CTNF 18/690,292 CTNF 84704 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims This Non-Final action is in reply to the application filed 12/11/2025. Claims 1-14 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-14 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-12 are directed to a system, claim 13 is directed to a process (method), claim 14 is directed to a non-transitory computer readable storage medium. Thus, each of the claims fall within one of the four statutory categories. Step 2A-Prong 1: Claim 1 recites in part, “ A device management system comprising: a memory storing instructions; and at least one processor configured to execute the instructions to receive, in a concealed form, a parameter relating to an operation status of a semiconductor manufacturing device , wherein the parameter is used in an analysis relating to indication maintenance of the semiconductor manufacturing device ; perform an analysis, by a secure calculation which uses the received parameter in the concealed form and which is relating to indication maintenance of a component of the semiconductor manufacturing device; and output an analysis result of the indication maintenance of the component ” The underlined limitations above demonstrate independent claim 1 is directed toward the abstract idea for receiving a parameter relating to an operation status of a semiconductor manufacturing device relating to indication maintenance, perform an analysis by a secure calculation and output an analysis result in a computing environment. Applicant’s specification emphasizes a device management system and method whereby the state of a semiconductor manufacturing device is measured and monitored and maintenance is indicated regarding the deterioration state of the equipment and replacing or repairing the components (¶21). Representative Claim 1 is considered an abstract idea because the underlined limitations pertains to (i) mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion) with the exception of “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication management system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14], a human being with a pen and paper can receive, in a concealed form, a parameter relating to an operation status of a semiconductor manufacturing device … relating to indication maintenance … perform an analysis, by a secure calculation … and output an analysis result of the indication maintenance of the component ”; and (ii) managing personal behavior or relationships or interactions between people (including social activities , teaching, and following rules or instructions ) “ receive, in a concealed form, a parameter relating to an operation status of a semiconductor manufacturing device … perform an analysis, by a secure calculation … and output an analysis result of the indication maintenance of the component ” and therefore also directed to the certain methods of organizing human activity groupings of abstract ideas. Hence, the claim recites an abstract idea--see MPEP 2106.04(II). Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication management system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14], merely provide an abstract-idea based solution using data gathering and analysis; merely provide instructions for organizing human interactions, and implement the abstract idea recited above utilizing the “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication management system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14], as tools to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h). These elements do not impose any meaningful limits on practicing the abstract idea—see MPEP 2106.05(g). Independent claim 1 fails to operate the recited “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication maintenance system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14], (which are merely standard computer technology and hardware/software components- see applicant’s disclosure ¶8: “An indication maintenance system …includes: one or more semiconductor manufacturer servers; and a device management system”; ¶16:“the semiconductor manufacturing device according to the first example embodiment of the present disclosure is achieved by a computer device 500 including a processor…the device management system 100 includes a memory such as a central processing unit (VPU) 501, a read only memory (ROM) 502, and a random access memory (RAM) 503, a storage device 505 such as a hard disk that stores a program 504, a communication interface (I/F) 508 for network connection, and an input/output interface 511 that inputs and outputs data”; ¶20: “The device management system 100 may be implemented by one physically coupled device or may be implemented by a plurality of devices”; ¶21: “The semiconductor manufacturing device refers to a general device used for manufacturing a semiconductor …The indication maintenance means, for example, measuring and monitoring the state of the semiconductor manufacturing device, grasping or indicating the deterioration state of the equipment, and replacing or repairing the components”; ¶22: “The component used in the semiconductor manufacturing device is, for example, a component that particularly affects the yield and the accuracy of the manufactured semiconductor among components used in the semiconductor manufacturing device. Examples of the component used in the semiconductor manufacturing device include a heating lamp, a light source, an ion source, a turbo molecular pump, a vacuum valve, and a chamber”) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for receiving a parameter relating to an operation status of a semiconductor manufacturing device relating to indication maintenance, perform an analysis by a secure calculation and output an analysis result in a computing environment —see MPEP 2106.05(a). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). Applicant’s limitations as recited above do nothing more than supplement the abstract using generic computer and networking components performing generic computer functions (receive, analyze, output, store, conceal transmit) such that it amounts to no more than mere instruction to apply the exception using a generic computer component -see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h). Independent claims 12-14 recite substantially similar limitations as independent claim 1, therefore they are also directed to the same abstract idea. Dependent claims 2-11 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2-11, recite additional data gathering and processing steps (integrating, analyzing, output, generate). For example dependent claims 2, 6 and 8, 10, 11 recite in part, “wherein the at least one processor is further configured to execute”, claim 3 recites in part, “wherein each of the plurality of servers is”, claims 4 and 5 recite in part, “wherein the parameter is a parameter defined by”, claim 7 recites in part, “wherein the learned model is a model that”, claim 9 recites in part, “ wherein the secure calculation is”, which are still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components. The additional elements in the dependent claims, “learned model” [claim 6] only serves to further limit the abstract idea utilizing “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication maintenance system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14] as a tool, and to generally link the use of the abstract idea to a particular technological environment; hence are nonetheless directed towards fundamentally the same abstract idea as their respective independent claim since they fail to impose any meaningful limits on practicing the abstract idea-see applicant’s disclosure, ¶58: “the learned model is a model that inputs the parameter and outputs necessity of maintenance of a component in the semiconductor manufacturing device… the learned model generates a model for estimating necessity of maintenance of a component of the semiconductor manufacturing device based on a relationship between a parameter acquired in a past and necessity of maintenance” Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea. Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, the additional elements “a device management system”, “a memory”, “at least one processor” [claim 1]; “indication maintenance system”, “one or more semiconductor manufacturer servers” [claim 12]; “semiconductor manufacturing device”, “components” [claim 13]; “a non-transitory recording medium”, “computer” [claim 14], amounts to no more than mere instructions to apply the exception using a generic computer component which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea). Further, giving the broadest reasonable interpretation of the claim limitations in light of the specification, applicant’s “learned model” amounts to no more than applying the judicial exception using generic computing components for carrying out the method/system steps linking the use of the judicial exception to a computing environment. In this case, the “learned model” is generically used to further process /transmit/communicate received data utilizing rules logic and fails to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea- and amounts to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. There is no improvement to the “learned model” which is merely used generically to further transmit/communicate received data via a processor component (computer system). Hence, the 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. Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea receiving a parameter relating to an operation status of a semiconductor manufacturing device relating to indication maintenance, perform an analysis by a secure calculation and output an analysis result in a computing environment. Hence, claims 1-14 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101. See 2019 PEG and MPEP 2106. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-20-02-aia AIA This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. 07-21-aia AIA Claim s 1-5 and 9-14 are rejected under 35 U.S.C. 103 as being unpatentable over Karasawa, US Patent Application Publication No US 2007/0179751 in view of Kawamoto et al., US Patent Application Publication No US A1 2014/0012862A1 . With respect to claims 1 and 12-14, Karasawa discloses, A device management system comprising: a memory storing instructions; and at least one processor configured to execute the instructions to receive, in a concealed form, a parameter relating to an operation status of a semiconductor manufacturing device (Abstract: “A vendor-side computer 26 obtains operating state data obtained by a monitoring device 18 provided in an apparatus 10 via a communication line 100, and monitors the operating state of the apparatus 10 from a remote location”; ¶21: “The apparatus may be a manufacturing apparatus for semiconductor devices or liquid crystal display devices”) wherein the parameter is used in an analysis relating to indication maintenance of the semiconductor manufacturing device ;(Abstract: “Maintenance data at a part replacing time is transmitted from the apparatus 10, and the vendor-side computer 26 that has received maintenance data calculates an optimal replacement period of a part based on this”; ¶21: “The apparatus may be a manufacturing apparatus for semiconductor devices or liquid crystal display devices”) output an analysis result of the indication maintenance of the component” (¶67: “Operating state data that is transmitted to the plant-side computer 11 from the apparatus 10 via the plant internal wiring network 12 is also transmitted to the vendor via the communication line 100. Moreover, maintenance data relating to the part replacement that is periodically transmitted from the apparatus 10 is also transmitted to the vendor. Additionally, operating state data and maintenance data may be obtained by connection to the plant-side computer 11 from the vendor”; Fig 4, ¶84: “During operation of the apparatus 10, the vendor-side computer 26 reads each parameter of the standard profiles illustrated in FIG. 4 according to the process chart illustrated in FIG. 5, and compares the change profile of each parameter in actual operating state data received from the apparatus 10 with the read standard profile to monitor the status of the apparatus 10. When an error between the actual change profile of each parameter and the standard profile is not within, for example, 5%, the vendor-side computer 26 judges that the apparatus 10 is in an abnormal status”; ¶100: “when it is determined that the difference between the measured profile and the reference profile is not within the predetermined error range (step S15; No), the vendor-side computer 26 judges that trouble has occurred in the apparatus 10. Then, the vendor-side computer 26 reports the trouble occurrence and