DETAILED ACTION
Applicant’s response filed 05/18/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 on 05/18/2026 has been entered.
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 .
Claim Status
Claim 22 is newly added by Applicant.
Claim 10 is cancelled by Applicant.
Claims 1-9 and 11-22 are currently pending and are herein under examination.
Claims 1-9 and 11-22 are rejected.
Claim 5 is objected.
Priority
The instant application claims domestic benefit to US Provisional Application No. 63/171,559 filed 04/06/2021. The claim to domestic benefit is acknowledged. As such, the effective filing date for claims 1-9 and 11-22 is 04/06/2021.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 19 and 21 under 35 USC 112(b) is withdrawn in view of claim amendment.
35 USC 103
The rejection of claims 1-8, 11-17 and 21 under 35 U.S.C. 103 as being unpatentable over Huang et al. in view of Alshammari et al. and MacKenzie is withdrawn in view of claim amendment.
The rejection of claims 9 and 18-19 under 35 U.S.C. 103 as being unpatentable over Huang et al. in view of Alshammari et al. and MacKenzie and in further view of Perkins et al. is withdrawn in view of claim amendment.
The rejection of claim 20 under 35 U.S.C. 103 as being unpatentable over Huang et al. in view of Alshammari et al., MacKenzie, and Perkins et al. is withdrawn in view of claim amendment.
Claim Objections
Claim 5 is objected to because of the following informality: Claim 5 recites “second_risk” which should be “second risk”. Appropriate correction is required.
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 1-9 and 11-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-9, 11-13 and 21-22 recite a system, claims 14-19 recite a method, and claim 20 recite a system. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “… determine a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes during a stage of a journey, wherein the risk index for each node is based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node; … aggregate the risk indices for the nodes to determine a risk value associated with the stage of the journey; and wherein each node in the chain of nodes is within the same building or vehicle.”
Claim 3 recites “wherein the stage of the journey is within an airport.”
Claim 4 recites “… determine a risk index associated with the risk of exposure to the health threat for each node in the chain of nodes during a second stage of the journey, the second stage representing one of transport to the airport, at least one flight segment departing from the airport, at least one flight segment arriving to the airport, or transport from the airport.”
Claim 5 recites “wherein the risk value is a first risk value … aggregate the risk indices for the nodes during the second stage of the journey …”
Claim 6 recites “wherein the health threat prevalence in the jurisdiction associated with a node of the chain of nodes is a weighted sum of a prevalence value of local jurisdiction that encompasses the airport and respective prevalence values of jurisdictions from which flight passengers arrive at the airport.”
Claim 7 recites “wherein the risk index for each node is also based on a behavior parameter indicative of a measure of behavioral compliance of people in the respective node to the set of one or more control measures in effect.”
Claim 8 recites “wherein the risk index for each node is also based on air flow characteristics of the node.”
Claim 9 recites “… determine an adverse impact associated with implementing the set of control measures, and to calculate a net value for the set of one or more control measures based on the risk value and the adverse impact.”
Claim 11 recites “… determine the chain of nodes by predicting movement and activity of a traveler during the stage of the journey.”
Claim 12 recites “wherein the risk index that is determined for each node is a relative risk index that represents a risk of exposure at the respective node compared to a risk of exposure at one or more benchmark locations outside of the stage of the journey.”
Claim 13 recites “wherein the set of one or more control measures in effect includes multiple different types of control measures in effect, such that the risk index for each node is based on the multiple different types of the control measures in effect.”
Claim 14 recites “determining, via … a model, a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes within the building or vehicle during a stage of a journey, wherein the risk index for each node is determined based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node; aggregating the risk indices for the nodes to determine a risk value associated with the stage of the journey”
Claim 16 recites “wherein the stage of the journey is within an airport, and the method further comprises determining a risk index associated with the risk of exposure to the health threat for each of node in the chain of nodes during second stage of the journey, the second stage representing one of transport to the airport, at least one flight segment departing from the airport, at least one flight segment arriving to the airport, or transport from the airport.”
Claim 17 recites “wherein the risk index for each node is also determined based on a behavior parameter indicative of a measure of behavioral compliance of people in the respective node to the set of one or more control measures.”
Claim 18 recites “determining an adverse impact associated with implementing the set of control measures; and calculating a net value for the set of control measures based on the risk value and the adverse impact.”
Claim 19 recites “comparing the net value for a set of control measures to other net values associated with the stage of the journey that are calculated based on different sets of control measures, and selecting the set of control measures that has a greater net value as a recommended set of control measures.”
Claim 20 recites “… determine a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes during a journey within the same building or vehicle, wherein the risk index for each node is based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node; … aggregate the risk indices for the nodes to determine a risk value associated with the journey and the set of one or more control measures, and to determine an adverse impact associated with implementing the set of one or more control measures; … calculate a net value for the set of control measures based on the risk value and the adverse impact.”
Claim 22 recites “wherein the chain of nodes includes a node that is at least one of a check-in line of an airport, a security screening of the airport, a waiting area of the airport, a lavatory of the airport, or a restaurant at the airport.”
Limitations reciting a mental process.
The above cited limitations in claims 1, 4-9, 11-14 and 16-20 are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraph below discusses the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
The BRI of claims 1, 4-5, 7-8, 13-14, 16-17 and 20 of calculating a risk index based on occupancy characteristics, control measures, a health threat prevalence, a behavioral parameter, and airflow characteristics includes performing calculations using the equations provided in para. [55-69], wherein the risk indices can be aggregated through weighted means as taught in para. [21].
