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 .
This action is final.
This action is in response to communications filed on 06/29/2026.
Claims 1, 3, 4-6 are pending and have been considered.
Objection on Specification regarding length of abstract is withdrawn.
The 35 U.S.C. 112(b) rejection of Claims 1- 6 is withdrawn in view of the amendments.
The claims are rejected under 35 USC 101 as being directed to non-statutory subject matter, a judicial exception, an abstract idea (mathematical concept), without significantly more. The amendment, has not overcome the rejection (the added limitation recites an abstract idea).
Rejection of Claims 1, 2, 4, 6 rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) is withdrawn in view of the amendments
Rejection of claims 5, rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) in further view of Maddineli et Pavoni, US 20130103627 A1 (“MAD”) is withdrawn in view of the amendments
New rejections are made in view of the amendments:
Claims 1, 2, 4 are rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021
Claim 5 rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021 in further view of Maddineli et Pavoni, US 20130103627 A1 (“MAD”)
Claim 6 rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021 in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”)
Response to Amendment/Arguments
The Amendment and Arguments filed on 06/29/2026 have been considered. Claim 6 has been canceled. Amended claims 1,2, 4, 5, 6 are pending and have been considered. The Objection on Specification regarding length of abstract is withdrawn. The 35 U.S.C. 112(b) rejection of the pending claims is withdrawn in view of the amendments.
Claims 1, 2, 4-6 are rejected under 35 USC 101 as being directed to non-statutory subject matter, a judicial exception, an abstract idea (mathematical concept), without significantly more. The amendment, has not overcome the rejection (the added limitation recites an abstract idea).
Applicant states that “One way in which a claim can integrate a judicial exception into a practical application is by reciting an additional element or combination of elements that amount to an improvement to a technology or technical field. (MPEP § 2106.05(a)(II).) Amended claim 1 recites (among other limitations), "selecting a candidate oil list for OAC production based on the calculated probability of fitting." These limitations represent additional elements that amount to an improvement in the technical field of oil asphalt cement (OAC) production.”
While reciting an additional element or combination of elements that amount to an improvement to a technology or technical field is indeed the guidance of (MPEP § 2106.05(a)(II)) the guidance specifies the additional elements. The added limitation, "selecting a candidate oil list for OAC production based on the calculated probability of fitting" is interpreted as a mental process, an abstract idea, and not an additional element. For example, a person can make the selection in the mind, or with the help of pen and paper the, to select the list by observing and making a judgement on the calculated probability of fitting. Therefore, unfortunately, the added limitation cannot be relied on, and the additional elements in the claim (to which this limitation is not a part of) are not able to integrate the abstract idea into a practical application. The additional elements in the claim also do not provide significantly more.
Therefore, claims 1,2 , 4-6 remain rejected under 35 USC 101.
In view of the amendments the previous rejections under 35 USC 103 of claims 1, 2, 4-6 are withdrawn. New rejections are made under 35 USC 103 in view of the amendments and new search for prior art. The rejections are properly made final.
Priority
The application claims priority to the Brazilian Application BR10 2021 0258780, filed on 12/20/2021. The priority is acknowledged. Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). No translation of the application has been provided.
Withdrawn Objections/Rejections
The Objection to the Specification is withdrawn
The Rejections of claims 1,2, 4-6 under 35 USC 112(b) are withdrawn in view of the Amendments.
Rejection of Claims 1, 2, 4, 6 rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) is withdrawn in view of the amendments
Rejection of claims 5, rejected under 35 U.S.C. 103 as being unpatentable over Kriz et al US 20140180650 A1 (“KRI”) in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”) in further view of Maddineli et Pavoni, US 20130103627 A1 (“MAD”) is withdrawn in view of the amendments
Claim Interpretation
Regarding the claim recitation of product/databases names: “BDEMQ” “BDAP”, “OAC”, “LOGISTICA”. The Examiner interprets the use of these as limitations indicating databases which contain a specific type of data, (e.g. compositions and volumes processed in the refineries) - as data sources for the composites that go into asphalt production.
