DETAILED ACTION
This Final Office action is responsive to the claims filed on February 4, 2026.
Claims 1-20 are under examination.
The drawings are objected to.
Claims 1-15 are rejected under 35 USC 112(a).
Claims 1-20 are rejected under 35 USC 112(b).
Claims 1-20 are rejected under 35 USC 101.
Response To Arguments/Amendments
Claim Objections: The Applicant’s arguments and amendments have been considered and are persuasive. The objections are withdrawn.
35 USC 112(b) Rejections: The Applicant’s arguments and amendments have been considered and are mostly persuasive. There are a few lingering 35 USC 112(b) rejections from the prior action, but most have been overcome. There are no specific arguments from the Applicant to address.
35 USC 101 Rejections: The Applicant’s argument and amendments have been considered but are not persuasive. The Applicant’s arguments will be addressed in the order presented in the response.
Alleged Improvement To Computer Technology: The Applicant alleges that the recited judicial exceptions in the claims, as amended, are integrated into the practical application because the specification discloses, “[t]he application of the job observations to the job design can reduce the number of revisions to the job design and increase the efficiency of the job design process.” However, many of the steps of the methods, as amended, are not even explicitly conducted by a computer. Without this explicit recitation, they are not only susceptible to being interpreted as mental processes and/or mathematic concepts, but they are also mere organizing of human activity. Also, the purported advantage is not explicitly stated in the claim. Further, applying existing knowledge to a problem using lessons learned is a longstanding practice and is well-understood, routine and conventional, as demonstrated by many references presented on the record. Labeling records with descriptive labels (e.g., groups of keywords) for later comparison and use is also a longstanding practice demonstrated by the cited references. That one uses metadata tags specifically, rather than descriptive labels or keywords, or uses a generic machine learning model trained by adjusting mathematical numbers in a machine learning model rather than a mind changing neuronal connections is largely immaterial to this inquiry to the extent that the any specific improvement to the adjusting of the mathematical numbers is not recited in the claims. The Applicant has failed to assert on the record which computerized elements recited in the method represent an improvement to computing itself, rather than just automating the longstanding practice of learning from past mistakes.
Accordingly, the 35 USC 101 rejections are maintained.
35 USC 103 Rejections: The Applicant’s amendments and arguments have been considered and are persuasive. The art rejections are withdrawn.
NOTE ON CLAIM CONSTRUCTION
Claim 3 recites “the observation object comprises a wellbore treatment, a servicing equipment, a pumping procedure, a wellsite environment, a downhole environment, or combinations thereof.” By principles of claim construction, it is likely that this claim, by using the plural, “combinations,” excludes a single combination of the elements recited. The term, “combinations thereof,” appears to be largely superfluous because the alternative language of the claim would include such combinations without explicit recitation. In fact, the explicit recitation of the plural combinations may eliminate a single combination of the elements from the scope of the claim by virtue of claim construction. In order to avoid prosecution history estoppel, it may be advisable to now add the limitation, “a combination thereof” in the alternative to the list, in addition to the recited “combinations thereof.” This is neither a rejection nor objection. This is merely a note made of record.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. As is demonstrated with respect to the 35 USC 112(b) rejection, the relationships between certain elements, such as the claimed machine learning model, machine learning classifier, machine learning process, (e.g., vis a vis the managing application), job observation, observation description, observation object, metadata tags, training metadata tags, classification grade, error value, level 1 and level 2 designs, and procedural steps are unclear. Therefore, the claimed machine learning model, machine learning classifier, machine learning process, job observation, observation description, observation object, metadata tags, training metadata tags, classification grade, error value, level 1 and level 2 designs, retrieving steps, identifying steps, comparing steps, applying steps, generating steps, validating steps, training steps, determining steps, recommending steps, continuing steps must be shown or the feature(s) canceled from the claim(s). No new matter should be entered. That is, the support for the new drawing elements must derive from the existing specification without embellishment and must be demonstrated for this objection to be withdrawn.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “metadata tags referring to content of the training set of job observations.” Claim 9 recites, “metadata tags referring to content of a training set of job observations.” The Applicant states in the Remarks/Arguments (Page 17 of the Response) that the support for this amendment can be found in paragraphs [0052], [0056]-[0058], and [0088]. However, those paragraphs do not teach that the metadata tags refer to training data. Therefore, claims 1 and 9 recite new matter. 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.
Claims 1-20 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.
Machine Learning Process
Claim 16 still recites “the machine learning process.” This has been objected to for lack of primary antecedence. Also, the 35 USC 112(b) rejection from the prior action still applies to this recitation of the machine learning process vis a vis the recited machine learning model. It appears this continued inclusion was in error, as the antecedence is incorrect. This appears both in the “determining… a probability” step and the “recommending … the portion” step.
Machine Learning Classifier v. Machine Learning Model and Validating By Comparing and Reducing Error
The Applicant has increased the clarity of the claim by removing the term machine learning process. This does not resolve the issue of clarifying the relationship between the amended machine learning model and the recited machine learning classifier. Claim 1 recites “comparing, by the machine learning classifier, the first job observation to a training set of job observations.” Claim 1 distinguishes between the machine learning classifier and the machine learning model. Claim 16 recites that the machine learning model conducts this same comparison. It is unclear what the claims now mean by a machine learning classifier. For example, some machine learning models are classifiers. That is, they output a classification based on input. However, it is unclear how the claimed comparison would then be performed. In the art, there is no machine learning element that conducts such a direct comparison, nor is such a machine learning model or method demonstrated in the Applicant’s specification. The machine learning model is trained to have its weights adjusted based on training data or other learning method. Then, when used to generate inferences, the machine learning model receives inference data and outputs a classification, without any direct comparison of training and inference inputs or scores generated therefrom. The term comparing is unclear in this context. Also, it is unclear what the machine learning classifier actually does or whether it is actually a machine learning element by the standards a person of ordinary skill would apply. Is it merely a direct comparator that involves no machine learning elements, such that it is merely a machine learning element to the extent that it prepares data for a machine learning model, or is it a machine learning model itself. In light of recent decisions/opinions, this distinction has become a meaningful one, because the decisions/opinions have been interpreted to attribute special meaning to the term “machine learning” as a “technology,” regardless of the apparently facile technological distinction from the underlying math. This will also make determining 35 USC 101 eligibility difficult, as the claim terms make the scope of the claim indefinite.
Also, claim 16 recites that the machine learning model both validates the metadata tags by comparing classification grades to determine an error value and to then somehow reduce the error value. Claim 9 recites something analogous. However, machine learning models do not do this. During training, the training elements use an objective/loss function to determine an error to mathematically propagate through the mathematical weights of the mathematical machine learning model. The training elements continue to make these error determinations/validations to reduce the error so that the machine learning model is trained. The machine leaning model itself is a black box that receives input and outputs output. That is, the model is validated. It does not do the validating.
