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 .
The following NON-FINAL Office Actin is in response to Applicant’s communication filed 11/07/2024 regarding 18/940,799. The following is the first action on the merits.
Status of Claim(s)
Claim(s) 1-20 is/are currently pending and are rejected as follows.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is/are directed towards a judicial exception (i.e. law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim(s) 1-20 are directed towards an invention for the receiving of data detected by a sensor, processing the data using one or more machine learning models to determine one or more likelihoods that the data includes evidence of a pest, determining whether the data includes evidence of the pest based on the one or more likelihoods, and displaying based on the determination the data that includes evidence of a pest and an identifier for the pest on a screen. These actions fall under a subject matter grouping which the courts have considered ineligible (Organizing Human Activity and Mental Process). These claims do not integrate the abstract idea into a practical application, and do not include additional elements that provide an inventive concept (are sufficient to amount to significantly more than the abstract idea).
Under Step 1 of the Alice/Mayo framework it must be considered whether the claims are directed to one of the four statutory categories of invention. Claim(s) 1-18 is directed towards an apparatus. Claim(s) 19 is directed towards a product. Claim(s) 20 is directed towards a method. Accordingly, the claims fall within the four statutory categories of invention, (apparatus, product, and method) and will be further analyzed under Step 2 of the Alice/Mayo framework.
Under Step 2, Prong One, of the Alice/Mayo framework it must be considered whether the claims recite any abstract ideas.
Independent claims 1, and 19-20 recite an invention for the receiving of data detected by a sensor, processing the data using one or more machine learning models to determine one or more likelihoods that the data includes evidence of a pest, determining whether the data includes evidence of the pest based on the one or more likelihoods, and displaying based on the determination the data that includes evidence of a pest and an identifier for the pest on a screen recite the abstract ideas of Organizing Human Activity and a Mental Process in the following limitations:
Receiving data detected by a sensor
Processing the data using one or more machine learning models,…[[and]] determining one or more likelihoods that the data includes evidence of a pest
Determining whether the data includes the evidence of the pest based on the one or more likelihoods
Displaying, in response to determining that the data includes evidence of the pest, an identifier for the pest…
Dependent claim(s) 2-18 merely further limit the abstract idea and are subject to the same rationale expressed above.
Under Step 2A, Prong Two, any additional elements are recited
Independent claim(s) 1, and 19-20 recite:
A sensor
A processor
A memory
A non-transitory computer readable storage medium
A machine learning model
An electronic display
Dependent claim 7 recites:
A mobile application
A mobile computing device
Dependent claim 8 recites:
A hardware server device
Dependent claim 9 recites:
A satellite
Dependent claim 10-13 recites:
A vehicle
An unmanned aircraft
Farming equipment
A tractor
These additional elements, considered both individually and as an ordered pair do no more than represent mere instructions to implement the abstract idea ("apply it" compute (See MPEP 2106.05(f)). Additionally, the claims represent insignificant extra solution activity (See MPEP 2106.05(g)). These elements are recited with a high degree of generality, and the specification sets forth the general purpose nature of the technologies required to implement the invention (emphasis added).
Support for this determination can found in Paragraph(s) [0012]-[0017], [0034]-[0037], [0047], and [0056]-[0067] of Applicant’s specification.
Under Step 2B eligibility analysis evaluates whether the claims as a whole amounts to significantly more than the recited exception, i.e. whether any additional element, or combination of elements, adds an inventive concept to the claims (MPEP 2106.05). As explained with respect to Step 2A, Prong Two, there are several additional elements. The sensor, processor, memory, non-transitory computer readable storage medium, machine learning mode, electronic display, mobile application, mobile device, hardware server device, satellite, vehicle, tractor, and unmanned aircraft are all, at best, the equivalent of merely adding the words "apply it" to the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (See MPEP 2106.05(f). Further the processor, memory, hardware server device, satellite, and mobile device represent insignificant extra solution activity (See MPEP 2106.05(g)), specifically that of mere data gathering which is known to be well-understood, routine, or conventional within the art (See MPEP 2106.05(d)(II)). Insignificant extra solution activity, especially that which is well-understood, routine, or conventional in the art does not provide an inventive concept. Even when considered in combination, these additional elements to are not deemed to be sufficient enough to provide an inventive concept onto the abstract idea, therefore, they are not eligible. (Alice Corp., 134 S. Ct. at 2358 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984 (warning against a §101 that turns on "the draftsman's art")).
