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
IDS filed 10/21/2024 is being considered by the examiner
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"; and
(C) the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word "means" (or "step") in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word "means" (or "step") in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word "means" (or "step") are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step") are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word "means," but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) is/are: "receiving, by a digital data gatherer" and "comparing, by a digital data analyzer" in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 are directed towards the statutory category of a machine. Claims 11-15 are directed towards the statutory category of a process. Claims 16-20 are directed towards the statutory category of an article of manufacture.
With regard to claim 1:
Step 2A Prong 1: This claim is direct to a judicial exception.
generate a … molding process fingerprint associated with the mold based on the received first information (mental process - a person can manually look at the information and determine a fingerprint)
compare the received first information to the generated … molding process fingerprint associated with the mold (mental process - a person can manually compare the fingerprint to received information)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
Additional elements:
a memory; and a processor coupled to the memory (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f))
receive, from a sensor implemented on a mold, first information associated with the mold (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g))
generate a digital molding process fingerprint (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a computer to generate the fingerprint without technical details)
store, in the memory, the generated digital molding process fingerprint (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a computer to store the fingerprint without technical details)
receive, from the sensor, second information associated with the mold (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g))
trigger a recommended action for the mold (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g) – data presentation)
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a memory; and a processor coupled to the memory (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f))
receive, from a sensor implemented on a mold, first information associated with the mold (MPEP 2106.05(d)(II) indicates that merely "storing and retrieving information in memory" and/or "receiving or transmitting data over a network" are well-understood, routine, conventional functions when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer)
generate a digital molding process fingerprint (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a computer to generate the fingerprint without technical details)
store, in the memory, the generated digital molding process fingerprint (adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) – high level use of a computer to store the fingerprint without technical details)
receive, from the sensor, second information associated with the mold (MPEP 2106.05(d)(II) indicates that merely "storing and retrieving information in memory" and/or "receiving or transmitting data over a network" are well-understood, routine, conventional functions when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer)
trigger a recommended action for the mold (MPEP 2106.05(d)(II) indicates that merely "storing and retrieving information in memory" and/or "receiving or transmitting data over a network" are well-understood, routine, conventional functions when they are claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the presentation step is well-understood, routine, conventional activity is supported under Berkheimer)
Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With regard to claims 2-10: the claims merely elaborate on what data is collected, how the determination is made, what generic computer component is utilized, or where the results are applied; thus, the additional limitations do not integrate the judicial exception into a practical application and do not amount to significantly more than the judicial exception.
With regard to claim 11-20, the claims recite elements similar to those presented in claims 1-10; therefore, claims 11-20 are rejected along the same grounds under 35 U.S.C. 101 as claims 1-10.
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 5, 6, and 13-20 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 5 recites, "the received information" in lines 4 and 11. It is unclear whether "the received information" refers to the received first information or the received second information. Therefore, the claim is indefinite. The examiner interprets "the received information" as the received second information.
Claim 13 recites, "the received information" in lines 2 and 7. It is unclear whether "the received information" refers to the received first information or the received second information. Therefore, the claim is indefinite. The examiner interprets "the received information" as the received second information.
Claim 15 recites, "presents at least one of the generated digital molding process fingerprint" in line 2. However, claim 11 recites, "a digital molding process fingerprint." It is unclear whether the fingerprint in claim 15 refers to the fingerprint in claim 11 or to another fingerprint. Therefore, the claim is indefinite.
Claim 16 recites, "the received information" in line 14. It is unclear whether "the received information" refers to the received first information or the received second information. Therefore, the claim is indefinite. The examiner interprets "the received information" as the received second information.
Claim 16 recites, "presents at least one of the generated digital molding process fingerprint" in line 20. However, claim 16 recites, "a digital molding process fingerprint" in line 7. It is unclear whether the fingerprint in line 20 refers to the fingerprint in line 7 or to another fingerprint. Therefore, the claim is indefinite.
Claim 17 recites, "the received information" in line 4. It is unclear whether "the received information" refers to the received first information or the received second information. Therefore, the claim is indefinite. The examiner interprets "the received information" as the received second information.
