DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/19/2025 has been entered.
Status of Claims
The following is a Non-Final Office Action in response to Applicant’s response received on 11/19/2025.
Claims 1, 9, 19, and 20 are amended. Claims 7 and 8 are cancelled. Claims 1-6 and 9-20 are considered in this Office Action. Claims 1-6 and 9-20 are currently pending.
Response to Arguments
Applicant’s arguments with respect to the 103 rejections to claims have considered, however, they are primarily raised in light of applicant’s amendments, and therefore, an updated 35 U.S.C. 103 will address applicant’s amendments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 10-13, 15-17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over J. Eric Pike (US 2020/0219037 A1, hereinafter “Pike”) in view of Chris A. Sabillon (NPL: “Audio-Based Productivity Forecasting of Construction Cyclic Activities”, published 2017, hereinafter “Sabillon”) in view of Hoffmann (US 2023/0093585 A1, hereinafter “Hoffmann”) in view of Travis Davis (EP 3872722 A1, hereinafter “Davis”) in view of Zhijun Cai (US 2018/0056839 A1, hereinafter “Cai”).
Claim 1/19/20
Pike teaches:
A computer implemented method ([0013] The system typically includes a processor, a memory, and a network communication device. The system also typically includes an analysis module stored in the memory and executable by the processor. [0022] a non-transitory computer-readable medium comprising computer-readable instructions), comprising: creating a schedule by a scheduling software, wherein creating the schedule (Figure 4 and [0074] describes for the selected time period, yard, and crew work type, the graphical user interface provides: the quantity of crews, average yard, job, and travel time, information about the quantity of tasks completed, and information about experienced delays) comprises:
receiving a project or certain activities, wherein the project or certain activities includes a set of information that describes requirements of the project or certain activities ([0074] graphical user interface that may be used to provide aggregate information. The graphical user interface depicted in FIG. 4 allows a user to select a time period (e.g., week or month), yard, and crew work type for which the user desires to see aggregate information);
receiving a dataset of available heavy equipment associated with the project or certain activities ([0034] A telematics dataset may include information about a single “event” or multiple “events.” Such “events” may include: a change in speed, a change in heading/direction of travel, and/or use of certain components of the equipment (e.g., engine or PTO). A telematic dataset may include information about the events that occurred since the last telematic dataset was transmitted. In some instances, an equipment telematic dataset may be generated and transmitted each time a piece of equipment (e.g., a telematics device associated with the piece of equipment) identifies an “event.” [0039] collecting and analyzing equipment telematic data in order to determine when such equipment is being used at a job site. By comparing such location information with the location of known job sites, the system is able to determine whether, at any particular time, the equipment (as well as personnel associated with such equipment) is being used to perform work at a job site or is engaged in other activities (e.g., collecting materials at a yard, traveling to a job site, break time, and the like). By knowing the amount of time spent at a job site, as compared to time spent on other activities, the system may determine the productivity of the equipment (as well as personnel associated with such equipment));
selecting a set of heavy equipment from the dataset of available heavy equipment, wherein each heavy equipment in the set of heavy equipment includes at least one sensor ([0042] the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data);
receiving sensor data from the at least one sensor for each heavy equipment in the set of heavy equipment ([0042] the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets), wherein the sensor data comprises metadata including timestamps, location information, and contextual information associated with the sensor data([0034] telematic data corresponding to a moment in time. For example, an equipment telematic dataset may include, among other things: an identifier of the applicable equipment (e.g., name, serial number, or other identifier), location information (e.g., GPS coordinates) for the equipment, data related to the use of an engine/motor of the equipment, data related to the use of tools, such as integrated components of the equipment or handheld tools, and/or a time (timestamp)at which the equipment generated or transmitted the telematic dataset. An equipment telematic dataset may be generated and transmitted each time a piece of equipment (e.g., a telematics device associated with the piece of equipment) identifies an “event.”);
outputting a set of productivity rates for at least a portion of the set of heavy equipment ([0077] The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.)).