Appropriate clarification by amendment and argument, as well as appropriate support is required.
Dependent claims that depend from the rejected claims are rejected based on their dependency.
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.
NOTE ON AUTOMATION: The Applicant’s amendments have withdrawn explicit automation from the claims. To this end, each of the steps that no longer recite that a computer element conducted the steps is sufficiently broad to encompass organizing human activity. The Applicant is invited to amend, based on support in the specification, any of these elements to render them other than organizing human activity, rather than elements of the judicial exception.
NOTE ON LONGSTANDING PRACTICE/WURC: Employing lessons learned to improve a job design and reduce design time is a longstanding practice that often comes in the form of heuristics presented in engineering texts and instruction manuals. Similarly, it is well-understood, routine, and conventional activity, as evidenced by the following references of record: US 20230022567 A1 to Cheng et al,.; US 20200332627 A1 to Tang; NPL: “Organizing lessons learned practice for product–service innovation” by Chirumalla; NPL: “A lessons-learned tool for organizational learning in construction” by Eken et al.; NPL: “Resolving Redundancy: A Recurring Problem in a Lessons Learned System” by Everett et al.; NPL: “Investigating the Benefits of Applying Artificial Intelligence Techniques to Enhance Learning Experiences in Capstone” by Gonzalez et al.; NPL: “Implementing Knowledge-Sharing Systems and Establishing a Culture to Share Lessons Learned Within a Multidisciplinary Company Enhancing Effective Knowledge Transfer” by Hinze et al.; NPL: “Communicate Lessons, Exchange Advice, Record (CLEAR) Database Development” by Jaselskis et al.; NPL: “Learning Object Metadata: An Empirical Investigation and Lessons Learned” by Najjar et al. NPL: “Incidents Investigations and Learning Approach in Oil & Gas Industry” by Al-Qubaisi; NPL: “Knowledge Management Metamodel from Social Analysis of Lessons Learned Recorded in the Cloud” by Quintero et al.; NPL: “Intelligent lessons learned systems” by Weber et al.; NPL: “Nine simple ways to make it easier to (re)use your data” by White et al.; NPL: “Computational narrative mapping for the acquisition and representation of lessons learned knowledge” by Young et al.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Independent Claims
Claim 1 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
Specifically, Claim 1 recites:
Identifying […] a format of the first job observation, wherein the format comprises the observation description and the observation object; (Evaluation, Mental Process – This step requires identifying a format that includes an observation description and an observation object. A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
comparing […] the first job observation to a training set of job observations; (Evaluation, Mental Process – This step requires comparing job observations. A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
identifying […] [descriptors] referring to the content of the training set of job observations; (Evaluation, Mental Process – This step requires identifying a metadata tag/descriptor/label that describes a characteristic of the job. A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
applying […] a combination of [labels] to the first job observation; (Evaluation, Mental Process – This step associates metadata tags/descriptors with a job, e.g., based on an objective rubric. A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
generating […] a classification grade by searching a database for the first job observation with a search criterion comprising the [labels]; (Evaluation, Mental Process – This step generates a grade for the job observation as it relates to a set of metadata tags, e.g., based on an objective rubric. A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
validating […] the [labels] by comparing a first classification grade using a first combination of [labels] to a second classification grade using a second combination of [labels] to determine an error value; and (Evaluation, Mental Process – This step repeats the other steps for validation data and compares the resulting grades to determine an error value (e.g., a loss value). A person can do this in their mind and may have to in order to label the training data. MPEP 2106.04(a)(2)(Ill))
The claim recites evaluations, which are mental processes, which comprise an abstract idea.
Claim 1 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
The additional limitations:
retrieving […] a first job observation comprising an observation description and an observation object;
The retrieving is mere data gathering, which is insignificant extra-solution activity similar to the MPEP 2106.05(g) examples: “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Therefore, this step, under MPEP 2106.05(g) fails to integrate the abstract idea into a practical application.
[…] by a/the machine learning process […]
[…] by a machine learning classifier […]
[…] by a machine learning process executing on a computer system […]
[…] metadata tags […]
[…] training the machine learning process to reduce the error value by modifying the first combination or the second combination.
Conducting machine learning at a high level of generality is utilizing generic computing elements. Here, the use of the machine learning model in the abstract idea steps is a mere apply it in a computer environment. Similarly, the training the machine learning process to reduce the error value by modifying the model input (e.g., with different training data) is just a generic minimization of loss/cost/error used in all machine learning training methods. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
Therefore, there are no additional limitations in claim 1 that integrate the abstract idea into a practical application at Step 2A, Prong 2.
Claim 1 does not integrate the abstract idea into a practical application and is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
retrieving […] a first job observation comprising an observation description and an observation object;
The retrieving is also well-understood, routine and conventional (WURC) activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” and “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Further, as previously demonstrated, this step is insignificant extra-solution activity under MPEP 2106.05(g). Therefore, this step, under MPEP 2106.05(d) and MPEP 2106.05(g), fails to combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept.
[…] by a/the machine learning process […]
[…] by a machine learning classifier […]
[…] by a machine learning process executing on a computer system […]
[…] metadata tags […]
[…] training the machine learning process to reduce the error value by modifying the first combination or the second combination.
Conducting machine learning at a high level of generality is utilizing generic computing elements. Here, the use of the machine learning model in the abstract idea steps is a mere apply it in a computer environment. Similarly, the training the machine learning process to reduce the error value by modifying the model input (e.g., with different training data) is just a generic minimization of loss/cost/error used in all machine learning training methods. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f) fail to combine with other elements of the claim to provide significantly more that would confer an inventive concept.
Therefore, there are no additional limitations in claim 1 that combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claim 1 is ineligible.
Claim 9 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
Specifically, Claim 9 recites:
designing […] a job design, wherein the job design comprises an inventory of materials, a pumping procedure, an inventory of pumping equipment, or combinations thereof; (Evaluation, Mental Process – People can design job designs in their minds or with aid of pen and paper. Wellbores were drilled with planning well before this application and before computers were available. MPEP 2106.04(a)(2)(Ill))
applying […] a set of [labels] referring to content of a training set of job observations to the job design by comparing the job design to a training set of job designs; (Evaluation, Mental Process – Applying a set of labels based on objective criteria can be conducted in the mind. MPEP 2106.04(a)(2)(Ill))
comparing the set of [labels] of the job design to a database of job observations; (Evaluation, Mental Process – This step compares metatags/labels of characteristics of a job. A person can do this in their mind and had to prior to the invention of computers. MPEP 2106.04(a)(2)(Ill))
retrieving a set of relevant job observations from the database in response to a comparison value exceeding a threshold value; (Evaluation, Mental Process – This step compares assessed a grade of a label description of a job observation (e.g., comprised of metadata tags) to a threshold to determine which job observations apply to a job. MPEP 2106.04(a)(2)(Ill) – Note that the actual retrieval itself is also insignificant extra-solution activity and WURC for the same reason as the other receiving steps.)
