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 .
DETAILED ACTION
Arguments and amendments filed 9/3/2025 have been examined.
Claims 1 and 3 are amended.
Claim 6 is cancelled.
In this Office Action, claims 1-5, 7-8 are currently pending.
This Office Action is Final.
Response to Arguments
Applicant’s arguments with respect to claim(s) and the prior are rejection under 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed concerning rejections under 35 USC 101 have been fully considered but they are not persuasive.
As to the argument:
“Applicant respectfully contends that amended claim 1 does not fall into a judicial exception,
such as an abstract idea because the features recited in amended claim 1 cannot practically be certain methods of organizing human activity. Hence, Applicant respectfully submits that amended claim 1 as a whole is not directed to an abstract idea. See Step 2A, Prong One analysis stated in MPEP § 2164.04(a)(l). Further, even if amended claim 1 is considered to be directed to an abstract idea, Applicant respectfully contends that amended claim 1 integrates the alleged abstract idea into a practical application by demonstrating a technical improvement in an existing technology, such as improving the accuracy of the data extraction process. In particular, amended claim 1 specifically recites, inter alia, that the operation of automatically adding information to the database, and the operations of evaluation. Applicant respectfully submits that these features demonstrate a technical improvement, such as improving recognition rate and the accuracy of the data extraction process, as described in, for example, paragraph [70] and [78] of the original specification.”;
The Examiner respectfully disagrees.
As to Applicants assertion above, “In particular, amended claim 1 specifically recites, inter alia, that the operation of automatically adding information to the database, and the operations of evaluation”; however, here, nowhere does Applicant specially explain or claim the
algorithm or technique that allows the system to perform “automatically adding information”, note specifically the related rejection below under 35 USC 112, concerning the repeated use of “and/or” language in claim 1 (see “performing selecting only composition and/or property pairs”); which renders the claimed steps indefinite, and thus similarly unspecific for the purposes of analysis under 35 USC 101 abstract idea analysis; nor does Applicant explain or claim what algorithm performs the “automatically adding information” (see for example “performing selecting only composition and/or property pairs”). This blanket/generic claiming of
operations (where the algorithm mentioned in the claim is not claimed, identified, or explained")
is analogous to case law where the claims have been found to be abstract. Please see
Clarilogic v. Freeform Holdings (Court of Appeals for the Federal Circuit 2016-1781,
decided March 15, 2017), which recites at page 7:
"Peculiar to this case is that the algorithm engine mentioned in the claim is not
claimed, identified, or explained. To be sure, claiming an algorithm does not
alone render subject matter patent eligible. See Gottschalk v. Benson, 409 U.S.
63, 71-72 (1972). But a method for collection, analysis, and generation of
information reports, where the claims are not limited to how the collected
information is analyzed or reformed, is the height of abstraction.".
Similarly to the claims in Clarilogic, instant claim 1 is not limited to how the collected information is used for “automatically adding information”; or what algorithm performs the “automatically adding information” using collected information. Thus, to quote the court in Clarilogic,
"where the claims are not limited to how the collected information is analyzed or
reformed, is the height of abstraction".
Thus this argument is moot, the examiner remains unconvinced and the rejection under 35 USC 101 remains.
As to the argument that:
“Accordingly, Applicant respectfully contends that amended claim 1 specifically and practically sets forth the features that demonstrate how the alleged abstract idea is applied in a way that provides concrete benefits or solves specific problems in the relevant field. Hence, Applicant respectfully submits that amended claim 1 as a whole integrates the alleged abstract idea into a practical application. See Step 2A, Prong Two analysis stated in MPEP § 2106.04(d) Additionally, Applicant respectfully contends that, as discussed above, amended claim 1 provides a technical improvement in the relevant field and that the specification as filed identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim or identifies technical improvements realized by the claim over the prior art, thereby adding inventive concepts to the alleged abstract idea. Therefore, Applicant respectfully submits that amended claim 1 recites significantly more than a judicial exception. See Step 2B analysis stated in MPEP § 2106.05(a).
For at least the above reasons, Applicant respectfully submits that amended independent claim 1 is subject matter eligible and that dependent claims 2-5, 7 and 8 are subject matter eligible at least by virtue of their dependence on amended independent claim 1. Thus, Applicant submits that the rejection of claims 1-5, 7 and 8 under 35 U.S.C. § 101 is overcome. The cancelation of claim 6 renders the rejection of claim 6 moot. Accordingly, Applicant respectfully requests reconsideration and withdrawal of the rejection under 35 U.S.C. § 101.”
