Patent Application 16258319 - MULTI-LEVEL LABORATORY-BASED SURVEILLANCE SYSTEM

Title: MULTI-LEVEL, LABORATORY-BASED SURVEILLANCE SYSTEM FOR DETECTION OF INTRAOPERATIVE "ESKAPE" BACTERIAL PATHOGENS FOR HCAI PREVENTION

Application Information

Invention Title: MULTI-LEVEL, LABORATORY-BASED SURVEILLANCE SYSTEM FOR DETECTION OF INTRAOPERATIVE "ESKAPE" BACTERIAL PATHOGENS FOR HCAI PREVENTION
Application Number: 16258319
Submission Date: 2025-05-14T00:00:00.000Z
Effective Filing Date: 2019-01-25T00:00:00.000Z
Filing Date: 2019-01-25T00:00:00.000Z
National Class: 702
National Sub-Class: 019000
Examiner Employee Number: 73817
Art Unit: 1686
Tech Center: 1600

Rejection Summary

102 Rejections: 0
103 Rejections: 1

Cited Patents

The following patents were cited in the rejection:

US 0103671🔗
US 0259729🔗

Office Action Text

DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s amendment and response, filed 3/11/2025, have been carefully considered, but are not completely persuasive.
Claims 1-2, 5-6, 8-17, 19-20, 22 are pending in this application. Claims 14-17, 19 stand withdrawn from consideration as being drawn to a non-elected invention. Claim 22 is newly added. Claims 3-4, 7, 18 and 21 have been canceled. Claims 1-2, 5-6, 8-13, 20 are under examination.
The examiner notes that new claim 22 may have been submitted in response to the comments made in the previous action for the elucidation of the necessary and sufficient steps required to achieve the alleged improvement, however this claim goes well beyond what has been examined, and appears to more logically fall within previously restricted Group II, claims 14-19, “a system for screening patients” comprising certain computer elements, computational steps, and computer modules which could be used to carry out the newly added computer process steps of claim 22.
Newly submitted claim 22 is directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: Claim 22 sets forth a method of prevention of infection (as opposed to tracking bacterial transmission), by executing at least 5 separate computer-implemented processes, to “identify clinically relevant structural variants and consequences at the nucleotide level that identify new, optimal targets for diagnostics and therapeutics to inhibit bacterial transmission, virulence and resistance.” This claim sets forth a multiplicity of steps, elements and procedures not required for the elected and examined invention. (Claim 1 of Group I did not require a computer).
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claim 22 is withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.

Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art.
In the claims, the acronym ESKAPE continues to be interpreted as the specific bacterial pathogens:
“S. aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, and Enterobacter spp. organisms that are particularly successful in causing patient harm.” (p1).
Claim Objections
Claim 1 is objected to because of the following informalities: the abbreviation for Electronic Medical Record is “EMR” and not ERM as amended into claim 1. Appropriate correction is required.

Claim Rejections - 35 USC § 112
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 1-2, 5-6, 8-13, 20 remain 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.
The claims have been heavily amended, partly in response to the previous rejection.
Applicant is reminded to use proper underlining and strike-through elements when amending claims. For example, “accessing electronically an Electronic Medical Record…” is an entirely new limitation, not previously present in claim 1, and should have been underlined in its entirety. Failure to adhere to the requirements may result in a reply being deemed non-responsive.
The metes and bounds of claim 1 are unclear. The claim fails to particularly point out and distinctly claim how any of the steps of claim 1 lead to tracking the bacterial transmission in any healthcare setting, when only the patient(s) who provided the sample(s) are provided possible treatments. The claim fails to set forth any information regarding the incidence of infection in any population, nor any specific way to track it, nor how to determine whether treating the individual patient actually leads to infection reduction in the desired population. There is no clear link between obtaining the various information, obtaining the samples, screening the obtained information and samples, and the goal of tracking transmission. The claim merely provides the treatment, but does not clearly treat the patient.
Further in claim 1 the metes and bounds of the screening step remain unclear. The metes and bounds of the processing remain completely unclear. No specific process is set forth which is to be carried out on the infection risk information, nor is any specific process set forth to be carried out on the physical sample cultures from the patient, nor the “physical samples” from the various listed environments. Each of these elements require differing types of processing, depending on the end goal of the analysis. Even with a goal of “preventing bacteria transmission”, one of skill in the art would not be apprised as to when the “first infection risk information and the second risk information” are “positive” in said screening step”. It is entirely unclear what data would qualify a patient as being “at risk for ESKAPE infection”. It is unclear what information is required to make the decision as to the presence of risk. It is further unclear at what point a patient is “at risk” or “at high risk” for ESKAPE infection based on the screening of the samples, as no particular screening is defined by the claim. The samples are merely “screened”. It is entirely unclear what processes are required in the screening steps. Processing data and processing samples do not comprise similar steps. While breadth of a limitation is not the same as indefiniteness, one of skill would not be apprised as to the particular processes to be applied to the information and the physical samples in order to obtain the desired results. The nature of the data processing and the analysis of the samples are each disparate in nature, and highly depend on the nature of the overall analysis or diagnostic question. Further, the processing fails to set forth how any patient is determined to have a high risk of development of an infection. It is entirely unclear what elements are required by Applicant’s process to make this risk analysis calculation. The newly added limitation to the screening step fails to remedy this deficiency, as it merely sets forth an undisclosed “dynamic set of criteria derived from data in the database…” without actually delineating what that set of criteria contains, and how the criteria is applied to the data at hand in claim 1. This further fails to separate “risk” from “high risk” as previously set forth. The metes and bounds of “a dynamic set of criteria derived from data in the database” are entirely unclear. The limitation lists sources of data, but fails to set forth the actual criteria, what specific data is derived from the sources, and how to analyze the information “dynamically” to determine whether the patient is at “high risk”. Merely reciting the source of unspecified data is not a recitation of actual statistical or information analysis steps, required to determine a level of risk. Additionally, none of this information is clearly obtained in step 1, merely sourced. The EMR data of the patient is obtained, but unspecified as to what data resides in the record and what is required for the “dynamic” criteria. The database to which the EMR data is uploaded has no particular structure or set of parameters, or previously entered data. The EMR of other patients, or other infection events are not clearly provided by claim 1, nor is it clear what that information would comprise. No dynamic analysis steps are actually carried out, or listed. Dynamic means that something can change over time. It is unclear what aspects of all the data gathered is considered to be dynamic, or how one would change any given set of parameters or criteria to meet the goal of the claim.
The metes and bounds of the newly added final limitation are unclear. The final limitation now reads: “at each of previous steps, adding patient infection event data resulting from the step to the database, such that a computer processes the data real-time to update the dynamic set of criteria.”
When considering “each of the previous steps” it would appear this is redundant for the newly added “accessing electronically an electronic medical record (ERM) [sic] of the patient and automatically uploading data from the ERM [sic] into a database” limitation. It is unclear whether any “patient infection event data” is present in the EMR, as at this point in the claim, no tests have been performed indicating an infection event.
When considering the next step, “obtaining first infection risk information” or “obtaining a second infection risk information” neither step appears to provide “patient infection event data”. These steps obtain information from the environment, not the patient.
When considering the steps of obtaining the first or second samples, and placing them in collection kits- merely collecting the sample and placing it in a kit does not yet provide any “patient infection event data” as these samples have not yet been screened or processed by a laboratory. It is not until the screening step where the first culture is processed for “development of infection” that any “patient infection event data” is generated, which could then be added to any database.
Therefore, the use of “at each of the previous steps” appears to be incorrect, an inapplicable to each of the previous steps as set forth in claim 1.
Further in claim 1, it is unclear what data to add from the second portion of the screening step “as having a high risk for development of infection from the second culture…” to the database, as the nature of this information is not clearly provided, as set forth above. No actual criteria are enumerated, such that the appropriate data or parameters could be obtained and saved to the database. No actual dynamic algorithmic processes are enumerated or required. The use of “real-time” in this limitation does not clearly provide any of the required criteria or processes, and appears to merely include routine data processing as data is added. It is further unclear how the steps of claim 1 now meet the amended goal of “tracking bacteria transmission”.
The metes and bounds of claim 5 remain unclear. The claim recites that the identifying step “is performed by software” and identifies a set of events or concepts of transmission, infection, particular pathogens, other pathogens, and concepts of genetic clonal transmission, and concepts of “clinical relevance” which are entirely unclear. There is no computer implementation for claim 1, and there is no indication as to how any software is to be implemented in the steps of claim 1. No particular software is described. The concepts and items recited are set forth as desired results, and not as specific, positive active steps which would result in the identification of any of the listed elements. Results-based language is not a limitation describing how to obtain those results. Further, none of the listed elements have any particular link to determining whether a patient or patients is at high risk of developing infection, nor is there any particular link to the goals of “tracking bacterial transmission” as set forth in claim 1, from which claim 5 ultimately depends. No particular results are described such that any of the required goals can be achieved.
Applicant’s arguments
Applicant’s arguments have been carefully considered but are not persuasive. Applicant’s amendments have removed certain aspects of the previous rejection, but elements of indefiniteness remain or were introduced by those amendments.
With respect to limitations from the specification, claims are read in light of the specification, limitations from the specification cannot be read into the claims.
Applicant is encouraged to clearly and concisely set forth the necessary and sufficient limitations required to achieve the listed goal of the claims, without open ended phrasing and in a clear order of operation.
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 1-2, 5-6, 8-13, 20 remain rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 for the most current and complete guidelines in the analysis of patent eligible subject matter.
With respect to step (1): yes, the claims are drawn to a statutory category and recite methods of tracking bacterial transmission.
With respect to step (2A) (1): yes, the claims recite an abstract idea, law of nature and/or natural phenomenon. The claims recite an abstract idea of tracking transmission of bacteria in patients or a hospital setting, utilizing screening steps, identification of high-risk patients, providing medication to prevent or treat infection, sampling the environment and patient, resulting in a reduction in transmission. The claims further recite certain methods of organizing human activity, as the steps are routinely performed by medical and/or hospital staff. "Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04).
Claim 1 is independent.
Mental Processes, Mathematic Concepts or Elements in Addition (EIA) within claim 1:
“1. (Currently amended) A method of tracking bacteria dissemination comprising:
identifying a patient at risk of developing postoperative infections, wherein said identifying comprises:
accessing electronically an Electronic Medical Record (ERM) of the patient and automatically uploading data from the ERM into a database.
(EIA- data gathering step of unspecified EMR data, utilizing a general-purpose computer)

