Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of Claims
The present Office Action is pursuant to Applicant’s communication on 11-29-2024 amending claim(s) 1-3, and 7-17 and cancelling claim(s) 4-6; current application filed on 11-29-2024. This application is a 371 of PCT/EP2023/065782 06/13/2023.
Information Disclosure Statement
The information disclosure statement(s) (IDS) filed on 11-29-2024, have been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 7-15 is/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.
Step 1
Claim(s) 1-3 and 7-15 is/are within the four statutory categories. Claim(s) 1-3 and 7-15 is/are drawn to an method1, computer program product2 and system3 which means that said claims(s) is/are within the four statutory categories (i.e. process). However, as will be shown below, arguendo, Aforementioned claim(s) is/are nonetheless unpatentable under 35 U.S.C. 101.
Prong 1 of Step 2A
Claim(s) 1, 13, 14, 15, which is/are representative of the inventive concept, recite(s):
1. A computer-implemented method for monitoring pathogen load in livestock, comprising:
a) receiving a request from an input/output device of a user to connect to an operating unit, wherein the request comprises the input of the user identity;
b) the user request of a) initiates the operating unit to perform the following; c) receiving or retrieving data from the input/output device of a), wherein the data comprises;
C1) data Dpat being indicative for the identity and load of one or more pathogens in a livestock, wherein the identity and the load of the pathogen are comprised in the data Dpat;
C2) identification Ifarm of a farm, and/or farmhouse of the livestock of Cl); and C3) the time point Tdata of generation of the data of C1),
d) evaluating the data of C1), by a method comprising:
d1) pulling a risk matrix from a database, wherein the risk matrix comprises, for each pathogen, a value vD for the limit of detection of the pathogen, a value vL for the lower limit of the occurrence of a disease caused by the pathogen, and a value vT for the threshold of the occurrence of a disease caused by the pathogen, and wherein; vL is nxv D with n being a real number larger than 1,1, wherein the value for n is given by the likelihood of the pathogen in question to cause an infectious disease a value for the pathogen load of not more than vD indicates that the risk for an infectious disease caused by the pathogen in question is unlikely, a value for the pathogen load being larger than vD and not more than vindicates that the risk for an infectious disease caused by the pathogen in question is rare, a value for the pathogen load being larger than vD and not more than v indicates that the risk for an infectious disease caused by the pathogen in question is possible, and a value for the pathogen load being larger than v r indicates that the risk for an infectious disease caused by the pathogen in question is certain;
d2) for every pathogen in the data Dpat reading off the values v n, v v and v r from the risk matrix of d1);
d3) for every pathogen, comparing the data Dpat indicative for the pathogen load) with the values vD, vL and VT of d2) to find out the probability of any of the pathogens of Cl) to cause a disease;
d4) storing the data Dpat, the identification/farm of the farm, and/or farmhouse, and the time point Tdata of generation of the data DPat, received or retrieved in c) together with the finding of d3) in a dataset Duser, for the user of a); and
d5) when the method is repeatedly carried out for the same user identity, storing the data Dpat, the identification Ifarm of the farm, and/or farmhouse, and the latest time point T data of generation of the data, received or retrieved in c), together with the finding of d3) in the same dataset Duser as in d4);
e) the user request of a) initiates the operating unit to perform the following:
e1) for each farm and/or farmhouse of the user of a), pulling the data Dpat, the findings and the latest time point T data of generation of the data stored in d4) or d5) from the dataset Duser for the user;
e2) for each farm and/or farmhouse, grouping the data Dpat, findings and latest time point T data pulled in e1) as individual reports in a list, with the reports having the latest time points T data at the top of the list;
e3) sending the list of reports obtained in e2) to the input/output device of the user,
e4) receiving a selection of a report from the input/ output device of the user;
e5) for the report selected in e4), plotting the load of the livestock with every pathogen in the data Dpat for each time point T data of generation of the data Dpat into a diagram;
e6) pulling the risk matrix of dl) from a database;
e7) for every pathogen in the data Dpat reading off the value(s) vL and/or Vr for the one or more pathogens from the risk matrix of dl); and
e8) plotting the value(s) vL and/or vT read off in e7) into the diagram obtained in e5).
