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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 16-30 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.
With respect to claims 16 and 27, the applicant claims “perform and/or simulate a chromatography process”. It is not clear to the examiner what the applicant is trying to convey with said limitation. What is the difference between performing and simulating if the steps are the same. The metes and bounds of the claimed limitation are vague and ill-defined rendering the claim indefinite. According to the examiner’s best knowledge, the claim limitation will be treated as “perform a chromatography process”.
Claims 17-26, and 28-30 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being dependent on rejected independent claim 16 and for failing to cure the deficiencies listed above.
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 therefore, subject to the conditions and requirements of this title.
Claim 28 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because as recited, the claim is related to a computer program product, i.e. directed to software per se.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 16-23, and 25-30 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hubbuch et al US 2018/0339244 A1 (hence Hubbuch).
In re claims 16, and 27-30 Hubbuch discloses controlling at least one multi-column chromatography arrangement for the continuous process of purifying biopharmaceuticals (Abstract) and teaches the following:
obtaining, from the chromatography system, a current state of the chromatography system, the current state including one or more values of one or more state parameters, the one or more state parameters including one or more quantities of one or more substances present in the chromatography system (Paragraph 0012 “detecting at least one multivariate signal by means of at least one detector”, and Paragraphs 0065-0075 “pH value, conductivity, etc.”);
determining one or more values of one or more control parameters for the chromatography system according to a policy that is configured to map the current state to a corresponding action representing the one or more values of the one or more control parameters (Paragraph 0113 “calculating new optimized process parameters by means of a mathematical model” and Paragraph 0114 “setting the control element on the basis of the calculated process parameters in order to obtain new optimized process parameters”),
the one or more control parameters including at least: a position of a valve comprised in the chromatography system and/or a pump speed of a pump comprised in the chromatography system (Paragraph 0079 “the deconvoluted signal is used to control valves and/or pumps”, Paragraph 0135 “a corresponding valve can be connected as control element and the pump operation of the feed be terminated at a suitable point of time”, and Paragraph 0148 “valve 40 can be controlled”);
and controlling the chromatography system using the one or more determined values of the one or more control parameters (Paragraph 0148 as recited above), wherein the policy is generated according to a machine learning algorithm that uses state-action pairs for training (Paragraphs 0086-0088 “A training and validation of the mathematical model can take place by means of a statistical method”); and wherein the state-action pairs are obtained at least in part by physically performing and/or simulating the chromatography process by the chromatography system (Paragraph 0088 “A training and validation of the mathematical model can take place by means of several chromatography runs”), each of the state-action pairs including: a state of the chromatography system including one or more values of the one or more state parameters at a particular point in time (Paragraph 0014 “at least one process parameter”); and an action including one or more values of the one or more control parameters, the chromatography system being controlled, in response to the state, using the one or more values included in the action (Paragraph 0014 “for controlling the purification process”)
In re claim 17, Hubbuch discloses the following:
receiving the state-action pairs; and generating the policy according to the machine learning algorithm, using the received state-action pairs (Paragraphs 0010-0014, and 0086-0088)
In re claim 18, Hubbuch discloses the following:
updating the policy using the current state and the corresponding action including the one or more determined values of the one or more control parameters (Paragraph 0014)
In re claim 19, Hubbuch discloses the following:
wherein the one or more quantities of the one or more substances present in the chromatography system include one or more of the following: one or more quantities of the one or more substances flowing into one or more chromatographic beds comprised in the chromatography system; one or more quantities of the one or more substances flowing out of the one or more chromatographic beds; one or more quantities of the one or more substances within the one or more chromatographic beds; one or more quantities of the one or more substances within one or more of vessels comprised in the chromatography system, wherein the one or more state parameters may include at least one parameter based on two or more of the quantities listed above (Paragraphs 0060-0062, and 0065-0075)
In re claim 20, Hubbuch discloses the following:
wherein the one or more control parameters further include one or more flow rates of one or more kinds of media flowing into and/or out of one or more of the following: at least one of one or more chromatographic beds of the chromatography system; at least one of vessels comprised in the chromatography system; at least one of one or more flow controllers comprised in the chromatography system; and wherein the one or more control parameters may further include one or more of the following: a temperature in the chromatography system; pH of a mobile phase in the chromatography system; and salinity of the mobile phase in the chromatography system (Paragraphs 0065-0075 and Paragraph 0153 “the flow rate”)
In re claim 21, Hubbuch discloses the following:
wherein the one or more state parameters further include one or more of the following: a temperature at a specified point of the chromatography system; pH of media in a specified portion of the chromatography system; one or more parameters relating to specifications of the one or more chromatographic beds, one or more vessels comprised in the chromatography system and/or one or more connections between the vessels; one or more maximum flow rates for one or more kinds of media that flow into the one or more chromatographic beds; one or more upstream parameters; one or more parameters relating to feed media, wash media and/or elute media used in the chromatography process; conductivity; absorption of effluent; target protein content; concentration of coeluting contaminants; product concentration; purity; and yield (Paragraphs 0065-0075)
In re claim 22, Hubbuch discloses the following:
wherein the machine learning algorithm includes one of, or a combination of two or more of, the following: reinforcement learning; deep reinforcement learning; supervised learning; semi-supervised learning; self-supervised learning; imitation learning; and transfer learning (Paragraph 0087)
In re claim 23, Hubbuch discloses the following:
wherein the machine learning algorithm includes the reinforcement learning or the deep reinforcement learning (Paragraph 0087); and wherein a reward in the reinforcement learning or the deep reinforcement learning is calculated using one or more of the following: at least one of the one or more values of the one or more state parameters (Paragraphs 0065-0075); a value representing a flow rate of the target compound flowing into one or more chromatographic beds of the chromatography system; a value representing a flow rate of the target compound flowing out of one or more chromatographic beds of the chromatography system; a value representing a quantity of a target compound for the chromatography process in or flowing into a product vessel comprised in the chromatography system; a value representing a quantity of the target compound in or flowing into a waste vessel comprised in the chromatography system; a value representing a quantity of spent media in or flowing into the product vessel, the spent media including substances other than the target compound; a value representing a quantity of the spent media in or flowing into the waste vessel
In re claim 25, Hubbuch discloses the following:
wherein the machine learning algorithm includes the supervised learning or the semi-supervised learning; and wherein at least a part of the state-action pairs is defined by an expert of the chromatography process (Paragraphs 0086-0088)
In re claim 26, Hubbuch discloses the following:
wherein the chromatography system comprises: a chromatography device configured to physically perform the chromatography process; and a simulation system that is configured to simulate the chromatography process and that is implemented by a processor and a storage medium, wherein said controlling of the chromatography system includes controlling the chromatography device comprised in the chromatography system using the one or more determined values of the one or more control parameters (Fig.2 and Paragraph 0131)
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.
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.
Claim(s) 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hubbuch in view of NPL “Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control”, published 09/20/2021 (hence Joshi).
In re claim 24, Hubbuch discloses the claimed invention as recited above but doesn’t explicitly teach the following:
wherein the machine learning algorithm includes the deep reinforcement learning involving an actor-critic method that uses: an actor network comprising a first neural network to be trained to represent the policy, the first neural network being configured to take a state as an input and an action as an output; and a critic network comprising a second neural network to be trained to estimate the reward gained by a state-action pair, the critic network being used to train the actor network to output an action that yield high rewards in response to a state input to the actor network
Nevertheless, Joshi discloses an actor-critic RL algorithm, namely, twin actor twin delayed deep deterministic policy gradient (TATD3), by incorporating twin actor networks in the existing twin-delayed deep deterministic policy gradient (TD3) algorithm for the continuous control (Abstract) and teaches the following:
wherein the machine learning algorithm includes the deep reinforcement learning involving an actor-critic method that uses: an actor network comprising a first neural network to be trained to represent the policy, the first neural network being configured to take a state as an input and an action as an output; and a critic network comprising a second neural network to be trained to estimate the reward gained by a state-action pair, the critic network being used to train the actor network to output an action that yield high rewards in response to a state input to the actor network (Page ii)
It would have been obvious to one having ordinary skills in the art at the time the invention was filed to have modified the Hubbuch reference to include an actor-critic network, as taught by Joshi, in order to balance exploration and exploitation efficiently, leading to faster and more stable learning than using just one network.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Rendl US 2022/0229028 A1 discloses obtaining a pressure characteristic indicative for a pressure of the sample storage portion, obtaining a displacement characteristic indicative for a displacement volume of the volume displacement device, when the volume displacement device is fluidically connected to the sample storage portion and based on the obtained pressure characteristic and the obtained measured displacement characteristic the method comprises determining at least one result.
Widehammar et al US 2023/0028051 A1 discloses a liquid chromatography system configured to operate with at least one column and configured for purification of a sample comprising a target product using a predefined process.
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/RAMI KHATIB/Primary Examiner, Art Unit 3669
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