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 .
This Final Office Action is responsive to Applicant's amendment filed on 10 February 2026. Applicant’s amendment on 10 February 2026 amended Claims 1, 12, 14, and 18. Currently Claims 1-6, 8-20 are pending and have been examined. Claim 7 has been canceled. The Examiner notes that the 101 rejection has been maintained.
Response to Arguments
Applicant's arguments filed 10 February 2026 have been fully considered but they are not persuasive.
The Applicant argues on page 12 that the “independent claim 1 (and similarly independent claims 12 and 18) is amended to recite: "receiving first observation data from multiple workers that includes a date of an observation and at least one other [type of observation data]" and "concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data."
The Examiner respectfully disagrees;
In response to the argument the rejection under 35 U.S.C. 101 is maintained. Applicant argues that the amendments reciting (1) "receiving first observation data from multiple workers" and (2) "concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data" are sufficient to overcome the rejection. The Examiner respectfully disagrees, and the rejection is maintained for the following reasons.
Regarding the "from multiple workers" limitation, the specification at par. [0032] confirms that receiving observation data from multiple workers is inherent to the operation of the system and simply defines the source of data being collected. Under controlling precedent, specifying a particular data source or type of data to be used does not integrate an abstract idea into a practical application. See Electric Power Group, LLC v. Alstom S.A.; Ameranth,; see also MPEP 2106.05(g) (identifying "selecting a particular data source or type of data to be manipulated" as a form of insignificant extra-solution activity). The limitation does nothing more than narrow the field of use to a particular worksite context where observations are gathered from multiple workers, which does not provide a meaningful technical limitation on the claim as a whole.
Regarding the "concurrently combining" limitation, the specification at par. [0049] expressly describes this combining/compression step as a way to reorganize observation data "for more optimal use in training" the machine-learning model, explaining that it allows the system to reduce the number of overall entries while correctly representing the types of data. This is, at its core, a data organization and manipulation step that serves to improve the accuracy and efficiency of the downstream statistical prediction which is the abstract idea itself. An improvement to the accuracy or efficiency of a mathematical or statistical prediction is not an improvement to technology or a technical field. See In re Bd. of Trs. of Leland Stanford Junior Univ., (Stanford II) (concluding that claims are ineligible when the improvement in "the accuracy of a mathematically calculated statistical prediction" is an improvement to the abstract idea, i.e., mathematical calculations, rather than an improvement to another technology); see also MPEP 2106.05(a), subsection II ("it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology"). The "concurrently combining" step is therefore more appropriately characterized as a data pre-processing step that is ancillary to the core abstract idea, and amounts to insignificant pre-solution activity under MPEP 2106.05(g).
Furthermore, this situation is distinguishable from Ex Parte Desjardins (PTAB, Sept. 26, 2025) (precedential), where the claims were found eligible because they reflected an improvement to how the machine learning model itself operates specifically, adjusting parameter values in a way that protects previously learned task performance during subsequent training, thereby overcoming the problem of "catastrophic forgetting" inherent in continual learning systems. That improvement was intrinsic to the operation of the machine learning model itself. Here, in contrast, the "concurrently combining" step is merely a data aggregation technique applied upstream of the incident predictor that organizes inputs to be fed into the model; it does not change how the incident predictor or machine learning model functions or operates internally. The claim reflects no analogous structural or functional improvement to the incident predictor or to any other technological component. The incident predictor itself as described in the specification at par. [0040]-[0054] operates using conventional machine learning techniques (e.g., random forest, XGBoost, logistic regression) on a generic computer processor, and the "concurrently combining" step simply reduces the number of data entries passed to that conventional system. Accordingly, viewed individually and in combination, the amended limitations do not integrate the abstract idea into a practical application at Step 2A, Prong Two, nor do they provide significantly more than the abstract idea under Step 2B. The rejection has been maintained.
The Applicant argues on pg. 13 that the “amended independent claims l, 12, and 18 are not directed to a mental process. The M.P.E.P. states that "claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind." (M.P.E.P. 2106.04(a)(2)(III)(A)). A human cannot practically perform, in the human mind, the concurrent/simultaneous combining of multiple types of observation data from multiple workers. At best, a human can only practically combine one type of observation data from one worker with the same type of observation data from another worker. It is simply not possible for the human mind to combine, at the same time/simultaneously, multiple types of observation data from multiple workers with each other. Thus, amended claims l, 12, and 18 are not directed to a mental process.”
