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 .
Election/Restrictions
Applicant’s election without traverse of Species I and corresponding to claims 1 -10 and 21 - 30 in the reply filed on 8/14/2026 is acknowledged.
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 – 10 and 21 - 30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
Claims 1 – 10 are directed to a method. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Claims 21 – 30 recites “one or more computer-readable storage media” that store a software program performing a function. The Specification fails to expressly limit the recited “media” to a statutory embodiment. Thus, the plain and ordinary meaning of the recited "media" includes signals, carrier waves, etc. Accordingly, the recited “one or more computer-readable storage media” are not a process, a machine, a manufacture or a composition of matter, and claims 21 - 30 fail to recite statutory subject matter as defined in 35 U.S.C. 101.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
upon creation of a new business record in a table, identifying a machine learning model stored in a database and associated with the table;
generating an input record including the new business record for input into the machine learning model, wherein generating the input record comprises, at least in part, cleaning the new business record for use in the machine learning model;
providing the input record as input into the machine learning model; and receiving an output from the machine learning model.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person receives new information, organizes or cleans it, applies a set of rules or learned knowledge to it and determines a result.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
loading the machine learning model;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The database is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
loading the machine learning model;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The database is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 1, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein identifying the machine learning model comprises:
querying a model definition database for a model definition corresponding to the table;
fetching the model definition corresponding to the table; and
fetching the machine learning model based on information in the model definition.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The recitation of model definition amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein identifying the machine learning model comprises:
querying a model definition database for a model definition corresponding to the table;
fetching the model definition corresponding to the table; and
fetching the machine learning model based on information in the model definition.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The recitation of model definition amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 2, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of
wherein the model definition comprises a name, a type, an identity of one or more data sources, one or more attributes, and one or more join definitions.
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein the model definition comprises a name, a type, an identity of one or more data sources, one or more attributes, and one or more join definitions.
amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 4,
Step 2A, Prong One
The claim recites the abstract idea described above in claim 2, but does not recite any other abstract ideas or any other judicial exceptions.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the machine learning model is stored as a serializable model object
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements of:
wherein the machine learning model is stored as a serializable model object
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
building one or more join definitions according to the model definition.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can create new/updated explanations based on the original or past explanations
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim recites in part:
wherein cleaning the new business record for use in the machine learning model comprises one or more of: vectorizing, transforming, formatting, mapping, plumbing, pipelining, and quantifying the new business record in preparation for running it through the machine learning model.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can alter the records to put it in a format to be understood by the machine learning model.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim recites in part:
wherein receiving an output from the machine learning model comprises one or more of: populating an existing field in a table, adding a new field to a data source, tuning a business parameter, and providing operational insights via a user interface.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can add another row or column to a table.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim recites in part:
wherein the machine learning model comprises a model name, a model type, a specified algorithm, a last trained date, and an identity of an associated datastore.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can name the model anything they want to.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim recites in part:
generating business feedback based on the output
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can create a report to give feedback to better improve the business.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
As to claim 10,
Step 2A, Prong One
The claim recites in part:
providing the business feedback to at least one business analytics environment.
As drafted and under its broadest reasonable interpretation, these limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, a person can create a report to give feedback to better improve the business.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The claim does not include additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception itself.
Step 2B
The claim does not include additional elements that are sufficient to amount to “significantly more” to the judicial exception.
Claim 21 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The one or more computer-readable storage media, a processing system, and database is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 22 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 23 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 24 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 25 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 27 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 28 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 29 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 30 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The computing apparatus, one or more computer-readable storage media, a processing system, and database is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
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 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.
Claim(s) 1 – 10 and 21 - 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chu et al (US 2006/0173906) in view of Kirsche et al (US 11,182,695).
