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 .
DETAILED ACTION
This communication is a Non-Final Office Action in response to communications received on 4/9/26.
Claims 6, 13 have been previously cancelled.
Claims 1, 8, 15 have been amended.
Therefore, Claims 1-5, 7-12, 14-20 are now pending and have been addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/9/26 has been entered.
Response to Amendment
Applicant has amended Claim 15 to overcome the 35 U.S.C 112b rejections. Examiner withdraws the 35 U.S.C. 112b rejections with respect to these and all depending claims unless otherwise indicated.
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 1-5, 7-12, 14-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-5, 7 are directed to a method and claims 8-12, 14, 15-20 are directed to a construction control system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-5,7-12,14-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 8 and 15 recite methods that facilitates a construction project, comprising: obtaining contract data of the construction project, the contract data comprising information of a contractor, a construction contract, a construction site, and construction activities of the construction project; obtaining, based on the contract data, historical data related to the construction project to form a training dataset; obtaining IoT sensor data of the construction site; training dataset including the contract data, the historical data and IoT sensor data, determining, from the IoT sensor data, at least one site-safety metric for at least one zone of the construction site, the at least one site-safety metric comprising at least one of worker density, machinery density, worker-to-machinery proximity, or worker and machinery movement flow; generating, using the contract data, historical data, sensor data as input, a plurality of insight data of the construction project; generating a contractor improvisation index by aggregating the plurality of insight data, the contractor improvision index quantifying a predicted likelihood of contractor improvisation in the construction project; and facilitating, based on the contractor improvisation index, the construction project; sending in response to the contractor improvisation index exceeding a pre-determined threshold, an alert to a user; initiating, based on the at least one site- safety metric and in response to the contractor improvisation index exceeding a pre-determined threshold, a mitigation action of the construction project, the mitigation action comprising at least one of reducing worker or machinery density in a zone, enforcing a stop work order, or dispatching additional flag men.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), including interactions between person and computer) and mathematical calculations (determine metrics; contractor improvisation index), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as training machine learning models, ML models, supervised learning technique, a computer processor; and memory, construction control system, sensor, weighted aggregation function), the claims are directed to facilitating a construction project based on insights and improvisation index. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing improvisation index for project. In particular, the claims only recites the additional element – training machine learning models, ML models, supervised learning technique, a computer processor; and memory, construction control system, sensor, weighted aggregation function. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The limitation of “training, using the training dataset, a plurality of machine learning (ML) models” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; facilitating a construction project based on insights. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to, training machine learning models, ML models, supervised learning technique, a computer processor; and memory, construction control system, sensor, weighted aggregation function, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0056]-[0057] details “ The computer (402)also includes a memory [0018] Machine-learned model types may include, but are not limited to, k-means, k-nearest neighbors, neural networks, logistic regression, random forests, generalized linear models, and Bayesian regression. [0038]index presented to user on dashboard. Spec [0038] &[0046] an alert is sent to a user in response to the contractor improvisation index exceeding a pre-determined threshold.” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and are well-understood, routine and conventional limitations that amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claim does not amount to significantly more than the abstract idea itself.
Dependent claims 2-5, 7, 9-12, 14, and 16-20 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as representative claims 1, 8 and 15.