Dependent claim(s) 2-6, and 14-18 do not disclose any further additional elements and are rejected for the same reasons expressed above.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-9, 14-17, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Files (US 10,524,461 B1)
Claim(s) 1, and 19-20 –
Files discloses the following limitations:
A sensor (Files: Column 2 lines 21-43, “The systems and techniques herein describe a pest detector that can detect and identify pests and send an alert to a computing device associated with a homeowner or a pest control service. The pest detector may include one or more sensors, such as, for example, a motion sensor, an imaging sensor (e.g., a camera), an audio transducer (e.g., microphone), a structured light sensor, an ultrasound sensor, an infrared imaging sensor, a temperature (e.g., a thermistor) sensor, an ultrasonic sensor, a capacitive sensor, a micropower impulse radar sensor, a global positioning satellite (GPS) sensor, an altimeter (e.g., to detect which floor of a building the detector has been placed, based on altitude), mmWave, and the like. Structured light involves projecting a known pattern (e.g., a grid or horizontal bars) of light on to an area (e.g., detection zone). The way in which the light deforms when striking the area enables a vision system (e.g., imaging sensor(s) and software) to determine the depth and surface information associated with a pest in the area. An mmWave sensor is able to detect objects (e.g., pests) and provide a range, a velocity, and an angle of each of the objects. An mmwave sensor operates in the spectrum between 30 GHz and 300 GHz.”)
An electronic display screen (Files: Column 15 lines 54 – 67, “FIG. 11 illustrates an example configuration of the computing device 1100 that can be used to implement the systems and techniques described herein, including the detector 100, the server 602, and one or more of the computing devices 628. The computing device 1100 may include one or more processors 1102, a memory 1104, communication interfaces 1106, a display device 1108, other input/output (I/O) devices 1110, and one or more mass storage devices 1112, configured to communicate with each other, such as via system buses 1114 or other suitable connection. The system buses 1114 may include multiple buses, such as memory device buses, storage device buses, power buses, video signal buses, and the like. A single bus is illustrated in FIG. 11 purely for ease of understanding.”)
A processor (Files: Column 16 lines 1 – 13, “The processors 1102 are one or more hardware devices that may include a single processing unit or a number of processing units, all of which may include single or multiple computing units or multiple cores. The processors 1102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, graphics processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 1102 may be configured to fetch and execute computer-readable instructions stored in the memory 1104, mass storage devices 1112, or other computer-readable media.”)
A memory storing computer instructions (Files: Column 16 lines 14 – 33, “Memory 1104 and mass storage devices 1112 are examples of computer storage media (e.g., memory storage devices) for storing instructions that can be executed by the processor 1102 to perform the various functions described herein. For example, memory 1104 may include both volatile memory and non-volatile memory (e.g., RAM, ROM, or the like) devices. Further, mass storage devices 1112 may include hard disk drives, solid-state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CD, DVD), a storage array, a network attached storage, a storage area network, or the like. Both memory 1104 and mass storage devices 1112 may be collectively referred to as memory or computer storage media herein, and may be a media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processor 1102 as a particular machine configured for carrying out the operations and functions described in the implementations herein.”)
A non-transitory computer readable storage medium (Files: Column 16 lines 14 – 33, “Memory 1104 and mass storage devices 1112 are examples of computer storage media (e.g., memory storage devices) for storing instructions that can be executed by the processor 1102 to perform the various functions described herein. For example, memory 1104 may include both volatile memory and non-volatile memory (e.g., RAM, ROM, or the like) devices. Further, mass storage devices 1112 may include hard disk drives, solid-state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CD, DVD), a storage array, a network attached storage, a storage area network, or the like. Both memory 1104 and mass storage devices 1112 may be collectively referred to as memory or computer storage media herein, and may be a media capable of storing computer-readable, processor-executable program instructions as computer program code that can be executed by the processor 1102 as a particular machine configured for carrying out the operations and functions described in the implementations herein.”)
Receiving data detected by a sensor (Files: Column 2 lines 21-43, “The systems and techniques herein describe a pest detector that can detect and identify pests and send an alert to a computing device associated with a homeowner or a pest control service. The pest detector may include one or more sensors, such as, for example, a motion sensor, an imaging sensor (e.g., a camera), an audio transducer (e.g., microphone), a structured light sensor, an ultrasound sensor, an infrared imaging sensor, a temperature (e.g., a thermistor) sensor, an ultrasonic sensor, a capacitive sensor, a micropower impulse radar sensor, a global positioning satellite (GPS) sensor, an altimeter (e.g., to detect which floor of a building the detector has been placed, based on altitude), mmWave, and the like. Structured light involves projecting a known pattern (e.g., a grid or horizontal bars) of light on to an area (e.g., detection zone). The way in which the light deforms when striking the area enables a vision system (e.g., imaging sensor(s) and software) to determine the depth and surface information associated with a pest in the area. An mmWave sensor is able to detect objects (e.g., pests) and provide a range, a velocity, and an angle of each of the objects. An mmwave sensor operates in the spectrum between 30 GHz and 300 GHz.”)
