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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 04/16/2026 has been entered and considered. Rejections and/or objections not reiterated from the previous office action mailed 01/27/2026 are hereby withdrawn. The following rejections and/or objections are either newly applied or are reiterated and are the only rejections and/or objections presently applied to the instant application.
Status of the Claims
Claims 1-2 and 4-8 are pending and under consideration in this action. Claim 3 was previously canceled.
Priority
This application claims foreign priority from Republic of Korea Application KR1020200187051, filed 12/30/2020, as reflected in the filing receipt mailed 1/11/2022. Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-2 and 4-8 is 12/30/2020.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1 contains an extra “and” between the limitations “generating, by the user terminal, a first output signal…” and “generating, by the user terminal, a second output signal…”, which should be removed for clarity, as these are not the last two claim limitations.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2 and 4-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitations “applying, by the server, the recycled ABS composition information to a first machine learning model to acquire an index related to a characteristic of the recycled ABS”; “wherein the first machine learning model is a model which has performed machine learning on a relationship between recycled ABS composition information and indices related to the characteristic of recycled ABS”; “applying, by the server, the index related to the characteristic of the recycled ABS and the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to a second machine learning model to acquire an index related to the property of the mixed material of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU and a reliability of the index”; and “the second machine learning model is a model which has performed machine learning on relationships between indices related to the property of mixed materials and the index related to the characteristic of the recycled ABS, content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU”.
MPEP § 2161.01(I) recites:
“Original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. … When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing.”
For these claim elements, it appears the disclosure “does not sufficiently describe how the function is performed or the result is achieved”. The instant Specification (see Pg. 22, Lines 11-18) recites that the machine learning model may be a neural network-based model. For example, a deep neural network (DNN), a recurrent neural network (RNN), a long short-term memory (LSTM) model, a bidirectional recurrent deep neural network (BRDNN), a convolutional neural network (CNN), etc. may be used as the machine learning model, but the machine learning model is not limited thereto. Additionally, Figs. 5 and 6 disclose a flow chart including the input and output for the first and second machine learning models. However, the disclosure is silent on, the structure of the neural network required to determine the relationship between recycled ABS composition information and indices related to the characteristic of recycled ABS or the relationship between indices related to the property of mixed materials and the index related to the characteristic of the recycled ABS and content ratios of the recycled ABS, as disclosed in claim 1. Accordingly, the disclosure is not commensurate with the written description scope of the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2 and 4-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite both (1) mathematical concepts (mathematical relationships, formulas or equations, or mathematical calculations) and (2) mental processes, i.e., concepts performed in the human mind (including observations, evaluations, judgements or opinions) (see MPEP § 2106.04(a)).
Step 1:
In the instant application, claims 1-2 and 4-8 are directed towards a method, which falls into one of the categories of statutory subject matter (Step 1: YES).
Step 2A, Prong One:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong One). The following instant claims recite limitations that equate to one or more categories of judicial exceptions:
Claim 1 recites a mental process (i.e., an evaluation of whether values are in a range) in “determining, by the server, that the ABS product is recyclable in response to determining that the first mass ratio is within a first ratio range and a second mass ratio is within a second ratio range”; a mathematical concept (i.e., a difference or absolute value calculation to be less than or equal to a threshold) in “in response to determining that an absolute value of a difference between the first mass ratio and the third mass ratio is equal to or less than a threshold mass ratio and an absolute value of a difference between the second mass ratio and the fourth mass ratio is equal to or less than the threshold mass ratio”; a mathematical concept (i.e., using a machine learning model to determine an index) in “applying, by the server, the recycled ABS composition information to a first machine learning model to acquire an index related to a characteristic of the recycled ABS”; a mathematical concept (i.e., using a machine learning model to determine an index) in “applying, by the server, the index related to the characteristic of the recycled ABS and the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to a second machine learning model to acquire an index related to the property of the mixed material of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU and a reliability of the index”; a mental process (i.e., an evaluation of the index to determine a range) in “determining, by the user terminal, a range of the property of the mixed material corresponding to the index related to the property of the mixed material”; a mental process (i.e., an evaluation of whether or not the index is lower than a threshold) in “generating, by the user terminal, a first output signal representing that it is necessary to perform an experiment to confirm the property of the mixed material when the reliability of the index is lower than a first threshold reliability”; a mental process (i.e., an evaluation of whether a property is within a range and an index is higher than a threshold) in “generating, by the user terminal, a second output signal representing that it is necessary to perform an additional experiment when the expected property information of the mixed material is within the range of the property of the mixed material and the reliability of the index is higher than a second threshold reliability”; a mathematical concept in “wherein the first machine learning model is a model which has performed machine learning on a relationship between recycled ABS composition information and indices related to the characteristic of recycled ABS”; a mathematical concept in “the second machine learning model is a model which has performed machine learning on relationships between indices related to the property of mixed materials and the index related to the characteristic of the recycled ABS, content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU”; a mental process (i.e., an observation of how the index correlates with a degree of breakage) in “wherein the index related to the characteristic of the recycled ABS includes information related to a degree of breakage of polymer chains made of acrylonitrile, butadiene, and styrene included in the recycled ABS”; and a mental process (i.e., an evaluation of indexes compared to thresholds) in “in response to determining that the reliability of the index is not lower than the first threshold reliability and that the reliability of the index is not higher than a second threshold reliability”.