[…]
generating […] a level two job design by modifying the level one job design with one or more relevant job observations in response to a probability value for the level two job design achieving a job objective with the one or more relevant job observations being greater than the probability value for the level one job design achieving a job objective without the one or more relevant job observations; and (Evaluation, Mental Process – This step improves an existing job design with observations from past job designs that are retrieved (based on having a high score of relation. This can be accomplished in the mind by remembering aspects/observations of prior jobs (e.g., a high porosity medium (which could be a label represented in a metadata tag)) and improving a new job based on those observations. MPEP 2106.04(a)(2)(Ill))
[…]
[…] comparing a first classification grade using a first combination of [labels] used to form a group of [labels] to a second classification grade using a second combination of [labels] used to form the group of labels, to determine an error value, (Evaluation, Mental Process – This step compares classification grades to determine error values, which is practically performable in the mind or with the aid of pen and paper. Therefore, this is an evaluation, a mental process, an abstract idea.)
The claim recites evaluations, which are mental, processes, abstract ideas.
Claim 9 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
The additional limitations include:
[…] by a managing application executing on a computer system[…]
[…] by the managing application […]
[…] a machine learning model […]
[…] metadata tags […]
[…] by the managing application […]
[…] machine learning model […]
[…] machine learning model validates by […]
[…] wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Using machine learning or other applications at a high level of generality is utilizing generic computing elements. Further, validating a machine learning model and training a machine learning model by reducing the error in its outputs are both generic machine learning technology elements. Also, the metadata tags, as recited, are generic computing elements that represent labels of characteristics of the data to which they refer. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f), fail to integrate the abstract idea into a practical application.
alerting, by the managing application, a user device to the relevant job observations from the database;
The alerting step is insignificant extra-solution activity similar to the MPEP 2106.05(g) example, “a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” Because the alerting step is insignificant extra-solution activity, under MPEP 2106.05(g) it fails to integrate the abstract idea into a practical application.
placing wellbore treatment in the wellbore in accordance with the level two job design.
The placing step is similar to the 2106.05(f) “apply it” example, “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” The placing step is insignificant extra-solution activity similar to the MPEP 2106.05(g) example, “Cutting hair after first determining the hair style” (Also from the Brown case). Because the placing step is an “apply it” step and is insignificant extra-solution activity, under MPEP 2106.05(g) it fails to integrate the abstract idea into a practical application.
Claim 9 fails to provide any additional limitations that integrate the abstract idea into a practical application.
Claim 9 is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The additional limitations include:
[…] by a managing application executing on a computer system[…]
[…] by the managing application […]
[…] a machine learning model […]
[…] metadata tags […]
[…] by the managing application […]
[…] machine learning model […]
[…] machine learning model validates by […]
[…] wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Using machine learning or other applications at a high level of generality is utilizing generic computing elements. Further, validating a machine learning model and training a machine learning model by reducing the error in its outputs are both generic machine learning technology elements. Also, the metadata tags, as recited, are generic computing elements that represent labels of characteristics of the data to which they refer. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f), fail to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
alerting, by the managing application, a user device to the relevant job observations from the database;
The alerting step is well-understood, routine, and conventional (WURC) activity similar to the MPEP 2106.05(d) example, “i. Receiving or transmitting data over a network.” Because the alerting step is WURC and insignificant extra-solution activity, under MPEP 2106.05(d) and MPEP 2106.05(g), it fails to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
placing wellbore treatment in the wellbore in accordance with the level two job design.
The placing step is similar to the 2106.05(f) “apply it” example, “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” Because the placing step is an “apply it” step and is insignificant extra-solution activity, under MPEP 2106.05(f) and MPEP 2106.05(g), it fails to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
Therefore, there are no additional limitations in claim 9 that combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claim 9 is ineligible.
Claim 16 (Statutory Category – Process)
Step 2A – Prong 1: Judicial Exception Recited?
Yes, the claims recite a mental process and a mathematical operation, which are abstract ideas.
Specifically, Claim 16 recites:
generating […] a job observation; (Evaluation, Mental Process – People can mentally make job observations. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
comparing […] a combination of [labels] of the job observation to the combinations of [labels] of a plurality of historical job observations in a database […] (Evaluation, Mental Process – People can mentally compare labels applied to jobs. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
retrieving […] a set of historical job observations from the database with the combination of [labels] that exceed a comparison threshold value; (Evaluation, Mental Process – People can mentally determine which jobs observation grades exceed a threshold. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill). Also note, the retrieval of the data itself is insignificant extra-solution activity and WURC for at least the same reasons as the basic receiving/retrieving steps.)
determining […] a portion of historical pumping procedure that corresponds to the job observation by comparing the set of historical job designs and historical job reports that correspond to the historical job observations from the database; (Evaluation, Mental Process – A person can correlate jobs and observations from experience, mentally. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
determining […] a probability of achieving the job objective with the portion of the historical pumping procedure based on […] by accessing the job reports within the database; (Evaluation, Mental Process – A person can determine a probability of achieving a job objective using objective metrics mentally or which aid of pen, paper, and calculator. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
modifying […] the portion of the pumping procedure that corresponds to the job observation with the portion of the historical pumping procedure that corresponds with the historical job observation; (Evaluation, Mental Process – A person can determine to change a job based on lessons from the past or by referencing manuals with expertise from others. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
recommending, by the machine learning process, the portion of the pumping procedure that corresponds with the job observation be replaced with the portion of the historical pumping procedure to increase a probability score above a threshold value; (Evaluation, Mental Process – A person can determine to change a job objective based on objective rubrics, including probabilities, that would yield an improvement to a job. This is an evaluation, a mental process. MPEP 2106.04(a)(2)(Ill))
comparing a first classification grade using a first combination of the metadata tags used to form a group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the group of metadata tags to determine an error value […] (Evaluation, Mental Process – This step compares classification grades to determine error values, which is practically performable in the mind or with the aid of pen and paper. Therefore, this is an evaluation, a mental process, an abstract idea.)
The claim recites evaluations, which are mental processes, which comprise an abstract idea.
Claim 16 recites an abstract idea.
Step 2A – Prong 2: Integrated into a Practical Solution?
No.
