DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. § 101 are directed to an abstract idea without significantly more.
The claims do not provide significantly more than the judicial exception under the subject matter eligibility two-part statutory analysis, as provided below.
Regarding Step 1,
Step 1 addresses whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter according to MPEP §2106.03. The claims fall within one of the four statutory categories.
Regarding Step 2A [prong 1],
The claimed invention recites an abstract idea according to MPEP §2106.04. Independent claim 1, also representative of independent claims 9 for the same abstract features, is underlined below which recite the following claim limitations, as an abstract idea.
Claims 1, 11 and 16: A method for asset analysis:
receiving first input data for a plurality of first assets; receiving second input data for a plurality of second assets; receiving a request to screen one or more of the second assets, wherein the request is to detect an anomaly and/or to improve a performance of one or more of the second assets; selecting one or more screening tools based upon the request determining an order to apply the one or more selected screening tools based upon the first input data, the second input data, and the request; and screening one or more of the second assets using the one or more selected screening tools in the order.
The underlined claim limitations, under its broadest reasonable interpretation, fall under “Certain Methods of Organizing Human Activities” grouping of abstract ideas, and includes at least managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP §2106.04(a)(2)(II).
But for the recitation of generic implementation of computer system components, the claimed invention merely recites a process for managing personal behavior/relationships or interactions between people because the claimed steps recite asset analysis by screening (oil well) assets according to rules and screening for detecting an anomaly or improve performance. Accordingly, since the claimed invention describes a process that falls under “Certain Methods of Organizing Human Activities” grouping, the claimed invention recites an abstract idea.
Regarding Step 2A [prong 2],
The judicial exception is not integrated into a practical application according to MPEP §2106.04(d). Claims 1 and 9 include the following additional elements:
A computing system comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
building or training a large language model (LLM) based upon the first input data; using the LLM.
In particular, the additional elements cited above beyond the abstract idea are recited at a high-level of generality and simply equivalent to a generic recitation and basic functionality that amount to no more than mere instructions to apply the judicial exception using generic computer technology components.
The claimed invention merely provides an abstract-idea-based-solution implemented with generic computer processes and components recited at a high-level of generality (receiving, storing, determining, and comparing data) using computer instructions to implement the abstract idea on a computer, and merely “apply it” without any meaningful technological limits or any improvement to technology, technical field or improvement to the functioning of the computer itself.
Additionally, receiving first input data for a plurality of first assets; receiving second input data for a plurality of second assets; receiving a request to screen one or more of the second assets amounts to data gathering and selecting a particular data source or type of data to be manipulated, thus does not add any meaningful limitations, and since receiving, storing and transmitting data is considered one of the most basic functions of a computer, these additional elements are deemed as insignificant extra-solution activity to the judicial exception. The legal precedent in Electric Power Group and Ultramercial cited in MPEP 2106.05(g) indicate that selecting information, based on types of information and availability of information for collection, analysis and display, and requiring a request from a user to view an advertisement and restricting public access, are all insignificant extra-solution activity.
Therefore, the additional elements fail to integrate the recited abstract idea into any practical application since they do not impose any non-generic meaningful limits on practicing the abstract idea. Thus, the claimed invention is directed to an abstract idea.
Regarding Step 2B,
The claimed invention does not include additional elements that are sufficient to amount to significantly more than the judicial exception. See MPEP §2106.05.
As discussed above, the claimed additional elements recited above amounts to no more than mere instructions to implement the abstract idea by adding the words “apply it” using generic computer components and functionality. See MPEP §2106.05(h). Mere instructions to apply the judicial exception using generic computer components are insufficient to provide an inventive concept. Furthermore, the claimed additional elements merely limit the abstract idea to be executed in a computer environment, thus do nothing more than generally linking the use of a judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Additionally, re-evaluating the insignificant extra-solution activities listed above, it is determined that they are also well-understood, routine, and conventional, as well. See MPEP 2106.05(d). The legal precedent in Ultramercial, Versata, Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that storing and retrieving information in memory, as well as receipt and transmission of information over a computer network, and updating an activity log are a well-understood, routine, and conventional functions claimed in a generic manner, as is the case here. See also Trading Techs. Int’l, Inc. v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019) (data gathering and displaying are well-understood, routine, and conventional activities) and also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (“That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive”).
