Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
INFORMATION DISCLOSURE STATEMENT
The information disclosure statement (IDS) submitted on 8/8/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
DOUBLE PATENTING
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over U.S. Patent 12,067,707
As to claims 1 & 11, instant application discloses receiving image information from an image sensor configured to capture information defining an image of the physical environment; generate computer vision output information based on the image information; receive a stream of information including the computer vision output information and location information generated by a wireless sensing device, the location information characterizing location of a user or an object within the physical environment; generating an input feature dataset based on the stream of information; processing the input feature dataset using a machine learning algorithm trained model to generate a safety related result and feedback data for the user or the object; and delivering the feedback data to the user or the object via a mobile tag device deployed to the user or the object. (U.S. Patent 12,067,707- Claim 1)
As to claims 2-10 & 12-20, these claims are rejected due to their dependence on claims 1 & 11 and are rejected for the same reasons.
CLAIM REJECTIONS - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to as ineligible under subject eligibility test. In the Subject Matter Eligibility Test for Products and Processes (Federal Register, Vol. 79, No. 241, dated Tuesday, December 16, 2014, page 74621), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional device elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1 & 11
Step 1
This step inquires “is the claim to a process, article of machine, manufacture or composition of matter?” Yes,
Claims 1 - “Systems” are machines.
Claim 11 – “Method” is a process.
Step 2A - Prong 1
This step inquires “does the claim recite an abstract idea, law or natural phenomenon”. This claim appears to directed to an abstract idea.
Abstract ideas fall under three categories:
(1) Mathematical Concepts;
(2) Certain Methods of Organizing Human Activity;
(3) Mental Processes
The limitation of “an image sensor configured to capture information defining an image of the physical environment; a wireless sensing device configured to generate location information characterizing location of a user or an object within the physical environment; and one or more hardware processors configured by machine-readable instructions to: receive the image information from the image sensor; generate computer vision output information based on the image information; receive a stream of information including the computer vision output information and the location information generated by the wireless sensing device; generate an input feature dataset based on the stream of information; process the input feature dataset using a machine learning algorithm trained model to generate a safety related result and feedback data for the user or the object; and deliver the feedback data to the user or the object via a mobile tag device deployed to the user or the object.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity) but for the recitation of generic computer components. That is, other than reciting “image sensor; wireless sensing device; one or more hardware processors” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “image sensor; wireless sensing device; one or more hardware processors” language, “capture, generate, receive, proves, deliver” in the context of this claim encompasses covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity).
STEP 2A – PRONG 1 - CONCLUSION
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2
This step inquires “does the claim recite additional elements that integrate the judicial exception into a practical application”. This judicial exception is not integrated into a practical application. In particular, the claim recites three additional element – using a “image sensor; wireless sensing device; one or more hardware processors” to perform “capture, generate, receive, proves, deliver “steps. The “image sensor; wireless sensing device; one or more hardware processors” are recited at a high-level of generality (i.e., as a generic processor) “an image sensor configured to capture information defining an image of the physical environment; a wireless sensing device configured to generate location information characterizing location of a user or an object within the physical environment; and one or more hardware processors configured by machine-readable instructions to: receive the image information from the image sensor; generate computer vision output information based on the image information; receive a stream of information including the computer vision output information and the location information generated by the wireless sensing device; generate an input feature dataset based on the stream of information; process the input feature dataset using a machine learning algorithm trained model to generate a safety related result and feedback data for the user or the object; and deliver the feedback data to the user or the object via a mobile tag device deployed to the user or the object.” such that it amounts no more than mere instructions to apply the exception using a generic computer component.
