electronic device, and/or the second user's mobile electronic device received a strong signal (e.g., above a predetermined or dynamically determined signal strength threshold) via BLE transmission from a target user's mobile electronic device for more than a predetermined or dynamically determined threshold amount of time (e.g., 30 minutes), with the threshold amount of time calculated either continuously or intermittently (e.g., cumulatively allowing for interruptions), during a date/time range specified via the system dashboard. A medium proximity score may be assigned when a second user's mobile electronic device detects signals that overlap with signals detected by a target user (e.g., overlapping in identity, strength profile, and/or time) in a significant manner (e.g., exceeding a signal strength threshold and/or a time threshold) in a first instance but in an insignificant manner (e.g., not exceeding one or both of a signal strength threshold and/or a time threshold) in a second instance. A medium proximity score may also be assigned when a signal strength of a BLE signal detected from a target user's mobile electronic device is strong in one instance (e.g., exceeding a strength threshold) but is weak in another instance. A low proximity score may be assigned when ambient WiFi/Bluetooth/BLE signals detected by a second user's mobile electronic device have any non-zero signal overlap with the signal scans detected by (or BLE signals broadcast from) the target user's mobile electronic device within a predefined or dynamically determined time window (e.g., 4 hours). (Thus, in some instances, users who were never in the same place at the same time may nonetheless be assigned a low proximity score if they were in the same place within a threshold time window of one another.) In some embodiments, users not meeting the criteria for high, medium, or low proximity score may not be assigned any proximity score…”; in addition see at least [0090] via: “…the system may then calculate an overall risk classification or proximity classification based on one or more weighted sums of the score vector components. The high-risk vector component may be weighted most heavily while the low-risk vector component may be weighted least heavily. The weighted sum of the vector components may be compared to one or more predefined thresholds to determine whether the system should indicate an overall “high risk”/“high proximity score,” “medium risk”/“medium proximity score,” or “low risk”/“low proximity score.”..”; in addition see at least [0058] via: “… the system may generate one or more notifications regarding one or more of the users who have been proximate to an exposed or infected user. For example, the system may generate a report with respect to all users who have been in contact with an exposed user, and/or may generate an alert/waning to users whose risk (e.g., disease risk, exposure risk, and/or proximity score) meets predefined risk criteria (e.g., for users whose risk is classified as “medium risk” or “high risk” and/or whose proximity scores are classified as “high” or “medium”)….”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the calculation of a proximity score. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
Regarding claims 6 and 13 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claims 1 and 7 respectively. Merjanian is silent the following claim that is taught by Mesirow:
wherein the central computer is configured to create a contact database of contacts between the asset and the one or more additional assets for automatic contact tracing. (See at least [0046] via: “…mobile electronic devices participating in the contact tracing system may be configured to perform two kinds of signal scans for the purpose of collecting signals to be used in building a database of signal data. First, each mobile electronic device may perform periodic scans for signals, such as ambient WiFi signals and/or Bluetooth signals, emitted by other devices such as WiFi hot spots, IOT devices, or the like. For example, a device may periodically (e.g., every 10 minutes or at any other predetermined interval or in accordance with dynamic triggering) scan ambient WiFi and Bluetooth signals and send signal scan reports along with the device's AdID, user identifier (e.g., UUID), unique BLE identifier, and/or location information to a server associated with data storage and/or contact analysis…”; in addition see at least [0129] via: “…At block 308, in some embodiments, in response to receiving the indication to trace the contacts of the user, the system retrieves at least a portion of the signal data from the signal database 124, the retrieved signal data indicating which of the plurality of mobile electronic devices have been proximate to the first mobile electronic device (e.g., the data indicating which devices meet signal identity, signal strength, signal detection time, and/or signal detection duration criteria). In some embodiments, retrieving the signal data for a user may require the system to first obtain the user identifier (e.g., AdID) used by the database storing the signal data, such that the system may identify the relevant signal data to extract from the database. In the example of system 100, database 124 stores the signal data. In some embodiments, the system may look up the identifier associated with the user in a separate database (e.g., database 136 in system 100), such as an enterprise database maintained separately from the database storing the signal data..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding a contact tracing system, configured to collect signals of specific mobile devices linked to an asset to be used in building a database of signal data. The combination of Merjanian and Mesirow is useful in better identifying, quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases between two or more assets/people by tracking the infection level of each asset and determining exposure level between assets based on their interaction in order to minimize the spread of diseases within certain geolocations.
