DETAILED ACTION
This office action is in response to applicant’s communication filed 12/08/2025.
Claim(s) 1-15 have been considered.
- Claim(s) 1-15 are pending.
- No claim(s) has/have been canceled.
- No claim(s) has/have been added.
- Claim(s) 1, 3, 10, and 13 has/have been amended.
- Claim(s) 1-15 have been rejected as described below.
- This action is MADE FINAL.
Specification
The amendment made to the disclosure (amended title) filed 12/08/2025 is accepted and acknowledged by the examiner for this stage of prosecution.
Claim Interpretation
As noted in previous office action: Examiner would like to note that many terms/phrases in the current claim language appears to be broadly applicable in many types of technologies/arts. Accordingly, to promote compact prosecution to apply prior art, examiner has interpreted such terms/phrases in light of various examples (and their equivalents) provided in the specification. For example, under broadest reasonable interpretation, “genetic procedure” within the scope of the claims has been interpreted as any type of computational procedure performed for various purposes in light of applicant specification, 0011-12. In light of the same paragraphs, examiner has interpreted “chromosome” as data, a “packing” as an organization of objects, etc.
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(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method (process), which is a statutory category of invention.
However, claim 1 recites, “generating … chromosomes in a genetic procedure, wherein each chromosome indicates a packing of objects in a volume, wherein the packing indicates an organization of the objects in the volume; determining … a stagnant generation quantity that indicates an amount of generations subsequent to a leading generation with a leading chromosome score; and terminating … the genetic procedure based on the stagnant generation quantity;” “ranking … each chromosome relative to other chromosomes based on a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;” and “selecting … the packing that is represented by the chromosome that is ranked the best in satisfying the objectives;”. These limitation(s) fall(s) into the “mental process” group of abstract ideas, because the recited step(s) of generating, determining, terminating, ranking and selecting each appear to be an observation/evaluation and judgement that can be performed in the human mind (and/or written with a pen on a paper) based on writing up some data, comparing some known/available data to determine further data, writing up instruction to end/terminate etc. See specification 0011-12 and 0026 for the broad examples and descriptions for these terms/phrases (See also “Claim interpretation” section above). These/This limitation(s) therefore recite(s) concept(s) performed in the human mind and/or can be written down with a pen on a paper. Note, the courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind. (See MPEP 2106.04(a)(2)). Thus, the claim recites a mental process. Thus, this/these limitation(s) fall(s) into the “mental processes” grouping of abstract ideas in 2019 PEG Section I, 84 Fed. Reg. at 52.
Besides the abstract ideas, claim now recites additional limitation(s) “and manufacturing, by the processor, the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute the selected packing to organize the objects in the volume and manufacture the objects based on the selected packing.” and also recites a processor performing the above steps. The limitations do not integrate the invention into a practical application because the processor is recited at a high level of generality and is merely invoking computer components as a tool. This/these element(s) is/are general purpose computer/computer component or other machinery that are used in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or being considered as simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP 2106.05(f)). Note, manufacturing by the processor by sending instructions to an additive manufacturing device is considered as applying computers or other machinery merely as a tool to perform an existing process. Therefore, the claim(s) is/are not patent eligible. Thus, this judicial exception is not integrated into a practical application. Accordingly, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception due to the same reasons as stated above.
Therefore, the claim is not patent eligible.
Similar analysis is applicable to independent claim(s) 10 and 13 and these are thus also not patent eligible. The statutory categories of claims 10 and 13 are different – these are apparatus /medium claims. Also note, the mere nominal recitation of a generic processor coupled to memory to perform these determination/termination/ranking/selecting steps does not take the claim limitation out of the mental processes grouping (similarly, for user interface to display in claim 10, see further details below in the analysis of claim 11).
Therefore, the claim(s) is/are not patent eligible.
Claim 2 depends from claim 1, thus includes the abstract idea of claim 1. Further, the additional limitation(s) is/are mere expansion of the abstract idea (comparing the stagnant generation quantity to a stagnant generation threshold) and no significant additional elements provided.
Claim 3 is/are rejected based on similar reasoning – an expansion of the mental step of terminating based on some conditions, where this/these limitation(s) just describe(s) the data type further that are to be manipulated and no significant additional elements provided.
Claim 4-8 is/are rejected based on similar reasonings for claim 3
Claim 9 is also expansion of abstract idea by including more mental steps such as determinations by comparing, hence is/are rejected similarly.
Claim 11 depends from claim 10, thus includes the abstract idea of claim 10. Further, the additional limitation(s) include user interface having control options and data shown. The limitations do not integrate the invention into a practical application because the displaying step (using display and/or computing components recited in high level of generality – note, applicant disclosure broadly mentions display via generic inputs, for example, and is merely invoking computer components as a tool. This/these element(s) is/are general purpose computer/computer component or other machinery that are used in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or being considered as simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) and thus does not integrate a judicial exception into a practical application or provide significantly more (MPEP 2106.05(f)). Therefore, the claim(s) is/are not patent eligible.