The Examiner respectfully disagrees.
As to Applicant’s assertion above “amended claim 1 provides a technical improvement in the relevant field and that the specification as filed identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim or identifies technical improvements realized by the claim over the prior art, thereby adding inventive concepts to the alleged abstract idea”; nowhere does Applicant specifically explain or connect the above assertions to the claim language, i.e. as to the assertions:
“amended claim 1 provides a technical improvement in the relevant field” ;
nowhere does Applicant explain what the technical improvement is, nor what claim limitations are related to the technical improvement; nor what specific “relevant field” is;
“and that the specification as filed identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim”; again nowhere does Applicant explain or identify the “unconventional technical solution expressed in the claim” nor does Applicant identify or explain what specific portions/limitations of “the specification as filed identifies a technical problem and explains the details”; thus this argument is moot, the examiner remains unconvinced and the rejection under 35 USC 101 remains.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed:
KR10-2023-0110403 REPUBLIC OF KOREA Filing Date 08/23/2023;
and
KR10-2024-0019940 REPUBLIC OF KOREA Filing Date 02/08/2024;
It is noted Applicant has filed two “Interim Copy of the Foreign Priority Document” on 9/3/2025;
however, that applicant has still not filed a certified copy of the above application(s) as required by 37 CFR 1.55.
Additionally, please note the two “PRIORITY DOCUMENT EXCHANGE FAILURE STATUS REPORT”(s), both dated 1/23/2025, noting:
“An attempt by the Office to electronically retrieve, under the priority document exchange program, the foreign application 10-2023-0110403 to which priority is claimed has FAILED on 01/23/2025.”;
and
“An attempt by the Office to electronically retrieve, under the priority document exchange program, the foreign application 10-2024-0019940 to which priority is claimed has FAILED on 01/23/2025.”; and
“Useful information is provided at the Electronic Priority Document Exchange (PDX) Program Website (https://www.uspto.gov/patents/basics/international-protection/electronic-priority-document-exchange-pdx), including practice tips for priority document exchange
(https://www.uspto.gov/patents/basics/international-protection/electronic-priority-document-exchange-pdx#Practice%20tips).
For further questions or assistance, please contact the Patent Electronic Business Center (EBC):
EBC Customer Support Center 1-866-217-9197 (toll-free)
571-272-4100 (local) M-F 6AM - Midnight (Eastern Time)
PDX@uspto.gov( email)”
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 1-5 and 7-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the phrase(s) “and/or” in the following limitations:
"selecting only composition and/or property pairs"; (Claim 1, lines 28-29)
“from a composition table and/or a property table,” (Claim 1, Lines 29-30)
“adding the selected composition and/or property pairs”; (Claim 1 line 30)
Render(s) the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
The dependent claims inherit and do not correct the above defects, and thus are also rejected under 35 USC 112
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 1-5, 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more.
Claim 1 recites: (Step 2a, Prong One)
extracting data from a document to build a formulation database.
The limitation of extracting data from a document to build a formulation database, as drafted,
is a process that, under its broadest reasonable interpretation, covers performance of the
limitation in the mind but for the recitation of generic computer components. That is, other than
reciting a generic processors/device, nothing in the claim element precludes the step from
practically being performed in the mind. For example, but for the processors language,
“extracting” in the context of this claim encompasses the user manually determining
a generic “formulation database” using a generic “documents” with data and generic “extracting” steps. Similarly, the limitation(s) of generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the processors language, generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding in the context of this claim encompasses the user manually receiving generic “documents” and “data” and performing generic “sorting” and “extracting” steps. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)).
Further, these concepts also recite “Certain Methods of Organizing Human Activity”; (such as
commercial or legal interactions (including agreements in the form of contracts; legal
obligations; advertising, marketing or sales activities or behaviors; business relations) where
performing generic exporting steps of generic profiles using generic transformations and
templates is a method of human activity in commercial or legal interactions.
