DETAILED ACTION
VACATE PREVIOUS ACTION
Examiner vacates the action of 06/05/2026 and has issued this action to account for the amendments received on 05/06/2025.
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/28/2025 and 08/06/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Specification
The amendments to the specification filed 2/28/2025 have been entered.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 30-57 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 30 recites the limitation “the computing system” in line 5 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claims 31-57 are rejected based on their dependency on claim 30.
Claim Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 7-29 and 34-57 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The claims either 1) recite multiple dependent claims which depend from other multiple dependent claims or 2) depend on claims with this issue. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Based on this above, these claims are not given art rejections at the present moment as Examiner is unable to properly examine these claims.
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 1-6, 8, 10-17, 21, 23-25, 28-35, 37, 39-46, 50, 52-54, and 57 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 1-6, 8, 10-17, 21, 23-25, and 28-29 are drawn to a method and claims 30-35, 37, 39-46, 50, 52-54, and 57 are drawn to a system, each of which is within the four statutory categories. Claims 1-6, 8, 10-17, 21, 23-25, 28-35, 37, 39-46, 50, 52-54, and 57 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a method for infection tracking and monitoring in a healthcare facility or a healthcare system, the method comprising:
1) --accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, the current information comprising: patient data, healthcare worker data, instrument data, and infection data; the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; and the infection data including: cases of infections among patients including, for each case: type of infection, and date of diagnosis;
2) --storing the current information in a) an infection monitoring and tracking (IMaT) database;
3) --accessing, receiving, or generating additional current information including updated patient data, updated healthcare worker data, updated instrument data, and updated infection data periodically or on demand and storing the additional current information in the IMaT database the current information and the additional current information identified herein as collected information;
4) --determining interaction data for each patient from the collected information based on documented interactions between the patient and one or more healthcare workers, based on documented uses of one or more instruments on the patient, and based on spatial location, date, and time overlap between the patient and the one or more healthcare workers and storing the determined interaction data in the IMaT database;
5) --identifying potentially related infections based, at least in part, on the infection data;
6) --determining one or more likely infection propagation paths based, at least in part, on the identified potentially related infections and the interaction data; and
7) --generating a dynamic interactive user interface enabling a user to view and interact with visualizations of infection monitoring and tracking results and/or generating a report on infection status including infection monitoring and tracking results where the infection monitoring and tracking results include the determined one or more likely infection propagation paths.
Claim 1 recites, in part, performing the steps of 2) storing the current information in an infection monitoring and tracking (IMaT) database (when pen and paper or mental storage), 3) accessing, receiving, or generating additional current information including updated patient data, updated healthcare worker data, updated instrument data, and updated infection data periodically or on demand and storing the additional current information in the IMaT database the current information and the additional current information identified herein as collected information, 4) determining interaction data for each patient from the collected information based on documented interactions between the patient and one or more healthcare workers, based on documented uses of one or more instruments on the patient, and based on spatial location, date, and time overlap between the patient and the one or more healthcare workers and storing the determined interaction data in the IMaT database, 5) identifying potentially related infections based, at least in part, on the infection data, 6) determining one or more likely infection propagation paths based, at least in part, on the identified potentially related infections and the interaction data, and 7) generating a dynamic interactive user interface enabling a user to view and interact with visualizations of infection monitoring and tracking results and/or generating a report on infection status including infection monitoring and tracking results where the infection monitoring and tracking results include the determined one or more likely infection propagation paths. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes contact tracing and how one could share that information with another. Independent claim 30 recites similar limitations and is also directed to an abstract idea under the same analysis.