Claims 2-4, 9-11, 16-18 recite historical incident data of the contractor and related historical construction projects; and historical contract data of the related historical construction projects; generates sensor data of the construction site, wherein the historical data further comprises historical sensor data of the related historical construction projects to form the training dataset, and wherein the sensor data is used as additional input to the plurality of ML models to generate the plurality of insight data of the construction project; wherein the plurality of insight data comprise incidents insights from the related historical construction projects, contextual insights from sensor data of the construction site, and contractual insights from the construction contract, and wherein the incidents insights, the contextual insights, and the contractual insights are respectively generated by an incidents model, a contextual model, and a large language model (LLM) of the plurality of ML models. These limitations are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The additional elements of “one or more sensors disposed at the construction site, incidents model, a contextual model, and a large language model (LLM) of the plurality of ML models” are recites at apply it level and amount to no more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Claims 5,7, 12, 14, 19-20 recites presenting, using a construction control dashboard, the contractor improvisation index to a user; sending, in response to the contractor improvisation index exceeding a pre- determined threshold, an alert to a user; and initiating, by the user in response to the alert, a mitigation action of the construction project; wherein the construction project comprises constructing one of a well system, a pipeline network, and a processing plant. These limitations of further recite presenting/sending data alert which are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The claims recite same additional elements as independent claims, and amount to no more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system is merely being used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Examiner Note: Subject matter free of prior art
Regarding Claims 1, 8 and 15, Doyle (US 2022/0358429 A1) discloses the method to facilitate a construction project ([0003] Construction and similar projects may involve a variety of different types of parties each having associated with different sets of data, including contractors, sub-contractors, suppliers, property owners, lenders, etc. Such a project may generally entail the various parties providing information regarding one or more associated contracts, each of which may include information that needs to be verified, validated, and assessed.], comprising:
Doyle discloses obtaining, using a computer processor contract data of the construction project (Fig 3 # 31 receive electronic file of project contract [0051]In step 310, one or more electronic files 210 may be provided from a contract device 110 via communication network 105 to assessment server 115. The electronic files 210 may include the content (e.g., text) of a project contract, which assessment server 115 may analyze to identify project parameters of different types in step 320), the contract data comprising information of a contractor, a construction contract, a construction site, and construction activities of the construction project ([0003] Construction and similar projects may involve a variety of different types of parties each having associated with different sets of data, including contractors, sub-contractors, suppliers, property owners, lenders, etc. Such a project may generally entail the various parties providing information regarding one or more associated contracts, each of which may include information that needs to be verified, validated, and assessed, [0036] the electronic files may include text of a contract related to a project involving one or more other entities.);
Doyle discloses obtaining, using the computer processor and based on the contract data, historical data related to the construction project to form a training dataset (Fig 1 # 135 historical data, [0039] Historical data 135 may be inclusive of any type of data storage device used to store and manage historical data 135. Historical data 135 may be stored in similar fashion and/or in association with data regarding current and ongoing projects. Project data may include data regarding the various entities involved in a project, various parameters and components of the project, associated project score(s), and metrics regarding approvals/disapprovals or successful/unsuccessful completion of the project);
Doyle discloses training, using the training dataset, a plurality of machine learning (ML) models ([0040] Operating in conjunction with the historical data 135, AI/machine learning 140 may be used to identify patterns and trends associated with approved and/or successful projects, as well as patterns and trends associated with disapproved and/or unsuccessful projects. Based on such historical data 135, AI/machine learning 140 may assess historical projects in accordance with one or more scoring algorithms to identify a recommended threshold above which the projects tend to be associated with indicators of approvals and/or success.);
Doyle discloses generating, using the contract data as input to the plurality of ML models, a plurality of insight data of the construction project ([0040] AI/machine learning 140 may identify that approved and/or successful projects are associated with project scores above a certain threshold level and that such project scores are associated with certain project parameters (and measurements thereof). Metadata regarding project-related communications, interactions, and online operations may also be included in and stored as historical data 135. Based on such historical data 135, AI/machine learning 140 may assess historical projects in accordance with one or more scoring algorithms to identify a recommended threshold above which the projects tend to be associated with indicators of approvals and/or success.);
Doyle discloses generating a project score by aggregating the plurality of insight data ([0041] AI/machine learning 140 may also be applied to current projects to select one or more appropriate scoring algorithms 145 and predict whether a given current project is likely to be (or should be) approved and/or successful in comparison to similar or comparable historical projects.); and
Doyle discloses facilitating, based on the project score, the construction project ([0041] AI/machine learning 140 may further be used to identify recommended ways (e.g., in accordance with the same or different scoring algorithms 145) in which a current project may be modified to improves its odds of approval and/or success. Such recommendations may entail additional information be provided or actions be taken, which may be associated with changes to one or more project scores (improvisation index) in accordance with one or more scoring algorithms 145.).
Doyle does not specifically teach generating a contractor improvisation index by aggregating the plurality of insight data using a weighted aggregation function, the contractor improvision index quantifying a predicted likelihood of contractor improvisation in the construction project; facilitating, based on the contractor improvisation index, the construction project obtaining, using the computer processor and one or more sensors disposed at the construction site, IoT sensor data of the construction site; generating using the computer processor and the contract data, the historical data and IOT sensor data as input to ML models, a plurality of insights data including incident insights, contextual insights and contractual insights; wherein facilitating the construction project comprises: sending, using the computer processor and in response to the contractor improvisation index exceeding a pre-determined threshold, an alert to a user; and initiating, automatically using the computer processor, a mitigation action of the construction project, the mitigation action comprising at least one of reducing worker or machinery density in a zone, enforcing a stop work order, or dispatching additional flag men.