Claim 2 recites a mathematical concept (i.e., using data to generate the machine learning model) in “performing, by the server, machine learning on relationships between the plurality of pieces of past recycled ABS composition information and the indices related to the characteristic of the plurality of pieces of past recycled ABS to generate the first machine learning model”.
Claim 4 recites a mathematical concept (i.e., using data to generate the machine learning model) in “performing, by the server, machine learning on the indices related to the characteristic of the plurality of pieces of past recycled ABS, the content ratios of the plurality of pieces of past recycled ABS, the content ratios of the plurality of pieces of past general ABS, the content ratios of the plurality of pieces of past CNT, the content ratios of the plurality of pieces of past CF, the content ratios of the plurality of pieces of past recycled TPU, and the indices related to the property of the plurality of past mixed materials to generate the second machine learning model”.
Claim 5 recites a mathematical concept (i.e., using an algorithm to determine the similarity) in “determining a similarity between the distribution of the molecular weights of the molecules included in the recycled ABS and the distribution of the molecular weights of the molecules included in the general ABS”.
Claim 6 recites a mathematical concept (i.e., determining the maximum number in the distribution) in “determining a first molecular weight (A2) having a maximum number (A1) in the distribution of the molecular weights of the molecules included in the recycled ABS”, a mathematical concept (i.e., determining the maximum number in the distribution) in “determining a second molecular weight (B2) having a maximum number (B1) in the distribution of the molecular weights of the molecules included in the general ABS”, and a mathematical concept (i.e., calculating similarity with a formula) in “determining the similarity between the distributions as follows: the similarity between the distributions = 1/((((A2 – B2)2/(A22 + B22))1/2 + (((A1 – B1)2/(A12 + B12))1/2)”.
Claim 8 recites a mental process (i.e., an evaluation of whether the content ratio is below a threshold) in “wherein the content ratio of the general ABS is one or less which is a ratio of a mass of the general ABS to a mass of the recycled ABS”, “the content ratio of the CNT is 1/20 or less which is a ratio of a mass of the CNT to the mass of the recycled ABS”, “the content ratio of the CF is 1/5 or less which is a ratio of a mass of the CF to the mass of the recycled ABS”, and “the content ratio of the recycled TPU is 2/3 or less which is a ratio of a mass of the recycled TPU to the mass of the recycled ABS”.
These recitations are similar to the concepts of collecting information, and displaying certain results of the collection and analysis is Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)), and organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) that the courts have identified as concepts that can be practically performed in the human mind or mathematical relationships.
