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finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/05/2026 has been entered.
Response to Amendment
The amendments filed 02/05/2026 have been entered. Claims 1-5 remain pending in the application.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1-5 under 35 U.S.C 103 filed 11/06/2025 have been considered and are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintain.
The applicant argues that the combination of references fails to teach or suggest the amended limitations of claim 1. Specifically, Applicant contends that Kanazawa merely predicts material properties based on a material list and does not perform supervised learning to output properties of a semiconductor device using a step list of a semiconductor elements as claimed. Applicant further asserts that Streetman, while describing semiconductor material properties, recognizes that devices characteristics depend on additional factors (e.g., carrier concentration, temperature, impurity) and therefore does not render the claimed invention obvious. According to Applicant, the amended claim requires that the step list include “a kind and a condition of a treatment of each step”, reflecting process-related factors beyond material properties, which the cited references allegedly neither teach nor suggest, nor would have been motivated by a person ordinary skilled in the art to combine absent impermissible hindsight.
The examiner respectfully disagrees. Applicant’s arguments are not persuasive. Kanazawa discloses at paragraph 42 “FIG. 4 illustrates the format of the data stored in the case-by-case material database 107. As illustrated in FIG. 4, the data includes ..., experiment conditions 404 and 405”, and paragraph 44 “Each of the case data items is data in which at least one of a target material, the definition of material properties, a preparation subject of the material, a preparation purpose of the material, a preparation time of the material, a preparation facility of the material, and the like is different, and for example, is data of an experiment result relevant to different themes. Accordingly, the definition or type of material structures, production experiment conditions, and material properties may be different for each of the cases. Appendant information such as a preparation subject, a preparation purpose, a preparation time, a preparation facility, and a theme of data, for example, may be stored in association with the case data, as text information to be capable of being referred to or searched by the user.” Kanazawa expressly teaches that the case data used for machine learning includes “experiment conditions” (¶ 42) and further discloses that the case data may differ in “production experiment conditions” and may include information such as “preparation subject, preparation purpose, preparation time, and preparation facility” (¶ 44). Under the broadest reasonable interpretation, the recited “kind of a treatment” encompasses categorical information describing how a material or semiconductor element is prepared or processed, while the recited “condition of a treatment” encompasses process parameters associated with such preparation. Kanazawa’s experiment/production conditions reasonably correspond to the claimed treatment conditions, and the preparation-related information reasonably corresponds to the claimed treatment kind under the broadest reasonable interpretation. Moreover, Kanazawa teaches using such case data in a supervised learning framework to predict material properties, such that a person ordinary skilled in the art would have found it obvious to apply Kanazawa’s condition-aware prediction framework to semiconductor elements as taught by Streetman, because material properties, including those of semiconductor materials, are known to depend on preparation and processing conditions, such that incorporating Kanazawa’s experiment conditions and preparation-related information into Streetman’s semiconductor material datasets would have been a predictable and routine optimization for improving material property prediction. Accordingly, the cited combination teaches or at least suggest the amended limitation requiring the step list to comprise a kind and a condition of a treatment of each step, and the rejection is therefore maintained.
Claim Rejections - 35 USC § 103
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.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Kanazawa et.al (US 20220359047 A1) in view of Streetman et.al (NPL: NPL: Semiconductors and Transistors)
Regarding claim 1,
Kanazawa teaches a part of the 1st limitation “ A property prediction system ... the property prediction system performing learning of supervised learning on the basis of a learning data set and making an inference of properties of a semiconductor element from prediction data on the basis of a result of the learning” (paragraph 35 “a device for predicting a material property”, paragraph 59 “In step S1005, the material property prediction unit 109 outputs a material property prediction value output by the prediction model to the display unit 110”, and paragraph 69 “a prediction model 1208 is learned by using a set of the feature quantities 1206 and data 1207 of the material physical properties A as the training data ... In the prediction model 1208, for example, RNN or DNN may be used, and in the learning, known supervised learning may be used.” Kanazawa discloses a material property prediction device and method. Within the disclosure, Kanazawa discloses perform the learning using training data, performing supervised learning, and produce an output of material property prediction value based on the learning from the training data, wherein the material may be a semiconductor element based on the teaching combination below.)