Processing the data using one or more machine learning models, each of the one or more machine learning models determining one or more likelihoods that the data includes evidence of a pest (Files: Column 2 line 61 – Column 3 line 15, “The pest detector may use the gathered data (e.g., digital image, movement information, and the like) associated with the potential pest to determine whether the data indicates a pest and if so, identify the pest using a machine learning (ML) algorithm. The ML algorithm may use, for example, a support vector machine or other type of classifier, clustering, Bayesian network, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, or the like. The ML may be trained to recognize multiple types of pests and to ignore data indicative of humans or pets (e.g., dogs and cats). In some cases, the type of pests that the ML can recognize may be based on the geographic region in which the pest detector is placed. For example, the pest detector may determine a location of the pest detector and download data associated with pests found in a region that encompasses the detector's location. To illustrate, a pest detector located in the southwestern United States may download an ML capable of detecting scorpions and snakes that are common to the local region, in addition to detecting ants, cockroaches, mice, rats, and other pests that are common to all geographic areas.”; Column 5 line 65 – Column 6 line 31, “For example, a pest detector may include one or more sensors, one or more processors, and computer-readable storage media to store instructions executable by the one or more processors to perform various operations. The operations may include receiving sensor data from one or more sensors. The sensor data may include a set of (e.g., one or more) digital images, a digital audio recording, or both. For example, a motion sensor may detect movement associated with a pest and an imaging sensor may capture one or more digital images of the pest. The one or more sensors may include at least one of: a motion sensor, an imaging sensor, a microphone, a structured light sensor, an ultrasound sensor, a temperature sensor, an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor. The operations may include using a machine learning algorithm to determine that the sensor data indicates a presence of a pest, and sending a notification message to a computing device. The machine learning algorithm may determine a type of the pest. For example, the machine learning algorithm may determine whether the pest is a cockroach, a mouse, or a rat. The notification message may include at least a portion of the sensor data (e.g., a digital image of the pest). The operations may include visually indicating, using an external indicator light of the detector, that the pest was detected. The operations may include storing the sensor data in a memory of the detector to create stored data and sending the stored data to a server. The operations may include receiving ambient light data from an ambient light sensor of the detector, determining that the ambient light data satisfies a predetermined threshold, and transitioning the detector from an active mode to a low-power mode. A particular sensor of the one or more sensors may include an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor, e.g., a sensor capable of detecting movement within a wall.”; Column 11 line 12 – Column 12 line 4, “The servers 602 may be hardware servers, cloud-based servers, or a combination of both. The servers 602 may store a remote ML algorithm 618 and a database 620. The remote ML 618 may be much larger and more sophisticated and may be capable of recognizing many more pests than the ML 612 used by the detector 100. The server 602 may receive and store the data 626 in the database 620. The database 620 may store data received from multiple detectors deployed in multiple geographic regions over a long period of time. In contrast, the stored data 614 in the detector 100 may have a limited size and may store data acquired over a relatively short period of time. If the data 626 indicates that the detector 100 was unable to recognize the pest, the data 626 may be added to the database 620 and the remote ML 618 may retrain the machine learning algorithm (e.g., used by a detector) using at least a portion of the database 620 to create a new detector ML 622. The server 602 may send an update 630 that includes the new detector ML 622 to one or more detectors via the network 106. For example, if the server 602 determines that a particular pest that was relatively absent has now become prevalent in a particular geographic region, the server 602 may create and send the new detector ML 622 to detectors located in the particular geographic region. In this way, the server 602 may continually provide update the detectors 100 to detect new and evolving pests (e.g., bigger mice, smaller cockroaches). The remote ML 618 may perform an analysis 636 of the data 626 received from detectors located in a particular structure (e.g., detectors in the same house, warehouse, industrial plant, restaurant, apartment building, or the like) and provide the analysis 636 to the computing devices 628. The app 634 may display the analysis 636, including predictions pertaining to the detected pests. The sensors 108 may include a temperature sensitive sensor, such as, for example, a thermistor and a humidity sensor (e.g., using capacitive, resistive, or thermal conductivity technology). The temperature sensitive sensor may capture temperature data and the humidity sensor may capture humidity data and send the captured data to the ML 612. The remote ML 618 may be trained to consider temperature and humidity and make predictions based on the temperature data and the humidity data. For example, for detectors that are placed outside, the remote ML 618 may make predictions based on current weather conditions, including temperature, humidity, and weather forecasts e.g., “Scorpions are predicted because the temperature is greater than X degrees”, “Crickets are predicted because the temperature is greater than X degrees and the humidity is less than Y”, and so on. The predictions may be based on (1) previous data gathered under similar conditions (e.g., temperature X, humidity Y for Z length of time usually cause the number of cockroaches to increase significantly) and (2) data gathered from detectors located nearby (e.g., several of your neighbors have experienced an increase in ant activity in the past few days). The server 602 may aggregate data from multiple detectors deployed in multiple locations (e.g., houses or buildings) and make predictions. For example, increased activity in multiple buildings that are in close proximity to each other may cause the remote machine learning 618 to predict a large scale infestation spanning the multiple buildings.”)