obtaining a first infection risk information identifying a pre-operative arena selected from the group consisting of: quick care outpatient units, emergency departments, intensive care units, hospital wards, and a primary care office; and
(EIA- data gathering step to obtain unspecified “first infection risk information”)

obtaining a first sample from the patient selected from the group consisting of: a nasopharyngeal swab, an axillary sample, an inguinal swab, a rectal swab, a blood sample, a sputum sample, a wound sample, a urine sample and a stool sample;
(EIA- data gathering step, obtaining samples by any known means)

wherein said obtaining the first sample comprises collecting a first culture by placing the first sample from the patient into a collection kit comprising one or more reservoirs;
(EIA- modifies the previous data gathering step with a direction of where to put the sample)

obtaining a second infection risk information identifying an intra-operative arena selected from the group consisting of: surgical suites, operating rooms, and anesthesia work environments; and
(EIA- data gathering step, obtaining unspecified information by any means)

obtaining a second sample selected from the group consisting of: air, patient skin contact sites, provider hands, instrumentation, equipment, and/or tools that are near the patient including any of scalpels, saws, forceps, clamps, surfaces, tubing, syringes, vials, syringe connection ports, catheters, a second blood sample, a second sputum sample, a second wound sample, a second urine sample, and a second stool sample of the patient;
(EIA- data gathering step, obtaining additional samples by any known means)

wherein said obtaining the second sample comprises collecting a second culture by placing the second sample into the collection kit comprising one or more reservoirs;
(EIA- modifies the previous data gathering step with a direction of where to put the sample)

screening the first and second cultures for: (i) development of infection from the first culture, and (ii) as having a high risk for development of infection from the second culture, the high risk being defined as meeting a dynamic set of criteria derived from data in the database relating to information from the EMR of the patient, the EMR of other patients, and other infection events occurring in the pre-operative and/or intra-operative arenas, for any of Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Enterobacter sp. (ESKAPE); and
(EIA- data gathering step, analysis of the samples gathered, to determine i) development of infection in the first sample, by any laboratory analysis that can identify the presence of infection; and a mental process step, in a computing environment or using a computer as a tool, of ii) screening for “having a high risk for development of infection from the second culture” using data analysis from previously gathered EMR and other data)

identifying the patient as being at risk for ESKAPE infection based on said screening, wherein the patient identified as being at risk are is positive for (i) or (ii) in said screening step; and
(Mental process- identification the patient is at risk by observing the information gathered, and the results of the sample screening, and making a judgement as to whether the information reflects risk)

providing the one or more patient identified as being at risk for any of ESKAPE infection with one or more treatments capable of treating or preventing said infection;
(EIA- provision of a generically described treatment “capable of treating or preventing”)

at each of the previous steps, adding patient infection event data resulting from the step to the database, such that a computer processes the data real-time to update the dynamic set of criteria.
(Mental Process, in a computing environment, or using a computer as a tool, of database annotation.)

It is noted that all steps of claim 1 represent steps of organizing human activity, as all steps are routinely performed by doctors, nurses and other hospital staff in their routine duties of treating patients.
Therefore, the claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE).
With respect to step 2A (2): NO. The claims must therefore be examined further to determine whether they integrate that JE into a practical application (MPEP 2106.04(d). The claimed additional elements are analyzed alone or in combination to determine if the JE is integrated into a practical application (MPEP 2106.04(d)(I.); MPEP 2106.05(a-c, e, f and h)).
Claim 1 recites additional elements that are not a JE, identified above as EIA, all related to data gathering.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. Data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. Data gathering steps are not sufficient to integrate a JE into a practical application. The additional limitation must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).

