DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments and remarks filed (6 – 22 – 2026) have been fully considered but they are not persuasiveApplicant argues…
Regarding claim 1 – 6 as being rejected under 35 U.S.C. §101 as being directed to an abstract idea without significantly more. Independent claim 1 is amended to include the subject matter of canceled claim 5 to recite, inter alia, the following feature: the circuitry is further configured to acquire the first data in a first molding cycle of the injection molding machine and subsequently acquire the first data in a second molding cycle of the injection molding machine, input the first data of each of the first molding cycle and the second molding cycle into the trained model and acquire, as the first value, quantitative data representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle, the trained model being trained with the training data combining the second data in a third cycle, the second data in a fourth cycle, and the second value, the second value representing a difference in evaluation between the product in the third cycle and the product in the fourth cycle as quantitative data, and determine whether the abnormality has occurred in the detection results of the sensor, based on the acquired quantitative data representing the difference in evaluation between the product of the first molding cycle and the product of the second molding cycle.
Chen merely illustrates inferring the value of burr and comparing the inferred value with a predetermined value (2 mm) to identify whether the molding product with the inferred value meets the acceptance condition. Chen does not teach or suggest “acquir[ing] … quantitative data representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle”.
The Office Action appears to equate Chen’s quantitative index or quantitative results to the recited “first value representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle as quantitative data”. Chen, however, does not teach or suggest that the quantitative index or quantitative results includes “the first value representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle as quantitative data”. Hence, the above-noted feature of claim 1 is a distinction over Chen.
Applicant further argues that none of the other applied references make up for the deficiency of Chen / Chen as modified.
This is not found to be persuasive because…
Applicant newly amended features still directed to mental steps, namely receive, observe, or log sensor values by looking at a machine. Applicant’s amendments are directed towards and focusing on the result (acquiring data) rather than how the specific structural configuration or hardware state changes the operation of the machine. Merely reciting "circuitry configured to" acquire data generally does not overcome a Section 101 mental-steps rejection if the physical components are described at a high level of generality and act as generic tools for a routine function. To avoid a mental process rejection, the claim must specify an unconventional technological improvement or a concrete physical interaction
As detailed in the action of (3 – 23 – 2026 Chen states on ([0040]) that the actual injection molding process 100 inputs the applied molding conditions MCA and the actual sensing data DSD into the injection molding process state observation unit 200, wherein the applied molding conditions MCA and the actual sensing data DSD are also stored in the data storage device 10 DS. ([0040]) adding that the sensing data is the result (obtained consequently). The sensing data may include the sensing data related to equipment, such as the sensing data of molding equipment, the sensing data of peripheral devices, sensing data of mold interior features, etc. Where, the sensing data being inputted into the injection molding process state observation unit 200 provides for and acts as applicant’s acquir[ing] … quantitative data representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle.
As detailed ([0040]) teaches that the sensing data is the result (obtained consequently). The sensing data may include the sensing data related to equipment, such as the sensing data of molding equipment, the sensing data of peripheral devices, sensing data of mold interior features, etc. With ([0040]) noting that according to the examples described above, the injection molding process state observation unit 200 may further comprise an acceptance state identifier 250, and the acceptance state identifier 250 identifies the results of the actual qualities collected and analyzed by the molding quality input selector 260 and also identifies the results of quantitative characteristics inferred by the molding quality inference engine 230 for a quality identification. Therefore, the acceptance state identifier 250 can be used for determining if a molding product with the actual qualities collected and analyzed by the molding quality input selector 260 (i.e. the actual qualities from the molding product inspection system 210) and the results of quantitative characteristics (inferred by the molding quality inference engine 230) is an accepted product in the acceptance state or not. ([0043]) notes that the actual qualities through measurement (e.g. the results of the molding items for the product as sampled can be obtained through the measurement at related equipment). Where determine the actual qualities provides for and acts as applicant’s the first value representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle as quantitative data.