The BRI of claim 6 includes performing calculations to derive a weighted sum.
The BRI of claims 9 and 18 includes determining a quantitative measure of economic and operation impact associated with a set of control measures such as by a cost associated with implementing and regulating a control measure [25]. The BRI also includes subtracting the adverse impact 614 from the risk reduction 610 to derive a net value, as recited in para. [53].
The BRI of claim 11 includes predicting that a person who bought a plane ticket will go through security, wait at a terminal, and board a plane.
The BRI of claim 12 includes comparing risk indices between two locations and determining whether one node has a higher or lower associated risk, as stated in para. [20]. The specification also states “The value of the relative risk index may be a percentage or multiple of a value representing the risk of exposure at the benchmark location” [45].
The BRI of claim 19 includes comparing numbers and making a selection, which a human can practically perform.
Limitations reciting a mathematical concept.
The above cited limitations in claims 1, 4-9, 13-14, 16-18 and 20 equate to a mathematical concept because these limitations are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. Specifically, the BRI of calculating a risk index based on occupancy characteristics, control measures, a health threat prevalence, a behavioral parameter, and airflow characteristics includes performing calculations using the equations provided in para. [55-69], wherein the risk indexes can be aggregated through weighted means as taught in para. [21]. It also includes performing calculations to derive a weighted sum and calculating a net value by subtracting numbers.
Limitations included in the recited judicial exception.
The limitations in claims 1, 14 and 20 of the nodes being within the same building or vehicle, claim 3 of the stage of the journey being in an airport, and claim 22 of the nodes being within an airport are included in the judicial exception of determining risk indices because they further limit how the indices are determined.
As such, claims 1-9 and 11-22 recite an abstract idea (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “A system comprising: one or more processors configured to … wherein the one or more processors are further configured to … generate a control signal to notify a user of the risk value associated with the stage of the journey.”
Claim 2 recites “a user interface on a display device, wherein the one or more processors are configured to generate the control signal to display the risk value associated with the stage on the user interface.”
Claim 4 recites “wherein the one or more processors are further configured to …”
Claim 5 recites “wherein the one or more processors are further configured to … generate a control signal to notify the user of the risk value associated with the second stage of the journey.”
Claim 9 recites “wherein the one or more processors are further configured to …”
Claim 11 recites “wherein the one or more processors are configured to …”
Claims 2-13 recite “the system”.
Claim 14 recites “… computer-implemented … generating a control signal to notify a user of the risk value associated with the stage of the journey.”
Claim 15 recites “displaying the risk value associated with the stage within a user interface on a display device.”
Claim 20 recites “A system comprising: a display device; and one or more processors communicatively connected to the display device, the one or more processors configured to … wherein the one or more processors are further configured to … wherein the one or more processors are configured to … display the net value on the display device.”
Claim 21 recites “wherein the one or more processors are further configured to update the user interface in real-time based on the control signal.”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitations in claims 1-13 and 21 of the system comprising one or more processors, in claim 14 of computer-implemented, in claims 2 and 15 of a user interface on a display device, in claim 20 of the system comprising a display device and one or more processors, these limitations require nothing more than a generic computer and/or generic computing system. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claims 1-2, 5, 14-15 and 20 of generating a control signal to notify a user of the risk value associated with the first/second stage of the journey and generating a control signal to display the risk value associated with the first/second stage of the journey on the user interface. These limitations equate to insignificant, extra-solution activity of necessary data outputting. These limitations merely output the judicial exception (i.e., the risk value and the net value).
The limitation in claim 21 is being interpreted as intended use and is not required to be performed. As such, claim 21 is not being analyzed under Step 2A, Prong 2.
As such, claims 1-9 and 11-22 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Regarding the above cited limitations in claims 1-13 and 21 of the system comprising one or more processors configured to, in claim 14 of computer-implemented, in claims 2 and 15 of a user interface on a display device, in claim 20 of the system comprising a display device and one or more processors configured to, these limitations require nothing more than a generic computer and/ or generic computing system. Therefore these limitations equate to instructions to implement an abstract idea in a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Regarding the above cited limitations in claims 1-2, 5, 14-15 and 20 of generating a control signal and displaying the risk and net value on a user interface or display device, these limitations equate to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
The limitation in claim 21 is being interpreted as intended use and is not required to be performed. As such, claim 21 is not being analyzed under Step 2B.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they all equate to WURC functions/components of a generic computer and/or generic computing system. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-9 and 11-21 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 05/18/2026 have been fully considered but they are not persuasive.
Applicant argues improvement in the field of health threats within public transportation systems, such as when taking a commercial flight (pg. 7, last para. - pg. 8, para. 1). Applicant’s argument is not persuasive because:
The improvement is a result of the judicial exception itself. Specifically, claim 1 limitations of "determine a risk index" and "aggregate the risk indices … to determine a risk value associated with the stage of the journey". MPEP 2106.05(a).II recites "an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." The additional element in claim 1 of "one or more computer processors" equates to mere instructions to implement the abstract idea on a generic computer, which does not provide a practical application (MPEP 2106.05(f)). The additional element in claim 1 of "generating a control signal to notify a user" equates to insignificant, extra-solution activity of necessary data outputting because it outputs the result of the judicial exception, which does not provide a practical application (MPEP 2106.05(g)(3)).
Furthermore, the alleged improvement for solving problems associated with health threats within public transportation systems is not commensurate in scope with claim 1 because it has not been limited to public transportation systems. Although claims 3 and 22 further limit the journey to be inside an airport and the nodes to be at specific locations in the airport, these claims are included in the judicial exception recited in claim 1 of determining and aggregating risk indices.