Claim 1 recites the additional non-abstract element of “and providing the one or more patients identified as being at risk for a particular post-operative infection with one or more treatments capable of treating or preventing said infection;” which is a step related to treatment or prophylaxis of a medical condition.
This step does not practically apply the JE, as the treatments are merely provided to the patient, not administered to the patient, and merely have the capability to treat or prevent infection, and are not required to actually prevent or treat an infection. The treatments are not specified as any particular type of treatment, such as antibiotics. Having the capability of possibly treating or preventing an infection does not necessarily lead to the reduction of an incidence in infection if the patient does not actually carry out the treatment. This limitation fails to meet the requirements for a “particular treatment and prophylaxis” as required by MPEP 2106.04(d)(2): “in order to qualify as a "treatment" or "prophylaxis" limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition. An example of such a limitation is a step of "administering amazonic acid to a patient" or a step of "administering a course of plasmapheresis to a patient." If the limitation does not actually provide a treatment or prophylaxis, e.g., it is merely an intended use of the claimed invention or a field of use limitation, then it cannot integrate a judicial exception under the "treatment or prophylaxis" consideration.”

Claim 1 implies the use of a general-purpose computer system and now recites new computer-based elements such as a database to access and obtain EMR data, and “a computer” that processes the data “real-time…” Claim 5 recites “software that identifies” a set of events, which implies the use of a general-purpose computer system. Claim 20 recites linking samples to “data acquired in any of the preceding steps” which implies the use of a general-purpose computer system.
The claims do not describe any specific computational steps by which the claimed computer elements perform or carry out the JE, nor do they provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a generic computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of a computer (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.

Dependent claims 2, 5-6, 8-13, 20 have been analyzed with respect to 2A-2.
Dependent claim 2 adds non-abstract limitations which are directed to the output of results.
Claim 13 is directed to “producing an archive” of physical samples, which is post-solution insignificant activity.
Dependent claim 8 performs unspecified “genomic analysis” on any discovered transmitted bacteria, which is a data gathering step using laboratory processes.
Claims 9-11, 20 set forth data gathering steps (laboratory processes), as well as additional abstract limitations, and output limitations.

Each step of data gathering is pre-solution insignificant activity, MPEP 2106.05(g) citing OIP Tech Inc v. Amazon.com, Inc.
The output of results is post-solution insignificant activity, MPEP 2106.05(g) citing Apple v. Ameranth Inc., collectively they are extra-solution activity.
Dependent claims 5-6, 9-12, 20 each add an abstract limitation to the identified JE reciting additional mental processes, mathematic concepts, or steps of organizing human behavior. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that exception.
As set forth above, dependent claims 5, and 20 set forth a non-abstract limitation directed to additional computer system elements, or the general-purpose computer.
The computer elements of the claims do not provide improvements to the functioning of a computer (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.).

As set forth above, none of the elements in addition to the JE are sufficient to integrate that JE into a practical application.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO. Because the claims recite a JE, and do not integrate that JE into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the JE amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim 1: The additional element of data gathering does not rise to the level of significantly more than the judicial exception.
Kriesworth (US 7,349,808 B1 of record) discloses obtaining or receiving patient EMR information, infection risk information, and patient / environmental samples meeting the broadest reasonable interpretation of those limitations.
Walker et al. (US 2015/0259729 A1 Sept 17, 2015) obtains patient information meeting the BRI of EMR data, infection risk information and patient / environmental samples meeting the BRI of those limitations.
Pederson et al (US 2002/0103671 A1, Aug. 1 2002) provides obtaining patient information meeting the BRI of EMR data, infection risk information and patient / environmental samples meeting the BRI of those limitations.
Diekma (of record) discloses obtaining patient information meeting the BRI of EMR data, infection risk data, and samples meeting the BRI of those limitations.

These elements meet the broadest reasonable interpretation of the claimed data gathering steps. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
Activities such as data gathering do not improve the functioning of the computer itself, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).

With respect to steps of treatment or prophylaxis in claim 1:
Rosenfeld (US 6,804,656 B1, Oct 12 2004) provides the patient with one or more treatments “capable of treating or preventing said infection.”
Kriesworth (of record) provides the patient with one or more treatments “capable of treating or preventing said infection.”
Olsen (US 7,447,643 B1, Nov 4 2008) provides the patient with one or more treatments “capable of treating or preventing said infection.”
Diekma (of record) provides the patient with one or more treatments “capable of treating or preventing said infection.”

As such, providing access to generically described treatments “capable of treating or preventing” infection is routine, well understood and conventional in the art of surveillance systems for reducing the incidence of infection. MPEP 2106.05(d) citing Alice Corp. (573 US at 225, 110 USPQ2d at 1984). This element does not provide improvements to any other technology or technological field (MPEP2106.05(a)), it does not improve the functioning of a computer, nor does it require a particular machine, effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). The treatment steps are recited at a high level of generality, and are insufficient to provide significantly more.

With respect to claim 1: the implied or recited computer related elements or the general-purpose computer systems do not rise to the level of significantly more than the judicial exception.
Kriesworth (of record) provides general purpose computer systems meeting the BRI of the computer-related elements.
Walker (of record) provides general purpose computer systems meeting the BRI of the computer-related elements.
Rosenfeld (of record) provides general purpose computer systems meeting the BRI of the computer-related elements.
Olsen (of record) provides general purpose computer systems meeting the BRI of the computer-related elements.
Diekma (of record) provides general purpose computer systems meeting the BRI of the computer-related elements.

Each sets forth computer systems including input, output, processors, memory, databases, etc. These computer systems or computing elements meet the BRI of the claimed or implied system elements. As such, the prior art recognizes that these computing elements are routine, well understood and conventional in the art.
These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not effect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).

Dependent claims 2, 5-6, 8-13, 20 have been analyzed with respect to step 2B.
Dependent claim 2 adds non-abstract limitations which are directed to the output of results.
Claim 13 is directed to “producing an archive” of physical samples, which is post-solution insignificant activity.
Dependent claim 8 performs routine laboratory tests, which are data gathering steps.
Claims 9-11, 20 set forth data gathering steps, as well as additional abstract limitations, and output limitations.

Each step of data gathering is pre-solution insignificant activity, MPEP 2106.05(g) citing OIP Tech Inc v. Amazon.com, Inc. The output of results is post-solution insignificant activity, MPEP 2106.05(g) citing Apple v. Ameranth Inc., collectively they are extra-solution activity. Each of the above cited references provide data gathering, kits for carrying out laboratory processes, and output limitations. As such these elements are each routine, well understood and conventional in the art and fail to rise to the level of significantly more.

Dependent claims 5-6, 9-12, 20 each add an abstract limitation to the identified JE requiring additional mental processes, mathematic concepts, or steps of managing human behavior. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that exception.
Dependent claims 5, and 20 set forth a non-abstract limitation directed to additional computer system elements, or the general-purpose computer. Each of the identified references above provide equivalent computer system elements.
The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). As such, none of these limitations rise to the level of significantly more than the JE as they are routine, well understood and conventional limitations in the art.