Regarding claim 12 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claim 7. Merjanian also teaches:
further comprising generating an alert if the contact is determined to be a close contact indicative of spreading disease. (See at least [0034] via: “…a user may select an icon or menu item on the GUI of the Epidemic App to activate a resource of an unexposed and uninfected mobile node 124 to assist the user 124′ in avoiding the exposed and infected node 122 (including user 122′) or an area (e.g., predefined set of boundaries) that the exposed node 122 is within or has recently been within. For instance, a user 124′ may activate a speaker of an unexposed node 124 to propagate an alarm or warning sound to help the user 124′ avoid the exposed node 122 (and the user 122′ associated with the exposed node 122) as they near the location identified by system 100. System 100 may be configured to provide audible or visual directions (through the aforementioned speaker or via the display of the mobile node 124) to give directions to the user to help them avoid the exposed node 122 (and the user 122′ associated with the exposed node 122) as the move about or proceed along a path toward their destination (which path and/or destination may or may not be known to the mobile node). Alternatively, an administrator node 150 associated with epidemic management entity 140 may activate a light source (e.g., an LED) of the unexposed node 124 to propagate a flashing light to help notify the user at an unexposed node 124 of their proximity to an exposed node 122 or an area (e.g., predefined set of boundaries) the exposed node 122 is within or has been within. In one embodiment, EME 140 may store and analyze past, present, and predicted locations of infected and unexposed nodes. For example, notifications directed to unexposed nodes 120 may contain information regarding locations exposed node 122 has traversed in the past 3 hours, 24 hours, the past week (e.g., a historical threshold of 1 week), or other time interval depending on the amount of information desired to be stored for a given scenario (e.g., for bacterial infections where the bacteria can survive in air particulates for up to 24 hours, the historical threshold may be set to 24 hours or longer, for instance). In another example, notifications directed to unexposed nodes 120 may contain information regarding current proximity of exposed nodes 122, previous pathways or routes upon which exposed nodes 122 traveled during a relevant time period, and predicted locations of exposed nodes 122 that should be avoided by the unexposed nodes 124…”; in addition see at least [0039] via: “…The number of nodes within the geofence at a given time, or that have passed through the geofenced zone within a given time, may also be determined and compared to a threshold. The threshold may be set based on the number of people within or who have passed through a given space. If the number of people surpasses this threshold, an alert or notification may be provided to users and/or to EME 240 for recordation and analysis. The status of the nodes and their users (e.g., as exposed or unexposed, infected or uninfected),..”)
Regarding claim 14 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claim 7. Merjanian also teaches:
further comprising creating a geo-fence within the designated area defining the designated area using a plurality of aggregators, each aggregator capable of communicating with neighboring aggregators and with the central computer over a network. (See at least [0021] via: “… The disclosed systems and methods may track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto). Both methods may be applied to presently disclosed technology at preexisting beacons and/or newly installed beacons at known or recordable locations of interest…”; in addition see at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented. Specifically, FIG. 2 provides an exemplary real-world implementation of the system 100 conceptually illustrated in FIG. 1. The environment 200 may include multiple nodes or computing devices (e.g., a server/cloud services 240, … beacons 230A, 230B, 230C, 230E, 230E) and/or other components… Connections between the components may form a mesh network, with individual components operating as a node within the mesh network. A node within the mesh network may refer to a connection point that can receive, generate, store, and/or transmit information along one or more network routes… The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210. … The location coordinates of the stationary nodes may be determined with a high level of specificity… and the coordinates (or other location information) of such nodes may be stored in a memory location within the system 200 (e.g., a memory at servers 240 hosting an EME 140, for example). As such, the exact location of the stationary nodes may be known to the system and used as the basis for highly reliable location determinations made concerning other elements of the system such as mobile nodes (e.g., via trilateration, triangulation, signal strength derivations, signal direction information, etc.). ..”; ;in addition see at least [claim 7] via: “… generating a set of virtual boundaries based in part on the known location of the at least one stationary node, the at least one stationary node comprising a plurality of proximity beacons..”)
Claims 4-5, 10-11, 15-20 are rejected under 35 U.S.C. 103 as being un-patentable by Merjanian in view of Mesirow in further view of Correnti et.al (US 20220293278 A1) hereinafter “Correnti”
Regarding claims 4 and 10 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claims 1 and 7 respectively. Merjanian is silent the following claim that is taught by Correnti::
wherein the central computer is configured to calculate a contact duration score using timestamps of all the signals received by the plurality of aggregators in the designated area. (See at least [0042] via: “… the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”; in addition see at least [0064] via: “…the exposure evaluation module 124 can aggregate multiple risk scores that a person A can accumulate through a set of potential exposures to infected people resulting in multiple exposure events. When the person's aggregated risk score reaches a threshold score, the contact tracing system 102 can provide a notification to the person including information of the aggregated risk score..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the calculation of a risk score based on a duration of proximity. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
Regarding claims 5 and 11 Merjanian and Mesirow teach the invention as claimed and detailed above with respect to claims 1 and 7 respectively and Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claims 4 and 10 respectively. Merjanian is silent the following claim that is taught by Correnti:
wherein the central computer is configured to compare the contact duration score with a stored duration threshold value to determine if the asset and the additional asset are in contact long enough to be indicative of spreading disease. (See at least [0042] via: “…the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the determination of a risk score based on the comparison of duration of proximity to a threshold. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed between two or more persons that may be infected depending on whether the duration of proximity score is above or below a given threshold in order to minimize the spread of diseases within certain geolocations.