Applicant argues that the claims are different from Electric Power Group because they analyze data from new sources and thus equate to an inventive concept and/or integrate into a practical application (pg. 8, para. 3) (pg. 10, para. 1). Applicant's argument is not persuasive because:
Claim 1 does not require that the nodes be any of the listed sources stated by Applicant. Claim 22 requires at least one node be from the listed sources stated by Applicant. Claim 22 is part of the recited judicial exception in claim 1 of determining and aggregating risk indices from nodes.
Regarding analyzing data and information from sources that have never been analyzed, the source of the data collected is part of the judicial exception for determining and aggregating risk indices from nodes. Under Step 2A, Prong 2, MPEP 2106.05(a).II recites "an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." Under Step 2B, MPEP 2106.05.I recites “[a]n inventive concept ‘cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself.’” As such, the abstract ideas of determining and aggregating risk indices from data collected from new sources, when the new sources themselves recite an abstract idea, cannot provide a practical application or significantly more. The abstract idea cannot integrate itself into a practical application or itself provide significantly more.
Additionally, MPEP 2106.04.I recites “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions.”
Applicant’s remarks regarding the August 2025 Memo are noted (pg. 8, last para. – pg. 9).
Applicant argues that (i) collecting information when recited as an abstract idea is not relevant under Step 2A, Prong 2 or Step 2B, (ii) collecting data can be the basis for finding a claim eligible under Step 2A, Prong 2 and Step 2B, and (iii) compares instant claim 1 to SME Example 40 (pg. 10, para. 2 - pg. 11, para. 1). Applicant's argument is persuasive in part because:
Examiner agrees that collecting information when recited as an abstract idea is not a relevant inquiry under Step 2A, Prong 2 or Step 2B, which evaluates the additional elements of the claims.
Example 40 is not analogous to instant claim 1 because claim 1 does not recite additional elements pertaining to collecting data. This is distinct from the collection steps in Example 40 which recited additional elements.
Applicant’s arguments regarding claims 14 and 20 are not persuasive for the same reasons described above regarding claim 1 (pg. 11, para. 2).
Applicant argues for claim 21 that a human cannot update a signal in real-time (pg. 11, para. 3). Applicant’s argument is persuasive in part because:
Examiner agrees that claim 21 recites an additional element. However, claim 21 equates to mere data outputting under Step 2A, Prong 2 and to transmitting data over a network under Step 2B.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8, 11-17 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Huang”; NPL ref. 2 on IDS filed 04/06/2023; previously cited) in view of Alshammari et al. (“Alshammari”; NPL ref. 3 on IDS filed 04/06/2023; previously cited), MacKenzie (Risk analysis 34, no. 12 (2014): 2143-2162; previously cited on PTO892 mailed 11/04/2025), and Malaviya (US 2017/0024531 A1; previously cited on PTO892 mailed 03/09/2026).
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
This rejection is newly recited as necessitated by claim amendment.
Claims 1 and 14:
A system comprising: one or more processors configured to determine a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes during a stage of a journey,
Huang discloses a web-based geographic information system called the vector-borne disease airline importation risk (VBD-AIR) that defines the role of airports and airlines in the transmission/spread of vector-born disease (abstract). Huang teaches “VBD-AIR is designed to be a flexible tool that combines multiple geospatial datasets to inform on the relative risks between differing airports, flight routes, times of year, diseases, and their vectors, in promoting the movement of passengers infected by vector-borne diseases and the vectors that spread these diseases” (pg. 6, col. 1, para. 3) (Figure 5).
wherein the risk index for each node is based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node;
Huang calculates three risk assessments (risk index) called imported vector-borne disease case risk assessment, onward transmission risk assessments, and imported vector risk assessments (pg. 7, sec. Risk assessments). These risks are calculated using schedule incoming flight routes in 2011 and the traffic capacity on these routes (occupancy characteristics) (pg. 7, sec. Risk assessments) as well as “disease/vector prevalence at origin locations” (a health threat prevalence in a jurisdiction associated with the node) (pg. 7, col. 1).
However, Huang does not teach that the risk assessments are based on a set of one or more control measures to reduce the risk of exposure in a node.
Alshammari discusses the effect that global mass gatherings have on importation/exportation of infectious disease to and from host countries by international participants (abstract). Alshammari teaches “a computational epidemic simulation framework to simulate disease transmission from the arrival, to the departure of international participants in the global event of Hajj” (abstract). The framework includes an agent-based epidemic framework that “includes parameters to assign vaccination status and preventative actions for each individual. The vaccination coverage, the proportion of participants following a preventative behavior, and the effectiveness of each measure are input parameters for the framework and can be modified depending on the data. In the framework, the effect of each preventative measure is simulated by reducing the transmission probability by the effectiveness or efficiency of the applied measure” (pg. 224, para. 1; sec. 3.2).
It would have been prima facie obvious to have modified Huang’s method of calculating a risk based on cities with a population over 50,000 located near an airport and disease distribution maps depicting prevalence by using a third variable based on preventative measures as taught by Alshammari. Alshammari teaches that agent-based simulations take into account preventative measures performed by an individual and “can be used to simulate disease transmission at an individual level providing a realistic representation of epidemic progress among individuals” (pg. 291, para. 2). This aligns with Huang who is directed to epidemiology, particularly transmission and spread of vector-borne disease in airlines and airports (abstract) (pg. 2, col. 1, para. 1).