While Pike teaches [0076] aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects. [0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.), it does not explicitly teach the following, however, analogous reference, Hoffmann teaches:
performing a beamforming technique to extract sound and/or kinematic patterns from the sensor data by spatially filtering and localizing desired sound from a variety of other unwanted sound sources in an environment ([0037] The audio controller 150 may receive data from the sensor array (e.g., acoustic sensors 180) and create a mapping of sound sources in the local area of the audio system. The audio controller 150 may create a sound filter to spatialize a virtual sound source at a position that is not co-located with sound sources in the local area. The filtered and spatialized virtual sound source is output through the transducer array (e.g., speakers 160). The audio controller 150 may additionally receive input from the imaging devices 130 or position sensors 190 and process the input data to calculate the spatializing sound filter. [0058] The beamforming module 270 is configured to process one or more ATFs to selectively emphasize sounds from sound sources within a certain area while de-emphasizing sounds from other areas. In analyzing sounds detected by the sensor array 220, the beamforming module 270 may combine information from different acoustic sensors to emphasize sound associated from a particular region of the local area while deemphasizing sound that is from outside of the region. The beamforming module 270 may isolate an audio signal associated with sound from a particular sound source from other sound sources in the local area based on, e.g., different DOA estimates from the DOA estimation module 240 and the tracking module 260).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike with Hoffmann to performing a beamforming technique to extract sound and/or kinematic patterns from the sensor data by spatially filtering and localizing desired sound from a variety of other unwanted sound sources in an environment, because it will increase the amount of audio information collected and the sensitivity and/or accuracy of the information [0035].
While Pike teaches [0076] aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects. [0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.), it does not explicitly teach the following, however, analogous reference in fleet management, Sabillon teaches:
pre-processing the sound and/or kinematic patterns by converting the sound and/or kinematic patterns into a set of images by using a Short Time Fourier Transform (pg. 25 discloses the processing module consists of two steps: pre-processing to remove unwanted noise and to extract a period information and feature extraction to identify characteristic features of the audio sample and create a vector. Pg. 27 The STFT consists on dividing a long-time signal into shorter portions or bins through a windowing approach and calculating the Fourier transform for each of these bins. Pg. 33 Audio and acceleration data were recorded and processed to evaluate the accuracy of activity classification using support vector machines (SVM). For audio data, frequency feature extraction was executed using the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT), while pg. 17 Specific movement patterns are then related to a specific activity. These activity-related patterns are trained to a computer through a supervised machine learning algorithm. Once several activities have been trained, a library has been created. This library is, finally, validated and used for real-time activity recognition. pg. 34 The audio signal portion of this study begins with on-site audio recording of construction equipment under normal operation. Then, audio recordings are fed to a machine learning framework consisting of: audio enhancement through a de-noising algorithm, time-frequency representation of the audio signal through the STFT and the CWT for comparison, library generation for posterior labeling using the SVM classifier, and high-level activity label acquisition through a window filtering approach);
inputting the set of images into a machine learning model (pg. 34 The audio signal portion of this study begins with on-site audio recording of construction equipment under normal operation. Then, audio recordings are fed to a machine learning framework consisting of: audio enhancement through a de-noising algorithm, time-frequency representation of the audio signal through the STFT and the CWT for comparison, library generation for posterior labeling using the SVM classifier, and high-level activity label acquisition through a window filtering approach. Further see figures 3.1 and 3.2);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike and Hoffmann with Sabillon to include extracting sound and/or kinematic patterns from the sensor data using a beamforming technique, wherein the beamforming technique comprises a method of spatial filtering or localization of desired sound from a variety of other unwanted sound sources in an environment, pre-processing the sound and/or kinematic patterns by converting the sound and/or kinematic patterns into a set of images by using a Short Time Fourier Transform, and inputting the set of images into a machine learning model, because it will provide overall efficient and clear objective of a project progress based on the productivity of equipment and other resources using telematics sensors data and machine learning model by evaluating cycle time estimation accuracy (pg. 49).