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.
Claims 1, 2, 5, 8, 9, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Meneses et al. [US Pub. 2025/0001661] ("Meneses") in view of Burns [US Pub. 2022/0097273] ("Burns").
With regard to claim 1, Meneses teaches a system, comprising:
a memory; and a processor coupled to the memory ("The computing system 150 may include one or more processor(s) 152 and a memory 154 [par. 0079]") and configured to:
receive, from a sensor implemented on a mold ("sensors for integrating into the injection mold [par. 0070]" and "the computing system 150 is configured to receive, from the sensor system 110, data associated with operating the injection mold [par. 0080]"), first information associated with the mold ("obtaining, using a sensor system, operational data associated with operating an injection mold during an injection molding process [par. 0088]"), the received first information comprising at least one of measured plastic melt pressure, plastic melt temperature, water line temperature ("water line temperature data [par. 0058]"), blow air temperature, exhaust, or unscrewing/ejection force data;
("a predictive model of the process parameter [par. 0090]");
store, in the memory, the ("one or more machine-readable memories, storing machine-readable instructions 156 for execution by the processor(s) 152 for … implementing machine learning and/or artificial intelligence for use in analyzing sensor data and monitoring process parameters of an injection molding process [par. 0079]");
receive, from the sensor, second information associated with the mold, the received second information comprising at least one of mold cycle count, mold cycle time, updated measured plastic melt pressure, updated plastic melt temperature, updated water line temperature ("water line temperature data [par. 0058];" and see [fig. 3] where the receiving step (320) is repeated to obtained updated operational data), updated blow air temperature, updated exhaust, or updated unscrewing/ejection force data;
compare the received first information to the ("Operation 330 may include monitoring, at the computing system, a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter [par. 0090]"); and
based on the comparison indicating a change in state of the mold, trigger a recommended action for the mold ("Operation 350 may include transmitting the output, when the process parameter exceeds an operational range determined by the predictive model [par. 0092];" Meneses does not explicitly teach to trigger a recommended action for the mold. However, Meneses does teach real-time alerts [par. 0072] via a visual indicator or an electronic message [par. 0083] and further teaches where the operational data received can be used to provide an indication of when future maintenance may be required or when failure may occur [par. 0082]. Menesis further teaches, "Compared to known approaches, embodiments of the present disclosure may provide improved data collection, improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]." It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have modified Meneses' teachings, to include providing a recommended action, for the benefit of providing a user an indication of when future maintenance may be required or when a failure may occur).
Note: claim is presented in the alternative.
Although Meneses teaches where the digital molding process fingerprint can be derived from mold flow analysis and based on various models [par. 0081] and implemented with machine learning or AI [par. 0079], Meneses does not explicitly teach generating the digital molding process fingerprint based on the received first information.
In an analogous art (molding machines), Burns teaches generating a digital molding process fingerprint based on received first information ("the controller 50 may analyze any environmental sensors 52 to set the initial values for the one or more control parameters [par. 0032]" and "the controller 50 determines the initial values by inputting the model data and/or the sensor data into a machine learning model [par. 0033]").
Burns further teaches, "[b]ased on the trained relationships between the model data and/or the sensor data, the machine learning model may generate a set of initial values that minimizes the error between the expected operation of the injection molding machine 10 and the injection pattern indicated by the injection cycle and/or produces more consistent molded parts [par. 0033]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included Burns' teachings of basing a fingerprint on received first information, with the teachings of Meneses, for the benefit of obtaining initial values that minimize the error between expected and produced parts.
With regard to claim 2, the combination above teaches the system of claim 1. Meneses in the combination further teaches wherein the stored digital molding process fingerprint is associated with one or more of an injection mold machine ("injection molding processes [par. 0002]"), injection stretch blow mold machine, injection blow mold machine, or extrusion blow mold machine.