This is unpersuasive because as explained above there was not found to be deficiency in Chen / Chen as modified.
Claim Rejections - 35 USC § 101
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. [FP 7.05 and 7.05.016 with explanation provided].
Claims(s) 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recites various limitations including “input the first data into a trained model and acquire a first value from the trained model” and “the training data combining second data and a second value, the second data indicating detection results from the sensor in a time series” and “determine whether an abnormality has occurred in the detection results of the sensor, based on the acquired first value by the inferring unit.” These limitations as drafted are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than potentially reciting “circuitry” (see claim 21) nothing in the claim element precludes the step from practically being performed in the mind but for the recitation of generic computer components. For example, but for the “circuitry” language, “acquire” , “input” , “evaluation”, and/or “detection“ in the context of this claim encompasses the user manually calculating the amount of use of each icon. Similarly, the limitation of ranking the icons based on the determined amount of use, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “circuitry” and “acquire” , “input” , “evaluation”, and/or “detection“ in the context of this claim encompasses the user thinking that assembling module comprising one assembly mold and at least one combination mold. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using “circuity” to perform “acquire” , “input” , “evaluation”, and/or “detection“ steps. The “circuity“ in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using an “circuity” to perform the “acquire” , “input” , “evaluation”, and/or “detection“ steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, as drafted the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
A.) Claim(s) 1 &6, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20200202235 A1, hereinafter Chen) in view of Otsuki et al. (US 20220324149 A1, hereinafter Otsuki)Regarding claim 1,
A monitoring device for an injection molding machine, the monitoring device comprising:
A monitoring device for an injection molding machine, the monitoring device comprising:
circuitry configured to acquire first data from a sensor provided in an injection molding machine,
the first data indicating detection results in a time series;
input the first data into a trained model and
acquire a first value from the trained model,
the first value representing an evaluation of a product,
the trained model being trained by machine learning with training data,
the training data combining second data and a second value,
the second data indicating detection results from the sensor in a time series, and
the second value representing an evaluation of the product produced by the injection molding machine; and
determine whether an abnormality has occurred in the detection results of the sensor, based on the acquired first value,
wherein the circuitry is further configured to acquire the first data in a first molding cycle of the injection molding machine and
subsequently acquire the first data in a second molding cycle of the injection molding machine,
input the first data of each of the first molding cycle and the second molding cycle into the trained model and acquire, as the first value,
quantitative data representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle,
the trained model being trained with the training data combining the second data in a third cycle,
the second data in a fourth cycle, and the second value, the second value representing a difference in evaluation between the product in the third cycle and the product in the fourth cycle as quantitative data, and
determine whether the abnormality has occurred in the detection results of the sensor, based on the acquired quantitative data representing the difference in evaluation between the product of the first molding cycle and the product of the second molding cycle.
Chen teaches the following:
([0040]) teaches that as shown in (Fig. 3), the injection molding production line to which the model-based machine learning system of the embodiment is introduced comprises an actual injection molding process 100. The recommended molding conditions MCR (e.g. a combination of several molding parameters) from the injection molding condition optimizer 310 can be inputted into the actual injection molding process 100. Also, the actual injection molding process 100 inputs the applied molding conditions MCA and the actual sensing data DSD into the injection molding process state observation unit 200, wherein the applied molding conditions MCA and the actual sensing data DSD are also stored in the data storage device 10 DS. ([0015]) teaches that in one example, the production raw data may comprise production rounds of actual injection molding, actual molding conditions, actual sensing data and quality states of actual products. As such, the injection molding process state observation unit 200 provides for circuitry configured to acquire first data from a sensor provided in an injection molding machine.
([0033]) teaches that after the injection molding production line is operated for a period of time, the injection molding process emulator 400 and the injection molding process state observation unit 200 can be updated by observing the production results of the actual products or by setting scheduled time. As such, the first data indicating detection results.