The broadest reasonable interpretation applied to Claim 1 limitations is as follows (italics, below claim recitation):
(i) obtaining industrial and oil information from one or more databases,
wherein the industrial and oil information comprises compositions and volumes
processed in refineries, asphalt storage and production information, laboratory
analyses results, properties of oils used in oil asphalt cement (OAC) campaigns,
pre-salt loads of oil streams, or combinations thereof;
Obtain from records/databases information about oils used in OAC operations
(ii) integrating, using a data analysis platform, the industrial and oil
information to obtain machine learning information comprising oils used in OAC
campaigns, properties of oil lists, production and refining routes, product
properties, indices for evaluating product fitting, operational difficulties, or
combinations thereof;
Preprocess data, such as refinery composition processing data, oil properties, crude streams or logistic information, using any data processing tool to obtain information for machine learning
(iii) mapping shipments to refinery tanks to identify an OAC production
route;
Use data that allows to associate/cluster by production operations/path
(iv) determining product fitting for samples in OAC campaigns by
comparing viscosity and penetration values obtained at mapped sampling points
with a current OAC specification;
Compare data of viscosity and penetration in production paths with respective process target data
(v) training a machine learning model with the machine learning
information;
Train a machine learning (ML) model
(vi) calculating, using the machine learning model and the identified OAC
production route, a probability of fitting or not fitting OAC for given oil lists; and
Use ML, for oil data in given production paths, to calculate the probability of meeting specification
(vii) selecting a candidate oil list for OAC production based on the
calculated probability of fitting.
Select oil parameters that provide higher probability of meeting specifications.
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, 4-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, a judicial exception (abstract idea) without significantly more.
(S1) Prima facie, claims 1, 2 4-6 are each directed to a statutory category of invention: process (Claims directed to a method).
Independent claim
(S2A1) Claim 1 limitations recites mathematical concepts and mental processes, and thus the claim recites an abstract idea, as shown below.
Using the BRI interpretation above the following steps, bolded, recited abstract ideas.
iii) mapping/associating/grouping by production path, can be done mentally or with pen and paper, by observing values and making a judgement on the meaning of those values that match a specific production path/route;
(iv) a comparison process for values of viscosity and penetration in production paths with respective process target data, can be done in the mind or using pen on paper
(vi) calculating, using the machine learning model and the identified OAC
production route, a probability of fitting or not fitting OAC for given oil lists; It recites a mathematical concept, calculation of a probability. Even if it were not an abstract idea, calculation of a probability with ML would be clearly WURC – no specific mechanism was applied, just generic ML (also specified as known in the specification, with multiple algorithms offering very similar results)
(vii) selecting a candidate oil list for OAC production based on the
calculated probability of fitting. A mental process, a person can observe, evaluate and decide the selection of items satisfying a condition.
Summarized in a single abstract idea (as per MPEP guidance) this is a process to identify data for production paths, calculate probabilities of elements in a list to match target specifications, select list elements based on calculated probabilities
(S2A2) The additional elements in I, ii and v are Insignificant Extra (Pre-Solution and/or Post-Solution) Activity (MPEP 2106.05(g)) limitations of data gathering and data manipulation. These additional elements alone or in combination are not sufficient to integrated the abstract idea into a practical application. The claim is directed to an abstract idea.
(S2B) The additional limitations of Insignificant Extra (Pre-Solution and/or Post-Solution) Activity (MPEP 2106.05(g)), i.e. the limitations of data gathering and data manipulation recited at high level of generality were found by the courts to be Well-Understood, Routine and Conventional (see MPEP § 2106.05(d)(ll)). Thus these additional al elements, alone, in combination – or the claim as a whole do not provide significantly more. The claim is thus ineligible under 35 US 101.
Dependent claims
Claim 2 is interpreted as adding the following limitation to claim 1:
At step (i) Information is obtained from databases containing refinery composition processing data, oil properties, crude streams or logistic information.
Claim 4 is interpreted as adding the following limitation to claim 1:
the machine learning uses Logistic Regression, hierarchical or multi-level, Gaussian Processes frameworks, Neural Networks, Support Vector Machines (SVM), and/or Random Forests.
Claim 5 is interpreted as adding the following limitation to claim 1.
variables used by machine learning are a numerical parameter for oil density or classification (often API gravity), amount or fraction of saturated hydrocarbons, fraction or amount of aromatics, asphaltenes insoluble in n-heptane, carbon residue – measure of coke-forming tendency, and the oil viscosity parameters
Claim 6 is interpreted as adding the following limitation to claim 1.
At step (iii) the model is implemented in software, including a web-based statistical computing platform, and spreadsheet
Claim 2, in the BRI presented above, only limits claim elements in the parent claim, specifically limits from which kinds of databases the information is obtained from.
Claim 4, in the BRI presented above, only limits claim elements in the parent claim, specifically limits the types of machine learning methods used.
Claim 5, in the BRI presented above, only limits claim elements in the parent claim, specifically limits
Variables and parameters used in machine learning
Claim 6, in the BRI presented above, only limits claim elements in the parent claim, specifically limits
the implementation computational platform and tools to web-based statistical computing platform, and spreadsheet interface, and using the model to compute probabilities of suitability of oils
These further elements in the dependent claims only limit other claim elements, as indicated above, by describing their nature, structure and/or content, thus further limiting the form of the transactions that are acted upon in the parent claim. The nature, form or structure of these elements themselves do not provide more than a general link to a technological environment and do not practically or significantly alter how the identified abstract idea would be performed.