Regarding claim 15 Merjanian teaches:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to one or more assets to a central computer, each of the one or more assets including one or more tags positioned thereon for communicating with the plurality of aggregators in a designated area, the one or more assets including at least a first asset and a second asset; (See at least [0021] via: “… track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto). Both methods may be applied to presently disclosed technology at preexisting beacons and/or newly installed beacons at known or recordable locations of interest…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented. Specifically, FIG. 2 provides an exemplary real-world implementation of the system 100 conceptually illustrated in FIG. 1. The environment 200 may include multiple nodes or computing devices (e.g., a server/cloud services 240, user devices 220A, 220B, 220C, 220D, 220E, 220F, 220G, 220H, assets or equipment 230F, beacons 230A, 230B, 230C, 230E, 230E) and/or other components. In one embodiment, user devices 220A-220H may be mobile computing devices such as, for example, smartphones (as shown in FIGS. 1 and 2), tablets, netbooks, laptop computers, or any other mobile node able to communicate over a wired or wireless network. Any two or more components of the environment 200 may be communicatively connected to each other. Connections between the components may form a mesh network, with individual components operating as a node within the mesh network. A node within the mesh network may refer to a connection point that can receive, generate, store, and/or transmit information along one or more network routes. One or more of the mobile phones shown within environment 200 may operate as mobile nodes of the environment 200 and may be tracked to determine if they have entered, within, or left a designated area or zone of interest. The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210. Alternatively, a single beacon, such as beacon 230E in FIG. 2, may be used to dynamically generate the geofence 210 as a radius around the point location of beacon 230E. The area of the geofence 210 may be based on beacons already disposed on the premises of an organization, beacons placed in position for ad hoc events or meetings, and/or a combination thereof. The location coordinates of the stationary nodes may be determined with a high level of specificity (with a smaller margin of error than a mobile phone may be able to produce with its native GPS module), and the coordinates (or other location information) of such nodes may be stored in a memory location within the system 200 (e.g., a memory at servers 240 hosting an EME 140, for example). As such, the exact location of the stationary nodes may be known to the system and used as the basis for highly reliable location determinations made concerning other elements of the system such as mobile nodes (e.g., via trilateration, triangulation, signal strength derivations, signal direction information, etc.). ..”; in addition see at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components. These components and their associated functionality may be embodied in non-transitory computer readable medium including instructions implemented by a processor of a mobile node (e.g., a downloaded epidemic “app”), a remote server, cloud-based services and/or a combination thereof. As a non-limiting example, processing components for contact trace engine 142, population monitor 144, and asset track engine 146 may be implemented entirely in a cloud-based platform such as Amazon Web Services (“AWS”) or similar platform. In this example, AWS may host one or more of the dedicated functions attributed to the EME 140 illustrated in FIG. 1. For example, mobile nodes 120A and 120B may be configured to transmit health, location, or other relevant data over BLE and/or a network 160 to the EME 140 hosted on AWS. The data received from the mobile nodes 120A and 120B may be used, directly or indirectly, as the input for components 142, 144, and 146. EME 140, with one or more of its components, analyzes the received data to identify events, patterns or trends concerning the data, e.g., the spread of an infectious disease. For example, the received data may be analyzed to identify events, patterns, and/or trends of users associated with mobile nodes 120A and 120B that may have been exposed to an infectious agent. As shown, the communication between one or more of: mobile nodes 120A-120B, stationary nodes 130A-130C, EME 140, and administration nodes components 150, may occur, directly or indirectly, over any one or more communications links (e.g., wired or wireless connections) including via one or more networks 160 (e.g., cellular network, Bluetooth® network, ZigBee® network, Wi-Fi® network, etc.), inclusive of the hardware and software required to establish such a communications link (e.g., communications interfaces such as cellular chipsets, Bluetooth® modules, ZigBee® modules, Wi-Fi® modules, etc.)…”)
However Merjanian is silent the following limitations taught by Mesirow:
determining, by a central computer, a proximity score related to a proximity between the first asset and the second asset based on the plurality of parameters from the plurality of parameters, the contact status being a normalized combination of aggregated duration and proximity score of the asset. (See at least [0028] via: “…generating the quantification of risk comprises: generating a vector comprising a plurality of vector components, wherein each of the plurality of vector components corresponds is computed based on comparing the signal data for to a respective predefined component threshold; calculating a weighted sum of the vector components; comparing the weighted sum of the vector components to a predefined threshold to determine a risk category...”; in addition see at least [0055] via: “…Once signal data for the target user has been retrieved, the system may use the signal data to determine which other users (e.g., which other user devices) have been proximate to the target user during the target time period. For each such proximate user, the system may calculate a quantification and/or characterization of risk (e.g., a quantification of exposure risk, contamination risk, infection risk, and/or disease risk), such as a numerical risk score (e.g., a “proximity score”), a risk classification (e.g., high risk, medium risk, low risk), and/or a proximity classification (e.g., high proximity, medium proximity, low proximity). In some embodiments, a quantification and/or characterization of risk (e.g., exposure risk or disease risk) may include and/or be provided as a quantification and/or characterization of time in proximity and/or closeness of physical proximity, such as a proximity score. The quantification and/or characterization of risk and/or proximity score may be calculated in accordance with one or more predefined algorithms and/or using one or more machine learning algorithms. In some embodiments, the calculation may be based on a number of times a user was proximate to an exposed user, a time at which a user was proximate to an exposed user, a length of time over which a user was proximate to an exposed user, and/or a closeness of physical proximity (e.g., closeness of physical distance, for example calculated and/or inferred based on signal strength) between a user and an exposed user and/or between devices with which the user and the exposed user are associated. In some embodiments, a quantified and/or characterized risk may be determined with respect to a specific disease and/or pathogen, and the system may be configured to calculate different risks (e.g., exposure risks, contamination risks, infection risks, disease