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 .
Status of the Claims
Claims 1-23 are pending and examined herein.
No claims are canceled.
Priority
As detailed on the 22 September 2023 filing receipt, the application claims priority as early as 14 September 2022 to provisional application 63/406,499. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Information Disclosure Statement
Information disclosure statement (IDS) was filed on 29 September 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the references are being considered by the examiner.
Specification
The disclosure is objected to because of the following informality: the articles “a” and “the” are both used, and one must be selected for grammatical correctness (pg. 36, lines 9 and 11).
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 and 7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites PIPER as a collection method for the microbial data, but the explanation for the PIPER abbreviation occurs in neither the claims nor the specification, rendering it unclear.
Claim 8 recites “the animal production process” but an animal production process is not introduced in claim 1, and so the term has unclear antecedence. The term is introduced in claim 7, and so this rejection may be remedied by reciting dependence on claim 7.
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 1-23 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements individually and in combination, are directed to a judicial exception at Step 2A, Prong 2, and the additional elements of the claims, considered individually and in combination, do not provide significantly more at Step 2B than the abstract idea of microbial risk assessment.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-22) and a computer system (claim 23), each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Claims 1 and 23 recite identifying the microbial and non-microbial data as corresponding the entity or biological processes associated with the entity, where making such an identification is a step practically performed in the human mind.
Claims 1 and 23 recite recites mapping the data to risk vectors, which is interpreted as a step of data evaluation and organization and thus a mental process.
Claims 1 and 23 recite recites determining risk vector ratings, where determining a rating may be understood as a mental step of qualifying a vector with a categorical rating or a mathematical step of rating it based on a calculation.
Claims 1 and 23 recite recites determining microbial risk assessment which is indicative of risk of the processes to the entity, where assessment of risk a data judgment and thus a mental process.
Claims 1 and 23 recite recites determining an expected loss value for the one or more biological processes associated with the entity, where determining a value suggests a mathematical calculation.
Claim 2 recites how the received data was collected and is still directed to data per se and thus abstract.
Claim 3 recites when the received data was collected and is still directed to data per se and thus abstract.
Claims 4-5 recite data about Coccidia and Salmonella, respectively and thus are still directed to data per se.
Claim 6 recites additional details regarding the non-microbial data, where data per se is abstract.
Claims 7-8 recite additional details regarding the biological processes, where data per se is abstract.
Claims 9-10 recite additional information about the risk vectors, where the risk vectors are data and data per se is abstract.
Claim 11 recites applying a transformation of the data, which is interpreted as a numerical operation and thus a mathematical concept.
Claim 12 recites the transformation is normalization, and data normalization is a mathematical concept.
Claim 13 recites a partially pooled Bayesian model and statistical distribution, and thus recites mathematical concepts.
Claim 14 recites mapping data, which is a mental step of data organization, and determining a risk vector based at least on weights, which is interpreted as a mathematical concept.
Claim 15 recites modifying weight factors, where weights are numerical and modifying numbers is a verbal description of a mathematical concept.
Claim 16 recites the microbial and/or non-microbial data is time series data, where data per se is abstract. Claim 16 also recites generating vector ratings, where generating a rating may be performed mathematically based on the data or quantitatively given and thus a mental step of data evaluation.
Claim 17 recites generating a time-series of risk ratings, where generating ratings may be performed mathematically based on the data or quantitatively given and thus a mental step of data evaluation.
Claim 18 recites a loss calculation model comprising a learning method, a Bayesian predictive function, a non-Bayesian predictive function, and an artificial intelligence technique, and thus recites mathematical concepts.
Claim 19 recites generating a written report, where writing is a human behavior and thus abstract.
Thus, the claims recite abstract ideas and thus must be examined further to determine whether elements in addition to the abstract ideas integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). [Step 2A Prong One: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Because the claims recite judicial exceptions, direction under Step 2A Prong Two provides that the claims must be examined further to determine whether they recite elements in addition to the abstract ideas which integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(III)).
Claim 1 recites a computer and receiving data.
Claims 11-12 and 18 recites providing data to a model.
Claim 20 recites providing information via user interface and sending information.
Claim 21 also recites sending information.
The computer claimed in the preamble of claim 1 is interpreted as a general purpose computer. The claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)). Similarly, the user interface is interpreted as a computer display and also part of a general purpose computer.
The claim elements comprising sending and receiving data are insignificant extra-solution activity required as input for the mental and mathematical steps or output to share the results of the abstract steps and thus do not integrate the abstract ideas into a practical application (MPEP 2106.05(g)). If interpreted as an element in addition to the abstract ideas, the generating of a written report would also be outputting of the results and thus insignificant extra-solution activity.