The Examiner respectfully disagrees;
The Examiner notes that the rejection under 35 U.S.C. § 101 is maintained. Applicant argues that because the "concurrently combining" limitation cannot practically be performed in the human mind, the claims are not directed to a mental process and therefore do not recite an abstract idea. This argument is not persuasive for the reasons set forth below, and the rejection is maintained in its entirety. The Examiner acknowledges the guidance in MPEP 2106.04(a)(2)(III)(A) that claims do not recite a mental process when they contain limitations that cannot practically be performed in the human mind, and the Examiner does not dispute that the "concurrently combining" step, considered in isolation, may present a challenge under the mental process grouping alone. However, Applicant's argument fundamentally mischaracterizes the basis of the rejection. The rejection is not premised solely upon the mental process grouping. The claims independently recite abstract ideas falling within the mathematical concepts grouping specifically mathematical relationships, mathematical calculations, and the statistical modeling and feature extraction operations at the core of all three independent claims and this independent basis for the rejection is wholly unaddressed by Applicant's argument.
As stated in MPEP 2106.04(a)(2), the three enumerated groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, and mental processes) are not mutually exclusive. A claim limitation need only fall within at least one grouping for a claim to recite an abstract idea at Step 2A, Prong One, and the Examiner is to identify all applicable groupings to the extent possible. The core operative limitations of claims 1, 12, and 18 namely, identifying and extracting "features that are most indicative of the occurrence of the reported unsafe incident" from types of project data and observation data, and "predicting...the likelihood of an occurrence of a first future unsafe incident" are directed to mathematical calculations and mathematical relationships. These operations constitute precisely the type of subject matter that courts have consistently identified as abstract ideas within the mathematical concepts grouping. See SAP Am., Inc. v. InvestPic, LLC, (claims to "a series of mathematical calculations based on selected information" are abstract); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., (claims directed to "a process of organizing information through mathematical correlations" are abstract); In re Bd. of Trs. of Leland Stanford Junior Univ., (statistical prediction based on mathematical calculations is abstract). These limitations fall squarely within the mathematical concepts grouping independent of any analysis under the mental process grouping, and this basis for the rejection stands unrebutted by Applicant's argument.
Moreover, even accepting Applicant's contention regarding the "concurrently combining" step, that step itself aggregating multiple data records by applying a common date value to reduce the total number of entries constitutes a data organization process through mathematical correlation and is itself a form of mathematical calculation. As the Federal Circuit explained in Digitech, organizing information through mathematical correlations is an abstract idea. The fact that this step may require computational resources and cannot be performed by the unaided human mind does not, by itself, remove it from the abstract idea analysis, as the courts do not distinguish between abstract ideas performed mentally and those implemented on a computer. See Versata Dev. Group v. SAP Am., Inc.; MPEP 2106.04(a)(2)(III)(C). Accordingly, the argument that the claims are not directed to a mental process, even if accepted as to the "concurrently combining" step alone, does not overcome the independent and primary basis for the rejection that the claims recite abstract ideas within the mathematical concepts grouping. The rejection is therefore maintained.
The Applicant argues on pg. 13 that “Even if the independent claims are characterized as reciting the judicial exception (a characterization to which Applicant does not acquiesce), the claims as a whole each integrate the judicial exception into a practical application of the judicial exception by providing a technical solution to a defined and nonabstract problem. The amended independent claims as a whole each integrate the system/method into a practical application by reciting a specific technical solution by collecting a wide variety of data regarding a project, efficiently combining that data to reduce a number of overall entries of that data, extracting features from that data that are most indicative of the occurrence of an unsafe incident, and predicting the likelihood oof another occurrence of an unsafe incident. Thus, the amended independent claims each integrate the judicial exception into a practical application”.
The Examiner respectfully disagrees;
With respect to the argument the Examiner notes that while the Applicant argues that the amended independent claims, when viewed as a whole, integrate the recited judicial exception into a practical application by providing a "specific technical solution to a defined and nonabstract problem," characterized as: (1) collecting a wide variety of data regarding a project, (2) efficiently combining that data to reduce overall entries, (3) extracting features most indicative of the occurrence of an unsafe incident, and (4) predicting the likelihood of another occurrence of an unsafe incident. For the reasons set forth below, this characterization does not establish integration into a practical application, and the rejection is maintained.
