DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. This Office action is responsive to the Request for Continued Examination (RCE) filed under 37 CFR §1.53(d) for the instant application on August 17, 2026. Applicants have properly set forth the RCE, which has been entered into the application, and an examination on the merits follows herewith.
Claims 1-4, 6-8, 10, 13-14, 16-17, and 19-20 have been examined and rejected. This Office action is responsive to the amendment filed on August 17, 2026, which has been entered in the above identified application.
Claim Objections
3. The correction to claim 17 has been approved, and the objection to the claim is withdrawn.
4. Claims 3 and 16 are objected to because of the following informalities:
On [line 17] of claim 1, Examiner suggests changing “the treatment device” to --the first treatment device--.
On [line 3] of claim 3, Examiner suggests changing “is treated are assigned” to --is treated is assigned--.
In [lines 15-18] of claim 16, Examiner suggests changing “for generating at least one identification information for the one of the containers and a variable which is characteristic of a performance of these containers can be uniquely identified and brought into association with one another” to add a comma as follows: --for generating at least one identification information for the one of the containers, and a variable which is characteristic of a performance of these containers can be uniquely identified and brought into association with one another--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
5. 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.
6. Claims 1-4, 6-8, 10, 13-14, 16-17, and 19-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
6-1. Claim 1 recites the limitation “the containers” in [line 6] of the claim. It is unclear whether “the containers” refers to the containers in [line 1] or [line 5] of the claim. If Applicant intended the containers recited in [line 5] should refer to the containers in [line 1], Examiner suggests changing “wherein individual containers are inspected” in [line 5] to --wherein each of the containers is inspected--. This would resolve the issue with [line 6] of the claim.
6-2. Claim 1 recites the limitation “the at least one value characteristic of a performance of these containers” in [line 9] of the claim. The claim had only previously recited at least one value characteristic of a performance of one of the containers (see [lines 5-6]), not of multiple containers. Therefore, it is unclear what the claim is referring to when reciting “the at least one value characteristic of a performance of these containers” (emphasis added).
6-3. Claim 1 recites the limitation “the measurement” in [line 13] of the claim. There is insufficient antecedent basis for this limitation. Although the claim recites “measurement data” in [line 12], no actual measurement is claimed as being performed. Examiner suggests changing “measurement data that cannot be recorded inline and in real time” to --measurement data that cannot be recorded inline and in real time by a measurement-- to provide proper antecedent basis.
6-4. Claim 1 recites the limitation “or are so technologically complex that they cannot be carried out inline” in [lines 13-14] of the claim. It is unclear what is being referred to that are so technologically complex, and what the pronoun “they” is referring to.
6-5. Claim 1 recites the limitation “the containers to be inspected” in [line 16] of the claim. There is insufficient antecedent basis for this limitation. The claim had only previously recited “wherein individual containers are inspected after their treatment” in [lines 5-6]. Examiner suggest changing “the containers to be inspected” to --the individual containers that are inspected after their treatment--.
6-6. Claim 1 recites the limitation “the production data of a container” in [line 18] of the claim. There is insufficient antecedent basis for this limitation. It appears “the production data of a container” in [line 18] may be referring to the production data in [line 4] of the claim. However, the production data in [line 4] is not claimed as being tied to any container and therefore, cannot provide antecedent basis for “the production data of a container’ in [line 18] of the claim.
6-7. Claim 10 dependent on claim 9, which has been canceled. For purposes of a prior art rejection, Examiner assumes claim 10 depends upon claim 1.
6-8. Claim 14 dependent on claim 9, which has been canceled. For purposes of a prior art rejection, Examiner assumes claim 14 depends upon claim 1.
6-9. Claim 16 recites the limitation “and has a discharge device arranged downstream” in [lines 8-9] of the claim. The limitation does not clearly recite what has a discharge device arranged downstream.
6-10. Claim 16 recites the limitation “the one of the containers” in [line 16] of the claim. There is insufficient antecedent basis for this limitation.
6-11. Claim 16 recites “the one of the containers” in [line 16] and then refers to it as “these containers” in [line 17]. It is unclear what containers are being referred to when reciting “these containers.”
6-12. Claim 16 recites the limitation “the measurement” in [line 19] of the claim. There is insufficient antecedent basis for this limitation. Although the claim recites “measurement data” in [line 18], no actual measurement is claimed as being performed. Examiner suggests changing “measurement data that cannot be recorded inline and in real time” to --measurement data that cannot be recorded inline and in real time by a measurement-- to provide proper antecedent basis.