The additional limitations:
[…] by a/the managing application executing on a User Equipment (UE) […]
[…] by the managing application […]
[…] metadata tags […]
[…] wherein the metadata tags are applied by a machine learning model […]
[…] by the machine learning process […]
[…] wherein the machine learning model validates by […]
[…] wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Using machine learning or other applications at a high level of generality is utilizing generic computing elements. Further, validating a machine learning model and training a machine learning model by reducing the error in its outputs are both generic machine learning technology elements. Also, the metadata tags, as recited, are generic computing elements that represent labels of characteristics of the data to which they refer. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f), fail to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
retrieving […] a job design comprising a pumping procedure, a bill of materials, an inventory of assigned pumping units, an inventory of downhole tools, an inventory of various chemicals, or combinations thereof, wherein the pumping procedure comprises a series of sequential stages to achieve a job objective;
transporting the job design to a wellsite;
beginning the pumping procedure by [an operator] to the pumping units;
These are regular elements of setting up a pumping assembly at a wellbore prior to a determination to change a procedure. This is pre-solution insignificant extra-solution activity because, under MPEP 2106.05(g) example, starting a pumping procedure is insignificant to the determinations of the abstract idea (e.g., the determinations of the abstract idea do not involve or rely on anything having to do with these steps. That an existing job is used for a pumping procedure has no effect on whether the determinations are made to adjust the job. The job existed in data before, and that same job is the job that is adjusted. Also, placing these steps that are done with an existing job that would have been done if the job were ever used is an “apply it”operation under 2106.05(f).
retrieving […] one or more datasets of periodic pumping data indicative of the pumping procedure;
receiving […] at least one dataset indicative of a change to the pumping procedure;
The retrieving and receiving steps are mere data gathering similar to the MPEP 2106.05(g) examples: “iv. Obtaining information about transactions using the Internet to verify credit card transactions” “v. Consulting and updating an activity log” “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display.” Therefore, under MPEP 2106.05(g), the retrieving and receiving steps fail to integrate the abstract idea into a particular application.
continuing the pumping procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.
The continuing step merely continues the already changed pumping procedure, which is similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” Also, the continuing step is insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style” (also from Brown).
Therefore, there are no additional limitations in claim 16 that integrate the abstract idea into a practical application under Step 2A, Prong Two.
Claim 16 does not integrate the abstract idea into a practical application and is directed to the abstract idea.
Step 2B: Claim provides an Inventive Concept?
No.
The additional limitations:
[…] by a/the managing application executing on a User Equipment (UE) […]
[…] by the managing application […]
[…] metadata tags […]
[…] wherein the metadata tags are applied by a machine learning model […]
[…] by the machine learning process […]
[…] wherein the machine learning model validates by […]
[…] wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Using machine learning or other applications at a high level of generality is utilizing generic computing elements. Further, validating a machine learning model and training a machine learning model by reducing the error in its outputs are both generic machine learning technology elements. Also, the metadata tags, as recited, are generic computing elements that represent labels of characteristics of the data to which they refer. Therefore, these elements are generic computing elements, and, under MPEP 2106.05(f), fail to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
retrieving […] a job design comprising a pumping procedure, a bill of materials, an inventory of assigned pumping units, an inventory of downhole tools, an inventory of various chemicals, or combinations thereof, wherein the pumping procedure comprises a series of sequential stages to achieve a job objective;
transporting the job design to a wellsite;
beginning the pumping procedure by [an operator] to the pumping units;
Also, these steps that are done with an existing job that would have been done if the job were ever used is an “apply it”operation under 2106.05(f). Further, these are regular elements of setting up a pumping assembly at a wellbore prior to a determination to change a procedure making them WURC activities. Because these are “apply it steps” and are WURC activities and were also previously demonstrated to be insignificant extra-solution activity, under MPEP 2106.05(d), 2106.05(f) and 2106.05(g), they fail to combine with other claim elements to provide significantly more than the abstract idea that would confer an inventive concept. References demonstrating the WURC nature of the activities include:
API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
retrieving […] one or more datasets of periodic pumping data indicative of the pumping procedure;
receiving […] at least one dataset indicative of a change to the pumping procedure;
The retrieving and receiving steps are WURC activity similar to the MPEP 2106.05(d) examples: “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price.” Also, as previously demonstrated, these steps are insignificant extra-solution activity. Therefore, under MPEP 2106.05(d) and MPEP 2106.05(g), the retrieving and receiving steps fail to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
continuing the pumping procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.
The continuing step merely continues the already changed pumping procedure, which is similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” Also, the continuing step is WURC (see evidence in bullet point). Because the continuing step is an apply it step, WURC, and, as previously demonstrated, insignificant extra-solution activity, under MPEP 2106.05(d), 2106.05(f), and 2106.05(g), these features fail to combine with other claims elements to provide significantly more than the abstract idea that would confer an inventive concept.
API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
Therefore, there are no additional limitations in claim 16 that combine with the other elements of the claim to provide significantly more than the abstract idea that would confer an inventive concept at Step 2B.
Claim 16 is ineligible.
Dependent Claims
The dependent claims 2-8 and 10-15
Claim 2
the observation description comprises a text description, picture description, video description, at least one dataset, or combinations thereof, and wherein the at least one dataset is a dataset of measured field data, a dataset of periodic data, or combinations thereof.
This claim describes the nature of data used in the method, but the nature of the data merely limits the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B. Should it be found otherwise, the features of claim 2 are merely elements of the limitations that generated or received them, and are either elements of the abstract idea or additional limitations recited in the claim(s) from which this claim depends.
Claim 2 fails to recite any additional limitations that confer eligibility.
Claim 2 is ineligible.
Claim 3
the observation object comprises a wellbore treatment, a servicing equipment, a pumping procedure, a wellsite environment, a downhole environment, or combinations thereof.
This claim describes the nature of data used in the method, but the nature of the data merely limits the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B. Should it be found otherwise, the features of claim 3 are merely elements of the limitations that generated or received them, and are either elements of the abstract idea or additional limitations recited in the claim(s) from which this claim depends.
Claim 3 fails to recite any additional limitations that confer eligibility.
Claim 3 is ineligible.
Claim 4
the first group of metadata tags comprises at least two categories of metadata tags.
As previously demonstrated, metadata tags are described at a high level, so they are generic computing elements. Under MPEP 2106.05(f), they do not confer eligibility at either Step 2A, Prong 2, or at Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 4 fails to recite any additional limitations that confer eligibility.
Claim 4 is ineligible.
Claim 5
generating, by the machine learning classifier, a second job observation by applying at least one additional metadata tag to the first job observation by comparing a training set of job observations comprising training metadata tags to the second job observation comprising the observation description and the observation object.
The generating step is tantamount to using descriptive labels to find similar files in a filing cabinet and making comparisons based on data to consider (“inputs”). People have conducted these evaluations in their minds with the aid of pen and paper, long before the invention of the computer and machine learning algorithms. This makes the element a mental process, an abstract idea, and provides no additional limitations that could confer eligibility at Step 2A, Prong 2, or Step 2B.
