Prosecution Insights
Last updated: April 19, 2026
Application No. 17/454,392

OPTIMIZATION SUPPORT DEVICE, METHOD, AND PROGRAM

Non-Final OA §103
Filed
Nov 10, 2021
Examiner
FOLLANSBEE, YVONNE TRANG
Art Unit
2117
Tech Center
2100 — Computer Architecture & Software
Assignee
Fujifilm Corporation
OA Round
5 (Non-Final)
57%
Grant Probability
Moderate
5-6
OA Rounds
3y 2m
To Grant
84%
With Interview

Examiner Intelligence

Grants 57% of resolved cases
57%
Career Allow Rate
60 granted / 105 resolved
+2.1% vs TC avg
Strong +26% interview lift
Without
With
+26.4%
Interview Lift
resolved cases with interview
Typical timeline
3y 2m
Avg Prosecution
33 currently pending
Career history
138
Total Applications
across all art units

Statute-Specific Performance

§101
16.0%
-24.0% vs TC avg
§103
50.2%
+10.2% vs TC avg
§102
22.2%
-17.8% vs TC avg
§112
7.7%
-32.3% vs TC avg
Black line = Tech Center average estimate • Based on career data from 105 resolved cases

Office Action

§103
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 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/19/2025 has been entered. Response to Amendment This Office Action has been issued in response to amendment filed 12/19/2025. Response to Arguments Applicant's arguments filed 12/19/2025 have been carefully and fully considered. With respect to applicant’s argument of the remarks on the USC 103 rejection which recites: “Applicant contends that the cited references fail to disclose the features of new claim 21. Accordingly, Applicant respectfully submits that new claim 21 is in condition for allowance”. Examiner notes that in light of the amendments the new claim 21 is rejected under Zubarev in view of Kinlen. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Zubarev et al., (US20200401110, herein Zubarev), in view of Kinlen et al., (US20190062502A1, herein Kinlen). Regarding claim 21, Zubarev teaches A production facility comprises a production device and a support device (Fig. 1), wherein the production device comprises a flow reactor and a controller ([0037] chemical reactor 108 control settings (e.g., “X” can represent a first control setting and/or “X′” can represent a second control setting, [0005] a computer program product for controlling a chemical reactor to produce a polymer is provide … The program instructions can be executable by a processor to cause the processor to generate), wherein the support device is configured to determine a target operating condition parameter for a flow reaction process performed by the flow reactor (Fig. 1, [0042] the prediction component 402 can generate one or more recommended chemical reactor 108 control settings that can be predicted to achieve a synthesis of one or more polymers within the target polymer class when implemented by the one or more chemical reactors 108.), the operation of each part of the flow reactor is controlled by the controller ([0005] a computer program product for controlling a chemical reactor to produce a polymer is provide … The program instructions can be executable by a processor to cause the processor to generate, [0015] FIG. 10 illustrates a flow diagram of an example, non-limiting method that can facilitate controlling one or more chemical reactors to generate one or more polymer compounds having one or more target characteristics based), and the controller is connected to the support device (Fig. 1)… wherein the reaction section comprises a first detection unit configured to detect the state of the mixed raw material in the reaction section and output the result of the state of the mixed raw material to the support device as teacher data ([0062] the method 1000 can comprise monitoring the characteristics of polymers synthesized by the operation of the one or more chemical reactors 108 to further train the system 100 and/or increase the accuracy of generated recommended chemical reactor 108 control settings), wherein the collecting section comprises a second detection unit configured to detect the quality of the product ([0068] the one or more chemical reactors 108 can comprise one or more sensors to measure and/or detect one or more physical and/or chemical properties of the one or more synthesized polymers. Example sensors can include, but are not limited to: timers, thermometers, calorimeters, spectroscopic equipment, equipment for mechanical testing, biochemical assays, a combination thereof, and/or the like, [0069] the method 1100 can comprise determining (e.g., via verification component 704), by the system 100, whether the one or more characteristics are within a permissible range defined by the one or more target polymer characteristics. For example, the determining at 1112 can comprise analyzing the one or more measurements and/or detections generated at 1110 to determine whether the one or more synthesized polymers belong to the target polymer class), which is the processing result of the flow reaction, and outputs the detected result to the support device as teacher data ([0070] the method 1100 can comprise updating (e.g., via update component 802), by the system 100, the reactor training data based on the one or more recommended chemical reactor 108 control settings. Further, the updating at 1114 can comprise updating one or more training datasets 122 based on the one or more measurements and/or detections generated at 1110) , wherein the support device comprises: a first conversion unit configured to convert an operating condition parameter indicating an operating condition of the flow reaction process into a state parameter, the operating condition parameter being a parameter for setting a reaction condition of the flow reaction process and for driving each part of the flow reactor, the state parameter being a parameter indicating physical properties of mixed raw material obtained when the mixed raw material is reacted (Fig. 9-10, Fig. 11 1102 &1104 [0007] FIG. 2 illustrates a block diagram of an example, non-limiting system that can perform one or more machine