the status to the plant 101 via the input/output device 25 of the plant-side computer 11, and reads contact information such as an e-mail address of a (person) in charge of maintenance at the manufacturer side (delivery destination) from the contact information database 34, and informs the person in charge of maintenance of trouble occurrence (step S16)”) one or more semiconductor manufacturer servers; and a device management system, wherein the one or more semiconductor manufacturer servers each include comprising: a memory storing instructions; and at least one processor configured to execute the instructions to: (¶58: “The apparatuses 10 are production apparatuses that are supplied by one or a plurality of vendor supply. The apparatuses 10 are computers (servers) that store various data of, for example, pre-process devices (film forming device, heat processing device, etc.) and post-process devices (mounting unit, test apparatus, etc.) of a semiconductor manufacturing apparatus”; ¶59: “As illustrated in FIG. 1, the apparatus 10 includes a central processing unit 13, a monitoring section 14, a communication control device 15, an input/output control section 16, and a storage section 17”; ¶60: “The central processing unit 13 includes RAM, ROM and the like (not shown), and performs predetermined processing to control the entire operation of the apparatus 10”) store a parameter relating to an operation status of a semiconductor manufacturing device; (Abstract: “A vendor-side computer 26 obtains operating state data obtained by a monitoring device 18 provided in an apparatus 10 via a communication line 100, and monitors the operating state of the apparatus 10 from a remote location”; ¶57: “The plant 101 is, for example, a semiconductor manufacturing plant and includes, in its interior, apparatuses 10, a plant-side computer 11, and a plant internal wiring network that connects each apparatus 10 and the plant-side computer 11”; ¶58: “The apparatuses 10 are production apparatuses that are supplied by one or a plurality of vendor supply. The apparatuses 10 are computers (servers) that store various data of, for example, pre-process devices (film forming device, heat processing device, etc.) and post-process devices (mounting unit, test apparatus, etc.) of a semiconductor manufacturing apparatus”; Fig 2, Fig 3, ¶76-¶80; ¶78: “FIG. 2 illustrates an example of operating status data stored in the history information database 32. As illustrated in FIG. 2, in the history information database 32, operating state data is collected for each delivery destination (manufacturer) of the apparatus 10, each plant to be used, and each individual (serial number). Operating state data is physical data representing the operating state of the apparatus 10, for example, a physical quantity based on parameters of temperature, pressure, gas flow in the chamber in a predetermined processing step provided to a processing object by the apparatus 10. Operating state data is obtained by the monitoring device 18 of the apparatus 10, and is periodically transmitted to the vendor-side computer 26. In the history information database 32, for example, data as shown by *1 in FIG. 2 is stored as temperature data”) Karasawa discloses all of the above limitations, Karasawa does not distinctly disclose the following limitations, but Kawamoto however as shown discloses, perform an analysis, by a secure calculation which uses the received parameter in the concealed form and which is relating to indication maintenance of a component of the semiconductor manufacturing device (¶34: “The reception unit may receive external data held by an external apparatus and a request for the sample data relating to relevant data relevant to the external data in the database. In this case, the calculation unit may calculate the frequency function with a combination of the external data and the relevant data as the one or more attribute values. The generation unit may generate the sample data including the combination of the external data and the relevant data as the one or more sample attribute values on the basis of the frequency function calculated”; ¶37: “The generation of the sample data for the combination of the external data and the relevant data described above may be executed on the basis of the multi-party protocol. As a result, it is possible to attain the data providing system useful for the data provider and the data user”; ¶38: “The reception unit may receive the external data encrypted by fully homomorphic encryption. In this case, the information processing apparatus may further include an encryption unit configured to encrypt the relevant data by the fully homomorphic encryption. Further, the calculation unit may calculate the frequency function in relation to a combination of the external data encrypted and the relevant data encrypted. The generation unit may generate, on the basis of the frequency function calculated, the sample data relating to the combination of the external data encrypted and the relevant data encrypted”; Fig 18A, Fig 18B, ¶207: “A reception unit 311 of the data providing apparatus 310 receives the request for the pseudo sample data 350 (Step 303). The data providing apparatus 310 transmits, to the data reception apparatus 320, a request for encrypted external data for creating the pseudo sample data 350 (Step 304)”; ¶210: “An encryption unit of the data reception apparatus 320 encrypts the external data obtained… the external data is encrypted by fully homomorphic encryption… the encryption unit has a key storage unit, and in the key storage unit, a public key and a secret key are stored. The public key is used to execute the encryption of the external data (Step 307)”; ¶211: “By the fully homomorphic encryption, a sum or product calculation is possible in the encrypted state, and in the case of an algorithm which can be subjected to a logic, it is possible to obtain an output result of the algorithm with an input value concealed”; ¶222: for the combination of the external data and the relevant data relating thereto, the pseudo sample data 350 is generated. As a result, it is possible to generate the pseudo sample data 350 for the correlation between the data relevant to each other, for example. It is also possible to have the correlation between data held by a plurality of data providers, for example. As a result, it is possible to attain the data providing system 300 useful for