Thus, the claims recite elements in addition to the abstract ideas which do not integrate the abstract ideas into a practical application, and must be examined further to determine whether elements in addition to the abstract ideas provide significantly more (MPEP 2106.05). [Step 2A Prong Two: Yes]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself. Step 2B of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Claim 1 recites a computer and receiving data.
Claims 11-12 and 18 recites providing data to a model.
Claim 20 recites providing information via user interface and sending information.
Claim 21 also recites sending information.
The claims recite a computer, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions, which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)). Storing data on a computer is a conventional computer function (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; MPEP 2106.05(d)). Display steps are interpreted as insignificant extra-solution activity (MPEP 2106.05(g)) which do not impose meaningful limits on the claim, here displaying an output of the analysis (Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55; MPEP 2106.05(g)).
The courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (storing and retrieving information in memory), as discussed in MPEP 2106.05(d)(II)(i)). Generating a written report, if interpreted as a data outputting step, would also be data necessary data outputting performed by a computing system and thus conventional.
Therefore, the recited additional elements, alone or in combination, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-11, 16-18, 20-21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Carrique-Mas (Epidemiology and Infection 137(6): 837-846, 2009; newly cited) in view of Nganje (Agribusiness 22(4): 475-489, 2006; newly cited).
Claim 1 recites receiving, from one or more computing systems, microbial data and non-microbial data and identifying the microbial data and the non-microbial data as corresponding to the entity and one or more biological processes associated with the entity.
Carrique-Mas teaches a model investigating Salmonella serovar and system of manure removal defined by type of house, which are considered microbial and non-microbial data.
Claim 1 recites mapping the microbial data and the non-microbial data to one or more risk vectors
corresponding to the one or more biological processes, wherein each of the one or more risk vectors impact a state of a biological process of the one or more biological processes.
Carrique-Mas teaches the data is related to pathogen clearance and rodent presence (Tables 4 and 5), which are interpreted as pathogen control programs and comorbidity with the presence of rodents.
Claim 1 recites determining, based on the microbial and non-microbial data, one or more risk vector ratings for the one or more risk vectors, each of the one or more risk vector ratings being mapped
to a respective risk vector of the one or more risk vectors.
Carrique-Mas teaches a rodent score associated with the S. enteritidis presence and type of physical structure the poultry are in (Table 5), where there was a significant difference between the rodent scores in some combinations, and the significance is interpreted as a rating.
Claim 1 recites determining, based on the one or more risk vector ratings, the microbial risk assessment for the entity, wherein the microbial risk assessment comprises a microbial risk rating, wherein the microbial risk assessment is indicative of a risk of the one or more biological processes to the entity.
Carrique-Mas teaches assessments as findings related to the risk of persistence pathogen presence given Salmonella serovars and building type, such as longer persistence of S. enteritidis in houses with a deep pit (pg. 842, col. 2, second paragraph).
Claim 1 recites determining, based on the microbial risk assessment, an expected loss value for the one or more biological processes associated with the entity.
Carrique-Mas does not teach the effect of the microbial risk assessment as an expected loss for the entity associated with the biological process.
Nganje teaches assessing food risk in monetary terms (abstract) and “economic losses due to food safety risk and microbial outbreak” (pg. 475, first paragraph).
Claim 23 recites a system comprising computing devices for carrying out the steps of claim 1.
While Carrique-Mas and Nganje do not teach a computing device to perform the method steps, generally automating an activity and the related automation and computational environment would have been prima facie obvious to a person having ordinary skill in the art (MPEP 2114(III) pertains).
Claim 2 recites the microbial data comprises data collected from at least one of: direct or indirect quantification, culture, sequencing, most-probable-number, secretion assays, polymerase chain reaction (PCR), PIPER, immunoassays, whole genome sequencing, metagenomic sequencing, and next generation sequencing.
Carrique-Mas teaches culturing Salmonella (pg. 838, col. 2, last paragraph).
Claim 3 recites the microbial data is sampled at a first time and a second time based on a microbial lifecycle corresponding to the one or more biological processes.
Carrique-Mas teaches sampling intervals following a first “time zero” sampling (pg. 839, col. 1, third paragraph), suggesting at least two sampling points.
Claim 5 recites the microbial data comprises microbial serotype data for Salmonella.
Carrique-Mas teaches Salmonella serovar as a variable investigated for the model (pg. 839, col. 2, second paragraph).
Claim 6 recites the non-microbial data comprises at least one of: physical structure data for the one or more biological processes, temporal data, spatial data, business operations and financial data, imagery and remote sensing data, economic data, management practices for the one or more biological processes, sanitation practices for the one or more biological processes, pathogen control program data for the one or more biological processes, data corresponding to an output of the one or more biological processes, efficiency data for the one or more biological processes, and environmental data for the one or more biological processes.