Note, claim 10 also has mere mention of generic user interface and claim 12 as well. Thus, claims 10-12 all are rejected by the similar reasons for the user interface explanation above.
Claims 14-15 is/are also expansion of abstract idea by including expansion on the mental steps by providing conditions, hence is/are rejected similar to claims 2-8.
Accordingly, claim(s) 1-15 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeng (WO 2019117963 Al – Provided in IDS by applicant) in view of Howard (US 20220250261 A1).
Regarding claim 1, Zeng teaches:
A method, comprising:
generating, by a processor, (see 0028 for processor and execution of instructions.) chromosomes in a genetic procedure, wherein each chromosome indicates a packing of objects in a volume, (0038 teaches, some examples of meta-heuristic processes that may be applied for parts packing include: genetic processes, simulated annealing, ant colony optimization, particle swarm optimization, etc.; 0039 teaches example involving the use of a genetic process, each population may be composed of many individuals (or creatures or phenotypes). Each individual may represent a complete parts packing solution. Each individual may be represented by chromosomes that encode an option for parts placement (e.g., three floating-point values for the x, y, and z coordinates of a part’s center of mass; three floating-point values for rotational angles representing the part’s orientation; and a Boolean value to indicate if the part is added to the build volume successfully). The population may be initialized by random-number generators. Note, these examples align with examples provided in applicant specification, 0011-12.) wherein the packing indicates an organization of the objects in the volume; (0041-46 teach this through various examples. For instance, 0041 teaches the 3D-printing system determining a collision-free packing of 3D objects in a build envelope and then 0045-46 teach the placement, orientation, shape and number of parts in the build volume including multiple regions. Note, these examples align with description/examples provided for object packing in applicant specification, 0006-08.)
determining, by the processor, a stagnant generation quantity that indicates an amount of generations subsequent to a leading generation with a leading chromosome score; (0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). More specifically, see 0041 - For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” Note, the fitness improvement rate stagnating here is an example of a stagnant generation quantity. See other examples related to scores being used for determination of packing solutions in 0065-67.)
and terminating, by the processor, the genetic procedure based on the stagnant generation quantity; (0041 teaches, “In some examples, termination of a genetic process may be determined based on a total allowed computing time and/or the rate of fitness improvement. For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” Note, the fitness improvement rate stagnating here is an example of a stagnant generation quantity.)
While Zeng teaches a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives and manufacturing, by the processor, the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute the … packing to organize the objects in the volume and manufacture the objects (As above, see 0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). See other examples related to scores being used for determination of packing solutions in 0065-67. See 0021-24 for instructions for 3D printing processes.),
Zeng does not explicitly disclose the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
ranking, by the processor, each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;]
selecting, by the processor, the packing that is represented by the chromosome that is ranked the best in satisfying the objectives;
[and manufacturing, by the processor, the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing.
Howard explicitly teaches the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
ranking, by the processor, each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;] (0159-0171 teach ranking the individuals in the population by a multi-objective computational algorithm. See 0164 for ranking, for example - The population is then fitness-ranked for each objective m. Each individual is assigned a crowding distance d.sub.c per m, as the difference between the fitnesses of its immediate neighbours, normalised in the range of observed fitnesses for that objective. The first and last individuals in each front have d.sub.c=∞. The fronts F.sub.x are ranked based the summed d.sub.c of every individual in the front, and the new population is recreated to the original population size N by adding individuals from the lowest-ranked F.sub.x to the highest in turn, in order of descending d.sub.c. A generation consists of the above-described steps. These steps are repeated G times, until a satisfactory level of performance, or some computational budget, is reached.)
selecting, by the processor, the packing that is represented by the chromosome that is ranked the best in satisfying the objectives; (Besides above, see 0163-67 teach such fitness-ranking based selection of packing that best satisfies the objectives, for example, 0165 teach determining optimal packings using the above. Another example is in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
[and manufacturing, by the processor, the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing. (As above in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
Accordingly, as Zeng and Howard are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of fitness-based ranking of population to determine optimal packing(s) allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, as taught by Howard to the genetic procedure and control system with ability to determine packing solutions based on multi-objective scoring for an individual into a fitness measurement as taught by Zeng. Such a combination would have enabled taking advantage of the well-known features of controlling an additive printing machine based on the packing element configuration and manipulator shape to manufacture the robot manipulator, which would have delivered an optimal manipulator function by allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, for example, as evident in Howard, abstract, 0170-71, etc.
