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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/14/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract of the disclosure is objected to because line 3 recites “process, may”, which should be “process may”. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
Para[0050] lines 4-5 recite “an injection pore”, which should be “the injection pore”.
Para[0077] line 9 recites “transceiver 134” which should be “transceiver 136”.
Para[0081] line 3 recites “the computing system 110” which should be “the computing system 150”.
Para[0090] line 10 recites “input”, which should be “output”.
Para[0095] line 3 recites “CPU 402”, which should be “processor 402”.
Appropriate correction is required.
Claim Objections
Claims 1, 32, and 40 are objected to because of the following informalities:
Claim 1 line 11 recites “based on monitoring”, which should be “based on the monitoring”.
Claim 32 line 6 recites “the first and second injection mold pressure sensors”, which should be “the first injection mold pressure sensor and the second injection mold pressure sensor”.
Claim 32 line 5 recites “an injection pore”, which should be “the injection pore”. It is unclear if there are two injection ports in Claim 32. Examiner will interpret “an injection pore” as “the injection pore”, similarly to the limitation of “the injection pore” in Claim 6 line 5.
Claim 32 line 8 recites “the first and second pressure data”, which should be “the first pressure data and the second pressure data”.
Claim 40 line 3 recites “a water line”, which should be “the water line”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 6-7, 11-12, 15-16, 19-20, 26, and 39-40 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 lines 11-12 and Claim 26 lines 8-9 recite “for transmitting to an electronic device”. It is unclear if “an electronic device” is part of the monitoring system in Claims 1 and 26. Therefore, Claims 1 and further dependent claims 2-3, 6-7, 11-12, 15-16, and 19-20 and Claim 26 are indefinite.
Claim 7 lines 5-6 recite “the first sensor data and the second sensor data”. There is insufficient antecedent basis for “the first sensor data” and “the second sensor data” in the claim.
Claim 26 line 9 recites “the computing system” There is insufficient antecedent basis for this limitation in the claim.
Claim 39 line 3 recites “the water line”. There is insufficient antecedent basis for this limitation in the claim. Therefore, Claim 39 and further dependent Claim 40 are indefinite.
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, 26-28, 35, and 39 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
With regards to Claim 1,
A monitoring system for monitoring operation of an injection mold during an injection molding process, the monitoring system comprising:
a sensor system configured for integration into the injection mold, the sensor system configured to obtain, from the injection mold, operational data associated with the injection molding process, and
a computing system in communication with the sensor system, the computing system being configured to:
receive, from the sensor system, the operational data;
monitor a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter, and
generate, based on monitoring the process parameter, an output for transmitting to an electronic device in communication with the computing system to provide a monitoring status indication.
With regards to Claim 26,
A non-transitory computer-readable storage medium having instructions stored thereon, that when executed by a processor, perform a computer-implemented method for monitoring an injection molding process for an injection mold, the method comprising:
receiving, from a sensor system integrated into the injection mold, operational data associated with the injection molding process;
monitoring a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter, and
generating, based on the monitoring of the process parameter, an output for transmitting to an electronic device in communication with the computing system to provide a monitoring status indication.
With regards to Claim 27,
A method for monitoring operation of an injection mold during an injection molding process, comprising:
generating, from a sensor system integrated into the injection mold, operational data associated with the injection molding process;
receiving the operational data, from the sensor system, at a computing system in communication with the sensor system;
monitoring, at the computing system, a process parameter associated with the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter;
generating, based on the monitoring of the process parameter, an output having a monitoring status indication, and
transmitting the output to an electronic device in communication with the computing system.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claims, the highlighted portions constitute an abstract idea because, under a broadest reasonable interpretation, they recite limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, they fall into the grouping of subject matter that, when recited as such in a claim limitation, cover performing mathematics or mental steps. The claim limitations merely indicate a field of use or technological environment, which is a monitoring system for an injection mold, in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the highlighted portions do not integrate the abstract idea into a practical application.