STEP 2A – PRONG 2 - CONCLUSION
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The critical inquiry here is does the claim recite additional elements that amount to “significantly more” than the judicial exception? The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a “image sensor; wireless sensing device; one or more hardware processors” to perform “capture, generate, receive, proves, deliver steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As to claims 2 & 12, this claim is directed to generic computer components (“computer vision software/model”), mental process (“yes”) and insignificant extra-solution activity (“generating descriptive information from sensor data.”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 3 & 13, this claim is directed to generic computer components (“computer vision software/model”), mental process (“yes”) and insignificant extra-solution activity (“labeling/classifying observed action.”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 4 & 14, this claim is directed to generic computer components (“mobile tag, reference point devices, wireless sensing devices, image sensor”) and insignificant extra-solution activity (“data gathering”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 5 & 15, this claim is directed to generic computer components (“wireless sensing device”), mental process (“motion observation”) and insignificant extra-solution activity (“collecting motion information is extra solution data gathering”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 6 & 16, this claim is directed to generic computer components (“display, speaker, haptic device, mobile tag, processor”), mental process (“yes”) and insignificant extra-solution activity (“yes, enforcing safety operations/guiding behaviors”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 7 & 17, this claim is directed to mental process (“yes”) and insignificant extra-solution activity (“characterizing a detected condition/change”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 8 & 18, this claim is directed to generic computer components (“mobile tag”), mental process (“a person could align records by identity/time at a high level, though not at sensor stream scale”) and insignificant extra-solution activity (“pre processing/data organization before the ML safety result”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 9 & 19, this claim is directed to generic computer components (“processor, control signal output, mobile tag or user device”), mental process (“yes, instructing/guiding user behavior”) and insignificant extra-solution activity (“yes, control signals directs user behavior”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 10 & 20, this claim is directed to generic computer components (“image sensor, CV/ML model, processor, mobile tag alert”), mental process (“yes, a human supervisor can observe user conduct, judge whether it violates a safety rule and issue an alert.”) and insignificant extra-solution activity (“yes, alerting is outputting the result of the judgment.”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
CLAIM REJECTIONS - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-6, 8, 10-13, 15-16 & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. “A FRAMEWORK OF ON-SITE CONSTRUCTION SAFETY MANAGEMENT USING COMPUTER VISION AND REAL-TIME LOCATION SYSTEM” Published 2019 in view of Cobb (U.S. Publication 2016/0350908) & Myers et al. (U.S. Publication 2019/0012895)
As to claims 1 & 11, Zhang discloses a multimodal system for managing safety in a physical environment comprising (Abstract & Page 327 discloses In this paper, a framework is proposed for an on-site construction safety management system using Fast R-Convolution Neural Network (CNN)–based computer vison and Bluetooth Low Energy (BLE)–based real-time location system (RTLS). Page 328 Introduction discloses this paper proposes an approach that combines CV and RTLS for proactive on-site safety management, where unsafe behaviours are detected by a CV system and people in danger are located by a RTLS and warned via several methods.): an image sensor configured to capture information defining an image of the physical environment (P. 328 discloses CV has the potential to understand images or videos obtained at complex and dynamic construction sites. Inexpensive 2D video cameras and surveillance systems are now standard equipment at most construction sites for a number of reasons, including documentation of construction activity (Everett et al., 1998) and security (Yang et al., 2010).); a wireless sensing device configured to generate location information characterizing location of a user or an object within the physical environment (P. 329 discloses a RTLS can be used to identify/track the location of workers, materials, and equipment at a construction site in real time (Li et al., 2016). The RTLS module in this framework locates and tracks the movement of workers, materials, and equipment with an acceptable accuracy. Through outdoor RTLS (e.g., GPS), indoor RTLS (e.g., RFID-based system), or mixed RTLS (e.g., UWB system), local coordinates of an object being tracked are calculated by the Received Signal Strength (RSS) method.); and (partly discloses) one or more hardware processors configured by machine-readable