DETAILED ACTION
This office action is in response to amendments and arguments filed 6/16/2026. Claims 5 – 8 have been amended and are discussed on the merits below.
Response to Arguments
Regarding the rejection under 35 USC 101:
Applicant argues that the limitation added to claim 5 - the processor is configured to group pieces of monitoring data having high correlation coefficient into the same group, and extract only one piece of monitoring data having the highest influence from the same group, when extracting monitoring data having a high influence on the defect type – introduces a specific, concrete technical improvement to the pass/fail determination system of the injection molding machine that goes well beyond what can be performed in the human mind (second paragraph on page 5).
Examiner respectfully disagrees. Grouping and extracting data are understood to be evaluations and judgements which are well within the scope of what may reasonably be done in the human mind. A human can mentally decide which pieces of data are sufficiently correlated so as to be considered a group. Likewise, a human can mentally assess/observe which piece of data has the most influence. Therefore this new limitation is considered, in Step 2A, prong 1, to be a recitation of additional abstract ideas.
Applicant argues that this limitation “prevents the pass/fail determination from being skewed by correlated data, …As a result, the accuracy of the pass/fail determination is further improved…”
As for the improvement allegation, Applicant’s alleged improvement is NOT better functioning of the injection molding system, but a better pass/fail assessment of the injection molding system. The molding system itself is unchanged. Naturally, operational changes might be implemented as a result of the pass/fail determination but these are not recited by the claim.
Applicant argues that “This grouping operation” is not a mental process, explaining that “It is a specific, technically grounded data-processing operation that involves calculating correlation coefficients between different types of monitoring data acquired from sensors on an injection molding machine, forming groups based on those coefficients, and selectively extracting only one piece of monitoring data per group” (bottom of page 5 of remarks).
Respectfully, the claim does not recite such specificity. However, even if it did:
“calculating correlation coefficients” would be a mathematical concept, which also falls under the abstract idea grouping;
grouping is a mental recognition of similarities (addressed above); and
“selectively extracting” is a decision which simply identifies or recognizes a select piece of data. Note that “extract” is a rather nebulous term; however, Meriam-Webster (“Extract.” Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriam-webster.com/dictionary/extract. Accessed 22 Sep. 2026.) defines it as “to draw forth (as by research)” or “to determine (a mathematical root) by calculation” or “to select (excerpts) and copy out or cite”. That is, “extract” is simply the result of research, a determination, or a selection. The term does not require anything to be moved, transformed, controlled, etc.
Any sensors implied by Applicant’s argument are not explicitly recited and would likely amount to mere data gathering in a conventional manner.
Applicant argues that “The claim…narrowly claims a particular use of grouping monitoring data by correlation coefficient in the specific context of extracting high-influence monitoring data for pass/fail determination of molded articles. This targeted, application-specific claim scope distinguishes the present claims from claims that merely recite a mathematical formula applied to generic data” (middle of page 6).
It has been held that “An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment" (Intellectual Ventures I LLC v. Capital One Bank (USA), N.A., 792 F.3d 1363, 1366, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)). Similarly, it has been held that “a claim directed to a judicial exception cannot be made eligible ‘simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use.’ Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself and cannot integrate a judicial exception into a practical application.” (MPEP 2106.05(h))
Applicant argues that “[the claims] recite an inventive concept that amounts to significantly more than the alleged abstract idea. The specific combination of (i) extracting monitoring data associated with defective and non-defective articles from basic data of an injection molding machine, (ii) grouping pieces of monitoring data having high correlation coefficient into the same group, (iii) extracting only one piece of monitoring data having the highest influence from each group, and (iv) using the resulting ungrouped high-influence monitoring data to calculate a molding parameter for pass/fail determination, represents a non-conventional, technologically specific arrangement of steps that is not well-understood, routine, or conventional activity in the field.” (bottom of page 6 to top of page 7).
This argument is not persuasive. The consideration undertaken in step 2B is whether the claim recites any additional elements that amount to significantly more than the abstract idea. All the limitations which Applicant has listed have been deemed recitations of an abstract idea. They are not additional elements. Hence, under current eligibility guidance, they are not considered in step 2B.
Regarding the rejection under 35 USC 102
Applicant argues that “Nigle does not describe or suggest any grouping of monitoring data based on correlation coefficients” (bottom of page 7).