risks) for a single user for different diseases and/or pathogens…”; in addition see at least [0056] via: “…the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user. In some embodiments, the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users. For example, duration of contact may be estimated based on observed overlapping time between two mobile electronic devices and may be incorporated with frequency of overlap. Physical proximity (e.g., physical distance) may be estimated based on signal strength (e.g., RSSI) of BLE signals transmitted by nearby mobile electronic devices. If a second user's mobile electronic device observes a similar set of ambient WiFi and/or Bluetooth signals as an infected user's mobile electronic device for an overlapped time, or the second user's mobile electronic device detects the infected user's BLE signal, the system may determine that the second user has been in contact with the infected user. In some embodiments, a second user may be determined to be at a higher risk (e.g., higher proximity score) if the second user's mobile electronic device detected similar and/or overlapping signals with an infected user with a higher signal strength and/or for a longer period of time..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the calculation of a proximity score. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations
the algorithm comparing the proximity score to a threshold proximity value stored on the central computer (See at least [0056] via: “…the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user. In some embodiments, the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users; in addition see at least [0057] via: “…proximity characterizations and/or risk characterizations may classify one or more users/devices into “high proximity score,” “medium proximity score,” and “low proximity score” categories as follows. A high proximity score may be assigned when a second user's mobile electronic device scanned similar ambient WiFi/Bluetooth signals as those signals scanned by a target user's mobile electronic device, and/or the second user's mobile electronic device received a strong signal (e.g., above a predetermined or dynamically determined signal strength threshold) via BLE transmission from a target user's mobile electronic device for more than a predetermined or dynamically determined threshold amount of time (e.g., 30 minutes), with the threshold amount of time calculated either continuously or intermittently (e.g., cumulatively allowing for interruptions), during a date/time range specified via the system dashboard. A medium proximity score may be assigned when a second user's mobile electronic device detects signals that overlap with signals detected by a target user (e.g., overlapping in identity, strength profile, and/or time) in a significant manner (e.g., exceeding a signal strength threshold and/or a time threshold) in a first instance but in an insignificant manner (e.g., not exceeding one or both of a signal strength threshold and/or a time threshold) in a second instance. A medium proximity score may also be assigned when a signal strength of a BLE signal detected from a target user's mobile electronic device is strong in one instance (e.g., exceeding a strength threshold) but is weak in another instance. A low proximity score may be assigned when ambient WiFi/Bluetooth/BLE signals detected by a second user's mobile electronic device have any non-zero signal overlap with the signal scans detected by (or BLE signals broadcast from) the target user's mobile electronic device within a predefined or dynamically determined time window (e.g., 4 hours). (Thus, in some instances, users who were never in the same place at the same time may nonetheless be assigned a low proximity score if they were in the same place within a threshold time window of one another.) In some embodiments, users not meeting the criteria for high, medium, or low proximity score may not be assigned any proximity score…”; in addition see at least [0090] via: “…the system may then calculate an overall risk classification or proximity classification based on one or more weighted sums of the score vector components. The high-risk vector component may be weighted most heavily while the low-risk vector component may be weighted least heavily. The weighted sum of the vector components may be compared to one or more predefined thresholds to determine whether the system should indicate an overall “high risk”/“high proximity score,” “medium risk”/“medium proximity score,” or “low risk”/“low proximity score.”..”; in addition see at least [0058] via: “… the system may generate one or more notifications regarding one or more of the users who have been proximate to an exposed or infected user. For example, the system may generate a report with respect to all users who have been in contact with an exposed user, and/or may generate an alert/waning to users whose risk (e.g., disease risk, exposure risk, and/or proximity score) meets predefined risk criteria (e.g., for users whose risk is classified as “medium risk” or “high risk” and/or whose proximity scores are classified as “high” or “medium”)….”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the determination of a comparison between a proximity score and a threshold. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected on whether the proximity score is above or below a given threshold in order to minimize the spread of diseases within certain geolocations.
However Merjanian and Mesirow are silent the following limitations taught by Correnti:
determining, by a central computer, a duration score related to a duration of time the first asset and the second asset are in the designated area based on the plurality of parameters from the plurality of parameters; (See at least [0042] via: “…the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Mesirow to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the calculation of a risk score based on a duration of proximity. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
determining a contact status of the first asset and the second asset based on the proximity score and the duration score using an algorithm on the central computer; (See at least [0042] via: “… the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”; in addition see at least [0064] via: “…the exposure evaluation module 124 can aggregate multiple risk scores that a person A can accumulate through a set of potential exposures to infected people resulting in multiple exposure events. When the person's aggregated risk score reaches a threshold score, the contact tracing system 102 can provide a notification to the person including information of the aggregated risk score..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Mesirow to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the determination of a risk score compared to a threshold based on the proximity and duration of two or more people. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed and proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
comparing the duration score to a threshold duration value stored on the central computer (See at least [0042] via: “… the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”; in addition see at least [0064] via: “…the exposure evaluation module 124 can aggregate multiple risk scores that a person A can accumulate through a set of potential exposures to infected people resulting in multiple exposure events. When the person's aggregated risk score reaches a threshold score, the contact tracing system 102 can provide a notification to the person including information of the aggregated risk score..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Mesirow to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the determination of a risk score compared to a threshold based on the proximity and duration of two or more people. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed and proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations
Regarding claim 16: Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claim 15. Merjanian and Mesirow are silent the following claim that is taught by Correnti:
wherein the contact status is determined to be a close contact when the proximity score exceeds the threshold proximity value and the duration score exceeds the threshold duration value, indicating a contact that is indicative of spreading disease. (See at least [0042] via: “… the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”; in addition see at least [0064] via: “…the exposure evaluation module 124 can aggregate multiple risk scores that a person A can accumulate through a set of potential exposures to infected people resulting in multiple exposure events. When the person's aggregated risk score reaches a threshold score, the contact tracing system 102 can provide a notification to the person including information of the aggregated risk score..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Mesirow to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the determination of a risk score compared to a threshold based on the proximity and duration of two or more people. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed and proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
Regarding claim 17 Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claim 15. Merjanian and Correnti are silent the following claim that is taught by Mesirow:
wherein the central computer is configured to create a contact database of close contacts between the first asset and the second asset for automatic contact tracing. (See at least [0046] via: “…mobile electronic devices participating in the contact tracing system may be configured to perform two kinds of signal scans for the purpose of collecting signals to be used in building a database of signal data. First, each mobile electronic device may perform periodic scans for signals, such as ambient WiFi signals and/or Bluetooth signals, emitted by other devices such as WiFi hot spots, IOT devices, or the like. For example, a device may periodically (e.g., every 10 minutes or at any other predetermined interval or in accordance with dynamic triggering) scan ambient WiFi and Bluetooth signals and send signal scan reports along with the device's AdID, user identifier (e.g., UUID), unique BLE identifier, and/or location information to a server associated with data storage and/or contact analysis…”; in addition see at least [0129] via: “…At block 308, in some embodiments, in response to receiving the indication to trace the contacts of the user, the system retrieves at least a portion of the signal data from the signal database 124, the retrieved signal data indicating which of the plurality of mobile electronic devices have been proximate to the first mobile electronic device (e.g., the data indicating which devices meet signal identity, signal strength, signal detection time, and/or signal detection duration criteria). In some embodiments, retrieving the signal data for a user may require the system to first obtain the user identifier (e.g., AdID) used by the database storing the signal data, such that the system may identify the relevant signal data to extract from the database. In the example of system 100, database 124 stores the signal data. In some embodiments, the system may look up the identifier associated with the user in a separate database (e.g., database 136 in system 100), such as an enterprise database maintained separately from the database storing the signal data..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Corenti to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding a contact tracing system, configured to collect signals of specific mobile devices linked to an asset to be used in building a database of signal data. The combination of Merjanian and Mesirow is useful in better identifying, quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases between two or more assets/people by tracking the infection level of each asset and determining exposure level between assets based on their interaction in order to minimize the spread of diseases within certain geolocations.
Regarding claim 18 Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claim 15. Merjanian also teaches:
further comprising creating a geo-fence within the designated area defining the designated area using a plurality of aggregators, each aggregator capable of communicating with neighboring aggregators and with the central computer over a network. (See at least [0021] via: “… The disclosed systems and methods may track contacts among and/or between individuals within a designated area and/or designated time frame (e.g., work hours). In one embodiment, the designated area can be formed by a geofence that acts as a virtual boundary for a real-world geographic area. A geofence may be dynamically generated, as in a radius around a point location (e.g., a beacon, a gateway, or other node), and/or a geofence can be a predefined set of boundaries (e.g., several beacons, or a boundary relative thereto). Both methods may be applied to presently disclosed technology at preexisting beacons and/or newly installed beacons at known or recordable locations of interest…”; in addition see at least [0028] via: “…These mobile nodes 120A-120B may be in bidirectional communication with emergency management entity (“EME”) 140 which may include functionality for tracing (e.g., contact trace engine 142), population density monitoring 144, asset tracking 146, and other components…”; in addition see at least [0035] via: “…FIG. 2 shows an example environment 200 in which one or more embodiments of the technology disclosed herein may be implemented. Specifically, FIG. 2 provides an exemplary real-world implementation of the system 100 conceptually illustrated in FIG. 1. The environment 200 may include multiple nodes or computing devices (e.g., a server/cloud services 240, … beacons 230A, 230B, 230C, 230E, 230E) and/or other components… Connections between the components may form a mesh network, with individual components operating as a node within the mesh network. A node within the mesh network may refer to a connection point that can receive, generate, store, and/or transmit information along one or more network routes… The area may be defined by a geofence 210 that serves as a 2-D or 3-D virtual boundary. The geofence 210 can be created by stationary nodes (e.g., beacons, gateways, and the like) disposed at the periphery of a desired area. For example, as illustrated in FIG. 2, beacons 230A-230D may be used to define one or more borders or boundaries of geofence 210. … The location coordinates of the stationary nodes may be determined with a high level of specificity… and the coordinates (or other location information) of such nodes may be stored in a memory location within the system 200 (e.g., a memory at servers 240 hosting an EME 140, for example). As such, the exact location of the stationary nodes may be known to the system and used as the basis for highly reliable location determinations made concerning other elements of the system such as mobile nodes (e.g., via trilateration, triangulation, signal strength derivations, signal direction information, etc.). ..”; ;in addition see at least [claim 7] via: “… generating a set of virtual boundaries based in part on the known location of the at least one stationary node, the at least one stationary node comprising a plurality of proximity beacons..”)