There would have been a reasonable expectation of success for calculating a risk by combining the preventative measures of Alshammari to the datasets of Huang because Huang teaches “It is envisioned that future research … will build upon VBD-AIR to continue to improve quantification of these aspects, drawing on newly-developed … mathematical models of transmission, to provide an evidence base to enable airports, airlines, and public health officials to assess the appropriateness and efficacy of current control, surveillance and treatment practices, and tailor strategies to these differing risk profiles for each disease, route and airport” (pg. 12, col. 2, para. 2). There is also a reasonable expectation because Huang recites “additional risk-modifying factors such as actual passenger numbers, traveler activities and prophylaxis use, seasonal variations in disease transmission, chartered flights or multiple stopovers” (pg. 7, col. 1, para. 3). Therefore, VBD-AIR can be modified to incorporate mathematical models of transmission such as the agent-based simulation framework of Alshammari that takes into account individual preventative measures.
wherein the one or more processors are further configured to aggregate the risk indices for the nodes to determine a risk value associated with the stage of the journey,
Huang discloses three risk assessments calculated for each airport (pg. 7, col. 1, para. 1) (Figures 4 and 5). However, Huang does not aggregate the three risk assessments to derive a risk value for each airport.
Mackenzie summarizes risk using risk measures and risk indices (abstract) and aggregates risk factors together (pg. 17, sec. 3.4).
It would have been prima facie obvious to have modified Huang’s method of risk values associated with nodes along a flight path into a single measure of risk as taught by MacKenzie. MacKenzie recites “For the risk index to be additive, MLB must equal 0. The appealing feature of this property is that an organization can separately measure multiple risks and add the risks together to obtain a numerical measure of the organization’s risk” (pg. 23, para. 2). Although this quote is directed toward an organization’s risk, MacKenzie states that risk indices are often used for disease risk (pg. 4, para. 1). MacKenzie also states that combining risk measurements “limits the risk measures to expected values or expected exponential disutility” (pg. 25, para. 1).
There would have been a reasonable expectation of success for aggregating risks along a proposed flight into a single risk because MacKenzie states that this can be achieved through an additive risk measure/index by combining the two or more risks together (pg. 23, para. 2).
and to generate a control signal to notify a user of the risk value associated with the stage of the journey.
Huang teaches “The VBD-AIR tool takes the form of an interactive online interface and is
targeted at users with interests in specific airports or regions, and the risks to those locations of vector-borne disease importation and onward spread, or exotic vector importation and establishment” (pg. 2, col. 2, last para.). Figure 5 shows an example of displaying risks associated with airports and flight routes. However, Huang does not teach the aggregated risk. MacKenzie teaches the aggregated risk (pg. 23, para. 2). The above combination of MacKenzie and Huang teach displaying the aggregated risk value in Figures 4 and 5 of Huang.
wherein each node in the chain of nodes is within the same building or vehicle.
Huang recites “The VBD-AIR … is targeted at users with interests in specific airports or regions, and the risks to those locations of vector-borne disease importation and onward spread, or exotic vector importation and establishment” (pg. 2, col. 2, last para.). Figure 5 shows direct flights to a selected airport, wherein the airport is the node with the risk indices as indicated by “measures are calculated for the selected airport, disease and month” (pg. 7-8, sec. Risk assessments).
However, Huang does not teach that an airport has multiple nodes.
Malaviya discloses methods for real-time contact tracing within a healthcare facility (abstract). Pathways comprised of nodes are generated in a healthcare facility such as a hospital (each node in a chain of nodes is within the same building) [19] [45] [118] [134]. Infection risk levels are calculated for each node [7] [92] (claim 14).
It would have been prima facie obvious to have modified Huang’s method for calculating disease risk assessments for an airport based on flight data by also dividing the airport into multiple nodes and calculating a risk assessment for each node, as taught by Malaviya. Malaviya teaches that their method optimizes which pathway should be taken from one location to another [80]. This teaching when combined with Huang produces an advantage of finding an optimized route within an airport to avoid disease. This aligns with Huang’s motivation for examining the role of airports in transmission and spread of disease (abstract).
One of ordinary skill in the art would have had a reasonable expectation of success to generate nodes with the same airport because Malaviya demonstrates that a building can be deconstructed into nodes [45] [134]. One of ordinary skill would recognize that an airport building can be deconstructed into nodes.
Regarding claim 14, the only difference between claim 1 and 14 is that claim 14 discloses “via a computer-implemented model”. Huang teaches this limitation by reciting “VBD-AIR utilizes an entity-relationship model to integrate data sources from airport locations and air routes, disease and vector distributions, global climate data, and global land-based travel time data” (pg. 3, col. 1, para. 1), wherein VBD-AIR calculates the risk assessments (Figures 4 and 5). The same prima facie case for obviousness applied to claim 1 applies to claim 14.
Claims 2-8, 11-13 and 15-17:
Regarding claims 2-3 and 15, Huang shows in Figure 5 the display output of VBD-AIR to a user, wherein the display output shows the risk assessments for airports (node and building) and flight routes.
Regarding claims 4 and 16, the broadest reasonable interpretation includes performing the same procedure as described in claims 1 and 14 on another set of airports and flight routes. Huang discloses that the risk assessments are calculated for various airports and flight routes (Figures 4 and 5). Figure 4 shows a user selecting an airport with an output being the top 10 routes based on the risk assessments.
Regarding claim 5, the broadest reasonable interpretation of claim 5 includes performing the same procedures recited in claim 1 but for other airports and flight routes (the nodes during the second stage of the journey). Thus, the same prima facie case for obvious above regarding claim 1 applies to claim 5.