, d.) & e.) (Abstract) teaches a molding condition optimization model constructed in the injection molding condition optimizer is trained according to the injection molding process state as determined. ([0017]) teaches according to one embodiment, the injection molding process state observation unit 200 determines an injection molding process state Sk according to the molding conditions MC as inputted, the set of emulated sensing data DES and a quality state, wherein the quality state at least comprises an acceptance state. ([0037]) teaches that the molding condition optimization model constructed in the injection molding process optimization unit 300 comprises several sets of correspondence relationships between at least one molding process state and adjustment of corresponding molding conditions. ([0042]) teaches that the injection molding process state observation unit 200 comprises at least an acceptance state inference engine 240. The acceptance state inference engine 240 as shown in (Fig. 3) infers the actual sensing data DSD outputted by the actual injection molding process 100 according to an acceptance state classification model (based on the set of training data or a set of renewed data) constructed in the acceptance state inference engine 240, thereby inferring a qualitative characteristic of a molding product with the actual sensing data DSD. Therefore, the quality state at least comprising an acceptance state of a molding product from the actual injection molding process 100 can be analyzed and determined by the injection molding process state observation unit 200. As such, the injection molding process state observation unit 200 comprises a state inference engine 240 that provides for the inputting the first data into a trained model and generates a first value acceptance state / the quality state from the trained model, with the first value representing an evaluation of a product from the actual injection molding process 100 which is then analyzed and determined as acceptable or not by the injection molding process state observation unit 200. Which may or may not result in adjustments to the actual injection molding process 100 conditions.
([0021]) teaches It is noted that the injection molding process state observation unit 200 may comprise other inference engine(s) and/or selector(s) related to the quantitative quality and/or qualitative quality. ([0021]]) adding that examples of the models commonly constructed in the inference engines include methods of Support Vector Classifier, Linear Discriminant, Nearest Neighbors, Decision Tree, Random Forest, and Neural Network for data classification and analysis. However, the methods of the models applicable to the embodiment are not limited to the methods described above. ([0037]) teaches that a neural network or the likes can be applied as the molding condition optimization model for recommending optimized molding conditions. Furthermore, the molding condition optimization model as constructed can be automatically or manually updated by a user as needed, and there is no limitation to the update frequency. ([0049]) teaches that the injection molding condition optimizer 310 may train the molding condition optimization model as shown in (Fig. 2), depending on actual conditions in the practical application. Details of the training have been described above and are not redundantly repeated. With ([0013]) noting that (Fig. 2) depicts an exemplary model construction and learning flow of a model-based machine learning system As such, the trained model is understood to be trained by a neural network / machine learning with training data.
, h.) & i.) ([0015]) teaches that the production raw data may comprise production rounds of actual injection molding, actual molding conditions, actual sensing data and quality states of actual product. ([0023]) teaches that the quality state obtained from the injection molding process state observation unit 200 comprises not only the result of acceptance state (inferred by at least the acceptance state inference engine 240), but also a result of quantitative characteristic (of molding quality) inferred by the molding quality inference engine 230. ([0035]) teaches that completeness of the training of the molding condition optimization model for each round of molding injection means that a set of molding condition from the injection molding condition optimizer 310 is initially inputted into to the injection molding process emulator 400, and the optimization procedure of molding condition starts based on the existing molding condition optimization model. If the module 280 determines that the molding product with the set of molding conditions is not in the acceptance state, another set of molding conditions would be recommended and inputted into the injection molding process emulator 400 to proceed the optimization procedure until the module 280 determines that the molding product with the recommended set of molding conditions is in the acceptance state, and the training of the molding condition optimization model in this round of molding injection is completed. Subsequently, the injection molding condition optimizer 310 selects a new set of molding conditions to train the molding condition optimization model again for the next round of molding injection. Initially, it may require many times (such as 20 times or more) of recommendations and adjustments of the molding conditions to obtain a molding product with the last recommended set of molding conditions in the acceptance state as determined by the module 280, so as to complete the training of the molding condition optimization model for one round of molding injection. As the number of training rounds increases, the number of the times for adjusting the molding conditions required to complete each round of molding injection decreases (i.e., the number of the times for adjusting the molding conditions required to complete each round of molding injection is gradually converged) since the system has learned how to adjust and select the molding conditions corresponding to the injection molding state from the records in the past training rounds. As such, the training data is understood to comprise multiple values including a second data and a second value representing the acceptance state / quality state of the injection molded article, the date being used to complete the training of the molding condition optimization model.