Regarding claim 19 Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claim 15. Merjanian and Correnti are silent the following claim that is taught by Mesirow:
wherein the central computer is configured to calculate a proximity score using one or more signals from the sensors as captured by each aggregator, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the first asset and a second numerical vector related to the second asset during the target time period. For each such proximate user, the system may calculate a quantification and/or characterization of risk (e.g., a quantification of exposure risk, contamination risk, infection risk, and/or disease risk), such as a numerical risk score (e.g., a “proximity score”), a risk classification (e.g., high risk, medium risk, low risk), and/or a proximity classification (e.g., high proximity, medium proximity, low proximity). In some embodiments, a quantification and/or characterization of risk (e.g., exposure risk or disease risk) may include and/or be provided as a quantification and/or characterization of time in proximity and/or closeness of physical proximity, such as a proximity score. The quantification and/or characterization of risk and/or proximity score may be calculated in accordance with one or more predefined algorithms and/or using one or more machine learning algorithms. In some embodiments, the calculation may be based on a number of times a user was proximate to an exposed user, a time at which a user was proximate to an exposed user, a length of time over which a user was proximate to an exposed user, and/or a closeness of physical proximity (e.g., closeness of physical distance, for example calculated and/or inferred based on signal strength) between a user and an exposed user and/or between devices with which the user and the exposed user are associated. In some embodiments, a quantified and/or characterized risk may be determined with respect to a specific disease and/or pathogen, and the system may be configured to calculate different risks (e.g., exposure risks, contamination risks, infection risks, disease risks) for a single user for different diseases and/or pathogens…:; in addition see at least [0056] via: “…the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user. In some embodiments, the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users. For example, duration of contact may be estimated based on observed overlapping time between two mobile electronic devices and may be incorporated with frequency of overlap. Physical proximity (e.g., physical distance) may be estimated based on signal strength (e.g., RSSI) of BLE signals transmitted by nearby mobile electronic devices. If a second user's mobile electronic device observes a similar set of ambient WiFi and/or Bluetooth signals as an infected user's mobile electronic device for an overlapped time, or the second user's mobile electronic device detects the infected user's BLE signal, the system may determine that the second user has been in contact with the infected user. In some embodiments, a second user may be determined to be at a higher risk (e.g., higher proximity score) if the second user's mobile electronic device detected similar and/or overlapping signals with an infected user with a higher signal strength and/or for a longer period of time..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Corenti to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the calculation of a proximity score. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
Regarding claim 20 Merjanian, Mesirow and Correnti teach the invention as claimed and detailed above with respect to claims 15 & 19. Merjanian and Mesirow are silent the following claim that is taught by Correnti::
wherein the central computer is configured to calculate a contact duration score using timestamps of the one or more signals received by the plurality of aggregators in the designated area. (See at least [0042] via: “… the exposure evaluation module 124 can determine a risk score based on a duration of exposure of each person at the property 104 to the infected person 109. Duration of exposure can be determined, for example, by video analytics (e.g., observing the duration of proximity), phone to phone BLE detection (e.g., how long was there at least a threshold amount of signal exchange), access control data (e.g., two people rode an elevator together based on key card access), and the like. The contact tracing system 102 can determine, based on the aggregated sensor data 112, that a person A was within a threshold proximity of the infected person (e.g., within 6 feet of the infected person) for a threshold duration, and determine, based on this determined proximity and duration, a risk score for person A. In one example, video analytics and access control data can be utilized to determine how long person A was on a shared elevator ride with the infected person 109, e.g., which floors each person got on/off the elevator to determine an overlap time. A lower risk score can be assessed for person A for a duration of proximity that is less than a threshold duration, and a higher risk score can be assessed for a person A for a duration of proximity that is greater than a threshold duration…”; in addition see at least [0064] via: “…the exposure evaluation module 124 can aggregate multiple risk scores that a person A can accumulate through a set of potential exposures to infected people resulting in multiple exposure events. When the person's aggregated risk score reaches a threshold score, the contact tracing system 102 can provide a notification to the person including information of the aggregated risk score..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian and Mesirow to incorporate the teachings of Correnti. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Correnti’s teaching regarding the calculation of a risk score based on a duration of proximity. The combination of Merjanian and Correnti is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” to infectious diseases as a function of amount of time exposed between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety
Vo (US 20210385621 A1) -Method And System For Mapping Persons And Resources -teaches: Methods and systems are provided for mapping persons or resources within an environment. One application of the methods and systems provided is contact tracing..
Mc Namara (US 20210313075 A1) - SYSTEMS AND METHODS FOR CONTAGIOUS DISEASE RISK MANAGEMENT -teaches: A building system of a building, the building system including one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to receive occupancy data of occupants from an occupant tracking system, the occupancy data indicating locations of the occupants within a building space of the building. The instructions cause the one or more processors to determine, based on the occupancy data, whether one or more occupants of the occupants have violated a social distancing policy that reduces a spread of an infectious disease within the building based on the locations of at least two of the occupants, the social distancing policy based on one or more characteristics of the building space and perform one or more operations to improve compliance with the social distancing policy within the building.
Jain (US 11342051 B1) - Infectious Disease Monitoring Using Location Information And Surveys -teaches: automated contact tracing using multiple data sources. In some implementations, a system uses location data generated based on one or more types of signals, such as GPS signals, WI-FI signals, signals from cellular base stations, signals from short-range wireless technology (e.g., BLUETOOTH), and so on. The system also prompts users for information regarding their locations and the conditions present at the locations, either at the time a user is present or later. With this information, the system compares the tracked locations for different individuals to identify instances of contacts in which criteria for disease transmission potential are met, e.g., when two individuals have certain levels of proximity and timing. Detected instances of contacts can be used to inform individuals of exposure to a disease, as well as to notify public health authorities.
Response to Arguments
Applicant's arguments filed 12-17-2025, have been fully considered but not found
persuasive.