With regard to claim 5, the combination above teaches the system of claim 1. Meneses in the combination further teaches wherein:
to compare the received first information to the generated digital molding process fingerprint associated with the mold, the processor is further configured to:
determine a degree of difference between the received information and the generated digital molding process fingerprint ("monitoring, at the computing system, a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter [par. 0090]" and "an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), the degree of difference indicating the state of the mold ("the computing system 150 may analyze the operational data with respect to a predictive model to determine whether the injection molding process is proceeding as expected [par. 0080]"); and
identify at least one aspect of the received second information as contributing to the change in the state of the mold ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"); and
the processor is further configured to generate, using an artificial intelligence (AI) model ("The computing system 150 may include one or more processor(s) 152 and a memory 154, such as or one or more machine-readable memories, storing machine-readable instructions 156 for execution by the processor(s) 152 for use analyzing sensor data and monitoring process parameters of an injection molding process, such as for example, implementing machine learning and/or artificial intelligence for use in analyzing sensor data and monitoring process parameters of an injection molding process [para. 0079]"), a time-and condition-based alert for intervention on the mold ("improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]" and "the output is a status indication of the injection molding process or a process parameter of the injection molding process. In an embodiment, the output is an audio signal, a visual indicator, or an electronic message [par. 0083]") based on
i) the determined degree of difference between the received information and the generated digital molding process fingerprint ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), and
ii) the identified at least one aspect of the received second information as contributing to the change in the state of the mold ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]").
With regard to claim 8, the combination above teaches the system of claim 1. Meneses in the combination further teaches wherein the processor is further configured to:
output, to at least one of an external device or a user interface device ("the output is an audio signal, a visual indicator, or an electronic message. For example, the computing system 150 may be in communication with an electronic device 170, such as a smart phone having an associated display 172 and speakers 174 [par. 0083]"),
an alert indicating the triggered recommended action ("the output is a status indication of the injection molding process or a process parameter of the injection molding process [par. 0083]" and "improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]"),
data indicating the change in the state of the mold ("the output is a status indication of the injection molding process or a process parameter of the injection molding process [par. 0083]" and "the computing system 150 may analyze the operational data with respect to a predictive model to determine whether the injection molding process is proceeding as expected [par. 0080]"), and
the received second information identified as contributing to the change in the state of the mold ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]").
With regard to claim 9, the combination above teaches the system of claim 1. Meneses in the combination further teaches wherein the sensor includes a plurality of sensors, each sensor of the plurality of sensors associated with a different portion of the mold ("the sensors are disposed in a plurality of devices and/or locations within the injection mold from one another [par. 0085]").
With regard to claim 11, the combination above teaches claim 1. Claim 11 recites limitations having the same scope as those pertaining to claim 1; therefore, claim 11 is rejected along the same grounds as claim 1.
With regard to claim 13, the combination above teaches claim 5. Claim 13 recites limitations having the same scope as those pertaining to claim 5; therefore, claim 13 is rejected along the same grounds as claim 5.
Claims 3, 4, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns further in view of Ferdinandsen et al. [US Pat. 5,697,424] ("Ferdinandsen").
With regard to claim 3, the combination of Meneses and Burns teaches the system of claim 1. Although the combination teaches comparing operational data with ideal operational data [par. 0071] the combination does not explicitly teach to receive, from an image capturing device implemented on the mold, a first image of the mold; and generate the digital molding process fingerprint based at least in part on the received first image of the mold.
In an analogous art (molding), Ferdinandsen teaches to receive, from an image capturing device implemented on a mold, a first image of the mold; and generate a reference based at least in part on the received first image of the mold ("at least one camera, having a field of vision disposed such that said field of vision encompasses an aspect of interest of an operation being carried out in one of the stations, for producing corresponding camera image information regarding said aspect of interest, comparing means for comparing said camera image information to previously stored image information corresponding to a desired performance of said aspect [col. 3 lines 2-9 ]").
Ferdinandsen further teaches, "producing an output in accordance therewith, and control means for evaluating said output and, if the said output reveals a difference beyond a predetermined acceptable level, for transmitting a corrective command to the at least one station [col. 3 lines 9-13]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have modified Meneses' teachings of comparing operational data with ideal data, with Ferdinandsen's teachings of capturing images, for the benefit of determining whether discrepancies exist based on additional data.