While Pike teaches [0025] the telematics processing system 106 determines a machine utilization pattern for the machines based on the telematics data. For example, a machine learning model (such as a neural network) can be applied to estimate each machine's utilization pattern based on telematics data (i.e., telemetry data). As an example, an excavator can have a use pattern of activities including 50% mass excavation, 20% grading, and 30% tracking (i.e., traveling from place to place). [0026] These models may differentiate between, for example, mass excavation, dirt moving, trenching, scraping, grading, loading, tracking, or idle time. Models may supplement standard telematics data with additional sensors to measure the intensity of use. The resulting machine utilization patterns, or activity data, can be provided to the machine modification identification system 100 to identify machine modifications that would improve a machine's productivity for an identified application, it does not explicitly teach the following, however, analogous reference, Davis teaches:
mapping the sensor data to one profile from a group of profiles based on the sensor data and the metadata, wherein each profile is mapped to a respective machine learning model from a group of machine learning models for at least one heavy equipment selected from the set of heavy equipment ([0003] storing, in a memory accessible to the server end, a software solution database containing a plurality of software solutions corresponding to different work machine task profiles. [0012] The software solutions each correspond to a particular work machine task profile. [0013] the task-specific data may include sensor readings or input collected by sensors onboard the work machine. [0014] the server end may generate the search criteria to include a geographical origin of the software solution request, which may be specified in the request or instead determined from geo-tagged metadata, the general location of an Internet Protocol (IP) address, or the like, where machine-specific data contained in the work machine profile database may be recalled utilizing identifying information extracted from the software solution request, with such machine-specific data then utilized in constructing the search criteria. The server end may then utilize the search criteria to search the software solution database for an optimal-fit software solution corresponding to the search criteria. [0015] A software solution is considered an "optimal-fit software solution" when an adequate match is found between the search criteria and the work machine task profile corresponding to the software solution. [0027] each software solution held within the database 58 is linked to or associated with a unique work vehicle task profile. Such software applications are advantageously, although not necessarily, realized as machine learning algorithms).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, and Sabillon with Davis to mapping the sensor data to one profile from a group of profiles based on the sensor data and the metadata, wherein each profile is mapped to a respective machine learning model from a group of machine learning models for at least one heavy equipment selected from the set of heavy equipment, because it will aid in enhancing work machine performance and efficiency, while reducing operator workload [0065].
While Pike teaches [0076] aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects. [0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.), it does not explicitly teach the following, however, analogous reference in fleet management, Cai teaches:
estimating,[…], a set of cycle times based on the set of information related to the project or the certain activities([0021] The signals underlying the time trace shown for illustration in FIG. 2 are provided to a controller, which is programmed to and operates to analyze the signals and automatically discern and catalog various operating parameters of the machine 100 according to the frequency, severity and duration of each operating segment to infer or estimate various efficiency parameters for the particular machine, while [0027] At a basic level, the controller 300 provides the acceleration signal to a cycle analyzer function 308. The analyzer function 308 operates to first filter and analyze the signal 302 to determine a vibration presence and frequency, for example, using a fast Fourier transform or similar operation, and to record the same with respect to time to create a time trace. This information may then be used to identify a work segment of truck, which is put into context and compared to a normal work cycle of the truck. For example, a dumping segment is typically expected to follow a loading segment and a hauling segment. That time trace is then compared to known or learned time traces to determine a match with a predefined trace);
estimating a set of productivity rates based on the set of cycle times ([0022]- [0023] In another example, the total productive utilization of the machine with respect to time lapsed can also be calculated. As shown in the graphs 326 of FIG. 4, the average cycle time for various cycles is measured, in this fashion, the truck spends an average of between 0.5 and 0.6 hours waiting, which represents 12% of the truck's up-time, with the remaining time being spent in LHD cycles and miscellaneous tasks. In general, the controller can calculate a time distribution for the various cycle types, as shown in FIG. 5A, a total segment time for each cycle or segment type, as shown in FIG. 5B, and also the average segment time for each segment type, as shown in FIG. 5C);
and automatically updating the schedule by the scheduling software based on the set of productivity rates ([0022] Various parameters and metrics of machine operation can be inferred, estimated or calculated on the basis of the vertical acceleration signal provided by the sensor 200 for a particular machine. For example, as shown in the charts 320 of FIG. 3, a controller may count the number of cycles during which the machine is working in a productive fashion including loading, hauling and dumping (LHD) cycles, and also non-productive segments such as waiting stopped or performing miscellaneous tasks. The counting of productive cycles 322 and non-productive segments 324, in the aggregate, can provide an indication of utilization of the machine for efficiency determinations. In the charts of FIG. 3, for example, 89% of the cycles executed by the machine are productive. This information can be used to modify the machine's schedule or to adjust the number of machines operating at a jobsite to increase overall efficiency of the work operation by the operator of the site).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, and Sabillon with Cai to estimating a set of cycle times based on the set of information related to the project or the certain activities, estimating a set of productivity rates based on the set of cycle times, and automatically updating the schedule by the scheduling software based on the set of productivity rates, because it will provide overall efficient and clear objective of a project progress based on the productivity of equipment and other resources[0002-0003].
