Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the RCE filed 05 November 2025:
Claims 1,8,15 are amended
Claims 1-5,7-12,14-20 are pending
Information Disclosure Statement
The Information Disclosure Statement(s) (lDS) submitted on 25 November 2025 is/are in compliance with the provisions of 37 CFR 1.97 and has/have been fully considered by the Examiner.
Claim Objections
Claim 7,14 is objected to because of the following informalities:
Claim 7 depends on Claim 6, which has been cancelled
Claim 14 depends on Claim 13, which has been cancelled.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 8, 15 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The limitation of “generating, by one or more processors, a first alert indicating the unified risk factor for the individual, and controlling, by one or more processors, additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location” represents new matter.
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 1-5,7-12,14-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1,8,15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a computer-implemented method, program product and a computer system, which are within a statutory category. The limitations of:
Claims 1, 8 and 15 (Claim 15 being representative)
receive individual profile data comprising individual attributes corresponding to an individual, the individual attributes being weighted and comprising demographic data, medical data, and individual location data;
determine an individual risk factor based at least on the individual profile data, wherein the individual risk factor is an aggregate of the weighted individual attributes corresponding to the individual;
detect one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles;
determine a location risk factor for the location based at least on one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location;
determine a unified risk factor for the individual at the location, wherein the unified risk factor is an aggregate of the individual risk factor and the location risk factor; and
responsive to the unified risk factor satisfying a first condition:
generate a first alert indicating the unified risk factor for the individual.
as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to determine infection risk levels for individuals and locations in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “retrieving, determining, detecting, generating and controlling” as indicated supra. Other than reciting generic computer components (discussed infra), i.e., a system implemented by a processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional element of a computer system comprising computer processors and computer readable storage media having program, as well as program instructions to control additional sanitation of the location configured to sanitize surface contacts at the location instructions that implements the identified abstract idea. The computer system comprising computer processors and computer readable storage media is not described by the applicant and is recited at a high-level of generality (i.e., a generic server performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, the “controlling” represents an apply it step as well, i.e., apply the results of the abstraction. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims further recite the additional element of IOT devices. The IOT devices merely generally links the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer system comprising computer processors and computer readable storage media having program instructions, as well as program instructions to control additional sanitation of the location configured to sanitize surface contacts at the location to perform the noted steps amounts to no more than mere instructions to apply the exception, either “apply it” on a generic computer or apply the results of the abstract idea. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Mere instructions to apply the results of the abstract idea cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of IOT devices was determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2-5,9-12,14,16-20 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2,9,16 merely describe(s) positive case location attributes, which further defines the abstract idea.
Claim(s) 3,10,17 merely describe(s) location risk factors, which further defines the abstract idea.
Claim(s) 4,11,18 merely describe(s) location risk factors, which further defines the abstract idea.
Claim(s) 5,12 merely describe(s) a time weight, which further defines the abstract idea.
Claim(s) 6 merely describe(s)…, which further defines the abstract idea.
Claim(s) 7,14,20 merely describe(s) what happens above a predetermined threshold, which further defines the abstract idea.
Claim(s) 19 merely repeat(s) Claims(s) 1, 5, which further defines the abstract idea.
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 Examiner notes that the rejection will reference the translated documents (attached) corresponding to any foreign documents recited in the rejection.
Claims 1,4,7-8,11,14-15,18-20 is/are rejected under 35 U.S.C. 103(a) as being unpatentable over WEI et al (Foreign Publication WO-2020144247-A1 in view of Jain et al (US Publication No. 11056242) in view of Baarman et al (US Publication No. 20230014466).