With regard to claim 4, the combination above teaches the system of claim 3. Ferdinandsen in the combination further teaches wherein the processor is further configured to:
receive, from the image capturing device implemented on the mold, a second image of the mold; compare the received second image of the mold to the received first image of the mold; and
based on the comparison of the received second image to the received first image indicating a visual change in the mold, trigger a second recommended action for the mold ("at least one camera, having a field of vision disposed such that said field of vision encompasses an aspect of interest of an operation being carried out in one of the stations, for producing corresponding camera image information regarding said aspect of interest, comparing means for comparing said camera image information to previously stored image information corresponding to a desired performance of said aspect, and for producing an output in accordance therewith, and control means for evaluating said output and, if the said output reveals a difference beyond a predetermined acceptable level, for transmitting a corrective command to the at least one station, said control means transmitting a command [col. 3 lines 2-9 ]").
With regard to claim 12, the combination above teaches claims 3 and 4. Claim 12 recites limitations having the same scope as those pertaining to claims 3 and 4; therefore, claim 12 is rejected along the same grounds as claims 3 and 4.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns further in view of Mizutani [US Pub. 2024/0083354].
With regard to claim 6, the combination of Meneses and Burns teaches the system of claim 5. Although Meneses in the combination teaches the generated time-and condition-based alert ("improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]" and "the output is a status indication of the injection molding process or a process parameter of the injection molding process. In an embodiment, the output is an audio signal, a visual indicator, or an electronic message [par. 0083]") indicating a degree of difference ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"),
the combination does not explicitly teach wherein the processor is further configured to:
based on the generated time-and condition-based alert indicating a first degree of difference associated with a first state, trigger a first recommended action, wherein the first recommended action for invention is a maintenance action of the mold; and
based on the generated time-and condition-based alert indicating a second degree of difference associated with a second state, trigger a second recommended action, wherein the second recommended action for intervention is a replacement of the mold.
In the same field of endeavor (determining maintenance and replacement), Mizutani teaches based on a first degree associated with a first state, trigger a first recommended action, wherein the first recommended action for invention is a maintenance action of an item ("when the degree of deterioration of the lens 72 is low such as when the lens 72 is clouded, the deterioration can be improved by just maintaining the lens 72 without replacing the lens 72 [par. 0061]"); and
based on a second degree associated with a second state, trigger a second recommended action, wherein the second recommended action for intervention is a replacement of an item ("when the degree of deterioration of the lens 72 is high such as when the lens 72 is yellowed, the lens 72 needs to be replaced [par. 0061]").
Mizutani further teaches, "providing different proposals depending on the degree of deterioration of the lens makes it easier for the user to estimate the cost required to improve the aged deterioration of the lens. [par. 0010]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included Mizutani's teachings of determining whether to repair or replace an item based on a measured degree, with the teachings of Meneses, for the benefit of reducing costs by repairing an item instead of replacing an item.
With regard to claim 14, the combination above teaches claim 6. Claim 14 recites limitations having the same scope as those pertaining to claim 6; therefore, claim 14 is rejected along the same grounds as claim 6.
Claims 7, 15-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns further in view of Nakamura et al. [US Pub. 2025/0026058] ("Nakamura").
With regard to claim 7, the combination of Meneses and Burns teaches the system of claim 1. Meneses in the combination teaches the system further comprising a user interface device ("the computing system 150 may be in communication with an electronic device 170, such as a smart phone having an associated display 172 and speakers 174 [par. 0083]") configured to:
the generated digital molding process fingerprint for the mold,
the received second information associated with the mold ("an output in accordance with the present disclosure may include an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), and
the triggered recommended action for the mold.
The combination does not explicitly teach to display a dashboard.
In an analogous art (molding), Nakamura teaches to display a dashboard ("The display input unit 130 includes a display device such as a liquid crystal display, and can be used by a molding worker who uses the molding condition derivation device 100, to confirm progress of molding condition adjustment and perform setting and operation through a graphical user interface (GUI) [par. 0060]" and "The measured quality value is sent to the display input unit 130 by means of transmission through a network or a graphical user interface (GUI) input by a molding worker [par. 0086]").