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification, and are determined to be directed to mental processes that in the simplest embodiments are not too complex to practically perform in the human mind. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Specifically, claim 1 involves nothing more than determining that the ABS product is recyclable based on evaluating ratio ranges; determining differences in mass ratios compared to thresholds; applying a machine learning model to determine an index; applying a machine learning model to determine a reliability of an index; determining property ranges of mixed materials; and generating output signals based on a comparison to thresholds. The steps reciting determining differences in mass ratios compared to thresholds, applying a machine learning model to determine an index, and applying a machine learning model to determine a reliability of an index are, under the BRI, performed using mathematical operations. The instant Specification (see Pg. 22, Lines 11-18) discloses that the machine learning model may be built in consideration of the field of application of the learning model, and may be a neural network-based model. For example, a convolutional neural network may be used, but the machine learning model is not limited thereto. However, as disclosed under Claim Rejections - 35 USC § 112(a) above, the disclosure does not provide, for example, a structure of the neural network for either the first or second machine learning model. Therefore, any machine learning model, performed using mathematical operations, can be used. Additionally, since there are no specifics in the methodology, determining that the ABS product is recyclable based on evaluating ratio ranges, determining property ranges of mixed materials, and generating output signals based on a comparison to thresholds, are something that under BRI, one could perform mentally. Therefore, the claimed steps are not further defined beyond something that reads on performing a calculation using a computer as a tool, and merely looking at data and making a determination. As such, said steps are directed to judicial exceptions. The instant claims must therefore be examined further to determine whether they integrate the abstract idea into a practical application (Step 2A, Prong One: YES).
Step 2A, Prong Two:
In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP § 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP § 2106.04(d)(I)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP § 2106.04(d)(III)). The following independent claims recite limitations that equate to additional elements:
Claim 1 recites “a server for analyzing data using a machine learning model”; “a user terminal for receiving an input of a user and transmitting the input to the server”; “a data collection unit for collecting data”; “transmitting, by the user terminal, information related to an ABS product to the server”; “acquiring, by the server, a first mass ratio of a mass of acrylonitrile to a mass of butadiene based on the information related to the ABS product”; “acquiring, by the server, a second mass ratio of a mass of styrene to a mass of butadiene based on the information related to the ABS product”; “generating recycled ABS from the ABS product”; “acquiring, by the data collection unit, a third mass ratio of a mass of acrylonitrile to a mass of butadiene included in the recycled ABS recycled from the ABS product”; “acquiring, by the data collection unit, a fourth mass ratio of a mass of styrene to a mass of butadiene included in the recycled ABS”; “performing, by a gel permeation chromatography equipment of the data collection unit, gel permeation chromatography (GPC) analysis on the recycled ABS to acquire recycled ABS composition information including a recycled ABS-related number average molecular weight and a recycled ABS-related weight average molecular weight”; “transmitting, by the data collection unit, the recycled ABS composition information to the server”; “acquiring, by the user terminal, content ratios of the recycled ABS, general ABS, carbon nanotubes (CNT), carbon fiber (CF), and recycled thermoplastic polyurethane (TPU) included in a mixed material and expected property information of the mixed material”; “transmitting, by the user terminal, the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to the server”; “transmitting, by the server, the index related to the property of the mixed material and the reliability of the index to the user terminal”; and “outputting a third output signal indicating that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property”.
Regarding the above cited limitations in claim 1 of (i) a server for analyzing data using a machine learning model, (ii) a user terminal for receiving an input of a user and transmitting the input to the server, and (iii) a data collection unit for collecting data. These limitations require only a generic computer component, which does not improve computer technology. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claim 1 of (vi) transmitting, by the user terminal, information related to an ABS product to the server; (v) acquiring, by the server, a first mass ratio of a mass of acrylonitrile to a mass of butadiene based on the information related to the ABS product; (vi) acquiring, by the server, a second mass ratio of a mass of styrene to a mass of butadiene based on the information related to the ABS product; (vii) generating recycled ABS from the ABS product; (viii) acquiring, by the data collection unit, a third mass ratio of a mass of acrylonitrile to a mass of butadiene included in the recycled ABS recycled from the ABS product; (ix) acquiring, by the data collection unit, a fourth mass ratio of a mass of styrene to a mass of butadiene included in the recycled ABS; (x) performing, by a gel permeation chromatography equipment of the data collection unit, gel permeation chromatography (GPC) analysis on the recycled ABS to acquire recycled ABS composition information including a recycled ABS-related number average molecular weight and a recycled ABS-related weight average molecular weight; (xi) transmitting, by the data collection unit, the recycled ABS composition information to the server; (xii) acquiring, by the user terminal, content ratios of the recycled ABS, general ABS, carbon nanotubes (CNT), carbon fiber (CF), and recycled thermoplastic polyurethane (TPU) included in a mixed material and expected property information of the mixed material; (xiii) transmitting, by the user terminal, the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to the server; and (xiv) transmitting, by the server, the index related to the property of the mixed material and the reliability of the index to the user terminal. These limitations equate to insignificant, extra-solution activity of mere data gathering because these limitations gather data before or after the recited judicial exceptions of applying the recycled ABS composition information to a first machine learning model and applying the index related to the characteristic of the recycled ABS and the content ratios to a second machine learning model (see MPEP § 2106.04(d)).
