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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been received.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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:
Claim 1 recites “interface node,” which is generic placeholder for “means,” followed by the functional language “generate a job script …” without reciting sufficient structure to perform the claimed generation. Interface node functions are described in the specification with respect to interface node 1, which is part of a computer with a processor and a computer-readable medium (see par. [0131]). Accordingly, “interface node” is interpreted as interface node 1 and equivalents.
Claim 1 recites “master node,” which is generic placeholder for “means,” followed by the functional language “generate multiple job objects …” without reciting sufficient structure to perform the claimed generation. Master node functions are described in the specification with respect to master node 2, which is part of a computer with a processor and a computer-readable medium (see par. [0131]). Accordingly, “master node” is interpreted as master node 2 and equivalents.
Claim 1 recites “module node,” which is generic placeholder for “means,” followed by the functional language “perform the experiment process …” without reciting sufficient structure to perform the claimed performance. Module node functions are described in the specification with respect to module node 3, which is the equipment that performs the experiment (see par. [0064]). Accordingly, “module node” is interpreted as module node 3 and equivalents.
Claim 7 recites “resource manager,” which is generic placeholder for “means,” followed by the functional language “receive, from each of the multiple module nodes, information …” without reciting sufficient structure to perform the claimed managing. Resource manager functions are described in the specification with respect to resource manager 22 which is part of master node 2 and thus part of a computer with a processor and a computer-readable medium (see par. [0131]). Accordingly, “resource manager” is interpreted as resource manager 22 and equivalents.
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.
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 1-17 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 recites “generate a job script that records name of each of multiple modules selected by a user among multiple modules into each of which multiple unit processes performed in a laboratory, is modularized as each experiment process by grouping the multiple unit processes.” Claim 16 recites a similar feature. It is unclear and thus indefinite as to what “into each of which multiple unit processes performed in a laboratory, is modularized as each experiment process by grouping the multiple unit processes” means. Appropriate correction and/or explanation is required. Dependent claims 2-15 and 17 are rejected based on their dependency.
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.
Claim 17 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because claim 17 is directed to a “computer-readable recording medium in which a program causing a computer to execute the laboratory operation method of claim 16 is recorded” without positively reciting any structural features. “Products that do not have a physical or tangible form” are not directed to a statutory category. See MPEP § 2106.03. Here, claim 17 is directed to non-statutory subject matter because a computer storage medium does not require a physical or tangible form. See MPEP § 2106.03.II. To overcome the rejection, based on the disclosure in par. [0131], Applicant may wish to recite “A tangible non-transitory computer-readable recording medium ….”
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.
(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.
Claims 1-3, 5, 7-9, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2023/0004886 to Biggers et al. (“Biggers”).
Regarding claim 1:
A laboratory operation system (See Biggers at Abstract and Figs. 2, and 3, system 200, 300) comprising:
an interface node configured to generate a job script that records name of each of multiple modules selected by a user among multiple modules into each of which multiple unit processes performed in a laboratory, is modularized as each experiment process by grouping the multiple unit processes (Biggers discloses a server 204 (“interface node”) that implements the method 100, which, inter alia, includes process 125 for receiving requests for one or more experimental workflows that are selected by a user (“multiple modules selected by a user” that corresponds to “experiment process”) and process 140 for generating a set of instructions for the selected experimental request (“job script that records name of each of multiple modules selected by a user among multiple modules”). Fig. 4 shows that the workflows are in the form of JSON scripts (“job script”). The experimental workflows include machine executable codes for execution by one or more devices at the laboratories (“modularized as each experiment process by grouping the multiple unit processes”). Biggers also discloses that the experimental workflows are identified (“name”) as “A,” “B,” and “C.” See Biggers at pars. [0032]-[0036], [0046]-[0047], and [0069]-[0070] and Figs. 1-5; see also pars. [0049]-[0060]; see also pars. [0077]-[0113] and Figs. 6-10.);
a master node configured to generate multiple job objects corresponding to multiple job scripts from the multiple job scripts including the generated job script and schedule an execution sequence of the generated multiple job object (Biggers disclose a server 204 (“master node”) that receives “one or more experimental requests for the one or more experimental workflows” and that the “experimental workflow is transformed into one or more machine executable codes [“generate multiple job objects”) for execution by one or more devices at one or more remote laboratories.” See Biggers at par. [0034] and [0070] and Figs. 1 and 5. Fig. 4 shows that the workflows are in the form of JSON scripts (“multiple job scripts”). Biggers also discloses that the server 204 includes a process 145 that determines “a plurality of sequence schedules for completing the set of instructions” sent to the remote laboratories (“schedule an execution sequence of the generated multiple job object”). See Biggers at par. [0039] and Fig. 1.); and
multiple module nodes configured to perform the experiment process of each of the multiple modules for each of the multiple job objects based on process conditions of the experiment process of each of the multiple modules presented by each of the multiple job objects according to the execution sequence of the multiple job objects (Biggers discloses that the experimental requests are sent to remote laboratories (“multiple module nodes”) for execution. See Biggers at Abstract and par. [0070] and Fig. 5; see also pars. [0034]-[0060] and Figs. 1-4; see also pars. [0077]-[0113] and Figs. 6-10. Biggers also discloses that sequence schedules are “based upon various constraints [(“process conditions of the experiment process”)], such as one or more available devices, one or more available materials, one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc.), and one or more experimental constraints (e.g., biological design constraints.” See Biggers at par. [0039].).