Considered as an ordered combination, the additional elements are claimed at a high-level of generality and add nothing that is not already present when the steps are considered separately. The sequence of the claimed limitations is equally generic and otherwise held to be abstract since the combination of these additional elements is no more than mere instructions to apply the judicial exception using generic computer components operating in their ordinary and generic capacities of what is typically expected of computers receiving, storing and updating data, and receiving and transmitting data between generic computer devices. The claimed invention is not patent eligible because the additional elements are merely invoked as tools to execute the abstract idea and thus are insufficient to amount to an inventive concept significantly more than the judicial exception.
As for dependent claims, they merely further narrow and reiterate the same abstract ideas for storing and updating data, and receiving and transmitting data using generic data storage and transmittal techniques with the same additional elements as recited above which provide nothing more than applying the abstract idea using generic computer technology components. Furthermore dependent claims comprise the following additional elements: displaying a result of screening [displaying data]. These additional elements do not provide any improvement to technology, technical field or improvement to the functioning of the computer itself, and at best simply applying the abstract idea executed in a general-purpose computer environment. Therefore the dependent claims are also directed to ineligible subject matter since they do not provide significantly more than the abstract idea itself.
Thus, after considering all claim elements in Claims 1-20 both individually and as an ordered combination, it has been determined that the claimed invention as a whole, is not enough to transform the abstract idea into a patent-eligible invention since nothing in the claim limitations provide significantly more than the abstract idea under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(b) the invention was patented or described in a printed publication in this or a foreign country or in public use or on sale in this country, more than one year prior to the date of application for patent in the United States.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14 and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GUPTA (US20210230981).
Regarding Claims 1 & 11 and 16,
Gupta discloses:
A method and a computing system and non-transitory computer readable medium for performing asset analysis, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations ([0003-0004] A method and a computing system that includes one or more processors, and a memory system including one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors cause the computing system to perform operations; Abstract and Figs. 1-15 for Oilfield data file classification and information processing systems using machine learning and [0056] an AI engine to generate actionable insights on oilfield data by increasing data utilization and automated data mining) comprising:
receiving first input data for a plurality of first assets; building or training a large language model (LLM) based upon the first input data, and receiving second input data for a plurality of second assets ([0038-0042] In FIG. 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, entity objects, property objects that may represent wells, surfaces, bodies, reservoirs, etc. Geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators and equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155 and other equipment 156 may be located remote from a well site and include sensing, and detecting well data, see also Fig 2 and [0048-0054] data ingestion phase 202, [0070-0075] larger training data sets can be used to improve accuracy and to train a multi-layer (e.g., three-layer) neural network multi-class language classification model, [0023] machine learning model can classify and tag subsets of training data and implement a clustering algorithm, [0044-0046] Natural language processing (NLP) and machine learning language model (LLM) can enable data ingestion and insight generation using oil field data. A machine learning technique can be trained to predict oil well intervention categories and other categories for extracting resources from geological reservoirs. Machine learning technique can be trained to identify a pattern and context of repeating words. Domain-led autonomous management of oil and gas fields may involve the use of AI (artificial intelligence) to collect data across various sources and also generate insights from historical data in order to enhance oil production operations, operating expense reduction, and turnaround time for workover planning and oil field optimization, see also [0055-0058] detecting all written data, including handwriting with optical character recognition techniques and contextualization of keywords such as common oil and gas terms, and the data quality rules can be used for removing outliers from time series data, handling missing data, removing stop words, and using stem words in unstructured data, and natural language processing enabled learning, and deep learning for ingesting, organizing, and interpreting such large datasets. Natural language processing may facilitate automatically understanding years of field history and heterogeneous production records, including extracting the relevant oilfield data from free-text fields and translating data into a standardized