Depending claims 2-6, 8, 10-17, 21, 23-25, 28-29, 31-35, 37, 39-46, 50, 52-54, and 57 include all of the limitations of claims 1 and 30, and therefore likewise incorporate the above described abstract idea. Depending claims 6 and 35 add the additional steps of “receive a selection of a graphical representation of contacts between two individual infected patients identified as having a potentially related infection from a user and to display details regarding the contact based on receiving the selection”, “receive input from a user regarding at least one parameter for identifying potentially related infections based on the infection data and to display a resulting change to the graphical representations of contacts and/or to the patients identified as having potentially related infections”, or “a combination thereof”; claims 8 and 37 add the additional steps of “wherein the collected data is preprocessed, normalized, or both prior to being stored in the IMaT database” and “wherein the collected data stored in the IMaT database is analyzed to generate enriched data and wherein the enriched data includes at least some of the interaction data”; claims 10 and 39 add the additional step of “wherein the method includes generating one or more spatially implicit models based, at least in part, on the infection data and the patient data”; claims 12 and 41 add the additional step of “wherein the method includes generating one or more spatially explicit models based, at least in part, on patient data, interaction data, and the identified potentially related infections”; claims 14 and 43 add the additional step of “generating or issuing an alert or notification”; claims 23 and 52 add the additional steps of “detection of the appearance of a pathogen having a combination of resistances that has not been previously observed in known pathogens”, “detection of an infection with an increase in resistance to a drug larger than a determination error for the drug”, “detection of a non-cultivable pathogen not matching known microbial strains”, “detection of a pathogen with a genotype not matching previous known data is detected”, “detection of a pathogen not matching previous known data as determined by genomic sequencing”, or “detection of a pathogen not matching previous known data as detected by a genomic-based technology”; claims 25 and 54 add the additional steps of “generating a pathogen propagator's report” or “generating a key propagators report” or “generating a recommended list of one or more infections for molecular analysis” or “identifying a one or more infections as a priority for analysis and generating or transmitting an order for a molecular analysis of the one or more infections” or “a combination thereof”. Claims 4-6 and 33-35 further elaborate upon elements of the display. Claim 29 shifts the statutory class and does not add additional functionality to the claims. Additionally, the limitations of depending claims 2-5, 8, 10-17, 21, 23-25, 28-29, 31-34, 37, 39-46, 50, 52-54, and 57 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-3, 11, 13, 15-17, 21, 24, 28, 31-32, 40, 42, 44-46, 50, and 57 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 30 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) an infection monitoring and tracking (IMaT) database and b) one or more processors configured to execute instructions that, when executed by the one or more processors, cause the computing system to perform functions (in claim 30) to perform the claimed steps.
Claim 1 also includes the additional element step of 1) “accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, the current information comprising: patient data, healthcare worker data, instrument data, and infection data; the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; and the infection data including: cases of infections among patients including, for each case: type of infection, and date of diagnosis.”
The a) infection monitoring and tracking (IMaT) database and b) one or more processors configured to execute instructions that, when executed by the one or more processors, cause the computing system to perform functions in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraphs [0201] and [0208] where there are generic computing components for these elements, see MPEP 2106.05(f)).
Additionally, the additional element step of 1) “accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, the current information comprising: patient data, healthcare worker data, instrument data, and infection data; the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; and the infection data including: cases of infections among patients including, for each case: type of infection, and date of diagnosis” adds insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do 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 elements of using a) an infection monitoring and tracking (IMaT) database and b) one or more processors configured to execute instructions that, when executed by the one or more processors, cause the computing system to perform functions to perform the claimed steps and the additional element step of 1) “accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, the current information comprising: patient data, healthcare worker data, instrument data, and infection data; the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; and the infection data including: cases of infections among patients including, for each case: type of infection, and date of diagnosis” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mental steps, certain method steps of organizing human activity, or certain mathematical steps. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates an interface utilizing a) an infection monitoring and tracking (IMaT) database and b) one or more processors configured to execute instructions, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Additionally, the additional element step of 1) “accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, the current information comprising: patient data, healthcare worker data, instrument data, and infection data; the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; and the infection data including: cases of infections among patients including, for each case: type of infection, and date of diagnosis” adds insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives current information, and transmits the data to a database over a network, for example the Internet.
Mere instructions to apply an exception using a generic computer component, a general linking to a particular technological field, and insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-6, 8, 10-17, 21, 23-25, 28-35, 37, 39-46, 50, 52-54, and 57 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 12-14, 25, 28-31, 41-43, 54, and 57 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 2017/0024531 to Malaviya.