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract ideas of a mental process and/or a certain method of organizing human activity because they recite a process that could be practically performed in the human mind (i.e. observations, evaluations, judgments, and/or opinions – in this case plotting values associated with risk of pathogens loads values associated with livestock of a farm, or using a pen and paper, but for the recitation of generic computer components (i.e. the computer), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract ideas are deemed “additional elements,” and will be discussed in further detail below.
Dependent claim(s) 2-7 and 9-10, include other limitations, for example:
2. The method of claim 1, wherein the livestock is one or more of cattle, sheep, goats, pigs, equine, and poultry.
3. The method of claim 1, wherein the data of Cl) comprises the concentration of a pathogen per mass, and/or the number of DNA copies of a pathogen per mass.
7. The method of claim 1, further comprising: e9) if applicable, receiving a read confirmation for the report selected in e4) from the input/output device of the user, and storing the read confirmation for the report together with the corresponding Dpat, findings and latest time point T data in the dataset D user' and elO) when e) is carried out repeatedly, el) further comprises pulling a read confirmation from the dataset Duser' and e2) further comprises ranking all reports without a read confirmation in a first list, with the reports having the latest time points at the top of the first list, and all other reports in a second list, with the reports having the latest time points at the top of the second list.
8. The method of claim 1, wherein the report of e2) further comprises the dates for scheduled samplings based on the dates of time point Tdata of C3).
9. The method according to claim 1, further comprising: f) planning a new cycle for monitoring the pathogen load in livestock, comprising: fl) receiving a selection or the input of the farm and/or farmhouse for which a new cycle is to be planned; f2) receiving a selection or the input of the start date for the new cycle; and f3) receiving a section or the input of the livestock age at the time of starting the new cycle and the scope of the new cycle.
10. The method of claim 9, wherein the f2) further comprises the indication of the cycles for monitoring the pathogen load in livestock which are active.
11. The method of claim 1, further comprising: g) receiving a request from the input/output device of a user to aggregate reports, comprising:
g1) for all farms and/or farmhouses of the user, pulling the load of the livestock with each pathogen in each farm and/or farmhouse from the data Dpat from the dataset Duser, forming the mean of the load with each pathogen, to get the mean pathogen status for all farms and/or farmhouses.
12. The method of claim 1, further comprising: g) receiving a request from the input/output device of a user to aggregate reports, comprising: g1) for all farms and/or farmhouses of the user, pulling the load of the livestock with each pathogen in each farm and/or farmhouse from the data Dpat from the dataset Duser, forming the mean of the load with each pathogen, to get the mean pathogen status for all farms and/or farmhouses; g2) pulling the risk matrix from a database; and g3) for all farms and/or farmhouses, reading off the values v L and/or v r for each pathogen in data Dpat and forming the mean of the values v L and/or v r to get the mean condition risk V L.mean and/or V r.mean for all farms and/or farm-houses.
However these dependent claims only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claim(s) 2-3 and 7-12 are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claim(s) 2-3 and 7-12 are nonetheless directed towards fundamentally the same abstract idea as independent Claim(s) 1, 13, 14, 15.
Prong 2 of Step 2A
Claim(s) 1, 13, 14, 15 is/are not integrated into a practical application because the additional elements (i.e. comprising non-underlined limitations above – in this case input/output device, database) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a computer, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see Page(s) 15 of the present Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g).
Additionally, dependent claim(s) 2-3 and 7-12 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not include any additional elements beyond those already recited in independent Claim(s) 1, 13, 14, 15, hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claim(s) 1, 13, 14, 15 do/does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case the input/output device, database), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
Page(s) 15 of the Specification discloses that the additional elements (i.e. the computer) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing or uploading media;
Dependent claim(s) 2-3 and 7-12 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the limitations of the aforementioned dependent claims amount to no more than generally linking the abstract idea to a particular technological environment or field of use, and/or do not recite any additional elements not already recited in independent Claim(s) 1, 13, 14, 15 hence does not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claim(s) 1-3 and 7-15 is/are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The second paragraph of 35 U.S.C. 112 is directed to requirements for the claims:
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.