Examiner respectfully observes that the amended claim does not recite grouping “based on” correlation coefficients. The claim recites “group pieces of monitoring data having high correlation coefficient into the same group”. This means that the data simply needs to have a “high” correlation coefficient. Further, the claim is silent regarding the treatment of pieces of monitoring data with other than “high” correlation coefficients. Therefore, the BRI of this limitation does not account for any grouping “based on” correlation.
Applicant argues that Nigl’s “comparisons between operation parameters and thresholds are performed for each individual operational parameter…Nigl’s approach treats each operation parameter independently without any consideration of correlations between different parameters” (top of page 8 of remarks).
Respectfully, this argument does not reflect the claim’s language. The only threshold comparison in the claim is also done with respect to an individual parameter (“compare the calculated molding parameter with a determination threshold being a threshold for the molding parameter”). There is no threshold comparison in the claim that considers correlations as argued.
As far as the new limitation is concerned, Nigl also discloses:
grouping parameters ([0037]: “a plurality of measured values and actual values from production plants with devices for producing products be collected and made available in a database”, i.e. grouped in a database).
Such parameters may demonstrate correlations ([0024]: “a correlation between the quality parameter or the quality parameters and the machine parameter or the machine parameters can be determined. These correlations can be both causal relationships and indirect correlations which are linked to one another via a third quantity, for example.” [0027]: “correlations between the actual values of several machine parameters be determined, since it is conceivable that several machine parameters likewise influence each other.”).
Such correlations being describable by an underlying correlation coefficient (note that the claim does not require that the coefficients be determined; only that they exist).
Such parameters also being known to have various levels of influence on product defects ([0028]: “predict the extent to which changing one machine parameter affects a different machine parameter, and, subsequently, also the extent to which a quality parameter may change.” [0033]: “It goes without saying that several different combinations of target values of machine parameters can be suitable for achieving a certain target value of a quality parameter” where achieving or not achieving a certain target value of a quality parameter corresponds to a non-defective or defective product, respectively).
The various levels of influence being known ([0060-63]: “a weighting of its influence on each quality parameter can be assigned according to its correlation with the one or more quality parameters of the quality data record…the greater or better the correlation between a machine parameter and a quality parameter, the greater the influence of a change in a corresponding machine parameter on the respective quality parameter…the machine parameter with the greatest influence on a respective quality parameter…lowest possible influence on other machine or quality parameters, be determined.”)
Examiner maintains that this anticipates the claim to the extent that the new limitation can be understood.
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 – 8, the claims 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.
With regard to claim 5, the newly added limitation of claim 5 recites “wherein the processor is configured to group pieces of monitoring data having high [1] correlation [2] coefficient [3] into the same group, and extract only one piece of monitoring data [4] having the highest influence from the same group, when extracting monitoring data having a high influence on the defect type [4].”
[1] The term “high” in claim 5 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
[2] It is not clear what correlation is being evaluated. That is, it is not clear whether the claim intends the correlation of (i) one datapoint to another datapoint, (ii) one datapoint to a defect occurrence, (iii) one datapoint to a defect type, (iv) a type of data to a type of defect, (v) a type of data to another type of data, among other potential options. The specification does not clarify. In [0095], the spec suggests that the correlation pertains to the correspondence between a piece of monitoring data and a defect type which is indicative of influence on the defect type. This matches interpretation (iii) above. However, the very next paragraph, [0096], discloses “a correlation coefficient between different types of monitoring data is calculated” – this matches interpretation (v) above - and the pieces of data are grouped based on the correlation coefficient being “high”, concluding with “…according to the correlation coefficient between individual pieces of monitoring data” – this matches interpretation (i) above. Because of the variety of ways that disclosure has phrased this step, Examiner is unable to ascertain which correlation is required by the claim.
[3] It is noted that “coefficient” is singular in the claim. Examiner is unable to ascertain whether this is a typographical error (“pieces of monitoring data having high correlation coefficients”) or whether the claim requires a single correlation coefficient for multiple pieces of data. This adds another layer of confusion to the previously described “correlation” interpretation.
[4] It is unclear whether the phrase “when extracting monitoring data having a high influence on the defect type” is meant to refer to the previously recited limitation “extract a plurality of pieces of the monitoring data as high-influence monitoring data” or if it is introducing another step. The wording is so similar that Examiner will infer that they are intended to be the same functional limitation. In that case, it is unclear how the phrase “extract only one piece of monitoring data” fits into the step which requires extracting a plurality of pieces of monitoring data. The claim language is contradictory.