Regarding claim 2:
The laboratory operation system of claim 1, wherein the master node schedules the execution sequence of the generated multiple job objects based on an available resource amount of at least one experimental device used in an experiment process of a module to be first executed among the multiple modules of the multiple job objects (Biggers discloses that “the plurality of sequence schedules are determined based upon various constraints, such as one or more available devices, one or more available materials, one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc.), and one or more experimental constraints (e.g., biological design constraints) … [and that] an optimal sequence schedule is determined based on the various constraints.” See Biggers at pars. [0039]-[0040] and [0070] and Fig. 5. The “module to be first executed” will correspond to the workflow that is scheduled to be executed first based on the optimized schedule.).
Regarding claim 3:
The laboratory operation system of claim 2, wherein the master node schedules the execution sequence of the generated multiple job objects according to a sequence in which the generated multiple job objects satisfy a condition for an available resource amount of at least one experimental device used in the experiment process of the module to be first executed (As discussed in claim 2, the “optimal sequence schedule is determined based on the various constraints,” which can be based on available resources such as materials and/or devices. Thus, the optimal sequence will “satisfy a condition for an available resource amount.” See Biggers at pars. [0039]-[0040] and [0070] and Fig. 5.).
Regarding claim 5:
The laboratory operation system of claim 3, wherein the master node includes a job scheduler configured to schedule the execution sequence of the generated multiple job objects in a method of repeating a process of storing multiple job identifications (IDs) assigned to the multiple job objects in a waiting queue according to a generation sequence of the multiple job objects (Biggers discloses that server 204 (“master node”) includes “the experimental workflow module 312 [(“job scheduler”)] [that] includes functionalities that allow the user 302 to submit an experimental workflow to a queue for execution.” Biggers also discloses that the experimental workflows are identified as “A,” “B,” and “C” (“job identifications (IDs)”) and that “the plurality of sequence schedules are determined based upon various constraints.” Accordingly, Biggers discloses the claimed “job scheduler.” See Biggers at pars. [0039]-[0040], [0050], and [0055] and Figs. 2-3.), and
moving, to an executing queue, a job ID that first satisfies the condition for the available resource amount of at least one experimental device used in the module to be first executed among multiple IDs stored in the waiting queue, and at least one job object to which at least one job ID stored in the executing queue is assigned is executed in a sequence in which the at least one job ID is stored in the executing queue (As discussed above, Biggers discloses sending experimental workflows to a queue (“stored”) and determining an optimal sequence schedule based on a variety of constraints. Biggers also discloses that the “experimental workflow is transformed into one or more machine executable codes for execution by one or more devices at one or more remote laboratories.” See Biggers at pars. [0039]-[0040], [0050], [0055] [0070], [0085]-[0089] and Figs. 2-5 and 7. Thus, Biggers discloses the claimed “moving.”).
Regarding claim 7:
The laboratory operation system of claim 5, further comprising: a resource manager configured to receive, from each of the multiple module nodes, information of each module node including an available resource amount of at least one experiment device used in the experiment process of each of the multiple modules performed by each of the multiple module nodes and configured to update the available resource amount of the at least one experimental device used in the experiment process of each of the multiple modules performed by each of the multiple module nodes according to the received information of the each module node, wherein the job scheduler repeats a process of reading the available resource amount of the at least one experimental device used for the module to be first executed from the information of the each module node updated by the resource manager, and moving a job ID that first satisfies a condition for the read available resource amount to the executing queue (Biggers discloses that “the inventory management module 308 [(“resource manager”)] includes functionalities that enable the user 302 to perform multi-site management, material management, and/or sample tracking and provenance.” See Biggers at par. [0053]. As seen in Fig. 3, inventory management module 308 includes information regarding an available resource such as, for example, “Sample_1, Sample_2, and Sample_3” and provides a status (“configured to update”) for each resource such as “Available” and “Pending.” Biggers discloses that “the plurality of sequence schedules are determined based upon various constraints, such as one or more available devices, one or more available materials, one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc.), and one or more experimental constraints (e.g., biological design constraints) … [and that] an optimal sequence schedule is determined based on the various constraints.” See Biggers at pars. [0039]-[0040].). Thus, because Biggers discloses taking into account the available resources when determining a sequence schedule, Biggers discloses the claimed “resource manager.”).