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included a dashboard as taught by Nakamura, with the teachings of Meneses, for the benefit of providing a user with a greater amount of information.
With regard to claim 15, the combination above teaches claims 7 and 8. Claim 15 recites limitations having the same scope as those pertaining to claims 7 and 8; therefore, claim 15 is rejected along the same grounds as claims 7 and 8.
With regard to claim 16, Meneses teaches one or more non-transitory computer-readable media storing instructions that, when executed by a processor ("The computing system 150 may include one or more processor(s) 152 and a memory 154 [par. 0079]" and "The machine-readable medium can be any suitable tangible, non-transitory medium [par. 0103]"), cause the processor to:
receive, from a sensor implemented on a mold ("sensors for integrating into the injection mold [par. 0070]" and "the computing system 150 is configured to receive, from the sensor system 110, data associated with operating the injection mold [par. 0080]"), first information associated with the mold ("obtaining, using a sensor system, operational data associated with operating an injection mold during an injection molding process [par. 0088]"), the received first information comprising at least one of measured plastic melt pressure, plastic melt temperature, water line temperature ("water line temperature data [par. 0058]"), blow air temperature, exhaust, or unscrewing/ejection force data;
("a predictive model of the process parameter [par. 0090]");
store, in the memory, the("one or more machine-readable memories, storing machine-readable instructions 156 for execution by the processor(s) 152 for … implementing machine learning and/or artificial intelligence for use in analyzing sensor data and monitoring process parameters of an injection molding process [par. 0079]");
receive, from the sensor, second information associated with the mold, the received second information comprising at least one of mold cycle count, mold cycle time, updated measured plastic melt pressure, updated plastic melt temperature, updated water line temperature ("water line temperature data [par. 0058];" and see [fig. 3] where the receiving step (320) is repeated to obtained updated operational data), updated blow air temperature, updated exhaust, or updated unscrewing/ejection force data;
determine a degree of difference between the received information and the("monitoring, at the computing system, a process parameter of the injection molding process based on analyzing the operational data with respect to a predictive model of the process parameter [par. 0090]" and "an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), the degree of difference indicating a state of the mold ("the computing system 150 may analyze the operational data with respect to a predictive model to determine whether the injection molding process is proceeding as expected [par. 0080]");
identify at least one aspect of the received second information as contributing to the change in the state of the mold ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]");
based on the degree of difference indicating a change in the state of the mold, trigger a recommended action for the mold ("Operation 350 may include transmitting the output, when the process parameter exceeds an operational range determined by the predictive model [par. 0092];" Meneses does not explicitly teach to trigger a recommended action for the mold. However, Meneses does teach real-time alerts [par. 0072] via a visual indicator or an electronic message [par. 0083] and further teaches where the operational data received can be used to provide an indication of when future maintenance may be required or when failure may occur [par. 0082]. Menesis further teaches, "Compared to known approaches, embodiments of the present disclosure may provide improved data collection, improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]." It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have modified Meneses' teachings, to include providing a recommended action, for the benefit of providing a user an indication of when future maintenance may be required or when a failure may occur); and
display, on a user interface device ("the computing system 150 may be in communication with an electronic device 170, such as a smart phone having an associated display 172 and speakers 174 [par. 0083]"),
the generated digital molding process fingerprint for the mold,
the received second information associated with the mold ("an output in accordance with the present disclosure may include an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), and
the triggered recommended action for the mold.
Note: claim is presented in the alternative.
Although Meneses teaches where the digital molding process fingerprint can be derived from mold flow analysis and based on various models [par. 0081] and implemented with machine learning or AI [par. 0079], Meneses does not explicitly teach generating the digital molding process fingerprint based on the received first information.
In an analogous art (molding machines), Burns teaches generating a digital molding process fingerprint based on received first information ("the controller 50 may analyze any environmental sensors 52 to set the initial values for the one or more control parameters [par. 0032]" and "the controller 50 determines the initial values by inputting the model data and/or the sensor data into a machine learning model [par. 0033]").