With regard to claims 6 – 8, the claims depend from claim 5 and therefore inherit the deficiencies of claim 5 described above. The claims are therefore rejected under similar logic to claim 5 above.
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 5 – 8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 5 recites:
An injection molding machine pass/fail determination system comprising:
a processor configured to:
extract, from basic data in which a determination result as to whether a molded article formed by an injection molding machine is a non-defective article or a defective article is associated with monitoring data of the injection molding machine at a time of forming the molded article, a plurality of pieces of non-defective article monitoring data and a plurality of pieces of defective article monitoring data, the plurality of pieces of non-defective article monitoring data being the monitoring data at a time of forming the non-defective article, and the plurality of pieces of defective article monitoring data being the monitoring data at a time of forming the defective article;
extract a plurality of pieces of the monitoring data as high-influence monitoring data in descending order of influence on a defect type of the defective article among the plurality of pieces of the extracted monitoring data;
calculate a molding parameter, the molding parameter being a parameter to be used when pass/fail determination of the molded article during molding performed by the injection molding machine is performed based on the monitoring data of a same type as the high-influence monitoring data; and
compare the calculated molding parameter with a determination threshold being a threshold for the molding parameter, and determine whether the defective article has occurred in the molded article formed by the injection molding machine, wherein
the processor is configured to group pieces of monitoring data having high correlation coefficient into the same group, and extract only one piece of monitoring data having the highest influence from the same group, when extracting monitoring data having a high influence on the defect type.
101 Analysis - Step 1: Statutory category – Yes
The claim recites a system, thus, the claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
Each bolded limitation will be discussed briefly below:
extract, from basic data in which a determination result as to whether a molded article formed by an injection molding machine is a non-defective article or a defective article is associated with monitoring data of the injection molding machine at a time of forming the molded article, a plurality of pieces of non-defective article monitoring data and a plurality of pieces of defective article monitoring data, the plurality of pieces of non-defective article monitoring data being the monitoring data at a time of forming the non-defective article, and the plurality of pieces of defective article monitoring data being the monitoring data at a time of forming the defective article;
The above limitation amounts to extracting, from a known data set (monitoring data associated with non-defective and defective articles) a plurality of data associated with the non-defective monitoring data that occurred at a time a non-defective article was formed and a plurality of data associated with the defective monitoring data that occurred at a time the defective article was formed. This amounts to taking a known data set and dividing it based on known properties of the data set. Thus, the limitation as drafted is a simple process, that under broadest reasonable interpretation, covers performance of the limitation in the mind.
extract a plurality of pieces of the monitoring data as high-influence monitoring data in descending order of influence on a defect type of the defective article among the plurality of pieces of the extracted monitoring data;
The above limitation amounts to an evaluation of how particular pieces of data influences a particular defect type relative to the influence of other pieces of data on the defect type and ranking the data accordingly. This amounts to a simple evaluation, observation, or judgement. Thus, the limitation as drafted is a simple process, that under broadest reasonable interpretation, covers performance of the limitation in the mind.
calculate a molding parameter, the molding parameter being a parameter to be used when pass/fail determination of the molded article during molding performed by the injection molding machine is performed based on the monitoring data of a same type as the high-influence monitoring data;
This amounts to simply calculating a parameter, of the same type as the determined high influence monitoring data, for use in determining if the monitored process is producing a defective or non-defective article. This amounts to a simple calculation as to which data, or range of data, is indicative of producing a defective or non-defective article. This amounts to a determination capable of being performed in the human mind Thus, the limitation as drafted is a simple process, that under broadest reasonable interpretation, covers performance of the limitation in the mind.
compare the calculated molding parameter with a determination threshold being a threshold for the molding parameter, and determine whether the defective article has occurred in the molded article formed by the injection molding machine, wherein
This amounts to a simple comparison of the determined parameter above with a determined threshold of the parameter to determine if an article is defective or non-defective. This amounts to a determination capable of being performed in the human mind Thus, the limitation as drafted is a simple process, that under broadest reasonable interpretation, covers performance of the limitation in the mind.
group pieces of monitoring data having high correlation coefficient into the same group, and extract only one piece of monitoring data having the highest influence from the same group, when extracting monitoring data having a high influence on the defect type.