the data provider and the data user “; ¶223: “by the multi-party computation, the pseudo sample data 350 relating to the combination of the external data and the relevant data is generated. That is, the frequency function is calculated by the fitting or the maximum likelihood estimation method with the encrypted combination data as the attribute value. On the basis of the frequency function, the pseudo sample data 350 is generated. As a result, it is possible to generate, provide, and receive the pseudo sample data 350 with the data concealed with respect to each other”) conceal a parameter stored and transmit a parameter concealed to a device management system in a concealed form, and the device management system (¶34: “The reception unit may receive external data held by an external apparatus and a request for the sample data relating to relevant data relevant to the external data in the database. In this case, the calculation unit may calculate the frequency function with a combination of the external data and the relevant data as the one or more attribute values. The generation unit may generate the sample data including the combination of the external data and the relevant data as the one or more sample attribute values on the basis of the frequency function calculated”; ¶37: “The generation of the sample data for the combination of the external data and the relevant data described above may be executed on the basis of the multi-party protocol. As a result, it is possible to attain the data providing system useful for the data provider and the data user”; ¶38: “The reception unit may receive the external data encrypted by fully homomorphic encryption. In this case, the information processing apparatus may further include an encryption unit configured to encrypt the relevant data by the fully homomorphic encryption. Further, the calculation unit may calculate the frequency function in relation to a combination of the external data encrypted and the relevant data encrypted. The generation unit may generate, on the basis of the frequency function calculated, the sample data relating to the combination of the external data encrypted and the relevant data encrypted”; ¶220: “The transmission unit 315 transmits the generated pseudo sample data ((x1, y1), (x2, y2), . . . (xn, yn)) to the data reception apparatus 320 (Step 314). The data reception apparatus 320 receives the pseudo sample data ((x1, y1), (x2, y2), . . . (xn, yn)) (Step 315)”; ¶221: “A decoding unit of the data reception apparatus 320 decodes the pseudo sample data 350, which is the data encrypted. In this embodiment, the secret key stored in the key storage unit of the data reception apparatus 320 is used”; ¶222: “it is possible to generate the pseudo sample data 350 for the correlation between the data relevant to each other, for example. It is also possible to have the correlation between data held by a plurality of data providers, for example. As a result, it is possible to attain the data providing system 300 useful for the data provider and the data user”; ¶223: “by the multi-party computation, the pseudo sample data 350 relating to the combination of the external data and the relevant data is generated. That is, the frequency function is calculated by the fitting or the maximum likelihood estimation method with the encrypted combination data as the attribute value. On the basis of the frequency function, the pseudo sample data 350 is generated. As a result, it is possible to generate, provide, and receive the pseudo sample data 350 with the data concealed with respect to each other”) Karasawa teaches a method/system for improving productivity of apparatuses whereby a vendor-side computer obtains operating state data, and monitors the operating state of the apparatus from a remote location. Maintenance data at a part replacing time is transmitted from the apparatus, and the vendor side computer calculates an optimal replacement period of a part and sends to a plant. Kawamoto discloses an information processing apparatus including a calculation and generation unit. The calculation unit may select a predetermined model function and fit the predetermined model function to the ratio of the appearance count for each attribute value to calculate the frequency function. Kawamoto also discloses a data reception and data providing apparatus for aggregating, calculating and concealing data on the basis of a multi-party protocol whereby computations are executed while data of each party is concealed. Karasawa and Kawamoto are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa with the information processing method/system as taught by Kawamoto since it allows for receiving, correlating generating and providing useful data relevant to a plurality of data providers with the data concealed with respect to each other (¶206-¶224). With respect to claim 2, Karasawa and Kawamoto disclose all of the above limitations, Kawamoto further discloses, wherein the at least one processor is further configured to execute the instructions to integrate the plurality of parameters received by secure calculation in a concealed form in a case where the same type of parameters are received from the plurality of servers (¶34: “The reception unit may receive external data held by an external apparatus and a request for the sample data relating to relevant data relevant to the external data in the database. In this case, the calculation unit may calculate the frequency function with a combination of the external data and the relevant data as the one or more attribute values. The generation unit may generate the sample data including the combination of the external data and the relevant data as the one or more sample attribute values on the basis of the frequency function calculated”; ¶37: “The generation of the sample data for the combination of the external data and the relevant data described above may be executed on the basis of the multi-party protocol. As a result, it is possible to attain the data providing system useful for the data provider and the data user”; ¶38: “The reception unit may receive the external data encrypted by fully homomorphic encryption. In this case, the information processing apparatus may further include an encryption unit configured to encrypt the relevant data by the fully homomorphic encryption. Further, the calculation unit may calculate the frequency function in relation to a combination