In combination, the data gathering steps providing the information required, to be acted upon by the JE, performed in a generic computer or generic computing environment, and generic treatment steps fail to rise to the level of significantly more. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. The generic treatment step fails to provide a specific treatment or prophylactic. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions.
For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Applicant’s arguments:
Applicant’s arguments with respect to this rejection have been carefully considered but are not persuasive.
Applicant’s arguments that the steps do not fall within the categories of abstract ideas are not persuasive. Each limitation was specifically set forth as to what category of abstract ideas are encompassed, followed by detailed analysis of all the elements in addition. Mental steps are processes which can be carried out in the human mind, using a computer as a tool, or in a computing environment. None of the limitations require any activity for which the human mind is not equipped.
Applicant argues “given the monumental task of coordinating the flow of data between various laboratories and operative arenas across the globe and the ability to use this data to identify a patient as being high risk and requiring extraordinary care and monitoring, the invention of claim 1 now amounts something significantly more than streamlining a laborious task.” This argument is not persuasive. It is the limitations of the abstract idea/ judicial exception which provide parts of this argued improvement. Once the data is gathered, it is the “screening” and “identifying” steps which perform the identification of a patient as being at high risk.
An improvement in the judicial exception itself is not an improvement in the technology. For example, in In re Board of Trustees of Leland Stanford Junior University, 989 F.3d 1367, 1370, 1373 (Fed. Cir. 2021) (Stanford I), Applicant argued that the claimed process was an improvement over prior processes because it ‘‘yields a greater number of haplotype phase predictions,’’ but the Court found it was not ‘‘an improved technological process’’ and instead was an improved ‘‘mathematical process.’’ The court explained that such claims were directed to an abstract idea because they describe ‘‘mathematically calculating alleles’ haplotype phase,’’ like the ‘‘mathematical algorithms for performing calculations’’ in prior cases. Notably, the Federal Circuit found that the claims did not reflect an improvement to a technological process, which would render the claims eligible (FR89 no.137, p58137, 7/17/2024).
The improvement in identifying and tracking patients (carried out by the judicial exception) does not provide an improvement in the technology of obtaining information, obtaining samples, or performing certain known laboratory procedures. The data gathering elements are carried out, unchanged, whether or not the judicial exception is applied. (Cleveland Clinic Foundation: using well-known or standard laboratory techniques is not sufficient to show an improvement (MPEP2106.05(a)).
The improvement in identifying and tracking patients (achieved by the judicial exception) does not require a non-conventional interaction with a specific element of a computer as was required in Enfish. The improvement in identification and tracking patients (carried out by the judicial exception) does not improve the functionality of the computer itself as in Finjan, Visual Memory, or SRI Int’l. It does not provide an improvement in computer animation and use rules to automate a subjective task of humans to create a sequence of synchronized, animated characters as in McRo.
With respect to the identified elements in addition (EIA) to the JE, each has been addressed above.
In the claims, the EIA identified as data gathering steps do not affect how the steps of the abstract idea are performed, they provide the data which is acted upon by the limitations of the JE. These data gathering steps do not apply, rely on, or use the steps identified as making up the JE. Rather, the steps avail themselves of the data gathered. The data gathering in the claims constitutes insignificant pre-solution activity. See MPEP § 2106.05(g):
MPEP2106.05(g). “The term "extra-solution activity” can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim...”
“An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent.”
See also CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372 (Fed. Cir. 2011) ("[E]ven if some physical steps are required to obtain information from the database ... such data-gathering steps cannot alone confer patentability.").

That the claimed system may result in faster and more accurate identifications in large data sets does not take the claim out of the realm of the abstract.
"[R]elying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible." OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); see also Intellectual Ventures I LLC v. Erie Indemnity Co., 711 F. App'x 1012, 1017 (Fed. Cir. 2017) (unpublished) ("Though the claims purport to accelerate the process of finding errant files and to reduce error, we have held that speed and accuracy increases stemming from the ordinary capabilities of a general-purpose computer 'do[] not materially alter the patent eligibility of the claimed subject matter."'); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Can. (US.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) ("[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.").

In the claims at issue here, there is no such application of specifically claimed rules to produce an improved technological result as was provided by MCRO. The process of characterizing patient data, sample data and environmental data is not a technological process; it is information evaluation.
Claims that recite performing information analysis (e.g., statistical analyses and correlating information), as well as the collection and manipulation of information related to such analysis, have been determined by our reviewing court to be an abstract concept that is not patent eligible.
See SAP, 898 F.3d, 1165, 1167, 1168 (Claims reciting "[a] method for providing statistical analysis" (id. at 1165) were determined to be "directed to an abstract idea" (id. at 1168)); see also Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat'l Ass 'n, 776 F.3d 1343, 1345, 1347 (Fed. Cir. 2014) (finding the "claims generally recite ... extracting data ... [and] recognizing specific information from the extracted data" and that the "claims are drawn to the basic concept of data recognition").
"As many cases make clear, even if a process of collecting and analyzing information is limited to particular content or a particular source, that limitation does not make the collection and analysis other than abstract." SAP, 898 F.3d at 1168 (internal quotation marks omitted)).

Further, with respect to the arguments regarding the alleged improvement, it is unclear that the independent claims recite all the necessary and sufficient steps required to achieve that improvement. MPEP 2106.05(a): “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102- 03; DDR Holdings, 773F.3d at 1259, 113 USPQ2d at 1107.”
The MPEP sets forth that “if the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 CFR 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification.” Applicant’s arguments cannot take the place of evidence.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.

The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.

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.

Claim(s) 1-2, 5-6, 8-13, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kriesworth in view of Diekema.
Kriesworth et al. System and method for tracking and controlling infections US 7,349,808 Mar 25, 2008. (Of record, PTO-1449)
Diekema et al. (2015) Chapter 8: Prevention of Health Care Associated Infections. IN: Manual of clinical Microbiology, 11th edition, (Eds. Jorgensen et al.) p106-119.

The claims have been heavily amended but the basis of the rejection remains the same. The Examiner has labeled the steps with letters merely to assist in the structure of the rejection.
“1. (Currently amended) A method of tracking bacteria dissemination comprising:
a) identifying a patient at risk of developing postoperative infections, wherein said identifying comprises:
b) accessing electronically an Electronic Medical Record (ERM) of the patient and automatically uploading data from the ERM into a database.
c) obtaining a first infection risk information identifying a pre-operative arena selected from the group consisting of: quick care outpatient units, emergency departments, intensive care units, hospital wards, and a primary care office; and
d) obtaining a first sample from the patient selected from the group consisting of: a nasopharyngeal swab, an axillary sample, an inguinal swab, a rectal swab, a blood sample, a sputum sample, a wound sample, a urine sample and a stool sample;
e) wherein said obtaining the first sample comprises collecting a first culture by placing the first sample from the patient into a collection kit comprising one or more reservoirs;
f) obtaining a second infection risk information identifying an intra-operative arena selected from the group consisting of: surgical suites, operating rooms, and anesthesia work environments; and
g) obtaining a second sample selected from the group consisting of: air, patient skin contact sites, provider hands, instrumentation, equipment, and/or tools that are near the patient including any of scalpels, saws, forceps, clamps, surfaces, tubing, syringes, vials, syringe connection ports, catheters, a second blood sample, a second sputum sample, a second wound sample, a second urine sample, and a second stool sample of the patient;
h) wherein said obtaining the second sample comprises collecting a second culture by placing the second sample into the collection kit comprising one or more reservoirs;
i) screening the first and second cultures for: (i) development of infection from the first culture, and (ii) as having a high risk for development of infection from the second culture, the high risk being defined as meeting a dynamic set of criteria derived from data in the database relating to information from the EMR of the patient, the EMR of other patients, and other infection events occurring in the pre-operative and/or intra-operative arenas, for any of Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Enterobacter sp. (ESKAPE); and
j) identifying the patient as being at risk for ESKAPE infection based on said screening, wherein the patient identified as being at risk are is positive for (i) or (ii) in said screening step; and
k) providing the one or more patient identified as being at risk for any of ESKAPE infection with one or more treatments capable of treating or preventing said infection;
L) at each of the previous steps, adding patient infection event data resulting from the step to the database, such that a computer processes the data real-time to update the dynamic set of criteria.