Claim 2
Pike teaches:
The computer implemented method of claim 1, wherein each heavy equipment in the set of heavy equipment includes two sensors ([0042] the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets).
Claim 3
While Pike teaches [0042] the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets, it does not explicitly teach the following, however, analogous reference in fleet management, Sabillon teaches:
The computer implemented method of claim 2, wherein a first sensor is a microphone and a second sensor is an accelerometer (pg. 20 Microphones are the primary devices used for audio recording. Pg. 34 Audio data from individual pieces of equipment performing routing actions were taken using the XMOS xCORE-200 multichannel array microphone connected to a laptop computer on site. Pg. 46 Mobile phone micro-electro-mechanical-systems (MEMS) accelerometers were used to collect data and process it through a model similar to that used for audio signals, as depicted in Figure 3.8).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, and Cai with Sabillon to include a first sensor is a microphone and a second sensor is an accelerometer as part of the sensor taught in Pike, because in doing so it will provide accurate telematics data and provide efficient data regarding the productivity of the equipment (pg. 49).
Claim 4
While Pike teaches [0042] the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets, it does not explicitly teach the following, however, analogous reference Sabillon teaches:
The computer implemented method of claim 1, wherein the at least one sensor includes a microphone and an accelerometer (pg. 20 Microphones are the primary devices used for audio recording. Pg. 34 Audio data from individual pieces of equipment performing routing actions were taken using the XMOS xCORE-200 multichannel array microphone connected to a laptop computer on site. Pg. 46 Mobile phone micro-electro-mechanical-systems (MEMS) accelerometers were used to collect data and process it through a model similar to that used for audio signals, as depicted in Figure 3.8 ).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, and Cai with Sabillon to include the at least one sensor includes a microphone and an accelerometer as part of the sensor taught in Pike, because in doing so it will provide accurate telematics data and provide efficient data regarding the productivity of the equipment (pg. 49).
Claim 5
Pike teaches:
The computer implemented method of claim 1, wherein the sensor data is received by a wireless chipset that is configured to communicate with a microcontroller located within each heavy equipment in the set of heavy equipment ([0042] As illustrated in FIG. 1, the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets;[0043] The operating environment 100 also typically includes a system 200 for collecting and analyzing equipment telematic data transmitted by the equipment 150. The system 200 and the equipment 150 are typically in communication with a network 110, such as the Internet, wide area network, local area network, wireless telephone network, Bluetooth network, near field network, or any other form of contact or contactless network).
