DETAILED ACTION
Notice of 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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Withdrawal Objections and Rejections
Applicant's response, filed 12/25/2025, has been fully considered.
In view of the amendment and remarks from 12/25/2025, the objection to the claims and the rejection of the following claims are withdrawn:
claims 4 and 8-17 under 35 USC § 112b;
claims 1, 5-7, 8-10 and 13-22 under 35 U.S.C. § 102;
claims 1-23 under Double Patenting.
The following rejections and/or objections are either maintained or newly applied for claims 1-23. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 09/22/2025.
Status of the Claims
Claims 1-23 are pending.
Claims 1 and 23 are independent.
Claims 1-23 are rejected.
Priority
This US Application 17597489 (01/07/2022) is a 371 of PCT/US2020/036922 (06/10/2020) which claims benefit of US Application 62/877,885 (07/24/2019) and US Application 63/024,853 (05/14/2020); as reflected in the filing receipt mailed on 06/07/2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-23 is 07/24/2019.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/06/2025 and 12/15/2025 were considered.
Claim interpretation
112(f) interpretation of particular recitations
Recited instances of "optimization module" (claims 1, 4-5, 7, 21, and 23)
The above recitations include means (or an equivalent, nonce term, here "module") and function and/or result (here "optimization" interpreted as the function of optimization).
The above recitations are not sufficiently well-known and not accompanied by sufficient structure in the claims to prevent invoking. Therefore, each is interpreted as invoking.
Having invoked, each above recitation has been analyzed as clearly linking to sufficient structure in the specification, as supported at ([0007 and 0019]). Thus, the above recitations have been interpreted as properly invoking 112(f).
Claim Rejections - 35 USC § 112(b)
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 1-22 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
In claim 1, the relationship is unclear between the recited ", and types of tests and number..." (i.e. final element of the receiving step) and the preceding portions of the "receiving" step. The ", and types of tests and number..." appears to be the end of a list, but it is unclear what is the beginning of the list. There is a preceding list, but it already recites one instance of "and..."
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 1-23 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)).
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Claims 1-22 is directed to a 101 process, here a "method," with process steps such as "receiving."
Claim 23 is directed to a 101 machine or manufacture, here a "system," comprising at least one non-transitory element such as " a system controller."
[Step 1: claims 1-23: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
Mathematical concepts recited in instant claims 1-21 and 23, include the terms:
• "optimization method";
• "optimization objective functions";
• "optimization constraints…"; and
• "reagent pack optimization module".
Said terms are being identified as mathematical concepts. 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 having ordinary skill in the art. In this instant disclosure, [0029] describes a "mathematical formulation of the optimization method"; which indicates the use of math. Thus, the recited terms corresponds to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989).
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "determining … a reagent pack loading plan over the planning period" (claim 1) and
• "loading reagent packs" (claim 22).
Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of identifying a reagent pack loading plan over the planning period and analyze information about the decision regarding reagent pack loading.
Dependent claims 4, 18-19 recite further details about the "reagent pack optimization module"; dependent claims 8-20 recite further details about the variables and constraints for the "objective functions"; not reciting any additional non-abstract elements; all reciting further aspects of the information being analyzed, the manner in which that analysis is performed. Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The instant claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)).
[Step 2A Prong One: claims 1-23: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 5, 7, 21 and 23 recite additional elements that are not abstract ideas:
• "receiving, at a system controller, computer-readable data" (claim 1);
• the computer implemented identified description for the "reagent pack optimization module" (claims 1, 5, 7, 21 and 23); and
• "a system controller coupled to the plurality of analyzers … having computer executable instructions" (claim 23).
Dependent claims 3 and 22 recite further details about "loading reagent packs"; and as such reads on insignificant extra-solution activity.
The recited limitations in 1, 5, 7, 21 and 23 are interpreted to require the use of a computer. The use of a computer is broadly interpreted and not actually described in the claims or specification. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer.
Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions.
The recited claims read on data gathering activities or the type of data being gathered; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
Claims directed to "receiving" read on receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and outputting and therefore correspond to insignificant extra-solution activity.
There are no additional limitations to indicate details of exactly how the judicial exception is being integrated into a practical application. There are no additional limitations to indicate that the claimed computer, processor, or computer readable medium require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. MPEP 2106.05(b).
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)). The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc .... are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination it is not yet the case that any of the Step 2A, Prong Two considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the first consideration at MPEP 2106.04(d)(1), the record, including for example the specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field. The claims do not yet clearly result in such an improvement (e.g. specification: [005]).