Next, under Step 2A, Prong Two, we consider whether the claims that recite a judicial exception are integrated into a practical application. In this step, we evaluate whether the claims recite additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed system, non-transitory computer-readable storage medium, and method do not represent an improvement to another technology or technical field as the monitoring system was already manufactured before the method steps recited in the claims. Similarly, there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because generating, from a sensor system integrated into the injection mold, operational data associated with the injection molding process; receiving the operational data, from the sensor system, at a computing system in communication with the sensor system; generating, based on the monitoring of the process parameter, an output having a monitoring status indication, and transmitting the output to an electronic device in communication with the computing system is considered necessary data gathering and outputting. As recited in MPEP section 2106.05(g), necessary data gathering and outputting (i.e. receiving data and outputting data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a monitoring system, a computing system, an electronic device, and a non-transitory computer-readable memory having instructions stored thereon, that when executed by a processor, perform a computer-implemented method are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
The additional element for the sensor system integrated into the injection mold is well-understood and conventional in the relevant art based on the prior art of record (see below). Additionally, the sensor system in the claims are recited in generality and represent insignificant field of use limitations that are not meaningful to indicate a practical application. Therefore, the claims are not patent eligible.
Claims 20, 28, 35, and 39 are further directed to abstract ideas and are rejected under 35 U.S.C. 101.
Claims 2-3, 6-7, 11-12, 15-16, 19, 29, 32, 36, and 40 integrate the abstract concept into a practical application and are not rejected under 35 U.S.C. 101.
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, 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.
Claim(s) 1-3, 11-12, 19-20, 26-29, and 35-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gergov (US 20190005164 A1) in view of Shimada (US 20200307055 A1).
With regards to Claim 1, Gergov teaches
a sensor system configured for integration into the injection mold (See Para[0018] “Sensors (i.e. a sensor system) are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold (i.e. configured for integration into the injection mold).”), the sensor system configured to obtain, from the injection mold, operational data associated with the injection molding process (See Abstract “The method also includes injecting the injection material into the physical mold (i.e. the injection mold) at a physical flow rate (i.e. the injection molding process) corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors (i.e. the sensor system configured to obtain, from the injection mold, operational data associated with the injection molding process, and the pressure, volume, and temperature data from the physical sensors is operational data as it corresponds to the operation of the injection molding process).”), and
a computing system in communication with the sensor system, the computing system being configured to (See Para[0063] “The controller 240 is configured to (i.e. a computing system, the computing system being configured to) receive pressure, volume, and temperature information (i.e. the operational data) from the sensors (i.e. a computing system in communication with the sensor system)”):
receive, from the sensor system, the operational data (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information (i.e. the operational data) from the sensors (i.e. receive, from the sensor system, the operational data)”);
monitor a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter (See Para[0063] “The controller 240 compares this received information (i.e. analyzing the operational data) to pressure, volume, and temperature curves (i.e. monitor a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter, where the pressure, volume, and temperature curves are the predictive model for a given process parameter of pressure, volume or temperature).” The operational data comes from the injection molding process, which also defines the process parameter, see Abstract “The method also includes injecting the injection material into the physical mold (i.e. the injection molding process) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature (i.e. the operational data, and the parameters of pressure, volume, and temperature are the process parameters) of the injection material by the physical sensors”), and
generate, based on monitoring the process parameter, an output for transmitting with the computing system to provide a monitoring status indication (See Fig. 5 step 540, where an output is generated regarding a decision of yes/no (i.e. an output which is a monitoring status indication) of whether the Sensed PVT matches PVT Curves (i.e. generate, based on monitoring the process parameter, an output to provide a monitoring status indication, where PVT are the pressure, volume, and temperature process parameters monitored, so one of the three is the process parameter, and the monitoring status indication is the yes/no output of step 540). The output is transmitted with the computing system, as the controller 240 is the computing system that transmits and provides the output of the monitoring status indication, see Para[0063] “The controller 240 compares this received information to pressure, volume, and temperature curves (i.e. step 540 in Fig. 5). If the monitored pressure, volume, or temperature of the injection material deviates from the pressure, volume, and temperature curves by more than a predetermined amount, the controller 240 can adjust the flow rate of the injection material (i.e. step 550 in Fig. 5).”).
Gergov is silent to the language of
transmitting to an electronic device in communication with the computing system.
Shimada teaches
transmitting to an electronic device in communication with the computing system (See Para[0036] “The corrected analysis conditions can be also outputted to a display (i.e. transmitting to an electronic device in communication with the computing system, where the computing system is the computer 10, see Fig. 2) or an information processor, which is not illustrated.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein transmitting to an electronic device in communication with the computing system is done like in Shimada in order to have a more compartmentalized design that can have specialized parts for processing and displaying results.