Carrique-Mas teaches “the system of manure removal, defined by type of house as house with a scraper, manure belt, step- cage ('A-frame') and non-cage (i.e. barn or free range)” (pg. 839, col. 2, second paragraph), where the type of house is considered physical structure data.
Claim 7 recites the one or more biological processes comprises at least one of: a food production supply chain process, a crop production process, an animal production process, a pharmaceutical production process, a chemical manufacturing process, a feed production process, and a medical device manufacturing process.
Carrique-Mas teaches egg production by hens (pg. 837, col. 2, first paragraph), which is an animal production process.
Claim 8 recites the animal production process comprises a poultry production process.
Carrique-Mas teaches egg production by hens (pg. 837, col. 2, first paragraph), which is considered to be a type of poultry production.
Claim 9 recites the one or more risk vectors comprise at least one of: a microbial load, a rate of change of the microbial load, a microbial species, a microbial subtype, a microbial serotype, a microbial strain, comorbidities of an output corresponding to the one or more biological processes, efficacy of pathogen control programs for the one or more biological processes, environmental data for the one or more biological processes, efficiency data for the one or more biological processes, historical performance data for the one or more biological processes, economic data, management practices for the one or more biological processes, and an affiliate microbial risk rating for an affiliate entity having a business relationship with the entity.
Carrique-Mas teaches risk as rodents and rodent control for associated pathogens (pg. 844, col. 1, third paragraph), where control of rodents and subsequent presence of rodents is a risk of spreading pathogens and thus comorbid.
Claim 10 recites each of the risk vector ratings indicates a risk of the respective risk vector to at least one of the one or more biological processes.
Carrique-Mas teaches significant time to clear incidence of Salmonella, where time to clear is significantly higher given certain conditions of Salmonella and housing, and significance is a rating.
Claim 11 recites the determining the one or more risk vector ratings for the one or more risk vectors further comprises at least one of: (i) applying a transformation to the microbial data and/or the non-microbial data to determine a first risk vector rating of the one or more risk vector ratings; and
(ii) providing the microbial data and/or the non-microbial data as an input to a model configured to determine a second risk vector rating of the one or more risk vector rating.
Carrique-Mas teaches transforming the serovar data and housing data to determine coefficients, and the coefficients are determined to be significant or not (Table 4). The coefficients are used to determine survival time or time to clearance (Table 5).
Claim 16 recites at least one of the microbial data and non-microbial data comprises time-series data, and wherein the determining the one or more risk vector ratings for the one or more risk vectors comprises: generating, based on the time-series data, a time-series of risk vector ratings for the one
or more risk vectors.
Carrique-Mas teaches probability of infection as a risk rating interpreted as corresponding to efficacy of pathogen control with respect to multiple time points, where the time points are the time after enrollment (Fig. 1).
Claim 17 recites the determining the microbial risk assessment for the entity further comprises:
generating, based on the time-series of risk vector ratings, a time-series of microbial risk ratings, wherein the time-series of microbial risk ratings comprises the microbial risk rating.
The median time to clearance (Table 5) is derived from the time series data indicating infection probability following enrollment (Fig. 2).
Claim 18 recites the determining the expected loss value for the entity further comprises: providing the microbial risk rating as an input to loss calculation model configured to generate the expected loss value, where the loss calculation model comprises at least one of: an unaided learning method, a Bayesian predictive function, a non-Bayesian predictive function, and an artificial intelligence technique.
Nganje teaches a non-Bayesian prediction of food safety losses (pg. 487, third paragraph).
Claim 20 recites generating a written report comprising the microbial risk assessment.
Nganje teaches generating a report of the value at risk (pg. 483, Section 4).
Claim 21 recites at least one of: (i) providing the microbial risk assessment via a user interface; and (ii) causing sending of a message comprising the microbial risk assessment.
Nganje teaches at least providing risk to firm management (pg. 476, last paragraph) and showing the risk in a table (Table 3).
Combining Carrique-Mas and Nganje
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Nganje with that of Carrique-Mas because while Carrique-Mas discusses risk of Salmonella for companies, such as banning of the sale of eggs from positive flocks, which would be financially detrimental to the company (abstract), a numerical value is not determined based on the risk, which is taught by Nganje as assessing food safety risk in monetary terms, which is valuable as a risk measurement and management tool for companies (pg. 487, Section 5). Both Carrique-Mas and Nganje are directed to the shared field of endeavor of Salmonella risk determination for poultry farming, and thus their combination would be prima facie obvious.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Carrique-Mas in view of Nganje as applied to claims 1-3, 5-11, 16-18, 20-21, and 23 above and further in view of Decanini (US 20170258899 A1; newly cited).