[Step 2A Prong Two: claims 1-23: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1, 5, 7, 21 and 23 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
When the claims are considered as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing. See MPEP 2106.05(a) and 2106.05(h).
[Step 2B: claims 1-23: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For these reasons, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not constitute significantly more than the abstract idea, so instant claims 1-23 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 12/11/2025 have been fully considered but are not persuasive for the reasons below:
It appears that pg. 12 para. 4 represents the only Applicant remarks specific to 101 and the instant claims:
Applicant respectfully disagrees with these rejections but
has nonetheless amended independent claims 1 and 23, as
discussed below in connection with the§§ 102/103 rejections, in
order to further prosecution and further make clear that the
claims are not directed to an abstract idea.
It appears that these remarks address Step 2A, Prong One – the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c)).
The following constitutes the identified mathematical concepts in claims 1-21 and 23: "optimization method"; "optimization objective functions"; "optimization constraints…"; and "reagent pack optimization module". In this instant disclosure, [0029] describes a "mathematical formulation of the optimization method" which supports the identification of math being applied.
Furthermore, the recited "determining … a loading plan" and "loading the reagent packs" are being identified as mental processes because a human mind is sufficiently capable of identifying a reagent pack loading plan over the planning period and analyze information about the decision regarding reagent pack loading.
Therefore the claims are directed to an abstract idea. Finally, the amendments to independent claims 1-23 does not render the rejection under 35 U.S.C. § 101 moot as it has been explained in detail in this action.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
A. Claims 1-23 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sonmez ("Reagent Usage Optimization In High Volume Diagnostics Testing" Dissertation MIT 2018) as evidenced by Pedroso ("Mathematical Optimization Documentation" GitHub (2017)) in view of Sepulveda ("The ideal laboratory information system" Archives of Pathology and Laboratory Medicine 137.8:1129-1140 (2013)) in view of Garg ("Multi-objective reliability-redundancy allocation problem using particle swarm optimization" Computers & Industrial Engineering 64:247–255 (2013)), as cited on the attached Form PTO-892 (9/22/2025).
Sonmez discloses a method for reagent usage optimization in high-volume diagnostics testing (pg. 3 para. 1). Bullet points indicate the teachings of the instant features over the prior art. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
Claim 1 recites:
receiving, at a system controller, computer-readable data comprising an inventory of a plurality of analyzers and respective fixed test menus thereof included within the diagnostic laboratory system, the plurality of analyzers including a number of reagent pack mounting spaces, and types of tests and number of the tests to be performed on samples by the diagnostic laboratory system over a planning period
• Sonmez teaches a mathematical model used to achieve optimal workload distribution of different assays over multiple analyzers in a single site (i.e. inventory of a plurality of analyzers) (pg. 49 para. 2); wherein the testing platform is capable of processing more than thousand patient tests per day (i.e. receiving data) (pg. 11 para. 2); wherein a laboratory information system (i.e. system controller) that identifies the tests ordered for each patient sample through the automated barcode reading system integrated in the analyzer (pg. 17 para. 3); wherein diagnostics testing platforms use reagents to perform tests at predefined frequencies (i.e. tests by the diagnostic laboratory system over a planning period) (pg. 65 para. 1);
• Sonmez does not teach "fixed test menus," "a number of reagent pack mounting spaces" and "types of tests and number of the tests to be performed". However, Sepulveda teaches the ideal laboratory information system designed to optimize not only laboratory operations (pg. 1129 col. 1 para. 1); wherein the menu must be consistent (i.e. fixed assay menu) (pg. 1132 col. 1 para. 2) and customize date intervals and time scales, test groups (i.e. types of tests), reagent cartridge, reagent lot number, (i.e. number of the tests ordered and the type of the tests to be run on the plurality of analyzers) (pg. 1137 col. 2para. 9).
determining, via a reagent pack optimization module executing on the system controller, a reagent pack loading plan over the planning period, the reagent pack loading plan including a number of reagent packs to be loaded on the plurality of analyzers, the determining based on the computer- readable data received at the system controller
• Sonmez teaches a method for reagent usage optimization in high-volume diagnostics testing (pg. 3 para. 1); wherein a linear programming mathematical optimization targets to minimize quality control testing and instrument calibrations by optimizing the load distribution over instruments in laboratory sites (i.e. optimization module executing on the system controller) (pg. 49 para. 3-4); wherein loading reagent kits are determined and loaded based on the optimization model (i.e. determining reagent pack loading plan) (pg. 17 para. 3).
one or more of the following optimization constraints: at least one of the plurality of analyzers is required to perform each of the types of tests included in the computer- readable data received at the system controller; a predetermined minimum number of the plurality of analyzers is required to perform a particular type of test included in the computer-readable data received at the system controller; the number of reagent packs to be loaded on the plurality of analyzers is not greater than the number of reagent pack mounting spaces at the plurality of analyzer; an amount of reagent volume required for performing the tests included in the computer-readable data received at the system controller is less than a total amount of reagent volume stored at the plurality of analyzers; a total number of stops made by the samples in the diagnostic laboratory system over the planning period is minimized; and all of the tests included in the computer-readable data received at the system controller are performed within the planning period.