of the external data encrypted and the relevant data encrypted. The generation unit may generate, on the basis of the frequency function calculated, the sample data relating to the combination of the external data encrypted and the relevant data encrypted”; ¶223: “by the multi-party computation, the pseudo sample data 350 relating to the combination of the external data and the relevant data is generated. That is, the frequency function is calculated by the fitting or the maximum likelihood estimation method with the encrypted combination data as the attribute value. On the basis of the frequency function, the pseudo sample data 350 is generated. As a result, it is possible to generate, provide, and receive the pseudo sample data 350 with the data concealed with respect to each other”) Karasawa and Kawamoto are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa with the information processing method/system as taught by Kawamoto since it allows for receiving, correlating generating and providing useful data relevant to a plurality of data providers with the data concealed with respect to each other (¶206-¶224). Karasawa further discloses, analyze indication maintenance of components of a semiconductor manufacturing device using the parameters integrated (¶58: “The apparatuses 10 are production apparatuses that are supplied by one or a plurality of vendor supply. The apparatuses 10 are computers (servers) that store various data of, for example, pre-process devices (film forming device, heat processing device, etc.) and post-process devices (mounting unit, test apparatus, etc.) of a semiconductor manufacturing apparatus”; ¶78: “As illustrated in FIG. 2, in the history information database 32, operating state data is collected for each deliver y destination (manufacturer) of the apparatus 10, each plant to be used, and each individual (serial number). Operating state data is physical data representing the operating state of the apparatus 10, for example, a physical quantity based on parameters of temperature, pressure, gas flow in the chamber in a predetermined processing step provided to a processing object by the apparatus 10. Operating state data is obtained by the monitoring device 18 of the apparatus 10, and is periodically transmitted to the vendor-side computer 26. In the history information database 32, for example, data as shown by *1 in FIG. 2 is stored as temperature data”) With respect to claim 3, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein each of the plurality of servers is a server owned by a different semiconductor manufacturer (¶58: “The apparatuses 10 are production apparatuses that are supplied by one or a plurality of vendor supply. The apparatuses 10 are computers (servers) that store various data of, for example, pre-process devices (film forming device, heat processing device, etc.) and post-process devices (mounting unit, test apparatus, etc.) of a semiconductor manufacturing apparatus”) With respect to claim 4, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein the parameter is a parameter defined by a difference from a reference value (¶84: “During operation of the apparatus 10, the vendor-side computer 26 reads each parameter of the standard profiles illustrated in FIG. 4 according to the process chart illustrated in FIG. 5, and compares the change profile of each parameter in actual operating state data received from the apparatus 10 with the read standard profile to monitor the status of the apparatus 10. When an error between the actual change profile of each parameter and the standard profile is not within, for example, 5%, the vendor-side computer 26 judges that the apparatus 10 is in an abnormal status”) With respect to claim 5, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein the parameter is a parameter relating to an operation status of a film formation related device (¶84: ““During operation of the apparatus 10, the vendor-side computer 26 reads each parameter of the standard profiles illustrated in FIG. 4 according to the process chart illustrated in FIG. 5, and compares the change profile of each parameter in actual operating state data received from the apparatus 10 with the read standard profile to monitor the status of the apparatus 10. When an error between the actual change profile of each parameter and the standard profile is not within, for example, 5%, the vendor-side computer 26 judges that the apparatus 10 is in an abnormal status”; Fig 12-Fig 15, ¶117: “The apparatus 10 includes n CVD (Chemical Vapor Deposition) devices 36 (36.sub.1 to 36.sub.n), other devices (m oxidizing devices 37 (37.sub.1 to 37.sub.m)), and j measuring devices 41 (41.sub.1 to 41.sub.j). n CVD (Chemical Vapor Deposition) devices 36 (36.sub.1 to 36.sub.n) have substantially the same structure, and each contains a processing object such as a semiconductor wafer, and performs film forming processing on the processing object by CVD”; ¶124: In the history information database 32, operating state data, maintenance data and measurement data of wafer W are stored. The operating state data and maintenance data are substantially the same as the data structure of the aforementioned system. However, in operating state data, the number of RAN times (RAN time) indicating that how often (or how many times) the operation is carried out after a predetermined part replacement or cleaning, is stored…Measurement data is data such as a thickness of a film formed on the wafer W by the CVD device 36, uniformity of the film, and the like, and is stored to correspond to operating state data as illustrated in FIG. 14. Measurement data is measured by a measuring device 41 and transmitted to the vendor-side computer 26 periodically”; ¶128: “During the monitoring operation, the vendor-side computer 26 receives real-time operating state data of the CVD apparatus 36 that the monitoring devices 18 such as temperature sensors S1 to S5 obtain (step S11). Then, the vendor-side computer 26 stores received operating state data in the history information database 32 (step S12)”; ¶129: “The vendor-side computer 26 reads a predetermined parameter, for example, data based on temperature (change profile) in operating state data received from the history information database 32 (step S13). The vendor-side computer 26 compares the reference profile relating to the temperature stored in the profile information database 33 with the profile of read (measured) data (step S14). Here, in the present embodiment, in order that the processing result becomes uniform between the surfaces between the wafers W and in the surfaces, a reference profile (temperature recipe) adjusted in advance is prepared for each zone, and the measured profile and the reference profile are compared with each other for each zone”) With respect to claim 9, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein the secure calculation is a secure variation calculation (¶34: “The reception unit may receive external data held by an external apparatus and a request for the sample data relating to relevant data relevant to the external data in the database. In this case, the calculation unit may calculate the frequency function with a combination of the external data and the relevant data as the one or more attribute values. The generation unit may generate the sample data including the combination of the external data and the relevant data as the one or more sample attribute values on the basis of the frequency function calculated”; ¶37: “The generation of the sample data for the combination of the external data and the relevant data described above may be executed on the basis of the multi-party protocol. As a result, it is possible to attain the data providing system useful for the data provider and the data user”; ¶38: “The reception unit may receive the external data encrypted by fully homomorphic encryption… the information processing apparatus may further include an encryption unit configured to encrypt the relevant data by the fully homomorphic encryption… the calculation unit may calculate the frequency function in relation to a combination of the external data encrypted and the relevant data encrypted. The generation unit may generate, on the basis of the frequency function calculated, the sample data relating to the combination of the external data encrypted and the relevant data encrypted”; ¶220: “The transmission unit 315 transmits the generated pseudo sample data ((x1, y1), (x2, y2), . . . (xn, yn)) to the data reception apparatus 320 (Step 314). The data reception apparatus 320 receives the pseudo sample data ((x1, y1), (x2, y2), . . . (xn, yn)) (Step 315)”; ¶221: “A decoding unit of the data reception apparatus 320 decodes the pseudo sample data 350, which is the data encrypted. In this embodiment, the secret key stored in the key storage unit of the data reception apparatus 320 is used”; ¶222: “it is possible to generate the pseudo sample data 350 for the correlation between the data relevant to each other, for example. It is also possible to have the correlation between data held by a plurality of data providers… As a result, it is possible to attain the data providing system 300 useful for the data provider and the data user”; ¶223: “by the multi-party computation, the pseudo sample data 350 relating to the combination of the external data and the relevant data is generated. That is, the frequency function is calculated by the fitting or the maximum likelihood estimation method with the encrypted combination data as the attribute value. On the basis of the frequency function, the pseudo sample data 350 is generated. As a result, it is possible to generate, provide, and receive the pseudo sample data 350 with the data concealed with respect to each other”) Karasawa and Kawamoto are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa with the information processing method/system as taught by Kawamoto since it allows for receiving, correlating generating and providing useful data relevant to a plurality of data providers with the data concealed with respect to each other (¶206-¶224). With respect to claim 10, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein the at least one processor is further configured to execute the instructions to: make an arrangement relating to maintenance of the component based on a result analyzed (¶24: “When the monitoring means detects trouble of the apparatus to specify a trouble part of the apparatus, the monitoring means may perform order processing or order preparation processing for a replacement part of the trouble part”; ¶37: “order processing or order preparation processing for a replaced part may be performed based on the maintenance data”; ¶85: “When receiving maintenance data (part replacement information) from the apparatus 10, the vendor-side computer 26 performs order processing or order preparation processing. Namely, the vendor-side computer 26 sends order information of the relevant part to the supplier that supplies the part with reference to the contact information database 34 illustrated in FIG. 6”) With respect to claim 11, Karasawa and Kawamoto disclose all of the above limitations, Karasawa further discloses, wherein the at least one processor is further configured to execute the instructions to: order necessary components in the semiconductor manufacturing device (¶85: “When receiving maintenance data (part replacement information) from the apparatus 10, the vendor-side computer 26 performs order processing or order preparation processing. Namely, the vendor-side computer 26 sends order information of the relevant part to the supplier that supplies the part with reference to the contact information database 34 illustrated in FIG. 6”; ¶86: “when detecting abnormity of the apparatus 10 during the monitoring operation and judging that replacement for a specific part is needed, the vendor-side computer 26 sends order information of the relevant part to the supplier with reference to the contact information database 34 (order processing or order preparation processing)”) 07-21-aia AIA Claim s 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Karasawa, Kawamoto in further view of Oberoi et al., US Patent Application Publication No US 2021/0398831 A1 . With respect to claim 6, Karasawa and Kawamoto disclose all of the above limitations, the combination of Karasawa and Kawasmoto does not distinctly describe the following limitations but Oberoi however as shown discloses, wherein the at least one processor is further configured to execute the instructions to: analyze indication maintenance of components of the semiconductor manufacturing device by using a learned model (¶7: “Particular embodiments provide virtual attendants and virtual consultants (bots) assistance on semiconductor equipment. Particular embodiments include using software bots, artificial intelligence (AI), machine learning (ML), and natural-language processing (NLP) on semiconductor-manufacturing tools”; ¶20: “the semiconductor-manufacturing system includes a controller configured to operate the processing components… the semiconductor-manufacturing system includes a controller configured to operate the processing components. The semiconductor-manufacturing system includes a text bot in communication with the semiconductor-manufacturing system. The text bot can have various alternative architectures… an on-tool bot can address one group or type of inquiry (such as diagnostic information), while a remote server-based bot can access deep learning and network data, as well as data from other tools within an integration flow to predict failures and suggest actions for optimization”; ¶21: “the text bot is configured to return or respond to inquiries from users (at-tool users or remote users). The text bot can also execute actions on the tool such as wafer processing or tool maintenance. By way of a non-limiting example, the text bot can be used for fault detection and classification (FDC)”; ¶23: “AI translations can assist with distributing Copy Exact periodic maintenance and with Best Known Methods (BKMs) for operation and maintenance. Headlock remote pointing can assist with Copy Exact error recovery. In particular embodiments, semiconductor-manufacturing systems have AR user hardware. Both tool use and tool maintenance/repair can be captured and delivered to users via video in AR or virtual-reality (VR) systems. Particular embodiments can include unstructured AR video acquisition learning. Embodiments can include digital transformation automation augmentation”; ¶24: “Deep learning and AI analysis can correlate particular actions (as well as corresponding video) to a best-known method for highest yield, longest time between failure, and so forth”; ¶31: “Data pre-processing can also include diagnostic data learning. This can include data structuring, creating a model with an application program interface for queries to trigger alarms based on parameters or pull graphs on demand. Data analytics can be used to address creep, drift, or abnormal alarms with push notification”; ¶47: “the AI engine on a semiconductor-manufacturing system is configured to learn through structured learning how to best optimize input and output data and operational efficacy of that particular semiconductor-manufacturing system. The AI engine on a tool (or in network communication with the tool) is configured to link relevant yield data available elsewhere in a semiconductor fabrication facility (or facilities) to optimize a function of that tool. In particular embodiments, on-tool automated assistants (text bots, NLP bots, language bots, AI engines) can function as a first point of information and resource before escalating to field service engineering”) Oberai teaches automated assistance in a semiconductor manufacturing environment utilizing software bots, artificial intelligence (AI) engines, machine learning (ML) programs, and natural language processing on semiconductor manufacturing tolls to provide user assistance and automated optimization via user communication devices. Oberai discloses that an artificial intelligence engine in communication with one or more semiconductor-manufacturing systems has access to a process-data store that comprises data extracted and transformed from corresponding operation manuals; and is configured to monitor operation of the one or more semiconductor-manufacturing systems and predict failure conditions. Oberai further teaches providing recommended tool-operation parameters to increase device yield compared with observed tool operation parameters. Oberai also discloses that bots can be implemented with AI engines to assist with periodic maintenance, best known methods for operation, predict failures and suggest actions for optimization. Karasawa, Kawamoto and Oberai are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more (semiconductor) attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa, the information processing method/system of Kawamoto and the machine learning/artificial intelligence techniques as taught by Oberai to learn through structured learning how to best optimize input and output data and operational efficacy of a particular semiconductor-manufacturing system (¶20-¶24,¶31, ¶47). With respect to claim 7, Karasawa, Kawamoto and Oberai disclose all of the above limitations, Oberai discloses, wherein the learned model is a model that inputs the parameter and outputs necessity of maintenance of a component in the semiconductor manufacturing device (¶23: “AI translations can assist with distributing Copy Exact periodic maintenance and with Best Known Methods (BKMs) for operation and maintenance. Headlock remote pointing can assist with Copy Exact error recovery. In particular embodiments, semiconductor-manufacturing systems have AR user hardware. Both tool use and tool maintenance/repair can be captured and delivered to users via video in AR or virtual-reality (VR) systems. Particular embodiments can include unstructured AR video acquisition learning. Embodiments can include digital transformation automation augmentation”; ¶31: “For data ingestion, one or more data servers can be configured to receive various raw data. Data can be consumed from any tool-related resource… Data is extracted, transformed and loaded from structured and unstructured sources. A data pre-processing step can be executed. This can include key pair indexing, sentence splitting, tokenization, part-of-speech tagging, parentheses correction, et cetera. Data pre-processing can also include diagnostic data learning. This can include data structuring, creating a model with an application program interface for queries to trigger alarms based on parameters or pull graphs on demand. Data analytics can be used to address creep, drift, or abnormal alarms with push notification”; ¶32: “After data pre-processing, model selection can be executed. This can include topic modeling, machine translation, dialog systems analysis, query ranking, and question answers. Model selection and creation can be fed into a knowledge graph or matrix”; ¶33: “a conversational AI engine can access any or all of these models and systems in responding to user queries, commands, and actions. In some embodiments, the AI engine can monitor tool usage, recipe selection, operating parameters, and