Kriesworth is directed to:
“a system and method for performing real-time infection control over a computer network. The method comprises obtaining a sample of a microorganism at a health care facility, sequencing a first region of a nucleic acid from the microorganism sample, comparing the first sequenced region with historical sequence data stored in a database, determining a measure of phylogenetic relatedness between the microorganism sample and historical samples stored in the database, and providing infection control information based on the phylogenetic relatedness determination to the health care facility, thereby allowing the health care facility to use the infection control information to control or prevent the spread of an infection” (Abstract).

With respect to claim 1, Kriesworth identifies: 1. (Currently amended) A method of tracking bacteria dissemination comprising:
a) identifying a patient at risk of developing postoperative infections, wherein said identifying comprises:
b) accessing electronically an Electronic Medical Record (ERM) of the patient and automatically uploading data from the ERM into a database.

Kriesworth provides central database 128, which stores digital data from a variety of sources. The broadest reasonable interpretation of an electronic medical record, is stored data about the patient, which can comprise medical data, personal data, et al. Kriesworth discloses obtaining and uploading EMR-type data to the database.
“(18) Central database 128 is located in data storage device 126. Central database 128 stores digital sequence data received from sequencer 146. Central database 128 also stores various types of information received from the various health care facilities…
(22) Patient medical history data 138 contains data about patients such as where they previously have been hospitalized and the types of procedures that have been done. This type of data is useful in determining where a patient may have previously picked up an infectious agent, and determining how an infection may have been transmitted.
(23) Patient infection information data 140 stores updated medical information pertaining to a patient who has obtained an infection. For example, data 140 could store that a particular patient acquired an infection in a hospital during heart surgery. Data 140 includes the time and the location that an infection was acquired. Data 140 also stores updated data pertaining to a patient's medical condition after obtaining the infection, for example, whether the patient died after three weeks, or recovered after one week, etc. This information is useful in looking for correlates between a disease syndrome and a strain subtype. Additional phenotypic assays to determine toxin production, heavy metal resistances and capsule subtypes, as examples, will also be added to the strain database and update properties and virulence data 136.” (all from column 7)
“FIGS. 2 (2A and 2B) depicts a flowchart illustrating a method of the present invention for performing infection control using the system architecture of FIG. 1. In step 200, a patient is admitted to a health care facility such as a hospital. In step 202, a medical history is obtained from the patient. The medical history can be obtained by asking the patient a series of questions. The medical history will include factors that will determine the risk level of the patient for carrying a particular microorganism. For example, the patient can be asked whether he or she has been hospitalized recently, for how long, what kind of procedure, what foreign countries he or she has visited, etc. After obtaining the answers to these questions, the risk level of the patient for carrying a potentially infectious agent can be determined.”
With respect to: “c) obtaining a first infection risk information identifying a pre-operative arena selected from the group consisting of: quick care outpatient units, emergency departments, intensive care units, hospital wards, and a primary care office; and

Kriesworth obtains infection risk information from environmental areas, such as a primary care office, emergency department, hospital wards, et al. This limitation merely requires obtaining information from one of the listed pre-operative arena sites.
“FIG. 1 depicts a blocking diagram illustrating a system architecture suitable for implementing the infection control system of the present invention. As shown in FIG. 1, various terminals at a number of health care facilities such as hospital terminal 102, a physician's office terminal 106, long term care facility terminal 110, and laboratory terminal 114 all communicate with an infection control facility 148 via a network 100. Other institutions or entities involved in infection control can also connect to infection control facility 148 via network 100… Infection control facility 148 then informs the health care facilities of potential outbreak problems and provides infection control information.” (col 6).

With respect to: “d) obtaining a first sample from the patient selected from the group consisting of: a nasopharyngeal swab, an axillary sample, an inguinal swab, a rectal swab, a blood sample, a sputum sample, a wound sample, a urine sample and a stool sample;
e) wherein said obtaining the first sample comprises collecting a first culture by placing the first sample from the patient into a collection kit comprising one or more reservoirs;”

Kriesworth provides obtaining patient samples at Fig 2A element 204, as well as at col 8-9.
“In step 204, a sample is taken from the patient. For example, the patient can be swabbed orally, nasally or rectally. In step 206, the sample is sent to a laboratory for analysis, such as laboratory 114 shown in FIG. 1… A sample can be taken from a patient in step 204 every time that a patient in the health care facility acquires an infection. Alternatively, a sample can be taken from a patient in step 204 every time that a patient is admitted to the hospital or health care facility; i.e. a isolate is taken from every patient who is admitted regardless of whether they have an infection or have a high-risk of infection.
As an alternative method, a sample can be taken only from patients who are determined to have a high risk of infection (e.g. patients who have been hospitalized recently or traveled internationally recently).” (col 8-9)

With respect to: “f) obtaining a second infection risk information identifying an intra-operative arena selected from the group consisting of: surgical suites, operating rooms, and anesthesia work environments; and
g) obtaining a second sample selected from the group consisting of: air, patient skin contact sites, provider hands, instrumentation, equipment, and/or tools that are near the patient including any of scalpels, saws, forceps, clamps, surfaces, tubing, syringes, vials, syringe connection ports, catheters, a second blood sample, a second sputum sample, a second wound sample, a second urine sample, and a second stool sample of the patient;
h) wherein said obtaining the second sample comprises collecting a second culture by placing the second sample into the collection kit comprising one or more reservoirs;”

Kriesworth provides obtaining additional risk information from the environment, and supplemental samples at Fig 2, and col 8-9:
“when an infectious isolate is obtained from a patient, other individual, or a piece of equipment, a first desired region of the DNA is sequenced and stored in DNA region one sequence data 130.”
“In step 204, samples could be taken from objects instead of people. For example, a piece of equipment such as a dialysis machine might harbor microorganisms. A sample could be obtained from the dialysis machine.”

This meets the limitation of a second sample from a patient, from the environment, a provider (other individual), operating room, or hospital ward as required by claim 1.
With respect to: “i) screening the first and second cultures for: (i) development of infection from the first culture, and (ii) as having a high risk for development of infection from the second culture, the high risk being defined as meeting a dynamic set of criteria derived from data in the database relating to information from the EMR of the patient, the EMR of other patients, and other infection events occurring in the pre-operative and/or intra-operative arenas, for any of Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Enterobacter sp. (ESKAPE); and”