As per claim 1, Malaviya teaches a method for infection tracking and monitoring in a healthcare facility or a healthcare system, the method comprising:
--accessing or receiving current information from electronic medical records or an electronic medical records system and a laboratory information system, (see: FIG. 1 and paragraph [0041] where real-time data is being accessed from record (104) or other systems such as 102, 104, and 106) the current information comprising: patient data, (see: paragraphs [0160] – [0161] where there is current information in the form of patient data (EHR data)) healthcare worker data, (see: paragraphs [0160] – [0161] where there is healthcare worker data in the form of job responsibilities) instrument data, (see: paragraphs [0160] – [0161] where there is instrument record logs) and infection data; (see: paragraph [0070] where there is infection information which is received) the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; (see: paragraphs [0107] – [0108] and [0161] where the patient data includes a profile for the patient including the individual (identifier), demographic data (age), medical history data (any health data), and admission data (ADT data including admission, discharge, etc.)) the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; (see: paragraphs [0058], [0063], and [0114] where there is information taken from sensors and a contextual profile about a worker including an identifier (name) and a role) the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; (see: paragraphs [0160] and [0176] where there is an instrumental record logs (instrument and usage)) and the infection data including: cases of infections among patients including, for each case: type of infection, (see: paragraph [0180] where there is a type of infection being received) and date of diagnosis; (see: paragraphs [0064] where there is duration of a disorder/disease)
--storing the current information in an infection monitoring and tracking (IMaT) database; (see: paragraph [0147] and FIG. 2 where there is storing of this information in a database (250))
--accessing, receiving, or generating additional current information including updated patient data, updated healthcare worker data, updated instrument data, and updated infection data periodically or on demand and storing the additional current information in the IMaT database the current information and the additional current information identified herein as collected information; (see: paragraphs [0020] and [0058] where there is updating of the collected information from above in a near-real time or real-time manner)
--determining interaction data for each patient from the collected information based on documented interactions between the patient and one or more healthcare workers, (see: paragraphs [0173] – [0176] where there is a determination of which entities (workers) interacted with the infected individual) based on documented uses of one or more instruments on the patient, (see: paragraphs [0173] – [0176] where there is a determination of which entities (instruments) interacted with the infected individual) and based on spatial location, date, and time overlap between the patient and the one or more healthcare workers and storing the determined interaction data in the IMaT database; (see: paragraphs [0173] – [0176] where there is location information and period of time (date and time overlap) used to determine interactions between entities and the infected)
--identifying potentially related infections based, at least in part, on the infection data; (see: paragraph [0176] where there is identification of entities that have come into proximate area of infection to determine if the entity is infected)
--determining one or more likely infection propagation paths based, at least in part, on the identified potentially related infections and the interaction data; (see: paragraphs [0173] – [0176] where there is investigating of infection spread to determine an infected entity) and
--generating a dynamic interactive user interface enabling a user to view and interact with visualizations of infection monitoring and tracking results and/or generating a report on infection status including infection monitoring and tracking results where the infection monitoring and tracking results include the determined one or more likely infection propagation paths (see: paragraphs [0150] – [0151] where there is a report generation engine which enable a user to view and interact with a map. The map here reports on the status of infection and tracks/maps the pathway as a heat map as shown in paragraph [0055]).
As per claim 2, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya further teaches wherein the infection data further comprises microbial strain characterization of the infection for at least some of the infections (see: paragraph [0177] where there is a microbial strain characterization of influenza, ebolavirus, etc.).
As per claim 12, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya further teaches wherein the method includes generating one or more spatially explicit models based, at least in part, on patient data, interaction data, and the identified potentially related infections (see: paragraph [0054] where there is a virtual cloud of dust which is the spatially explicit model which is generated and this is based in part of the patient, where they interact, and where other infected patients are (potentially related infections)).
As per claim 13, Malaviya teaches the method of claim 12, see discussion of claim 12. Malaviya further teaches wherein the likely propagation paths are determined based, at least in part, on the potentially related infections and at least one of the one or more spatially explicit models (see: paragraph [0054] where there is a path being determined in the form of a virtual cloud of dust. This is based on other infected patients are (potentially related infections) and a spatially explicit model which defines the cloud).
As per claim 14, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach further comprising generating or issuing an alert or notification (see: paragraphs [0092] and [0119] where there is generation of an alert).