Accordingly, the claim recites an abstract idea.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites one additional element – using a processor with a device/method to perform both the generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding; and extracting steps. The processor with a device/method in both steps is recited at a high level of generality (i.e., as a generic processor performing a generic computer function of “extracting”) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Accordingly, this additional element does not integrate the abstract idea into a practical
application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that
are sufficient to amount to significantly more than the judicial exception.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of a processor with a device/method to perform both the generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding; and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
Referring to claim 2, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “extracting text segments in the document; predicting a class related to the formulation for the extracted text segments using an object name recognition model;
and storing a paragraph including a sentence including information including at least one of the composition and property of the formulation”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “extracting text segments in the document; predicting a class related to the formulation for the extracted text segments using an object name recognition model; and storing a paragraph including a sentence including information including at least one of the composition and property of the formulation” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “extracting text segments in the document; predicting a class related to the formulation for the extracted text segments using an object name recognition model; and storing a paragraph including a sentence including information including at least one of the composition and property of the formulation” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 3, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “checking the locations of tables in the document using a pretrained
table-transformer model; setting a window of a preset size at the checked locations of the tables; extracting contents of a table included in the window; selecting a table including a content including a keyword related to the formulation from the extracted contents using a
Tesseract OCR model; and storing the selected table”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “checking the locations of tables in the document using a pretrained table-transformer model; setting a window of a preset size at the checked locations of the tables; extracting contents of a table included in the window; selecting a table including a content including a keyword related to the formulation from the extracted contents using a
Tesseract OCR model; and storing the selected table” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “checking the locations of tables in the document using a pretrained table-transformer model; setting a window of a preset size at the checked locations of the tables; extracting contents of a table included in the window; selecting a table including a content including a keyword related to the formulation from the extracted contents using a Tesseract OCR model; and storing the selected table” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 4, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “converting selected table images into an HTML format using a table structure recognition model; and converting and storing a table to fit the format of the existing database using an HTML parser”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “converting selected table images into an HTML format using a table structure recognition model; and converting and storing a table to fit the format of the existing database using an HTML parser” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “converting selected table images into an HTML format using a table structure recognition model; and converting and storing a table to fit the format of the existing database using an HTML parser” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 5, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating composite formulation data from original data using a tabular data generator; comparing a conditional distribution between the original data and the composite formulation data using a discriminator to output a similarity; and sampling composite data based on the similarity”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating composite formulation data from original data using a tabular data generator; comparing a conditional distribution between the original data and the composite formulation data using a discriminator to output a similarity; and sampling composite data based on the similarity” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “generating composite formulation data from original data using a tabular data generator; comparing a conditional distribution between the original data and the composite formulation data using a discriminator to output a similarity; and sampling composite data based on the similarity” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 7, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “generating, in an actor network, third data for a new compound formulation based on first data for a target property and second data for an existing compound formulation;
assigning a reward score based on a similarity between a property value of the new compound formulation of the third data and the target property; and feeding back, in a critic network, an expected value of how close the new formulation changed compared to an existing formulation has improved to the target property based on the reward score to the actor network”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “generating, in an actor network, third data for a new compound formulation based on first data for a target property and second data for an existing compound formulation; assigning a reward score based on a similarity between a property value of the new compound formulation of the third data and the target property; and feeding back, in a critic network, an expected value of how close the new formulation changed compared to an existing formulation has improved to the target property based on the reward score to the actor network” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “generating, in an actor network, third data for a new compound formulation based on first data for a target property and second data for an existing compound formulation; assigning a reward score based on a similarity between a property value of the new compound formulation of the third data and the target property; and feeding back, in a critic network, an expected value of how close the new formulation changed compared to an existing formulation has improved to the target property based on the reward score to the actor network” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Referring to claim 8, (Step 2a, Prong One) this further merely performs an additional abstract
mental step of “a memory that stores one or more instructions; and a processor that executes the one or more instructions stored in the memory, wherein the processor executes the one or more instructions”.
(Step 2a, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim only
recites the additional elements of “a memory that stores one or more instructions; and a processor that executes the one or more instructions stored in the memory, wherein the processor executes the one or more instructions” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
(Step 2b)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception. As discussed above with respect to integration of the abstract
idea into a practical application, the additional element of using “a memory that stores one or more instructions; and a processor that executes the one or more instructions stored in the memory, wherein the processor executes the one or more instructions” steps to perform both the aforementioned generating; predicting; optimizing; extracting; extracting; checking; sorting; extracting; extracting; extracting; collecting; storing; evaluating; determining and adding and extracting steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim(s) is/are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 5, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Hong et al. US Pub. No. 2022/0397886 A1, in view of Dorsett et al., US Pub. No. 2002/0128734 A1, in view of Asahara et al., US Pub. No. 2022/0358438 A1, in view of Kakuda et al., US Pub. No. 2024/0378355 A1.