As to claim 1, Chu et al figure 1 shows and teaches a method of operating at least one server, the method comprising:
upon creation of a new business record in a table, identifying a machine learning model stored in a database and associated with the table (paragraph [0034]…The model creator 12 determines the specifications for a particular data mining run, generates the corresponding model based on the specification, and then stores the model in his or her individual project folder 20 ; paragraph [0036]… The end-user 16 is the person who is interested in using the models in the model repository 24. The end-user 16 could also be a model creator 12, although not all end-users will be creating models. The end-user 16 accesses the model repository 24 and searches for an appropriate model 23A, 23B, 23N2 by examining the one or more index structures 26, 28, 30)(Examiner’s Note: “model creator 12 determines the specifications for a particular data mining run, generates the corresponding model based on the specification, and then stores the model in his or her individual project folder 20” reads on “upon creation of a new business record in a table” ; “The end-user 16 could also be a model creator 12… The end-user 16 accesses the model repository 24 and searches for an appropriate model 23A, 23B, 23N2 by examining the one or more index structures 26, 28, 30” reads on “identifying a machine learning model stored in a database and associated with the table”);
loading the machine learning model (paragraph [0037]…The end-user 16 may also be an end-user application program that programmatically searches for and retrieves an appropriate model from the model repository 24);
generating an input record including the new business record for input into the machine learning model, wherein generating the input record comprises, at least in part,
cleaning the new business record for use in the machine learning model (paragraph [0052]…The tree-type index 28A is preferably constructed using every splitting variable in the model repository 24. There are preferably two formats for the tree-type index 28. The format that is most comfortable for people to work with (such as, index 28A), if browsing the index, may or may not be the format that gives the best performance (such as, index 28B) to an application that may be automatically searching for and retrieving models from the model repository 24)(Examiner’s Note: “There are preferably two formats for the tree-type index 28” reads on “generating an input record including the new business record for input into the machine learning model, wherein generating the input record comprises, at least in part, cleaning the new business record for use in the machine learning model”);
providing the input record as input into the machine learning model (paragraph [0053]…The first format 28A is shown in FIG. 4, as described above. The second format 28B is a table that has as many rows per model as the model has splitting variables. This second format 28B is shown in FIG. 5, and includes two columns, a first column 60 that identifies the model, and a second column 62 that identifies the splitting variable. If the model's identification is not unique within the model repository, then an additional column is used to identify the project for which the model was generated. In this second format, if a model has four splitting variables, then the model has four rows in the table); and
receiving an output from the machine learning model (paragraph [0036]…By supplying search parameters and then comparing these search parameters against the attributes stored in the index structures, the end-user 16 is able to find one or more useful models. Having found a useful model, the end-user 16 may then obtain a copy of the information contained in the model. A special graphical user interface could be provided to the end-user 16 in order to facilitate the search and retrieval process with the model repository 24. The graphical user interface can be used to send a search and/or retrieval request to the model repository 24 over a network, such as a local, wide area, or global (e.g., Internet) network).
Chu et al fails to explicitly show/teach that the model is machine learning model.
However, Kirsche et al teaches models are machine learning model (column 28, lines 25 – 45… when creating an Experiment of a Model, clients may specify model parameters that include relevant data to make a Run of specified execution engine. For example, for R on PySpark execution engine, model parameters may include an R script that will be sourced to produce the scores, an RData binary file that contains the serialized model object that will be loaded prior to the scoring, and any additional side data in the form of RData files. For the Foundry execution engine, model parameters may contain the type of the model, random forest or logistic regression, and a set of hyper-parameters specific to the model, such as the number of trees and maximum depth of each tree in a random forest, or the strength of L1 and L2 regularization for logistic regression. Hyper-tuning may be implemented for these models).
Therefore, it would have obvious for one having ordinary skill in the art, at the time the invention was made, for Chu et al’s model to be a machine learning model, as in Kirsche et al, for the purpose of machine learning model lifecycle management.
As to claim 2, Chu et al figure 1 shows and teaches the method wherein identifying the machine learning model comprises:
querying a model definition database for a model definition corresponding to the table (paragraph [0013]…a user application program may automatically query the model repository in order to find and extract information from a particular model stored in the model repository);
fetching the model definition corresponding to the table (paragraph [0046]… Model descriptors are additional attributes that are associated with the models in the model repository 24, and also may be used in the main index 26, which can be searched by a user in order to find and retrieve a particular model 23 or set of models); and
fetching the machine learning model based on information in the model definition (paragraph [0046]…Descriptors can be assigned at the project level, the diagram level, and/or at the model level. Descriptors can be manually associated with the models in the project folder 20 by any of the system users 12, 14, 16).
As to claim 3, Chu et al figure 1 shows and teaches the method wherein
the model definition comprises a name, a type, an identity of one or more data sources, one or more attributes, and one or more join definitions (paragraph [0046]… Model descriptors are additional attributes that are associated with the models in the model repository 24, and also may be used in the main index 26, which can be searched by a user in order to find and retrieve a particular model 23 or set of models).