Regarding claim 8:
The laboratory operation system of claim 1, wherein the interface node selects a model presenting a process condition of the experiment process of each of the selected multiple modules according to information input by a user and generates the job script that records the process condition of the experiment process according to the selected model (Biggers discloses a protocol browser module 304 with configuration module 306 that allows the user to manage the workflows, including materials management such as selecting parameters and samples (“selects a model presenting a process condition of the experiment process” and then “submit configured experimental workflows with selected samples for validation.” See Biggers at pars. [0050]-[0054] and Fig. 2. Biggers discloses that the experimental workflow can then be selected for execution by generating a set of instructions using process 140 (“generates the job script that records the process condition of the experiment process according to the selected model”). See Biggers at pars. [0032]-[0036], [0050]-[0054] and Figs. 1-4.), and
the master node generates a job object including the selected model and determines the process condition of the experiment process of each of the multiple modules presented by the model included in the generated job object as a process condition of an experiment process of each of the multiple modules of the generated job object (Biggers disclose a server 204 (“master node”) that receives “one or more experimental requests for the one or more experimental workflows” and that the “experimental workflow is transformed into one or more machine executable codes [“generate a job object”) for execution by one or more devices at one or more remote laboratories.” See Biggers at par. [0034] and [0070] and Figs. 1 and 5.).
Regarding claim 9:
The laboratory operation system of claim 8, wherein, when the selected model is a manual model that manually determines the process condition of the experiment process of each of the selected multiple modules and presents the manually determined process condition, the master node determines values of multiple process parameters of each experiment process recorded in the generated job script as values of multiple process parameters of the experiment process of each of the selected multiple modules (Biggers discloses that the user 302 can use inventory management module 308 to manually select the samples for the experimental workflow (“the selected model is a manual model”). See Biggers at par. [0053].).
Regarding claim 16:
A laboratory operation method comprising: generating a job script that records name of each of multiple modules selected by a user among multiple modules into each of which multiple unit processes performed in a laboratory, is modularized as each experiment process by grouping the multiple unit processes; generating multiple job objects corresponding to multiple job scripts from the multiple job scripts including the generated job script and scheduling an execution sequence of the generated multiple job objects; and performing the experiment process of each of the multiple modules for each of the multiple job objects based on process conditions of the experiment process of each of the multiple modules presented by each of the multiple job objects according to the execution sequence of the multiple job objects (See analysis in claim 1.).
Regarding claim 17:
A computer-readable recording medium in which a program causing a computer to execute the laboratory operation method of claim 16 is recorded (See Biggers at par. [0012] (disclosing a non-transitory computer-readable medium). See analysis in claim 1 for the method steps recited in clam 16.).
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.
Claims 4, 6, and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Biggers.
Regarding claim 4:
The laboratory operation system of claim 3, wherein the master node schedules the execution sequence of the generated multiple job objects according to a sequence in which the generated multiple job objects satisfy a condition for the available resource amount of the at least one experimental device used in the experiment process of the module to be first executed and whether the module to be first executed owns a task causing a bottleneck (Biggers discloses that “the plurality of sequence schedules are determined based upon various constraints, such as one or more available devices, one or more available materials, one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc.), and one or more experimental constraints (e.g., biological design constraints) … [and that] an optimal sequence schedule is determined based on the various constraints.” See Biggers at pars. [0039]-[0040] and [0070] and Fig. 5.). Thus, Biggers discloses optimizing a sequence schedule by taking into account conditions that could create a “bottleneck” such as, e.g., one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc. Based on this disclosure, it would have been obvious and one skilled in the art would have been motivated to select an optimal sequence schedule that is focused on, e.g., minimizing execution time, according to known techniques to achieve predictable results. Therefore, under the optimal sequence schedule, the workflow (“module”) that is selected to be executed first will include a task that causes a “bottleneck” in the sense that using another sequence of workflows (and associated tasks) will increase the execution time.).