data, ML model can be a neural network or language classification model, language model can classify and tag subsets of training data and implement a clustering algorithm, [0044-0046] Natural language processing (NLP) and machine learning language model (LLM) can enable data ingestion and insight generation using oil field data, [0117] training and implementing word2vec model (LLM), [0106-0110] Word feature extraction module uses the organized data from the data extraction module and clustering module may execute the machine learning algorithm. The ML algorithm follows iterative steps, assigning data points as centroids and finding distances of other data points to the centroids, the iterative clustering continues, until the number of data points which have distances greater than 1 from their respective centroids is minimum. This small quantity of data points are considered as outliers, see also [0060-0062] [0086-0087] and [0112-0117]);
receiving a request to screen one or more of the second assets, wherein the request is to detect an anomaly or to improve a performance of one or more of the second assets ([0025] a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, [0060-0066] Machine learning techniques can be used in oil field optimization 208 to recommend actions, diagnose anomalies, and discover patterns in real-time. AI search engine 308 is implemented to search with data enrichment module 400 configured to extract data based on context, perform fact extraction, obtain correlation statistics and calculate key performance indicators, [0070-0079] NLP-enabled machine learning from associated free text expressed as graphs, and calculation and visualization of performance indicators to identify wells that were candidates for performance improvement, and performance indicators help generate actionable insights for field operations to increase production and/or choose an intervention mechanism and beneficial practices, and also understand bottlenecks, learn best practices from the past operations so that they can refine and optimize their present and planned interventions operation strategy (e.g., in a field optimization phase as shown in FIG. 2), and comparing data across a wide variety of wells, well events, such as equipment failure, [0091-0092] Providing well workovers and interventions, [0106-0110] Word feature extraction module uses the organized data from the data extraction module and clustering module may execute the machine learning algorithm. The user request can specify a subset of data from the repository to be included in the structured data object);
selecting one or more screening tools using the LLM based upon the request; determining an order to apply the one or more selected screening tools based upon the first input data, the second input data, and the request ([0065] search engine 308 can also return the files based on the order of importance and relevance of the search criteria, such as descending order, [0067] search engine can be used on any dataset to extract any kind of files like workover reports, completion reports, frac reports, and the data enrichment module 400 may include a fact extraction module that extracts entities from these files in a key value manner wherein from these workover files it extracts values of attributes like well name, date of workover, type of intervention, cost related to the workover etc. and organizes and aggregates this extracted information of each well over time and across wells in the field in a structured chronological order. The module 400 may then form associations between this structured information and the production time series data, [0116] a search engine can score and rank the relevance of each document based on the matrix, the matrix including input data and prompt); and
screening one or more of the second assets using the one or more selected screening tools in the order ([0032] the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130 [0120] executing an oil and gas data instruction based on the one or more clusters, the oil and gas data instruction can include aggregating data that share one of the one or more clusters and include generating a second structured data object including data from the aggregated data, [0045] identify a source of data that includes workover information and extract workover and cost information from the data sources. In some embodiments, a machine learning technique can be trained to predict well intervention categories and determine a return on investment from workovers and rank the workovers based on a production improvement and a payout time, [0065] The search engine 308 can also return the files based on the order of importance and relevance of the search criteria and the results of the search engine 308 can be ranked by order of importance).
Claims 2 and 12. Gupta discloses wherein the first input data comprises time series data, images, production performance, energy consumption, temperature, pressure, flow rate, vibration, speed, water cut, gas-oil ratio, valve or actuator positions, corrosion and/or erosion status, noise levels, radiation levels, tank levels, uptime status, choke settings, or a combination thereof, and wherein the first input data also comprises training manuals for the first assets, operation manuals for the first assets, maintenance history for the first assets, or a combination thereof ([0037] input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, [0042-0044] ingested data may be in the form of well designs, well plans, drilling logs, geological data, wireline or other types of well logs, workover reports, production data, offset well data).