Determining whether the data includes the evidence of the pest based on the one or more likelihoods (Files: Column 2 line 61 – Column 3 line 15, “The pest detector may use the gathered data (e.g., digital image, movement information, and the like) associated with the potential pest to determine whether the data indicates a pest and if so, identify the pest using a machine learning (ML) algorithm. The ML algorithm may use, for example, a support vector machine or other type of classifier, clustering, Bayesian network, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, or the like. The ML may be trained to recognize multiple types of pests and to ignore data indicative of humans or pets (e.g., dogs and cats). In some cases, the type of pests that the ML can recognize may be based on the geographic region in which the pest detector is placed. For example, the pest detector may determine a location of the pest detector and download data associated with pests found in a region that encompasses the detector's location. To illustrate, a pest detector located in the southwestern United States may download an ML capable of detecting scorpions and snakes that are common to the local region, in addition to detecting ants, cockroaches, mice, rats, and other pests that are common to all geographic areas.”; Column 5 line 65 – Column 6 line 31, “For example, a pest detector may include one or more sensors, one or more processors, and computer-readable storage media to store instructions executable by the one or more processors to perform various operations. The operations may include receiving sensor data from one or more sensors. The sensor data may include a set of (e.g., one or more) digital images, a digital audio recording, or both. For example, a motion sensor may detect movement associated with a pest and an imaging sensor may capture one or more digital images of the pest. The one or more sensors may include at least one of: a motion sensor, an imaging sensor, a microphone, a structured light sensor, an ultrasound sensor, a temperature sensor, an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor. The operations may include using a machine learning algorithm to determine that the sensor data indicates a presence of a pest, and sending a notification message to a computing device. The machine learning algorithm may determine a type of the pest. For example, the machine learning algorithm may determine whether the pest is a cockroach, a mouse, or a rat. The notification message may include at least a portion of the sensor data (e.g., a digital image of the pest). The operations may include visually indicating, using an external indicator light of the detector, that the pest was detected. The operations may include storing the sensor data in a memory of the detector to create stored data and sending the stored data to a server. The operations may include receiving ambient light data from an ambient light sensor of the detector, determining that the ambient light data satisfies a predetermined threshold, and transitioning the detector from an active mode to a low-power mode. A particular sensor of the one or more sensors may include an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor, e.g., a sensor capable of detecting movement within a wall.”; Column 11 line 12 – Column 12 line 4, “The servers 602 may be hardware servers, cloud-based servers, or a combination of both. The servers 602 may store a remote ML algorithm 618 and a database 620. The remote ML 618 may be much larger and more sophisticated and may be capable of recognizing many more pests than the ML 612 used by the detector 100. The server 602 may receive and store the data 626 in the database 620. The database 620 may store data received from multiple detectors deployed in multiple geographic regions over a long period of time. In contrast, the stored data 614 in the detector 100 may have a limited size and may store data acquired over a relatively short period of time. If the data 626 indicates that the detector 100 was unable to recognize the pest, the data 626 may be added to the database 620 and the remote ML 618 may retrain the machine learning algorithm (e.g., used by a detector) using at least a portion of the database 620 to create a new detector ML 622. The server 602 may send an update 630 that includes the new detector ML 622 to one or more detectors via the network 106. For example, if the server 602 determines that a particular pest that was relatively absent has now become prevalent in a particular geographic region, the server 602 may create and send the new detector ML 622 to detectors located in the particular geographic region. In this way, the server 602 may continually provide update the detectors 100 to detect new and evolving pests (e.g., bigger mice, smaller cockroaches). The remote ML 618 may perform an analysis 636 of the data 626 received from detectors located in a particular structure (e.g., detectors in the same house, warehouse, industrial plant, restaurant, apartment building, or the like) and provide the analysis 636 to the computing devices 628. The app 634 may display the analysis 636, including predictions pertaining to the detected pests. The sensors 108 may include a temperature sensitive sensor, such as, for example, a thermistor and a humidity sensor (e.g., using capacitive, resistive, or thermal conductivity technology). The temperature sensitive sensor may capture temperature data and the humidity sensor may capture humidity data and send the captured data to the ML 612. The remote ML 618 may be trained to consider temperature and humidity and make predictions based on the temperature data and the humidity data. For example, for detectors that are placed outside, the remote ML 618 may make predictions based on current weather conditions, including temperature, humidity, and weather forecasts e.g., “Scorpions are predicted because the temperature is greater than X degrees”, “Crickets are predicted because the temperature is greater than X degrees and the humidity is less than Y”, and so on. The predictions may be based on (1) previous data gathered under similar conditions (e.g., temperature X, humidity Y for Z length of time usually cause the number of cockroaches to increase significantly) and (2) data gathered from detectors located nearby (e.g., several of your neighbors have experienced an increase in ant activity in the past few days). The server 602 may aggregate data from multiple detectors deployed in multiple locations (e.g., houses or buildings) and make predictions. For example, increased activity in multiple buildings that are in close proximity to each other may cause the remote machine learning 618 to predict a large scale infestation spanning the multiple buildings.”)