As to claim 4, Chu et al figure 1 shows and teaches the method wherein the machine learning model is stored as a serializable model object (column 28, lines 25 – 45… when creating an Experiment of a Model, clients may specify model parameters that include relevant data to make a Run of specified execution engine. For example, for R on PySpark execution engine, model parameters may include an R script that will be sourced to produce the scores, an RData binary file that contains the serialized model object that will be loaded prior to the scoring, and any additional side data in the form of RData files. For the Foundry execution engine, model parameters may contain the type of the model, random forest or logistic regression, and a set of hyper-parameters specific to the model, such as the number of trees and maximum depth of each tree in a random forest, or the strength of L1 and L2 regularization for logistic regression. Hyper-tuning may be implemented for these models).
As to claim 5, Chu et al figure 1 shows and teaches the method wherein the method further comprises building one or more join definitions according to information in the model definition (paragraph [0046]… Descriptors can be manually associated with the models in the project folder 20 by any of the system users 12, 14, 16. A descriptor preferably includes a variable-value pair, such as "site=Chicago" or "size=100,000". In these examples, site is a variable and Chicago is its value, and size is a variable and 100,000 is its value. The variable-value pairs may be manually specified by one of the system users 12, 14, 16 via a graphical user interface element, such as a pop-up window, notes tab, or other graphical data entry means, for selecting the particular project, diagram or model, and then for entering the appropriate descriptor).
As to claim 6, Chu et al figure 1 shows and teaches the method wherein cleaning the new business record for use in the machine learning model comprises one or more of: vectorizing, transforming, formatting, mapping, plumbing, pipelining, and quantifying the new business record in preparation for running it through the machine learning model (paragraph [0052]…The tree-type index 28A is preferably constructed using every splitting variable in the model repository 24. There are preferably two formats for the tree-type index 28. The format that is most comfortable for people to work with (such as, index 28A), if browsing the index, may or may not be the format that gives the best performance (such as, index 28B) to an application that may be automatically searching for and retrieving models from the model repository 24).
As to claim 7, Chu et al figure 1 shows and teaches the method wherein receiving an output from the machine learning model comprises one or more of: populating an existing field in a table, adding a new field to a data source, tuning a business parameter, and providing operational insights via a user interface (paragraph [0036]…By supplying search parameters and then comparing these search parameters against the attributes stored in the index structures, the end-user 16 is able to find one or more useful models. Having found a useful model, the end-user 16 may then obtain a copy of the information contained in the model. A special graphical user interface could be provided to the end-user 16 in order to facilitate the search and retrieval process with the model repository 24. The graphical user interface can be used to send a search and/or retrieval request to the model repository 24 over a network, such as a local, wide area, or global (e.g., Internet) network).
As to claim 8, Chu et al figure 1 shows and teaches the method wherein the machine learning model comprises a model name, a model type, a specified algorithm, a last trained date, and an identity of an associated datastore (paragraph [0032]… [0032] With reference back to FIG. 1, for each level of the model repository structure, one or more additional descriptive attributes may be associated with the models. The attributes provide descriptive information about the model that can be used to identify a particular model in the model repository 24 via a search and retrieval process. These attributes may be automatically associated with the models by the data mining application 18, or by the model repository facility 18A when the model is exported to the model repository 24. In addition, any of the system users 12, 14, 16 may associate additional attributes with the models. The model attributes may be assigned at the project level, the diagram level, or at the individual model level).
As to claim 9, Chu et al figure 1 shows and teaches the method further comprising generating business feedback based on the output (paragraph [0006]…As a result of this data explosion, data mining software has been developed. A data mining software application can search through the large volumes of data stored in the data warehouse and can identify patterns in the data using a variety of pattern-finding algorithms. These patterns are then used by the business analyst in order to make business recommendations).
As to claim 10, Chu et al figure 1 shows and teaches the method further comprising providing the business feedback to at least one business analytics environment (paragraph [0006]…As a result of this data explosion, data mining software has been developed. A data mining software application can search through the large volumes of data stored in the data warehouse and can identify patterns in the data using a variety of pattern-finding algorithms. These patterns are then used by the business analyst in order to make business recommendations).
Claim 21 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 22 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 23 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 24 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 25 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 27 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 28 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 29 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 30 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
Prosecution Insights