Claims 3 and 13. Gupta discloses wherein the first assets comprise one or more wells, compressors, pumps, tanks, separators, production manifolds, artificial lifts, electrical submersible pump, a gas lifts, plunger lifts, rod pump prime movers, or a combination thereof ([0009] FIG. 2 illustrates a block diagram of a method for data organization and oilfield insight generation, [0012] FIG. 5 illustrates oil production with episodic well intervention and time series model and forecast of each production segment, [0016] FIG. 9 illustrates a flowchart of a method for ingesting large amounts of oilfield data of various different types and using the ingested data for oilfield management and evaluation, [0042] collecting and/or using any type of oilfield data, which may include seismic data, borehole tool data (e.g., wireline, drilling, or fracturing), and surface equipment data, e.g., drilling rig data or artificial lift pump data).
Claim 4, 5 and 17. Gupta discloses wherein selecting the one or more screening tools comprises: interpreting the request; classifying the request into a domain-specific methodology, wherein the request is classified after the request is interpreted; and selecting the one or more screening tools based upon the domain-specific methodology ([0038] the model simulation layer 180 may provide domain objects 182, [0046] Embodiments of the present disclosure may employ autonomous systems or semi-autonomous systems, e.g., artificial intelligence or “AI”. Domain-led autonomous management of oil and gas fields may involve interactions among multiple agents and systems that use AI to collect data across complex information sources and generate insights from historical data in order to enhance production operations, operating expense reduction, and turnaround time for workover planning and field optimization, [0049] domain data processing information related to a field's production potential to support a go/no-go decision to undertake a certain activity for the project, [0096] classifying clusters, wherein each cluster may also be created and visualized, each being unique in its corpus and significant of a specialized domain using the cluster centroid together with the word cloud for each cluster which can be used by domain methodology to quickly classify the whole cluster set, and selecting keyword search for glossary-based search terms can include search terms configured to identify a particular type of data based on the classification)
wherein the one or more screening tools comprise: a first of the one or more screening tools configured to detect the anomaly and/or the performance; a second of the one or more screening tools configured to determine a cause of the anomaly and/or the performance; a third of the one or more screening tools configured to determine a remedy for the anomaly and/or improve the performance; and a fourth of the one or more screening tools configured to predict an outcome after the remedy and/or improvement is implemented, wherein the prediction comprises an economic analysis ([0045] determine a return on investment from workovers and rank the workovers based on a production improvement and a payout time, [0061-0062] provides tools and capabilities to organize and contextualize historical data related to workover interventions, model workover upside based on production and economic potential, identify bottlenecks and learn best practices from historical workover operations using natural language processing and machine learning, and he output from machine learning techniques can be used in field optimization 208 to recommend actions, diagnose anomalies, and discover patterns in real-time. Field optimization may include understanding the impact of historical field interventions to predict production and economic performance of future workovers, which may assist in selecting a beneficial and economical workover type and timeline for oil wells, [0074] This model assists in determining and quantifying production and economic upside due to each intervention. In this manner, economic metrics (e.g., return on investment) may be estimated for each workover).
Claims 6 and 18. Gupta discloses wherein the order is also determined based upon an amount of time, detail, and/or effort to implement the remedy and/or to improve the performance, an expense to implement the remedy and/or to improve the performance, a type of the remedy or the improvement, a likelihood of a risk of the anomaly, an impact of the remedy, weights, custom rules, or equations to calculate an indicator for the order, or a combination thereof ([0049] processing information related to a field's production potential to support a go/no-go decision to undertake a certain activity for the project. Such activities for which a go/no-go decision may be made include drilling operations, treatment operations, intervention operations, workover operations, artificial lift selections, production, well designs, etc., e.g., generally anything for which the likelihood of a financial return may be evaluated, e.g., in terms of cost versus production, [0079] here the well event represents a paid-for activity, e.g., maintenance or a workover, the area 509 may represent a return on the investment, both in time and cost. This can be conducted for each of the zones 501-505. Moreover, a trend to the returns from the well events (e.g., diminishing) may facilitate making a forecast on a return of a subsequent paid-for well events (e.g., workovers). This may facilitate determining whether to conduct a workover, and what type to perform, e.g., depending on the expected return. Further, by comparing data across a wide variety of wells, well events, such as equipment failure, may be expected and the costs associated therewith accounted for, [0080] his change may be calculated based on historical data, if the historical data is parsed and available, as described above. FIG. 6 illustrates an example of several such well events. For example, correcting a bad pump can provide a range of returns, from e.g., about 50% to about 200% increase, while ported rods can have a net positive or a net negative effect, as highlighted).