As to claim 1, Hong discloses
an operating method, performed by one or more processors of an electronic device,
(Hong [0001, 0063, 0096])
the operating method comprising:
extracting data from a document to build a formulation database;
(Hong teaches collecting/receiving input chemical material/chemical formulation information dataset including chemical material Information for a database that stores the chemical material data, i.e. “extracting data from a document to build a formulation database” see [0051] The data reception unit 110 may collect or receive a input chemical material dataset including chemical material information, chemical composition information, chemical formulation information, and/or chemical property information
of the chemical formulation. In the input chemical material dataset, information other than the above-mentioned information may be added when the information is required for deriving a chemical formulation, or a part of the above-mentioned information may be excluded. The input
chemical material dataset can be reduced in dimension and complexity of input data by weighting a principal variable or a principal component using feature importance or principal
component analysis (PCA).
see also [0060] The database 170 may store at least one or more of the first chemical material data, the predicting model, the validation model, and the chemical formulation data of the
first to third groups, and further store other information including various parameters generated in an automated chemical formulation process.;
see also [0009] a data reception unit that receives a first chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof; )
generating composite formulation data based on the formulation database;
(Hong also teaches chemical composition information/ chemical formulation information, i.e. generating composite formulation data see [0009] a data reception unit that receives a first chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof;)
predicting, using a formulation property prediction model that infers a property change according to the composition of a formulation, the property of a compound from the formulation information of the compound based on the composite formulation data;
(Hong teaches a predicting model/ a formulation prediction unit for target properties , i.e. “predicting, using a formulation property prediction model that infers a property change” [0010] a predicting model generation unit that trains a first machine learning model using the input chemical material dataset to generate a predicting model for predicting a chemical formulation based on target property information of a target material;
[0011] a formulation prediction unit that sets a boundary condition based on the first chemical material dataset, generates a new input dataset including the chemical material information, chemical composition information, and chemical formulation information within the
boundary condition, inputs the new input dataset to the predicting model, and sets predetermined one or more pieces of target property information to perform prediction,
thereby outputting chemical formulation data of a first group;
see also [0052] The predicting model generation unit 120 may train a first machine learning model using the input chemical material dataset to generate a predicting model for predicting
a chemical formulation based on target property information of a target material. Here, a "target material" refers to a chemical material exhibiting a physical property specified by a user. A "target property" refers to one or more features required for a chemical material to be obtained by the user, and may be one or more of physical and/or chemical features. )
and
optimizing a formulation using a formulation optimization model that generates a new compound formulation suitable for a target property in a compound formulation,
(Hong [0052] The predicting model generation unit 120 may train a first machine learning model using the input chemical material dataset to generate a predicting model for predicting
a chemical formulation based on target property information of a target material. Here, a "target material" refers to a chemical material exhibiting a physical property specified by a user. A "target property" refers to one or more features required for a chemical material to be obtained by the user, and may be one or more of physical and/or chemical features.)
Hong does not disclose:
wherein the building of the formulation database comprises:
extracting a paragraph related to a formulation from text included in the document; and
extracting data related to the formulation from a table included in the document, and
wherein the extracting of data related to the formulation comprises:
checking a row having composition information or property
information in a table included in at least one of an HTML document and an XML document;
sorting example numbers assigned to classify experimental examples in a table head, the composition information, and the property information, respectively, to generate a composition data set and a property data set;
extracting a pair of composition information and property information whose example numbers match from the composition data set and the property data set;
extracting information including the name, unit, and amount of a raw material from the pair of the composition information and the property information;
extracting information including the name, value, unit, property experimental method, and property experimental condition of a property from the pair of the composition information and the property information; and
automatically adding information including the name, unit, and amount of the raw material and information including the name, value, unit, property experimental method, and property experimental condition of the property to an existing database;
However, Dorsett discloses:
wherein the building of the formulation database comprises:
extracting a paragraph related to a formulation from text included in the document;
(Dorsett teaches extracting object descriptions, i.e. a paragraph related to formulation see [0047] As described above, client processes 140 receive or generate data derived from an experiment and package that data using known techniques in a format (e.g., an XML data stream) for communication to database server process 130. Upon receiving such a communication from a client process 140, database server process 130 parses the incoming data stream to extract object descriptions from the data stream.;
See also [0020] FIGS. 4A-4B illustrate a portion of an XML document defining a data object representing an experimental data set and a relational database table into which such an
object is mapped, respectively.)