Burns further teaches, "[b]ased on the trained relationships between the model data and/or the sensor data, the machine learning model may generate a set of initial values that minimizes the error between the expected operation of the injection molding machine 10 and the injection pattern indicated by the injection cycle and/or produces more consistent molded parts [par. 0033]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included Burns' teachings of basing a fingerprint on received first information, with the teachings of Meneses, for the benefit of obtaining initial values that minimize the error between expected and produced parts.
The combination does not explicitly teach a dashboard.
In an analogous art (molding), Nakamura teaches a dashboard ("The display input unit 130 includes a display device such as a liquid crystal display, and can be used by a molding worker who uses the molding condition derivation device 100, to confirm progress of molding condition adjustment and perform setting and operation through a graphical user interface (GUI) [par. 0060]" and "The measured quality value is sent to the display input unit 130 by means of transmission through a network or a graphical user interface (GUI) input by a molding worker [par. 0086]").
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included a dashboard as taught by Nakamura, with the teachings of Meneses, for the benefit of providing a user with a greater amount of information.
With regard to claim 17, the combination above teaches the one or more non-transitory computer-readable media of claim 16. Meneses in the combination teaches the media further storing instructions that, when executed by the processor, cause the processor to:
generate, using an artificial intelligence (AI) model ("The computing system 150 may include one or more processor(s) 152 and a memory 154, such as or one or more machine-readable memories, storing machine-readable instructions 156 for execution by the processor(s) 152 for use analyzing sensor data and monitoring process parameters of an injection molding process, such as for example, implementing machine learning and/or artificial intelligence for use in analyzing sensor data and monitoring process parameters of an injection molding process [para. 0079]"), a time-and condition-based alert for intervention on the mold ("improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]" and "the output is a status indication of the injection molding process or a process parameter of the injection molding process. In an embodiment, the output is an audio signal, a visual indicator, or an electronic message [par. 0083]") based on
i) the determined degree of difference between the received information and the generated digital molding process fingerprint ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"), and
ii) the identified at least one aspect of the received second information as contributing to the change in the state of the mold ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]").
With regard to claim 20, the combination above teaches the one or more non-transitory computer-readable media of claim 16. Meneses in the combination further teaches wherein the stored digital molding process fingerprint is associated with one or more of an injection mold machine ("injection molding processes [par. 0002]"), injection stretch blow mold machine, injection blow mold machine, or extrusion blow mold machine.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns further in view of Rakshit et al. [US Pub. 2025/0001695] ("Rakshit").
With regard to claim 10, the combination of Meneses and Burns teaches the system of claim 1. The combination does not explicitly teach wherein the sensor is an internet of things (IoT) sensor.
In the same field of endeavor (manufacturing), Rakshit teaches wherein a sensor is an internet of things (IoT) sensor ("IoT sensor set 325 is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector [par. 0058]").
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have substituted the IoT sensor as taught by Rakshit, as the sensor taught by Meneses, since the IoT sensor would still function in Meneses' system the same way that it does in Rakshit's system and the combination would have predictably allowed the sensor to take parameters of the mold.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns in view of Nakamura further in view of Mizutani.
With regard to claim 18, the combination of Meneses, Burns, and Nakamura teaches the one or more non-transitory computer-readable media of claim 17. Although Meneses in the combination teaches the generated time-and condition-based alert ("improve predictions for faults or required maintenance, real-time alerts, and more accurate projections as to performance for an injection molding process [par. 0072]" and "the output is a status indication of the injection molding process or a process parameter of the injection molding process. In an embodiment, the output is an audio signal, a visual indicator, or an electronic message [par. 0083]") indicating a degree of difference ("an indication that the injection molding process is operating at, above, or below, a particular process parameter. For example, a pressure reading at an injection pore to an injection mold may reveal that an input pressure is below an expected input pressure wherein the output may include an indication of a low input pressure [par. 0091]"),
the combination does not explicitly teach based on the generated time-and condition-based alert indicating a first degree of difference associated with a first state, trigger a first recommended action, wherein the first recommended action for invention is a maintenance action of the mold; and based on the generated time-and condition-based alert indicating a second degree of difference associated with a second state, trigger a second recommended action, wherein the second recommended action for intervention is a replacement of the mold.