As per claim 25, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach further comprising:
(i) generating a pathogen propagator's report; (see: paragraph [0098] where there is generation of a report (pathogen propagator’s report)) or
(ii) generating a key propagators report; or
(iii) generating a recommended list of one or more infections for molecular analysis; or
(iv) identifying a one or more infections as a priority for analysis and generating or transmitting an order for a molecular analysis of the one or more infections; or
(v) a combination thereof.
As per claim 28, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach wherein updated patient data, updated healthcare worker data, updated instrument data, and updated infection data are obtained periodically or on demand at least on a daily basis or more frequently than a daily basis; (see: paragraph [0111] where the data is updated in near real time, which is more frequently than a daily basis) and
--wherein the determining of interaction data for each patient occurs on a daily basis or more frequently than a daily basis (see: paragraph [0181] where there is updating of an overall risk score as conditions change over time. This means that the interaction data for each patient occurs on a frequent basis.).
As per claim 29, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya further teaches a non-transitory computer readable medium comprising instructions that, when executed by a computing system including one or more processors, cause the computing system to perform the method of claim 1 (see: paragraph [0095] where there are such non-transitory instructions being executed by a processor).
As per claim 30, Malaviya teaches a system for infection tracking and monitoring for a healthcare facility or a healthcare system, the system including:
--at least one database including an infection monitoring and tracking (IMaT) database; (see: paragraph [0147] and FIG. 2 where there is a database (250)) and
--one or more processors configured to execute instructions (see: paragraph [0049] where there is a processor) that, when executed by the one or more processors, cause the computing system to:
--access or receive current information from electronic medical records or an electronic medical records system and a laboratory information system, (see: FIG. 1 and paragraph [0041] where real-time data is being accessed from record (104) or other systems such as 102, 104, and 106) the current information comprising: patient data, (see: paragraphs [0160] – [0161] where there is current information in the form of patient data (EHR data)) healthcare worker data, (see: paragraphs [0160] – [0161] where there is healthcare worker data in the form of job responsibilities) instrument data, (see: paragraphs [0160] – [0161] where there is instrument record logs) and infection data; (see: paragraph [0070] where there is infection information which is received) the patient data including, for each patient identified as having an infection: an identifier for the patient, demographic information, medical history, and admission information where admitted; (see: paragraphs [0107] – [0108] and [0161] where the patient data includes a profile for the patient including the individual (identifier), demographic data (age), medical history data (any health data), and admission data (ADT data including admission, discharge, etc.)) the healthcare worker data including, for each healthcare worker: an identifier for the healthcare worker, a role or position of the healthcare worker; (see: paragraphs [0058], [0063], and [0114] where there is information taken from sensors and a contextual profile about a worker including an identifier (name) and a role) the instrument data including for each instrument: an identifier for the instrument and record information about the usage of the instrument; (see: paragraphs [0160] and [0176] where there is an instrumental record logs (instrument and usage)) and the infection data including: cases of infections among patients including, for each case: type of infection, (see: paragraph [0180] where there is a type of infection being received) and date of diagnosis; (see: paragraphs [0064] where there is duration of a disorder/disease)
--store the current information in the IMaT database; (see: paragraph [0147] and FIG. 2 where there is storing of this information in a database (250))
--access, receive, or generate additional current information including updated patient data, updated healthcare worker data, updated instrument data, and updated infection data periodically or on demand and storing the additional current information in the IMaT database, the current information and the additional current information identified herein as collected information; (see: paragraphs [0020] and [0058] where there is updating of the collected information from above in a near-real time or real-time manner)
--determine interaction data for each patient from the collected information based on documented interactions between the patient and one or more healthcare workers, (see: paragraphs [0173] – [0176] where there is a determination of which entities (workers) interacted with the infected individual) based on documented uses of one or more instruments on the patient, (see: paragraphs [0173] – [0176] where there is a determination of which entities (instruments) interacted with the infected individual) and based on spatial location, date, and time overlap between the patient and the one or more healthcare workers and storing the determined interaction data in the IMaT database; (see: paragraphs [0173] – [0176] where there is location information and period of time (date and time overlap) used to determine interactions between entities and the infected)
--identify potentially related infections based, at least in part, on the infection data; (see: paragraph [0176] where there is identification of entities that have come into proximate area of infection to determine if the entity is infected)
--determine one or more likely infection propagation paths based, at least in part, on the identified potentially related infections and the interaction data; (see: paragraphs [0173] – [0176] where there is investigating of infection spread to determine an infected entity) and
--generate a dynamic interactive user interface enabling a user to view and interact with visualizations of infection monitoring and tracking results and/or generating a report on infection status including infection monitoring and tracking results where the infection monitoring and tracking results include the determined one or more likely infection propagation paths (see: paragraphs [0150] – [0151] where there is a report generation engine which enable a user to view and interact with a map. The map here reports on the status of infection and tracks/maps the pathway as a heat map as shown in paragraph [0055]).