As an initial matter, the "problem" asserted by Applicant predicting the likelihood of safety incidents at a project worksite to prevent injuries, fines, and property damage is a real-world safety management and risk-mitigation problem, not a technological problem. The specification expressly frames the problem in those terms at par. [0003] and [0021], describing the issue as the occurrence of unsafe incidents resulting in "injuries to workers, fines, lawsuits, damage to property, missed project deadline, and other issues," with the benefit being that "proper safety protocols can be established and/or followed." This is not a problem rooted in computer technology; it is a field-specific operational problem to which the abstract idea of collecting, analyzing, and predicting from data has been applied. The mere application of an abstract idea to a specific field of use or technological environment even one with real-world consequences does not integrate the exception into a practical application. See Parker v. Flook, (limiting mathematical formula to petrochemical industry insufficient); Electric Power Group, LLC v. Alstom S.A., (limiting collection, analysis, and display of information to the power grid domain is a field-of-use limitation that does not provide meaningful limits on the claim); MPEP 2106.05(h).
Examining each of Applicant's four asserted elements of the "technical solution" in turn: First, "collecting a wide variety of data regarding a project" the receiving steps for project data, observation data, and incident data are well-established instances of pre-solution data gathering, which courts and the Office consistently treat as insignificant extra-solution activity. MPEP 2106.05(g). The breadth and variety of data types collected does not transform this from data gathering into a technological improvement; it merely defines the scope of information gathered within the worksite safety field of use, analogous to selecting "information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display," which the Federal Circuit held was insufficient in Electric Power Group.
Second, "efficiently combining that data to reduce overall entries" as addressed in the response to Argument 1, this is a data organization and compression step, and the specification at par. [0049] expressly characterizes it as serving to make data more optimal "for use in training" the machine-learning model and achieving more accurate predictions. Any efficiency gained through the combining step goes directly to improving the accuracy or efficiency of the statistical prediction which is the abstract idea not to improving the operation of the computer, the incident predictor's architecture, or any other technology. An improvement to the abstract idea itself is not an improvement to technology. In re Bd. of Trs. of Leland Stanford Junior Univ.; MPEP 2106.05(a), subsection II.
Third, "extracting features most indicative of the occurrence of an unsafe incident" this step is the core of the recited abstract idea, not an additional element that integrates it. The claims recite this step at a high level of generality simply stating the desired result of identifying features "most indicative" of the unsafe incident without reciting any specific technical mechanism by which this extraction is accomplished at the claim level. This is precisely the result-oriented claiming the Federal Circuit cautioned against, as it effectively covers any solution to the identified problem without restricting how that solution is to be accomplished. Electric Power Group. Contrast this with Ex Parte Desjardins (PTAB Sept. 26, 2025) (precedential), where the claims reflected an improvement to how the machine learning model itself operates by reciting the specific mechanism of "adjusting first values of a plurality of parameters to optimize performance... while protecting performance...on the first machine learning task," which was found to reflect the disclosed technological improvement to the ML model in the claim language itself. Here, no analogous specific mechanism is recited in the claims that would reflect a disclosed improvement to the incident predictor or to any computer component.
Fourth, "predicting the likelihood of another occurrence of an unsafe incident" this is simply the output of the abstract idea itself, a statistical prediction. The specification at par. [0053] describes the machine-learning model as using entirely conventional techniques including "support vector machines, discriminant analysis, naïve bayes, nearest neighbor... linear regression, GLM, SVR, GPR... random decision forest, random forest, neural networks... XGBoost, logistic regression, and time series forecasting." Applying these well-known, generic techniques to the worksite safety domain, executed on a generic computer processor as described at par. [0041]-[0043], does not reflect an improvement to the functioning of the computer or to any other technology or technical field. See TLI Commc'ns LLC v. AV Auto. LLC, (computer invoked merely as a tool to execute abstract idea). Viewed individually and in combination, the four elements Applicant identifies as constituting a "specific technical solution" collectively amount to no more than the abstract idea of gathering worksite safety data, processing it through a generic statistical model, and outputting a prediction precisely the type of claim the courts have found remains directed to a judicial exception. Electric Power Group,. The rejection is therefore maintained.