Claim(s) 13-15 is/are rejected as failing to define the invention in the manner required by 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Dependent claim(s) 13-15 which depend from independent claim(s) 1 as follows:
Claim(s) 13 recite(s) “A computer program product comprising code portions adapted for performing the method of claim 1…”; [It is unclear whether claim 13 is a method or computer program product. The Office interprets that this limitation is a independent claim that needs to be rewritten it as an independent claim with appropriate limitations in view of claim(s) 1.]
Claim(s) 14 recite(s) “…A computer program product stored on a computer usable medium, comprising computer readable program means for causing a computer to perform the method of claim 1”; [It is unclear whether claim 14 is a method or computer program product. The Office interprets that this limitation is a independent claim that needs to be rewritten it as an independent claim with appropriate limitations in view of claim(s) 1.]
Claim(s) 15 recites “A system for monitoring pathogen load in livestock, comprising an operating unit adapted to perform the method according to claim 1”; [It is unclear whether claim 15 is a system. The Office interprets that this limitation is a independent claim that needs to be rewritten it as an independent claim with appropriate limitations in view of claim(s) 1.]
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.
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 date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-3 and 7-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teng (US 2013/0275316) in view of Abudayyeh (US 11,104,937).
Regarding claim(s) 1, 13, 14, 15, Teng discloses: A computer-implemented method for monitoring pathogen load in livestock, A computer program product comprising code portions adapted for performing when the program is loaded into a computer device, A computer program product stored on a computer usable medium, comprising computer readable program means for causing a computer to perform, A system for monitoring pathogen load in livestock, comprising an operating unit adapted to perform, comprising [¶54: processor, memory with instructions as depicted in ¶66]:
a) receiving a request from an input/output device of a user to connect to an operating unit, wherein the request comprises the input of the user identity (i.e., facilitating authentication via login of users); [¶124]
b) the user request of a) initiates the operating unit to perform the following:
c) receiving or retrieving data from the input/output device of a), [¶183: retrieving unique information] wherein the data comprises:
C1) data Dpat being indicative for the identity and load of one or more pathogens in a livestock, wherein the identity and the load of the pathogen are comprised in the data Dpat (i.e., data including identity of pathogen); [¶¶217, 218: data collection regarding an animal population, pathogen, event, venue, data necessary for analysis for prediction a risk, a statistical probability, as depicted in 154: databases employed in analyzing pathologies associated with a pathogen(s), wherein as depicted in ¶156, “pathologies, risks charted in terms of particular risk, and particular percentage of occurrence of a particular pathology”, employing analytical tools such as “risk charts”, detailing “health factors”, wherein each of the “factors may be identifiable by animal, by population, or the like”]
C2) identification Ifarm of a farm, and/or farmhouse of the livestock of Cl) (i.e., data having identifiers for a farm and/or livestock for identifying); [¶166, 183] and
C3) the time point Tdata of generation of the data of Cl) (i.e., embedding of information including time); [¶¶164, 165 associated with “specific location” that is “bound to a particular time period”]
Regarding [c], Teng does not explicitly disclose as disclosed by Abudayyeh load (i.e., values associated determination of a presence of an infectious disease in a sample, corresponding to a positive or negative result); [13:40-53: a matrix associated with a heatmap as depicted in FIG 60E]
Teng does not explicitly disclose as disclosed by Abudayyeh:
d) evaluating the data of Cl), by a method comprising:
d1) pulling a risk matrix from a database, wherein the risk matrix comprises, for each pathogen, a value vD for the limit of detection of the pathogen, a value vL for the lower limit of the occurrence of a disease caused by the pathogen, and a value vT for the threshold of the occurrence of a disease caused by the pathogen, and wherein:
