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 .
Claim Rejections - 35 USC § 103
Claim(s) 1,3,7,8,10,11,12,13 is/are rejected under 35 U.S.C. 102(a1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Wang CN 16568914.
Wang CN 16568914 (listed 1449) teaches a method of obtaining measurements of a body of water by an autonomous marine vessel (“intelligent cruise ship can be under the remote control mode”, Figure 3) in that body of water, the autonomous marine vessel comprising a control module (Control host 8, Figure 3), and the method comprising:
providing the control module with data providing operation of the autonomous marine vessel in a predefined area of an electronic map (monitoring center user can “directly in the control mainframe 8 present cruise line” per Preferred Embodiment; system has “GPS locating and intelligent guide mechanism comprises GPS receiver”),
providing the control module with instructions to operate in an area of the electronic map to perform a measurement of a body of water (control host 8 controls receives/analyzes data from “water quality detecting instrument” tagged “detecting probe” that’s not shown in Figure 3) ,
obtaining a predefined model data of a first water measurement device
(“b, monitoring data model to analyze the intelligent cruise ship equipped with abnormal water quality analysis model software, the quality abnormality analysis model software can store the quality monitoring data, and can rapidly analyze the monitored data in accordance with a model of historical data and construct, normal, continuing cruise is not normal, entering step c” (Wang)),
generating a first primary control signal for the first water measurement device and performing a first movement of a first water testing part in a first position relative to the body of water (Figure 3’s vessel moves to location where a test is taken),
receiving a first sensor data from the first water measurement device (“water quality detecting instrument” takes a measurement),
generating first comparison data where the first sensor data (Xn) is compared with the predefined model data of the first water measurement device (to obtain Xn-X normal value)
(“the better method is that in the step b, the quality abnormality analysis model software is the model algorithm determines whether it is abnormal data, the basic principle of the model algorithm is | (Xn-X normal value) | /X normal value Y, which is the abnormal data. X is a water quality monitoring, Y is empirical value, n is sampling time of monitoring. X normal value according to actual historical temporal data, spatial data and empirical value, Y value determined by the instrument error, season temperature and field conditions” (Wang)),
generating a second primary control signal for the first water measurement device based on the first comparison (as action/subtraction of Xn-X normal value assures that the measuring instrument is at least then available for second measurement),
performing a second movement of the first water testing part to a second position relative to the body of water, where the second position is different from the first position (Wang teaches relocating the testing location as he completes measurements Xn-Xnormal value),
receiving a second sensor (i.e. Xn+1) data from the first water measurement device, and
generating a second comparison data where the second sensor data is compared with the predefined model data of the first water measurement device (i.e. Xn+1 – Xnormal value)
(“the better method is that in the step b, the quality abnormality analysis model software is the model algorithm determines whether it is abnormal data, the basic principle of the model algorithm is | (Xn+1 - X normal value) | /X normal value Y, which is the abnormal data. X is a water quality monitoring, Y is empirical value, n is sampling time of monitoring. X normal value according to actual historical temporal data, spatial data and empirical value, Y value determined by the instrument error, season temperature and field conditions” (Wang))
As to claims 1,10,12,13, either Wang teaches generating a second primary control signal for the water measurement device based on the first comparison with Xn as such assures that the measurement device is then available for a second measurement Xn+1, or in the alternative, one or ordinary skill would recognize that timing insures that measurements are taken at properly identified positions of the vessel.
As to claim 3, Figure 2 indicates that the GPS measurements/readings are correlated with taken data.
“(1), intelligent cruise ship carried on water quality detecting instrument selection permanganate index analyzer. (2) establishing a city transit river water quality abnormality analysis model according to the historical time data, space data and empirical value X city transit river of the season normal value is 4.5. The instrument stability and scene conditions, determining the empirical ratio value Y is 10%. city transit river water quality abnormality analysis model [Xn-4.5 | /4.5 = 10% is the abnormal point. (3), set at 3 m distance to cruise and every 500 meters for a water quality monitoring. The experiment result is shown in FIG. 2.” (Wang)
As to claim 7:
“control host 8 determines the position of this invention according to the positioning information of the GPS receiver, the control 8” (Wang)
As to claim 8, the many measured values may subsequently be employed as “normal” values, or in the alternative, one of ordinary skill recognizes that acceptable water values change with rule changes.
As to claim 11, there are sample containers 20.
Claim Rejections - 35 USC § 103
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as obvious over Wang CN 16568914.
As to claim 9, either Wang’s “water quality detecting instrument” (ex. “sewage” (Wang)) is one of the listed sensors, or in the alternative, one of ordinary skill would recognizes that water quality sensors suggest employing light, odor, optical, oxygen related) sensors.