The remaining Applicant's arguments filed 10 February 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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 1-6, and 8-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claim(s) 1-6, and 8-20 as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. The claim(s) 1-6, and 8-20 is/are directed to the abstract idea of predicting safety incidents based upon observation and project data. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself. Claim(s) (1-6, and 8-20) is/are directed to an abstract idea without significantly more.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claim(s) (1-6, 8-11 and 18-20) is/are directed to a method, and claim(s) (12-17) is/ are directed to a system and therefore the claims recites a series of steps and, therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) mental process. Specifically, the independent claims 1, 12, and 18 recite a mental process: as drafted, the claim recites the limitation of predicting of safety incidents based upon observation data which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for a processor language, the claim encompasses the user manually analyzing data to determine the predicting of safety incidents. The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. It has been established by ongoing guidance that claims that contain a generic processor are still viewed as mental process when they contain limitations that can practically be performed in the human mind, however this is different for instance when the human mind is not equipped to perform the claim limitations (network monitoring, data encryption for communication, and rendering images). Therefore, these limitations are viewed a mental process. Additionally, with regard to the instant application the Examiner has reviewed the disclosure and determined that the underlying claimed invention is described as a concept that is performed in the human mind and/or with the aid of a pen and paper, and thus it is viewed that the applicant is merely claiming that concept performed 1) on a generic computer, 2) in a computer environment or 3) is merely using a computer as a tool to perform the concept, and therefore is considered to recite a mental process.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally that data receiving steps required to use the predicting do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the predicting step. The processor in the step is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (predicting safety incident based on observation data). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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. The claim is directed to the abstract idea.
The claim recites the additional element(s): receiving first project data, receiving first observation data, and receiving first incident data performs the predicting step. The receiving steps are recited at a high level of generality (i.e., as a general means of receiving data for use in the predicting step), and amounts to mere data receiving, which is a form of insignificant extra-solution activity. The processor that performs the predicting step is also recited at a high level of generality, and merely automates the predicting step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, where it is not clear that the specification sets forth an improvement in technology, the claim must reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
For further clarification the Examiner points out that the claim(s) 1-6, and 8-20 recite(s) receiving project data, receiving observation data, receiving incident information, and predicting the likelihood which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for receiving and predicting which is the abstract idea steps of valuing an idea (predicting safety incidents) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to predict safety incidents). Using a computer to receiving and predicting the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The predicting the likelihood of a safety incident would clearly be to a mental activity that a company would go through in order to decide how anticipate safety incidents. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine how to predict safety incidents based upon observation and project data:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 5, 6, 11, 16, 19, and 20 recite limitations which further limit the claimed analysis of data.
Claims 2-4, 9, 13-15, and 17 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Grattie which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 8 and 10 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information about the prediction of safety incidents. This is not a technical or technological problem but is rather in the realm of incident prevention in the work place and therefore an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
Additionally, with respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the current claims receiving that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication such as the currently cited prior art Grattie provides those extra solution activities and is viewed as a form of publication which also includes a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry. The claim is ineligible.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
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 may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-6, and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gattie (U.S. Patent Publication 2017/0357923 A1) in view of Grant et al. (U.S. Patent Publication 2021/0182748 A1) (hereafter Grant) in further view of SAMSON (U.S. Patent Publication 2020/0042915 A1) (hereafter Samson).
Referring to Claim 1, Gattie teaches a method of predicting the likelihood of an unsafe incident occurring at a first worksite, said method comprising:
receiving first project data that includes at least one of the following types: a cost of the first project; a projected start date of the first project; an actual start date of the first project; a projected completion date of the first project; a size of the first worksite of the first project; a location of the first worksite; a contract type of a contract under which work at the first worksite is performed; a procurement method of the contract; a value of the contract; an insurance type of a workers’ compensation policy covering work performed at the first project; a policy holder of the workers’ compensation policy; and a number of change orders performed at the first worksite (see; par. [0003] and par. [0044] of Gattie teaches collecting data related to the predicting of incidents using where the some of the data is cost of the project).