Displaying, in response to determining that the data includes the evidence of the pest, an identifier for the pest on an electronic display screen (Files: Column 14 lines 30 – 56, “At 902, sensor data may be received from one or more sensors. At 904, a determination may be made whether the sensor data indicates a presence of a pest. If a determination is made, at 904, that “no” the sensor data does not indicate the presence of a pest, then the process may proceed back to 902. If a determination is made, at 904, that “yes” the sensor data indicates the presence of a pest, then the process may proceed to 906, where a type of the pest may be determined. For example, in FIG. 6, the sensors 108 may detect the presence of a pest and capture the sensor data 632 associated with the pest. The sensors 108 may send the sensor data 632 to the processors 604. The processors 604 may determine whether the sensor data 632 indicates the presence of a pest. For example, if the sensor data 632 indicates the presence of a human or a pet (e.g., cat or dog), then the sensor data 632 may be discarded and the processors 604 may wait to receive additional sensor data. If the sensor data 632 indicates the presence of a pest, then the process may determine, using the ML 612, a type of the pest. For example, the ML 612 may be trained to recognize (e.g., classify or predict) one of multiple types of pests based on the sensor data 632. At 908, a notification may be sent (e.g., to a computing device). For example, in FIG. 6, the detector 100 may send the notification 624 indicating that a particular type of pest was detected to one or more of the computing devices 628. The UI 702 may provide an audible (and/or visual indication) that the notification 624 was received.”; Column 15 lines 34 – 44, “At 1010, the UI may display one or more predictions associated with the pests that were detected. At 1012, the UI may display one or more suggestions. For example, in FIG. 6, the remote ML 618 may analyze at least a portion of the data stored in the database 620 to create the analysis 636. The server 602 may send the analysis 636 to the computing devices 628 for display by the app 634. The analysis 636 may include the predictions 716 and the suggestions 718 of FIG. 7. The predictions 716 may include predictions on particular location(s) where pests are predicted to be nesting based on pest movement.”)
Claim(s) 2 –
Files discloses the limitations of claim 1
Files further discloses the following:
Determining an action to mitigate one or more effects of the pest and displaying the action on the electronic display screen (Files: Column 4 line 44 – Column 5 line 11, “An app (created by a manufacturer of the detector) may be downloaded and installed on a user device, such as a computing device associated with an occupant of a home, a warehouse staff member, a pest control service, or the like. The app may display a user interface (UI) to display data received from multiple detectors in a particular location, such as a house, a warehouse, an industrial plant, or another type of building or set of buildings. For example, the UI may display an approximate floor plan of the particular location and an approximate location of each detector within the floor plan. The UI may display data associated with each detector, such as a mode (e.g., detection mode or low-power mode), network connectivity (e.g., connected to or disconnected from a network), whether or not the detector has detected a pest (e.g., green indicates no pests detected, red indicates one or more pests were detected), and other information associated with the detector. The UI may display one or more predictions and/or suggestions made by the ML algorithm. For example, the predictions may include that a particular type of pest appears to nesting in a particular location. To illustrate, the multiple detectors in the location may, after detecting a pest, determine a direction in which the pest is travelling and predict where the pests are nesting (e.g., breeding) based on the direction data. The ML may make suggestions such as “add a detector in this room and on this wall” to provide additional data, “add a detector with a back-facing sensor in this location” to determine (e.g., confirm) whether a pest is nesting behind a particular wall, and the like. The UI may enable a user to view the data gathered by each detector, such as a digital image of the pest captured by the detector. The UI may provide information as to nearby (e.g., within a predetermined radius from a current location of the device on which the app is installed) pest control service providers and enable the user to request a quote for pest control services.”)