Claim 10
Pike teaches:
The computer implemented method of claim 1, further comprising: receiving a set of operators, wherein each operator in the set of operators is assigned to at least one heavy equipment in the set of heavy equipment ([0074] FIG. 4 depicts an exemplary graphical user interface that may be used to provide aggregate information. The graphical user interface depicted in FIG. 4 allows a user to select a time period (e.g., week or month), yard, and crew work type for which the user desires to see aggregate information. For the selected time period, yard, and crew work type, the graphical user interface provides: the quantity of crews, average yard, job, and travel time, information about the quantity of tasks completed, and information about experienced delays. [0072] equipment records may be analyzed by the system 200 to determine aggregate productivity and other information related to various pieces of equipment and personnel. Information may be aggregated for: a single piece of equipment over multiple time periods (e.g., over multiple days); individual personnel (e.g., a crew leader) or a group of personnel (e.g., a crew) over multiple time periods. [0041] Telematic data may also be combined with other information in equipment records. Such other information may include: identities of personnel associated with (e.g., assigned to) such equipment, such as the leader of a crew assigned to use the equipment; the number of worked hours submitted by such personnel (e.g., on a timesheet); a project identifier (e.g., project number) for which the equipment was used during the applicable time period; a line of business of an entity for which the equipment was used during the applicable time period; the type of work performed by the equipment and/or personnel; ); and selecting a portion of operators from the set of operators([0074] FIG. 4 depicts an exemplary graphical user interface that may be used to provide aggregate information. The graphical user interface depicted in FIG. 4 allows a user to select a time period (e.g., week or month), yard, and crew work type for which the user desires to see aggregate information. For the selected time period, yard, and crew work type, the graphical user interface provides: the quantity of crews, average yard, job, and travel time, information about the quantity of tasks completed, and information about experienced delays).
Claim 11
Pike teaches:
The computer implemented method of claim 10, wherein the set of productivity rates is based on the portion of operators ([0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.)).
Claim 12
While Pike teaches [0041] telematic data may also be combined with other information in equipment records. Such other information may include: identities of personnel associated with (e.g., assigned to) such equipment, such as the leader of a crew assigned to use the equipment; the number of worked hours submitted by such personnel (e.g., on a timesheet); a project identifier (e.g., project number) for which the equipment was used during the applicable time period; a line of business of an entity for which the equipment was used during the applicable time period; the type of work performed by the equipment and/or personnel; it does not explicitly teach the following, however, analogous reference in fleet management, Sabillon teaches:
The computer implemented method of claim 1, wherein each heavy equipment in the set of heavy equipment is categorized with an equipment type (pg. 55, pg. 71, and Table 4.6 categorize equipment by type such as Komatsu PC200/ Excavatin and JD 50G Backhoe/ Clearing).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, and Cai with Sabillon to include each heavy equipment in the set of heavy equipment is categorized with an equipment type, because it will provide overall efficient and clear objective of a project progress based on the productivity of equipment and other resources using equipment type.
Claim 13
While Pike teaches [0041] telematic data may also be combined with other information in equipment records. Such other information may include: identities of personnel associated with (e.g., assigned to) such equipment, such as the leader of a crew assigned to use the equipment; the number of worked hours submitted by such personnel (e.g., on a timesheet); a project identifier (e.g., project number) for which the equipment was used during the applicable time period; a line of business of an entity for which the equipment was used during the applicable time period; the type of work performed by the equipment and/or personnel; it does not explicitly teach the following, however, analogous reference in fleet management, Sabillon teaches:
The computer implemented method of claim 12, wherein the equipment type includes at least one of standard, wheeled, long-reach, and backhoe excavator (pg. 55, pg. 71, and Table 4.6 categorize equipment by type such as Komatsu PC200/ Excavatin and JD 50G Backhoe/ Clearing).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, and Cai with Sabillon to include the equipment type includes at least one of standard, wheeled, long-reach, and backhoe excavator, because it will provide overall efficient and clear objective of a project progress based on the productivity of equipment and other resources using equipment type.
Claim 15
Pike teaches:
The computer implemented method of claim 1, further comprising sending a notification ([0070] the system 200 may transmit an alert to one or more users regarding any flagged equipment records so that such users may further investigate the cause of the flagged anomalies).
Claim 16
Pike teaches
The computer implemented method of claim 15, wherein the notification indicates a delayed project ([0048] In connection with its analysis of telematic data from the equipment 150, the system 200 typically generates equipment records. An “equipment record” typically includes information related to the use of a particular piece of equipment during a particular time period (e.g., during a particular day). An equipment record may also include information related to the personnel using the equipment, the yard to which such equipment is assigned, the identity of projects worked on by the equipment/personnel, the tasks completed by the equipment/personnel during the time period, delays experienced during the time period, mileage, and weather during the time period.[0068]- [0070] the system 200 may transmit an alert to one or more users regarding any flagged equipment records so that such users may further investigate the cause of the flagged anomalies while [0076] Aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects).