Gattie does not explicitly disclose the following limitations, however,
Grant teaches receiving first incident data that includes at least one of the following types: a date of a reported unsafe incident associated with the first project; a severity of the unsafe incident; a category of worker associated with the unsafe incident; a project associated with the unsafe incident; a category of activity associated with the unsafe incident; a total number of reported unsafe incidents associated with the first project; a first variable indicating if the reported unsafe incident occurred on the project on a particular date; a second variable indicating the severity of the reported unsafe incident occurring on the project on a particular date; and a third variable indicating if no reported unsafe incident occurred on the project on a particular date (see; par. [0018] of Grant teaches collecting information related to predicting incidents including the severity of the hazard), and
from features that include at least one of the types of first observation data and the types of first project data that are most indicative of the occurrence of the reported unsafe incident, predicting, by an incident predictor, the likelihood of an occurrence of a first future unsafe incident at the first project (see; par. [0077] of Grant teaches the collection of safety observations that are both positive and negative, par. [0019] the observation data is used to predict the likelihood of incidents).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose receiving first incident data that includes at least one of the following types: a date of a reported unsafe incident associated with the first project; a severity of the unsafe incident; a category of worker associated with the unsafe incident; a project associated with the unsafe incident; a category of activity associated with the unsafe incident; a total number of reported unsafe incidents associated with the first project; a first variable indicating if the reported unsafe incident occurred on the project on a particular date; a second variable indicating the severity of the reported unsafe incident occurring on the project on a particular date; and a third variable indicating if no reported unsafe incident occurred on the project on a particular date, and from features that include at least one of the types of first observation data and the types of first project data that are most indicative of the occurrence of the reported unsafe incident, predicting, by an incident predictor, the likelihood of an occurrence of a first future unsafe incident at the first project.
Grant discloses receiving first incident data that includes at least one of the following types: a date of a reported unsafe incident associated with the first project; a severity of the unsafe incident; a category of worker associated with the unsafe incident; a project associated with the unsafe incident; a category of activity associated with the unsafe incident; a total number of reported unsafe incidents associated with the first project; a first variable indicating if the reported unsafe incident occurred on the project on a particular date; a second variable indicating the severity of the reported unsafe incident occurring on the project on a particular date; and a third variable indicating if no reported unsafe incident occurred on the project on a particular date, and from features that include at least one of the types of first observation data and the types of first project data that are most indicative of the occurrence of the reported unsafe incident, predicting, by an incident predictor, the likelihood of an occurrence of a first future unsafe incident at the first project.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie receiving first incident data that includes at least one of the following types: a date of a reported unsafe incident associated with the first project; a severity of the unsafe incident; a category of worker associated with the unsafe incident; a project associated with the unsafe incident; a category of activity associated with the unsafe incident; a total number of reported unsafe incidents associated with the first project; a first variable indicating if the reported unsafe incident occurred on the project on a particular date; a second variable indicating the severity of the reported unsafe incident occurring on the project on a particular date; and a third variable indicating if no reported unsafe incident occurred on the project on a particular date, and from features that include at least one of the types of first observation data and the types of first project data that are most indicative of the occurrence of the reported unsafe incident, predicting, by an incident predictor, the likelihood of an occurrence of a first future unsafe incident at the first project as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Gattie in view of Grant does not explicitly disclose the following limitation, however,
Samson teaches receiving first observation data from multiple workers that includes a date of observation and at least one other of the following types: a safety category of the observation, the safety category identifying the observation as one of a safe observation or an unsafe observation; a category of worker that was observed; a category of activity that was observed; a cause of the activity that was observed; a number of observations made on the date of the observation; a ratio of safe observations to unsafe observations; a day of week of the date of the observation; a difference in time between the date of the observation and a date of recordation of the observation; and a difference in time between the date of the observation and a date of corrective action (see; par. [0021] of Samson teaches receiving accident incident reports daily (i.e. date of observation) worksite assessments and analyzing the workers and the activity performed), and
concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data (see; par. [0021] of Samson teaches crate a matrix (i.e. combining) the risk evaluation data, par. [0026] collect data to perform predictive analysis and problem solve the observation data).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Samson teaches collaboration system for construction management utilizing shared computing platforms including safety incident analysis and as it is comparable in certain respects to Gattie and Grant which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie and Grant discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie and Grant does not explicitly disclose receiving first observation data from multiple workers that includes a date of observation and at least one other of the following types: a safety category of the observation, the safety category identifying the observation as one of a safe observation or an unsafe observation; a category of worker that was observed; a category of activity that was observed; a cause of the activity that was observed; a number of observations made on the date of the observation; a ratio of safe observations to unsafe observations; a day of week of the date of the observation; a difference in time between the date of the observation and a date of recordation of the observation; and a difference in time between the date of the observation and a date of corrective action and concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data.