Claim 4 recites the microbial data comprises microbial load data for Coccidia.
Carrique-Mas and Nganje do not teach microbial load data for Coccidia.
Decanini teaches bacteria load with respect to Coccidia as relevant for poultry infection (paragraph [10]).
Combining Carrique-Mas, Nganje, and Decanini
Carrique-Mas and Nganje teach microbial contamination or infection of poultry by Salmonella. Nganje teaches other microbial outbreaks have also contributed to food recalls for poultry products (abstract). Decanini teaches increasing bacterial load and animal diseases (paragraph [7]), including discussion of both Salmonella (paragraph [9]) and Coccidia (paragraph [10]) was infecting poultry. Given the discussion of these two microbes, they are considered analogous as microbial data for microbial risk in poultry and thus would be considered by one having ordinary skill in the art as a simple substitution at the effective filing date to yield a predictable result (MPEP 2143).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Carrique-Mas in view of Nganje as applied to claims 1-3, 5-11, 16-18, 20-21, and 23 above and further in view of Quinn (Experimental Design and Data Analysis for Biologists, Cambridge University Press, 552 pgs., 2002; newly cited).
Claim 12 recites the transformation comprises a normalization technique.
Quinn teaches normalizing transformations for biological data (pg. 207, col. 1, first paragraph).
Combining Carrique-Mas, Nganje, and Quinn
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works with those of Quinn because Quinn teaches well-known treatments of biological data, including normalization, with the rationale being such a transformation makes variances more similar between groups (pg. 207, col. 1, first paragraph). As such a mathematical step is well known for treatment of biological data, it would be prima facie obvious to apply to the biological data in the form of microbial data as taught by Carrique-Mas and Nganje.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Carrique-Mas in view of Nganje as applied to claims 1-3, 5-11, 16-18, 20-21, and 23 above and further in view of Jones (US 20060115559 A1; newly cited).
Claim 19 recites a financial product is derived from the expected loss value, wherein the financial product comprises at least one of: a risk transfer product, a credit product, a derivative product, a valuation product, and a securitization product.
Jones teaches recall insurance to cover loss revenue and cost (paragraph [23]), which is interpreted as a risk transfer product.
Combining Carrique-Mas, Nganje, and Jones
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works with those of Jones because Jones teaches economic impacts of food recall (paragraph [23]), also taught by Nganje as recall having monetary risk to companies, with companies carrying recall insurance to cover lost revenue and costs. The recited risk transfer is interpreted as insurance, as risk transfer, given its broadest reasonable interpretation, is shifting losses from one party to another. Carrique-Mas, Nganje, and Jones are all directed to the shared field of endeavor of contaminant detection and assessment in the food industry and their combination is prima facie obvious.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Carrique-Mas in view of Nganje as applied to claims 1-3, 5-11, 16-18, 20-21, and 23 above and further in view of Shirani (International Journal of Economics and Management Engineering 9(9): 3136-3140, 2015; newly cited).
Claim 22 recites causing sending of at least one of the microbial risk assessment or the expected loss value to an affiliate entity having a business relationship with the entity.
Shirani teaches food supply chain safety (abstract) and particularly alerting stakeholders to problems in the system, including producer, retailer, and transporter (pg. 3139, col. 2, second paragraph) which are all considered to be business affiliates.
Combining Carrique-Mas, Nganje, and Shirani
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the previously combined works with those of Shirani because Shirani teaches alerting stakeholders may lower risk and costs, allow recall more quickly, lower customer risk, and improve buyer trust (pg. 3139, col. 2, second paragraph). The instantly combined art is directed to the shared field of endeavor of food safety, including Salmonella contamination, and such a combination would be prima facie obvious.
Subject Matter Free of the Prior Art
Claim 13 recites the model comprises a partially pooled Bayesian model, and wherein the method further comprises: determining the second risk vector rating of the one or more risk vector ratings by providing the microbial data as an input to the partially pooled Bayesian model, wherein the microbial data corresponds to an expected statistical distribution. Claim 14 recites the model comprises a categorical assessment model, and wherein the method further comprises: mapping the microbial data to one or more of a plurality of categorical risk tiers of the categorical assessment model, each of the plurality of categorical risk tiers corresponding to a respective weight factor; and determining the second risk vector rating of the one or more risk vector ratings based on the microbial data and the weight factors. Claim 15 recites modifying the weight factors based on nonmicrobial data. The prior art does not teach or fairly suggest application of a partially pooled Bayesian model for assessing a risk vector rating corresponding to an expected distribution or weight factors based on non-microbial factors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert J Kallal whose telephone number is (571)272-6252. The examiner can normally be reached Monday through Friday 8 AM - 4 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.J.K./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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