Regarding Claim 1
WEI teaches a computer-implemented method, comprising:
receiving, by one or more processors, individual profile data comprising individual attributes corresponding to an individual, the individual attributes being weighted and comprising demographic data, medical data, and individual location data [WEI at Para. 0014 teaches determining, by the processor, when the patients are indoors based upon the received C02 level data, calculating a for each patient a ventilation rate based upon the received C02 level data and the local outdoor pollution levels, and calculating an indoor pollution exposure for each based upon pollution data for the patient’s location; and calculating, by the processor, the augmented health risk score for each patient based upon the outdoor pollution exposure, indoor pollution exposure, a medical history of the patient, and demographic information of the patient];
determining, by one or more processors, an individual risk factor based at least on the individual profile data, wherein the individual risk factor is an aggregate of the weighted individual attributes corresponding to the individual [WEI at Para. 0014];
determining a unified risk factor for the individual at the location, wherein the unified risk factor is an aggregate of the individual risk factor and the location risk factor [WEI at Para. 0014];
generating, by one or more processors, a first alert indicating the unified risk factor for the individual [WEI at Para. 0008 teaches various embodiments are described, further including providing the user with an alert based upon their location when their augmented health risk score exceeds a threshold level], … [ … ]
WEI does not teach detecting, by one or more processors, one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles;
determining, by one or more processors, a location risk factor for the location based at least on the one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location;
and responsive to the unified risk factor satisfying a first condition:
and controlling, by one or more processors, additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Jain teaches detecting, by one or more processors, one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles [Jain at Col 6-7 Lines 60-67, 1 teaches in general, behavior can be tracked to allow detection of certain types of signs, symptoms, effects of disease and of disease treatment, including for items that are not readily detectable with physiological sensing alone. User behavior can be inferred from passively sensed data, such as location tracking, movement tracking, device pose and device usage, sounds detected with a microphone (e.g., detecting a user coughing), etc];
determining, by one or more processors, a location risk factor for the location based at least on the one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location [Jain at Col 114 Lines 39-44 teaches the process 2200 includes assigning disease transmission scores corresponding to location tags (step 2208). Each location tag for a visit can be assigned one or more disease transmission scores indicative of the level or risk that the visit represents in terms of potentially exposing others who concurrently or subsequently visited the location; Jain at Col 10 Lines 3-10 teaches in another general aspect, a method performed by one or more computers includes: receiving monitoring data for a community generated using mobile devices of individuals in the community, the monitoring data comprising location tracking data that indicates locations visited by the individuals; accessing community data for the community that describes characteristics of the community and a geographic region associated with the community];
and responsive to the unified risk factor satisfying a first condition [Jain at Col 11 Lines 47-53 teaches in some implementations, generating the indication of one or more regions of elevated potential for disease transmission comprises: identifying locations in community that have a corresponding disease transmission score that satisfies a threshold; and generating data that designates the identified locations as regions of elevated potential for disease transmission]:
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine individual risk factors of WEI with the location risk factors of Jain with the motivation to improve a community’s efforts to contain and eliminate a disease.
WEI/Jain do not teach and controlling, by one or more processors, additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Baarman teaches and controlling, by one or more processors, additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location [Barrman at Para. 0056 teaches the various aspects and embodiments of the UV disinfection platform provide an intelligent, automated UV-C light disinfection system that can continuously monitor surfaces to detect when disinfection is warranted and automatically disinfects hundreds of times per day without any assistance from staff. Bacteria levels can be reduced by more than 99 percent on surfaces equipped with the disinfection platform; Baarman at Para. 0167 teaches for example, the amount of touch necessary to trigger a dirty flag may be changed depending on external factors, such as a risk score, traffic level near that disinfection device, or other factors associated with that particular location, room, or building. Such change can be implemented automatically or manually, for example via the communication system, at a periodic maintenance session where one or a group of disinfection devices can be adjusted based on collected data and other factors].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of WEI/Jain with the IOT device of Baarman with the motivation to improve patient safety.
Regarding Claim 4
WEI/Jain/Baarman teach the computer-implemented method of claim 1,
WEI/Jain/Baarman further teach wherein determining the individual risk factor further comprises:
assigning, by one or more processors, an individual attribute risk level score to each of the individual attributes [Jain at Col 62 Lines 31-40 teaches FIG. 9B shows another example user interface 950 that shows different questions and corresponding answers from the user 102a, along with score weightings for the user's answers that together result in a significant level of risk of contracting COVID-19, and so the system determines to send the user 102a a testing kit to test for the disease. For example, the score weights for each answer can be added and compared to a threshold, and when score satisfies the threshold, the system determines to send a testing kit for the disease (interpreted as individual attributes)];
generating, by one or more processors, a weighted attribute risk level score by applying an attribute weight to each corresponding individual attribute [Jain at Col 62 Lines 31-40];
and aggregating, by one or more processors, the weighted attribute risk level score for each of the individual attributes to determine the individual risk factor [Jain at Col 62 Lines 31-40].