and
extracting data related to the formulation from a table included in the document,
(Dorsett teaches extracting objects/entities, i.e. extracting data related to the formulation from a table see [0038] Database server process 130 parses the data, extracting for each object represented in the XML stream an
object type and optionally one or more associated object properties (step 250), as will be described in more detail below.; see also [0012] Parsing the representation can
include identifying each of a plurality of XML entities in an XML stream, each entity having associated content; mapping each XML entity into a corresponding object property;
and assigning the content associated with an XML entity to a database table based on the corresponding object property.)
and
wherein the extracting of data related to the formulation comprises:
checking a row having composition information or property information in a table included in at least one of an HTML document and an XML document;
(Dorsett teaches mapping rows/columns properties to the database, i.e. “checking a row having composition information or property information” see [0048] In one implementation,
database server process 130 maps classes (e.g., the Experiment class and any subclasses, the Element class, etc.), to database tables, with each row representing an individual instance of the class or classes in the table (e.g., an experiment identified by a unique ExperimentID) and columns corresponding to class properties (e.g., for an experiment, the individual ExperimentID primary key, project name, experiment name, notebook number or the like, although not
all properties need necessarily be saved in database 180))
sorting example numbers assigned to classify experimental examples in a table head, the composition information, and the property information, respectively,
(Dorsett teaches extracting ordering see [0071] In particular, the presence or absence and ordering of entities in the XML document can represent the ordering of (and hierarchies in)
the experimental procedure;
see also [0064] 2. The ability to use the Experiment object itself to determine and represent the time-ordered sequence of all laboratory experiments related to a combinatorial library.)
to generate a composition data set and a property data set;
(Dorsett teaches experiments/composition data and various properties data sets see
[0043] Experiment objects representing ( e.g., referring to) the library. Optionally, experiments that result in reformulation or other alteration of the library ( e.g., experiments that
would result in a significantly different composition of matter and/or phase) can result in the creation of a new library.;
see also [0043] In addition to properties inherited from the Experiment base class, a Synthesis object has additional properties denoting the library's geometry (e.g., the number of rows
and columns in the matrix representing the library of materials), and may also have properties corresponding to the methods used to prepare the library, such as data for reagents, processing conditions and the like.;
see also [0040] When it has received the necessary information, database server process 130
creates an Experiment object representing the preparation of the library-e.g., a Synthesis object, as described below. Optionally, database server process 130 can also create a separate object representing each new library (such as, for example, a Library object having properties specifying, e.g., LibraryID, library geometry, the identity of materials making up the library and the like).)
extracting a pair of composition information and property information whose example numbers match from the composition data set and the property data set;
(Dorsett teaches extracting experiments IDs/experiment objects and matching experiment object data, i.e. “composition information and property information whose example numbers match from the composition data set and the property data set”
See [0051] Assuming the user "jsmith" has appropriate system authorization, after parsing this object in step 250, database server process 130 assigns an Experiment ID and Keyword
IDs and in step 260 stores the information as follows (in EXPERIMENT and KEYWORD tables);
See also [0065] The first benefit means that since the Experiment object contains attributes common to all experiments (such as library ID, date and time, notebook number and page,
staff member, and keywords), it is possible to simply query the database for all experiments performed on a given library (for example) and immediately produce a list.;
see also [0092] Database server process 130 searches the appropriate tables in database 180 for any record that satisfies the specified search terms, and assembles the results into the form of a list object (step 780). The list object can subsequently be used by user interface
program 160 to, for example, present the number of hits returned by the user's query, and it can be independently stored in database 180 for sharing with other users or for later use.)
extracting information including the name, unit, and amount of a raw material from the pair of the composition information and the property information;
(Dorsett [0065] The first benefit means that since the Experiment object contains attributes common to all experiments (such as library ID, date and time, notebook number and page,
staff member, and keywords), it is possible to simply query the database for all experiments performed on a given library (for example) and immediately produce a list;
see also [0032] Laboratory data management system 100 is configured to manage data generated during the course of the experiments performed by laboratory apparatus 150. An
experiment is performed on a library of materials. As used in this specification, a library of materials is a matrix having two or more members, generally containing some variance in chemical or material composition, amount, reaction conditions, and/or processing conditions. A member, in turn, represents a single constituent, location, or position in a library containing one set of chemicals or materials subject to one set of reaction or processing conditions.)