vL is nxvD with n being a real number larger than 1, wherein the value for n is given by the likelihood of the pathogen in question to cause an infectious disease a value for the pathogen load of not more than vD indicates that the risk for an infectious disease caused by the pathogen in question is unlikely, a value for the pathogen load being larger than vD and not more than vindicates that the risk for an infectious disease caused by the pathogen in question is rare, a value for the pathogen load being larger than vD and not more than v indicates that the risk for an infectious disease caused by the pathogen in question is possible, and a value for the pathogen load being larger than vT indicates that the risk for an infectious disease caused by the pathogen in question is certain (i.e., wherein threshold limits represented by bounds encapsulated within statistical p-value significance thresholds are employed when sample comparisons are made to a PCR-amplified genotype standard, to determine a positive or negative pathogen result, associated with a matrix related to color scales, consistent with Applicant Specification, page 12, lines 1-25); [13:40-53, FIG 60E]
d2) for every pathogen in the data Dpat reading off the values vD, vL and vT from the risk matrix of d1); [13:40-53, FIG 60E: comparing pathogens to standard genotype]
d3) for every pathogen, comparing the data Dpat indicative for the pathogen load) with the values vD, vL and VT of d2) to find out the probability of any of the pathogens of Cl) to cause a disease (i.e., comparing genotype to a statistical significance measure, p-values to determine probability of a positive or negative result); [13:40-53, FIG 60E]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Teng, including mechanism(s) [c], [f]-[h] as taught by Abudayyeh. One of ordinary skill would have been so motivated to employ said mechanism(s) to provide statistical matches useful for ascertaining a positive or negative determination of a pathogen’s presence in a sample. [13:40-53, FIG 60E]
Teng discloses:
d4) storing the data Dpat, the identification/farm of the farm, and/or farmhouse, and the time point Tdata of generation of the data DPat, received or retrieved in c) together with the finding of d3) in a dataset Duser, for the user of a) (i.e., storing data associated with farm facilities and data incorporating temporal information); [¶33: storing data including temporal data as depicted in ¶39] and
d5) when the method is repeatedly carried out for the same user identity, storing the data Dpat, the identification Ifarm of the farm, and/or farmhouse, and the latest time point T data of generation of the data, received or retrieved in c), together with the finding of d3) in the same dataset Duser as in d4) ) (i.e., storing data associated with farm facilities); [¶¶33, 39]
e) the user request of a) initiates the operating unit to perform the following:
e1) for each farm and/or farmhouse of the user of a), pulling the data Dpat, the findings and the latest time point T data of generation of the data stored in d4) or d5) from the dataset Duser for the user (i.e., generating analytic products associated with farm data and uploaded data); [¶¶106, 193: “configure[ing] reports to be generated” at “farms” at myriad locations and uploaded data as depicted in ¶33]
e2) for each farm and/or farmhouse, grouping the data Dpat, findings and latest time point T data pulled in e1) as individual reports in a list, with the reports having the latest time points T data at the top of the list (i.e., generating reports linked to farm facilities); [¶¶106, 193: “configure[ing] reports to be generated” at “farms” at myriad locations]
e3) sending the list of reports obtained in e2) to the input/output device of the user, e4) receiving a selection of a report from the input/ output device of the user (i.e., delivering reports as attachments to farms); [¶¶106, 193]
e5) for the report selected in e4), plotting the load of the livestock with every pathogen in the data Dpat for each time point T data of generation of the data Dpat into a diagram (i.e., visual integration corresponding to superimposing or embedding); [¶¶164, 165 associated with “specific location” that is “bound to a particular time period”]
e6) pulling the risk matrix of dl) from a database; [¶154: databases employed in analyzing pathologies associated with a pathogen(s), wherein as depicted in ¶156, “pathologies, risks charted in terms of particular risk, and particular percentage of occurrence of a particular pathology”, employing analytical tools such as “risk charts”, detailing “health factors”, wherein each of the “factors may be identifiable by animal, by population, or the like”]
Teng does not explicitly disclose as disclosed by Abudayyeh:
e7) for every pathogen in the data Dpat reading off the value(s) vL and/or VT for the one or more pathogens from the risk matrix of d1) (employing a heatmap to read off colors related to a positive or negative result); [13:40-53, FIG 60E]
and e8) plotting the value(s) vL and/or vT read off in e7) into the diagram obtained in e5) (i.e., employing a heatmap to read off values associated with colors corresponding to a positive or negative result); [13:40-53, FIG 60E]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Teng, including mechanism(s) [p]-[q] as taught by Abudayyeh. One of ordinary skill would have been so motivated to employ said mechanism(s) to provide statistical matches useful for ascertaining a positive or negative determination of a pathogen’s presence in a sample. [13:40-53, FIG 60E]
Regarding claim(s) 2, Teng-Abudayyeh as a combination discloses: The method of claim 1, Teng disclosing: wherein the livestock is one or more of cattle, sheep, goats, pigs, equine, and poultry. [¶94: cattle]
Regarding claim(s) 3, Teng-Abudayyeh as a combination discloses: The method of claim 1, Abudayyeh disclosing [a]: wherein the data of C1) comprises the concentration of a pathogen per mass, and/or the number of DNA copies of a pathogen per mass (i.e., wherein data includes DNA templates associated with created copies). [57:5-30]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Teng, including mechanism(s) [a] as taught by Abudayyeh. One of ordinary skill would have been so motivated to employ said mechanism(s) to provide statistical matches useful for ascertaining a positive or negative determination of a pathogen’s presence in a sample. [13:40-53, FIG 60E]
Regarding claim(s) 7, Teng-Abudayyeh as a combination discloses: The method of claim 1, Teng disclosing further comprising:
e9) if applicable, receiving a read confirmation for the report selected in e4) from the input/output device of the user, and storing the read confirmation for the report together with the corresponding Dpat, findings and latest time point Tdata in the dataset Duser, (i.e., storing data associated with farm facilities and data incorporating temporal information); [¶33: storing data including temporal data as depicted in ¶39] and
e10) when e) is carried out repeatedly, el) further comprises pulling a read confirmation from the dataset Duser and e2) further comprises ranking all reports without a read confirmation in a first list, with the reports having the latest time points at the top of the first list, and all other reports in a second list, with the reports having the latest time points at the top of the second list (i.e., storing data associated with farm facilities and data incorporating temporal information); [¶33: storing data including temporal data as depicted in ¶39]
Regarding claim(s) 8, Teng-Abudayyeh as a combination discloses: The method of claim 1, Teng disclosing wherein the report of e2) further comprises the dates for scheduled samplings based on the dates of time point Tdata of C3) (i.e., including date schedules associated with farm activities). [¶91]
Regarding claim(s) 9, Teng-Abudayyeh as a combination discloses: The method according to claim 1, Teng disclosing: further comprising:
f) planning a new cycle for monitoring the pathogen load in livestock, comprising:
f1) receiving a selection or the input of the farm and/or farmhouse for which a new cycle is to be planned; [¶93: cycles associated with tracking to monitor and track communicable disease outbreaks as depicted in ¶189, performing planning “based on dates” associated with “potential or actual exposures” as depicted in ¶214]
f2) receiving a selection or the input of the start date for the new cycle; and f3) receiving a section or the input of the livestock age at the time of starting the new cycle and the scope of the new cycle. [¶93: cycles associated with tracking to monitor and track communicable disease outbreaks as depicted in ¶189, performing planning “based on dates” associated with “potential or actual exposures” as depicted in ¶214 and further incorporating age of livestock associated with “birth date” of animals, as depicted in ¶90]
Regarding claim(s) 10, Teng-Abudayyeh as a combination discloses: The method of claim 9, Teng disclosing: wherein the f2) further comprises the indication of the cycles for monitoring the pathogen load in livestock which are active (i.e., cycles are included for farm maintenance). [¶93: cycles associated with tracking to monitor and track communicable disease outbreaks as depicted in ¶189]