([0023]) teaches that the injection molding process state observation unit 200 further comprises a molding quality inference engine 230. ([0024]) teaches that the molding quality inference engine 230 compares the molding item quality inference model to the set of emulated sensing data DES generated by the injection molding process emulator 400, thereby inferring the result of quantitative characteristic of the molding product with the set of emulated sensing data DES. As such, the state observation unit 200 comprisingthe molding quality inference engine 230 determines whether an abnormality / quality (of the injection molded item) has occurred in the detection results of the sensor, based on the acquired first value by the inferring unit.
([0040]) teaches that the injection molding condition optimizer 310 can be inputted into the actual injection molding process 100. Also, the actual injection molding process 100 inputs the applied molding conditions MCA and the actual sensing data DSD into the injection molding process state observation unit 200, wherein the applied molding conditions MCA and the actual sensing data DSD are also stored in the data storage device 10 DS. Where, the circuitry acquires the first data in a first molding cycle of the injection molding machine.
([0035]) teaches completeness of the training of the molding condition optimization model for each round of molding injection means that a set of molding condition from the injection molding condition optimizer 310 is initially inputted into to the injection molding process emulator 400, and the optimization procedure of molding condition starts based on the existing molding condition optimization model. Where, multiple round of molding injection data are understood to be collected and provides for collecting the first data in a second molding cycle of the injection molding machine.
([0035]) teaches that completeness of the training of the molding condition optimization model for each round of molding injection means that a set of molding condition from the injection molding condition optimizer 310 is initially inputted into to the injection molding process emulator 400, and the optimization procedure of molding condition starts based on the existing molding condition optimization model. If the module 280 determines that the molding product with the set of molding conditions is not in the acceptance state, another set of molding conditions would be recommended and inputted into the injection molding process emulator 400 to proceed the optimization procedure until the module 280 determines that the molding product with the recommended set of molding conditions is in the acceptance state, and the training of the molding condition optimization model in this round of molding injection is completed. Where, inputting the first data of each of the first molding cycle and the second molding cycle into the trained model and acquiring an acceptance state / quality state representing applicant’s first value is understood to be disclosed.