With regards to Claim 2, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the sensor system comprises: an injection mold pressure sensor configured to generate pressure data (See Para[0018] “Sensors (i.e. wherein the sensor system comprises:) are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure (i.e. configured to generate pressure data) inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold (i.e. a sensor system integrated into the injection mold).” As the sensor within the sensor system detects pressure and its location is in the injection mold, it is an injection mold pressure sensor.) wherein the operational data comprises the pressure data (See Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold (i.e. the injection mold) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors (i.e. wherein the operational data comprises the pressure data).” Since the pressure data is included in the sensor data gathered during the injection molding process, it is part of the operational data.).
With regards to Claim 3, Gergov and Shimada teach the limitations of Claim 2. Gergov further teaches
wherein the predictive model is based on an analysis of the injection mold and the injection molding process (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection mold and the injection molding process, where the injection mold is analyzed as well since it is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore, a predictive model, which include one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection mold and the injection molding process, where the analysis involves the simulation.), and the process parameter is an expected injection mold pressure (See Para[0040] “Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore the expected injection mold pressure is the curve representing the pressure in the injection mold (See Para[0037] “As the molding system injects material into the cavity (i.e. the injection mold), the process controls 240 compare the pressure (i.e. a process parameter), volume, and temperature at sensor locations to the pressure, volume, and temperature curves.”), so the process parameter is the parameter of an expected injection mold pressure.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
With regards to Claim 11, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the sensor system comprises: an injection mold temperature sensor configured for displacement at a sensor location within the injection mold and for generating temperature data (See Para[0018] “Sensors (i.e. wherein the sensor system comprises:) are employed at various locations on the injection molding device 100 or molds 110 to measure temperature (i.e. an injection mold temperature sensor configured for displacement at a sensor location within the injection mold and for generating temperature data. This injection mold temperature sensor is configured for displacement at a sensor location within the injection mold, see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”) ”), wherein the operational data of the sensor system comprises the temperature data (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure (i.e. the operational data of the sensor system comprises the temperature data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)”).
With regards to Claim 12, Gergov and Shimada teach the limitations of Claim 11. Gergov further teaches
wherein the predictive model is based on an analysis of the injection molding process for the injection mold (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection molding process for the injection mold, where the injection mold is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore, a predictive model, which includes one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection molding process for the injection mold, where the analysis involves the simulation.), and the process parameter is an expected internal temperature of the injection mold (See Para[0040] “Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore the expected internal temperature of the injection mold is the curve representing the temperature in the injection mold (see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold” and see Para[0037] “As the molding system injects material into the cavity (i.e. the injection mold), the process controls 240 compare the pressure, volume, and temperature (i.e. a process parameter) at sensor locations to the pressure, volume, and temperature curves.”), so the process parameter is the parameter of an expected internal temperature of the injection mold.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
With regards to Claim 19, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the sensor system comprises at least one of a water line temperature sensor, a water line pressure sensor, an injection mold temperature sensor (See Para[0018] “Sensors (i.e. wherein the sensor system comprises at least one of) are employed at various locations on the injection molding device 100 or molds 110 to measure temperature (i.e. an injection mold temperature sensor is one of the sensors, since temperature is measured) and pressure”), an injection mold pressure sensor, a strain sensor, a deflection sensor, or a crash detection sensor (Examiner notes options are recited via “at least one of” and “or”. Optional limitations are considered non-limiting.).
With regards to Claim 20, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the process parameter comprises one of: an internal pressure of the injection mold, a temperature of the injection mold (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure (i.e. wherein the process parameter comprises one of) temperature (i.e. a temperature of the injection mold which is a parameter to be measured) and pressure”), a deflection of the injection mold, a strain of the injection mold, a cooling system temperature, a cooling system flow rate, a cooling system pressure, or a crash detection indicator (Examiner notes options are recited via “one of” and “or”. Optional limitations are considered non-limiting.).
