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 .
Response to Amendment
The Amendments filed 07/24/2026 responsive to the Office Action filed 04/24/2026 has been entered. Claims 1-3 and 5-7 have been amended. Claims 1-7 are pending in this application.
Response to Arguments
Applicant's arguments, filed 07/24/2026, with respect to the rejection of claim 1 under 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2 and 5-7 are rejected under 35 U.S.C. 103 as obvious over Shin (KR 102264066B1_ Machine Translation provided herewith) in view of Altonen et al. (US 2017/0274571A1).
With respect to claim 1, Shin teaches a molding condition setting method for an injection molding machine that molds a molded article (“a smart control system for an injection molding machine based on artificial intelligence and image analysis, and a method of operation thereof”, Pa [0022]), the molding condition setting method comprising:
acquiring a moisture content of a material used for molding the molded article (“detects the raw material humidity”, Pa [0025]);
setting a plurality of molding conditions for the molded article to be molded by the injection molding machine (“an injection environment condition setting unit (5000) connected to the injection central control unit (2000) above, which controls the raw material humidity, raw material temperature, injection nozzle temperature, injection speed, injection pressure, and mold temperature inside the injection machine according to the corresponding control signal.”, Pa [0025]);
after the acquiring of the moisture content, and the setting of the plurality of molding conditions, molding the molded article under the plurality of molding conditions by the injection molding machine injecting a plasticized material obtained by plasticizing the material into a mold under the moisture content acquired in the acquiring of the moisture content (“for molding applied to the injection machine”, Pa [0029]); and
after the molding, determining an optimum condition in the molding conditions under the environmental condition based on quality of the molded article molded in the molding (“to find and apply optimal conditions that match the reference value.”, Pa [0002]; “enables the production of optimal injection molded products without defects by learning and inspecting injection molded products produced by the injection molding machine using artificial intelligence”, Pa [0022]).
Shin further teaches that injection molding machines must always be managed to maintain a constant set temperature and prevent overheating and overcooling to prevent the production of defective products caused by temperature differences and changes, but they are also significantly affected by changes in the temperature and humidity of the surrounding environment and the dryness of the supplied raw materials (Pa [0006]), and teaches an injection molding external environment sensor unit (1000) installed outside the injection molding machine and detecting the ambient temperature, humidity, and an injection molding central control unit (2000) connected to the injection molding external environment sensor unit (1000), inputting the detected ambient temperature, humidity respectively, inputting the captured image signal of the injection molded product and the respective data detected from the internal environment of the injection molding machine, and outputting the corresponding control signal to each functional unit of the injection molding machine based on the results of comparative analysis by artificial intelligence with the respective reference values that are recorded and managed (Pa [0025]); and the injection external environment sensor unit (1000) may include: an external temperature detection unit (1010) and an external humidity detection unit (1020) installed outside the injection machine within a straight distance of 3 to 5 meters in accordance with the control signal of the injection central control unit (2000), thus one would have appreciated that the external temperature detection unit and the external humidity detection unit measure the ambient temperature and humidity outside the injection machine within the plant/facility/building.
However, Shin is silent to adjusting a temperature and humidity around the injection molding machine located inside of a cover; and after the adjusting of the temperature and the humidity, molding the molded article under the plurality of molding conditions and an environmental condition including the temperature and the humidity adjusted in the adjusting of the temperature and the humidity.
In the same field of endeavor, injection molding, Altonen teaches that injection molding machines allow an operator to modify and/or manipulate the operating parameters thereof, if an environmental factor such as a plant ambient temperature causes the injection molding machine to work harder to generate parts, the machine's operating load value over a given period of time will increase, this increase in the operational load value may eventually cause the machine to approach or exceed the maximum load value which may result in temporary or permanent machine failure, and prior to exceeding or even reaching this maximum load value, the machine may be pre-programmed to generate an alarm which prompts a machine operator to adjust operating variables as required to lower the operating load on the machine (Pa [0004]), and the operating parameters may be any combination of adjustments to the injection molding machine, and may include environmental conditions, some of which may be within the control of the molder, such as ambient temperature in a temperature-adjustable manufacturing facility (Pa [0012]). Altonen further teaches that the present invention is directed to the use of multiple controllers (i.e., a native controller and a retrofit controller) to effectively control operation of an injection molding machine (Pa [0008]), the retrofit controller is adapted to selectively operate the injection molding machine in a manner that allows the current load value to remain within a predetermined range below the maximum load value (Pa [0009]), the signal or signals from the native controller 140 may generally be used to control operation of the molding process such that variations in material viscosity, mold temperatures, melt temperatures, and other variations influencing filling rate are taken into account by the native controller 140, adjustments may be made by the native controller 140 in real time or in near-real time (that is, with a minimal delay between sensors 128, 129 sensing values and changes being made to the process), or corrections can be made in subsequent cycles, furthermore, several signals derived from any number of individual cycles may be used as a basis for making adjustments to the molding process (Pa [0041]), and the retrofit mold cycle 300 includes an operating sequence of injecting molten plastic 310, according to control 302 by the retrofit controller 150, and subsequently performing other functions according to control 301 by the native controller 140 (Pa [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Shin with the teachings of Altonen to incorporate the retrofit controller and perform the retrofit mold cycle by adjusting operating parameters including environment conditions such as ambient temperature and humidity before molding according to control by the native controller in order to allow the current load value to remain within a predetermined range below the maximum load value.