6-13. Claim 16 recites the limitation “or are so technologically complex that they cannot be carried out inline” in [lines 19-20] of the claim. It is unclear what is being referred to that are so technologically complex, and what the pronoun “they” is referring to.
6-14. Claim 16 recites the limitation “the production data” in [line 22] of the claim. It is unclear whether “the production data” refers to the determined production data in [line 8] or the production data of a container in [line 21].
Claim Rejections - 35 USC § 103
7. 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.
8. Claims 1-4, 6-8, 10, 13-14, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Feilloley et al (Pub. No. US 2019/0031383), in view of Linke et al (DE 102017120863), and further in view of Noone et al (Pub. No. US 2020/0166909).
8-1. Regarding claim 1, Feilloley teaches the claim for treating containers, wherein the containers are transported along a predetermined transport path by a transport device, by disclosing a container production facility [paragraph 6] that produces a series of containers from a series of preforms made of plastic materials where the series of containers are transported for treatment by a transport device [paragraphs 9-13, 60-61].
Feilloley teaches wherein the containers are treated in a predetermined manner by a first treatment device, by disclosing that stations are arranged to apply a particular treatment to the containers [paragraphs 59, 63, 68, 74, 77, 79].
Feilloley teaches wherein predetermined working parameters are used for the treatment of the containers, wherein production data or working parameters are determined, during operation, by disclosing that each station performs a particular treatment [paragraphs 59, 63, 70-72] and comprises machine parameters that may be adjusted [paragraph 40].
Feilloley teaches wherein individual containers are inspected after their treatment, by disclosing that containers may be tested to detect faults [paragraph 7] and such containers may be ejected from the production following a production fault, or for sampling [paragraph 90].
Although Feilloley discloses analyzing a fault in a container by testing the container offline because such a test is a long manual test that cannot be carried out online at the production rate, or because the test for detecting an origin of a fault is a test that destroys the container [Feilloley, paragraph 7], Feilloley does not expressly teach at least one value characteristic of a performance of one of the containers, referred to as performance data, is determined,… the performance data are measurement data that cannot be recorded inline and in real time as the containers are destroyed during the measurement or are so technologically complex that they cannot be carried out inline. Linke discloses a method and device for producing containers which make it possible to keep the variance between the containers produced with different forming stations as low as possible [paragraph 8]. The method performs metrological detection of properties of finished containers after removal of the containers from a transport stream, also known as offline measurements, and such containers are provided a marking to associate them with the respective forming stations they came from [paragraph 27]. These removed containers may undergo destructive measuring methods as well as measuring methods that take too long to be completed inline [paragraph 27, last sentence; paragraph 37]. Properties of a container are detected and compared with reference properties, and if deviations between the detected property and the reference property are determined, control measuring methods that parameters of the respective forming station influencing the property can be suitably adapted in order to achieve an improvement in the container quality [paragraphs 24, 30-31, 33, 69]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine performance data of for containers offline and use such data for adjustments to the system, as taught by Linke. This would help minimize the variance between containers.
Feilloley-Linke teach wherein the one container and the at least one value characteristic of a performance of these containers are unambiguously identified and associated with one another by identification information, by disclosing applying a marking to each container [Feilloley, paragraph 79; Linke, paragraph 27] where the marking comprises a number of pieces of information [Feilloley, paragraphs 36, 80-82] and compiling a mixed database that matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38].
Feilloley-Linke teaches wherein the containers to be inspected are discharged from a transport path of the containers downstream of the treatment device, whereby off-line measurements are carried out, by disclosing that the series of containers are transported for treatment by a transport device [Feilloley, paragraphs 9-13, 60-61]. Containers may be tested to detect faults [Feilloley, paragraph 7] and such containers may be ejected from the production following a production fault, or for sampling [Feilloley, paragraph 90; Linke, paragraphs 27, 37].
Feilloley-Linke teaches wherein production data of a container can be associated with its performance data, by disclosing for each individual container that is produced, recording a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container (i.e. production data) [Feilloley, paragraph 36; Linke, paragraph 28] and measuring properties of containers offline [Feilloley, paragraph 7; Linke, paragraphs 27, 37]. Both these types of data are associated with each other based on a marking that matches said container with each type of data [Feilloley, paragraphs 36-38; Linke, paragraph 27].