As per claim 31, Malaviya teaches the system of claim 30, see discussion of claim 30. Malaviya further teaches wherein the infection data further comprises microbial strain characterization of the infection for at least some of the infections (see: paragraph [0177] where there is a microbial strain characterization of influenza, ebolavirus, etc.).
As per claim 41, claim 41 is similar to claim 12 and is therefore rejected in a similar manner to claim 12.
As per claim 42, claim 42 is similar to claim 13 and is therefore rejected in a similar manner to claim 13.
As per claim 43, claim 43 is similar to claim 14 and is therefore rejected in a similar manner to claim 14.
As per claim 54, claim 54 is similar to claim 25 and is therefore rejected in a similar manner to claim 25.
As per claim 57, claim 57 is similar to claim 28 and is therefore rejected in a similar manner to claim 28.
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 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.
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.
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.
Claims 3 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S 2020/0048718 to Seligson et al.
As per claim 3, Malaviya teaches the method of claim 2, see discussion of claim 2. Malaviya fails to further, specifically teach wherein the microbial strain characterization of infection data includes antibiogram data, genome sequencing data, or both for at least some of the infections.
Seligson et al. teaches:
--wherein the microbial strain characterization of infection data includes antibiogram data, genome sequencing data, or both for at least some of the infections (see: paragraph [0004] where there is sequencing data).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to wherein the microbial strain characterization of infection data includes antibiogram data, genome sequencing data, or both for at least some of the infections as taught by Seligson et al. in the method as taught by Malaviya with the motivation(s) of allowing for detection of an infectious disease (see: paragraph [0004] of Seligson et al.).
As per claim 32, claim 32 is similar to claim 3 and is therefore rejected in a similar manner to claim 3 using the Malaviya and Seligson et al. references in combination.
Claims 4-6 and 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S 2021/0398659 to Sharma et al.
As per claim 4, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach further comprising generating a spatially explicit infection propagation view depicting spatial relationships between infected patients and a structure of at least a portion of a healthcare facility for display in the dynamic interactive user interface based on the determined likely propagation paths.
Sharma et al. teaches:
--further comprising generating a spatially explicit infection propagation view depicting spatial relationships between infected patients and a structure of at least a portion of a healthcare facility for display in the dynamic interactive user interface based on the determined likely propagation paths (see: FIG. 2 where there is such a spatially explicit view depicting infected patients and a structure of the floorplan of the facility. The routes/propagation paths are also displayed).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to generate a spatially explicit infection propagation view depicting spatial relationships between infected patients and a structure of at least a portion of a healthcare facility for display in the dynamic interactive user interface based on the determined likely propagation paths as taught by Sharma et al. in the method as taught by Malaviya with the motivation(s) of contact tracing (see: paragraph [0002] of Sharma et al.).
As per claim 5, Malaviya and Sharma et al. in combination teaches the method of claim 4, see discussion of claim 4. Sharma et al. further teaches wherein the spatially explicit infection propagation view includes graphical representations of contacts between individual infected patients identified as having a potentially related infection (see: FIG. 2 and paragraph [0044] where there is identification of persons who contacted the infected individual).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 4, and incorporated herein.
As per claim 6, Malaviya and Sharma et al. in combination teaches the method of claim 5, see discussion of claim 5. Sharma et al. further teaches wherein the dynamic interactive user interface is configured to:
--receive a selection of a graphical representation of contacts between two individual infected patients identified as having a potentially related infection from a user and to display details regarding the contact based on receiving the selection; (see: paragraph [0052] where there is an extended search where the user interface is able to receive a selection of a contact between individuals in order to display information associated with the second individual’s pathway)
--(ii) receive input from a user regarding at least one parameter for identifying potentially related infections based on the infection data and to display a resulting change to the graphical representations of contacts and/or to the patients identified as having potentially related infections; or
--(iii) a combination thereof.