Regarding claim 6:
The laboratory operation system of claim 5, wherein the job scheduler includes a job trigger configured to schedule the execution sequence of the generated multiple job objects in a method of repeating a process of moving, to the executing queue, a job ID that first satisfies the condition on an available resource amount of at least one experimental device used for the module to be first executed among the multiple IDs stored in the waiting queue and whether the module to be first executed owns a task causing a bottleneck (Biggers discloses that “the plurality of sequence schedules are determined [(“job trigger”)] based upon various constraints, such as one or more available devices, one or more available materials, one or more estimated durations of executing the set of instructions, one or more optimizations (e.g., minimum execution time, maximum throughput of instructions, maximum device usage, etc.), and one or more experimental constraints (e.g., biological design constraints) … [and that] an optimal sequence schedule is determined based on the various constraints.” See Biggers at pars. [0039]-[0040].). Thus, Biggers discloses the claimed “job trigger.” With respect to the claimed “module to be first executed owns a task causing a bottleneck,” see analysis in claim 4.).
Regarding claim 10:
The laboratory operation system of claim 8, wherein, when the selected model is an automatic model that automatically determines the process condition of the experiment process of each of the selected multiple modules and presents the automatically determined process condition, the master node determines values of multiple process parameters of each experiment process predicted by an artificial intelligence model corresponding to the automatic model as values of multiple process parameters of the experiment process of each of the selected multiple modules (Biggers discloses that the experimental workflow can be configurable “using customizable parameters.” See Biggers at pars. [0052]-[0054]. Biggers also discloses that “a programmable (software) agent can replace the user 302 in operating the system 300” and that “the programmable agent can use an API to select, configure, submit [e.g., ‘submit parameters to the experimental configuration module 306’] and/or fetch data.” See Biggers at par. [0064]. Biggers further discloses that “any variety of machine learning approaches may be performed on data generated in the system 200 to further inform and guide next steps in experimentation, improve performance, create new experiments, and/or analyze data across previously disparate scientific applications.” See Biggers at par. [0048]. Accordingly, based on the suggestion to use an API and to use machining learning in order to, e.g., “improve performance,” it would have been obvious and one skilled in the art would have been motivated to have the API use machining learning on the configurable parameters (“process condition of the experiment process”) of the experimental workflow to improve performance and/or create new experiments, according to known techniques to achieve predictable results.)
Regarding claim 11:
The laboratory operation system of claim 10, wherein the master node includes a job scheduler configured to generate the selected model, generate a process database in which information representing the experiment process of each of the selected multiple modules is recorded, and generate a job object including the generated model and the generated process database, and the information representing the experiment process of each of the selected multiple modules includes an execution sequence of the multiple modules and an execution sequence of multiple tasks for each module according to the information input by the user (Biggers discloses a server 204 (“master node”) that receives “one or more experimental requests for the one or more experimental workflows” and that the “experimental workflow is transformed into one or more machine executable codes for execution by one or more devices at one or more remote laboratories.” For example, Figs. 2 and 3 show that experimental configuration module 304, experimental workflow module 312 and experimental workflow status module 314, including data/results (“process database”) handle the process of receiving and configuring the experimental workflow (including data/results information), which corresponds to the “selected model” with “process database.” Then laboratory management module 318 of server 204 generates the machine executable code (“job object”), which includes the claimed “information.” Thus, modules 304, 312, 314, and 318 perform the functions of the claimed “job scheduler.” See Biggers at par. [0034], [0047]-[0057], and [0070] and Figs. 1-5; see also pars. [0077]-[0113] and Figs. 6-10.).
Regarding claim 12:
The laboratory operation system of claim 11, wherein the master node further includes: a task generator configured to generate multiple task recipes corresponding to recipes of multiple unit processes corresponding to the experiment process of each of the selected multiple modules for each of the selected multiple modules based on the process condition presented by the model of the generated job object and information recorded in a process database of the generated job object (Biggers discloses that experimental request management module 320 (“task generator”) “submit[s] experiments for execution and/or schedule experiments [(“generate multiple task recipes corresponding to recipes of multiple unit processes”)] for execution with the automation scheduling module 322.” See Biggers at par. [0058]-[0060] and Fig. 4.); and
a task scheduler configured to perform or stop a unit process according to each of the multiple task recipes generated for each of the multiple selected modules according to the available resource amount of the at least one experimental device used in the experiment process of each of the selected multiple modules (Biggers discloses that server 204 (“master node”) includes automation scheduling module 322 (“task scheduler”) that provides a task schedule for “experiment execution module 324 [that] provides detailed steps for execution [(“perform or stop”)] by human and/or robotic operators.” Biggers also discloses that management module 320 uses “feedback from the inventory management module 308” (“according to the available resource amount”). See Biggers at par. [0058]-[0060] and Fig. 4.).