Kriesworth provides screening cultures for development of infection, as well as reviewing data collected from the patient, other patient data, and other infection events as set forth at col 6-9:
“Infection control facility 148 sequences predetermined regions of DNA from infectious isolates received from various health care facilities. Infection control facility 148 stores and analyzes the sequence data, tracks the spread of infections, and predicts infection outbreaks… Patient medical history data 138 contains data about patients such as where they previously have been hospitalized and the types of procedures that have been done... FIGS. 2 (2A and 2B) depicts a flowchart illustrating a method of the present invention ... The medical history will include factors that will determine the risk level of the patient for carrying a particular microorganism. ... After obtaining the answers to these questions, the risk level of the patient for carrying a potentially infectious agent can be determined.”
“Infection control facility 148 includes a server 118 and a sequencer 146. Sequencer 146 sequences desired regions of DNA from infectious agents such as bacteria. The digital sequence data is then sent to server 118. Server 118 analyzes the digital sequence data and provides infection control information and warnings to hospital 102, physician's office 106, long term care facility 110, laboratory 114, and other facilities involved with infection control via network 100.”
“with respect to FIG. 2, when an infectious isolate is obtained from a patient, other individual, or a piece of equipment, a first desired region of the DNA is sequenced and stored in DNA region one sequence data 130. Similarly, DNA region 2 sequence data 132 stores the digital sequence data of a second desired sequenced region of the DNA of an infectious agent. DNA region 3 sequence data 134 stores the digital sequence data of a third desired sequenced region of the DNA of an infectious agent. Central database 128 can store any number of sequenced regions of the DNA, as will be discussed further with respect to FIGS. 2-3.
(20) Different organisms will have different predetermined regions of their respective DNA that are sequenced. For example, an isolate of S. aureus bacteria will have different regions that are sequenced than an isolate of E. facaelis. Each type of bacteria or other infectious agent will have predetermined regions that are used for sequencing. The way that those predetermined regions are chosen is described in more detail with respect to FIG. 2, step 214.
(21) Central database 128 also stores species/sub-species properties and virulence data 136. Data136 includes various properties of different species and subspecies of infectious agents. For example, data 136 can include phenotypic and biomedical properties, effects on patients, resistance to certain drugs, and other information about each individual subspecies of microorganism.”

With respect to “a dynamic set of criteria” the BRI of this term is met by the infection control facility which stores and analyzes the data, including information from a variety of sources, as claimed. This meets processing both information, and samples, as well as screening for development of infection, and the determination of a patient being at high risk of developing an infection.
“(37) Most hospitals today do not have their own sequencers. Therefore, in most cases the hospitals would send out their samples for analysis. However, in the future more and more hospitals will purchase their own sequencers. When this happens, all communications between the hospitals and infection control facility 148 can occur electronically via network 100. This will allow for rapid real-time infection control.” (column 9)

With respect to: “j) identifying the patient as being at risk for ESKAPE infection based on said screening, wherein the patient identified as being at risk are is positive for (i) or (ii) in said screening step; and”

Kriesworth provides this identification at col 6-9, the same citation as for the immediately preceding limitations i) and j).
With respect to: “k) providing the one or more patient identified as being at risk for any of ESKAPE infection with one or more treatments capable of treating or preventing said infection;”

Kriesworth provides treatment information to be given to the patient at Figure 2, element 228, and Col 8-9:
“In step 228, server 118 determines if the isolate taken from the patient is a virulent or dangerous strain. This can be determined from the virulence of identical or closely related strains. Central database 128 stores species/subspecies properties and virulence data 136 for various subspecies of bacteria. This data is used to distinguish between contaminating and infecting isolates and to distinguish between separate episodes of infection and relapse of disease. Data 136 links bacteria types with disease syndromes, such as cases of food poisoning and toxic shock syndrome. Data 136 can identify which subspecies are resistant to certain drugs, or which subspecies are treatable by certain drugs…
If the patient is determined to have a virulent strain, the strain can be treated and eliminated before the patient is admitted”

With respect to: “L) at each of the previous steps, adding patient infection event data resulting from the step to the database, such that a computer processes the data real-time to update the dynamic set of criteria.”

Kriesworth provides the infection control facility system to which all data is entered during the processes.
“(11) FIG. 1 depicts a blocking diagram illustrating a system architecture suitable for implementing the infection control system of the present invention. As shown in FIG. 1, various terminals at a number of health care facilities such as hospital terminal 102, a physician's office terminal 106, long term care facility terminal 110, and laboratory terminal 114 all communicate with an infection control facility 148 via a network 100. Other institutions or entities involved in infection control can also connect to infection control facility 148 via network 100.
“(13) Infection control facility 148 sequences predetermined regions of DNA from infectious isolates received from various health care facilities. Infection control facility 148 stores and analyzes the sequence data, tracks the spread of infections, and predicts infection outbreaks. Infection control facility 148 then informs the health care facilities of potential outbreak problems and provides infection control information. Other functions of infection control facility 148 will be described in more detail with respect to FIGS. 2-7.
“(15) Infection control facility 148 includes a server 118 and a sequencer 146. Sequencer 146 sequences desired regions of DNA from infectious agents such as bacteria. The digital sequence data is then sent to server 118. Server 118 analyzes the digital sequence data and provides infection control information and warnings to hospital 102, physician's office 106, long term care facility 110, laboratory 114, and other facilities involved with infection control via network 100.
(16) Server 118 contains a central processing unit (CPU) 124, a random access memory (RAM) 120, and a read only memory (ROM) 122. CPU 124 runs a software program for performing the method of the present invention described further below with respect to FIGS. 2-3.
(17) CPU 124 also connects to data storage device 126. Data storage device 126 can be any magnetic, optical, or other digital storage media. As will be understood by those skilled in the art, server 118 can be comprised of a combination of multiple servers working in conjunction. Similarly, data storage device 126 can be comprised of multiple data storage devices connected in parallel.
(18) Central database 128 is located in data storage device 126. Central database 128 stores digital sequence data received from sequencer 146. Central database 128 also stores various types of information received from the various health care facilities. CPU 124 analyzes the infection data stored in central database 128 for infection outbreak prediction and tracking. Some examples of the various types of data that are stored in central database 128 are shown in FIG. 1. These types of data are not exclusive, but are shown by way of example only.
(21) Central database 128 also stores species/sub-species properties and virulence data 136. Data 136 includes various properties of different species and subspecies of infectious agents. For example, data 136 can include phenotypic and biomedical properties, effects on patients, resistance to certain drugs, and other information about each individual subspecies of microorganism.
(22) Patient medical history data 138 contains data about patients such as where they previously have been hospitalized and the types of procedures that have been done. This type of data is useful in determining where a patient may have previously picked up an infectious agent, and determining how an infection may have been transmitted.
(23) Patient infection information data 140 stores updated medical information pertaining to a patient who has obtained an infection. For example, data 140 could store that a particular patient acquired an infection in a hospital during heart surgery. Data 140 includes the time and the location that an infection was acquired. Data 140 also stores updated data pertaining to a patient's medical condition after obtaining the infection, for example, whether the patient died after three weeks, or recovered after one week, etc. This information is useful in looking for correlates between a disease syndrome and a strain subtype. Additional phenotypic assays to determine toxin production, heavy metal resistances and capsule subtypes, as examples, will also be added to the strain database and update properties and virulence data 136.
(24) Species repeat sequence data 142 stores specific repeat sequences that have been identified for particular organisms in predetermined regions of the organism's DNA. These repeat sequences will be discussed more fully with respect to FIGS. 2-4.
(25) Health care facility data 144 contains information about various facilities communicating with server 118 such as hospital 102, physician's office 106, and long term care facility 110. Health care facility data 144 contains such information as addresses, number of patients, areas of infection control, contact information and similar types of information. Health care facility data 144 can also include internal maps of various health care facilities. As will be described later, these maps can be used to analyze the path of the spread of an infection within a facility.
(26) Some of the health care facilities also have local databases. FIG. 1 shows that hospital 102, long term care facility 110 and laboratory 114 include local databases 103, 111, and 115, respectively. The local databases can store local copies of selected infection control information and data contained in central database 128, so that the health care facility can access its local database for infection control information instead of having to access central database 128 via network 100. Accessing the local database can be useful for times when communication with the infection control facility 148 is unavailable or has been disrupted.
(27) The local database can be used to store private patient information such as the patient's name, social security number. The health care facility can send a patient's infection information and medical history data to infection control facility without sending the patient's name and social security number. Only the health care facility's local database stores the patient's name and social security number and any other private patient information. This helps to maintain the patient's privacy by refraining from the patient's private information over the network.”
“all communications between the hospitals and infection control facility 148 can occur electronically via network 100. This will allow for rapid real-time infection control.” (column 9)
Kriesworth provides information regarding S aureus, one of the ESKAPE bacterial strains listed in claim 1, and claim 1 now requires only one.
Kriesworth does not specifically set forth a kit containing a reservoir for obtaining patient samples.
Kriesworth does not teach all of the ESKAPE bacterial strains which are to be tested for in the identification of patients at risk of developing postoperative infections.
In the same field of endeavor, Diekema provides a review of health-care associated infections (HCAI or HAI), the surveillance thereof, and the prevention thereof. Table 2 of Diekema sets forth each of the listed ESKAPE pathogens as the most common hospital acquired infections from major infection sites, including surgical sites, central-line, ventilator-associated, and catheter associated infections. Diekema discloses strategies pursued in healthcare settings to track, mitigate and prevent outbreaks, or increases in incidence of infections with these pathogens, including the steps set forth by Kriesworth, above. These include processes documented and carried out by an Infection Prevention committee (p108-109); active HAI surveillance (p109-110) by analyzing infection risk information, as well as patient sample analysis information (including patient samples, environment samples, provider samples, et al), antibiotic usage; Process Surveillance (p110) including hand hygiene, use of sterile equipment and barriers, etc.; and Antimicrobial Stewardship (p110), relating to determining antibiotic resistance rates, tailoring antibiograms, determining susceptibility in patient cultures, et al.
Diekema discusses specimen collection and transport at page 108, col 2 and 110, col 2.
“The microbiology laboratory can benefit if the infection prevention staff understands the routine processes in microbiology, e.g., timelines for the processing of blood, wound, or urine cultures and related techniques (29). Specimen processing timelines enable infection prevention staff to understand various expectations of turnaround times (TATs) for specific results and time constraints of microbiology test services and can educate them as to when they can expect follow-up information for routine cultures… In addition, the microbiologist should inform the committee about changes in methods, reagents, or instrumentation that may substantially affect the laboratory's ability to detect and characterize HAI pathogens.”
“Many HAI pathogens (e.g., coagulase-negative staphylococci) also commonly colonize patients' skin or mucous membranes and can easily contaminate cultures if specimens are not collected or handled properly. If contaminants are mistakenly considered to be infecting organisms, IPs may inadvertently count these as HAIs, thereby inflating the infection rates (29, 59, 60). Consequently, the laboratory must provide guidance for acceptable collection and transport of clinical specimens (see chapter 18). Enforcement of rejection criteria and a quality assurance program with monitors for blood culture contamination rates and specimen transport time help to optimize the preanalytical phase of testing.”