Claim 17
While Pike teaches [0070] the system 200 may transmit an alert to one or more users regarding any flagged equipment records so that such users may further investigate the cause of the flagged anomalies, it does not explicitly teach the following, however Kobel teaches:
The computer implemented method of claim 15, wherein the notification indicates a heavy equipment in the set of heavy equipment is in an idle state ([0050] The control system 60 generates a range of inputs, outputs, and user interfaces. The inputs, outputs, and user interfaces may be related to a jobsite, a status of a piece of equipment, environmental conditions, equipment telematics, an equipment location, task instructions, sensor data, equipment consumables data (e.g., a fuel level, a condition of a battery), status, or sensor data from another connected piece of equipment, equipment operation, performance data, operating and idle time data).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike with Kobel to include the notification indicates a heavy equipment in the set of heavy equipment is in an idle state, because in doing so it will provide overall requirement of equipment need in order to allocate resources to complete the project accordingly.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Pike in view of Hoffmann in view of Sabillon in view of Davis in view of Cai, as applied in claim 5, and further in view of Jeff Jenkins (US 2021/0221312 A1, hereinafter “Jenkins”).
Claim 6
While Pike teaches [0042] as illustrated in FIG. 1, the operating environment 100 typically includes multiple pieces of equipment 150, illustrated by way of example in FIG. 1 as a truck 150A, a trailer 150B, and a tractor 150C. The equipment 150 may be able to generate and transmit equipment telematic datasets that include telematic data related to such equipment 150. Accordingly, each piece of equipment 150 may include one or more sensors (e.g., sensors for sensing the location, speed, heading, performance of integrated components, etc. of the equipment) for collecting telematic data, a controller for aggregating such telematic data and generating equipment telematic datasets, and a network interface for communicating such equipment telematic datasets;[0043] The operating environment 100 also typically includes a system 200 for collecting and analyzing equipment telematic data transmitted by the equipment 150. The system 200 and the equipment 150 are typically in communication with a network 110, such as the Internet, wide area network, local area network, wireless telephone network, Bluetooth network, near field network, or any other form of contact or contactless network, it does not explicitly teach the following, however, analogous reference Jenkins teaches:
The computer implemented method of claim 5, wherein the communication is established using a serial/parallel interface (SPI) standard ([0033] The vehicle telematics device 110 can include any of a variety of sensors and/or devices, including those described above with respect to the vehicle data bus and those described in more detail below, to obtain data regarding the status of the vehicle, and to obtain accelerometer data that can be used to detect an impact to the vehicle while the vehicle is parked. This data can be utilized in a variety of crash determination processes to determine if there has been an impact to the parked vehicle and/or whether the vehicle has been involved in a crash as described in more detail below. The set of sensors including the accelerometer can communicate with the microcontroller via an interface bus, which can be an SPI (Serial Peripheral Interface) bus, an I.sup.2C interface, among various other interfaces as appropriate to the requirements of specific applications).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, Sabillon, and Cai with Jenkins to include the communication is established using a serial/parallel interface (SPI) standard, because in doing so it will provide communication to transmit and receive data efficiently.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Pike in view of Hoffmann in view of Sabillon in view of Davis in view of Cai, as applied in claim 1, and further in view of Charles Howard Cella (US 2019/0041842 A1, hereinafter “Cella”).
Claim 9
While Pike teaches [0076] aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects. [0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.), it does not explicitly teach the following, however, analogous reference in fleet management, Cella teaches:
The computer implemented method of claim 1, wherein one of the group of machine learning models is a convolutional neural network ([0629] the machine learning model is a convolutional neural network).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, Sabillon, and Cai with Cella to include the machine learning model is a convolutional neural network, because in doing so it will provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments. [0012].