Feilloley-Linke teach wherein a model is created that combines the production data and the performance data, wherein the working parameters are adjusted based on the model to achieve optimal performance data, by disclosing a diagnostic step, during which the container data are compared against pre-set target values corresponding to measured values, and a detected or imminent malfunction at one or more of the forming and/or treatment stations and/or holding elements of the facility is determined [Feillloley, paragraph 39, lines 1-6]. The detection of an imminent fault can be detected by measuring a derivative of a container datum and/or a machine datum and by detecting that this derivative exceeds a pre-set threshold [Feilloley, paragraph 39, lines 6-9]. Properties of a container are compared with reference properties, and if deviations between the detected property and the reference property are determined, control measuring methods that parameters of the respective forming station influencing the property can be suitably adapted in order to achieve an improvement in the container quality [Linkie, paragraphs 24, 30-31, 33, 69].
Feilloley-Linke do not expressly teach wherein the determination of working parameters is performed with a neural network. Noone discloses a method for real-time adaptive control of a manufacturing process (i.e., for real-time adaptive control of a fabrication process) that uses a machine learning algorithm to provide output values to adjust one or more manufacturing process control parameters in real-time [paragraph 4]. The machine learning algorithm may be a deep convolutional neural network algorithm or a deep recurrent neural network [Paragraph 5, lines 72-80]. This would help provide more presence parameter optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a neural network to adapt parameters of the system, as taught by Noone. This would help provide more presence parameter optimization.
8-2. Regarding claim 2, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein the work parameters by which the one of the containers was treated are assigned to an inspected container and/or to at least one value characteristic of the performance, by disclosing that the mixed database matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38; Linke, paragraph 27]. The number of pieces of information comprise, on the one hand, the marking that matches the container, and, on the other hand, a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container [Feilloley, paragraph 36]. Such information includes the number of a particular treatment station the container has passed through [Feilloley, paragraph 81, lines 14-20] and thus, the machine parameters with which a treatment station is run and which the container has passed through is considered assigned to the container.
8-3. Regarding claim 3, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein environmental data and/or measurement data by which a container is treated are assigned to the one of the containers inspected and/or the at least one value characteristic of the performance, by disclosing that the mixed database matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38; Linke, paragraph 27]. The number of pieces of information comprise, on the one hand, the marking that matches the container, and, on the other hand, a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container [Feilloley, paragraph 36].
8-4. Regarding claim 4, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein a marking is applied to a container to be inspected, by disclosing applying a marking to each container [Feilloley, paragraph 79; Linke, paragraph 27]
8-5. Regarding claim 6, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein a point in time is detected at which a specific container is inspected and/or rejected from a transport path, by disclosing that the marking comprises time-stamping information relative to the movement at which the container is marked [Feilloley, paragraph 82] such that a complete traceability of the container in the facility is acquired [Feilloley, paragraph 87]. Peripheral marking stations are provided at locations where containers are removed from the facility outside of the normal production path, such as following a production fault, or for sampling [Feilloley, paragraph 90].
8-6. Regarding claim 7, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein the identification information contains a time stamp, by disclosing that the marking comprises time-stamping information relative to the movement at which the container is marked [Feilloley, paragraph 82].
8-7. Regarding claim 8, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein the identification information is stored, by disclosing compiling a mixed database that matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38].
8-8. Regarding claim 10, Feilloley-Linke-Noone teach all the limitations of claim 9, wherein the production data are selected from work parameters, environmental data, measurement data and combinations thereof, by disclosing analyzing the mixed database, during which at least one law of correlation between the container data and the machine data is established, and a correction step in which according to the correlation law(s), one or more likely adjusted machine parameters is/are calculated during the next production cycle for producing containers, one or more physical values of which will be closer to the pre-set target(s) than during the first step [Feilloley, paragraph 40].
8-9. Regarding claim 13, Feilloley-Linke-Noone teach all the limitations of claim 1, wherein the first treatment device is selected from a group of treatment devices comprising a heating device for heating plastic preforms, a forming device for forming plastic preforms into plastic containers, a labelling device for labelling containers, a filling device for filling containers, a printing device for printing containers and a closing device for closing containers and/or the treatment of the containers is selected from a group of treatment operations comprising heating of plastic containers, forming of plastic preforms into plastic containers, labelling of containers, filling of containers, printing of containers and closing of containers, by disclosing that stations comprise a heating station [Feilloley, paragraph 59], a forming machine [Feilloley, paragraph 63], a labeling unit [Feilloley, paragraph 70], a filling machine [paragraph 74], a capping station [Feilloley, paragraph 77], and a marking device [Feilloley, paragraph 79].