Regarding the above cited limitation in claim 1 of (xv) outputting a third output signal indicating that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property. This limitation equates to an extra-solution step of generally outputting a result, which is incidental to the primary process of using the first and second machine learning models to determine an index related to characteristics of the recycled ABS and reliability of the index (see MPEP § 2106.05(g)).
Additionally, none of the recited dependent claims recite additional elements which would integrate the judicial exception into a practical application. Specifically, claims 2, 4-5, and 7 recite data gathering steps analogous to claim 1 above. As such, claims 1-2 and 4-8 are directed to an abstract idea (Step 2A, Prong Two: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The instant independent claims recite the same additional elements described in Step 2A, Prong Two above.
Regarding the above cited limitations in claim 1 of (i) a server for analyzing data using a machine learning model, (ii) a user terminal for receiving an input of a user and transmitting the input to the server, and (iii) a data collection unit for collecting data.. These limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept (see MPEP § 2106.05(d) and MPEP § 2106.05(f)).
Regarding the above cited limitations in claim 1 of (vi) transmitting, by the user terminal, information related to an ABS product to the server; (v) acquiring, by the server, a first mass ratio of a mass of acrylonitrile to a mass of butadiene based on the information related to the ABS product; (vi) acquiring, by the server, a second mass ratio of a mass of styrene to a mass of butadiene based on the information related to the ABS product; (viii) acquiring, by the data collection unit, a third mass ratio of a mass of acrylonitrile to a mass of butadiene included in the recycled ABS recycled from the ABS product; (ix) acquiring, by the data collection unit, a fourth mass ratio of a mass of styrene to a mass of butadiene included in the recycled ABS; (xi) transmitting, by the data collection unit, the recycled ABS composition information to the server; (xii) acquiring, by the user terminal, content ratios of the recycled ABS, general ABS, carbon nanotubes (CNT), carbon fiber (CF), and recycled thermoplastic polyurethane (TPU) included in a mixed material and expected property information of the mixed material; (xiii) transmitting, by the user terminal, the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to the server; and (xiv) transmitting, by the server, the index related to the property of the mixed material and the reliability of the index to the user terminal. These limitations do not include any specific steps for acquiring or transmitting information related to the ABS product, recycled ABS composition information, content ratios, or the property indices. Under the BRI, these limitations are merely receiving data for the subsequent steps of applying the composition information to a first machine learning model, applying the indices to a second machine learning model, and determining output signals based on the machine learning models. Therefore, these limitations equate to receiving/transmitting data over a network, which the courts have established as a WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Regarding the above cited limitations in claim 1 of (vii) generating recycled ABS from the ABS product; and (x) performing, by a gel permeation chromatography equipment of the data collection unit, gel permeation chromatography (GPC) analysis on the recycled ABS to acquire recycled ABS composition information including a recycled ABS-related number average molecular weight and a recycled ABS-related weight average molecular weight. These limitations when viewed individually and in combination, are WURC limitations as taught by Bai et al. (Reprocessing Acrylonitrile–Butadiene–Styrene Plastics: Structure-Property Relationships. Polymer Engineering and Science 47(2): 120-130 (2007); previously cited). Bai et al. discloses that acrylonitrile-butadiene-styrene (ABS) plastics from computer equipment housings have been reprocessed, and analyzed for structural changes using infrared spectroscopy, gel permeation chromatography (GPC), and dynamic mechanical thermal analysis (Abstract). Bai et al. also discloses that the molecular weight distribution was analyzed at room temperature with a GPC system (limitations (vii) and (x)) (Pg. 122, Col. 1, Para. 2).
Regarding the above cited limitation in claim 1 of (xv) outputting a third output signal indicating that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property. This limitation equates to an extra-solution step of generally outputting a result, which is incidental to the primary process of using the first and second machine learning models to determine an index related to characteristics of the recycled ABS and reliability of the index. This post solution activity is analogous to the additional element of measuring metabolites of a drug administered to a patient in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 79, 101 USPQ2d 1961, 1968 (2012) and the post-solution activity of adjusting an alarm limit variable to a figure computed according to a mathematical formula in Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978) (see MPEP § 2106.05(g)).
These additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the instant claims do not amount to significantly more than the judicial exception itself (Step 2B: NO). As such, claims 1-2 and 4-8 are not patent eligible.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments filed 04/16/2026 have been fully considered but they are not persuasive.
1. Applicant argues that amended claim 1 integrates the alleged abstract idea into a practical application. Amended claim 1 recites “generating recycled ABS from the ABS product”, “generating, by the user terminal, a second output signal representing that it is necessary to perform an additional experiment when the expected property information of the mixed material is within the range of the property of the mixed material and the reliability of the index is higher than a second threshold reliability”, and “in response to determining that the reliability of the index is not lower than the first threshold reliability and that the reliability of the index is not higher than a second threshold reliability, outputting a third output signal indicating that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property”. The claimed invention claims a 'Practical Application' directed to solving a specific technical problem in the field of polymer recycling, going far beyond mere data processing. When the polymer chains within recycled ABS are broken, the resulting material may exhibit properties different from those of general ABS (see Paragraph 12 of the present application, US2022-0208311). The present invention is directed to solving this problem. More specifically, merely obtaining molecular weight distribution data through GPC analysis is insufficient to quantitatively determine the 'degree of breakage of polymer chains,' which is the critical variable in the recycling process. Chain breakage is the root cause of the unpredictable properties of mixed materials containing recycled ABS; however, this phenomenon lies within an 'invisible domain' that cannot be directly measured from GPC report values alone, nor can it be calculated through human mental processes. As a result of this limitation, countless experiments have been unavoidable in order to achieve target material properties for mixed materials containing recycled ABS (Applicant’s Remarks, Pg. 8-9).
It is respectfully submitted that this is not persuasive for the following reasons:
The limitation, indicated by Applicant of “generating, by the user terminal, a second output signal representing that it is necessary to perform an additional experiment when the expected property information of the mixed material is within the range of the property of the mixed material and the reliability of the index is higher than a second threshold reliability” has been identified as reciting a judicial exception in Step 2A, Prong One above. Under the BRI, this limitation is merely analyzing if the property is within a range and if the index is higher than a threshold to generate a signal, which are steps capable of being performed in the human mind. As described in MPEP § 2106.04(d)(II), the integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Therefore, this limitation is not considered as an improvement for by reducing the number of experiments to achieve target material properties for mixed materials containing recycled ABS.
Additionally, the limitations, indicated by Applicant, of (i) “generating recycled ABS from the ABS product” and (ii) “outputting a third output signal indicated that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property” have been identified as reciting additional elements in Step 2A, Prong Two above. As described in the rejection above, limitation (i) gathers data under Step 2A, Prong One and is a WURC limitation as taught by Bai et al. under Step 2B. Bai et al. discloses the reprocessing of ABS plastics as well as subsequent analysis using gel permeation chromatography. Additionally, under Step 2A, Prong Two, limitation (ii) is an extra solution activity of generally outputting a signal, which is incidental to the primary process of using the first and second machine learning models to determine an index related to characteristics of the recycled ABS and reliability of the index. This is analogous to the example of post-solution activity in MPEP § 2106.05(g): An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. Under Step 2B, this limitation is analogous to the extra-solution examples recited in MPEP § 2106.05(g): the additional element of measuring metabolites of a drug administered to a patient in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 79, 101 USPQ2d 1961, 1968 (2012) and the post-solution activity of adjusting an alarm limit variable to a figure computed according to a mathematical formula in Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Therefore, the limitations indicated by Applicant do not provide improvement by avoiding countless experiments in order to achieve target material properties for mixed materials containing recycled ABS, and this argument is not persuasive.
2. Applicant also argues that the claimed invention uses a first machine learning model to determine "the index related to a characteristic of the recycled ABS,'' which represents the degree of breakage of polymer chains - the very phenomenon that exists within this invisible domain. This constitutes a solution to a problem that could not previously be resolved. Furthermore, by applying "the index related to a characteristic of the recycled ABS" to a second machine learning model, the present invention is able to obtain the properties of mixed materials containing recycled ABS (e.g., at least one of tensile strength, impact strength, electrical conductivity, flexural strength, and hardness) - properties that were previously unpredictable. This constitutes an automatic generation of technically valuable results that previously required skilled practitioners to conduct numerous experiments, and thus has the same structure as the practical application recognized in McRO (Applicant’s Remarks, Pg. 9).