Claim(s) 3 –
Files discloses the limitations of claims 1-2
Files further discloses the following:
Wherein the action is determined using one or more additional machine learning models (Files: Column 5 line 12 – 47, “The data gathered by each detector (e.g., pest-related data) may be sent to a server (e.g., a cloud-based server). The server may thus receive data from multiple detectors in each of multiple locations (e.g., houses, warehouses, industrial plants, restaurants, grocery stores, and the like). The server may execute a second ML algorithm to provide additional analysis and prediction. For example, if multiple locations in a particular neighborhood of a city detect a particular pest, the server may proactively provide a suggestion (via the UI of the app) to users located in the particular neighborhood, e.g., “This particular pest has been detected in your neighborhood but has not yet been detected in your location (e.g., house). We recommend taking the following preventative measures to prevent this pest from becoming a problem.” In addition, if the detector is unable to identify a particular pest, the detector may send the data (e.g., digital image, audio recording, video recording, movement data, structured light data) associated with the particular pest to the server for further analysis. The server may determine the type of pest that the detector detected and send an update to the detector. For example, if a new pest is detected, the server may send an updated ML algorithm or an updated (or new) pest profile to detectors located in a same region. For example, if a particular region experiences unseasonably warm weather that causes a large number of a particular pest (e.g., locusts, crickets, or the like) to breed and the current version of the ML in the deployed detectors is not capable of detecting the particular pest, then after one or more detectors send pest-related data to the server, the server's ML algorithm may identify the pest, create an updated detector ML algorithm, and send (or provide for download) the updated ML algorithm to detectors in the particular region. Each detector may install the updated ML algorithm to enable each detector to detect the particular pest. In this way, if a new pest becomes prevalent in a particular region, the new pest can be detected by updating the ML algorithm used by each detector.”)
Claim(s) 6 –
Files discloses the limitations of claim 1
Files further discloses the following:
Wherein the pest comprises an insect (Files: Column 5 line 12 – 47, “The data gathered by each detector (e.g., pest-related data) may be sent to a server (e.g., a cloud-based server). The server may thus receive data from multiple detectors in each of multiple locations (e.g., houses, warehouses, industrial plants, restaurants, grocery stores, and the like). The server may execute a second ML algorithm to provide additional analysis and prediction. For example, if multiple locations in a particular neighborhood of a city detect a particular pest, the server may proactively provide a suggestion (via the UI of the app) to users located in the particular neighborhood, e.g., “This particular pest has been detected in your neighborhood but has not yet been detected in your location (e.g., house). We recommend taking the following preventative measures to prevent this pest from becoming a problem.” In addition, if the detector is unable to identify a particular pest, the detector may send the data (e.g., digital image, audio recording, video recording, movement data, structured light data) associated with the particular pest to the server for further analysis. The server may determine the type of pest that the detector detected and send an update to the detector. For example, if a new pest is detected, the server may send an updated ML algorithm or an updated (or new) pest profile to detectors located in a same region. For example, if a particular region experiences unseasonably warm weather that causes a large number of a particular pest (e.g., locusts, crickets, or the like) to breed and the current version of the ML in the deployed detectors is not capable of detecting the particular pest, then after one or more detectors send pest-related data to the server, the server's ML algorithm may identify the pest, create an updated detector ML algorithm, and send (or provide for download) the updated ML algorithm to detectors in the particular region. Each detector may install the updated ML algorithm to enable each detector to detect the particular pest. In this way, if a new pest becomes prevalent in a particular region, the new pest can be detected by updating the ML algorithm used by each detector.”)