Man et al. (US 2023/0394605 A1) teaches training, using training data machine learning models ([0076] one or more machine-learning models may be trained using historical data sets from past construction projects whose data objects have been manually curated and associated with locations); generating a risk index by aggregating the plurality of insight data ([0024] a computing platform may apply the machine-learning techniques noted above and determine that a certain type of data object, or combination of data objects (e.g., an RFI related to a specific building element), tends to be associated with a relatively high risk to a project budget, although relatively low risk to the project schedule or project safety. Risk quantification of this kind may be represented on a relative scale, such as a risk score (contractor index) from 0-100, among other possibilities (e.g., a green/yellow/red color-coded scale, etc.). [0026] an aggregate risk score may be estimated for a construction project based on the combined effect of the individual risk events that are present on the project at any given time. This type of project risk score may be normalized based on project size and project type, among other factors, [0027] a respective risk score may be estimated for individual construction entities (e.g., a project owner, an engineering firm, a general contractor, a subcontractor, etc.) based on their level of connectivity to various risk events in the construction knowledge graph.); facilitating, based on the risk index, the construction project ([0036] automatically generating the suggested action to be taken may involve automatically generating the suggested action to be taken based on determining that the second risk score for the second data object exceeds the threshold risk score, [0116] the computing platform 400 shown in FIG. 4, may generate a suggested action to be taken with respect to a data object, in order to reduce the risk associated with the data object and the construction project as a whole.); sending, in response to the improvisation index exceeding a pre- determined threshold, an alert to a user ([0095] the risk mitigation engine 440 may automatically generate a suggested action 441 if the risk score of a given data object is above a predetermined threshold value (e.g., a risk score of 50). the risk mitigation engine 440 may automatically generate a suggested action 441, which may take the form of an alert or similar notification indicating that the risk score associated with the data object exceeds the threshold.); and initiating, by the user in response to the alert, a mitigation action of the construction project ([0097] At block 312, the computing platform 400 may cause an indication of the suggested action 441 to be displayed at a client station of a user associated with the construction project., [0098] data regarding a user's response to the suggested action 441 may be provided to the risk assessment engine 430 as additional training data for the one or more prediction models. For example, data indicating that the user performed the suggested action 441 with respect to a given data object may be correlated with eventual outcome data regarding the identified risk. )
Kumar (US 2023/0153716 A1) teaches obtaining, using the computer processor and one or more sensors disposed at the construction site, IoT sensor data of the construction site ([0061] The monitor 118 is also programmed to receive data feeds from one or more external sources, such as on-site sensors or videos, and to store the data feeds in the data repository 130.); generating using the computer processor and IOT sensor data as input to ML models wherein the plurality of insight data ([0083] The input data sets may also include historical weather/climate patterns of the locality during the construction phase. As discussed herein, the monitor 118 of FIG. 1 may assist the Climate Analysis Module 202 to collect relevant input data sets. The Climate Analysis Module 202 can dynamically receive these inputs and provide recommendations that are optimized for weather-related events., [0084] the model ensemble 112 receives one or more input data sets for analysis of completion timelines based on legal contracts, regulatory requirements, etc., [0091] the model ensemble 112 receives one or more input data sets for analysis of task maps (listing of tasks/schedules), current labor availability and skills analysis, efficacy analysis (e.g., pace of completion, quality metrics, etc.), historical trends (e.g., past efficiencies, remarks, commentary, Cost of Poor Quality analysis, etc.) [0061] The monitor 118 is also programmed to receive data feeds from one or more external sources, such as on-site sensors or videos, and to store the data feeds in the data repository 130., [0081] The model ensemble 112 receives input data from the data repository 130 (training data).), [0069] if the construction objective was to honor the set construction completion date, then the system recommendation (insights) could have suggested adding additional construction workers, procuring material from a nearby supplier among other considerations) a mitigation action of the construction project, the mitigation action comprising at least one of reducing worker or machinery density in a zone, enforcing a stop work order, or dispatching additional flag men ([0069] if the construction objective was to honor the set construction completion date, then the system recommendation could have suggested adding additional construction workers, procuring material from a nearby supplier among other considerations.)