Moreover, under the broadest reasonable interpretation, the further elements in these dependents claims, respectively, do not perform any claimed method steps and are not part of the claimed system. They are outside the scope of the claimed invention and, as such, cannot change the nature of the identified abstract idea from a judicial exception into an eligible application, because they do not represent significantly more (than the identified abstract idea). In summary, in none of the claims there is an inventive concept beyond the judicial exception, and thus, when each of these claims is considered as a whole, it does not amount to significantly more than the judicial exception itself. Therefore, claims 2, 4-6 are deemed ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
i. Determining the scope and contents of the prior art.
ii. Ascertaining the differences between the prior art and the claims at issue.
iii. Resolving the level of ordinary skill in the pertinent art.
iv. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims that share substantially similar limitations (even though not verbatim) are grouped and analyzed together; the analysis is done on the claim with most comprehensive limitations. The parenthesis following a claim number indicates the parent claim.
Claims 1, 2, 4 are rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021
The claim recites in broadest reasonable interpretation, obtaining input data from relevant sources preprocess and select relevant training data based on measures indicating the quality of outputs, train a
machine learning with this data and use the trained model to select the suitable oils.
Several overlapping references teach the combination, these sources each teaching multiple aspects, yet leaving some gap. All references in the combination refer to asphalt/bitumen production with machine learning techniques or machine learning techniques broadly used in oil industry.
Re claim 1 SSE teaches :
The broadest reasonable interpretation applied to limitations of Claim 1 is as follows (italics, below claim recitation):
obtaining industrial and oil information from one or more databases, {Title: Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to
Empirical Models Ref. [29] presented an ANN model to predict the viscosity of crude oil. However, the model was limited to single-component heavy oil [24,29] reported a study that involved the prediction of the viscosity of heavy oils as well as of their mixtures with solvents under formation conditions [24]. To construct their model, they used only very large datasets,. A comparison was conducted to determine which approach was better between the empirical and machine learning approaches. We also introduced the use of ANNs to solve the coefficient problem associated with the RVDM model, since ANNs can also be saved and loaded whenever datasets of similar properties and within a similar range are to be used to make viscosity predictions.} databases interpreted as datasets industrial and oil information interpreted as the formation conditions. A POSITA would understand that the more data is used from available datasets the
(v) training a machine learning model with the machine learning information {{Title: Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models}
SSE does not teach however WIL teaches the use of historical data about the production
wherein the industrial and oil information comprises compositions and volumes processed in refineries, asphalt storage and production information, laboratory analyses results, properties of oils used in oil asphalt cement (OAC) campaigns, pre-salt loads of oil streams, or combinations thereof;,
(iii) mapping shipments to refinery tanks to identify an OAC production route;
{ FIG. 1 illustrates a flowchart of the workflow of one embodiment of the present invention; Fig 1 – Generate Historical Production Profiles.} FIG. 2 illustrates a graph of the general workflow of the history matching and forecast phase of an analysis of production for oil reservoirs; }
WIL teaches how to map the history of production aspects (as opposed to measurements) as input data for predicting properties, In BR history includes shipping route and tanks and therefore identification of production route.
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It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of SSE and WIL. One would have been motivated to do so, in order to obtain the advantage of using the already accumulated information in the databases and explit the modern techniques of data-driven modeling that provide superior results compared to models using only physical properties. Accordingly, the claimed subject matter would have been obvious over SSE/WIL.
While SSE/WIL teaches the use of neural networks for predicting viscosity and training of a machine learning it does not focus on the details of data preprocessing, feature engineering and validation and testing. Thus SSE/WIL does not explicitly disclose, however DOT discloses
(iv) determining product fitting for samples in OAC campaigns by comparing viscosity and penetration values obtained at mapped sampling points with a current OAC specification;; (vi) calculating, using the machine learning model and the identified OAC production route, a probability of fitting or not fitting OAC for given oil lists; and (vii) selecting a candidate oil list for OAC production based on the calculated probability of fitting. { see p17 3.1. Data preparation and preprocessing Data from the PA40 database for flexible pavement keel sections were used for developing the predictive models. Before ML training, various data preprocessing methods were applied to both the input and output data.. For each modeling approach, the required input and output vectors were created…
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determining product fitting for samples in OAC campaigns by comparing viscosity and penetration values obtained at mapped sampling points with a current OAC specification is interpreted as Feature Engineering; a conventional step in machine learning. The choice of viscosity and penetration values would have been normal to a POSITA, since it has been the main variables used in assessing quality since at least 1969, as also evidenced in the Specification by the Applicant [002] “Description of the State of the Art [0002] The technique or tool hitherto used for the evaluation of oils for Oil Asphalt Cement (OAC) is based on Shell's Heukelon diagram, from 1969, also known as Bitumen Test Data Chart (BTDC). This is an approach based on analysis of vacuum residues (VR) (penetration, viscosity and softening point), which evaluates and compares the consistency of VR (or VR mixtures) at different temperatures, indicating the suitability of the list for OAC production.”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of SSE/WIL and DOT. One would have been motivated to do so, to obtain the advantage of using the known methjods of preprocessing , feature selection and validation. Accordingly, the claimed subject matter would have been obvious over SSE/WIL/DOT.