Regarding claim 6, this claim is being interpreted as a product-by-process. MPEP 2113.I recites “The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process.” In this case, the “health threat prevalence in the jurisdiction associated with the node” is the product and the process is “a weighted sum of a prevalence value of local jurisdiction that encompasses the airport and respective prevalence values of jurisdictions from which flight passengers arrive at the airport”. Huang uses a dataset set called land-based travel-time which depicts travel times from an airport to a major city that has more than 50,000 people and is within 2 hours and 50km from the airport (pg. 6, col. 1, para. 3). The land-based travel-time dataset was combined with disease distribution maps, which contain disease prevalence (pg. 2, col. 2, para. 2), to calculate a maximum predicted disease risk within the 2 hours and 50km of each airport, wherein the maximum level of disease risk within these travel times and distances were assigned to each airport in all tool calculations (pg. 6, col. 1, para. 3). Huang teaches the product of a health threat prevalence in a jurisdiction associated with a node, even though the process of making the product differs from the claimed invention.
Regarding claims 7-8 and 17, Huang teaches that the risk assessments are calculated based on scheduled incoming flight routes in 2011, the traffic capacity on these routes, among other variables (pg. 7, sec. Risk assessment). However, Huang does not teach that the risk assessments are calculated based on behavioral compliance to control measures or based on air flow characteristics. Alshammari teaches that the agent-based epidemic framework uses as an input parameter “the proportion of participants following a preventative behavior” (pg. 224, para. 1) as well as contact model that takes into account how far disease droplets travel in the air when an infected person sneezes, coughs, talks or breaths and is represented as equation 1 in sec. 2.5.
It would have been prima facie obvious to have modified the method of Huang for calculating risk assessments by also accounting for the proportion of participants following a preventative behavior and disease droplet travel in air as taught by Alshammari. The motivation for doing so is taught by Alshammari who teaches that agent-based simulations take into account preventative measured performed by an individual and “can be used to simulate disease transmission at an individual level providing a realistic representation of epidemic progress among individuals” (pg. 291, para. 2). This motivation aligns with the teachings of Huang which are directed to epidemiology, particularly transmission and spread of vector-borne disease in airlines and airports (abstract) (pg. 2, col. 1, para. 1).
One of ordinary skill in the art would have had a reasonable expectation of success for calculating a risk by combining the preventative measures and disease droplet calculations of Alshammari to the datasets of Huang because Huang teaches mathematical models of transmission can be incorporated into VBD-AIR to tailor strategies to these differing risk profiles for each disease, route and airport (pg. 12, col. 2, para. 2). There is also a reasonable expectation of success because Huang also recites “additional risk-modifying factors such as actual passenger numbers, traveler activities and prophylaxis use, seasonal variations in disease transmission, chartered flights or multiple stopovers” (pg. 7, col. 1, para. 3). Therefore, it appears VBD-AIR can be modified to incorporate mathematical models of transmission such as the agent-based simulation framework to calculate risk as well as calculate the three risk assessments by including a parameter of preventative measures.
Regarding claim 11, Huang does not teach predicting movement and activity of a traveler during a state of a journey. Alshammari teaches a computational epidemic simulation framework that simulates disease outbreaks at global mass gatherings that includes a population model that simulates the movements and interactions of pilgrims throughout different stages (sec. 2.2 and sec. 3.1.1).
It would have been prima facie obvious to one of ordinary skill before the effective filing date of the instant invention to have modified the method of Huang for determining disease risk for travels by incorporating an agent-based epidemic framework that simulates travel interactions and movement, as taught by Alshammari, because Alshammari states that agent-based epidemic frameworks provide a realistic representation of epidemic progress among individuals (pg. 219, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success to incorporate the agent-based simulation framework of Alshammari into Huang because Huang states that VBD-AIR can incorporate mathematical models of transmission (pg. 12, col. 2, para. 2).
Regarding claim 12, Huang states that VBD-AIR generates relative risks between differing airports and flight routes (pg. 6, col. 1, last para.).
Regarding claim 13, Huang teaches that the risk assessments are calculated based on scheduled
incoming flight routes in 2011, the traffic capacity on these routes, among other variables (pg. 7, sec. Risk assessment). However, Huang does not teach that the risk assessments are calculated based on multiple different types of control measures. Alshammari teaches that the agent-based epidemic framework uses as an input parameters vaccination status and preventative actions for each individual (multiple different types of control measures) (pg. 224, para. 1).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Huang for calculating risk assessments by also accounting for vaccination status and preventative actions for each individual as taught by Alshammari. The motivation for doing so is taught by Alshammari who teaches that agent-based simulations “can be used to simulate disease transmission at an individual level providing a realistic representation of epidemic progress among individuals” (pg. 291, para. 2). This motivation aligns with the teachings of Huang which are directed to epidemiology, particularly transmission and spread of vector-borne disease in airlines and airports (abstract) (pg. 2, col. 1, para. 1).
One of ordinary skill in the art would have had a reasonable expectation of success for calculating a risk by combining the vaccination status and preventative actions of each individual of Alshammari to the datasets of Huang because Huang teaches mathematical models of transmission can be incorporated into VBD-AIR to tailor strategies to these differing risk profiles for each disease, route and airport (pg. 12, col. 2, para. 2). There is also a reasonable expectation of success because Huang also recites “additional risk-modifying factors such as actual passenger numbers, traveler activities and prophylaxis use, seasonal variations in disease transmission, chartered flights or multiple stopovers” (pg. 7, col. 1, para. 3). Therefore, it appears VBD-AIR can be modified to incorporate mathematical models of transmission such as the agent-based simulation framework to calculate risk as well as calculate the three risk assessments by including parameters of preventative actions and vaccination status.