Regarding Claim 7
The computer-implemented method of claim 6,
WEI/Jain/Baarman/McNamara teach further comprising:
detecting, by one or more processors, a change in one or more of the positive case location attributes and the individual attributes that exceeds a predetermined threshold [Jain at Col 85 Lines 57-64 teaches the computer system 110 then distributes interventions and/or assessments to individuals based on the risk factor indices (step 1470). For example, if the disease exposure score for a first user exceeds a threshold, an alert or recommendation can be provided to the first user. For example, the user can be warned of the high exposure level, asked about signs or symptoms of COVID-19, and be instructed to remain home for a period of time];
responsive to detecting the change, determining, by one or more processors, a second unified risk factor for the individual at the location based on the individual risk factor and the location risk factor [Jain at Col 85 Lines 57-64];
and responsive to the second unified risk factor satisfying the condition, generating, by one or more processors, the alert for one or more of the location and the individual [Jain at Col 80 Lines 49-58 teaches the computer system 110 and user devices can use the location tags to inform users of disease exposure risks. For example, the computer system 110 or the user device 1420 can obtain location monitoring data indicating a location or direction of travel of the user device 1420. When the user is detected to be near a tagged location (e.g., approaching the tagged location, within a predetermined distance of the tagged location, at the tagged location, etc.), the device 1420 provides a notification to alert the user to the current or upcoming risk].
Regarding Claim 8
WEI teaches a computer program product, comprising:
one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising [WEI at Para. 0067, 0069 (see Claim 1 for explanation)]:
program instructions to receive individual profile data comprising individual attributes corresponding to an individual, the individual attributes being weighted and comprising demographic data, medical data, and individual location data [WEI at Para. 0014 (see Claim 1 for explanation)];
program instructions to determine an individual risk factor based at least on the individual profile data, wherein the individual risk factor is an aggregate of the weighted individual attributes corresponding to the individual [WEI at Para. 0014 (see Claim 1 for explanation)];
program instructions to determine a unified risk factor for the individual at the location, wherein the unified risk factor is an aggregate of based on the individual risk factor and the location risk factor [WEI at Para. 0014 (see Claim 1 for explanation)];
program instructions to generate a first alert indicating the unified risk factor for the individual [WEI at Para. 0008 (see Claim 1 for explanation)],
WEI does not teach program instructions to detect one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles;
program instructions to determine a location risk factor for the location based at least on one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location;
and responsive to the unified risk factor satisfying a first condition:
program instructions to generate a first alert indicating the unified risk factor for the individual, and program instructions to trigger control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Jain teaches program instructions to detect one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles [Jain at Col 6-7 Lines 60-67,1 (see Claim 1 for explanation)];
program instructions to determine a location risk factor for the location based at least on one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location [Jain at Col 114 Lines 39-44, Col 10 Lines 3-10 (see Claim 1 for explanation)];
and responsive to the unified risk factor satisfying a first condition [Jain at Col 11 Lines 47-53 see Claim 1 for explanation)]:
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine individual risk factors of WEI with the location risk factors of Jain with the motivation to improve a community’s efforts to contain and eliminate a disease.
WEI/Jain do not teach and program instructions to trigger control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Baarman teaches and program instructions to trigger control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location [Baarman at Para. 0056, 0167 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of WEI/Jain with the IOT device of Baarman with the motivation to improve patient safety.