• Sonmez teaches that instances of high volume diagnostics testing platform are referred to as instruments and analyzers (i.e. computer- readable data) (pg. 13 para. 3) with 57 analyzers distributed over 7 sites (pg. 25 para. 3); wherein a mathematical model (i.e. system controller) is used to achieve an optimal workload distribution of different assays over multiple analyzers in a single site (i.e. plurality of analyzers is required to perform each of the types) (pg. 49 para. 3).
Claim 2 recites:
wherein each of the plurality of analyzers has a fixed assay menu over the planning period.
• Sepulveda teaches the recitation above as applied for claim 1.
Claim 3 recites:
wherein the reagent pack loading plan determines optimal placement in available reagent pack mounting spaces for reagent packs given the number of the tests ordered and the type of the tests to be run on the plurality of analyzers.
• Sonmez does not teach the recitation above. However, Sepulveda teaches the ideal laboratory information system designed to optimize not only laboratory operations (pg. 1129 col. 1 para. 1); wherein a control system determines proper use of reagents (pg. 1138 col. 2 para. 3) and a management system tracks reagents and quality control (pg. 1133 col. 1 para. 3).
Claim 4 recites:
wherein the optimization method utilizes demand data as an input to the reagent pack optimization module.
• Sonmez teaches a program "Load Distributer Tool" (pg. 55 para. 4) that uses Python a linear programming modeler package for Python (pg. 55 para. 5 Sonmez) to achieve operational efficiency (pg. 40 para. 3 Sonmez); wherein Python’s mathematical optimization methods use used in classical linear optimization problems (pg. 15 para. 2 Pedroso) with objective function using constraint requiring
demand volumes to be satisfied (pg. 16 para. 4 Pedroso) as evidenced by Pedroso.
Claim 5 recites:
wherein the reagent pack optimization module optimizes for one or more operational efficiency considerations.
• Sonmez teaches that reagent usage optimization strategy includes an optimized load distribution model in accordance with instrument capacity, load balancing constraints and merge rules (i.e. operational efficiency considerations) (pg. 63 para. 1) to mitigate unoptimized distribution of assays to instruments as causes of unnecessary reagent consumption (pg. 3 para. 2).
Claim 6 recites:
wherein the one or more operational efficiency considerations include one or more of: operation of the diagnostic laboratory system with a subset of the plurality of analyzers; load balancing between the plurality of analyzers; reduced turn-around time; efficient reagent usage; minimizing quality assurance costs; and providing improved robustness of the diagnostic laboratory system.
• Sonmez teaches that laboratory management targets to sustain load balancing across analyzers to prevent overloaded and under loaded analyzers (pg. 49 para. 2); wherein short turn-around time solutions, mitigation of reagent waste and cost effective solutions are considerations involved in healthcare diagnostic testing services (pg. 3 para. 1).
Claim 7 recites:
wherein reagent pack optimization module optimizes using one or more optimization objective functions.
• Sonmez teaches a linear mathematical programming for the optimization of a linear objective function subject to linear equality and linear inequality constraints (pg. 49 para. 3-4).
Claim 8 recites:
wherein the one or more optimization objective functions comprise one of: minimizing quality assurance costs, minimizing unmet capacity cost, maximizing test assignment redundancy, optimizing workload balance, minimizing sample visits, and minimizing total analyzers used.
• Sonmez teaches analytical model targets to minimize quality control testing (i.e. minimizing quality assurance costs) (pg. 49 para.3).
Claim 9 recites:
wherein one of the one or more optimization objective functions is operated to minimize quality assurance costs.
• Sonmez teaches the recitation above as applied for claim 8.
Claim 10 recites:
wherein one of the one or more optimization objective functions is operated to minimize unmet capacity cost.
• Sonmez teaches merge rules target to reduce labor, supply, QC and disposal costs (i.e. reading on minimizing unmet capacity cost) (pg. 56 para. 2); wherein the strategy includes maximize the impact of waste reduction efforts for any use of reagent that does not lead to patient test results being considered as reagent waste (i.e. minimizing unmet capacity cost) (pg. 11 para. 2).