Regarding claim 21, Huang teaches “The controller receives inputs from the users via html form and sends it to the model” (pg. 8, col. 1, para. 2). Huang also teaches that the WBD-AIR tool is automatically responsive to user input (Figure 5a) (pg. 9, col. 1, para. 3) (pg. pg. 10, col. 1, para. 2).
Claims 9 and 18-19 rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Huang”; NPL ref. 2 on IDS filed 04/06/2023; previously cited) in view of Alshammari et al. (“Alshammari”; NPL ref. 3 on IDS filed 04/06/2023; previously cited), MacKenzie (Risk analysis 34, no. 12 (2014): 2143-2162; previously cited on PTO892 mailed 11/04/2025), and Malaviya (US 2017/0024531 A1; previously cited on PTO892 mailed 03/09/2026) and in further view of Perkins et al. (In What is the effect of reduced street lighting on crime and road traffic injuries at night? A mixed-methods study. NIHR Journals Library, 2015; previously cited on PTO892 mailed 11/04/2025).
This rejection is newly recited as necessitated by claim amendment.
The limitations of claims 1 and 14 have been taught in the rejection above by Huang, Alshammari, MacKenzie and Malaviya.
Regarding claims 9 and 18, Alshammari teaches “the effect of each preventative measure is simulated by reducing the transmission probability by the effectiveness or efficiency of the applied measure” (adverse impact associated with implementing the set of control measures) (pg. 224, para. 1). However, Huang, Alshammari, MacKenzie and Malaviya do not calculate a net value for a control measure based one risk value and adverse impact.
Perkins teaches a cost-benefit analysis (CBA) regarding evaluating large-scale public health interventions (title). The CBA includes calculating a net present value (NPV), which is the sum of costs (adverse impact) and benefits (risk value).
It would have been prima facie obvious to have modified the method of Huang, Alshammari, and MacKenzie for generating risk values associated with control measures by performing a CBA for the control measures resulting in an NPV, as taught by Perkins. The motivation for doing so is taught by Perkins who recites “CBAs synthesize all costs and benefits of an intervention by valuing all costs and benefits in monetary units. In principle, this approach is more attractive, as all outcomes and inputs are valued in the same units, making comparisons more straightforward. When the calculation of all of the values of costs and benefits are carefully justified and made explicit, CBAs can lead to more transparent decision-making. Academics have, therefore, argued that more attention should be placed on the CBA framework when evaluating large-scale public policy interventions” (pg. 2, para. 2), particularly in public health (pg. 2, para. 3)
One of ordinary skill in the art would have had a reasonable expectation of success for performing a CBA of the control measures resulting in a NPV based on the difference between a risk value and cost of implementing a control measure because Perkins states that revealed and stated preference techniques can be used to monetize the effect of an intervention (pg. 2, para. 3). In the case of the combination, the control measures such as vaccinations and mask wearing, as taught by Alshammari, can be monetized to calculate the NPV.
Regarding claim 19, as discussed above regarding claim 18 Huang, Alshammari, MacKenzie, and Malaviya teach control measures and stages of journey. However, Huang, Alshammari, MacKenzie and Malaviya do not calculate net values for different sets of control measures to then compare the net values and select the control measure with the greatest net value.
Perkins teaches performing a CBA analysis and comparing the NPVs between a do-nothing scenario against the part-night lighting scenario (pg. 10, para. 3-4). Perkins determined based on the comparison that after 5 years the cost of street lighting provision in the part-night lighting scenario are greater than the do-nothing scenario (pg. 10, para. 4), and determined which solution has a greater net value based on the CBA (pg. 10, para. 4).
It would have been prima facie obvious to have modified the method of Huang, Alshammari, MacKenzie and Malaviya for incorporating individual preventative measures such as vaccination status and mask wearing when calculating disease transmission risk by determining NPVs for each control measure to then compare the net values of each CBA such that the best control measure is selected based on its benefits and costs, as taught by Perkins. Perkins teaches the motivation for doing so by reciting “There are four main types of economic evaluation currently used in public health: … CBA … CBA can lead to more transparent decision-making” (pg. 1, para. 3 – pg. 2, para. 2).
One of ordinary skill in the art would have had a reasonable expectation of success for performing a CBA using the data acquired from Huang and Alshammari because Perkins states that CBAs are performed using public health data (pg. 1, para. 3).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Huang”; NPL ref. 2 on IDS filed 04/06/2023; previously cited) in view of Alshammari et al. (“Alshammari”; NPL ref. 3 on IDS filed 04/06/2023; previously cited), MacKenzie (Risk analysis 34, no. 12 (2014): 2143-2162; previously cited on PTO892 mailed 11/04/2025), and Malaviya (US 2017/0024531 A1; previously cited on PTO892 mailed 03/09/2026) and in further view of Gaber et al. (Aviation, space, and environmental medicine 80, no. 7 (2009): 595-600; previously cited on PTO892 mailed 03/09/2026).
This rejection is newly recited as necessitated by claim amendment.
The limitations of claims 1-2 have been taught in the rejection above by Huang, Alshammari, MacKenzie and Malaviya.
Claim 22:
Huang teaches each airport as a node (Figure 5). However, Huang does not teach the locations recited in claim 22.