It is respectfully submitted that this is not persuasive for the following reasons:
MPEP § 2106.04(d)(II) recites:
The analysis under Step 2A Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon (including products of nature). Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations introduced in subsection I supra, and discussed in more detail in MPEP §§ 2106.04(d)(1), 2106.04(d)(2), 2106.05(a) through (c) and 2106.05(e) through (h).
The limitations for applying the first and second machine learning models, of "applying the recycled ABS composition information to a first machine learning model to acquire an index related to a characteristic of the recycled ABS", "applying the index related to the characteristic of the recycled ABS and the content ratios of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU to a second machine learning model to acquire an index related to the property of the mixed material of the recycled ABS, the general ABS, the CNT, the CF, and the recycled TPU and a reliability of the index" have been identified as reciting judicial exceptions in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, the recited limitations are not considered as an improvement.
Additionally, the instant claims are different from the improvements recited in McRO. In McRO, the Federal Circuit held claimed methods of automatic lip synchronization and facial expression animation using computer-implemented rules to be patent eligible under 35 U.S.C. 101, because they were not directed to an abstract idea. McRO, 837 F.3d at 1316, 120 USPQ2d at 1103. The basis for the McRO court's decision was that the claims were directed to an improvement in computer animation and thus did not recite a concept similar to previously identified abstract ideas. The court relied on the specification's explanation of how the claimed rules enabled the automation of specific animation tasks that previously could not be automated. The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process". This is different from the instant case, because no evidence has been presented to suggest that the computer itself has been altered in any way, i.e., by changing the functioning of a processor or by changing the way in which it stores or accesses memory. Nothing about the physical components of the computer nor the way the computer operates is changed by the machine learning limitations in claim 1. Therefore, the use of a computer to perform the limitations in claim 1 invokes the computer as a tool (see MPEP § 2106.05(a)(I)). This is different from McRO, where the computer was altered by improved animation techniques. This argument is thus not persuasive.
3. Applicant also argues that the Examiner classified the claim elements preceding and following the machine learning model steps as mere extra-solution activity. However, under MPEP § 2106.04(d), additional elements that meaningfully limit a judicial exception are recognized as constituting a practical application. In the present invention, (i) the acquisition of recycled ABS composition information through GPC analysis, (ii) the sequential application of the first and second machine learning models, and (iii) the generation of output signals based on the predicted properties are not independent elements. Rather, they form an ordered combination that is organically integrated under the single technical purpose of predicting the properties of a mixed material containing recycled ABS. This entire ordered combination achieves a substantive functional improvement in the specific technical field of recycled materials processing (Applicant’s Remarks, Pg. 9-10).
It is respectfully submitted that this is not persuasive for the following reasons:
Of the limitations (i), (ii), and (iii) indicated by Applicant immediately above, only limitation (i) recites an additional element. The limitations reciting the machine learning models ((ii) immediately above) have both been identified as reciting judicial exceptions (see Step 2A, Prong One and argument (2) above). The generation of the first and second output signals ((iii) immediately above) have also been identified as reciting judicial exceptions (see Step 2A, Prong One and argument (2) above). The acquisition of recycled ABS composition information through GPC analysis and the generation of the third output signal ((i) and (iii) immediately above) are data gathering and extra-solution activity, respectively (see Step 2A, Prong One and argument (1) above). Therefore, even when considered as an ordered combination, the additional elements are either gathering data or outputting a signal, both of which are incidental to the primary process of using the first and second machine learning models to determine an index related to characteristics of the recycled ABS and reliability of the index. This argument is thus not persuasive.