learning techniques using chemical reactor operation data and/or data regarding one or more polymers synthesized by the one or more chemical reactors in accordance with one or more embodiments described herein, [0008] FIG. 3 illustrates a diagram of an example, non-limiting adjacency matrix and/or latent embedding processes that can be generated by one or more machine learning techniques using chemical reactor operation data and/or data regarding one or more polymers synthesized by the one or more chemical reactors in accordance with one or more embodiments described herein) ; and a second conversion unit configured to convert the state parameter into a quality parameter indicating a quality of the product (Fig. 11 1110 & 1112 , [0069] the method 1100 can comprise determining (e.g., via verification component 704), by the system 100, whether the one or more characteristics are within a permissible range defined by the one or more target polymer characteristics. For example, the determining at 1112 can comprise analyzing the one or more measurements and/or detections generated at 1110 to determine whether the one or more synthesized polymers belong to the target polymer class, [0065] the method 1100 can comprise training (e.g., via training component 202), by the system 100, a machine learning algorithm based on the reactor training data via one or more adjacency matrices, wherein the reactor training data can regard the synthesis of one or more previously generated polymers), wherein the first conversion unit has a first learning model that has been trained using the result of the state of the mixed raw material to output the state parameter by inputting the operating condition parameter (Fig. 9-11 step 1104, , [0007] FIG. 2 illustrates a block diagram of an example, non-limiting system that can perform one or more machine learning techniques using chemical reactor operation data and/or data regarding one or more polymers synthesized by the one or more chemical reactors in accordance with one or more embodiments described herein, [0008] FIG. 3 illustrates a diagram of an example, non-limiting adjacency matrix and/or latent embedding processes that can be generated by one or more machine learning techniques using chemical reactor operation data and/or data regarding one or more polymers synthesized by the one or more chemical reactors in accordance with one or more embodiments described herein), and the second conversion unit has a second learning model that has been trained using the processing result of the flow reaction to output the quality parameter by inputting the state parameter (Fig. 11, [0069] the method 1100 can comprise determining (e.g., via verification component 704), by the system 100, whether the one or more characteristics are within a permissible range defined by the one or more target polymer characteristics. For example, the determining at 1112 can comprise analyzing the one or more measurements and/or detections generated at 1110 to determine whether the one or more synthesized polymers belong to the target polymer class, [0065] the method 1100 can comprise training (e.g., via training component 202), by the system 100, a machine learning algorithm based on the reactor training data via one or more adjacency matrices, wherein the reactor training data can regard the synthesis of one or more previously generated polymers, [0035] the training component 202 can analyze the one or more training datasets 122 using one or more machine learning algorithms, [0058] the verification component 704 can further determine whether the one or more synthesized polymers belong to the target polymer class based on the measured and/or detected polymer characteristics. As shown in FIG. 9, in various embodiments the recommended chemical reactor 108 control settings and/or the polymer characteristics of the synthesized polymers can further contribute to the one or more training processes 300 performed by the training component 202. For example, the update component 802 can populate and/or update the one or more training datasets 122 with the recommended chemical reactor 108 control settings and/or the polymer characteristics of the synthesized polymers; thereby, enabling the recommended chemical reactor 108 control settings and/or the polymer characteristics of the synthesized polymers to be included in one or more iterations of the one or more training processes 300 (e.g., included in one or more initial adjacency matrices 302 generated by the training component 202). wherein the support device further comprises: a third conversion unit configured to convert a target quality parameter into a state parameter based on a control parameter of the second learning model that has been trained to output the quality parameter by inputting the state parameter (Fig. 11, 1106 & 1112, [0016] FIG. 11 illustrates a flow diagram of an example, non-limiting method that can facilitate controlling one or more chemical reactors to generate one or more polymer compounds having one or more target characteristics based, for example, previous operation of one or more chemical reactors in accordance with one or more embodiments described herein, [0023] the trained recommendation components can generate one or more recommended chemical reactor control settings based on one or more target polymer characteristics, [0008] FIG. 3 illustrates a diagram of an example, non-limiting adjacency matrix and/or latent embedding processes that can be generated by one or more machine learning techniques using chemical reactor operation data and/or data regarding one or more polymers synthesized by the one or more chemical reactors in accordance with one or more embodiments described herein, [0023] the trained recommendation components can generate one or more recommended chemical reactor control settings based on one or more target polymer characteristics); and a fourth conversion unit configured to convert the state parameter into the target operating