Gaber screens for infectious disease at international airports (abstract). Disease transmission is more likely to occur in an airport before or after, rather than during, a flight (pg. 598, col 1, last para.). Before boarding an aircraft, transmission can occur in line at the check-in counter of an airport (pg. 598, col. 1, last para.). It would have been prima facie obvious to have used a check-in line at an airport, as taught by Gaber, as a node in the nodes of an airport as taught by Huang and Malaviya to calculate risk. Motivation for doing so is to track locations in an airport where disease is most likely to be transmitted as taught by Gaber (pg. 598, col. 1, last para.). There would have been a reasonable expectation of success to use check-in lines as nodes because it requires collecting the same data from nodes collected by Huang and Alshammari.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Huang et al. (“Huang”; NPL ref. 2 on IDS filed 04/06/2023; previously cited) in view of Alshammari et al. (“Alshammari”; NPL ref. 3 on IDS filed 04/06/2023; previously cited), MacKenzie (Risk analysis 34, no. 12 (2014): 2143-2162; previously cited on PTO892 mailed 11/04/2025), Perkins et al. (In What is the effect of reduced street lighting on crime and road traffic injuries at night? A mixed-methods study. NIHR Journals Library, 2015; previously cited on PTO892 mailed 11/04/2025), and Malaviya (US 2017/0024531 A1; previously cited on PTO892 mailed 03/09/2026).
This rejection is newly recited as necessitated by claim amendment.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claim 20:
A system comprising: a display device; and one or more processors communicatively connected to the display device, the one or more processors configured to determine a risk index associated with a risk of exposure to a health threat for each node within a chain of nodes during a journey within the same building or vehicle,
Huang discloses a web-based geographic information system called the vector-borne disease airline importation risk (VBD-AIR) tool aimed to help define the roles of airports and airlines in the transmission/spread of vector-born disease (abstract). VBD-AIR is an interactive online interface that users can access to display information (a display device; and one or more processors communicatively connected to the display device, the one or more processors configured to) (Figure 5). Huang teaches “VBD-AIR is designed to be a flexible tool that combines multiple geospatial datasets to inform on the relative risks between differing airports, flight routes, times of year, diseases, and their vectors, in promoting the movement of passengers infected by vector-borne diseases and the vectors that spread these diseases” (pg. 6, col. 1, para. 3) (Figure 5).
Huang recites “The VBD-AIR … is targeted at users with interests in specific airports or regions, and the risks to those locations of vector-borne disease importation and onward spread, or exotic vector importation and establishment.” Figure 5 shows disease risks between airports connected by flight paths, wherein each airport is a node.
However, Huang does not teach a single airport having a chain of nodes within it.
Malaviya discloses methods for real-time contact tracing within a healthcare facility (abstract). Pathways comprised of nodes are generated in a healthcare facility such as a hospital [19] [45] [118] [134]. Infection risk levels are calculated for each node [7] [92] (claim 14).
It would have been prima facie obvious to have modified Huang’s method for defining the role of airports in the transmission and spread of disease by calculating risk for each node in an airport, as taught by Malaviya. Malaviya teaches that their method optimizes which pathway should be taken from one location to another [80]. This teaching when combined with Huang produces an advantage of finding the best path in an airport to avoid disease exposure.
One of ordinary skill in the art would have had a reasonable expectation of success to generate nodes with the same airport because Malaviya demonstrates that a building can be deconstructed into nodes [45] [134]. One of skill would recognize that an airport building can also be deconstructed into nodes.
wherein the risk index for each node is based on occupancy characteristics of the node, a set of one or more control measures in effect to reduce the risk of exposure in the node, and a health threat prevalence in a jurisdiction associated with the node;
Huang calculates three risk assessments (risk index) called imported vector-borne disease case risk assessment, onward transmission risk assessments, and imported vector risk assessments (pg. 7, sec. Risk assessments). These risks are calculated using schedule incoming flight routes in 2011 and the traffic capacity on these routes (occupancy characteristics) (pg. 7, sec. Risk assessments) as well as “disease/vector prevalence at origin locations” (pg. 7, col. 1).
However, Huang does not teach that the risk assessments are based on a set of one or more control measures to reduce the risk of exposure in a node.
Alshammari discusses the effect that global mass gatherings have on importation/exportation of infectious disease to and from host countries by international participants (abstract). Alshammari teaches “a computational epidemic simulation framework to simulate disease transmission from the arrival, to the departure of international participants in the global event of Hajj” (abstract). The framework includes an agent-based epidemic framework that “includes parameters to assign vaccination status and preventative actions for each individual. The vaccination coverage, the proportion of participants following a preventative behavior, and the effectiveness of each measure are input parameters for the framework and can be modified depending on the data. In the framework, the effect of each preventative measure is simulated by reducing the transmission probability by the effectiveness or efficiency of the applied measure” (pg. 224, para. 1; sec. 3.2).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Huang for calculating a risk based on cities with a population over 50,000 located near an airport and disease distribution maps depicting prevalence by using a third variable based on preventative measures as taught by Alshammari. The motivation for doing so is taught by Alshammari who teaches that agent-based simulations take into account preventative measured performed by an individual and “can be used to simulate disease transmission at an individual level providing a realistic representation of epidemic progress among individuals” (pg. 291, para. 2). This motivation aligns with the teachings of Huang which are directed to epidemiology, particularly transmission and spread of vector-borne disease in airlines and airports (abstract) (pg. 2, col. 1, para. 1).