KR10-2024-0043276 discloses method of evaluating subcontract company in construction field using machine learning model.
Goel (US 2018/0349817) discusses change order (improvisation) and related change management workflow ([0076]). Subcontractor risk assessment bases on amount of activity and daily changes on a construction site and the range of work GCs perform, managing the subcontractors is an important but difficult task. (0161]
Driessnack (US 2012/0215574) discloses receiving performance data for a project, receiving risk data for the project, developing an estimate to complete (ETC) based on the performance data, adjusting the ETC based on the risk data, and developing an estimate at completion (EAC) based on the adjusted ETC.
Reichert (US11,416,958) discloses system monitors people, projects, objects and properties to validate the status and quality of work completed, services provided and safety within geofenced zones. The system provides for a business solution and consumer solution that receives data relating to tracking the onsite/offsite time of people assigned to perform a service or complete a task within a specified geofence location, time-interval visual status updates of work completion
NPL, Alhussein, “Improvisation in construction planning: An agent based simulation approach”, 2022 discusses influencing factors of improvisation in construction shown in Fig 1 page 22 to predict the outcome of different improvisational practices (Section 7.3)
Jiang, “Understanding megaproject improvisation”, 2023 discusses improvisation degree dimension (section 3.2.1). The next higher degree, swift improvisation, is a rapid response that deviates from existing plans or routines generally triggered by unexpected events emerging in megaprojects under high time constraint
However, the prior art fails to teach or suggest at least “determining, using the computer processor, from the IoT sensor data, at least one site-safety metric for at least one zone of the construction site, the at least one site-safety metric comprising at least one of worker density, machinery density, worker-to-machinery proximity, or worker and machinery movement flow; generating using the computer processor and the contract data, the historical data and IOT sensor data as input to ML models, a plurality of insights data including incident insights, contextual insights and contractual insights; generating a contractor improvisation index by aggregating the plurality of insight data using a weighted aggregation function, the contractor improvision index quantifying a predicted likelihood of contractor improvisation in the construction project”. The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims.
Response to Arguments
Applicant's arguments filed 4/9/26 have been fully considered but they are not persuasive.
Regarding 101 rejection, Applicant on page 11 of response states that claims integrate the exception into a practical application. Applicant further states that claims are similar to example 47, directed to using AI to detect malicious network packets. Examiner has considered all arguments and respectfully disagrees. The current claims are directed to facilitating a construction project based on insights and improvisation index. This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing improvisation index for project. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. The determining at least one site-safety metric… is an abstract idea of mathematical calculations. Further, initiating automatically based on safety metric and improvisation index exceeding threshold, a mitigation action…is recited at high level of generality. Spec [0038] &[0046] an alert is sent to a user in response to the contractor improvisation index exceeding a pre-determined threshold. The limitation of “training, using the training dataset, a plurality of machine learning (ML) models” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
Regarding example 47 claim 3, the claim recites a plurality of neurons, which are hardware components comprising a register and a microprocessor, and a plurality of synaptic circuits which together form an ANN. The claim does not recite any abstract ideas. The claimed invention reflects this improvement in the technical field of network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. However, current claims are not similar to example 47, claim 3, as claims do not provide improvement to the technical field or computer. The current claims are directed to abstract idea of organizing human activity and mathematical calculation as shown in rejection above. Further, claims recite additional elements that do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. In instant claims, the step of initiating automatically based on safety metric and improvisation score exceeding a threshold a mitigating action is abstract idea of organizing human activity and is recited at high level of generality. Spec [0038]&[0046] an alert is sent to a user in response to the contractor improvisation index exceeding a pre-determined threshold. The fact that certain functions are done “automatically” does not necessarily make the claim a technical solution, especially when the functions are still part of the abstract idea of “managing personal behavior or interactions or relationships between people.” Therefore, in view of the amended claim language, the claims still recite an abstract, therefore Step 2A Prong 1 results in a “yes”. Furthermore, the fact that initiating step is done automatically is still an “apply it” level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KR10-2024-0043276 discloses method of evaluating subcontract company in construction field using machine learning model.
Reichert (US11,416,958) discloses system monitors people, projects, objects and properties to validate the status and quality of work completed, services provided and safety within geofenced zones. The system provides for a business solution and consumer solution that receives data relating to tracking the onsite/offsite time of people assigned to perform a service or complete a task within a specified geofence location, time-interval visual status updates of work completion
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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