Regarding Claim 11
The Claim(s) 11 is/are analogous to Claim(s) 4, thus Claim(s) 11 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Regarding Claim 14
The Claim(s) 14 is/are analogous to Claim(s) 7, thus Claim(s) 14 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Regarding Claim 15
WEI teaches a computer system, comprising:
one or more computer processors [WEI at Para. 0067 (see Claim 1 for explanation)];
one or more computer readable storage media [WEI at Para. 0067, 0069 (see Claim 1 for explanation)];
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising [WEI at Para. 0067 (see Claim 1 for explanation)]:
program instructions to receive individual profile data comprising individual attributes corresponding to an individual, the individual attributes being weighted and comprising demographic data, medical data, and individual location data [WEI at Para. 0014 (see Claim 1 for explanation)];
program instructions to determine an individual risk factor based at least on the individual profile data, wherein the individual risk factor is an aggregate of the weighted individual attributes corresponding to the individual [WEI at Para. 0014 (see Claim 1 for explanation)];
program instructions to generate a first alert indicating the unified risk factor for the individual [WEI at Para. 0008 (see Claim 1 for explanation)],
program instructions to determine a unified risk factor for the individual at the location, wherein the unified risk factor is an aggregate of the individual risk factor and the location risk factor [WEI at Para. 0014 (see Claim 1 for explanation)];
WEI does not teach program instructions to detect one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles;
program instructions to determine a location risk factor for the location based at least on one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location;
and responsive to the unified risk factor satisfying a first condition:
and program instructions to control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Jain teaches program instructions to detect one or more health risk events from sensor data gathered by IoT devices at a location, wherein the one or more health risk events include sound data corresponding to a release of aerosolized particles [Jain at Col 6-7 Lines 60-67,1 (see Claim 1 for explanation)];
program instructions to determine a location risk factor for the location based at least on one or more detected health risk events at the location, wherein the location risk factor is an aggregate of weighted location attributes for the location [Jain at Col 6-7 Lines 60-67,1 (see Claim 1 for explanation)];
and responsive to the unified risk factor satisfying a first condition [Jain at Col 11 Lines 47-53 (see Claim 1 for explanation)]:
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine individual risk factors of WEI with the location risk factors of Jain with the motivation to improve a community’s efforts to contain and eliminate a disease.
WEI/Jain do not teach and program instructions to control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location.
Baarman teaches and program instructions to control additional sanitation of the location via an IoT device configured to sanitize surface contacts at the location [Baarman at Para. 0056, 0167 (see Claim 1 for explanation)].
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of WEI/Jain with the IOT device of Baarman with the motivation to improve patient safety.
Regarding Claim 18
The Claim(s) 18 is/are analogous to Claim(s) 4, thus Claim(s) 18 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4.
Regarding Claim 19
WEI/Jain/Baarman/McNamara teach the computer system of claim 16,
WEI/Jain/Baarman/McNamara further teach further comprising:
program instructions to determine a first unified risk factor for the individual at the location based on the individual risk factor and the location risk factor [WEI at Para. 0014 (see Claim 1 for explanation)]; and
program instructions to apply a time weight to the local individual risk level, wherein the time weight is directly proportional to a time duration the individual is present at the location Jain at Col 2 Lines 55-60, 4 Lines 64-66 (see Claim 5 for explanation)].
Regarding Claim 20
The Claim(s) 20 is/are analogous to Claim(s) 7, thus Claim(s) 20 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7.
Claims 2-3,5,9-10,12-16-17 rejected under 35 U.S.C. 103(a) as being unpatentable over WEI, Jain, Baarman as applied to claim 1, 8, 15 above, and further in view of McNamara et al (US Publication No. 20210313075).