Kanazawa teaches the 2nd limitation “wherein the property prediction system for a semiconductor element comprises a memory unit, an input unit, a processing unit, and an arithmetic unit” (paragraph 38 “Note that, the material property prediction device 101 is attained by a device including a processor, a memory, ... the material property prediction receiving unit 105 receive data input by the communication unit ... In addition, the autoencoder learning unit”. Kanazawa discloses the device may have a processor for processing, a memory, a communication unit to receive input data, and an autoencoder learning unit to perform the learning of data.)
Kanazawa teaches the 3rd limitation “a function of creating the learning data set from first data stored in the memory unit” (paragraph 38 “In addition, the autoencoder learning unit 104, the autoencoder 108, and the material property prediction unit 109 are executed by software processing in which a program stored in the memory is executed”, and paragraph 69 “A structural formula 1205 is acquired from the training data 1204 and input to the autoencoder 108 to obtain feature quantities 1206.” Kanazawa discloses the autoencoder learning unit to execute program instructions stored in the memory, wherein the executing of program instructions is to perform acquiring training data as input into the autoencoder to generate feature quantities as learning data for the prediction model. The training data suggest first data stored, as a person ordinary skilled in the art would have been able to configure the training data to be a first set of data within the storage that comprises of information of semiconductor element and properties based on the teaching combination below.)
Kanazawa teaches the 4th limitation “a function of creating the prediction data from second data supplied from the input unit” (paragraph 59 “inputs the feature quantities to the prediction model trained in previous step S1003 to predict the material property value with respect to the prediction target material. In step S1005, the material property prediction unit 109 outputs a material property prediction value output by the prediction model to the display unit 110.” Kanazawa discloses input the feature quantities to the prediction model to predict the material property value, wherein the feature quantities may be learning data and may be supplied from the input unit as configured by a person ordinary skilled in the art. After the feature quantities (learning data) is obtained from the process of the autoencoder above, it may be configured through the communication input unit to be inputted into the prediction model to create prediction value. The feature quantities may be feature quantities of second data comprising of semiconductor element based on the teaching combination below.)
Kanazawa teaches the 5th limitation “a function of converting qualitative data into quantitative data” (paragraph 59 “In step S1004, first, the material property prediction unit 109 inputs the structural formula in the material list of the prediction target to the autoencoder 108 to generate the feature quantities (the descriptor)” Kanazawa discloses a using the autoencoder to generate the structural formula in the material list of the prediction target into the feature quantities (the descriptor), wherein a person ordinary skilled in the art would recognize that the generating function of the autoencoder suggest the converting process.)
Kanazawa teaches the 6th limitation “a function of performing extraction or removal on the first data and the second data”. (paragraph 44 “In this case, the chemical space designation unit 103 includes a graphical user interface (GUI) for searching the case data with a keyword or the like. The user is capable of extracting the case data to be used by using a search function.” Kanazawa discloses a GUI for user to search a case data with a keyword. The user is capable of extracting the case data to be used by using a search function. Such case data may be data of first data or second data of semiconductor element and properties based on the teaching combination below.)