The above limitation amounts to an evaluation of how particular pieces of data are correlated. A correlation coefficient is simply a numerical expression of this correlation and is a mathematical concept (although the claim does not recite determining the coefficient). Extracting data having the highest influence amounts to a simple evaluation, observation, or judgement. Thus, the limitation as drafted is a simple process, that under broadest reasonable interpretation, covers performance of the limitation in the mind.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The above underlined limitation is a “processor”. Thus, this limitation amounts to merely the use of a general purpose computer as a tool to perform the abstract idea.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Thus, the claim is ineligible.
With regard to claim 6 – 8, the claims simply further expound upon the abstract idea(s) discussed above. There are no additional elements beyond the processor which has been addressed above.
Thus, claims 6 – 8 are also ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 5 – 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2020/221766 Nigl (as provided in applicant’s IDS)
Examiner’s Note: WO 2020221766 is not published in English. US 2022/0176604 is a US PgPub version of the document which is published in English. Thus, US 2022/0176604 is being used as an English translation of WO 2020221766 and all citation below are to the US PgPub for convenience and readability.
With regard to claim 5, Nigl discloses:
An injection molding machine pass/fail determination system comprising:
a processor ([0075]) configured to:
extract, from basic data in which a determination result as to whether a molded article formed by an injection molding machine is a non-defective article or a defective article is associated with monitoring data of the injection molding machine at a time of forming the molded article, a plurality of pieces of non-defective article monitoring data and a plurality of pieces of defective article monitoring data, the plurality of pieces of non-defective article monitoring data being the monitoring data at a time of forming the non-defective article, and the plurality of pieces of defective article monitoring data being the monitoring data at a time of forming the defective article (see [0009]-[0012]; [0023], [0088] - [0096]);
configured to extract a plurality of pieces of the monitoring data as high-influence monitoring data in descending order of influence on a defect type of the defective article among the plurality of pieces of the extracted monitoring data ([0013], [0024], [0027] – [0028], [0033] – [0034], [0040], [0052] – [0053], [0060] – [0063]);
calculate a molding parameter, the molding parameter being a parameter to be used when pass/fail determination of the molded article during molding performed by the injection molding machine is performed based on the monitoring data of a same type as the high-influence monitoring data ([0013] – [0014], [0025], [0033], [0040], [0054], [0099]); and
compare the calculated molding parameter calculated by the molding parameter calculation module with a determination threshold being a threshold for the molding parameter, and determine whether the defective article has occurred in the molded article formed by the injection molding machine ([0043], [0048], [0070] – [00712], [0101]), wherein
the processor is configured to group pieces of monitoring data ([0037]) having high correlation coefficient ([0024], [0027]) into the same group ([0037]), and extract only one piece of monitoring data having the highest influence from the same group, when extracting monitoring data having a high influence on the defect type ([0060] – [0063]).
With regard to claim 6, Nigl discloses:
The injection molding machine pass/fail determination system according to claim 5, wherein the processor is configured to: extract the non-defective article monitoring data and the defective article monitoring data for each of defect types of the defective article; calculate the molding parameter for each of the defect types; and determine whether the defective article has occurred in the molded article for each of the defect types (see [0013] – [0015], [0017], [0021], [0024] – [0025], [0033], [0040], [0043], [0052] - [0054], [0060], [0095], [0099]).
With regard to claim 7, Nigl discloses:
The injection molding machine pass/fail determination system according to claim 5, wherein the processor is further configured to extract the monitoring data having a highest abnormality degree when determining that the defective article has occurred in the molded article, and the processor is configured to extract, as the monitoring data having the highest abnormality degree, monitoring data having a largest deviation from the non-defective article monitoring data in the high-influence monitoring data, among a plurality of pieces of the monitoring data of the same type as a plurality of pieces of the high-influence monitoring data during molding performed by the injection molding machine (see [0025], [0027], [0052] – [0053], [0060] – [0065]).
With regard to claim 8, Nigl discloses:
The injection molding machine pass/fail determination system according to claim 5, wherein the processor is further configured to calculate a determination basis recommended value at the time of determining whether the defective article has occurred in the molded article based on the extracted non-defective article monitoring data, and the processor is configured to use the calculated determination basis recommended value, as the determination threshold ([0013] – [0014], [0025], [0033], [0040], [0043], [0048], [0054], [0070] – [0072], [0099], [0101]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM R MOTT whose telephone number is (571)270-5376. The examiner can normally be reached M-F 9 - 5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at (571) 272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