The motivations to combine the above-mentioned references are discussed in the rejection of claim 4, and incorporated herein.
As per claim 33, claim 33 is similar to claim 4 and is therefore rejected in a similar manner to claim 4 using the Malaviya and Sharma et al. references in combination.
As per claim 34, claim 34 is similar to claim 5 and is therefore rejected in a similar manner to claim 5 using the Malaviya and Sharma et al. references in combination.
As per claim 35, claim 35 is similar to claim 6 and is therefore rejected in a similar manner to claim 6 using the Malaviya and Sharma et al. references in combination.
Claims 8 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S. Patent No. 12,563,060 to Sobrier et al.
As per claim 8, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach wherein the collected data is preprocessed, normalized, or both prior to being stored in the IMaT database,
--wherein the collected data stored in the IMaT database is analyzed to generate enriched data and wherein the enriched data includes at least some of the interaction data.
Sobrier et al. teaches:
--wherein the collected data stored in the IMaT database is analyzed to generate enriched data and wherein the enriched data includes at least some of the interaction data (see: column 71, lines 1-17 where there is collected data and generation of an enriched data set which includes additional data. The additional data including interaction data was taught in the base references).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the collected data stored in the IMaT database is analyzed to generate enriched data and wherein the enriched data includes at least some of the interaction data as taught by Sobrier et al. in the method as taught by Malaviya with the motivation(s) of improving model accuracy faster (see: column 66, lines 17-37 of Sobrier et al.).
As per claim 37, claim 37 is similar to claim 8 and is therefore rejected in a similar manner to claim 8.
Claims 10-11 and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S 2024/0168020 to Kviatkovskyi.
As per claim 10, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach wherein the method includes generating one or more spatially implicit models based, at least in part, on the infection data and the patient data.
Kviatkovskyi teaches:
--wherein the method includes generating one or more spatially implicit models based, at least in part, on the infection data and the patient data (see: paragraph [0199] where there is a cluster analysis which is a generation of a spatially implicit model based on the patient’s data and infection data (all data stored in database in paragraph [0197])).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the method includes generating one or more spatially implicit models based, at least in part, on the infection data and the patient data as taught by Kviatkovskyi in the method as taught by Malaviya with the motivation(s) of detecting correlations in data (see: paragraph [0199] of Kviatkovskyi).
As per claim 11, Malaviya and Kviatkovskyi in combination teaches the method of claim 10, see discussion of claim 10. Malaviya may not further, specifically teach wherein identifying potentially related infections includes performing a clustering analysis based on one of the spatially implicit models (see: paragraph [0199] where there is identification of infections (variants) related to the patient’s disease).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 10, and incorporated herein.
As per claim 39, claim 39 is similar to claim 10 and is therefore rejected in a similar manner to claim 10.
As per claim 40, claim 40 is similar to claim 11 and is therefore rejected in a similar manner to claim 11.
Claims 15-16, 21, 23-24, 44-45, 50, and 52-53 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S 2023/0207091 to Joyce et al.
As per claim 15, Malaviya teaches the method of claim 1, see discussion of claim 1. Malaviya may not further, specifically teach:
(a) wherein the alert or notification is a rapid increase alert or notification; or
(b) wherein the alert or notification is an active propagator alert or notification; or
(c) wherein the alert or notification is an emerging pathogen alert or notification.
Joyce et al. teaches:
--(a) wherein the alert or notification is a rapid increase alert or notification; (see: paragraph [0026] where there is an alert with increasing frequency) or
--(b) wherein the alert or notification is an active propagator alert or notification; or
--(c) wherein the alert or notification is an emerging pathogen alert or notification.
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have (a) wherein the alert or notification is a rapid increase alert or notification, (b) wherein the alert or notification is an active propagator alert or notification, or (c) wherein the alert or notification is an emerging pathogen alert or notification as taught by Joyce et al. in the method as taught by Malaviya with the motivation(s) of improving patient adherence (see: paragraph [0125] of Joyce et al.).