condition parameter for obtaining the target quality parameter based on a control parameter of the first learning model that has been trained to output the state parameter by inputting the operating condition parameter (Fig. 11 1106 & 1108, [0035] the training component 202 can analyze the one or more training datasets 122 using one or more machine learning algorithms, [0004] a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data, [0003] The computer executable components can comprise a recommendation component that can generate a recommended chemical reactor control setting for inverse synthesis of a polymer based on a target polymer characteristic and reactor training data), wherein the support device is configured to output the target operating condition parameter to the controller, and wherein the controller is configured to control the operation of the flow reactor according to the target operating condition parameter, whereby a product having the target quality is produced ([0005] a computer program product for controlling a chemical reactor to produce a polymer is provided. The computer program product can comprise a computer readable storage medium having program instructions embodied therewith. The program instructions can be executable by a processor to cause the processor to generate, by a system operatively coupled to the processor, a recommended chemical reactor control setting for inverse synthesis of the polymer based on a target polymer characteristic and reactor training data) . Zubarev does not teach wherein the flow reactor comprises a first supply unit, a second supply unit, a reaction section, and a collecting section, … wherein in the flow reactor, the first supply unit and the second supply unit are respectively connected to upstream end parts of the reaction section by piping, and the collecting section is connected to a downstream end part of the reaction section by piping… wherein the first supply unit is configured to supply a first raw material of the flow reaction to the reaction section, wherein the second supply unit is configured to supply a second raw material of the flow reaction to the reaction section Kinlen teaches wherein the flow reactor comprises a first supply unit, a second supply unit, a reaction section, and a collecting section ([0041] FIG. 3 shows system 100 c that includes pumping equipment 15, 25, 55 for introducing reactants 10, 20, and 52 mixing units 30, 60, [0036] With reference to FIG. 1A, flow reactor system 100 shown. First reactant 10 and second reactant 20 are introduced to first mixing unit 30), …wherein in the flow reactor, the first supply unit and the second supply unit are respectively connected to upstream end parts of the reaction section by piping, and the collecting section is connected to a downstream end part of the reaction section by piping (Fig. 1A, [0013] a mixing chamber, the mixing chamber having an outlet fluidically coupled to the inlet of the tubing, and an inlet; and at least one fluid flow control device fluidically coupled to the inlet of the mixing chamber, [0015] the at least one fluid control device comprises a monomer fluid flow control device and an acid fluid flow control device)…, wherein the first supply unit is configured to supply a first raw material of the flow reaction to the reaction section, wherein the second supply unit is configured to supply a second raw material of the flow reaction to the reaction section (Fig. 1 Chemical Reactor(s), ([0041] FIG. 3 shows system 100 c that includes pumping equipment 15, 25, 55 for introducing reactants 10, 20, and 52 mixing units 30, 60, [0036] With reference to FIG. 1A, flow reactor system 100 shown. First reactant 10 and second reactant 20 are introduced to first mixing unit 30) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zubarev’s teaching of synthesis of polymers using flow reactors with Kinlen’s teaching of flow reactors synthesis of polymers using two supply units, reaction section, and collecting section connected by tubing. The combined teaching provides an expected result of synthesis of polymers using flow reactors having two supply units, reaction section, and collecting section connected by tubing. Therefore, one of ordinary skill in the art would be motivated for improving heat management as supported by Kinlen [0043]. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Kohl (US11065594) discloses continuous flow process for preparing conducting polymers. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YVONNE T FOLLANSBEE whose telephone number is (571)272-0634. The examiner can normally be reached on Monday - Friday 1pm - 9pm. 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, Robert Fennema can be reached on (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /YVONNE TRANG FOLLANSBEE/Examiner, Art Unit 2117 /Christopher E. Everett/Primary Examiner, Art Unit 2117
Read full office action

Prosecution Timeline

Nov 10, 2021
Application Filed
Feb 08, 2024
Non-Final Rejection — §103
May 02, 2024
Interview Requested
May 08, 2024
Examiner Interview Summary
Jun 17, 2024
Response Filed
Aug 28, 2024
Final Rejection — §103
Nov 18, 2024
Interview Requested
Nov 25, 2024
Applicant Interview (Telephonic)
Nov 25, 2024
Examiner Interview Summary
Jan 15, 2025
Applicant Interview (Telephonic)
Jan 16, 2025
Examiner Interview Summary
Mar 04, 2025
Request for Continued Examination
Mar 10, 2025
Response after Non-Final Action
Apr 15, 2025
Non-Final Rejection — §103
Jul 17, 2025
Response Filed
Sep 16, 2025
Final Rejection — §103
Dec 19, 2025
Request for Continued Examination
Jan 10, 2026
Response after Non-Final Action
Jan 28, 2026
Non-Final Rejection — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

5-6
Expected OA Rounds
57%
Grant Probability
84%
With Interview (+26.4%)
3y 2m
Median Time to Grant
High
PTA Risk
Based on 105 resolved cases by this examiner. Grant probability derived from career allow rate.

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