([0025]) teaches that if the value of burr (i.e. quantitative quality result) of a molding product as inferred by the molding quality inference engine 230 is greater than 2 mm, the acceptance state identifier 250 identifies that the molding product with that value related to the burr item does not meet the acceptance condition; if the value of burr of a molding product as inferred by the molding quality inference engine 230 is less than or equal to 2 mm, the acceptance state identifier 250 identifies that the molding product with that value related to the burr item does meet the acceptance condition. ([0040]) teaches that the sensing data is the result (obtained consequently). The sensing data may include the sensing data related to equipment, such as the sensing data of molding equipment, the sensing data of peripheral devices, sensing data of mold interior features, etc. With ([0040]) noting that according to the examples described above, the injection molding process state observation unit 200 may further comprise an acceptance state identifier 250, and the acceptance state identifier 250 identifies the results of the actual qualities collected and analyzed by the molding quality input selector 260 and also identifies the results of quantitative characteristics inferred by the molding quality inference engine 230 for a quality identification. Therefore, the acceptance state identifier 250 can be used for determining if a molding product with the actual qualities collected and analyzed by the molding quality input selector 260 (i.e. the actual qualities from the molding product inspection system 210) and the results of quantitative characteristics (inferred by the molding quality inference engine 230) is an accepted product in the acceptance state or not. ([0043]) notes that the actual qualities through measurement (e.g. the results of the molding items for the product as sampled can be obtained through the measurement at related equipment). ([0051]) teaches that the acceptance state AS (based on the qualitative characteristic data sources) and other qualitative results of the molding product; for example, if there are flow marks, spray marks, etc., the results as presented may follow a binary classification. In the embodiment, the quantitative index comprises the molding quality MQ (based on the quantitative characteristic data sources) and other quantitative results of the molding product; for example, the length of the burr of the molding product, the weight of the finished product, the size of the finished product, and the degree of warpage. Where determine the actual qualities provides for and acts as applicant’s the first value representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle as quantitative data.Additionally and/or alternatively. Where, determining the molding quality MQ (based on the quantitative characteristic data sources) and other quantitative results of the molding product; for example, the length of the burr of the molding product, the weight of the finished product, the size of the finished product, and the degree of warpage provides for the first value representing a difference in evaluation between the product of the first molding cycle and the product of the second molding cycle as quantitative data.
([0021]) teaches It is noted that the injection molding process state observation unit 200 may comprise other inference engine(s) and/or selector(s) related to the quantitative quality and/or qualitative quality. ([0021]]) adding that examples of the models commonly constructed in the inference engines include methods of Support Vector Classifier, Linear Discriminant, Nearest Neighbors, Decision Tree, Random Forest, and Neural Network for data classification and analysis. However, the methods of the models applicable to the embodiment are not limited to the methods described above. ([0037]) teaches that a neural network or the likes can be applied as the molding condition optimization model for recommending optimized molding conditions. Furthermore, the molding condition optimization model as constructed can be automatically or manually updated by a user as needed, and there is no limitation to the update frequency. ([0049]) teaches that the injection molding condition optimizer 310 may train the molding condition optimization model as shown in (Fig. 2), depending on actual conditions in the practical application. Details of the training have been described above and are not redundantly repeated. With ([0013]) noting that (Fig. 2) depicts an exemplary model construction and learning flow of a model-based machine learning system Where, the trained model is understood to be trained by a neural network / machine learning with the training data.
([0035]) teaches If the module 280 determines that the molding product with the set of molding conditions is not in the acceptance state, another set of molding conditions would be recommended and inputted into the injection molding process emulator 400 to proceed the optimization procedure until the module 280 determines that the molding product with the recommended set of molding conditions is in the acceptance state, and the training of the molding condition optimization model in this round of molding injection is completed. Subsequently, the injection molding condition optimizer 310 selects a new set of molding conditions to train the molding condition optimization model again for the next round of molding injection. Initially, it may require many times (such as 20 times or more) of recommendations and adjustments of the molding conditions to obtain a molding product with the last recommended set of molding conditions in the acceptance state as determined by the module 280, so as to complete the training of the molding condition optimization model for one round of molding injection. ([0037]) teaches that several sets of correspondence relationships are respectively expected values of the adjustment of corresponding molding conditions for producing products with acceptance conditions under the at least one molding process state as inputted. In one embodiment, a neural network or the likes can be applied as the molding condition optimization model for recommending optimized molding conditions. As such, the trained model being trained by machine learning with training data, the training data combining the second data in a third cycle, the second data in a fourth cycle, and the second value.