The machine learning process and metadata tags, as previously demonstrated, are generic computing elements that, under MPEP 2106.05(f), fail to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 5 fails to recite any additional limitations that confer eligibility.
Claim 5 is ineligible.
Claim 6
wherein the first classification grade and the second classification grade comprise ranking values of the search results.
The machine learning process and metadata tags, as previously demonstrated, are generic computing elements that, under MPEP 2106.05(f), fail to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 6 fails to recite any additional limitations that confer eligibility.
Claim 6 is ineligible.
Claim 7
wherein the ranking value of the search results is determined by a placement of a pending job observation.
Ranking chosen options based on scores is an evaluation that was done mentally, or with aid of pen and paper, prior to computers and machine learning models. This makes the element a mental process, an abstract idea, and provides no additional limitations that could confer eligibility at Step 2A, Prong 2, or Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 7 fails to recite any additional limitations that confer eligibility.
Claim 7 is ineligible.
Claim 8
retrieving, by a managing application, a job report and a corresponding job design from the database; and
This is mere data gathering and WURC for the same reasons as the retrieving step in independent claim 9 and fails to confer eligibility for at least the same reasons as that retrieving step.
generating, by the managing application the first job observation in response to a comparison value exceeding a threshold value, wherein the comparison value is determined by comparing the job report to the job design.
Comparing scores and qualitative information to determine a best option or an option that is sufficient based on a threshold is an evaluation that has been conducted since long before computers and machine learning models. This makes the element a mental process, an abstract idea, and provides no additional limitations that could confer eligibility at Step 2A, Prong 2, or Step 2B.
The managing application, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 8 fails to recite any additional limitations that confer eligibility.
Claim 8 is ineligible.
Claim 10
calculating, by the managing application, a bill of materials and an inventory of pumping equipment from the level two job design, and wherein the managing application modifies the level one job design to the level two job design with the bill of materials and the inventory of pumping equipment.
Performing a calculation is both a mathematical calculation and an evaluation that can be performed mentally, or with the aid of pen, paper, and/or a calculator. Further, modifying an approach to a job based on this calculation is an evaluation. These are mental processes and mathematical concepts, so the limitation fails to provide any additional limitations to confer eligibility at Step 2A, Prong 2 and Step 2B.
The managing application, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 10 fails to recite any additional limitations that confer eligibility.
Claim 10 is ineligible.
Claim 11
a machine learning classifier generates a level one job design by comparing the job design to a training set of job designs; (Selecting a design based on comparison) the machine learning classifier determines a set of metadata tags from the training set of job designs; (Determining labels for data) the machine learning classifier, applies a set of metadata tags to the level one job design by comparing the job design to training set of job designs; (Labeling data based on a comparison) the machine learning classifier identifies a set of relevant job observations by a comparison value with the database; (Ranks and chooses the best options e.g., based on a score) and the set of relevant job observations is retrieved in response to the comparison value exceeding a threshold limit. (Collecting data that satisfies an objective criterion)
All of these steps are evaluations that can be performed mentally or with aid of pen, paper, and/or a calculator. These are mental processes, abstract ideas, so the limitations fail to provide any additional limitations to confer eligibility at Step 2A, Prong 2 and Step 2B.
The machine learning process classifier, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 11 fails to recite any additional limitations that confer eligibility.
Claim 11 is ineligible.
Claim 12
comparing a first probability value for achieving a job objective by the job design to a second probability value for achieving the job objective by modifying the job design with at least one relevant job observation, wherein the relevant job observation is from the set of job observations retrieved from the database; and replacing the job design with the level one job design in response to the second probability value being greater than the first probability value.
Determining and Comparing objective scores for assessing the values of options for how to perform work is an evaluation that was conducted mentally and/or with the aid of pen, paper, and/or a calculator long before the existence of computers and machine learning models to determine what to do. These are mental processes, elements of the abstract idea, so the limitations fail to provide any additional limitations to confer eligibility at Step 2A, Prong 2 and Step 2B.
The machine learning process, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 12 fails to recite any additional limitations that confer eligibility.
Claim 12 is ineligible.
Claim 13
the job objective comprises wellbore isolation, a location of top of cement, a kick off plug, a shoe test, or a combination thereof.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B. The claims fails to provide any additional limitations that confer eligibility at either Step 2A, Prong 2, or Step B
Claim 13 fails to recite any additional limitations that confer eligibility.
Claim 13 is ineligible.
Claim 14
transporting a wellbore treatment blend and an inventory of pumping equipment to a wellsite, wherein the wellbore treatment blend is included in the level two job design; beginning a wellbore treatment procedure by the managing application; retrieving, by the managing application, one or more datasets of periodic pumping data indicative of the wellbore treatment procedure; mixing a wellbore treatment, by the pumping equipment, per the wellbore treatment procedure; pumping the wellbore treatment blend per the wellbore treatment procedure;
These are all apply it steps similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” These are all insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style.” These are both examples from Brown. Further, these are WURC under MPEP 2106.05(d). Because the features of the claim are apply it and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(f) and 2106.05(g). Because the features of the claim are WURC, apply it, and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(d), 2106.05(f), and 2106.05(d).
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
As Demonstrated In a 1993 Textbook called “Cementing Technology and Procedures” by Association de recherche sur les techniques d'exploitation de petrol. The following image from Chapter 2, Page 32 shows:
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This illustrates a wellsite, where equipment did not appear naturally as trees in a forest. Everything is transported to the wellsite because the equipment was not there naturally. If Applicant disagrees, Applicant is invited to demonstrate situations in which these elements naturally spring forth from the earth. Even still, the elements are transported from the earth. The slurry’s constituent elements (e.g., water and cement) are transported at the very least, from the cement silo to the slurry preparation tank and the pumping unit. (This illustrates the well-understood, routine, and conventional nature of the transporting steps) As is illustrated in the image from the textbook, The pumping unit is attached to the wellbore. It did not connect itself. (This illustrates the well-understood, routine, and conventional nature of the connecting step) There is a slurry preparation tank that mixes the slurry blend for the fluid loss control treatment. (This illustrates the well-understood, routine, and conventional nature of the mixing step) The mix is made to specification of a fluid loss control treatment plan, as is demonstrated in Chapter 1 of the same textbook, pages 4-16. (This illustrates the well-understood, routine, and conventional nature of the receiving step as it pertains specifically to the fluid loss control treatment) Further, receiving data is well-understood, routine, and conventional activity according to the examples in MPEP 2106.05(d) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iii. Electronic recordkeeping […] iv. Storing and retrieving information in memory). As for pumping, the pumping unit connected to the wellbore for the purpose of pumping slurry is there to pump. For example, see the same textbook on Page 33, with an image demonstrating the aforementioned pumping. Also, the Applicant is directed to the Chevron reference (e.g., pages 12 and 166) in the 35 USC 103 rejections.