Claim 11 recites:
wherein one of the one or more optimization objective functions is operated to maximize test assignment redundancy.
• Sonmez does not teach the recitation above. However, Garg teaches a method for solving multi-objective reliability-redundancy allocation problems (pg. 248 col. 2 para. 2) for mechanical systems to be operational and available for the maximum possible time to maximize the overall production and hence profit (pg. 247 col. 1 para. 1) by adding redundant components (i.e. maximizing test assignment redundancy) (pg. 247 col. 2 para. 1).
Claim 12 recites:
wherein the one of the optimization objective functions uses a redundancy factor for each assay.
• Sonmez does not teach the recitation above. However, Garg teaches a method for solving multi-objective reliability-redundancy allocation problems (pg. 248 col. 2 para. 2); wherein the goal is to maximize reliability defined by Rs (r, n) (pg. 249 col. 2 para. 2); wherein n is the vector variable of redundancy allocation for the system (i.e. redundancy factor) (pg. 249 col. 1 para. 3).
Claim 13 recites:
wherein one of the one or more optimization objective functions is operated to optimize workload balance.
• Sonmez teaches a Load Distributor Tool program to achieve a balanced load distribution among instruments on a site (i.e. optimizing workload balance) (pg. 56 para. 2).
Claim 14 recites:
wherein optimization of the workload balance is achieved by one or more of: operation-time balancing strives for equal processing times across all the plurality of analyzers; test-type balancing wherein workload of each test type is distributed equally across the plurality of analyzers that have that test type deployed thereon; and total workload balancing wherein a total number of tests to be performed should be balanced across all the plurality of analyzers.
• Sonmez teaches load balancing among instruments preventing overloaded and under-loaded instruments (pg. 61 para. 2); wherein a laboratory management targets to sustain load balancing across analyzers to prevent overloaded and under loaded analyzers (pg. 49 para. 1).
Claim 15 recites:
comprising integer, non-negative slack variables for one or more cost functions of the workload balance optimization.
• Sonmez teaches a program "Load Distributer Tool" (pg. 55 para. 4) that uses Python a linear programming modeler package for Python (pg. 55 para. 5 Sonmez) to achieve operational efficiency (pg. 40 para. 3 Sonmez); wherein Python’s mathematical optimization methods use mixed integer optimization (pg. 60 para. 3 Pedroso) and all zero (non-negative) slack variables for equality constraints (pg. 20 para. 1 Pedroso) as evidenced by Pedroso.
Claim 16 recites:
wherein one of the one or more optimization objective functions is operated to minimize total analyzer visits to be made by the samples.
• Sonmez teaches optimizing the load distribution over similar instruments (pg. 3 para. 3) lead to reduction of overall testing (i.e. minimizing total analyzers visits to be made and total number of analyzers used) (pg. 61 para. 1).
Claim 17 recites:
wherein one of the one or more optimization objective functions is operated to minimize a total number of the analyzers used.
• Sonmez teaches the recitation above as applied for claim 16.
Claim 19 recites:
wherein the determining is further based on a menu feasibility optimization constraint that ensures that at least some of the plurality of analyzers have reagent packs loaded thereon for all the types of tests and the number of the tests to be performed on the samples by the diagnostic laboratory system over the planning period.
• Sonmez teaches a program "Load Distributer Tool" that propose a feasible optimal solution (i.e. feasibility optimization) (pg. 55 para. 4); including optimal distribution of assays and billable test volumes to instruments on sites is modeled as a linear programming (LP) problem with instrument capacity, merge rules and load balancing as linear constraints (i.e. menu of constraints) (pg. 49 pg. 3); applied to diagnostics testing platforms that have similar operating principles, using reagents in performing tests and performing quality control tests at predefined frequencies (pg. 65 para. 1); to achieve an optimal workload distribution of different assays over multiple analyzers (pg. 49 para. 2); wherein the length of one run is dependent on the number of patient samples loaded to the instrument and number of tests ordered (i.e. plurality of analyzers have reagent packs loaded thereon for all the types of tests and the number of the tests to be performed on the samples by the diagnostic laboratory system over the planning period) (pg. 18 para. 1).
Claim 20 recites:
wherein the determining is further based on a capacity optimization constraint that addresses physical limitations of the diagnostic laboratory system selected from a group of: a number of the plurality of analyzers, throughput of the plurality of analyzers, and a quantity of available reagent packs.
• Sonmez teaches a capacity constraint to aid minimizing the total number of QC tests wherein the number of tests run on one instrument per days should not exceed a certain threshold (pg. 51 para. 5).
Claim 21 recites:
wherein the reagent pack optimization module comprises a mixed integer program that is optimized for operational efficiency.