With regards to Claim 26, Gergov teaches
receiving, from a sensor system integrated into the injection mold (See Para[0018] “Sensors (i.e. a sensor system) are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold (i.e. a sensor system integrated into the injection mold).”), operational data associated with the injection molding process (See Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold (i.e. the injection mold) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors (i.e. receiving, from a sensor system, operational data associated with the injection molding process, and the pressure, volume, and temperature data from the physical sensors is operational data as it corresponds to the operation of the injection molding process).”);
monitoring a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter (See Para[0063] “The controller 240 compares this received information (i.e. analyzing the operational data) to pressure, volume, and temperature curves (i.e monitoring a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter, where the pressure, volume, and temperature curves are the predictive model for a given process parameter of pressure, volume or temperature).” The operational data comes from the injection molding process, which also defines the process parameter, see Abstract “The method also includes injecting the injection material into the physical mold (i.e. the injection molding process) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature (i.e. the operational data, and the parameters of pressure, volume, and temperature are the process parameters) of the injection material by the physical sensors”), and
generating, based on the monitoring of the process parameter, an output for transmitting with the computing system to provide a monitoring status indication (See Fig. 5 step 540, where an output is generated regarding a decision of yes/no (i.e. an output which is a monitoring status indication) of whether the Sensed PVT matches PVT Curves (i.e. generating, based on the monitoring of the process parameter, an output for transmitting to provide a monitoring status indication, where PVT are the pressure, volume, and temperature process parameters monitored, so one of the three is the process parameter, and the monitoring status indication is the yes/no output of step 540). The output is transmitted with the computing system, as the controller 240 is the computing system that transmits and provides the output of the monitoring status indication, see Para[0063] “The controller 240 compares this received information to pressure, volume, and temperature curves (i.e. step 540 in Fig. 5). If the monitored pressure, volume, or temperature of the injection material deviates from the pressure, volume, and temperature curves by more than a predetermined amount, the controller 240 can adjust the flow rate of the injection material (i.e. step 550 in Fig. 5).”).
Gergov is silent to the language of
transmitting to an electronic device in communication with the computing system.
Shimada teaches
transmitting to an electronic device in communication with the computing system (See Para[0036] “The corrected analysis conditions can be also outputted to a display (i.e. transmitting to an electronic device in communication with the computing system, where the computing system is the computer 10, see Fig. 2) or an information processor, which is not illustrated.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein transmitting to an electronic device in communication with the computing system is done like in Shimada in order to have a more compartmentalized design that can have specialized parts for processing and displaying results.
With regards to Claim 27, Gergov teaches
generating, from a sensor system integrated into the injection mold (See Para[0018] “Sensors (i.e. a sensor system) are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold (i.e. a sensor system integrated into the injection mold).”), operational data associated with the injection molding process (See Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold (i.e. the injection mold) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors (i.e. generating, from a sensor system, operational data associated with the injection molding process, as the sensors first generate the operational data after sensing them, and the pressure, volume, and temperature data from the physical sensors is operational data as it corresponds to the operation of the injection molding process).”);
receiving the operational data, from the sensor system, at a computing system in communication with the sensor system (See Para[0063] “The controller 240 (i.e. a computing system) is configured to receive pressure, volume, and temperature information (i.e. the operational data) from the sensors (i.e. receiving the operational data, from the sensor system, at a computing system in communication with the sensor system)”);
monitoring, at the computing system, a process parameter associated with the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter (See Para[0063] “The controller 240 (i.e. the computing system) compares this received information (i.e. monitoring, at the computing system, a process parameter associated with the injection molding process based on analyzing the operational data. The operational data comes from the injection molding process, which also defines the process parameter, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature (i.e. the operational data, and the parameters of pressure, volume, and temperature are the process parameters) of the injection material by the physical sensors”) to pressure, volume, and temperature curves (i.e. monitoring, at the computing system, a process parameter associated with the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter, where the pressure, volume, and temperature curves are the predictive model for a given process parameter of pressure, volume, or temperature as parameters).”);
generating, based on the monitoring of the process parameter, an output having a monitoring status indication (See Fig. 5 step 540, where an output is generated regarding a decision of yes/no of whether the Sensed PVT matches PVT Curves (i.e. generating, based on the monitoring of the process parameter, an output having a monitoring status indication, where PVT are the pressure, volume, and temperature process parameters, so one of the three is the process parameter, and the monitoring status indication is the yes/no output of step 540)), and
the output (See Fig. 5 step 540, where an output (i.e. the output) is generated regarding a decision of yes/no of whether the Sensed PVT matches PVT Curves).