Kanazawa teaches the 9th limitation “wherein the qualitative data is a material name or a compositional formula of a material used in each step of the step lists” (paragraph 41 “the case-by-case material database 107 acquires the material experimental data from the experimental data receiving unit 106 and stores the material experimental data for each of the cases”, paragraph 42 “FIG. 4 illustrates the format of the data stored in the case-by-case material database 107... The structural formula of the compound can be simply represented by using a simplified molecular-input line-entry system (SMILES) format, but is not necessarily limited thereto”, and paragraph 53 “FIG. 9 is a data structure of the material list that is received by the material property prediction receiving unit 105. As illustrated in FIG. 9, the data includes a number 901 and structural formula information 902 of the compound.” Kanazawa discloses the data stored within the case-by-case material database comprising of structural formula of a compound represented by a SMILES format for each material, wherein the structural formula of a compound within the material database for each material is analogous to the compositional formula of the material within the claim, wherein the material may correspond to each material within the material list of the semiconductor as taught by Streetman based on the teaching combination below, such that these semiconductor materials may be represented by the SMILES format for machine learning prediction.)
Kanazawa teaches the 10th limitation “wherein the step lists in the first data comprises a kind and a condition of a treatment of each step in each semiconductor element” (paragraph 42 “FIG. 4 illustrates the format of the data stored in the case-by-case material database 107. As illustrated in FIG. 4, the data includes ..., experiment conditions 404 and 405.”, and paragraph 44 “Each of the case data items is data in which at least one of a target material, the definition of material properties, a preparation subject of the material, a preparation purpose of the material, a preparation time of the material, a preparation facility of the material, and the like is different, and for example, is data of an experiment result relevant to different themes. Accordingly, the definition or type of material structures, production experiment conditions, and material properties may be different for each of the cases. Appendant information such as a preparation subject, a preparation purpose, a preparation time, a preparation facility, and a theme of data, for example, may be stored in association with the case data, as text information to be capable of being referred to or searched by the user.” Kanazawa discloses the stored case data used for learning includes “experiment condition” and that the case data for each material may differ in “production experiment condition” and may include information such as “preparation subject”, “preparation purpose”, “preparation time”, and “preparation facility”. Under the broadest reasonable interpretation, the discloses experiment/production conditions correspond to the claimed condition of a treatment, as they represent process parameters under which the material is prepared. Likewise, the preparation-related information constitutes categorical information describing how the material is processed, which corresponds to the claimed kind of a treatment. In view of Streetman’s teaching of semiconductor elements and their properties below (e.g., table 3), in which each semiconductor material is associated with characteristic property information, thereby providing structure per-element suitable for use as the claimed step lists under the broadest reasonable interpretation, a person ordinary skilled in the art would have found it obvious to apply Kanazawa’s condition-aware prediction framework including condition information and preparation-related information in data utilized for prediction to such semiconductor elements because semiconductor materials are known to depend on processing and preparation conditions.)
Kanazawa teaches the 11th limitation “wherein the step list in the second data comprises a kind and a condition of a treatment of each step in the (m+1)-th semiconductor element” (paragraph 42 “FIG. 4 illustrates the format of the data stored in the case-by-case material database 107. As illustrated in FIG. 4, the data includes ..., experiment conditions 404 and 405.”, and paragraph 44 “Each of the case data items is data in which at least one of a target material, the definition of material properties, a preparation subject of the material, a preparation purpose of the material, a preparation time of the material, a preparation facility of the material, and the like is different, and for example, is data of an experiment result relevant to different themes. Accordingly, the definition or type of material structures, production experiment conditions, and material properties may be different for each of the cases. Appendant information such as a preparation subject, a preparation purpose, a preparation time, a preparation facility, and a theme of data, for example, may be stored in association with the case data, as text information to be capable of being referred to or searched by the user.” Kanazawa discloses the stored case data used for learning includes “experiment condition” and that the case data for each material may differ in “production experiment condition” and may include information such as “preparation subject”, “preparation purpose”, “preparation time”, and “preparation facility”. Under the broadest reasonable interpretation, the discloses experiment/production conditions correspond to the claimed condition of a treatment, as they represent process parameters under which the material is prepared. Likewise, the preparation-related information constitutes categorical information describing how the material is processed, which corresponds to the claimed kind of a treatment. In view of Streetman’s teaching of semiconductor materials (e.g., table 1), in which each semiconductor material is associated with elemental compound information, thereby providing structure per-element suitable for use as the claimed step lists under the broadest reasonable interpretation, a person ordinary skilled in the art would have found it obvious to apply Kanazawa’s condition-aware prediction framework including condition information and preparation-related information in data utilized for prediction to such semiconductor elements because semiconductor materials are known to depend on processing and preparation conditions.)