Regarding Claim 2
WEI/Jain/Baarman teach the computer-implemented method of claim 1,
WEI/Jain/Baarman further teach further comprising:
receiving, by one or more processors, location data corresponding to a positive case at the location, the location data comprising positive case location attributes comprising a local individual risk level, a location function, location size, location surface contact frequency, and location air access [Jain at Col 94 Lines 22-28 teaches Examples of these conditions include conditions at the location of a location tag, conditions at surrounding areas, whether the location an open area or an enclosed space, a level of airflow or ventilation, environmental information such as humidity and moisture levels, sanitization procedures at the location, and so on. Due to variation in the characteristics and conditions at different locations, the decrease in impact of an exposure event can vary for different locations (level of airflow or ventilation interpreted as location air access; sanitization procedures interpreted as location surface contact frequency); Jain at Col 71 Lines 13-19 teaches a model to predict the disease transmission risk of a location can use current information about the location (e.g., location characteristics, location type, etc.) and other current or recent data (e.g., user behavior patterns, community disease measures, disease prevention measures in place, etc.) to predict a score indicating the current disease transmission risk of the location; Jain at Col 81 Lines 20-27 teaches Other factors can be taken into account, such as whether the paths of movement of the users within the location crossed (e.g., if the two people passed through the same portion of a store), whether disease prevention measures such as masks were used, the duration of the visits, the activities performed (e.g., shopping, exercising at a gym, etc.), the characteristics of the location (e.g., building size, layout, occupancy at the time, etc.), etc (activities performed interpreted as location function; building size interpreted as location size)], … [ … ]
and determining, by one or more processors, the location risk factor for the location based at least on the location data [Jain at Col 2-3 Lines 48-67,1-5 teaches One of the significant advantages that the system can provide is to predictively localize hotspots of disease transmission risk with precision. Machine learning models can be trained using rich data about geography and behavior in different communities. The examples used for training can include data describing specific places, data characterizing occupancy and traffic over time, geographic relationships such as map data, and so on. The examples can also include individual and community-level disease outcomes (e.g., infections, hospitalizations, deaths, etc.) and monitoring data, such as location tracking data indicating locations that people visited and the times, durations, and activities of the visits. Contact tracing data, if available, can also be used to identify when and where different interactions leading to disease transmission occurred. With this data, machine learning techniques can train a model to incorporate relationships among travel patterns, location types, environmental factors, geographical factors, and other factors with disease transmission. For example, the model can learn to identify the relative risk of transmission posed by different types of locations when combined with different community characteristics and behavior or travel patterns. As a result, when the system assesses a community data set that has a location with this combination of factors, the model can indicate the high risk transmission risk for the location].
WEI/Jain/Baarman do not teach [ … ] … wherein the location surface contact frequency is a ratio of individuals within the location over a given time period that make contact with a common surface;
McNamara teaches [ … ] … wherein the location surface contact frequency is a ratio of individuals within the location over a given time period that make contact with a common surface [Mc Namara at Para. 0014 teaches in some embodiments, the instructions cause the one or more processors to determine occupancy levels in each space of spaces of the building over a historical time period based on the occupancy data and generate a heat map that that indicates historical utilization of each space of the spaces over the historical time period; Mc Namara at Para. 0067 teaches to provide a method of managing infection transmission risk of persons to other persons through direct person-to-person interaction, indirect transmission through sharing of spaces, and potential contamination of common surfaces, system 100 monitors the location and use of equipment, and the location of individuals and their interactions with different people, spaces, and equipment, to support social distancing recommendations and infection risk management practices in response to a current disease epidemic, to assess and record transmission risk events, and to track and record interactions and locations for the purposes of contact tracing (interpreted as common surfaces)];
It would have been prima facie obvious skill in the art, at the time of effective filing, to combine the references of WEI, Jain, Baarman with the surface contact frequency of McNamara with the motivation to improve future epidemic and pandemic planning.
Regarding Claim 3
WEI/Jain/Baarman/McNamara teach the computer-implemented method of claim 2,
WEI/Jain/Baarman/McNamara further teach wherein determining the location risk factor further comprises:
assigning, by one or more processors, a location risk level score to each of the positive case location attributes [Jain at Col 11 Lines 28-46 teaches in some implementations, the one or more predictive models comprise one or more machine learning models that have been trained to provide disease transmission scores indicative of a disease transmission potential for locations, the one or more machine learning models being trained based on training data examples for different locations, each training data example indicating one or more location characteristic s for a location, one or more community disease measures for a community that includes the location, and one or more behavior measures for visitors to the location. Generating the indication of one or more regions of elevated potential for disease transmission comprises using the one or more machine learning models to determine a disease transmission score for each of multiple locations in the community, each of the disease transmission scores being based on one or more location characteristic s for the corresponding location, one or more community disease measures for the community of the corresponding location, and one or more behavior measures for visitors to the corresponding location];
generating, by one or more processors, a weighted location risk level score by applying a location factor weight to each corresponding positive case location attribute [Jain at Col 84 Lines 3-5 teaches the aging of tags, e.g., weighting or scaling the scores based on the passage of time, can be dependent on the environment at the visited location];
and aggregating, by one or more processors, the weighted location risk level score for each of the positive case location attributes to determine the location risk factor [Jain at Col 11 Lines 28-46].