Claims 7 and 19. Gupta discloses wherein the screening is based upon a combination of rules, and wherein the rules dictate that the screening be performed for wells in a predetermined area, to the wells of a predetermined type, to compressors above or below predetermined compressor thresholds, or a combination thereof ([0072] NLP-enabled learning from associated free text expressed as graphs, and calculation and visualization of performance indicators to identify wells that were candidates for performance improvement, [0055] the data enrichment phase 204 may include determining data quality rules, key performance indicators, correlation statistics, contextualization techniques, and business intelligence techniques, among others. In some embodiments, the data quality rules can indicate a threshold).
Claims 8, 9 and 20. Gupta further comprising displaying a result of screening, wherein the result comprises a ranking of one or more of the second assets based upon the anomaly and/or the performance, the cause of the anomaly and/or the performance being below a performance threshold, a timeframe and/or expense to implement the remedy or improvement, the predicted outcome after implementing the remedy or the improvement, or a combination thereof ([0039] Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model, , [0055] the data enrichment phase 204 may include determining data quality rules, key performance indicators, correlation statistics, contextualization techniques, and business intelligence techniques, among others. In some embodiments, the data quality rules can indicate a threshold, [0062] field optimization 208 to recommend actions, diagnose anomalies, and discover patterns in real-time, [0092] a workover can be identified for a particular oil rig that resulted in an increased amount or flow of resources from a reservoir, [0045] determine a return on investment from workovers and rank the workovers based on a production improvement and a payout time, [0065] The search engine 308 can also return the files based on the order of importance and relevance of the search criteria and ranked by importance, see also [0116] scoring and ranking the relevance), and performing a wellsite action in response to the result, wherein the wellsite action comprises generating and/or transmitting a signal that instructs or causes a physical action to occur ([0062] field optimization 208 to recommend actions, diagnose anomalies, and discover patterns in real-time, [0069] workover report given is describing the workover job such as cause, observations, actions taken and impact, [0073] choose an appropriate intervention mechanism and replicate beneficial practices determined from historical learnings. In this manner, the knowledge generation phase 206 helps in automating the field optimization process in a data-driven, artificial-intelligence manner, [0089] the clusters may represent different actions, e.g., workovers, interventions, fracturing operations, drilling operations, production operations, completion operations).
Claim 10. Gupta discloses wherein the physical action improves the performance in one or more of the second assets, and wherein the physical action comprises performing setpoint changes, adjusting a speed, adjusting a pressure, adjusting a chemical dosage, or a combination thereof ([0060] Provide action to improve workover planning and operating expense spending by enabling rapid access to relevant content from historical records in an organized manner and learning patterns to better understand past strategies, capital spending, and make recommendations for improving production performance using an integrated workover plus operating expense digital workflow, [0078-0080] regressions 505-508 may represent “what if” scenarios, in particular, indicating an impact of the well events that were experienced, the production is changed by the well-event represented by the vertical line between the zones 501, 502. In this case, the production is increased, and thus this well event may be representative, e.g., of fixing a piece of equipment, and facilitate determining whether to conduct a workover, and what type to perform, e.g., depending on the expected return and the type of well event may result in a different change in production).
Claim 14. Gupta discloses herein the one or more screening tools comprise a time series anomaly detection tool ([0098] The data contained cross section diagrams, periodic charts and time series data where essential information , [0074] episodic interventions activities are connected to time series data, calculations may be performed to forecast and compare individual well production with and without workover intervention).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Note: In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over GUPTA in view of MILLER (US 20250139160).
