Prosecution Insights
Last updated: July 17, 2026
Application No. 18/512,562

AUTOMATED MEMBRANE CLEANING MODULE

Non-Final OA §101§102§103§112
Filed
Nov 17, 2023
Examiner
MENON, KRISHNAN S
Art Unit
1777
Tech Center
1700 — Chemical & Materials Engineering
Assignee
Blue Origin LLC
OA Round
1 (Non-Final)
60%
Grant Probability
Moderate
1-2
OA Rounds
7m
Est. Remaining
72%
With Interview

Examiner Intelligence

Grants 60% of resolved cases
60%
Career Allowance Rate
898 granted / 1500 resolved
-5.1% vs TC avg
Moderate +12% lift
Without
With
+11.8%
Interview Lift
resolved cases with interview
Typical timeline
3y 3m
Avg Prosecution
67 currently pending
Career history
1563
Total Applications
across all art units

Statute-Specific Performance

§101
0.5%
-39.5% vs TC avg
§103
83.6%
+43.6% vs TC avg
§102
7.6%
-32.4% vs TC avg
§112
4.4%
-35.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 1500 resolved cases

Office Action

§101 §102 §103 §112
DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Election/Restrictions Claims 10-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on. Arguments are (1) claims 1-9, while differing in scope, are not configured for a materially different purpose, and (2) no significant burden. Regarding the burden, based on the different classification and also the fact that the claims have different scopes, and the crowded nature of the art, there is a significant burden. Regarding the differing scope not for different purpose, applicant admits the scope of claims 1-9 are different from the rest. This requires different search and examination for each set of claims. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a water treatment unit and a membrane cleaning unit in claim 1; artificial intelligence model to determine .. cleaning strategies in claim 2; recirculation cycle, physical dislodging cycle, backwashing cycle and applied electric field cycle, all in claim 8 Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. Claims 1-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1: “the processed operational data” lacks antecedent basis. Claim 2: the repeated limitations, “the respective second membrane module,” lack antecedent bases. Also unclear is, which membrane module provides the training data – “the membrane module” or the “individual second membrane module.” Terms like “indication of …” are vague. Claim limitations “membrane cleaning unit” (claim 1) and “artificial intelligence (AI) model to determine …” (claim 2) invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Membrane cleaning unit: while not positively recited as a structural part, this claim element is significant in the claim mapping as a source of data. The disclosure provides no details on what is the membrane cleaning unit. Fig. 1 shows just a box with a label and a membrane shown inside the box. The description does not make it clear if this is just a part of the processor with executable software, or an actual cleaning unit having pumps, valves, and various cleaning devices and supplies to which the membrane may be relocated to, or cleaned in-situ by switching valves, etc. While the disclosure describes many different cleaning methods, it lacks a unified stricture that can be considered as “membrane cleaning unit.” AI model: The disclosure provides no details of the structure, algorithm or process steps involved in the AI model. Fig. 9 is described as a flow diagram to train an AI model, and a box 733 labeled ‘AI model trainer.’ Fig. 7 shows another box labeled ‘AI training unit.’ None of these provide any usable details on how the AI model works, or how one could use this AI model. Therefore, these claims are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims recite computer-executable instructions to estimate a cleaning strategy from historical cleaning data, operational sensor data, etc. Dependent claims also recite the data like conductivity, pressure drop, etc., types of fouling, and the types of cleaning cycles. However, the claims and the disclosure do not show how the executable instructions are developed, like any mathematical model, strategy algorithm or data-driven empirical model to how to select the type of cleaning cycle, when to move from normal operation to cleaning, what would make a successful cleaning, or when the membrane module be replaced instead of being cleaned. PNG media_image1.png 314 1026 media_image1.png Greyscale Figs. 9 (copied above) and 10 (copied below) are described as example block diagram for training AI model and example cleaning strategy selector respectively, but add no significant light into the issues raised. These figures only appear block diagrams with the abstract ideas labeled therein. Particularly, unclear what is inside boxes 732 and 733 of fig. 9, and 1006 of fig. 10. PNG media_image2.png 269 586 media_image2.png Greyscale Explanation of what the office is trying to raise: as examples, an empirical model approach would be an increase in differential pressure across the membrane beyond a threshold indicate a concentration polarization problem that may be remedied by a backwash. A pressure drop along the membrane from feed end to concentrate end beyond a threshold is indicative of particulate, organic and/or biological fouling that may require a chemical cleaning. Such a model would show graphical or tabular representation of usable data representing normal operating conditions and conditions that require cleaning and results of cleaning. A mathematical model would show an equation or equations and their solution using input variables and operational parameters to predict the output variables such as frequency, selection and strength of the cleaning cycles. No such details are disclosed, which prima facie, indicate that applicant was not in possession of the claims invention at the time of filing. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claim(s) recite(s) abstract ideas. This judicial exception is not integrated into a practical application because: see below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because: see below. The following is a step-wise analysis of judicial exception according to MPEP 2106: Step 1: claims 1-9 recite a system (an apparatus) for cleaning strategy which comprises a processor with a memory and instructions to execute a cleaning strategy. This meets step 1. Step 2A, prong 1: The claims are directed to abstract ideas because the computer-executable instructions are in the abstract. The membrane module, operational sensor data, historical cleaning data, characteristics of water treatment and membrane cleaning units, determining the cleaning cycles, estimating the cleaning strategy, a sequence of execution – all these are recited in the abstract. Step 2A, prong 2: while “cause the membrane cleaning unit to execute the cleaning strategy to clean the membrane module” reads as an action step, the entire instructions being in abstract would not make it practicable. “The membrane module” covers many different types of membranes for many different applications. The claims do not identify any practicable or tangible data and how they are manipulated. The determining step does not provide any methodology to determine how the cleaning cycles are determined using the data and how the cleaning strategy is estimated, as reasoned in the 35 USC 112(a) and (b) rejections above. Therefore, the claims do not have additional elements that integrate the abstract ideas into a practical application. Step 2B: Claims recite only the processor with memory as structural parts, which would not add to anything significantly more. Claims 2: using AI and machine learning using training data from the membrane or the second membrane, etc., do not add to any practical application beyond reciting abstract ideas. Claims 3-4 only recite more abstract variables. Claim 6 adds operational sensor data. Claim 7 adds computer-executable instructions to determine defined intensity of cleaning cycles, but does not provide how the determination is done. Claim 8: only defines cleaning cycles. Claim 9 only recites what entails in each named cleaning cycle. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 1-9 are rejected under 35 U.S.C. 102(a2) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over (Faramarzi et al (US 2023/0398498.) Faramarzi’s fig. 2 is copied herein for easy reference. This figure clearly teaches the claim elements in the instant claims. Fig. 1 teaches optimizing performance of an ultrafiltration membrane system. Fig. 3 shows implementation of models for AI using AI, ML and teaching data, Faramarzi teaches determining the cleaning strategy for various scenarios. Faramarzi uses sensor-collected data, historical data and membrane cleaning-recovery data for the purpose. Cleaning cycles involve chemical cleaning, backwashing, maintenance cleaning, etc. (see [0037], [0024], [0043].) While the teaching exemplifies ultrafiltration, Faramarzi teaching is applicable to other membrane processes as well, like reverse osmosis, microfiltration, nanofiltration etc. [0033]. PNG media_image3.png 649 982 media_image3.png Greyscale Membrane process variables: flux, transmembrane pressure, fouling resistance, etc. [0036], [0041]. Sensor data include pressure, temperature, flow rates, etc. [0052]. Second membrane resistance measurements as in claim 2: [0161]. Thus Faramarzi does not explicitly teach “a membrane cleaning unit,” though not positively recited as part of claim 1. Since applicant has no clear structural description of the membrane cleaning unit and the functions described are taught by Faramarzi, there is an implicit teaching in Faramarzi. "[I]n considering the disclosure of a reference, it is proper to take into account not only specific teachings of the reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom." In re Preda, 401 F.2d 825, 826, 159 USPQ 342, 344 (CCPA 1968. While the examiner believes that the claims are anticipated by Faramarzi, if one considers the teaching of Faramarzi as not literally mapping the claims, the teachings of Faramarzi at the least makes the claims obvious. Claim(s) 1-9 are rejected under 35 U.S.C. 102(a1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over (Hoek et al (WO 2016/025590.) PNG media_image4.png 565 984 media_image4.png Greyscale Hoek’s fig. 1 copied herein shows a membrane system including some cleaning units like clean-in-place. See the abstract and the figures. This system represents several membrane processes. See [0035] and [0041]. Table 1 shows the various parameters used in developing AI model [0130] and machine learning with teaching data (see fig. 6.) Sensor data – see [0035]. Historical – see stored flux data in [0175], and stored values in [0079] and fig. 6. The filtration management system details are in fig. 8. Table 2 shows flux decline and corresponding cleaning techniques. Paragraph [0111] teaches all the measured, monitored and collected data. [0112] teaches the various methodologies. [0113] teaches determining conditions of membranes and the process. Executable programs and computers – [0129]. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISHNAN S MENON whose telephone number is (571)272-1143. The examiner can normally be reached Flexible, but generally Monday-Friday: 8:00AM-4:30PM. 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, Prem C Singh can be reached at 571-272-6381. 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. /KRISHNAN S MENON/Primary Examiner, Art Unit 1777
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Prosecution Timeline

Nov 17, 2023
Application Filed
May 05, 2026
Non-Final Rejection mailed — §101, §102, §103 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

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Patent 12673887
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Patent 12663403
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Patent 12654139
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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
60%
Grant Probability
72%
With Interview (+11.8%)
3y 3m (~7m remaining)
Median Time to Grant
Low
PTA Risk
Based on 1500 resolved cases by this examiner. Grant probability derived from career allowance rate.

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