instructions to (p. 330 discloses CNN-based approach requires sufficient computing power to process large images, especially when dealing with a dataset that includes millions of images. Hardware improvement in GPUs has enabled effective training of large CNN networks by stacking multiple convolutional and pooling layers to recognize features not only from static images but also from video footage): receive the image information from the image sensor (p. 330 discloses Fig. 4 shows the result of object tracking. Fig. 5 discloses the result of action recognition); generate computer vision output information based on the image information (p. 327, Abstract discloses CV can detect on site entities, understand their spatial relations in a semantic way, and recognize actions of workers and construction equipment. Moreover, the trajectories of moving objects can be tracked, and the next location can be predicted. P. 328, 2.1 discloses the imagery data processed from these devices enable three main capabilities in construction safety and health monitoring: object detection, object tracking, and action recognition (Seo et al., 2015). Object detection refers to the capability of recognizing a variety of entities at a construction site such as workers, equipment, and materials. CV achieves object tracking by assigning consistent labels to detected objects and tracking them in a sequence of still images from a video feed
Action recognition requires the CV to understand what a worker or a piece of equipment is doing even if it remains in one place); receive a stream of information including the computer vision output information and the location information generated by the wireless sensing device (P. 328 discloses this paper proposes an approach that combines CV and RTLS for proactive on-site safety management, where unsafe behaviors are detected by a CV system and people in danger are located by a RTLS and warned via several methods. P. 330 discloses
Once it has been recognised by CV that a person is in danger and the locations of the relevant people, materials, and equipment, are determined, a personnel warning system is needed to take action to notify key people and eliminate the danger. By combining the CV and RTLS, the people to be notified (the worker and the excavator operator in the example) can be identified. As an illustration, a CNN-based CV approach, a BLE-based RTLS, and a Bluetooth-based warning system are presented as best practices in their respective modules.);
(partly discloses) generate an input feature dataset based on the stream of information (p. 330 discloses CNN is a CV approach for object detection and classification which can automatically extract a large number of image features and use those features for image classification. Treating an image as a matrix of pixels where each pixel represents one feature, LeCun et al. (1998) developed a CNN-based CV to process large images. The same authors further improved the R-CNN algorithm by feeding the input image to the CNN to generate a convolutional feature map.
The output of the RTLS is a dataset of time-stamped locations and a calculated object trajectory.);
(partly discloses) process the input feature dataset using a machine learning algorithm trained model to generate a safety related result and feedback data for the user or the object; (p. 327 discloses In this paper, a framework is proposed for an on-site construction safety management system using Fast R-Convolution Neural Network (CNN)–based computer vison and Bluetooth Low Energy (BLE)–based real-time location system (RTLS). P. 328 discloses This paper proposes an approach that combines CV and RTLS for proactive on-site safety management, where unsafe behaviours are detected by a CV system and people in danger are located by a RTLS and warned via several methods. P. 330 discloses CNN is a CV approach for object detection and classification which can automatically extract a large number of image features and use those features for image classification. Hardware improvement in GPUs has enabled effective training of large CNN networks by stacking multiple convolutional and pooling layers to recognize features not only from static images but also from video footage) and deliver the feedback data to the user or the object via a mobile tag device deployed to the user or the object (p 327 discloses workers involved in a potential construction hazard will be warned through a mobile application on their smartphones, via loud sounds and vibrations. P. 330 discloses methods to notify on-site workers include speakers built into hardhats, mobile phones, smart wrist bands, LED-equipped safety vests, and so on. Beacons are attached to workers, materials, and equipment to track their locations in real time. The Bluetooth beacons used in the RTLS are carried by workers and are paired with the workers’ mobile phones. Once a worker is identified as a person in danger, the system will send a command to the beacon which can trigger a loud sound and a vibration on the worker’s mobile phone.
)
Zhang is silent to one or more hardware processors configured by machine-readable instructions; generate an input feature dataset based on the stream of information; process the input feature dataset using a machine learning algorithm trained model.