Samson discloses receiving first observation data from multiple workers that includes a date of observation and at least one other of the following types: a safety category of the observation, the safety category identifying the observation as one of a safe observation or an unsafe observation; a category of worker that was observed; a category of activity that was observed; a cause of the activity that was observed; a number of observations made on the date of the observation; a ratio of safe observations to unsafe observations; a day of week of the date of the observation; a difference in time between the date of the observation and a date of recordation of the observation; and a difference in time between the date of the observation and a date of corrective action and concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie and Grant rreceiving first observation data from multiple workers that includes a date of observation and at least one other of the following types: a safety category of the observation, the safety category identifying the observation as one of a safe observation or an unsafe observation; a category of worker that was observed; a category of activity that was observed; a cause of the activity that was observed; a number of observations made on the date of the observation; a ratio of safe observations to unsafe observations; a day of week of the date of the observation; a difference in time between the date of the observation and a date of recordation of the observation; and a difference in time between the date of the observation and a date of corrective action and concurrently combining each type of the first observation data from the multiple workers that have the same date of the observation to reduce a number of overall entries of the first observation data as taught by Samson since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, Grant, and Samson teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie further discloses a method having the limitations of:
receiving first worker data that includes at least one of the following types: a number of workers in each worker category on the first worksite on each date; an age of the worker on the first worksite on each date; an amount of experience of each worker on the first worksite on each date; and a total number of workers on the first worksite on each date (see; par. [0064] of Gattie teaches receiving worker data including the number of tasks and staff per job).
receiving first weather data that includes at least one of the following types: current weather data; a date of the current weather data; forecasted weather data; and a date of the forecasted weather data (see; par. [0003] and par. [0033] of Gattie teaches receiving weather data and the date of the weather event).
wherein predicting the likelihood of the occurrence of the first future unsafe incident is determined from features that include at least one of the types of first observation data, the types of first project data, the types of first worker data, and the types of first weather data that are most indicative of the occurrence of the reported unsafe incident (see; par. [0003] of Gattie teaches the predicting of hazards based on weather data).
Referring to Claim 3, see discussion of claim 1 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie further discloses a method having the limitations of:
associating the first observation data with the first incident data by linking the date of the observation to the date of the reported unsafe incident (see; par. [0033] of Gattie teaches a weather hazard data as an observation data).
associating the first worker data with the first incident data by linking each date for which first worker data is received to the date of the reported unsafe incident (see; par. [0033] of Gattie teaches associated the dangerous situation of the employee date of incident).
associating the first weather data with the first incident data by linking each date for which first weather data is received to the date of the reported unsafe incident (see; par. [0038] of Gattie teaches incident reports weather safety, par. [0033] associating the date with the employee).
identifying, by the computer processor, the features that are most indicative of the occurrence of the reported unsafe incident (see; par. [0005] of Gattie teaches determining features of work associated with, par. [0007] the occurrence of the hazardous).
Referring to Claim 4, see discussion of claim 1 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie further discloses a method having the limitations of:
associating the first observation data with the first incident data by linking the date of the observation to the date of the reported unsafe incident (see; par. [0033] of Gattie teaches linking the observation data with the date of the incident).
identifying, by the computer processor, the features that are most indicative of the occurrence of the reported unsafe incident (see; par. [0007] of Gattie teaches par. [0007] of Gattie teaches tracking the performance of projects and anticipated potential hazards).
Referring to Claim 5, see discussion of claim 4 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches determining, by the machine-learning model, an importance of each of the features to the occurrence of the first future unsafe incident and weighting the features accordingly in predicting the likelihood of the occurrence of the first future unsafe incident (see; Abstract of Grant teaches a machine learning model, par. [0052] and par. [0064] determining a weighting factor feedback loop used to determine the probability and frequency based on weighting factors to predict, par. [0062] an incident possibility (i.e. unsafe incident)).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose determining, by the machine-learning model, an importance of each of the features to the occurrence of the first future unsafe incident and weighting the features accordingly in predicting the likelihood of the occurrence of the first future unsafe incident.
Grant discloses determining, by the machine-learning model, an importance of each of the features to the occurrence of the first future unsafe incident and weighting the features accordingly in predicting the likelihood of the occurrence of the first future unsafe incident.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie determining, by the machine-learning model, an importance of each of the features to the occurrence of the first future unsafe incident and weighting the features accordingly in predicting the likelihood of the occurrence of the first future unsafe incident as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 5 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches wherein the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the first observation data and the first project data that are most indicative of the occurrence of the reported unsafe incident (see; Abstract of Grant teaches using a machine learning model, par. [0020] where the model is a Random Forest Technique for determining the probability of an incident).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose wherein the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the first observation data and the first project data that are most indicative of the occurrence of the reported unsafe incident.