Gergov is silent to the language of
transmitting to an electronic device in communication with the computing system.
Shimada teaches
transmitting to an electronic device in communication with the computing system (See Para[0036] “The corrected analysis conditions can be also outputted to a display (i.e. transmitting to an electronic device in communication with the computing system, where the computing system is the computer 10, see Fig. 2) or an information processor, which is not illustrated.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein transmitting to an electronic device in communication with the computing system is done like in Shimada in order to have a more compartmentalized design that can have specialized parts for processing and displaying results.
With regards to Claim 28, Gergov and Shimada teach the limitations of Claim 27. Gergov further teaches
wherein the predictive model is based on an analysis of the injection molding process for the injection mold (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection molding process for the injection mold, where the injection mold is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore, a predictive model, which includes one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection molding process for the injection mold, where the analysis involves the simulation.), and the process parameter is an expected injection mold pressure (See Para[0040] “Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore, the expected injection mold pressure is the curve representing the pressure in the injection mold (see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold” and Para[0037] “As the molding system injects material into the cavity (i.e. the injection mold), the process controls 240 compare the pressure (i.e. a process parameter), volume, and temperature at sensor locations to the pressure, volume, and temperature curves.”), so the process parameter is the parameter of an expected injection mold pressure.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
With regards to Claim 29, Gergov and Shimada teach the limitations of Claim 28. Gergov further teaches
generating pressure data at an injection mold pressure sensor displaced within the injection mold (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure (i.e. generating pressure data at an injection mold pressure sensor, which is one of the sensors) inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.” As the sensor is within the sensor system, which is displaced within the injection mold, the injection mold pressure sensor is also inside the injection mold.);
wherein the sensor system includes the injection mold pressure sensor (See Para[0018] “Sensors (i.e. wherein the sensor system) are employed at various locations on the injection molding device 100 or molds 110 (i.e. the injection mold) to measure temperature and pressure (i.e. includes the injection mold pressure sensor, as at least one of the sensors amongst the sensors detect pressure for an injection mold) inside the device”), and wherein the operational data includes the pressure data (See Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold (i.e. the injection mold) at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors (i.e. and wherein the operational data includes the pressure data).” Since the pressure data is included in the sensor data gathered during the injection molding process, it is part of the operational data.).
With regards to Claim 35, Gergov and Shimada teach the limitations of Claim 27. Gergov further teaches
wherein the predictive model is based on an analysis of the injection molding process for the injection mold (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection molding process for the injection mold, where the injection mold is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created.” Therefore a predictive model, which includes one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection molding process for the injection mold, where the analysis involves the simulation.), and the process parameter is an expected internal temperature of the injection mold (See Para[0040] “Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore the expected internal temperature of the injection mold is the curve representing the temperature in the injection mold (see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold” and see Para[0037] “As the molding system injects material into the cavity (i.e. the injection mold), the process controls 240 compare the pressure, volume, and temperature (i.e. a process parameter) at sensor locations to the pressure, volume, and temperature curves.”), so the process parameter is the parameter of an expected internal temperature of the injection mold.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
With regards to Claim 36, Gergov and Shimada teach the limitations of Claim 35. Gergov further teaches
generating temperature data at a temperature sensor displaced at a sensor location within the injection mold (See Para[0018] “Sensors (i.e. one of which is a temperature sensor) are employed at various locations on the injection molding device 100 or molds 110 to measure temperature (i.e generating temperature data at a temperature sensor. This injection mold temperature sensor is displaced at a sensor location within the injection mold, see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”) ”);
wherein the sensor system includes the temperature sensor (See Para[0018] “Sensors (i.e. one of which is the temperature sensor and the entire set of sensors is the sensor system) are employed at various locations on the injection molding device 100 or molds 110 to measure temperature), and
wherein the operational data includes the temperature data (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure (i.e. wherein the operational data includes the temperature data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)”).
Claim(s) (6-7, 32) is/are rejected under 35 U.S.C. 103 as being unpatentable over Gergov (US 20190005164 A1) and Shimada (US 20200307055 A1) as applied to claim (1, 28) above, and further in view of Lin (US 20190389100 A1).