Diekema further discusses the integration of the sample testing facilities with Laboratory Information Systems, such as that disclosed by Kriesworth, at page 111, col 2.
“An information system that can perform prospective data mining and interface with other parts of the computerized patient record could help IPs perform surveillance, monitor patient-to-patient spread of pathogens and detect outbreaks early (44, 45, 71, 72).”
Diekema provides types of testing and screening processes of samples, and information for each of the ESKAPE pathogens set forth in claim 1, including AST, sequencing, MALDI-TOF MS, and other molecular or proteomic methods. See pages 110-115. Table 2 provides a list of each pathogen, and major infection sites. Figure 1 is an example of tracking bacterial transmission from MICU central-line samples. Table 4 is a set of steps taken in tracking nosocomial outbreaks. DNA testing, phenotype testing, biochemical profiling, banking samples, supplemental testing are all disclosed. Table 5 sets forth the ESKAPE pathogens, routine diagnostic procedures, and the optimum specimen to obtain, in order to achieve results useful in HAI tracking and prevention. Table 6 provides a variety of environmental sources for infection, the routine diagnostic procedures, and the optimum specimen to obtain.
In KSR Int 'l v. Teleflex, the Supreme Court, in rejecting the rigid application of the teaching, suggestion, and motivation test by the Federal Circuit, indicated that “The principles underlying [earlier] cases are instructive when the question is whether a patent claiming the combination of elements of prior art is obvious. When a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one. If a person of ordinary skill can implement a predictable variation, § 103 likely bars its patentability.” KSR Int'l v. Teleflex lnc., 127 S. Ct. 1727, 1740 (2007).
Applying the KSR standard of obviousness to Kriesworth and Diekema the examiner concludes that the combination of Diekema’s disclosure of each of the ESKAPE pathogens, methods of testing collected patient and environmental samples for those pathogens, as well as integrated laboratory information systems for processing data from the patient, and the processed samples with the specific processes of Kriesworth, represents a combination of known elements which yield the predictable result of infection reduction systems and methods wherein the dissemination of ESKAPE pathogens is tracked and ultimately reduced. The disclosure of the ESKAPE pathogens, and methods for identifying and analyzing ESKAPE pathogens by Diekema in this combination would further serve to achieve the predictable result of better application of suitable antimicrobial treatments to infected patients. Such a combination is merely a "predictable use of prior art elements according to their established functions." KSR Int’l 7, 127 S. Ct. at 1740.
With respect to claim 2, generating an alert and delivering the alert to a healthcare provider is disclosed by Kriesworth at col 5:
“The infection control facility then transmits infection control information based on the phylogenetic relatedness determination to the health care facility over the computer network, thereby allowing the health care facility to use the infection control information to control or prevent the spread of an infection… Another feature of the present invention includes determining whether the health care facility has a potential outbreak problem, and transmitting an outbreak warning to the health care facility.”

With respect to claim 5, Kriesworth provides “assessment and identification is performed by software what identifies one or more” events, including patients that become infected, bacterial transmission events, epidemiology, clonal transmission rates, linking transmission to infection, and identifying clinically relevant bacterial pathogens. S. aureus is a particular clinically pathogen discussed by Kriesworth. At col 6-7:
“Infection control facility 148 sequences predetermined regions of DNA from infectious isolates received from various health care facilities. Infection control facility 148 stores and analyzes the sequence data, tracks the spread of infections, and predicts infection outbreaks. Infection control facility 148 then informs the health care facilities of potential outbreak problems and provides infection control information… Central database 128 also stores species/sub-species properties and virulence data 136. Data 136 includes various properties of different species and subspecies of infectious agents. For example, data 136 can include phenotypic and biomedical properties, effects on patients, resistance to certain drugs, and other information about each individual subspecies of microorganism.”