With respect to claim 2, Shin as applied to claim 1 above further teaches that the setting of the plurality of molding conditions, a first molding condition and a second molding condition in which a first parameter, which is a parameter in the first molding condition, is different from that of the first molding condition are set, and the molding includes the injection molding machine molding a first molded article under the first molding condition, and the injection molding machine molding a second molded article under the second molding condition, and in the determining, the optimum condition is selected from the first molding condition and the second molding condition according to a comparison result between the first molded article and the second molded article (“it relates to a smart control system for an injection molding machine based on artificial intelligence and image analysis and a method of operation thereof, which processes an image of a product injected from an injection molding machine using a digital image processing (DIP) method and, when a deviation exceeding an allowable size relative to a reference value occurs continuously or multiple times, analyzes the external and internal factors of the injection molding machine using artificial intelligence to find and apply optimal conditions that match the reference value”, Pa [0002]; “a method of operation thereof, which enables the production of optimal injection molded products without defects by learning and inspecting injection molded products produced by the injection molding machine using artificial intelligence”, Pa [0022]).
With respect to claim 5, Shin as applied to claim 1 above further teaches setting a set value of a factor that is adjusted according to the environmental condition and that is related to molding of the molded article (“the recorded reference environmental value”, Pa [0032]), wherein in the setting of the plurality of molding conditions, each of the molding conditions is set such that an effective value of the factor in the molding is close to the set value (“determining whether the detected environmental value is larger or smaller than the recorded reference environmental value by monitoring and controlling each functional unit provided by the injection central control unit”, Pa [0032]), and the factor includes at least one of a temperature of the mold, a temperature of the plasticized material, a pressure of the plasticized material, an injection speed of the plasticized material (“raw material temperature, injection nozzle temperature, injection speed, injection pressure, and mold temperature inside the injection machine”, Pa [0025]).
With respect to claim 6, Shin as applied to claim 5 above further teaches that the molding includes monitoring the effective value, and storing, in a storage unit (“an injection molding central control unit (2000)”, Pa [0025]), first information in which the environmental condition, the molding condition, and the effective value when the molded article is molded under the molding condition under the environmental condition are associated with one another (“an injection molding central control unit (2000) connected to the injection molding external environment sensor unit (1000), inputting the detected ambient temperature, humidity, and operating fluid temperature respectively, inputting the captured image signal of the injection molded product and the respective data detected from the internal environment of the injection molding machine”, Pa [0025]; “determining whether the detected environmental value is larger or smaller than the recorded reference environmental value by monitoring and controlling each functional unit provided by the injection central control unit”, Pa [0032]).
With respect to claim 7, Shin as applied to claim 6 above further teaches molding the molded article under the optimum condition under the environmental condition by the injection molding machine, wherein the optimum condition is corrected based on the first information when the effective value varies outside a predetermined range (“The injection central control unit sequentially detects the surrounding environment values according to the adjustment order from the highest set order (S320), and determines whether each detected environment value is larger or smaller than the recorded reference environment value (S330).”, Pa [0108]; “If defects exceeding specifications occur consecutively more than a predetermined number of times or if the cumulative number of defective products is confirmed to exceed a predetermined number, it sequentially detects surrounding external environmental values and analyzes them using artificial intelligence to sequentially adjust the internal environmental values set in the injection molding machine, thereby enabling the mass production of optimal, defect free injection-molded products.”, Pa [0112]).
Claims 3 and 4 are rejected under 35 U.S.C. 103 as obvious over Shin (KR 102264066B1_ Machine Translation provided herewith) in view of Altonen et al. (US 2017/0274571A1) as applied to claim 1 above, and further in view of Suh et al. (US 4,352,059-of record).
With respect to claim 3, Shin as applied to claim 1 above further teaches a raw material humidity detection unit (4010) that detects the humidity of the raw material for injection supplied to the injection machine (Pa [0028]), but does not explicitly teach a spectrometer.
Suh relates to methods for determining the moisture content of material (co 1 li 6-7) and teaches that industrial NMR spectrometers is used for determination of moisture content of plastics, molding powders, fillers, etc. from the amplitude of the nuclear magnetic resonance (NMR) signal (co 2 li 41-45).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Shin with the teachings of Suh to use the NMR spectrometer in order to detect the moisture content of the raw material, since it has been held that the use of a known technique to improve similar devices (methods or products) in the same way is likely to be obvious. See KSR International Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395 – 97 (2007) (see MPEP § 2143, C.).
With respect to claim 4, Altonen as applied in the combination regarding claim 3 above further teaches that the injection unit 102 includes a hopper 106 adapted to accept material in the form of pellets 108 or any other suitable form (Pa [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Shin to provide the hopper to accept the raw material and dispose the spectrometer in the hopper in which the raw material is stored in order to detect the moisture content of the raw material in the hopper unit.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUNJU KIM whose telephone number is (571)270-1146. The examiner can normally be reached on 8:00-4:00 EST M-Th; Flexing Fri.
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/YUNJU KIM/Primary Examiner, Art Unit 1742
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