Claim(s) 7 –
Files disclose the limitations of claim 1
Files further discloses the following:
A mobile computing device, wherein the mobile computing device comprises the sensor, electronic display screen, processor, and the memory, and the computer program code comprises a mobile application executing on the mobile computing device (Files: Column 4 line 27 – Column 5 line 11 , “Each detector may include a wireless network interface (e.g., WiFi®, Bluetooth®, or the like) to enable the detector to communicate with (i) other detectors, (ii) an application (“app”) executing on a user's computing device, (iv) a cloud-based server, (iii) a pest services company, or any combination thereof. For example, the detector may create a mesh network with other detectors using a short distance networking protocol, such as, for example, Bluetooth®, ZigBee, or the like. As another example, the detector may communicate with other detectors, one or more user devices, a server, or other devices using WiFi® or another type of wireless networking technology. The detector may communicate data to an application executing on a user device, such as a smartphone, a tablet, or a virtual assistant enabled device (e.g., Amazon® Echo® or Alexa®, Google® Home, Apple® Homepod, or the like). An app (created by a manufacturer of the detector) may be downloaded and installed on a user device, such as a computing device associated with an occupant of a home, a warehouse staff member, a pest control service, or the like. The app may display a user interface (UI) to display data received from multiple detectors in a particular location, such as a house, a warehouse, an industrial plant, or another type of building or set of buildings. For example, the UI may display an approximate floor plan of the particular location and an approximate location of each detector within the floor plan. The UI may display data associated with each detector, such as a mode (e.g., detection mode or low-power mode), network connectivity (e.g., connected to or disconnected from a network), whether or not the detector has detected a pest (e.g., green indicates no pests detected, red indicates one or more pests were detected), and other information associated with the detector. The UI may display one or more predictions and/or suggestions made by the ML algorithm. For example, the predictions may include that a particular type of pest appears to nesting in a particular location. To illustrate, the multiple detectors in the location may, after detecting a pest, determine a direction in which the pest is travelling and predict where the pests are nesting (e.g., breeding) based on the direction data. The ML may make suggestions such as “add a detector in this room and on this wall” to provide additional data, “add a detector with a back-facing sensor in this location” to determine (e.g., confirm) whether a pest is nesting behind a particular wall, and the like. The UI may enable a user to view the data gathered by each detector, such as a digital image of the pest captured by the detector. The UI may provide information as to nearby (e.g., within a predetermined radius from a current location of the device on which the app is installed) pest control service providers and enable the user to request a quote for pest control services.”)
Claim(s) 8 –
Files discloses the limitations of claim 1
Files further discloses the following:
Wherein receiving the data comprises receiving, at a hardware server device over a data network, an upload of the data, the hardware server device over a data network, an upload of the data, the hardware server comprising the processor and the memory. (Files: Column 4 line 27 – Column 5 line 11 , “Each detector may include a wireless network interface (e.g., WiFi®, Bluetooth®, or the like) to enable the detector to communicate with (i) other detectors, (ii) an application (“app”) executing on a user's computing device, (iv) a cloud-based server, (iii) a pest services company, or any combination thereof. For example, the detector may create a mesh network with other detectors using a short distance networking protocol, such as, for example, Bluetooth®, ZigBee, or the like. As another example, the detector may communicate with other detectors, one or more user devices, a server, or other devices using WiFi® or another type of wireless networking technology. The detector may communicate data to an application executing on a user device, such as a smartphone, a tablet, or a virtual assistant enabled device (e.g., Amazon® Echo® or Alexa®, Google® Home, Apple® Homepod, or the like). An app (created by a manufacturer of the detector) may be downloaded and installed on a user device, such as a computing device associated with an occupant of a home, a warehouse staff member, a pest control service, or the like. The app may display a user interface (UI) to display data received from multiple detectors in a particular location, such as a house, a warehouse, an industrial plant, or another type of building or set of buildings. For example, the UI may display an approximate floor plan of the particular location and an approximate location of each detector within the floor plan. The UI may display data associated with each detector, such as a mode (e.g., detection mode or low-power mode), network connectivity (e.g., connected to or disconnected from a network), whether or not the detector has detected a pest (e.g., green indicates no pests detected, red indicates one or more pests were detected), and other information associated with the detector. The UI may display one or more predictions and/or suggestions made by the ML algorithm. For example, the predictions may include that a particular type of pest appears to nesting in a particular location. To illustrate, the multiple detectors in the location may, after detecting a pest, determine a direction in which the pest is travelling and predict where the pests are nesting (e.g., breeding) based on the direction data. The ML may make suggestions such as “add a detector in this room and on this wall” to provide additional data, “add a detector with a back-facing sensor in this location” to determine (e.g., confirm) whether a pest is nesting behind a particular wall, and the like. The UI may enable a user to view the data gathered by each detector, such as a digital image of the pest captured by the detector. The UI may provide information as to nearby (e.g., within a predetermined radius from a current location of the device on which the app is installed) pest control service providers and enable the user to request a quote for pest control services.”)