However, Cobb discloses one or more hardware processors configured by machine-readable instructions ([0022] discloses One embodiment of the invention is implemented as a program product for use with a computer system. [0023] discloses the computer program of the present invention is comprised typically of a multitude of instructions that will be translated by the native computer into a machine-readable format and hence executable instructions. [0024] discloses As shown, the behavior-recognition system 100 includes a video input source 105, a network 110, a computer system 115, and input and output devices 118. Illustratively, the computer system 115 includes a CPU 120, storage 125 (e.g., a disk drive, optical disk drive, floppy disk drive, and the like), and a memory 130 which includes both a computer vision engine 135 and a machine-learning engine 140.); generate an input feature dataset based on the stream of information;
([0040] discloses the context processor component 220 may package a stream of micro-feature vectors and kinematic observations of an object and output this to the machine-learning engine 140, e.g., at a rate of 5 Hz).[0041] discloses the computer vision engine 135 may take the output from the components 205, 210, 215, and 220 describing the motions and actions of the tracked objects in the scene and supply this information to the machine-learning engine 140.)
[0042] discloses micro-feature classifier 255 may subscribe to receive the micro-feature vectors output from the computer vision engine 135.)
process the input feature dataset using a machine learning algorithm trained model.
([0020] discloses in turn, a machine learning engine is configured to build models of certain behaviors within the scene based on the foreground blobs and extracted features, and determine whether observations indicate that the behavior of an object is anomalous or not, relative to the model. Machine learning engine may issue an alert when anomalous sea-surface oil is observed so that the oil may be investigated. [0029] discloses the machine-learning engine 140 may be configured to analyze the received data, cluster objects having similar visual and/or kinematic features, build semantic representations of events depicted in the video frames. Over time, the machine learning engine 140 learns expected patterns of behavior for objects that map to a given cluster. [0046] discloses the micro-feature classifier 255 may schedule a codelet 245 to evaluate the micro-feature vectors output by the computer vision engine 135. each micro-feature vector may be supplied to an input layer of the ART network. [0052] discloses a machine-learning video analytics system may be configured to use a computer vision engine to observe a scene, generate information streams of observed activity, and to pass the streams to a machine learning engine. Thereafter, when unexpected (i.e., abnormal or unusual) behavior is observed, alerts may be generated.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zhang’s disclosure to include the above limitations in order to generate and process machine readable feature data from construction site CV/RTLS safety management information and generate warnings or alerts.
Zhang in view of Cobb is silent to deliver the feedback data to the user or the object via a mobile tag device deployed to the user or the object.
However, Myers discloses to deliver the feedback data to the user or the object via a mobile tag device deployed to the user or the object. ([0007] discloses a system stores information indicative of real-time locations of wearable alert devices, receives information indicative of a potentially dangerous condition in a geographic area, determines which of the wearable alert devices are in the geographic area, and outputs alerts to a communications network for transmittal to the wearable alert devices in the geographic area. [0016] discloses The wearable alert device 100 includes a processing unit 110, a transmitter 120, a receiver 130, one or more antennas 123, memory 140, a power supply 150, and an output device 160. [0017] discloses The wearable alert device 100 may be incorporated into a piece of clothing such as a shoe, pants, a shirt, a jacket, a wristband, a watch, glasses, a hat, etc. Alternatively, the wearable alert device 100 may be wearable on the body of a user. [0022] discloses The output device 160 may be any suitable device that outputs an alert to a user. The alert may be visual, audible, haptic, etc. [0023] discloses the position sensors 170 may include any suitable device that determines the real-time or near real-time location, velocity, acceleration, and/or orientation of the wearable alert device 100. [0036] discloses The device location database 340 stores information indicative of the real-time locations of the wearable alert devices 100. [0040] discloses the wearable alert system 300 may receive an alert regarding a potentially dangerous condition (e.g., a weather alert 322, public safety alert 324, an environmental alert 326, etc.) in a geographic area and output an alert to the wearable alert devices 100 in the geographic area. [0055] discloses The wearable alert system 300 outputs alerts to a communications network 230 for transmittal to the wearable alert devices 100 that are located in the geographic area in step 410.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Zhang in view of Cobb’s disclosure to include the above limitations in order to transmit safety feedback to the wearable/mobile device of the user located in the dangerous area.