8-10. Regarding claim 14, Feilloley-Linke-Noone teach all the limitations of claim 9, wherein the production data are working parameters and/or, interference values or environmental parameters and/or measurement data, by disclosing that the information used in the model comprise, on the one hand, the marking that matches the container, and, on the other hand, a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container [Feilloley, paragraph 36].
8-11. Regarding claim 16, Feilloley teaches the claim of an apparatus for treating containers, having a transport device which transports the containers along a predetermined transport path, by disclosing a container production facility [paragraph 6] that produces of a series of containers from a series of preforms made of plastic materials where the series of containers are transported for treatment by a transport device [paragraphs 9-13, 60-61].
Feilloley teaches the apparatus having a first treatment device which treats the containers in a predetermined manner, by disclosing that stations are arranged to apply a particular treatment to the containers [paragraphs 59, 63, 68, 74, 77, 79].
Feilloley teaches wherein the first treatment device uses predetermined working parameters for the treatment of the containers, wherein production data or working parameters are determined, during operation, by disclosing that each station performs a particular treatment [paragraphs 59, 63, 70-72] and comprises machine parameters that may be adjusted [paragraph 40].
Feilloley teaches the apparatus has a discharge device arranged downstream of the first treatment device configured to discharge individual containers treated by the first treatment device from the transport path and/or an inspection device arranged downstream of the first treatment device in order to inspect the containers treated by the first treatment device, by disclosing a capping unit that removes the containers from the production facility [paragraphs 77-78]. Further, containers may be tested to detect faults [paragraph 7] and such containers may be ejected from the production following a production fault, or for sampling [paragraph 90].
Although Feilloley discloses analyzing a fault in a container by testing the container offline because such a test is a long manual test that cannot be carried out online at the production rate, or because the test for detecting an origin of a fault is a test that destroys the container [Feilloley, paragraph 7], Feilloley does not expressly teach whereby off-line measurements are carried out, referred to as performance data,… wherein the performance data are measurement data that cannot be recorded inline and in real time as the containers are destroyed during the measurement or are so technologically complex that they cannot be carried out inline. Linke discloses a method and device for producing containers which make it possible to keep the variance between the containers produced with different forming stations as low as possible [paragraph 8]. The method performs metrological detection of properties of finished containers after removal of the containers from a transport stream, also known as offline measurements, and such containers are provided a marking to associate them with the respective forming stations they came from [paragraph 27]. These removed containers may undergo destructive measuring methods as well as measuring methods that take too long to be completed inline [paragraph 27, last sentence; paragraph 37]. Properties of a container are detected and compared with reference properties, and if deviations between the detected property and the reference property are determined, control measuring methods that parameters of the respective forming station influencing the property can be suitably adapted in order to achieve an improvement in the container quality [paragraphs 24, 30-31, 33, 69]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine performance data of for containers offline and use such data for adjustments to the system, as taught by Linke. This would help minimize the variance between containers.
Feilloley-Linke teach wherein the apparatus has an information-generating device configured for generating at least one identification information for the one of the containers and a variable which is characteristic of a performance of these containers can be uniquely identified and brought into association with one another, by disclosing applying a marking to each container [Feilloley, paragraph 79; Linke, paragraph 27] where the marking comprises a number of pieces of information [Feilloley, paragraphs 36, 80-82]. One or more physical values of a container are determined [Feilloley, paragraph 37] and a mixed database is compiled that matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38].
Feilloley-Linke teach wherein the performance data are measurement data that cannot be recorded inline and in real time, by disclosing that containers may be tested to detect faults [paragraph 7] and such containers may be ejected from the production for sampling (i.e. taken offline) [paragraph 90] where one or more physical values of the container are measured (i.e. performance data) [paragraph 37] and matched with data collected while the container was in-line [paragraph 38] for use in detecting any imminent faults [paragraph 39].
Feilloley-Linke teach wherein production data of a container can be associated with its performance data, by disclosing for each individual container that is produced, recording a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container (i.e. production data) [Feilloley, paragraph 36; Linke, paragraph 28] and measuring properties of containers offline [Feilloley, paragraph 7; Linke, paragraphs 27, 37]. Both these types of data are associated with each other based on a marking that matches said container with each type of data [Feilloley, paragraphs 36-38; Linke, paragraph 27].