As per claim 16, Malaviya and Joyce et al. in combination teaches the method of claim 15, see discussion of claim 15. Joyce et al. further teaches wherein issuance of the rapid increase alert or notification is based on pathogen-specific detection criteria (see: paragraph [0026] where there is an increase for an alert frequency based on the criteria).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 15, and incorporated herein.
As per claim 21, Malaviya teaches the method of claim 15, see discussion of claim 15. Malaviya may not further, specifically teach wherein the active propagator alert or notification is issued based on a healthcare worker or an instrument being identified to have been in contact with more than a statistically expected number of patients with potentially related infections in a timeframe (does not need to be met based on claim 15 containing optionality).
As per claim 23, Malaviya teaches the method of claim 15, see discussion of claim 15. Malaviya may not further, specifically teach wherein issuance of the emerging pathogen alert or notification is based on one or more of:
--(i) detection of the appearance of a pathogen having a combination of resistances that has not been previously observed in known pathogens;
--(ii) detection of an infection with an increase in resistance to a drug larger than a determination error for the drug;
--(iii) detection of a non-cultivable pathogen not matching known microbial strains;
--(iv) detection of a pathogen with a genotype not matching previous known data is detected;
--(v) detection of a pathogen not matching previous known data as determined by genomic sequencing; or
--(vi) detection of a pathogen not matching previous known data as detected by a genomic-based technology (does not need to be met based on claim 15 containing optionality).
As per claim 24, Malaviya and Joyce et al. in combination teaches the method of claim 1, see discussion of claim 1. Malaviya further teaches wherein the alert or notification includes at least some or all of information on a microbial strain of infection, characteristics of a pathogen for the infections, a location involved, personnel involved, or an instrument involved (see: paragraphs [0046] and [0119] where there are alerts based on the sensed information related to an entity/object (instrument involved)).
As per claim 44, claim 44 is similar to claim 15 and is therefore rejected in a similar manner to claim 15.
As per claim 45, claim 45 is similar to claim 16 and is therefore rejected in a similar manner to claim 16.
As per claim 50, claim 50 is similar to claim 21 and is therefore rejected in a similar manner to claim 21.
As per claim 52, claim 52 is similar to claim 23 and is therefore rejected in a similar manner to claim 23.
As per claim 53, claim 53 is similar to claim 24 and is therefore rejected in a similar manner to claim 24.
Claims 17 and 46 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2017/0024531 to Malaviya in view of U.S 2023/0207091 to Joyce et al. as applied to claims 15 and 44, and further in view of U.S 2021/0296007 to Singh.
As per claim 17, Malaviya and Joyce et al. in combination teaches the method of claim 16, see discussion of claim 16. The combination may not further, specifically teach wherein the pathogen-specific detection criteria are based on a rate of increase for a specific pathogen being greater than a pathogen-specific threshold; and
--(a) wherein the pathogen-specific threshold is based on prior data for the pathogen; or
--(b) wherein the pathogen-specific threshold is based on prior data for infections for the pathogen at the healthcare facility or healthcare system.
Singh teaches:
--wherein the pathogen-specific detection criteria are based on a rate of increase for a specific pathogen being greater than a pathogen-specific threshold; (see: paragraph [0027] where there is criteria based on a rate of increase of a disease being greater than a threshold) and
--(a) wherein the pathogen-specific threshold is based on prior data for the pathogen; (see: paragraph [0027] where there is a threshold based on a population of data (prior data)) or
--(b) wherein the pathogen-specific threshold is based on prior data for infections for the pathogen at the healthcare facility or healthcare system.
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the pathogen-specific detection criteria are based on a rate of increase for a specific pathogen being greater than a pathogen-specific threshold and (a) wherein the pathogen-specific threshold is based on prior data for the pathogen or (b) wherein the pathogen-specific threshold is based on prior data for infections for the pathogen at the healthcare facility or healthcare system as taught by Singh in the method as taught by Malaviya and Joyce et al. in combination with the motivation(s) of helping rapid identification of diseases (see: paragraph [0027] of Singh).
As per claim 46, claim 46 is similar to claim 17 and is therefore rejected in a similar manner to claim 17.
Conclusion
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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