Claim Rejections - 35 USC § 103
Claim(s) 14,16,19,20,22,24,25 is/are rejected under 35 U.S.C. 102(a1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Wang CN 16568914.
Wang teaches an autonomous marine vessel configured for obtaining samples from a body of water, the marine vessel (intelligent cruise ship can be under the remote control mode”) comprising a processor (Control host, Figure 3), memory and an interface, wherein the processor is configured to:
provide data providing operation of the autonomous marine vessel in a predefined area of an electronic map (monitoring center use can “directly in the control mainframe 8 present cruise line” per Preferred Embodiment; system has “GPS” locating and intelligent guide mechanism comprises GPS receiver“),
provide instructions to operate in an area of the electronic map to perform a measurement of the body of water (control host 8 controls receives/analyzes data from “water quality detecting instrument” tagged as “detecting probe” that’s not shown in Figure 3”),
obtain predefined model data of a first water measurement device
(“b, monitoring data model to analyze the intelligent cruise ship equipped with abnormal water quality analysis model software, the quality abnormality analysis model software can store the quality monitoring data, and can rapidly analyze the monitored data in accordance with a model of historical data and construct, normal, continuing cruise is not normal, entering step c” (Wang)),
generate a first primary control signal for the first water measurement device and perform a first movement of a first water testing part in a first position relative to the body of water (Figure 3’s vessel moves to location where a test is taken),
receive a first sensor data from the first water measurement device (“water quality detecting instrument” takes a measurements”),
generate first comparison data where the first sensor data is compared with the predefined model data of the first water measurement device
(“the better method is that in the step b, the quality abnormality analysis model software is the model algorithm determines whether it is abnormal data, the basic principle of the model algorithm is | (Xn-X normal value) | /X normal value Y, which is the abnormal data. X is a water quality monitoring, Y is empirical value, n is sampling time of monitoring. X normal value according to actual historical temporal data, spatial data and empirical value, Y value determined by the instrument error, season temperature and field conditions” (Wang)),
generate a second primary control signal to the first water measurement device based on the first comparison (as action/subtraction of Xn-X normal value assures that the measuring instrument is at least then available for second measurement), and
perform a second movement of the first water testing part to a second position relative to the body of water, where the second position is different from the first position (Wang teaches relocating the testing location as he completes measurements Xn-Xnormal value),
receive a second sensor data (i.e. Xn+1) from the first water measurement device; and
generate a second comparison data where the second sensor data is compared with the predefined model data of the first water measurement device (i.e. Xn+1 – Xnormal value).
The controller 8 does not employ the phrase “provide instructions to operate”.
As to claims 14,22,24,25, either Wang’s controller employ instructions, or in the alternative, it would have been obvious to employ a processor that utilize instruction because sure a known to be effective. In addition, Wang teaches generating a second primary control signal for the water measurement device based on the first comparison with Xn as such assures that the measurement device is then available for a second measurement Xn+1, or in the alternative, one or ordinary skill would recognize that timing insures that measurements are taken at properly identified positions of the vessel.
As to claim 16, Figure 2 indicates that the GPS measurements/readings are correlated with taken data.
“(1), intelligent cruise ship carried on water quality detecting instrument selection permanganate index analyzer. (2) establishing a city transit river water quality abnormality analysis model according to the historical time data, space data and empirical value X city transit river of the season normal value is 4.5. The instrument stability and scene conditions, determining the empirical ratio value Y is 10%. city transit river water quality abnormality analysis model [Xn-4.5 | /4.5 = 10% is the abnormal point. (3), set at 3 m distance to cruise and every 500 meters for a water quality monitoring. The experiment result is shown in FIG. 2.” (Wang)
As to claim 19,
“control host 8 determines the position of this invention according to the positioning information of the GPS receiver, the control 8” (Wang)
As to claim 20, the many measured values may subsequently be employed as “normal” values, or in the alternative, one of ordinary skill recognizes that acceptable water values change with rule changes.
Claim Rejections - 35 USC § 103
Claim(s) 21,23 is/are rejected under 35 U.S.C. 103 as obvious over Wang CN 16568914.
As to claim 21, either Wang’s “water quality detecting instrument” (ex. “sewage” (Wang)) is one of the listed sensors, or in the alternative, one of ordinary skill would recognizes that water quality sensors suggest employing light, odor, optical, oxygen related) sensors.
As to claim 23, there are sample containers 20.
Allowable Subject Matter
Claims 2,4,6,15,17,18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT R RAEVIS whose telephone number is (571)272-2204. The examiner can normally be reached on Mon to Fri from 8am to 4pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kristina DeHerrera, can be reached at telephone number 303-297-4237. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT R RAEVIS/Primary Examiner, Art Unit 2855
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