([0025]) teaches that if the value of burr (i.e. quantitative quality result) of a molding product as inferred by the molding quality inference engine 230 is greater than 2 mm, the acceptance state identifier 250 identifies that the molding product with that value related to the burr item does not meet the acceptance condition; if the value of burr of a molding product as inferred by the molding quality inference engine 230 is less than or equal to 2 mm, the acceptance state identifier 250 identifies that the molding product with that value related to the burr item does meet the acceptance condition. ([0037]) notes that adjusting the molding conditions required for completing each round of molding injection can be achieved in 20 rounds, the training of the molding condition optimization model can be regarded as preliminarily completed, and the molding condition optimization model can be introduced into an injection molding production line. ([0051]) teaches that the acceptance state AS (based on the qualitative characteristic data sources) and other qualitative results of the molding product; for example, if there are flow marks, spray marks, etc., the results as presented may follow a binary classification. In the embodiment, the quantitative index comprises the molding quality MQ (based on the quantitative characteristic data sources) and other quantitative results of the molding product; for example, the length of the burr of the molding product, the weight of the finished product, the size of the finished product, and the degree of warpage. Where, determine the molding quality MQ based on the quantitative characteristic data sources in a reiterative process via a various amount of injection molding rounds provides for and acts as applicant’s determine whether the abnormality has occurred in the detection results of the sensor, based on the acquired quantitative data representing the difference in evaluation between the product of the first molding cycle and the product of the second molding cycle. Highlighting, that this reiterative process is understood to go on very numerous rounds (such as 20 times or more) of recommendations and adjustments of the molding conditions to obtain a molding product with the last recommended set of molding conditions in the acceptance state as determined by the module 280, so as to complete the training of the molding condition optimization model for one round of molding injection.
Regarding Claim 1, Chen is silent on details regarding the sensors utilized for obtaining the training data, and the training data comprising time series data. In analogous art for a molding machine management system includes a display section configured to display, along a common time axis, and provides for detecting weather an abnormality occurs in the injection molding machine, Otsuki suggests details regarding the sensors utilized for obtaining the training data, and the training data comprising time series data, and in this regard, Otsuki teaches the following:
Otsuki teaches the following:
([0018]) teaches the molding control section 21 has a function of controlling the operation of the entire injection molding machine 2 and executing injection molding and a function of performing communication with the peripheral equipment 3 and the management device 4 via a not-shown interface. ([0022]) teaches that the molding control section 21 has a function of acquiring various time series data concerning the injection molding using not-shown various sensors provided in the injection molding machine 2, for example, a weight sensor for detecting the weight of the material stored in the hopper 22, a pressure sensor for detecting the pressures of, for example, gas in the injecting section 23 and the material in the mold section 26, and a temperature sensor for detecting the temperatures of the injecting section 23, the mold section 26, and the like. As such, the molding control section 21 acts as applicant’s circuitry configured to acquire first data from a sensor provided in an injection molding machine.
& h.) ([0022]) teaches that the molding control section 21 has a function of acquiring various time series data concerning the injection molding using not-shown various sensors provided in the injection molding machine 2, for example, a weight sensor for detecting the weight of the material stored in the hopper 22, a pressure sensor for detecting the pressures of, for example, gas in the injecting section 23 and the material in the mold section 26, and a temperature sensor for detecting the temperatures of the injecting section 23, the mold section 26, and the like. As such, the molding control section 21 acquires data from sensors comprising and indicating detection results in a time series data concerning the injection molding.
([0023]) teaches the various time series data concerning the injection molding acquired by the molding control section 21 are transmitted from the molding control section 21 to the management device 4.
([0024]) teaches that the inspection device is, for example, an image inspection device and is used to determine, with an exterior inspection using images of molded products, for each of the molded products, presence or absence of an abnormality in an exterior.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the production method and apparatus for manufacturing an injection molding machine that utilizes model-based machine learning system for calculating optimum molding conditions includes a data storage device providing a set of training data of Chen. By modifying the injection molding machine to comprise various sensors and the data collected to comprise time series data as taught by Otsuki. Highlighting, one would be motivated to implement sensors as part of the injection molding machines as it provides for detecting and monitoring various aspects of the injection molding machine, including the weight of the material stored in the hopper 22, a pressure sensor for detecting the pressures of, for example, gas in the injecting section 23 and the material in the mold section 26, and a temperature sensor for detecting the temperatures of the injecting section 23, the mold section 26, ([0022]) and capturing and utilizing data that comprises time series data is it provides for monitoring and controlling the operation state of the peripheral equipment 3 is transmitted from the molding control section 21 to the management device , ([0025]). Accordingly, the use of known technique to improve similar devices (methods, or products) in the same way and/or the application of a known technique to a known device (method, or product) ready for improvement to yield predictable results provides for the recitation of KSR case law. Where, "A person of ordinary skill has good reason to pursue the known option within his or her technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense." KSR int'l Co. v. Teleflex Inc., 127 S. Ct. 1727, 82 USPQ2d 1385 (2007), MPEP 2143.