Regarding claim 13:
The laboratory operation system of claim 11, wherein the master node further includes: a task generator configured to generate a task recipe corresponding to a recipe of each of the multiple unit processes of the experiment process of each of the selected multiple modules for each of the multiple tasks for each module based on the process condition presented by the model of the generated job object and information recorded in a process database of the generated job object; and a task scheduler configured to determine execution or stop of each task according to each of the multiple task recipes generated for each of the selected multiple modules according to the available resource amount of the at least one experimental device used in the experiment process of each of the selected multiple modules (See analysis in claim 12.).
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Biggers in view of U.S. Patent Application Publication No. 2022/0057418 to Edwards et al. (“Edwards”).
Regarding claim 14:
The laboratory operation system of claim 13, wherein the master node further includes: an action translator configured to determine multiple actions of at least one experimental device used in the each task recipe according to a resource amount of at least one experimental device allocated to each task determined by the execution and a process condition recorded in a task recipe of each task determined by the execution; and an action scheduler configured to schedule an execution sequence of the determined multiple actions according to whether identical experimental devices are used simultaneously during execution of different job objects (Biggers discloses that “the experiment execution module 324 [(“action translator” and “action scheduler”)] includes functionalities to provide detailed experimental steps” (“schedule an execution sequence”) and “coordinate[] experimental tasks [(“actions”)] that are executable by various human and/or robotic resources.” See Biggers at par. [0060]. The task recipes are based on “feedback from the inventory management module 308” (“according to a resource amount”) and various constrains (“process condition”) on the sequence schedules, as discussed above. See Biggers at pars. [0039], [0058]-[0060] and Fig. 4.).
action scheduler configured to schedule an execution sequence of the determined multiple actions according to whether identical experimental devices are used simultaneously during execution of different job objects (Biggers does not explicitly disclose that the experimental tasks are scheduled “according to whether identical experimental devices are used simultaneously during execution of different job objects.” However, in a same field of endeavor, generating a schedule for an automated laboratory system (and thus analogous art), Edwards discloses a symmetry based optimizer that is configured such that “when more than one instance of a same protocol is to be carried out on processing units forming part of a same bank [(“identical experimental devices are used simultaneously”)], introduces additional precedence constraints having a zero weighting between identical processing steps in separate protocol instances [(“different job objects”)], thereby indicating that start times between identical processing steps can be swapped….” See Edwards at par. [0020]. Thus, Edwards disclose that the execution sequence of the tasks can be “according to whether identical experimental devices are used simultaneously during execution of different job object.” It would have been obvious and one skilled in the art would have been motivated to incorporate Edwards’ symmetry-based optimizer into the task scheduler of Biggers in order to “consider optimisation solutions in which it is possible to swap start times between identical activities to find schedules where those additional precedence constraints would have been held and all other constraints still valid.” See Edwards at par. [0020]; see also par. [0003] (disclosing that it is important to minimize time-to-result and increase throughput). Because both Biggers and Edwards relate to scheduling tasks for a laboratory, there would have been a reasonable chance of success. See § MPEP 2143.I.G.).
Regarding claim 15:
The laboratory operation system of claim 14, wherein one module node that performs an experiment process of one module among the multiple modules of the each job object among the multiple module nodes, performs an experiment process of the one module by receiving multiple action names listed according to the execution sequence of the multiple actions and information for performing actions corresponding to the multiple action names for each task of the one module from the master node, and by executing the actions corresponding to the multiple action names in a sequence in which the multiple action names are listed, according to the information for executing the actions corresponding to the multiple action names (Biggers discloses that “the experiment execution module 324 coordinates experimental tasks that are executable by various human and/or robotic resources … [and that] the experiment execution module 324 returns data, results, metadata, and/or logs captured during experiments.” To perform such coordination and monitoring on individual tasks (“actions”), the system of Biggers in view of Edwards will have a means for identifying individual tasks (“action names”). Accordingly, Biggers in view of Edwards renders obvious the claimed features.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 20210286604 to Jean Peccoud discloses optimizing laboratory processes.
U.S. Patent Application Publication No. 20230004885 to Biggers et al. discloses a system and method for processing experimental workflows at remote laboratories.
U.S. Patent Application Publication No. 20230004909 to Phanimukla et al. discloses a system and method for processing experimental workflows at remote laboratories.
U.S. Patent Application Publication No. 20210129327 to Dambman et al. discloses scheduling laboratory tasks and experiments.
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/B.K./Examiner, Art Unit 2116
/KENNETH M LO/ Supervisory Patent Examiner, Art Unit 2116
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