extracting information including the name, value, unit, property experimental method, and property experimental condition of a property from the pair of the composition information and the property information;
(Dorsett [0065] The first benefit means that since the Experiment object contains attributes common to all experiments (such as library ID, date and time, notebook number and page,
staff member, and keywords), it is possible to simply query the database for all experiments performed on a given library (for example) and immediately produce a list;
see also [0032] Laboratory data management system 100 is configured to manage data generated during the course of the experiments performed by laboratory apparatus 150. An
experiment is performed on a library of materials. As used in this specification, a library of materials is a matrix having two or more members, generally containing some variance in chemical or material composition, amount, reaction conditions, and/or processing conditions. A member, in turn, represents a single constituent, location, or position in a library containing one set of chemicals or materials subject to one set of reaction or processing conditions.)
and
automatically adding information including the name, unit, and amount of the raw material and information including the name, value, unit, property experimental method, and property experimental condition of the property to an existing database
(Dorsett teaches creating new libraries in a database with many different experiment properties/condition
See [0043] In one implementation, each library 300 is represented by at least one Experiment object 330, including a Synthesis object (instantiated from a Synthesis subclass of the Experiment base class) reflecting the library's preparation. In addition to properties inherited from the Experiment base class, a Synthesis object has additional properties denoting the library's geometry (e.g., the number of rows and columns in the matrix representing the library of materials), and may also have properties corresponding to the methods used to prepare the library, such as data for reagents, processing conditions and the like. Additional
experiments performed on the same library can result in multiple Experiment objects representing ( e.g., referring to) the library. Optionally, experiments that result in reformulation
or other alteration of the library ( e.g., experiments that would result in a significantly different composition of matter and/or phase) can result in the creation of a new
library.
[0032] Laboratory data management system 100 is configured to manage data generated during the course of the experiments performed by laboratory apparatus 150. An
experiment is performed on a library of materials. As used in this specification, a library of materials is a matrix having two or more members, generally containing some variance in chemical or material composition, amount, reaction conditions, and/or processing conditions. A member, in turn, represents a single constituent, location, or position in a library containing one set of chemicals or materials subject to one set of reaction or processing conditions.).
It would have been obvious to one having ordinary skill in the art at the time the time of the effective filing date to apply experiment library creation using experiment objects as taught by Dorsett to the system of Hong since it was known in the document processing art that it is useful to use an experiment object as a base class for all experiments where this provides several benefits, including: object-oriented design benefits of improving reusability in the sense that this base class identifies the common attributes for all laboratory experiments and the ability to use the Experiment object itself to determine and represent the time-ordered sequence of all laboratory experiments related to a combinatorial library where this means that since the Experiment object contains attributes common to all experiments (such as library ID, date and time, notebook number and page, staff member, and keywords), it is possible to simply query the database for all experiments performed on a given library (for example) and immediately produce a list and where this provides that since the actual name of the specific Experiment-derived class is stored using of the ClassName attribute in the SaveXML method as described above, it is possible to dynamically retrieve the specific experimental detail for any type of experiment by simply inspecting the value of the Experiment.ClassName property retrieved and using that name in a subsequent GetObject request where this permits the construction of universal user software that can view any experimental data records from the past, present and future definitions of experiment objects (Dorsett [0062-0066]).
Hong/Dorsett do not disclose:
by performing selecting only
composition and/or property pairs with matching example numbers from a composition table
and/or a property table, and automatically adding the selected composition and/or property pairs
to the existing database, and
wherein the predicting of the property of the compound comprises:
collecting learning data including original data and the composite formulation data;
evaluating a prediction result for each predicted property, by performing evaluating the
prediction result using a first performance evaluation index for a first predicted property
corresponding to a continuous variable, and evaluating the prediction result using a second
performance evaluation index different from the first performance evaluation index for a first
predicted property corresponding to a categorical variable
however, Asahara discloses:
by performing selecting only composition and/or property pairs with matching example numbers from a composition table and/or a property table,
(Asahara teaches generating cross-task compatible feature values using the experimental/material data table/DB , i