Transmission, and epidemiology are addressed beginning at col 7: “As bacteria cells reproduce, new generations of bacteria cells will contain new mutations (for the purposes of illustration, the discussion below will use the example of "bacteria;" however, the discussion applies to any microorganism). The more time that passes, the more the bacterial DNA will mutate. These mutations allow a path of infection to be traced… The goal behind sequencing the DNA is to distinguish epidemiologically related or clonal isolates, from unrelated isolates. Epidemiologically related isolates can be identified as being descendants from a common precursor cell, and as a consequence, their genomic "fingerprint" will be indistinguishable or similar from one another and recognizably different from unrelated or random isolates from the same species. By analyzing the epidemiological relatedness of the DNA of various isolates of bacteria, a path of transmission of infection can be determined. By analyzing a region of the DNA that is known to mutate, the bacterial isolate can be identified and compared to other subspecies of bacteria.” See also Fig 2 and its description at col 7: “For example, server 118 can determine that a hospital has had seven patients in the last month who have picked up the same or similar subspecies of S. aureus, and the infection is emanating from the burn ward.”

Diekema also discloses identifying infected patients, bacterial transmission events of ESKAPE pathogens, epidemiology of bacterial transmission events, clonal transmission events, and other clinically relevant bacterial pathogens, throughout.
With respect to claim 6, Kriesworth provides isolates that have hyper-virulence, hyper-resistance or are hyper-transmissible at col 5:
“Another feature of the present invention includes predicting the virulence and other properties of the sampled microorganism by retrieving the virulence data of similar microorganisms from the centralized database, and transmitting virulence information and other properties to the health care facility.”
And col 6-7: “Central database 128 also stores species/sub-species properties and virulence data 136. Data 136 includes various properties of different species and subspecies of infectious agents. For example, data 136 can include phenotypic and biomedical properties, effects on patients, resistance to certain drugs, and other information about each individual subspecies of microorganism… In step 228, server 118 determines if the isolate taken from the patient is a virulent or dangerous strain. This can be determined from the virulence of identical or closely related strains. Central database 128 stores species/subspecies properties and virulence data 136 for various subspecies of bacteria. This data is used to distinguish between contaminating and infecting isolates and to distinguish between separate episodes of infection and relapse of disease. Data 136 links bacteria types with disease syndromes, such as cases of food poisoning and toxic shock syndrome. Data 136 can identify which subspecies are resistant to certain drugs, or which subspecies are treatable by certain drugs. Thus, central database 128 is able to link genetic markers and clinical presentations to identify important correlates of disease.”

With respect to claim 8-9, Kriesworth provides genomic testing on bacterial isolates and identifying certain properties, and structural variations unique to clinically relevant pathogens:
“In step 208, if the hospital has its own sequencer, then in step 212 the hospital performs its own sequencing of the organism's DNA. The digital sequence data is then transmitted electronically to infection control facility 148 via network 100… In step 214, a first desired region of the DNA located between a first predetermined set of primers is then amplified by polymerase chain reaction (PCR) or similar technique… Two S. aureus genes, protein A (spa) and coagulase (coa), both conserved within the species, have variable short sequence repeat (SSR) regions that are constructed from closely related 24 and 81 bp tandem repeat units, respectively. In both genes, the in-frame SSR units are degenerative, variable in number, and variable in the order the repeat units are organized. The genetic alterations in the SSR regions include both point mutations and intragenic recombination that arise by slipped-strand mispairing during chromosomal replication, and together this region shows a high degree of polymorphism… In step 228, server 118 determines if the isolate taken from the patient is a virulent or dangerous strain. This can be determined from the virulence of identical or closely related strains. Central database 128 stores species/subspecies properties and virulence data 136 for various subspecies of bacteria. This data is used to distinguish between contaminating and infecting isolates and to distinguish between separate episodes of infection and relapse of disease. Data 136 links bacteria types with disease syndromes, such as cases of food poisoning and toxic shock syndrome. Data 136 can identify which subspecies are resistant to certain drugs, or which subspecies are treatable by certain drugs. Thus, central database 128 is able to link genetic markers and clinical presentations to identify important correlates of disease.”

The SSR and cassettes of Kriesworth represent genomic structural variations in S. aureus. Diekema provides molecular typing of ESKAPE pathogens to identify genomic changes, including structural variations, beginning at page 112, col 2.
With respect to claim 10, Kriesworth and Diekema provide all the steps as set forth above, and in figures 1 and 2, as set forth in the places cited for claims 8-9, et al.
With respect to claim 11, the steps of producing the archive sample are disclosed by Diekema and Kriesworth.
With respect to claim 12, Kreisworth provides clonal transmission information and identity of sequences at:
“The goal behind sequencing the DNA is to distinguish epidemiologically related or clonal isolates, from unrelated isolates. Epidemiologically related isolates can be identified as being descendants from a common precursor cell, and as a consequence, their genomic "fingerprint" will be indistinguishable or similar from one another and recognizably different from unrelated or random isolates from the same species.”

Diekema also discusses clonal transmission events, and MLST testing, throughout.
With respect to claim 13, Kriesworth provides generating archive samples, and collections of archive samples:
“320 isolates of S. aureus were typed by DNA sequence analysis of the X region of the protein A gene (spa). spa typing was compared to both phenotypic and molecular techniques for the ability to differentiate and categorize S. aureus strains into groups that correlate with epidemiological information. A collection of 59 isolates from the Centers for Disease Control and Prevention (CDC) was used to test for the ability to discriminate outbreak from epidemiologically unrelated strains. A separate collection of 261 isolates from a multicenter study of methicillin-resistant S. aureus in New York City was used to compare the ability of spa typing to group strains along clonal lines to that of the combination of PFGE and Southern hybridization. In the 320 isolates studies, spa typing identified 24 distinct repeat sequence types (also referred to herein as cassette types) and 33 different strain types (also referred to herein as subspecies). spa typing distinguished 27 of 29 related strains and did not provide a unique fingerprint for 4 unrelated strains from the four outbreaks of the CDC collection. In the NYC collection, spa typing provided a clonal assignment for 185 of 195 strains within the five major groups previously described.”

With respect to claim 20, Kriesworth provides all the steps recited for obtaining, testing, analyzing and archiving certain bacterial isolates, with links to relevant data, throughout. See Figures 2 and its description, identifying all the common laboratory practices required. Figure 3 sets forth the steps required to determine relatedness between isolates, Figs 4A and B disclose how structural isolates are identified. Fig 6 discloses steps required for subspeciation. As cited for Claim 13, archive isolates linked to various data are disclosed. Diekema provides similar disclosures for the rest of the ESKAPE pathogens.
Applicant’s arguments:
Applicant’s arguments have been fully considered but are not persuasive. The combination of Kriesworth and Diekema meet all the limitations of the rejected claims as pointed out above. The Surveillance system of Diekema and Kreisworth both provide dynamic databases and computer processes to collect and analyze a wide variety of information, from EMR, patient samples, environmental and personnel samples, known information about ESKAPE pathogens, laboratory test data, medical exam data, et al. Both Diekema and Kriesworth track bacterial transmission / dissemination in “real time.”
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the knowledge of clinicians related to ESKAPE pathogens, and a desire to reduce transmission or dissemination of ESKAPE bacteria would lead one of skill in the art to references related to ESKAPE pathogen management, and hospital infection reduction protocols, which would have provided the steps set forth in the rejected claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686