Applicant amended independent claims 1, 7 and dependent claims 4, 17, 19, 20 as posted in the above analysis with additions underlined and deletions as .
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 101.
Several steps are taken in the analysis as to whether an invention is rejected under 101. The first step is to determine if the claim falls within a statutory category. In this case it does for claims 1, 7 and 15 since the claims recite a system, method of “performing contact tracing”.
The second step under 2A prong one is to determine if the claims recite an abstract idea, which would be the case if the invention can be grouped as either: a) mathematical concepts; (b) mental processes; or (c) certain methods of organizing human activity (encompassing (i) fundamental economic principles, (ii) commercial or legal interactions or (iii) managing personal behavior or relationships or interactions between people). The current invention is classified as an abstract idea since it may be grouped as a mental process as it recites “performing contact tracing” or alternatively as belonging to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “performing contact tracing”.
The third step under 2A Prong Two is to determine if additional elements in the claim imposes a meaningful limit on the abstract idea in order to integrate it into a practical idea. The current invention does not represent a practical idea since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
the fourth step under 2B is to determine if additional elements of the claim provide an inventive concept. An invention may be classified as an inventive concept if a computer-implemented processes is determined to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic, and non-conventional even if generic computer operations on a generic computing device is used to implement the abstract idea. The current invention does not represent an inventive concept since the additional elements amount to mere instructions to implement an abstract idea on a computer, or merely use a generic computer as a tool to implement the abstract idea.
Step 2A Prong ONE
The Applicant argues that the invention does not recite an abstract idea since the claim with limitations cannot does not recite a mental process since it cannot practically be performed in the human mind. Accordingly, amended claims 1, 7 & 15 are not directed to an abstract idea.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Examiner explains the method to select the abstract idea, which is to strip the additional elements from the claims.
Regarding Claim 1:
As seen below the recited boldened words constitute the abstract idea after stripping the un-boldened additional elements of the amended limitations of claim 1
a plurality of aggregators configured to communicate in a meshed network to create a geo-fence within a designated area, each of the plurality of aggregators comprising a processor and a transceiver, each of the plurality of aggregators being configured to receive location information associated with a relative location of an asset based at least in part on information relating to the asset;
one or more sensors, each of the one or more sensors associated with the asset, the one or more sensors configured to communicate, with the plurality of aggregators in the designated area, the information relating to the asset regarding the relative location of the asset to each of the plurality of aggregators; and
a central computer in communication with the plurality of aggregators and configured to receive the information from at least one of the plurality of aggregators relating to a contact tracing event within the geo-fence within the designated area, the central computer configured to receive, from at least one of the plurality of aggregators, a plurality of parameters associated with the information relating to the
determine a contact status of the asset based on the plurality of parameters, the plurality of parameters including a duration of time the asset remained in the designated area and a distance between the asset and one or more additional assets in the designated area, the contact status being a normalized combination of aggregated duration and proximity score of the asset;
wherein the central computer is configured to calculate the proximity score using one or more signals from the one or more sensors as captured by each aggregator, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the asset and a second numerical vector related to one or the one of more additional assets.
The selected abstract idea (boldened limitations) of claim 1 belong to the grouping of mental processes under concepts performed in the human mind (including an observation, evaluation, judgement, opinion) as it recites: “performing contact tracing”. Alternatively, the claim belongs to certain methods of organizing human activity under managing personal behavior or relationships or interactions between people (including social activities, and following rules or instructions) as it recites: “performing contact tracing” (refer to MPP 2106.04(a)(2)). Accordingly, this claim recites an abstract idea.
Regarding Step 2A Prong Two.
The Applicant argues that even if claims 1, 7 and 15 were to recite an abstract idea they do recite additional elements that integrate the judicial exception into a practical application. Applicant submits that the additional elements recited in the claims do provide technical improvements that demonstrate integration of the alleged abstract idea into a practical application, and that Applicant's Specification does describe the technical improvements provided by the claims.
Specifically, the Applicant argues, based on the specification paragraphs [0048]-[0050] that the Application as filed identifies a particular technical problem in remote
localization whereby simply detecting the presence of a Bluetooth item cannot effectively detect a proximity of two or more assets. Further, the Application as filed identifies a particular solution of a network of aggregators along with a specialized algorithm to leverage detection parameters and thereby make contact tracing within a defined area possible. Reflecting such an improvement, amended claim 1 recites additional elements including, inter alia: The recited steps represent a specific ordered process that uses the claimed aggregators and central computer as a system-generated data structure to localize the assets. Therefore, the method of amended claim 1 further improves the computer-based technology of performing contact tracing by using the claimed aggregators. Hence, the Applicant argues that the present claims are more than mere "generic computer components with an unpatentable "algorithm," but rather, provide a particular solution of asset localization and contact tracing. Accordingly, amended independent claim I is patent eligible for at least the reasons detailed.
The Examiner disagrees since the Applicant’s arguments are not persuasive. The Applicant is interpreting the practical application of the invention colloquially. Instead, it should be analyzed as to whether the additional elements amount to more than mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. As a result, the Examiner restates that claims 1, 7 and 15 do not integrate the abstract idea into a practical application.