Regarding claim 2 SSE/WIL/DOT teaches the limitations of the parent claim. WIL further teaches
wherein one or more databases comprise: BDEMQ, BDAP, and LOGISTICA.
{Fig 1 – Generate Historical Production Profiles.} It would be obvious to a POSITA to use the history of production that come from the datasources that contain the relevant information. The parent claim already teaches the database with relevant information.
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The specification teaches : BDEMQ, BDAP, and LOGISTICA are the relevant historical data databases.
A POSITA would have found obvious, before the effective file of the invention, to use the databases that contain the relevant information. It would have been motivated to do so to obtain the large collection of historical data needed.
Regarding claim 4 SSE/WIL/DOT teaches the limitations of the parent claim. SSE further teaches
wherein the machine learning model uses Hierarchical Logistic Regression, Gaussian Processes, Neural Networks, Vectors Supported by Machines, and/or Random Forests.
{SSE title : Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models}
Claim 5 rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021 in further view of Maddineli et Pavoni, US 20130103627 A1 (“MAD”)
Regarding claim 5 SSE/WIL/DOT teach the limitations of the parent claim. SSE/WIL/DOT does not disclose, however MAD discloses
Wherein variables used by the machine learning model are a numerical parameter for oil density or classification (often API gravity), amount or fraction of saturated hydrocarbons, fraction or amount of aromatics, asphaltenes insoluble in n-heptane, carbon residue – measure of coke-forming tendency, and the oil viscosity parameters
{[Abstract] the most representative physico-chemical factors of crude oils; Claim 1 entering said selected values as input data for a multilayer neural network of the back propagation type, trained and optimized by means of genetic algorithms; predicting, by means of said trained and optimized neural network, at least one of the following physico-chemical factors of the unknown crude oil: TBP yield, API degree, viscosity, sulfur content, acidity, paraffin content, naphthene content, aromatic content, naphthene+2 aromatic content, smoke point, freezing point, cloud point, pour point, cetane index, Nickel content, Vanadium content, asphaltene content, or carbonaceous residue content.}
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of SSE/WIL/DOT with MAD. One would have been motivated to do so, in order to obtain the advantage of using the most important physico-chemical factors of the crude oil .
Accordingly, the claimed subject matter would have been obvious over SSE/WIL/DOT in further view of MAD.
Claim 6 rejected under 35 U.S.C. 103 as being unpatentable over Ssebadduka et al Artificial Neural Network Model Prediction of Bitumen/Light Oil Mixture Viscosity under Reservoir Temperature and Pressure Conditions as a Superior Alternative to Empirical Models, Energies 2021, 14, 8520 (“SSE”) in view of WO 2008036664 A2 WILKINSON (“WIL) in further view of DOT Application of Machine Learning Techniques to Pavement Performance Modeling, DOT/FAA/TC-21/44, Oct 2021 in view of Justo-Silva et al “ Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models, Sustainability, May 2021 (“JUS”)
Re Claim 6 SSE/WIL/DOT teaches the limitations of the parent claim including qualities properties of asphalt. JUS r teaches
Wherein the machine learning model is implanted in a web application, based on an R platform, and in an electronic spreadsheet
. { p12. the routines for its computation are available in a vast number of software packages. }the prediction is for binary outcome which is the interpretation for fitting or not fitting. Web application and R platform or spreadsheet are interpreted broadly as implementation in various software packages. A POSITA would have naturally chosen R first as being the main software for statistical processing. R is also available as anyone in the art knows, as a web-based application.
Thus it would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of SSE/WIL/DOT with teaching of JUS. One would have been motivated to do so, in order to obtain the advantage of using state of the art commercial tools of availability and accuracy and implement available software functions, such as logit to give the predicted binary answer
Accordingly, the claimed subject matter would have been obvious over SSE/WIL/DOT/JUS.
Prior art made of record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20140180650 A1
IT MI20072329 A1
WO 2019234762 A1
WO 2008135411 A1
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.S./Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
Prosecution Insights