In the same field of endeavor (determining maintenance and replacement), Mizutani teaches based on a first degree associated with a first state, trigger a first recommended action, wherein the first recommended action for invention is a maintenance action of an item ("when the degree of deterioration of the lens 72 is low such as when the lens 72 is clouded, the deterioration can be improved by just maintaining the lens 72 without replacing the lens 72 [par. 0061]"); and
based on a second degree associated with a second state, trigger a second recommended action, wherein the second recommended action for intervention is a replacement of an item ("when the degree of deterioration of the lens 72 is high such as when the lens 72 is yellowed, the lens 72 needs to be replaced [par. 0061]").
Mizutani further teaches, "providing different proposals depending on the degree of deterioration of the lens makes it easier for the user to estimate the cost required to improve the aged deterioration of the lens. [par. 0010]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have included Mizutani's teachings of determining whether to repair or replace an item based on a measured degree, with the teachings of Meneses, for the benefit of reducing costs by repairing an item instead of replacing an item.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Meneses in view of Burns in view of Nakamura further in view of Ferdinandsen.
With regard to claim 19, the combination of Meneses, Burns, and Nakamura teaches the one or more non-transitory computer-readable media of claim 16. Although the combination teaches comparing operational data with ideal operational data [par. 0071] the combination does not explicitly teach to receive, from an image capturing device implemented on the mold, a first image of the mold; generate the digital molding process fingerprint based at least in part on the received first image of the mold; receive, from the image capturing device implemented on the mold, a second image of the mold; compare, the received second image of the mold to the received first image of the mold; and based on the comparison of the received second image to the received first image indicating a visual change in the mold, trigger a second recommended action for the mold.
In an analogous art (molding), Ferdinandsen teaches to receive, from an image capturing device implemented on a mold, a first image of the mold; generate a reference based at least in part on the received first image of the mold ("at least one camera, having a field of vision disposed such that said field of vision encompasses an aspect of interest of an operation being carried out in one of the stations, for producing corresponding camera image information regarding said aspect of interest, comparing means for comparing said camera image information to previously stored image information corresponding to a desired performance of said aspect [col. 3 lines 2-9 ]").
receive, from the image capturing device implemented on the mold, a second image of the mold; compare the received second image of the mold to the received first image of the mold; and
based on the comparison of the received second image to the received first image indicating a visual change in the mold, trigger a second recommended action for the mold ("at least one camera, having a field of vision disposed such that said field of vision encompasses an aspect of interest of an operation being carried out in one of the stations, for producing corresponding camera image information regarding said aspect of interest, comparing means for comparing said camera image information to previously stored image information corresponding to a desired performance of said aspect, and for producing an output in accordance therewith, and control means for evaluating said output and, if the said output reveals a difference beyond a predetermined acceptable level, for transmitting a corrective command to the at least one station, said control means transmitting a command [col. 3 lines 2-9 ]").
Ferdinandsen further teaches, "producing an output in accordance therewith, and control means for evaluating said output and, if the said output reveals a difference beyond a predetermined acceptable level, for transmitting a corrective command to the at least one station [col. 3 lines 9-13]."
It would have been obvious to one of ordinary skill in the art at the time of filing the invention to have modified Meneses' teachings of comparing operational data with ideal data, with Ferdinandsen's teachings of capturing images, for the benefit of determining whether discrepancies exist based on additional data.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Collins et al. [US Pub. 2020/0086542] teaches a method for controlling an injection molding process based upon an actual plastic melt pressure including identifying an optimal actual plastic melt pressure curve over time based on a baseline cycle, and adjusting in a subsequent cycle an injection pressure in order to cause a monitored pressure of the molten thermoplastic material to follow the optimal actual plastic melt pressure curve over time.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT W CHANG whose telephone number is (571)270-1214. The examiner can normally be reached (M-F) 10:00 am - 6:00 pm.
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VINCENT WEN-LIANG CHANG
Examiner
Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
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