Neither claims 1, 7 and 15 recite additional elements that impose a meaningful limit on the abstract idea:
Claim 1 recites:
a plurality of aggregators configured to communicate in a meshed network
each of the plurality of aggregators comprising a processor and a transceiver, each of the plurality of aggregators being configured to receive location information
one or more sensors, each of the one or more sensors associated with the
one or more sensors configured to communicate, with the plurality of aggregators
a central computer in communication with the plurality of aggregators and configured to receive the information from at least one of the plurality of aggregators
the central computer configured to receive, from at least one of the plurality of aggregators, a plurality of parameters
wherein the central computer is configured to calculate the proximity score using one or more signals from the one or more sensors as captured by each aggregator;
Claim 7 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to an asset to a central computer;
determining a localization, by the central computer;
using an algorithm on the central computer;
proximity score is calculated using one or more signals from the one or more tags as captured by each aggregator
Claim 15 recites:
transmitting a plurality of parameters from one or more of a plurality of aggregators relating to one or more assets to a central computer;
determining, by a central computer, a proximity score;
determining, by a central computer, a duration score.
The additional elements as recited above amount to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Further support regarding mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea may be seen in paragraphs [004- 0022], [0080-00115] of the specification.. Accordingly, the claim as a whole does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
In order to integrate the abstract idea into a practical application the additional elements should be shown to impose a meaningful limit on the abstract idea. A colloquial interpretation of a practical application is not enough.
In order to integrate the abstract idea into a practical idea the Applicant could demonstrate at least one of the conditions enumerated below applies:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
The Applicant has not demonstrated any of the above listed conditions.
In response to applicant's arguments regarding claim rejection under 35 U.S.C § 103.
The Applicant argues that the following amended limitation of claims 1, 7 and 15 is not taught by any of the prior art used to reject the invention.
“the contact status being a normalized combination of aggregated duration and proximity score of the asset wherein the central computer is configured to calculate the proximity score using one or more signals from the one or more sensors as captured by each aggregator, the one or more signal being transformed into a single numerical vector, the proximity score being a similarity between a first numerical vector related to the of
The examiner disagrees since the Applicant’s argument is not persuasive.
Regarding the first limitation: Based on the following description in paragraph [0072] of the specification:
“normalized combination of each person's aggregated contact time and proximity score. This combination is parametrized with an alpha (a ) parameter (bounded between 0 and one) which weights the importance of proximity and time of contacts regarding the risk of transmission. The weighting is done with the following scheme: a.proximity + (1-a).duration. This alpha parameter is either manually tuned by users or automatically tuned through a subsequent optimization process. The higher is alpha, the more importance we give to the proximity score with respect to the time of contacts. For example, setting alpha to 0,75 means that the final risk score would be composed of 75% of the proximity score and 25% of the duration.”
The BRI interpretation of the amendment is that the normalized combination of the aggregated duration and proximity score of the asset is equivalent to weighted average or sum of both factors (duration and proximity).
Mesirow in [0028] recites: “generating the quantification of risk comprises: generating a vector comprising a plurality of vector components, wherein each of the plurality of vector components corresponds is computed based on comparing the signal data for to a respective predefined component threshold; calculating a weighted sum of the vector components; comparing the weighted sum of the vector components to a predefined threshold to determine a risk category...”. Furthermore Mesirow in [0056] recites: “the system may be configured to apply one or more algorithms to calculate a risk level and/or proximity score to determine which users should be classified as having a high proximity score, medium proximity score, or low proximity score with respect to the target user... the calculated proximity score may be a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal overlap and/or cross-device signal detection for the two users.”. In addition Mesirow in [0055] recites: “…Once signal data for the target user has been retrieved, the system may use the signal data to determine which other users (e.g., which other user devices) have been proximate to the target user during the target time period. For each such proximate user, the system may calculate a quantification and/or characterization of risk (e.g., a quantification of exposure risk, contamination risk, infection risk, and/or disease risk), such as a numerical risk score ..”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Merjanian to incorporate the teachings of Mesirow. Those in the art would have recognized that Merjanian’s teaching regarding methods to implement a comprehensive approach to the spread of infectious diseases including the acquisition and analysis of data to determine (1) a proximity to other nodes and users associated with respective nodes including historical paths/locations exposed to infectious agents, (2) a proximity within or to an area formed via a preexisting and/or ad hoc network of beacons, (3) if a number of nodes surpasses a predetermined threshold within an area, enabled by contact tracing, population density monitoring, and asset tracking using various forms of communication could be modified to include Mesirow’s teaching regarding the calculation of a proximity score. The combination of Merjanian and Mesirow is useful in better quantifying and characterizing the “exposure risk, contamination risk, infection risk, and/or disease risk” [Mesirow 0055] to infectious diseases as a function of proximity between two or more persons that may be infected in order to minimize the spread of diseases within certain geolocations.
The Examiner interprets Mesirow’s “calculated proximity score” or “risk” to be equivalent to the “contact status” of the amended limitation. Furthermore the Examiner interprets that the proximity score is a function of one or both of the duration and physical proximity (e.g., physical distance) of detected-signal, and that the quantification of risk may be calculated as a weighted sum of these signals.
For reasons of record and as set forth above, the examiner maintains the rejection of claims 1, 3-7, 9-20 as being directed to a judicial exception without significantly more, and thereby being directed to non-statutory subject matter under 35 USC §101, in addition to maintaining the rejection of claims 1, 3-7, 9-20 under 35 USC §103 based on prior art. In reaching this decision, the Examiner considered all evidence presented and all arguments actually made by Applicant.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE L MACCAGNO whose telephone number is (571)270-5408. The examiner can normally be reached M-F 8:00 to 5:00.
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687