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alerting, by the managing application, in response to at least one dataset of periodic pumping data indicative of the wellbore treatment procedure indicating a change to the wellbore treatment procedure; (Taking note if evaluation says to change) generating, by the managing application, a job observation; (Evaluating an observation about a job) comparing a combination of metadata tags of the job observation to the metadata tags of a plurality of historical job observations in a database; (Comparing descriptive labels on information in files from past jobs) calculating a probability score for achieving the job objective based on at least one of the historical job observations; (Calculating a probability mentally and as math) recommending modifying the job design of the wellbore treatment to increase the probability score above a threshold value; (Evaluating whether to change an approach to a job based on a comparison of a score with a threshold value) and
These are evaluations that can be performed mentally and/or with the aid of pen, paper, and/or a calculator, a mental process. Also, the calculating step is a mathematical calculation, a mathematical concept. Mental processes and mathematical concepts are abstract ideas that merge with the abstract idea of the claims from which the claim depends. The features provide no additional limitations that would confer eligibility at Step 2A, Prong 2 and Step 2B.
continuing the wellbore treatment procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.
This is an apply it step similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” This is insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style.” These are both examples from Brown. Further, this is WURC under MPEP 2106.05(d). Because the features of the claim are apply it and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(f) and 2106.05(g). Because the features of the claim are WURC, apply it, and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(d), 2106.05(f), and 2106.05(d).
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
The machine learning, managing application, and metadata tags, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 14 fails to recite any additional limitations that confer eligibility.
Claim 14 is ineligible.
Claim 15
transporting a downhole tool to a wellsite, wherein the downhole tool is included in the level two job design; beginning a wellbore treatment procedure by the managing application; coupling the downhole tool with a casing via the wellbore treatment procedure;
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
As Demonstrated In a 1993 Textbook called “Cementing Technology and Procedures” by Association de recherche sur les techniques d'exploitation de petrol. The following image from Chapter 2, Page 32 shows:
PNG
media_image1.png
807
597
media_image1.png
Greyscale
This illustrates a wellsite, where equipment did not appear naturally as trees in a forest. Everything is transported to the wellsite because the equipment was not there naturally. If Applicant disagrees, Applicant is invited to demonstrate situations in which these elements naturally spring forth from the earth. Even still, the elements are transported from the earth. The slurry’s constituent elements (e.g., water and cement) are transported at the very least, from the cement silo to the slurry preparation tank and the pumping unit. (This illustrates the well-understood, routine, and conventional nature of the transporting steps) As is illustrated in the image from the textbook, The pumping unit is attached to the wellbore. It did not connect itself. (This illustrates the well-understood, routine, and conventional nature of the connecting step) There is a slurry preparation tank that mixes the slurry blend for the fluid loss control treatment. (This illustrates the well-understood, routine, and conventional nature of the mixing step) The mix is made to specification of a fluid loss control treatment plan, as is demonstrated in Chapter 1 of the same textbook, pages 4-16. (This illustrates the well-understood, routine, and conventional nature of the receiving step as it pertains specifically to the fluid loss control treatment) Further, receiving data is well-understood, routine, and conventional activity according to the examples in MPEP 2106.05(d) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iii. Electronic recordkeeping […] iv. Storing and retrieving information in memory). As for pumping, the pumping unit connected to the wellbore for the purpose of pumping slurry is there to pump. For example, see the same textbook on Page 33, with an image demonstrating the aforementioned pumping. Also, the Applicant is directed to the Chevron reference (e.g., pages 12 and 166) in the 35 USC 103 rejections.
retrieving, by the managing application, one or more datasets of periodic pumping data indicative of the wellbore treatment procedure;
This is mere data gathering, which is insignificant extra-solution activity, and, under MPEP 2106.05(g) fails to confer eligibility at Step 2A, prong 2. It is also WURC similar to the MPEP 2106.05(d) examples “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” and “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”. Because this is WURC and insignificant extra-solution activity, it fails to confer eligibility at Step 2B.
alerting, by the managing application, in response to at least one dataset of periodic pumping data indicative of the wellbore treatment procedure indicating a change to the wellbore treatment procedure;
alerting is mere reporting of data which is extra-solution activity under MPEP 2106.05(g) (e.g., printing out a report or transmitting, storing, or displaying data) and is WURC (e.g., printing out a report or transmitting, storing, or displaying data).
generating, by the managing application, a job observation;
Generating an observation is practically performable in the mind or with the aid of pen and paper. Therefore, it is an evaluation, a mental process, an element of the abstract idea.
comparing, by a machine learning process a combination of metadata tags of the job observation to the metadata tags of a plurality of historical job observations in a database;
Comparing data labels is practically performable in the mind or with the aid of pen and paper, so it is a mental process, an abstract idea.
Doing so generically using metadata tags and a machine learning process is merely applying the mental process using generic computing elements recited at a high level, which fails to confer eligibility under MPEP 2106.05(f).
calculating a probability score for achieving the job objective based on a historical job observation;
Using numerical data to calculate probabilities is an evaluation, a mental process, and a mathematical calculation, a mathematical concept, that has been practically performable in the mind or with the aid of pen and paper since before the advent of the computer.
recommending modifying the job design of the wellbore treatment to increase the probability score above a threshold value; and
Providing a recommendation is mere data transfer which is insignificant extra-solution activity and WURC and, under MPEP 2106.05(g) and 2106.05(d), fails to confer eligibility.
Continuing the wellbore treatment procedure by the managing application in response to the probability score being above a threshold value for achieving the job objective.
This is an apply it step similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” This is insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style.” These are both examples from Brown. Further, this is WURC under MPEP 2106.05(d). Because the features of the claim are apply it and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(f) and 2106.05(g). Because the features of the claim are WURC, apply it, and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(d), 2106.05(f), and 2106.05(d).
This is also a repetition of the existing steps. Continuing to do something does not change anything even if its continuation is after some determination is made. For the determination to be relevant, there would have to be some reason that it would otherwise have ceased to operate.
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
The machine learning, managing application, and metadata tags, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 15 fails to provide any additional limitations that confer eligibility.
Claim 15 is ineligible.
Claim 17
determining, by the machine learning model, a set of metadata tags from a training set of job observations.
This is an evaluation of providing descriptive labels to job observations which has been performed in the mind or with aid of pen and paper long before computers and machine learning models. The evaluation is a mental process, an abstract idea, that merges with the abstract idea of the claim(s) from which this claim depends. This claim provides no additional limitations to confer eligibility at Step 2A, Prong 2 and Step 2B.