Feilloley-Linke teach wherein a model is created that combines the production data and the performance data, and wherein the working parameters are adjustable based on the model to achieve optimal performance data, by disclosing a diagnostic step, during which the container data are compared against pre-set target values corresponding to measured values, and a detected or imminent malfunction at one or more of the forming and/or treatment stations and/or holding elements of the facility is determined [Feillloley, paragraph 39, lines 1-6]. The detection of an imminent fault can be detected by measuring a derivative of a container datum and/or a machine datum and by detecting that this derivative exceeds a pre-set threshold [Feilloley, paragraph 39, lines 6-9]. Properties of a container are compared with reference properties, and if deviations between the detected property and the reference property are determined, control measuring methods that parameters of the respective forming station influencing the property can be suitably adapted in order to achieve an improvement in the container quality [Linkie, paragraphs 24, 30-31, 33, 69].
Feilloley-Linke do not expressly teach wherein the determination of working parameters is with a neural network. Noone discloses a method for real-time adaptive control of a manufacturing process (i.e., for real-time adaptive control of a fabrication process) that uses a machine learning algorithm to provide output values to adjust one or more manufacturing process control parameters in real-time [paragraph 4]. The machine learning algorithm may be a deep convolutional neural network algorithm or a deep recurrent neural network [Paragraph 5, lines 72-80]. This would help provide more presence parameter optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a neural network to adapt parameters of the system, as taught by Noone. This would help provide more presence parameter optimization.
8-12. Regarding claim 17, Feilloley-Linke-Noone teach all the limitations of claim 16, wherein the apparatus comprises an assignment device which assigns, to a container to be inspected and/or a value characteristic of the performance, working parameters used on a treated container, by disclosing that the mixed database matches, for each individually identified containers, the physical measurements of the container and the number of pieces of information [Feilloley, paragraph 38; Linke, paragraph 27]. The number of pieces of information comprise, on the one hand, the marking that matches the container, and, on the other hand, a series of specific data relative to the forming and/or treatment stations and/or relative to the elements that have held said container during its production, and/or a series of general data relative to the operation of the facility during the production of said container [Feilloley, paragraph 36]. Such information includes the number of a particular treatment station the container has passed through [Feilloley, paragraph 81, lines 14-20] and thus, the machine parameters with which a treatment station is run and which the container has passed through is considered assigned to the container.
8-13. Regarding claim 19, Feilloley-Linke-Noone teach all the limitations of claim 1, further wherein a model for controlling the first treatment device is derived from measured values determined during inspections, by disclosing using a machine learning algorithm to provide output values to adjust one or more manufacturing process control parameters in real-time [Noone, paragraph 4].
8-14. Regarding claim 20, Feilloley-Linke-Noone teach all the limitations of claim 16, further wherein a plurality of containers are inspected and a model for controlling the first treatment device is derived from measured values determined during inspections, by disclosing using a machine learning algorithm to provide output values to adjust one or more manufacturing process control parameters in real-time [Noone, paragraph 4].
Response to Arguments
9. The Examiner acknowledges the Applicant’s amendments to claims 1-3, 16, and 17, the cancellation of claims 9, 11-12, and 18, and the addition of claims 19 and 20.
Regarding independent claim 1, Applicant alleges that Feilloley et al (Pub. No. US 2019/0031383) does not teach or suggest the claims as amended. Examiner has rejected claim 1 under 35 U.S.C. 103 as being unpatentable over Feilloley et al (Pub. No. US 2019/0031383), in view of Linke et al (DE 102017120863), and further in view of Noone et al (Pub. No. US 2020/0166909). Applicant’s arguments have been considered but are moot in view of the new grounds of rejection.
Similar arguments have been presented for independent claim 16 and thus, Applicant’s arguments are not persuasive for the same reasons.
Applicant states that dependent claims 2-4, 6-8, 10, 13-14, 17, and 19-20 recite all the limitations of the independent claims, and thus, are allowable in view of the remarks set forth regarding independent claims 1 and 16. However, as discussed above, Feiloley, in view of Linke, and further in view of Noone are considered to teach claims 1 and 16, and consequently, claims 2-4, 6-8, 10, 13-14, 17, and 19-20 are rejected.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN H TAN whose telephone number is (571)272-8595. The examiner can normally be reached M-F 10AM-6PM.
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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