Regarding Claim 5
WEI/Jain/Baarman/McNamara teach the computer-implemented method of claim 2,
WEI/Jain/Baarman/McNamara further teach further comprising:
applying, by one or more processors, a time weight to the local individual risk level, wherein the time weight is directly proportional to a time duration the individual is present at the location [Jain at Col 2 Lines 55-60 teaches the examples can also include individual and community-level disease outcomes (e.g., infections, hospitalizations, deaths, etc.) and monitoring data, such as location tracking data indicating locations that people visited and the times, durations, and activities of the visits; Jain at Col 4 Lines 64-66 teaches exposure scores can be adjusted or weighted based on the amount of time elapsed between the visits of different people].
Regarding Claim 9
The Claim(s) 9 is/are analogous to Claim(s) 2, thus Claim(s) 9 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 10
The Claim(s) 10 is/are analogous to Claim(s) 3, thus Claim(s) 10 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Regarding Claim 12
The Claim(s) 12 is/are analogous to Claim(s) 5, thus Claim(s) 12 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 5.
Regarding Claim 16
The Claim(s) 16 is/are analogous to Claim(s) 2, thus Claim(s) 16 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 2.
Regarding Claim 17
The Claim(s) 17 is/are analogous to Claim(s) 3, thus Claim(s) 17 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 3.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims 1-5,7-12,14-20, the Examiner has considered the Applicant’s arguments; however the arguments are not persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons. Applicant argues:
Applicant respectfully asserts that independent claims 1, 8, and 15, as a whole, integrate any alleged judicial exception into a practical application by "[a]pplying 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, as discussed in MPEP § 2106.05(e)". In particular, the claims are directed towards a specifically designed algorithm to compute a unified risk score (i.e., infection risk probability) for an individual at a particular location, and step (f) is particularly directed towards controlling additional sanitation via an loT device configured to sanitize surface contacts based on the computed unified risk score for the individual at the particular location. This results in preventing and controlling the risk of infection for the individual at the particular location, as well as controlling the spread of infection to others.
Although the Current Office Action has determined that claim 1 includes limitations directed towards an abstract (i.e., certain methods of organizing human activity), Applicant respectfully asserts that at least the limitations of step (f) - "responsive to the unified risk factor satisfying a first condition generating, by one or more processors, a first alert indicating the unified risk factor for the individual, and controlling, by one or more processors, additional sanitation of the location via an loT device configured to sanitize surface contacts at the location" - is meaningful because it integrates the results of the unified risk score (i.e., infection risk probability analysis) for an individual at a particular location into a specific and tangible method (i.e., controlling additional sanitation of the location via an loT device configured to sanitize surface contacts at the location) that results in the limitations of claim 1, as a whole, moving from a method of organizing human activity to a specific application of disease prevention and control based on the infection risk probability analysis.
Applicant respectfully asserts that independent claim 1 (and similarly independent claims 8 and 15), as a whole, integrates the alleged judicial exception into a practical application under Step 2A, Prong Two by "[a]pplying 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.
Regarding (a-c), the Examiner respectfully disagrees. There is no connection in the claims between the
individual risk factors and the sanitization of surface contacts. Furthermore, the limitation of controlling additional sanitation via an IOT device is merely an apply it step. As such, the claims do not provide a practical application and/or significantly more.
Rejection under 35 U.S.C. § 102/103
Regarding the rejection of Claims 1-5,7-12,14-20, the Examiner has considered the Applicant’s arguments; however, these arguments are moot given the new grounds of rejection as afforded by the present RCE.
Conclusion
The prior art made of record and not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
ALMOGY et al (US Publication No. 20130318027) discloses a system and method to enable detection of viral infection by users of electronic communication devices.
VAN LIESHOUT et al (Foreign Publication WO-2017216056-A1) discloses a method for monitoring risk of infection.
Deros et al (US Publication No. 20200397936) discloses a system for use in the prevention of microorganisms and diseases caused by pathogens present within an interior space.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JONATHAN C EDOUARD/Examiner, Art Unit 3683
/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683