One of ordinary skill in the art would have had a reasonable expectation of success for calculating a risk by combining the preventative measures of Alshammari to the datasets of Huang because Huang teaches “It is envisioned that future research … will build upon VBD-AIR to continue to improve quantification of these aspects, drawing on newly-developed … mathematical models of transmission, to provide an evidence base to enable airports, airlines, and public health officials to assess the appropriateness and efficacy of current control, surveillance and treatment practices, and tailor strategies to these differing risk profiles for each disease, route and airport (pg. 12, col. 2, para. 2). There is also a reasonable expectation because Huang also recites “additional risk-modifying factors such as actual passenger numbers, traveler activities and prophylaxis use, seasonal variations in disease transmission, chartered flights or multiple stopovers” (pg. 7, col. 1, para. 3). Therefore, it appears VBD-AIR can be modified to incorporate mathematical models of transmission such as the agent-based simulation framework of Alshammari that takes into account individual preventative measures.
wherein the one or more processors are further configured to aggregate the risk indices for the nodes to determine a risk value associated with the journey and the set of one or more control measures, and
Huang discloses three risk assessments (pg. 7, col. 1, para. 1) and discloses different stages of a journey including flight origin and destination (stage of the journey) (Figures 4 and 5). However, Huang does not teach aggregating the three risk assessments to derived a risk value associated with a stage of a journey.
Mackenzie teaches summarizing risk using risk measures and risk indices (abstract) and teaches aggregating risk factors together (pg. 17, sec. 3.4).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the method of Huang by aggregating risks along a planned flight path into a single measure of risk as taught by MacKenzie. Motivation for the combination is taught by MacKenzie who recites “For the risk index to be additive, MLB must equal 0. The appealing feature of this property is that an organization can separately measure multiple risks and add the risks together to obtain a numerical measure of the organization’s risk” (pg. 23, para. 2). Although this quote is directed toward an organizations risk, MacKenzie states that risk indices are often used for disease risk (pg. 4, para. 1). MacKenzie also states that combining risk measurements “limits the risk measures to expected values or expected exponential disutility” (pg. 25, para. 1).
One of ordinary skill in the art would have had a reasonable expectation of success for aggregating different risk assessments of Huang into a single risk because MacKenzie states that this can be achieved through an additive risk measure/index by combining the two or more risks together (pg. 23, para. 2).
to determine an adverse impact associated with implementing the set of one or more control measures;
Huang does not teach this limitation.
Alshammari teaches “the effect of each preventative measure is simulated by reducing the transmission probability by the effectiveness or efficiency of the applied measure” (pg. 224, para. 1).
It would have been prima facie obvious to one of ordinary skill before the effective filing date of the instant invention to have modified the method of Huang for determining disease risk for travels by incorporating an agent-based epidemic framework that incorporates the effects of individual control measures such as vaccination status and mask wearing to determine transmission probability, as taught by Alshammari because Alshammari states that agent-based epidemic framework provides a realistic representation of epidemic progress among individuals (pg. 219, para. 2). One of ordinary skill in the art would have had a reasonable expectation of success to incorporated the agent-based simulation framework of Alshammari into Huang because Huang states that VBD-AIR can incorporate mathematical models of transmission (pg. 12, col. 2, para. 2).
wherein the one or more processors are configured to calculate a net value for the set of control measures based on the risk value and the adverse impact, and display the net value on the display device.
Huang teaches a display device and risk values but does not teach calculating net values for control measures based on the risk value and calculated adverse impacts. As discussed above, Alshammari teaches calculating an adverse impact for a control measure. However, neither Huang, Alshammari, or MacKenzie discuss generating a net value for control measures based on risk value and adverse impact.
Perkins teaches a cost-benefit analysis (CBA) regarding evaluating large-scale public health interventions (title). The CBA includes calculating a net present value (NPV), which is the sum of costs (adverse impact) and benefits (risk value).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the instant invention to have modified the method of Huang, Alshammari, and MacKenzie for generating risk values associated with control measures by performing a CBA for the control measures resulting in an NPV, as taught by Perkins. The motivation for doing so is taught by Perkins who recites “CBAs synthesize all costs and benefits of an intervention by valuing all costs and benefits in monetary units. In principle, this approach is more attractive, as all outcomes and inputs are valued in the same units, making comparisons more straightforward. When the calculation of all of the values of costs and benefits are carefully justified and made explicit, CBAs can lead to more transparent decision-making. Academics have, therefore, argued that more attention should be placed on the CBA framework when evaluating large-scale public policy interventions” (pg. 2, para. 2), particularly in public health (pg. 2, para. 3).
One of ordinary skill in the art would have had a reasonable expectation of success for performing a CBA of the control measures resulting in a NPV based on the difference between a risk value and cost of implementing a control measure because Perkins states that revealed and stated preference techniques can be used to monetize the effect of an intervention (pg. 2, para. 3). In the case of the combination, the control measures such as vaccinations and mask wearing, as taught by Alshammari, can be monetized to calculate the NPV.
Response to Arguments under 35 USC 103
Applicant's arguments filed 5/18/2026 have been fully considered but they are not persuasive.
Applicant argues that Huang’s Figure 5 does not show nodes within the same building or vehicle (pg. 11, para. 4 – pg. 14, para. 4). Applicant’s argument is not persuasive because:
Examiner agrees that Huang’s Figure 5 does not show multiple nodes within the same airport. However, the claim amendments necessitated a new ground of rejection that relies on a different combination of references not previously applied. It is now the combination of Huang et al. and Malaviya that teach the airport (the same building) of Huang having multiple nodes (i.e. a chain of nodes).
Conclusion
No claims are allowed.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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