The machine learning, managing application, and metadata tags, as previously demonstrated, are generic computing elements that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claims 17 fails to recite any additional limitations that confer eligibility.
Claim 17 is ineligible.
Claim 18
wherein the database is on a computer system, a local network, a local data source, or a remote data source; and wherein the remote data source is a server, a computer system, a virtual computer system, a virtual network function, or data storage device.
The database, computer system, local network, local data source, remote data source, server, computer system, virtual computer system, virtual network function, and data storage device are all generic computing elements recited at a high level of generality and fail, under MPEP 2106.05(f), to confer eligibility at Step 2A, Prong 2 and Step 2B.
Claim 18 fails to recite any additional limitations that confer eligibility.
Claim 18 is ineligible.
Claim 19
wherein the job observation comprises an operational dataset, a portion of the pumping procedure, a current step of the pumping procedure, a set of identification data, or combinations thereof.
These features describe the nature of data used in the method that merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 19 fails to recite any additional limitations that confer eligibility.
Claim 19 is ineligible.
Claim 20
transporting a modified job design to a wellsite, wherein the modified job design includes the job design and additional materials based on at least one historical job observation; beginning a wellbore treatment procedure by the managing application; coupling a downhole tool with a casing string via the wellbore treatment procedure;
These are all apply it steps similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” These are all insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style.” These are both examples from Brown. Further, these are WURC under MPEP 2106.05(d). Because the features of the claim are apply it and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(f) and 2106.05(g). Because the features of the claim are WURC, apply it, and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(d), 2106.05(f), and 2106.05(d).
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
As Demonstrated In a 1993 Textbook called “Cementing Technology and Procedures” by Association de recherche sur les techniques d'exploitation de petrol. The following image from Chapter 2, Page 32 shows:
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This illustrates a wellsite, where equipment did not appear naturally as trees in a forest. Everything is transported to the wellsite because the equipment was not there naturally. If Applicant disagrees, Applicant is invited to demonstrate situations in which these elements naturally spring forth from the earth. Even still, the elements are transported from the earth. The slurry’s constituent elements (e.g., water and cement) are transported at the very least, from the cement silo to the slurry preparation tank and the pumping unit. (This illustrates the well-understood, routine, and conventional nature of the transporting steps) As is illustrated in the image from the textbook, The pumping unit is attached to the wellbore. It did not connect itself. (This illustrates the well-understood, routine, and conventional nature of the connecting step) There is a slurry preparation tank that mixes the slurry blend for the fluid loss control treatment. (This illustrates the well-understood, routine, and conventional nature of the mixing step) The mix is made to specification of a fluid loss control treatment plan, as is demonstrated in Chapter 1 of the same textbook, pages 4-16. (This illustrates the well-understood, routine, and conventional nature of the receiving step as it pertains specifically to the fluid loss control treatment) Further, receiving data is well-understood, routine, and conventional activity according to the examples in MPEP 2106.05(d) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data […] iii. Electronic recordkeeping […] iv. Storing and retrieving information in memory). As for pumping, the pumping unit connected to the wellbore for the purpose of pumping slurry is there to pump. For example, see the same textbook on Page 33, with an image demonstrating the aforementioned pumping. Also, the Applicant is directed to the Chevron reference (e.g., pages 12 and 166) in the 35 USC 103 rejections.
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retrieving, by the managing application, one or more datasets of periodic pumping data indicative of the wellbore treatment procedure; receiving, by the managing application, at least one dataset indicative of a change to the pumping procedure; generating, by the managing application, a job observation;
This is mere data gathering, which is insignificant extra-solution activity, and, under MPEP 2106.05(g) fails to confer eligibility at Step 2A, prong 2. It is also WURC similar to the MPEP 2106.05(d) examples “i. Receiving or transmitting data over a network” “iii. Electronic recordkeeping” “iv. Storing and retrieving information in memory” and “vi. Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price”. Because this is WURC and insignificant extra-solution activity, it fails to confer eligibility at Step 2B.
alerting, by the managing application, in response to the job observation not corresponding to the historical job observation; (Taking note if evaluation says to change) calculating, by the machine learning model, the probability score for achieving the job objective by modifying a portion of the pumping procedure based on the at least one historical job observation; (Evaluating an observation about a job) recommending, by the machine learning model, one or more portions of the modified pumping procedures to replace one or more portions of the pumping procedure to increase the probability score above a threshold value; and ; (Evaluating whether to change an approach to a job based on a comparison of a score with a threshold value)
These are evaluations that can be performed mentally and/or with the aid of pen, paper, and/or a calculator, a mental process. Also, the calculating step is a mathematical calculation, a mathematical concept. Mental processes and mathematical concepts are abstract ideas that merge with the abstract idea of the claims from which the claim depends. The features provide no additional limitations that would confer eligibility at Step 2A, Prong 2 and Step 2B.
continuing the modified pumping procedure, by the managing application, in response to the probability score being above the threshold value for achieving the job objective.
This is an apply it step similar to the MPEP 2106.05(f) example: “A method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair.” This is insignificant extra-solution activity similar to the MPEP 2106.05(g) example: “Cutting hair after first determining the hair style.” These are both examples from Brown. Further, this is WURC under MPEP 2106.05(d). Because the features of the claim are apply it and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(f) and 2106.05(g). Because the features of the claim are WURC, apply it, and insignificant extra-solution activity, the features fail to confer eligibility at Step 2A, Prong 2, under MPEP 2106.05(d), 2106.05(f), and 2106.05(d).
This is also a repetition of existing steps that fails to confer eligibility for at least the same reasons.
WURC evidence - API Standard 65, Part 2, Second Edition, Dec. 2010, "Isolating Potential Flow Zones During Well Construction",§ 5.7.4 ,i 1, § 7.3 ,i 2, Appendix B, § B.2.4, the bulleted list for the cementing plan including "pump rates"; also§ 5.6.4 ,i 2: "Computer based thermal modeling programs may be used to develop cementing testing temperatures. Such programs require input information such as static temperature, formation and well fluid thermal characteristics, rheologies, estimated job volumes, planned pump rates and well geometry. The predictions generated by thermal modeling programs may vary significantly; operators may consider employing more than one thermal model to arrive at a cement test temperature schedule", and§ 5.6.5.5: "Some computer programs may be used to determine the type and volume of spacers to be pumped for drilling fluid removal and predict the degree of fluid (cement, spacer, drilling fluid) intermixing that may occur during placement.",§ 5.9.5 ,i,i 1-2 incl.: "Pumping the cement job with the designed pump rates is important but density control should not be sacrificed to obtain a planned rate"
With respect to transporting the blend, see§ 5.9 which details this, e.g.§ 5.9.2: "The service company providing the cement and/or cement blend should follow all established, documented company procedures to ensure that all received neat cement is within acceptable specifications upon arrival at the bulk plant", e.g.§ 5.9.2: "All cement blends should be stored and transported in properly maintained bulk storage tanks", e.g. page 80, table at the top of the page, the row for "Special Blending Mixing"
The machine learning, managing application, and metadata tags, as previously demonstrated, is a generic computing element that, under MPEP 2106.05(f), fails to confer eligibility at Step 2A, Prong 2, and Step 2B.