With regards to Claim 6, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the sensor system comprises (See Abstract “a plurality of physical sensors”):
a first injection mold pressure sensor (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”. Therefore, one of the sensors within the sensors is a first injection mold pressure sensor.) configured for displacement proximal (See Para[0018] “However, sensors may be employed at any location”), and
a second injection mold pressure sensor (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”. Therefore, one of the sensors within the sensors is a second injection mold pressure sensor.) configured for displacement proximal (See Para[0018] “However, sensors may be employed at any location”).
Gergov and Shimada are silent to the language of
to an input end of an injection pore of the injection mold and
to an output end of the injection pore of the injection mold.
Lin teaches
to an input end of an injection pore of the injection mold (See Abstract “The lower mold is a porous material and has a plurality of pores. Gas is pre-injected into the mold cavity through the gas passage and at least one air path to maintain a preset pressure inside the mold cavity. Gas is spewed out through the plurality of pores” (i.e. to an input end of an injection pore of the injection mold would correspond to the side of the pore where gas is travelling from). Therefore, Gergov can place a first injection mold pressure sensor in a region proximal to the input end of a pore by placing it a small distance above the input end.) and
to an output end of the injection pore of the injection mold (See Abstract “The lower mold is a porous material and has a plurality of pores. Gas is pre-injected into the mold cavity through the gas passage and at least one air path to maintain a preset pressure inside the mold cavity. Gas is spewed out through the plurality of pores” (i.e. to an output end of the injection pore of the injection mold would correspond to the side of the pore where gas is travelling to. Therefore, Gergov can place a second injection mold pressure sensor in a region proximal to the output end of a pore by placing it on the input end, making it close to the output end of the pore.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein to an input end of an injection pore of the injection mold and to an output end of the injection pore of the injection mold is used like in Lin in order to specify clearly where the pressure sensors in Gergov are located.
With regards to Claim 7, Gergov and Shimada teach the limitations of Claim 6. Gergov further teaches
the first injection mold pressure sensor is configured to generate first pressure data (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device.” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.” Therefore, the first injection mold pressure sensor is inside the injection mold and is configured to generate first pressure data as all the sensors in Gergov generate pressure data for each sensor.);
the second injection mold pressure sensor is configured to generate second pressure data (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device.” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.” Therefore, the second injection mold pressure sensor is inside the injection mold and is configured to generate second pressure data as all the sensors in Gergov generate pressure data for each sensor.), and
the operational data obtained by the sensor system comprises the first sensor data and the second sensor data (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information from the sensors (i.e. the operational data obtained by the sensor system comprises the first sensor data and the second sensor data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)” ).
With regards to Claim 32, Gergov and Shimada teach the limitations of Claim 28. Gergov further teaches
generating first pressure data at a first injection mold pressure sensor (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”. Therefore, one of the sensors within the sensors is generating first pressure data inside the mold at a first injection mold pressure sensor.) displaced proximal (See Para[0018] “However, sensors may be employed at any location”), and
generating second pressure data at a second injection mold pressure sensor (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device” and see Para[0021] “The sensor locations correspond to locations where physical sensors would be located in a physical mold.”. Therefore, one of the sensors within the sensors is generating second pressure data inside the mold at a second injection mold pressure sensor.) displaced proximal (See Para[0018] “However, sensors may be employed at any location”);
wherein the sensor system includes the first and second injection mold pressure sensors (See Para[0018] “Sensors are employed at various locations on the injection molding device 100 or molds 110 to measure temperature and pressure inside the device.” Since the first and second mold pressure sensors are part of this group of sensors, Gergov teaches a sensor system wherein the sensor system includes the first and second injection mold pressure sensors.), and
wherein the operational data includes the first and second pressure data (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information from the sensors (i.e. wherein the operational data includes the first and second pressure data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)” ).
Gergov and Shimada are silent to the language of
to an input end of an injection pore of the injection mold and
to an output end of an injection pore of the injection mold.