Grant discloses wherein the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the first observation data and the first project data that are most indicative of the occurrence of the reported unsafe incident.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie wherein the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the first observation data and the first project data that are most indicative of the occurrence of the reported unsafe incident as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, see discussion of claim 5 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches the type of features that are most indicative of the occurrence of the reported unsafe incident are saved by the incident predictor as predictive features (see; par. [0019] of Grant teaches the metric is determined to provide a predictor to determine the likelihood of an incident).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose the type of features that are most indicative of the occurrence of the reported unsafe incident are saved by the incident predictor as predictive features.
Grant discloses the type of features that are most indicative of the occurrence of the reported unsafe incident are saved by the incident predictor as predictive features.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie the type of features that are most indicative of the occurrence of the reported unsafe incident are saved by the incident predictor as predictive features as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 8 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches receiving second project data and second observation data from a second project physically remote from the first project (see; par. [0038] and par. [0077] of Grant teaches multiple incidents (i.e. second project) are collected and analyzed taking into account all observations), and
from the features that include at least one of the types of observation data and the types of project data that are identified as predictive features, predicting the likelihood of an occurrence of a second future unsafe incident at the second project (see; par. [0019] and [0077] of Grant teaches taking the data and predict the likelihood of an incident where the
Referring to Claim 10, see discussion of claim 1 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
the first incident data includes the severity of the incident, with the severity of the incident being one of the following: an Occupational Safety and Health Administration-recordable incident, a Days Away Restricted or Transferred-incident, and a lost-time incident (see; par. [0039] of Gattie teaches the incident data is related to a safety incident (i.e. Occupational Safety and Health Administration).
Referring to Claim 11, see discussion of claim 10 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches predicting, by the computer processor, the severity of the first future unsafe incident (see; par. [0018]-[0019] of Grant teaches a severity assessment of incidents to predict the likelihood of an incident).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose predicting, by the computer processor, the severity of the first future unsafe incident.
Grant discloses predicting, by the computer processor, the severity of the first future unsafe incident.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie predicting, by the computer processor, the severity of the first future unsafe incident as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, Gattie in view of Grant in further view of Samson teaches a system for predicting the likelihood of an unsafe incident occurring. Claim 12 recites the same or similar limitations as those addressed above in claim 1, Claim 12 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 13, see discussion of claim 12 above, while Gattie in view of Grant in further view of Samson teaches the system above Claim 13 recites the same or similar limitations as those addressed above in claim 2, Claim 13 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 14, see discussion of claim 12 above, while Gattie in view of Grant in further view of Samson teaches the system above Claim 14 recites the same or similar limitations as those addressed above in claim 3, Claim 14 is therefore rejected for the same or similar limitations as set forth above in claim 3, except for the following noted exception:
determine the type of first observation data and the type of first project data that are most indicative of the occurrence of the reported unsafe incident (see; par. [0005] of Gattie teaches determining features of work associated with, par. [0007] the occurrence of the hazardous).
Referring to Claim 15, see discussion of claim 12 above, while Gattie in view of Grant in further view of Samson teaches the system above, Gattie further disclose a method having the limitations of,
the incident predictor includes a machine-learning module configured to extract the features from the types of first project data and the types of first observation data that are identified by the machine-learning module as being most indicative of the occurrence of the first future unsafe incident (see; par. [0007] of Gattie teaches par. [0007] of Gattie teaches tracking the performance of projects and anticipated potential hazards).
Referring to Claim 16, see discussion of claim 15 above, while Gattie in view of Grant in further view of Samson teaches the system above Claim 16 recites the same or similar limitations as those addressed above in claim 5, Claim 16 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 17, see discussion of claim 12 above, while Gattie in view of Grant in further view of Samson teaches the system above, Gattie further disclose a method having the limitations of,
memory in communication with the incident predictor for storing the first project data, the first observation data, and the first incident data (see; par. [0008] of Gattie teaches observation data for incidents, par. [0028] utilizing data transfer medium).