Kanazawa teaches the 12th limitation “wherein the quantitative data is properties of an element and a composition” (paragraph 57 “In step S1003, the material property prediction unit 109 inputs the structural formula information of the material experimental data to the learned autoencoder 108 to generate feature quantities (a descriptor) of the compound” Kanazawa discloses generating feature quantities (a descriptor) of the structural formula of the compound, wherein the feature quantities (a descriptor) may suggests properties of an element and composition as recited within the claim based on the broadest reasonable interpretation.)
Kanazawa teaches the 13th limitation “wherein the arithmetic unit has a function of performing learning and inference of the supervised learning in order to output properties of a semiconductor device as a prediction result by using a step list of the semiconductor element” (paragraph 54 “Returning to FIG. 2, in step S206, the material property prediction unit 109 performs material property prediction and outputs a prediction result to the display unit 110”, and paragraph 69 “Then, a prediction model 1208 is learned by using a set of the feature quantities 1206 and data 1207 of the material physical properties A as the training data ... in the learning, known supervised learning may be used” Kanazawa discloses performing machine learning by using a prediction model that utilize supervised learning to predict the property of a material. One of ordinary skilled in the art may utilize the material table by Streetman based on the teaching combination below to utilize the material property prediction unit to perform material property prediction on these materials within the semiconductor.)
Kanazawa does not teach a part of the 1st limitation “...a semiconductor element...”. However, Streetman teaches this limitation (Page 2 column 2 “Semiconductors are found in column IV and neighboring columns of the periodic table (Table 1). The column-IV semiconductors silicon (Si) and germanium (Ge) are called elemental semiconductors because they are composed of single species of atoms. In addition to the elemental materials, compounds of column-Ill and column-V atoms, as well as certain combinations from columns II and VI, make up the intermetallic, or compound, semiconductors” Streetman discloses semiconductors, and transistor as well as semiconductor materials and properties. Within the disclosure, Streetman discloses element and compounds of semiconductor.)
Kanazawa does not teach the 7th limitation “wherein the first data comprises step lists of a first semiconductor element to an m-th semiconductor element (m is an integer of 2 or more) and properties of the first semiconductor element to the m-th semiconductor element”. However, Streetman teaches this limitation (page 11 table 3 “PROPERTIES OF SEMICONDUCTOR MATERIALS” Streetman discloses table 3 comprises of various semiconductor elements and their properties such as density or melting point. Under the broadest reasonable interpretation, the claimed step lists encompass structured process-related or material-related data entries associated with each semiconductor element. A person ordinary skilled in the art would have been able to configure the data from this table to be the first data stored of a machine learning model for property prediction through the teaching of Kanazawa based on the teaching combination below.)
Kanazawa does not teach the 8th limitation “...wherein the second data comprises a step list of an (m+1)-th semiconductor element”. However, Streetman teaches this limitation (page 3 table 1 “COMMON SEMICONDUCTOR MATERIALS” Streetman discloses table 1 comprises of various semiconductor elements. Under the broadest reasonable interpretation, the claimed step lists encompass structured process-related or material-related data entries associated with each semiconductor element. A person ordinary skilled in the art would have been able to configure the data from this table to be the second data stored of a machine learning model for property prediction through the teaching of Kanazawa based on the teaching combination below)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of a material property prediction device and method by Kanazawa with the teaching of semiconductors materials and properties by Streetman. The motivation to do so is referred to in Streetman’s disclosure (page 2-3 “Semiconductors are a group of materials that have electrical conductivities intermediate between those of metals and insulators. It is significant that the conductivity of these materials can be varied considerably by changes in temperature, optical excitation, and impurity content. This variability of electrical properties makes possible the wide range of modern electronic devices... Thus, the wide range of semiconductor materials offers considerable variety in properties and provides experts in electronic circuits and systems with much flexibility in the design of electronic functions.” Streetman discloses semiconductors, and transistor as well as semiconductor materials and properties. Each semiconductor material may have electrical conductivities be varied by their properties. Therefore, an embodiment of predicting the property may be useful with regard to the change of property of semiconductor materials. Kanazawa discloses an embodiment of property prediction for material. A person ordinary skilled in the art may incorporate the teaching by Kanazawa with the teaching of Streetman to obtain a system that may perform prediction on property of material of semiconductor using the embodiment of Kanazawa and dataset of Streetman.)