Lin teaches
to an input end of an injection pore of the injection mold (See Abstract “The lower mold is a porous material and has a plurality of pores. Gas is pre-injected into the mold cavity through the gas passage and at least one air path to maintain a preset pressure inside the mold cavity. Gas is spewed out through the plurality of pores” (i.e. to an input end of an injection pore of the injection mold would correspond to the side of the pore where gas is travelling from). Therefore, Gergov can place a first injection mold pressure sensor in a region proximal to the input end of a pore by placing it a small distance above the input end.) and
to an output end of an injection pore of the injection mold (See Abstract “The lower mold is a porous material and has a plurality of pores. Gas is pre-injected into the mold cavity through the gas passage and at least one air path to maintain a preset pressure inside the mold cavity. Gas is spewed out through the plurality of pores” (i.e. to an output end of an injection pore of the injection mold would correspond to the side of the pore where gas is travelling to. Therefore, Gergov can place a second injection mold pressure sensor in a region proximal to the output end of a pore by placing it on the input end, making it close to the output end of the pore.).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein to an input end of an injection pore of the injection mold and to an output end of an injection pore of the injection mold is used like in Lin in order to specify clearly where the pressure sensors in Gergov are located.
Claim(s) (15-16, 39-40) is/are rejected under 35 U.S.C. 103 as being unpatentable over Gergov (US 20190005164 A1) and Shimada (US 20200307055 A1) as applied to claim (1, 27) above, and further in view of Galt (US 20070294121 A1).
With regards to Claim 15, Gergov and Shimada teach the limitations of Claim 1. Gergov further teaches
wherein the sensor system comprises (See Abstract “a plurality of physical sensors”):
wherein the operational data of the sensor system comprises the data (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information from the sensors (i.e. wherein the operational data of the sensor system comprises the data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)” ).
Gergov and Shimada are silent to the language of
a water temperature sensor configured for displacement proximal to a water line of the injection mold and for generating water line temperature data
the water line temperature data.
Galt teaches
a water temperature sensor configured for displacement proximal to a water line of the injection mold and for generating water line temperature data (See Para[0040] “Sensors 208 (i.e. one of which is a water temperature sensor) monitor the temperature and flow rate of the supplied chilled water (i.e. for generating water line temperature data, as the chilled water is supplied via a water line (See Para[0034] “The molding system 100 includes a connection (i.e. a water line) to a supply 122. The supply 122 provides electrical power and chilled water to the molding system 100.” Therefore, the sensors 208 is configured for displacement proximal to a water line of the mold 104, which is the injection mold, see Figures 1 and 3) to cool the material in the injection mold).”), and
the water line temperature data (See Para[0040] “Sensors 208 monitor the temperature and flow rate of the supplied chilled water (i.e. the water line temperature data).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein a water temperature sensor configured for displacement proximal to a water line of the injection mold and for generating water line temperature data and the water line temperature data is used like in Galt in order to better characterize the behavior of the cooling elements in Gergov (See Gergov Para[0028] “Additional part parameters include any heating or cooling elements that aid in heat transfer.”)
With regards to Claim 16, Gergov and Shimada teach the limitations of Claim 15. Gergov further teaches
wherein the predictive model is based on an analysis of the injection molding process for the injection mold (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection molding process for the injection mold, as the injection mold is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created.” Therefore a predictive model, which includes one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection molding process for the injection mold, where the analysis involves the simulation.), and the process parameter is an expected temperature (See Para[0040] “Specifically, the pressure, volume, and temperature (i.e. a process parameter as a temperature parameter) are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore the process parameter is the parameter of an expected temperature, and the expected temperature is the curve representing the temperature.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
Gergov and Shimada are silent to the language of
a water line temperature of the water line.
Galt teaches
a water line temperature of the water line (See Para[0040] “Sensors 208 monitor the temperature (i.e. a water line temperature of the water line) and flow rate of the supplied chilled water (i.e. the chilled water is supplied via the water line (See Para[0034] “The molding system 100 includes a connection (i.e. the water line) to a supply 122. The supply 122 provides electrical power and chilled water to the molding system 100.”).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein a water line temperature of the water line is used like in Galt in order to better characterize the behavior of the cooling elements in Gergov (See Gergov Para[0028] “Additional part parameters include any heating or cooling elements that aid in heat transfer.”).