Referring to Claim 18, Gattie in view of Grant in further view of Samson teaches method for predicting the likelihood of a future unsafe incident occurring. Claim 18 recites the same or similar limitations as those addressed above in claim 1, Claim 18 is therefore rejected for the same reasons as set forth above in claim 1, except for the following noted exceptions;
extracting, by an incident predictor that includes a machine-learning model, features from the types of project data and the types of observation data that are most indicative of the occurrence of the future unsafe incident at the first worksite of the first project, wherein the extraction includes (see; par. [0062] of Gattie teaches analytics that use gathered data and analyzed by machine learning model and all the observation data in order to determine potential hazards).
receiving second project data associated with a second project, the second project data includes the plurality of types of project data having different values of project data as compared to the first project data (see; par. [0039]-[0040] of Gattie teaches collecting information about different types of project data values related to multiple projects (i.e. second project data)).
receiving second observation data associated with the second project, the second observation data includes the plurality of types of observation data having different values of observation data as compared to the first observation data (see; par. [0039]-[0040] of Gattie teaches collecting information about different types of project data in the form of observation data values related to multiple projects (i.e. second observation data)).
associating the second observation data with the first incident data by linking a date of the observation data to a date of the unsafe incidents (see; par. [0033] of Gattie teaches a weather hazard data as an observation data).
identifying predictive features that include the plurality of types of project data and the plurality of types of observation data that are most indicative of an occurrence of the unsafe incident at the second project (see; par. [0005] of Gattie teaches determining features of work associated with, par. [0007] the occurrence of the hazardous).
Gattie does not explicitly disclose the following limitations, however,
Grant teaches receiving second incident data associated with the second project, the second incident data includes incident data regarding unsafe incidents and safe incidents at the second project (see; par. [0019] of Grant teaches the metric is determined to provide a predictor to determine the likelihood of an incident), and
predicting, by the machine-learning model and dependent upon the predictive features that are most indicative of the occurrence of a future unsafe incident, the likelihood of the future unsafe incident occurring at a first worksite of a first project (see; par. [0018]-[0019] of Grant teaches a severity assessment of incidents to predict the likelihood of an incident).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose receiving second incident data associated with the second project, the second incident data includes incident data regarding unsafe incidents and safe incidents at the second project, and predicting, by the machine-learning model and dependent upon the predictive features that are most indicative of the occurrence of a future unsafe incident, the likelihood of the future unsafe incident occurring at a first worksite of a first project.
Grant discloses receiving second incident data associated with the second project, the second incident data includes incident data regarding unsafe incidents and safe incidents at the second project, and predicting, by the machine-learning model and dependent upon the predictive features that are most indicative of the occurrence of a future unsafe incident, the likelihood of the future unsafe incident occurring at a first worksite of a first project.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie receiving second incident data associated with the second project, the second incident data includes incident data regarding unsafe incidents and safe incidents at the second project, and predicting, by the machine-learning model and dependent upon the predictive features that are most indicative of the occurrence of a future unsafe incident, the likelihood of the future unsafe incident occurring at a first worksite of a first project as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 19, see discussion of claim 18 above, while Gattie in view of Grant in further view of Samson teaches the method above Claim 19 recites the same or similar limitations as those addressed above in claim 5, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 5.
Referring to Claim 20, see discussion of claim 18 above, while Gattie in view of Grant in further view of Samson teaches the method above, Gattie does not explicitly disclose a method having the limitations of, however,
Grant teaches the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the predictive features that are most indicative of the occurrence of the unsafe incident at the second project (see; Abstract of Grant teaches using a machine learning model, par. [0020] where the model is a Random Forest Technique for determining the probability of an incident).
The Examiner notes that Gattie teaches similar to the instant application teaches construction analytics to improve safety. Specifically, Gattie discloses the objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents it is therefore viewed as analogous art in the same field of endeavor. Additionally, Grant teaches workplace risk determination and scoring system and as it is comparable in certain respects to Gattie which construction analytics to improve safety as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Gattie discloses objectively evaluating data from past and current construction projects to drive decision making to produce favorable outcomes including with respect to safety incidents. Gattie does not explicitly disclose the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the predictive features that are most indicative of the occurrence of the unsafe incident at the second project.
Grant discloses the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the predictive features that are most indicative of the occurrence of the unsafe incident at the second project.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Gattie the machine-learning model uses a random forest technique, an extreme gradient boosting technique, or a time series forecasting technique to identify the predictive features that are most indicative of the occurrence of the unsafe incident at the second project as taught by Grant since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Gattie, and Grant teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Amigo et al. (U.S. Patent 12,190,391 B2) discloses a sensor based systems and methods for evaluating activity.
Kruckeberg (U.S. Patent 8,290,796 B1) discloses a system and method for reducing worker’s compensation insurance reportable incidents.
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.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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