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated.
Streetman teaches the limitation “wherein properties of the element are any one or more of an atomic number, a group, a period, an electron configuration, an atomic weight, an atomic radius as a covalent bond radius, a Van der Waals force radius, an ionic radius, or a metal bond radius, an atomic volume, an electronegativity, an ionized energy, an electron affinity, a dipole polarizability, an elemental melting point, an elemental boiling point, an elemental lattice constant, an elemental density, and an elemental heat conductivity” (page 7 table 3 “PROPERTIES OF SEMICONDUCTOR MATERIALS” Streetman discloses table 3 comprises of various semiconductor elements and their properties such as density or melting point or an electron configuration, which are one of the property of the element as recited within the claim.)
Claims 3, 4 are rejected under 35 U.S.C. 103 as being unpatentable over Kanazawa et.al (US 20220359047 A1 in view of Streetman et.al (NPL: NPL: Semiconductors and Transistors), further in view of Endo et.al (US 20200006567 A1)
Regarding claim 3 depends on claim 1, thus the rejection of claim 1 is incorporated.
Kanazawa/Streetman does not teach the limitation “wherein the properties of the semiconductor element are change in AVsh over time obtained by a reliability test (a +GBT stress test, a +DBT stress test, a -GBT stress test, a +DGBT stress test, a +BGBT stress test, or a -BGBT stress test).” However, Endo teaches the limitation (paragraph 656 “As the reliability test, a +GBT (Gate Bias Temperature) stress test was performed. The +GBT stress test is one of the most important reliability test items in the reliability test of a transistor.”, and paragraph 658 “Here, as indexes of the amount of change in electrical characteristics of the transistor caused by stress, ΔIds (%) which shows a rate of change in Ids and ΔVsh (V) which shows a rate of time-dependent change in Vsh were used.” Endo discloses embodiment of a semiconductor device. Within the disclosure, Endo discloses a transistor, which is an element of the semiconductor device. A +GBT reliability stress test is performed, wherein the transistor comprises of amount of change in electrical characteristics of the transistor caused by stress which is shown by a rate of change in ΔVsh (V) which shows a rate of time-dependent change in Vsh.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of a material property prediction device and method and the teaching of semiconductors materials and properties by Kanazawa/Streetman with the teaching of embodiment of a semiconductor device by Endo. The motivation to do so is referred to in Endo’s disclosure (paragraph 617 “In this example, the transistor 1000 included in the semiconductor device of one embodiment of the present invention illustrated in FIG. 1 was fabricated (Sample A). For comparison, a transistor that does not include the oxide 406d (S4) was also fabricated (Sample B). Measurement of the electrical characteristics and a reliability test were performed on each transistor.”, paragraph 663 “From the above results, it is confirmed that the density dependence of ΔVsh in Sample A is greatly improved as compared with that in Sample B.”, and paragraph 672 “From the above results, it was confirmed that the transistor having a structure including the S4 of one embodiment of the present invention has small density dependences of the initial characteristics of the transistor and the reliability, and thus has favorable transistor characteristics and high reliability.” Endo discloses the benefit of the invention through conducting example experiment with two semiconductor devices with each transistor, wherein one transistor does not include the oxide 406d (S4). The experiment with the reliability stress test showcases a result that the semiconductor device that include the oxide 406d (S4) is greatly improved as it has small density dependences of the initial characteristics of the transistor and the reliability, and thus has favorable transistor characteristics and high reliability. Therefore, a person ordinary skilled in the art can further incorporate the teaching of an oxide semiconductor as well as reliability stress test for amount of change in electrical characteristics to further test and improve the semiconductor embodiment as discloses within the teaching combination.)