4. Applicant also argues that MPEP Example 47 (Anomaly Detection, 2024) provides important guidance on Step 2A, Prong Two analysis for inventions that use machine learning models. In Example 47, the USPTO found that when an ANN is used to detect anomalies but the results are merely output without further application, a practical application is not established (Claim 2). In contrast, when the ANN's detection results are used to achieve a concrete and substantive improvement in a specific technical field - namely, network security - the judicial exception is integrated into a practical application (Claim 3). The USPTO articulated two requirements for recognizing a practical application: (i) the specification must describe the technical improvement, and (ii) the claims themselves must reflect that improvement (MPEP § 2106.04(d)(1)). The claimed invention satisfies both requirements. With respect to requirement (i), as described above, the specification clearly discloses the technical problem caused by the breakage of polymer chains in recycled ABS, which necessitated countless prior experiments, and the technical improvement achieved by the present invention in solving this problem (see Paragraph 12 of the present application, US2022-0208311). With respect to requirement (ii), contrary to the Examiner's assertion, the claims do reflect the disclosed improvement. Claim 1 explicitly recites: a first machine learning model trained on the relationship between recycled ABS composition information and the index related to the characteristic of recycled ABS; and a second machine learning model trained on the relationships between indices related to the property of mixed materials and the index related to the characteristic of the recycled ABS, together with the content ratios of the mixed material components. Additionally, Claim 1 recites the generation of a first output signal and a second output signal based on the index related to the property of the mixed material obtained through the second machine learning model. These output signals guide the practitioner on whether experimentation is warranted, thereby dramatically reducing the number of unnecessary experiments required in the recycled materials processing field (see Paragraph 155 of the present application, US2022-0208311). Just as in Example 47, Claim 3, where the ANN's detection results lead directly to concrete improvements in network security through real-time packet blocking, in the present invention, the outputs of the two machine learning models lead directly to concrete improvements in recycled materials processing (Applicant’s Remarks, Pg. 10-11).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in argument (1) above, the integration of judicial exceptions into a practical application can only be achieved by additional elements. The elements indicated by Applicant, of a first machine learning model trained on the relationship between recycled ABS composition information and the index related to the characteristic of recycled ABS; a second machine learning model trained on the relationships between indices related to the property of mixed materials and the index related to the characteristic of the recycled ABS, together with the content ratios of the mixed material components; and the generation of a first output signal and a second output signal based on the index related to the property of the mixed material obtained through the second machine learning model all recite judicial exceptions. Therefore, none of these limitations were considered for an improvement in recycled materials processing.
Additionally, as described in argument (1) above, the third output signal recites an extra-solution activity under Step 2A, Prong Two and Step 2B. Furthermore, the limitation “outputting a third output signal indicating that it is not necessary to perform an experiment for checking whether the recycled ABS has a target property” only requires the output of a signal, with the intended use of the signal to reduce the amount of subsequent experimentation. Therefore, this limitation also does not improve recycled materials processing and this argument is not persuasive.
5. Applicant also argues that the claimed invention includes a clear inventive concept that satisfies Step 2B of the Alice/Mayo test for the following reasons. The Federal Circuit held in BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC that even where individual elements are themselves generic or conventional, if they are combined in a specific arrangement or ordered combination that overcomes the limitations of prior art and provides a technological solution, an 'inventive concept' is recognized. Claim 1 of the present application includes, among other things, a configuration of: applying the recycled ABS composition information to a first machine learning model and a second machine learning model to derive "the index related to a characteristic of the recycled ABS," and further predicting the properties of the mixed material containing recycled ABS – a domain that was previously unpredictable. Because Claim 1 includes inventive configurations not found in any prior art, the claimed invention cannot constitute 'well-understood, routine, conventional (WURC)' activity in the relevant technical field. Accordingly, Claim 1 of the present application includes a sufficient inventive concept under Step 2B (Applicant’s Remarks, Pg. 11).
It is respectfully submitted that this is not persuasive for the following reasons:
As described in arguments (2) and (4) above, only additional elements are considered as an ordered combination to provide a technical solution. The limitations reciting the first machine learning model, the second machine learning model, and the determination of a range of a property of the mixed material have all been identified as reciting judicial exceptions in Step 2A, Prong One above. The integration of a judicial exception into a practical application can only be achieved by additional elements, not by a limitation that recites a judicial exception. Thus, the recited limitations are not considered as an improvement in recycled materials processing.
MPEP § 2106.05(I) recites:
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) ("The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces."). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103) is further discussed in MPEP § 2106.05(d).
Additionally, whether or not the claimed elements appear to be free of the prior art is distinct from subject matter eligibility under 35 U.S.C. 101. As described in the rejection, and arguments above, only additional elements can provide the technical improvements and amount to significantly more than the recited judicial exceptions. As described in arguments (1) and (2) above, the additional elements do not provide significantly more than the recited judicial exceptions. Therefore, this argument is not persuasive.