Claim(s) 9 –
Files discloses the limitations of claim 1
Files further discloses the following:
A satellite in orbit, the satellite comprising the sensor, the pest being within a range of the sensor from orbit (Files: Column2 lines 21 – 43, “The systems and techniques herein describe a pest detector that can detect and identify pests and send an alert to a computing device associated with a homeowner or a pest control service. The pest detector may include one or more sensors, such as, for example, a motion sensor, an imaging sensor (e.g., a camera), an audio transducer (e.g., microphone), a structured light sensor, an ultrasound sensor, an infrared imaging sensor, a temperature (e.g., a thermistor) sensor, an ultrasonic sensor, a capacitive sensor, a micropower impulse radar sensor, a global positioning satellite (GPS) sensor, an altimeter (e.g., to detect which floor of a building the detector has been placed, based on altitude), mmWave, and the like. Structured light involves projecting a known pattern (e.g., a grid or horizontal bars) of light on to an area (e.g., detection zone). The way in which the light deforms when striking the area enables a vision system (e.g., imaging sensor(s) and software) to determine the depth and surface information associated with a pest in the area. An mmWave sensor is able to detect objects (e.g., pests) and provide a range, a velocity, and an angle of each of the objects. An mmwave sensor operates in the spectrum between 30 GHz and 300 GHz.”; Column 4 line 27 – Column 5 line 11 , “Each detector may include a wireless network interface (e.g., WiFi®, Bluetooth®, or the like) to enable the detector to communicate with (i) other detectors, (ii) an application (“app”) executing on a user's computing device, (iv) a cloud-based server, (iii) a pest services company, or any combination thereof. For example, the detector may create a mesh network with other detectors using a short distance networking protocol, such as, for example, Bluetooth®, ZigBee, or the like. As another example, the detector may communicate with other detectors, one or more user devices, a server, or other devices using WiFi® or another type of wireless networking technology. The detector may communicate data to an application executing on a user device, such as a smartphone, a tablet, or a virtual assistant enabled device (e.g., Amazon® Echo® or Alexa®, Google® Home, Apple® Homepod, or the like). An app (created by a manufacturer of the detector) may be downloaded and installed on a user device, such as a computing device associated with an occupant of a home, a warehouse staff member, a pest control service, or the like. The app may display a user interface (UI) to display data received from multiple detectors in a particular location, such as a house, a warehouse, an industrial plant, or another type of building or set of buildings. For example, the UI may display an approximate floor plan of the particular location and an approximate location of each detector within the floor plan. The UI may display data associated with each detector, such as a mode (e.g., detection mode or low-power mode), network connectivity (e.g., connected to or disconnected from a network), whether or not the detector has detected a pest (e.g., green indicates no pests detected, red indicates one or more pests were detected), and other information associated with the detector. The UI may display one or more predictions and/or suggestions made by the ML algorithm. For example, the predictions may include that a particular type of pest appears to nesting in a particular location. To illustrate, the multiple detectors in the location may, after detecting a pest, determine a direction in which the pest is travelling and predict where the pests are nesting (e.g., breeding) based on the direction data. The ML may make suggestions such as “add a detector in this room and on this wall” to provide additional data, “add a detector with a back-facing sensor in this location” to determine (e.g., confirm) whether a pest is nesting behind a particular wall, and the like. The UI may enable a user to view the data gathered by each detector, such as a digital image of the pest captured by the detector. The UI may provide information as to nearby (e.g., within a predetermined radius from a current location of the device on which the app is installed) pest control service providers and enable the user to request a quote for pest control services.”)
Claim(s) 14 –
Files discloses the limitations of claim 1
Files further discloses the following:
Wherein the sensor comprises an image sensor and the data comprise one or more image data and video data (Files: Column2 lines 21 – 40, “The systems and techniques herein describe a pest detector that can detect and identify pests and send an alert to a computing device associated with a homeowner or a pest control service. The pest detector may include one or more sensors, such as, for example, a motion sensor, an imaging sensor (e.g., a camera), an audio transducer (e.g., microphone), a structured light sensor, an ultrasound sensor, an infrared imaging sensor, a temperature (e.g., a thermistor) sensor, an ultrasonic sensor, a capacitive sensor, a micropower impulse radar sensor, a global positioning satellite (GPS) sensor, an altimeter (e.g., to detect which floor of a building the detector has been placed, based on altitude), mmWave, and the like. Structured light involves projecting a known pattern (e.g., a grid or horizontal bars) of light on to an area (e.g., detection zone). The way in which the light deforms when striking the area enables a vision system (e.g., imaging sensor(s) and software) to determine the depth and surface information associated with a pest in the area. An mmWave sensor is able to detect objects (e.g., pests) and provide a range, a velocity, and an angle of each of the objects. An mmwave sensor operates in the spectrum between 30 GHz and 300 GHz. The pest detector m