Regarding claim 6 as applied to claim 1,
Wherein the circuitry is further configured to display, on a display device, one or more of determination results of the product by the circuitry or the first value, every time the product is produced by the injection molding machine.
Chen teaches the following:
([0045]) teaches that the external input unit 220 can be referred as an external acceptance state input unit. The inspection results of acceptance state inputted into the external input unit 220 are transmitted to the acceptance state input selector 270. Therefore, in one example, based on several different sources of qualitative characteristics related to the acceptance state (e.g. three sources as shown in FIG. 3), the acceptance state input selector 270 can determine the molding product having the qualitative characteristic corresponding to actual sensing data is an accepted product in the acceptance state or a defective product not in the acceptance state. As shown in FIG. 3, examples of the sources of qualitative characteristics related to the acceptance state for the acceptance state input selector 270 include the inspection results of acceptance state from the external input unit 220 (can be optionally updated in every round of molding injection), the qualitative characteristic of the molding product with the actual sensing data inferred by the acceptance state inference engine 240 (can be optionally updated in every round of molding injection), and the quality results of the molding product identified by the acceptance state identifier 250 (i.e. the quality result is obtained by inferring the quantitative result from the molding quality input selector 260), wherein those results are identified and determined by the acceptance state input selector 270. ([0050]) teaches that the acceptance state AS can be instantly displayed on the external input unit 220, and the user only needs to tag/mark the false predicted results of molding conditions, which can reduce the user's operation load. Also, the results of quantitative characteristics can be instantly displayed on the external input unit 220, and the acceptance identification of the molding product can be conducted by automatically comparing with the acceptance conditions inputted by the user, thereby reducing the loading on the user's operation. As such, the circuitry is further configured to display, on a display device, one or more of determination results of the product by the circuitry or the first value, is updated every time, in every round of molding injection the product is produced by the injection molding machine.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Asaoka et al. (US 20180281256 A1) – teaches in the (Abstract) A state determination apparatus for determining a state related to an abnormality of an injection molding machine based on an operation state of the injection molding machine includes a machine learning apparatus for learning the state related to the abnormality of the injection molding machine.
Atsushi Horiuchi (US 20200130246 A1) – teaches in the (Abstract) A state determination device acquires data on an injection molding machine and stores conditions for classifying the acquired data on the injection molding machine and a plurality of learning models. The state determination device further classifies the acquired data based on the stored classification conditions and settles a learning model to which the classified data are applied, among the plurality of stored learning models.
Yusuke Yamazaki (US 20240253285 A1) – teaches in the (Abstract) An injection molding assistance system includes: an information receiving unit, receiving trial molding conditions, article information, and molding defect types; a design variable determination unit, determining a part of molding conditions to be learned as design variables based on the article information and the molding defect types; a learning data generating unit, generating multiple learning data in which the design variables are uniformly distributed in a variable space.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrés E. Behrens Jr. whose telephone number is (571)-272-9096. The examiner can normally be reached on Monday - Friday 7:30 AM-5:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alison Hindenlang can be reached on (571)-270-7001. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Andrés E. Behrens Jr./Examiner, Art Unit 1741
/JaMel M Nelson/Primary Examiner, Art Unit 1743
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