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
Endo teaches the limitation “wherein the properties of the semiconductor element are Id-Vg characteristics or Id-Vd characteristics.” (paragraph 651 “The electrical characteristics of Sample A and Sample B were measured by measuring change in source-drain current (hereinafter referred to as a drain current Id) when a source-gate potential (hereinafter referred to as a gate potential Vg) ... That is, Id-Vg characteristics were measured.” Endo discloses the electrical characteristics of samples were measured by measuring change in source-drain current Id when a source-gate potential Vg, such that Id-Vg characteristics were measured.)
The motivation to combine the teaching of Kanazawa/Streetman with the teaching of Endo is similar to the motivation in claim 3 above.
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kanazawa et.al (US 20220359047 A1 in view of Streetman et.al (NPL: NPL: Semiconductors and Transistors), further in view of Tripashi et.al (NPL: Different Type of Feature Engineering Encoding Techniques for Categorical Variable Encoding)
Regarding claim 5 depends on claim 1, thus the rejection of claim 1 is incorporated.
Kanazawa/Streetman does not teach the limitation “wherein the processing unit has a function of quantifying the qualitative data using Label Encoding” However, Tripashi teaches the limitation (Label Encoding: — In this encoding each category is assigned a value from 1 through N (here N is the number of category for the feature) ... categories that have some ties or are close to each other lose some information after encoding.” Tripashi discloses various encoding methodologies, wherein one of the methodologies comprise of label encoding. The label encoding is a data preprocessing technique that converts categorical variables into numerical representations with unique integer. The label encoding technique may be performed toward the data of the structural formula of the compound of the semiconductor element to obtain a numerical representation for machine learning process based on the teaching combination below.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of a material property prediction device and method and the teaching of semiconductors materials and properties by Kanazawa/Streetman with the teaching of various encoding methodologies including label encoding by Tripashi. The motivation to do so is referred to in Tripashi’s disclosure (“In many practical data science activities, the data set will contain categorical variables. These variables are typically stored as text values”. Since machine learning is based on mathematical equations, it would cause a problem when we keep categorical variables as is... The algorithms that do not support categorical values, in that case, are left with encoding methodologies.” Tripashi discloses the necessity to encode categorial variables for machine learning. A person ordinary skilled in the art would recognize that the semiconductor element within the teaching combination of Kanazawa/Streetman, wherein the teaching combination is subjected to machine learning, may require encoding methodologies to encode data of semiconductor element and formula to further process the machine learning model. Therefore, a person ordinary skilled in the art would have been able to incorporate various encoding methodologies as disclosed by Tripashi into the teaching combination, wherein one of the methods can be label encoding to obtain numerical representations for various semiconductor elements within the table 1 and 3 as mentioned above.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190064751 A1 by Ohmori et.al – Ohmori discloses a method of retrieving a condition given to a semiconductor treatment apparatus, wherein the semiconductor treatment apparatus comprises many types of apparatus (e.g., a pattern processing apparatus, an ion implanting apparatus, a heating apparatus, ...) to predict a processing result of the semiconductor apparatus for performance consideration, which corresponds to the claimed kind and condition of treatment of semiconductor element as claimed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY TU DIEP whose telephone number is (703)756-1738. The examiner can normally be reached M-F 8-4:30.
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/DUY T DIEP/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123