Features describing the nature of data used in the method merely limit the abstract idea to a particular field of technology, and, under MPEP 2106.05(h), fail to confer eligibility at either Step 2A, Prong 2, or Step B.
Claim 20 is ineligible.
Claims Allowable Over Art
Claims 1-20 are allowable over the prior art. The prior art fails to teach at least the following analogous, amended claim features of the independent claims:
Claim 1:
validating the metadata tags by comparing a first classification grade using a first combination of the metadata tags used to form a second group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the second group of metadata tags to determine an error value; and
training the machine learning model to reduce the error value by modifying the first combination or the second combination.
Claim 9:
wherein the machine learning model validates the metadata tags by comparing a first classification grade using a first combination of the metadata tags to form a group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the group of metadata tags, to determine an error value, and
wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Claim 16:
wherein the machine learning model validates the metadata tags by comparing a first classification grade using a first combination of metadata tags used to form a group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the group of metadata tags, to determine an error value, and
wherein the machine learning model is trained to reduce the error value by modifying the first combination or the second combination.
Specifically, Heidari teaches training a machine learning model to act as a lessons-learned system in order to ensure that the lessons from past jobs and prior employees are readily transferable to future projects and employees. This information includes the claimed job observations and comparison of data.
Haiyeng teaches multilabel classification systems, such as the one in the claims that is configured to generate metadata tags. Haiyeng does not teach metadata tags.
NASA teaches automated generation of metadata tags using machine learning.
A new Chen reference, discovered in a search and presented on the record, teaches the automated determination of metadata tags using machine learning. Existing references on the record that were not applied as art, such as the Cheng and Tang references do not remedy the deficiencies.
Accordingly, none of the cited references appear to teach validating metadata tags by comparing a first classification grade using a first combination of the metadata tags used to form a second group of metadata tags to a second classification grade using a second combination of the metadata tags used to form the second group of metadata tags to determine an error value that is then used to train a machine learning model by reducing the error value by modifying one of the combinations of the metadata tags. Also, any combination of the references purported to teach this would require the use of impermissible hindsight.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lessons Learned Systems/Search-Based Modelling:
US 20230022567 A1 to Cheng et al,. (Teaches most of the features of the claims including a lessons learned system using tags)
US 20200332627 A1 to Tang et al. (Teaches most of the features of the claims)
NPL: “Home Applying Textual Case-Based Reasoning and Information Extraction in Lessons Learned Systems” by Ashley (Teaches using AI to extract descriptive data for a lessons learned system)
NPL: “Ranking with Query-Dependent Loss for Web Search” by Bian et al. (Teaches query dependent loss for training search based models)
NPL: “Organizing lessons learned practice for product–service innovation” by Chirumalla (Teaches automating a lessons learned system)
NPL: “A lessons-learned tool for organizational learning in construction” by Eken et al. (Teaches a lessons learned system with discussion of automation)
NPL: “Resolving Redundancy: A Recurring Problem in a Lessons Learned System” by Everett et al. (Teaches automating removal of redundancy in lesson learned systems)
NPL: “Investigating the Benefits of Applying Artificial Intelligence Techniques to Enhance Learning Experiences in Capstone” by Gonzalez et al. (Teaches using AI to benefit from lessons learned)
NPL: “Implementing Knowledge-Sharing Systems and Establishing a Culture to Share Lessons Learned Within a Multidisciplinary Company Enhancing Effective Knowledge Transfer” by Hinze et al. (Teaches lesson learned systems with automation)
NPL: “Communicate Lessons, Exchange Advice, Record (CLEAR) Database Development” by Jaselskis et al. (Teaches automation of a lessons learned system.)
NPL: “Text Classification Algorithms: A Survey” Kowsari et al. (Teaches the use of machine learning for text classification of documents)
NPL: “Efficient Optimization for Rank-based Loss Functions” by Mohapatra et al. (Teaches search-rank-based loss functions for ML model training)
NPL: “Learning Object Metadata: An Empirical Investigation and Lessons Learned” by Najjar et al. (Teaches using metadata in a lessons learned system for education)
NPL: “Incidents Investigations and Learning Approach in Oil & Gas Industry” by Al-Qubaisi (Teaches a lessons learned system for the oil and gas industry with automation)
NPL: “Knowledge Management Metamodel from Social Analysis of Lessons Learned Recorded in the Cloud” by Quintero et al. (Teaches using a metamodel for a lessons learned system)
NPL: “Intelligent lessons learned systems” by Weber et al. (Teaches lessons learned system and use of AI therewith)
NPL: “Nine simple ways to make it easier to (re)use your data” by White et al. (Teaches parameters of a lessons learned system)
NPL: “Computational narrative mapping for the acquisition and representation of lessons learned knowledge” by Young et al. (Teaches AI in lessons learned systems)
NPL: “
Fluid Treatments/Wellbore Context US 20090188718 A1 to Kaageson-Loe et al. (Teaches fluid loss treatment procedures)
US 20190032476 A1 to Yerubandi et al. (Teaches a simple fluid loss rate equation)
US 20130181155 A1 to Robison et al. (Teaches an equation for probability of jamming)
US 20090188718 A1 to Lucas et al. (Teaches methods for sensing downhole parameters)
US 20210255913 A1 to Singh et al. (Teaches prediction of equipment failures in a downhole environment)
NPL: “Composition and Properties of Drilling and Completion Fluids” by Caenn et al. (Teaches designing for filtration properties of filter cakes)
NPL “Jamming and critical outlet size in the discharge of a two-dimensional silo” by Janda et al. (Teaches parameters that are useful in determining jamming)
NPL “Flow Rate of Particles through Apertures Obtained from Self-Similar Density and Velocity Profiles” by Janda et al. (Teaches parameters that are useful in determining parameters of the claims)
NPL “An Engineered Approach to Design Biodegradables Solid Particulate Diverters: Jamming and Plugging” by Shahri et al. (Teaches equations for determining jamming)
NPL: “A probability-Based Pore Network Model of Particle Jamming in Porous Media” by Li et al. (Teaches a probability-based pore network model)
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/J.M.W./Examiner, Art Unit 2188 /RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188