6. Applicant argues that Claim 3 of MPEP Example 45 (Controller for Injection Mold) sets forth important legal principles regarding the recognition of an inventive concept at Step 2B. In Example 45, the USPTO recognized that a data-collection tool - specifically, the ARCXY thermocouple - that was known only in the aerospace field and was not conventionally used in injection molding, was not a WURC element when applied to an injection molding apparatus, and thus provided an inventive concept at Step 2B. The Examiner contends that GPC analysis is a WURC technique as taught by Bai et al. However, Bai et al. employed GPC only as a post-hoc analytical tool for examining structural changes in reprocessed ABS. The configuration of using GPC analysis results as input to a machine learning model to derive an index representing the degree of polymer chain breakage has not been disclosed in Bai et al. or any other prior art reference. This was in fact confirmed by the Examiner in withdrawing the § 103 rejection. That is, even if Bai et al. discloses the use of GPC, the manner in which GPC is used in the present invention is fundamentally different from that of Bai et al., and therefore cannot be considered WURC. It is logically inconsistent for the Examiner to characterize GPC as WURC under § 101, while simultaneously acknowledging under § 103 that no prior art discloses the same configuration of using GPC as input for a machine learning model, Accordingly, just as with the ARCXY thermocouple in Example 45, the use of GPC in the present invention is non-conventional in the relevant technical field, does not constitute a WURC element, and provides an inventive concept at Step 2B. Therefore, the subject matter eligibility of the present invention should be recognized (Applicant’s Remarks, Pg. 11-12).
It is respectfully submitted that this is not persuasive for the following reasons:
Under Step 2B above, the limitations disclosed by Bai et al. are “generating recycled ABS from the ABS product” and “performing, by a gel permeation chromatography equipment of the data collection unit, gel permeation chromatography (GPC) analysis on the recycled ABS to acquire recycled ABS composition information including a recycled ABS-related number average molecular weight and a recycled ABS-related weight average molecular weight”. Bai et al. discloses that ABS plastics from computer equipment housings have been reprocessed, and analyzed for structural changes using infrared spectroscopy, gel permeation chromatography (GPC), and dynamic mechanical thermal analysis (Abstract). Bai et al. also discloses that the molecular weight distribution was analyzed at room temperature with a GPC system (Pg. 122, Col. 1, Para. 2). As currently recited, these claim limitations do not require any specific steps for generating the recycled ABS from the ABS product, or, for example, any specific format for the molecular weight distribution or recycled ABS composition information obtained by performing GPC. Bai et al. was not relied upon to disclose the input to the machine-learning model to derive an index representing the degree of polymer chain breakage. Therefore, both “generating recycled ABS from the ABS product” and “performing, by a gel permeation chromatography equipment of the data collection unit, gel permeation chromatography (GPC) analysis on the recycled ABS to acquire recycled ABS composition information...” are WURC limitations as disclosed by Bai et al. Accordingly, the instant claims recite WURC limitations under Step 2B, and are therefore different from Example 45, which does not recite WURC limitations. This argument is thus not persuasive.
Conclusion
No claims allowed.
Claims 1-2 and 4-8 appear to be free from the prior art because the prior art does not fairly suggest or teach a method for estimating the properties of recycled acrylonitrile butadiene styrene (ABS), including steps of acquiring mass ratios, using gel permeation chromatography (GPC) to obtain ABS composition information, and using two machine learning models to predict the degree of breakage of polymer chains in the recycled ABS. The closest prior art is Choi et al. (Korean Application 20200052393A; previously cited). Choi et al. discloses physical property prediction system based on artificial intelligence for various composite resins. The system disclosed by Choi et al. includes a model generation unit, a property calculation unit, and an output unit that are wirelessly linked for acquiring and transmitting input or output composition/raw material data. However, Choi et al. does not teach that the property prediction is for recycled ABS, including the degree of breakage for individual components, the acquiring of specific mass ratios of the ABS product, or that the composition information is obtained by performing GPC analysis on the recycled ABS, as disclosed in instant claim 1. Claims 2 and 4-8 appear to be free from the prior art due to their dependency on claim 1.
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/D.P.S./Examiner, Art Unit 1687
/Lori A. Clow/Primary Examiner, Art Unit 1687
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