As to claims 2 & 12, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Zhang discloses wherein the computer vision output information includes a description of the physical environment. (p. 327, Abstract discloses CV can detect on site entities, understand their spatial relations in a semantic way, and recognize actions of workers and construction equipment. P. 328, 2.1 discloses the imagery data processed from these devices enable three main capabilities in construction safety and health monitoring: object detection, object tracking, and action recognition. P. 332 discloses using the Fast R-CNN method to analyze imagery data obtained from on-site cameras, the situation of the constriction scene is semantically understood: the entities on site and their spatial relations, the trajectories of moving objects and the trends of their movement, and the actions of workers and construction equipment.)
As to claims 3 & 13, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Zhang discloses wherein the computer vision output information includes an action identified for the object. (p. 327, Abstract discloses CV can detect on site entities, understand their spatial relations in a semantic way, and recognize actions of workers and construction equipment. P. 328 discloses Action recognition requires the CV to understand what a worker or a piece of equipment is doing even if it remains in one place. P. 330 & 5.1 discloses Fig. 5 shows the result of action recognition. P. 330 & 5.1 discloses
By matching features extracted from detected workers to a set of known action classification features, three types of action were recognized: standing (in green frames), stooping (in blue frames), and squatting (in red frames).)
As to claims 5 & 15, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Myers discloses wherein the wireless sensing device includes a motion sensor collecting motion information characterizing motion of the user. ([0016] discloses The wearable alert device 100 includes a processing unit 110, a transmitter 120, a receiver 130, one or more antennas 123, memory 140, a power supply 150, and an output device 160. The wearable alert device 100 may also include an energy harvesting device 152, one or more position sensors 170, one or more physiological sensors 180, and/or one or more environmental sensors 190 a. [0023] discloses the position sensors 170 may include any suitable device that determines the real-time or near real-time location, velocity, acceleration, and/or orientation of the wearable alert device 100. [0023] discloses The position sensors 170 may include, for example, a global positioning system (GPS) receiver, an altimeter, an accelerometer, a gyrometer, a magnetometer, a compass, an inclinometer, a device orientation sensor, etc. )
As to claims 6 & 16, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Zhang discloses wherein the feedback data is an alert, a warning, guidance, or displays of information for enforcing safety operations. (P. 327 & Abstract discloses workers involved in a potential construction hazard will be warned through a mobile application on their smartphones, via loud sounds and vibrations. Page 328 Introduction discloses this paper proposes an approach that combines CV and RTLS for proactive on-site safety management, where unsafe behaviours are detected by a CV system and people in danger are located by a RTLS and warned via several methods. P. 330 discloses methods to notify on-site workers include speakers built into hardhats, mobile phones, smart wrist bands, LED-equipped safety vests, and so on.), Also see Myers ([0022])
As to claims 8 & 18, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Zhang discloses wherein the mobile tag device is configured to track identity information of the user, wherein the input feature dataset is generated by aligning the data stream with respect to time and/or the identity information of the user. (P. 328, 330). See Myers ([0036, 0039, 0052]). See Cobb ([0040-0041])
As to claims 10 & 20, Zhang in view of Cobb & Myers discloses everything as disclosed in claims 1 & 11 respectively. In addition, Zhang discloses wherein the safety related result includes a detection of an action of the user that does not comply with a safety protocol and the feedback data includes an alert indicative of the detection. (P. 327-328, 330). Also, see Myers [0022, 0040]
CONCLUSION
No prior art has been found for claims 4, 7, 9, 14, 17 & 19-20 in their current form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Stephen P Coleman whose telephone number is (571)270-5931. The examiner can normally be reached Monday-Thursday 8AM-5PM.
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675
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