other actions, and then suggest to a user optimized recipes, warn or predict potential failures, recommend repairs to increase uptime, and other actions and suggestions to generally increase uptime and yield”; claim 8: “the user inquiry is associated with a trouble-shooting problem; and the conversational bot is configured to provide to respond to the user inquiry based on one or more of: a decision-making logical path analysis of historical logs; best known methods; or trouble-shooting decision-making-tree guides”) Karasawa, Kawamoto and Oberai are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more (semiconductor) attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa, the information processing method/system of Kawamoto and the machine learning/artificial intelligence techniques as taught by Oberai to monitor tool usage, recipe selection, operating parameters, and other actions, and then suggest to a user optimized recipes, warn or predict potential failures, recommend repairs to increase uptime, and other actions and suggestions to generally increase uptime and yield (¶20-¶24,¶31-¶33, claim 8) With respect to claim 8, Karasawa, Kawamoto and Oberai disclose all of the above limitations, Oberai further discloses, wherein the at least one processor is further configured to execute the instructions to generate a model for estimating necessity of maintenance of a component of the semiconductor manufacturing device based on a relationship between a parameter acquired in a past and necessity of maintenance. (¶23: “AI translations can assist with distributing Copy Exact periodic maintenance and with Best Known Methods (BKMs) for operation and maintenance. Headlock remote pointing can assist with Copy Exact error recovery. In particular embodiments, semiconductor-manufacturing systems have AR user hardware. Both tool use and tool maintenance/repair can be captured and delivered to users via video in AR or virtual-reality (VR) systems. Particular embodiments can include unstructured AR video acquisition learning. Embodiments can include digital transformation automation augmentation”; ¶31: “For data ingestion, one or more data servers can be configured to receive various raw data. Data can be consumed from any tool-related resource… Data is extracted, transformed and loaded from structured and unstructured sources. A data pre-processing step can be executed. This can include key pair indexing, sentence splitting, tokenization, part-of-speech tagging, parentheses correction, et cetera. Data pre-processing can also include diagnostic data learning. This can include data structuring, creating a model with an application program interface for queries to trigger alarms based on parameters or pull graphs on demand. Data analytics can be used to address creep, drift, or abnormal alarms with push notification”; ¶32: “After data pre-processing, model selection can be executed. This can include topic modeling, machine translation, dialog systems analysis, query ranking, and question answers. Model selection and creation can be fed into a knowledge graph or matrix”; ¶33: “a conversational AI engine can access any or all of these models and systems in responding to user queries, commands, and actions. In some embodiments, the AI engine can monitor tool usage, recipe selection, operating parameters, and other actions, and then suggest to a user optimized recipes, warn or predict potential failures, recommend repairs to increase uptime, and other actions and suggestions to generally increase uptime and yield”; claim 8: “the user inquiry is associated with a trouble-shooting problem; and the conversational bot is configured to provide to respond to the user inquiry based on one or more of: a decision-making logical path analysis of historical logs; best known methods; or trouble-shooting decision-making-tree guides”) Karasawa, Kawamoto and Oberai are directed to the same field of endeavor since they are related to receiving, correlating, analyzing and transmitting data/information based on one or more (semiconductor) attributes in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method/system for improving productivity of apparatuses of Karasawa, the information processing method/system of Kawamoto and the machine learning/artificial intelligence techniques as taught by Oberai since it allows for creating a model with an application program interface for queries to trigger alarms based on parameters or pull graphs on demand, and to monitor tool usage, recipe selection, operating parameters, and other actions, and then suggest to a user optimized recipes, warn or predict potential failures, recommend repairs to increase uptime, and other actions and suggestions to generally increase uptime and yield (¶20-¶24,¶31-¶33, claim 8) . 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fujikata, US Patent Application Publication No US 2018/0294174 A1, “SemiConductor Manufacturing Apparatus, Failure Prediction Method for SemiConductor Manufacturing Apparatus and Failure Prediction Program for SemiConductor Manufacturing Apparatus” relating to evaluating one or more measured feature quantities of a first device, to determine whether the first device is normal or abnormal, and process a failure prediction via a semiconductor manufacturing apparatus generated by machine learning based on a set of one or more feature quantities at normal time of the first device. Kita, US Patent No 12,007752 B2, “Information Processing Apparatus, Display Control Method, Storage Medium, Substrate Processing System, and Method for Manufacturing Article”, relating to managing the operating state of an apparatus subjected to periodic maintenance. Zheng (CN117473572A) “Data Calculating method, Device, Electronic Equipment and Storage Medium”, relating to a method/system for secure data calculation . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY L EVANS whose telephone number is (571)270-3929. The examiner can normally be reached M-F 730a-5p. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached at (571)272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KIMBERLY L EVANS/Examiner, Art Unit 3629 /NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626 Application/Control Number: 18/690,292 Page 2 Art Unit: 3629 Application/Control Number: 18/690,292 Page 3 Art Unit: 3629 Application/Control Number: 18/690,292 Page 4 Art Unit: 3629
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