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Pike in view of Hoffmann in view of Sabillon in view of Davis in view of Cai, as applied in claim 1, and further in view of Aditya Sen (US 2016/0171406 A1, hereinafter “Sen”).
Claim 14
While Pike teaches [0076] aggregate information may be analyzed to identify variables (e.g., weather, location, traffic, personnel, etc.) that drive productivity or delays. Based on variables determined to cause delays, the system 200 may be able to project expected delays for future projects. [0077] the system 200 may calculate the estimated productivity (e.g., expected quantity of various tasks) for particular equipment or personnel over a particular time period and compare the estimated productivity to the actual productivity (e.g., actual quantity of various tasks completed). The estimated productivity may be based on the average productivity across the entity, adjusted for various factors such as weather, delays, job site time, geographic region, yard, crew leader, and/or the like that may affect productivity. In this regard, regression analysis may be performed by the system 200 to create a model for calculating estimated productivity. The system 200 may then provide users with information regarding the estimated productivity and actual productivity (e.g., for a piece of equipment, group of personnel, type of task, etc.), it does not explicitly teach the following, however, analogous reference Sen teaches:
The computer implemented method of claim 1, further comprising estimating a total duration of the project or the certain activities based on the set of productivity rates ([0072] At block 930, a completion date for the entire project is forecasted by rolling up time durations and completion dates for all of the activities of the project (whether completed or not) across all of the categories of the project. In one embodiment, the schedule roll-up logic 120 is configured to determine a completion date for the entire project).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, Sabillon, and Cai with Sen to include estimating a total duration of the project or the certain activities based on the set of productivity rates, because it will provide overall efficient and clear objective of a project progress and timeline based on the productivity of equipment and other resources([0001]-[0002]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Pike in view of Hoffmann in view of Sabillon in view of Davis in view of Cai, as applied in claim 1, and further in view of Emily Zhao (US 2021/0171762 A1, hereinafter “Zhao”).
Claim 18
While Pike teaches [0070] the system 200 may transmit an alert to one or more users regarding any flagged equipment records so that such users may further investigate the cause of the flagged anomalies, it does not explicitly teach the following, however Zhao teaches:
The computer implemented method of claim 1, further comprising: determining a heavy equipment in the set of heavy equipment is in an idle state ([0085] In block 802, a system determines a status for each fleet vehicle of a plurality of fleet vehicles. For example, a status may be one or more of active (e.g., currently being operated), inactive (e.g., not deployed for operation), standby (e.g., deployed but not being currently operated), requires inspection, requires replacing a battery or other electrical/mechanical component, requires retrieval and/or replacement of the fleet vehicle, and/or requires relocation/deployment of the fleet vehicle to a new location); and transferring the heavy equipment in the set of heavy equipment to a second project or a second set of certain activities([0085] In block 802, a system determines a status for each fleet vehicle of a plurality of fleet vehicles. For example, a status may be one or more of active (e.g., currently being operated), inactive (e.g., not deployed for operation), standby (e.g., deployed but not being currently operated), requires inspection, requires replacing a battery or other electrical/mechanical component, requires retrieval and/or replacement of the fleet vehicle, and/or requires relocation/deployment of the fleet vehicle to a new location).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the teaching of Pike, Hoffmann, Davis, Sabillon, and Cai with Zhao to include determining a heavy equipment in the set of heavy equipment is in an idle state and transferring the heavy equipment in the set of heavy equipment to a second project or a second set of certain activities, because it will allow for dynamic service scheduling using real-time data to increase productivity and efficiency in servicing machinery ([0002]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20210182753 A1
WORK ORDER INTEGRATION SYSTEM
SHATTERS; Aaron R. et al.
US 20090063031 A1
Performance-based haulage management system
Greiner; Jonny Ray et al.
US 20140207512 A1
Timeline-Based Visual Dashboard for Construction
Khanzode; Atul et al.
NPL
Noise cancellation with Python and Fourier Transform
Piero Paialunga
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/REHAM K ABOUZAHRA/Examiner, Art Unit 3625