Regarding claim(s) 11, Teng-Abudayyeh as a combination discloses: The method of claim 1, Teng disclosing: further comprising: g) receiving a request from the input/output device of a user to aggregate reports, comprising:
G1) for all farms and/or farmhouses of the user, pulling the load of the livestock with each pathogen in each farm and/or farmhouse from the data Dpat from the dataset Duser, forming the mean of the load with each pathogen, to get the mean pathogen status for all farms and/or farmhouses (i.e., employing statistics). [¶154: employing a statistics module used in calculations, correlations, information tracked and managed in “reports” that may be generated on-demand as depicted in ¶193]
Regarding claim(s) 12, Teng-Abudayyeh as a combination discloses: The method of claim 1, Teng disclosing: further comprising: g) receiving a request from the input/output device of a user to aggregate reports, comprising:
g1) for all farms and/or farmhouses of the user, pulling the load of the livestock with each pathogen in each farm and/or farmhouse from the data Dpat from the dataset Duser, forming the mean of the load with each pathogen, to get the mean pathogen status for all farms and/or farmhouses (i.e., employing statistics modeling providing calculations in reports for each farm associated with evaluated risks); [¶154: employing a statistics module used in calculations, correlations, information tracked and managed in “reports” that may be generated on-demand as depicted in ¶193 to properly analyze, evaluate, judge, rate, notify, track, evaluate risk as depicted in ¶222]
g2) pulling the risk matrix from a database (i.e., employing tracking, analysis and risk assessments employing a database uploads); [¶85: employing a “database identifier” and “upload[s]” of information from the database as depicted in ¶127] and
Regarding [c], Teng discloses forming the mean of the values to get the mean condition (i.e., employing statistics modeling providing calculations in reports for each farm associated with evaluated risks); [¶154: employing a statistics module used in calculations, correlations, information tracked and managed in “reports” that may be generated on-demand as depicted in ¶193 to properly analyze, evaluate, judge, rate, notify, track, evaluate risk as depicted in ¶222]
Teng does not explicitly disclose as disclosed by Abudayyeh:
g3) for all farms and/or farmhouses, reading off the values vL and/or vT for each pathogen in data Dpat and forming the mean of the values vL and/or vT to get the mean condition risk VL,mean and/or VT,mean for all farms and/or farm-houses (i.e., employing a heatmap to read off values associated with colors corresponding to a positive or negative result); [13:40-53, FIG 60E]
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified Teng, including mechanism(s) [c] as taught by Abudayyeh. One of ordinary skill would have been so motivated to employ said mechanism(s) to provide statistical matches useful for ascertaining a positive or negative determination of a pathogen’s presence in a sample. [13:40-53, FIG
Conclusion
The prior art made of record4 and NOT relied upon is considered pertinent to applicant's disclosure: Xie (US 2018/0291473):
The present disclosure discloses a primer combination and GeXP detection method for simultaneously identifying eight kinds of bovine pathogens. The primer combination of the present disclosure consists of primer pair I, primer pair II, primer pair III, primer pair IV, primer pair, primer pair VI, primer pair VII and primer pair VIII. The present disclosure also discloses a GeXP detection method that can simultaneously identify bovine infectious diseases of foot-and-mouth disease virus, bluetongue virus, vesicular stomatitis virus, bovine viral diarrhea virus, bovine rotavirus, enterotoxigenic E. coli, infectious bovine rhinotracheitis virus and peste des petits ruminants virus. The GeXP detection method established can simultaneously identify the eight pathogens of bovine infectious diseases. The method has the characteristics of high throughput, high specificity and sensitivity, and can be used for the bovine epidemiological monitoring and the differential diagnosis of sudden epidemic situation, and guarantees the healthy development of cattle industry.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL EZEWOKO whose telephone number is 571 272 7850. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached on 571 270 5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-7850.
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/MICHAEL I EZEWOKO/Primary Examiner, Art Unit 3682
1 Claim(s) 1-3, 7-12
2 Claim(s) 13-14
3 Claim(s) 15
4 Please see Form 892 for complete listing
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