Claim 15. Gupta already discloses wherein the request is received of the LLM ([0034] framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity, [0062] the output from machine learning techniques can be used in field optimization 208 to recommend actions, diagnose anomalies, and discover patterns in real-time, [0065] The search engine 308 can also return the files based on the order of importance and relevance of the search criteria, [0044-0046] Natural language processing (NLP) and machine learning language model (LLM) can enable data ingestion and insight generation using oil field data. [0117] training and implementing word2vec model (LLM), [0106-0110] Word feature extraction module uses the organized data from the data extraction module and clustering module may execute the machine learning algorithm).
Gupta does not explicitly verbatim specify that the request of the search engine is a type of “CHATBOT” which Examiner asserts is just a generic interface that receives input and outputs results performed like the search engine interface.
Nonetheless, MILLER discloses wherein the request is received via a chatbot of the LLM ([0036] recommendations may be provided as part of an artificial intelligence (e.g., LLM-powered) chat between a user and a chatbot (e.g., in response to a user request for recommendations), [0174] the computerized assistant (e.g., an AI/LLM-powered chatbot), prompts the user to provide a request for assistance (e.g., for content recommendations).
Thus, it would have been obvious to one of ordinary skill in the art, before the effective
filing date of the invention, to modify the Gupta to incorporate Miller’s chatbot type LLM-based search request interface as taught by Miller. One of ordinary skill in the art would have been motivated to include receiving the request via a chatbot of the LLM for the benefit of “generate sophisticated user-specific customized content in substantially real time” (Miller; [0019]).
Relevant Prior Art
The relevant prior art made of record below is not relied upon but considered pertinent
to applicant's disclosure and can be found in the PTO-892 Notice of References Cited.
Relevant Prior Art References and Non-Patent Literature
US20210166184 Oil field resources are allocated using machine learning and optimization. The job schedule is generated using the set of priorities. An updated job schedule is presented based on the update to the priority.
US20220114302 Reservoir performance system
US20230017966 Well Construction Equipment Framework
US20190188584 Computer System And Method For Building And Deploying Models Predicting Plant Asset Failure
US20190187685 Methods and systems for data collection in tanks with future status prediction and load balancing
US20210133607 Systems and methods for self-learning artificial intelligence of things devices and services
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US20200370423 Controller optimization via reinforcement learning on asset
US20220269853 Domain-specific language interpreter and interactive visual interface for rapid screening
Rachapudi Venkata, Subba Ramarao, Reddicharla, Nagaraju, Alshehhi, Shamma Saeed, Utama, Indra, Al Nuimi, Saber Mubarak, Gönczi, David, Toumi, Oussema, Pechorskaya, Eleonora, Schweiger, Georg, and Franz Führer. "Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore." Paper presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, November 2021.
Conclusion
The relevant prior art made of record not relied upon but considered pertinent to applicant's disclosure can be found in the current and/or previous PTO-892 Notice of References Cited. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to LAURA YESILDAG whose direct telephone number is (571) 270-5066 and work schedule is generally Monday-Friday, from 9:00 AM - 5:00 PM ET.
In order to receive any email communication from the Examiner, filing for official authorization for Internet Communication is required. The authorization form can be accessed at https://www.uspto.gov/sites/default/files/documents/sb0439.pdf.
Examiner interviews can be requested by telephone or are available using the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the Examiner are unsuccessful, the Examiner’s Supervisor, LYNDA JASMIN, can be reached at (571) 272-6782 for any urgent matter that needs immediate attention. Additional information regarding the status of an application may be obtained from the USPTO Patent Center. For more information about the USPTO Patent Center, please access https://patentcenter.uspto.gov/ The Patent Center is available to all users for electronic filing and management of patent applications and can be contacted for questions at 1-866-217-9197 or 571-272-4100.
/LAURA YESILDAG/Primary Examiner, Art Unit 3629
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