• Sonmez teaches the recitation above as applied for claim 15.
Claim 22 recites:
comprising loading reagent packs on the plurality of analyzers according to the reagent pack loading plan over the planning period.
• Sonmez teaches a method for reagent usage optimization in high-volume diagnostics testing (pg. 3 para. 1); applied to diagnostics testing platforms using reagents for tests at predefined frequencies (i.e. over a planning period) (pg. 65 para. 1); wherein workload of different assays is distributed over multiple analyzers in a single site (pg. 49 para. 2); wherein the analytical phase of the laboratory testing includes adding samples and reagents (i.e. loading reagent packs on the plurality of analyzers according to the reagent pack loading plan) (pg. 12 para. 2).
Claim 23 recites:
a plurality of analyzers that are configured to perform tests on samples, each of the plurality of analyzers having a fixed menu and a number of reagent pack mounting spaces; and
a system controller coupled to the plurality of analyzers, the system controller comprising a reagent pack optimization module having computer executable instructions configured to cause the system controller to generate a reagent pack load plan for the diagnostic laboratory system over a planning period, the reagent pack load plan including a total number of reagent packs to be loaded on the plurality of analyzers, the system controller generating the reagent pack load plan based on receiving:
computer-readable data including an inventory of the plurality of analyzers, respective fixed test menus and respective number of reagent pack mounting spaces thereof, the computer-readable data also including types and number of tests to be performed on samples by the diagnostic laboratory system over a planning period
one or more of the following optimization constraints: at least one of the plurality of analyzers is required to perform each of the types of tests included in the computer- readable data received at the system controller; a predetermined minimum number of the plurality of analyzers is required to perform a particular type of test included in the computer-readable data received at the system controller; the number of reagent packs to be loaded on the plurality of analyzers is not greater than the number of reagent pack mounting spaces at the plurality of analyzer; an amount of reagent volume required for performing the tests included in the computer-readable data received at the system controller is less than a total amount of reagent volume stored at the plurality of analyzers; a total number of stops made by the samples in the diagnostic laboratory system over the planning period is minimized; and all of the tests included in the computer-readable data received at the system controller are performed within the planning period.
• Sonmez and Sepulveda teach the recitation above as applied for claim 1.
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-23, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Sonmez in view of Sepulveda and Garg because all references disclose methods for system's optimization. The motivation would have been to:
• optimizing the operation of laboratories by intelligent management of laboratory information (pg. 1 col. 1 para. 1 Sepulveda) and
• incorporate a method for solving multi-objective reliability-redundancy allocation problems (pg. 248 col. 2 para. 2 Garg).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the allelic imbalance analysis method of Sonmez to the methods by Sepulveda and Garg because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for system's optimization.
No prior art has been applied to the following claims
Claim 18 is free of the analogous art at least because close art, e.g. Sonmez, Sepulveda and Garg as cited on the attached Form PTO-892 (9/22/2025)., either individually or in obvious combination, does not teach the claim 1 recited combination of
determining is further based on a workflow continuity constraint that ensures continuous workflow of the diagnostic laboratory system without requiring change of the fixed test menus of the plurality of analyzers
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 102/103
The Remarks of 12/15/2025 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 14 para. 2-8 and pg. 15 para. 5-8 (emphasis added):
Sonmez as evidenced by Pedroso does not anticipate amended independent claim 1 … Applicant has also not found any disclosure in Sonmez of other constraints … Applicant has not found any disclosure or suggestion in Pedroso of "constraints" as recited in amended independent claim 1 ... Sepulveda .. does not make up for the deficiencies of Sonmez and Pedroso … Garg … does not make up for the deficiencies of Sonmez, Pedroso, and/or Sepulveda.
It appears that these remarks address the combination of references not teaching the amended recitations. While none of the references teach all claim limitations, and the examiner does not dispute Applicant's identification of material missing from each one, all the claim limitations are taught by the combination of references, as explained previously and as described in the above rejections. The amended claims 1 and 23 recite "one or more of the following optimization constraints". As such, Sonmez teaches that instances of high volume diagnostics testing platform are referred to as instruments and analyzers (i.e. computer- readable data) (pg. 13 para. 3) with 57 analyzers distributed over 7 sites (pg. 25 para. 3); wherein a mathematical model (i.e. system controller) is used to achieve an optimal workload distribution of different assays over multiple analyzers in a single site (i.e. plurality of analyzers is required to perform each of the types) (pg. 49 para. 3). Therefore, Sonmez teaches the required "one or more" of the described constraints.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM ET.
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, Olivia Wise can be reached at (571) 272-2249. 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.
/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685