With regards to Claim 39, Gergov and Shimada teach the limitations of Claim 27. Gergov further teaches
wherein the predictive model is based on an analysis of the injection molding process for the injection mold (See Para[0040] “When the system is used to simulate an injection molding process (i.e. based on an analysis of the injection molding process for the injection mold, as the injection mold is part of the injection molding process), data is recorded at each of the sensor locations. Such data recordation may be referred to as data capture by the virtual sensors in the solid model. Specifically, the pressure, volume, and temperature are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created.” Therefore a predictive model, which includes one of the pressure, volume, and temperature curves, is generated wherein the predictive model is based on an analysis of the injection molding process for the injection mold, where the analysis involves the simulation.), and the process parameter is an expected temperature (See Para[0040] “Specifically, the pressure, volume, and temperature (i.e. a process parameter as a temperature parameter) are recorded at the sensor locations during the simulation, so that pressure, volume, and temperature curves can be created. ” Therefore the process parameter is the parameter of an expected temperature, and the expected temperature is the curve representing the temperature.).
Gergov is silent to the language of
a mold flow analysis.
Shimada teaches
a mold flow analysis (See Para[0039] “The flow analysis system 3 has the function of analyzing, for example, a resin flow in the injection molding machine (i.e. a mold flow analysis)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov wherein a mold flow analysis is used like in Shimada in order to have a computationally effective method to determine a comparison standard for the process parameters in Gergov.
Gergov and Shimada are silent to the language of
a water line temperature of the water line.
Galt teaches
a water line temperature of the water line (See Para[0040] “Sensors 208 monitor the temperature (i.e. a water line temperature of the water line) and flow rate of the supplied chilled water (i.e. the chilled water is supplied via the water line (See Para[0034] “The molding system 100 includes a connection (i.e. the water line) to a supply 122. The supply 122 provides electrical power and chilled water to the molding system 100.”).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein a water line temperature of the water line is used like in Galt in order to better characterize the behavior of the cooling elements in Gergov (See Gergov Para[0028] “Additional part parameters include any heating or cooling elements that aid in heat transfer.”).
With regards to Claim 40, Gergov and Shimada teach the limitations of Claim 39. Gergov further teaches
wherein the sensor system includes the temperature sensor (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information from the sensors (i.e. wherein the sensor system includes the temperature sensor as the sensors is the sensor system).”), and
wherein the operational data includes the temperature data (See Para[0063] “The controller 240 is configured to receive pressure, volume, and temperature information from the sensors (i.e. wherein the operational data includes the temperature data, and this data is operational data as it corresponds to the operation of the injection molding process, see Abstract “The method also includes injecting the injection material (i.e. the injection molding process) into the physical mold at a physical flow rate corresponding to the desired flow rate profile and monitoring pressure, volume, and temperature of the injection material by the physical sensors.”)” ).
Gergov and Shimada are silent to the language of
generating water line temperature data at a water temperature sensor displaced proximal to a water line of the injection mold;
the water temperature sensor and
the water line temperature data.
Galt teaches
generating water line temperature data at a water temperature sensor displaced proximal to a water line of the injection mold (See Para[0040] “Sensors 208 (i.e. one of which is a water temperature sensor) monitor the temperature and flow rate of the supplied chilled water (i.e. generating water line temperature data, as the chilled water is supplied via a water line (See Para[0034] “The molding system 100 includes a connection (i.e. a water line) to a supply 122. The supply 122 provides electrical power and chilled water to the molding system 100.” Therefore, the sensors 208 is displaced proximal to a water line of the mold 104, which is the injection mold, see Figures 1 and 3) to cool the material in the injection mold).”);
the water temperature sensor (See Para[0040] “Sensors 208 (i.e. one of which is the water temperature sensor ) monitor the temperature and flow rate of the supplied chilled water.”) and
the water line temperature data (See Para[0040] “Sensors 208 monitor the temperature and flow rate of the supplied chilled water (i.e. the water line temperature data).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Gergov and Shimada wherein generating water line temperature data at a water temperature sensor displaced proximal to a water line of the injection mold, the water temperature sensor, and the water line temperature data is used like in Galt in order to better characterize the behavior of the cooling elements in Gergov (See Gergov Para[0028] “Additional part parameters include any heating or cooling elements that aid in heat transfer.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOSTOFA AHMED HISHAM whose telephone number is (571)272-8773. The examiner can normally be reached Monday - Friday, 7:00 a.m. - 4 p.m. ET.
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/MOSTOFA AHMED HISHAM/Examiner, Art Unit 2857
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
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