Regarding claim 13, Zeng teaches:
A non-transitory tangible computer-readable medium storing executable code, comprising:
code to cause a processor to produce a current generation of chromosomes using a genetic procedure, wherein each chromosome indicates a packing of objects in a volume, (0028 for processor and execution of instructions; 0038 teaches, some examples of meta-heuristic processes that may be applied for parts packing include: genetic processes, simulated annealing, ant colony optimization, particle swarm optimization, etc.; 0039 teaches example involving the use of a genetic process, each population may be composed of many individuals (or creatures or phenotypes). Each individual may represent a complete parts packing solution. Each individual may be represented by chromosomes that encode an option for parts placement (e.g., three floating-point values for the x, y, and z coordinates of a part’s center of mass; three floating-point values for rotational angles representing the part’s orientation; and a Boolean value to indicate if the part is added to the build volume successfully). The population may be initialized by random-number generators. Note, these examples align with examples provided in applicant specification, 0011-12.) wherein the packing indicates an organization of the objects in the volume; (0041-46 teach this through various examples. For instance, 0041 teaches the 3D-printing system determining a collision-free packing of 3D objects in a build envelope and then 0045-46 teach the placement, orientation, shape and number of parts in the build volume including multiple regions. Note, these examples align with description/examples provided for object packing in applicant specification, 0006-08.)
code to cause the processor to determine a current set of chromosome scores based on the current generation of chromosomes; (see below citation from 0040-41) for the generation and comparisons in details.) code to cause the processor to determine a stagnant generation quantity based on a comparison of a current best chromosome score to a previous leading chromosome score; (0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). More specifically, see 0041 - For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” Note, the fitness improvement rate stagnating here is an example of a stagnant generation quantity. See other examples related to scores being used for determination of packing solutions in 0065-67.)
and code to cause the processor to determine whether to terminate the genetic procedure based on the stagnant generation quantity; (0041 teaches, “In some examples, termination of a genetic process may be determined based on a total allowed computing time and/or the rate of fitness improvement. For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” Note, the fitness improvement rate stagnating here is an example of a stagnant generation quantity.)
While Zeng teaches a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives and manufacturing the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute the … packing to organize the objects in the volume and manufacture the objects (As above, see 0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). See other examples related to scores being used for determination of packing solutions in 0065-67. See 0021-24 for instructions for 3D printing processes.),
Zeng does not explicitly disclose the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
code to cause the processor to rank each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;]
code to cause the processor to select the packing that is represented by the chromosome that is ranked the best in satisfying the objectives;
[and code to cause the processor to manufacture the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing.
Howard explicitly teaches the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
code to cause the processor to rank each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;] (0159-0171 teach ranking the individuals in the population by a multi-objective computational algorithm. See 0164 for ranking, for example - The population is then fitness-ranked for each objective m. Each individual is assigned a crowding distance d.sub.c per m, as the difference between the fitnesses of its immediate neighbours, normalised in the range of observed fitnesses for that objective. The first and last individuals in each front have d.sub.c=∞. The fronts F.sub.x are ranked based the summed d.sub.c of every individual in the front, and the new population is recreated to the original population size N by adding individuals from the lowest-ranked F.sub.x to the highest in turn, in order of descending d.sub.c. A generation consists of the above-described steps. These steps are repeated G times, until a satisfactory level of performance, or some computational budget, is reached.)
code to cause the processor to select the packing that is represented by the chromosome that is ranked the best in satisfying the objectives; (Besides above, see 0163-67 teach such fitness-ranking based selection of packing that best satisfies the objectives, for example, 0165 teach determining optimal packings using the above. Another example is in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
[and code to cause the processor to manufacture the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing. (As above in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
Accordingly, as Zeng and Howard are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of fitness-based ranking of population to determine optimal packing(s) allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, as taught by Howard to the genetic procedure and control system with ability to determine packing solutions based on multi-objective scoring for an individual into a fitness measurement as taught by Zeng. Such a combination would have enabled taking advantage of the well-known features of controlling an additive printing machine based on the packing element configuration and manipulator shape to manufacture the robot manipulator, which would have delivered an optimal manipulator function by allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, for example, as evident in Howard, abstract, 0170-71, etc.
Regarding claim 14, Zeng and Howard teach all the elements of claim 13.
Zeng further teaches:
wherein the code to cause the processor to determine whether to terminate the genetic procedure comprises code to cause the processor to execute the genetic procedure until a logic rule is satisfied. (0041, as above, teaches, for example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted, emphasis added on the “when …” logic rule.)
Claim(s) 2-12, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zeng (WO 2019117963 Al – Provided in IDS by applicant) in view of Howard (US 20220250261 A1) in further view of Zhu (US 20070192263 A1).
Regarding claim 2, Zeng and Howard teach all the elements of claim 1.
While Zeng teaches “For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” (0041),
Zeng and Howard do not explicitly disclose the predetermined criteria to be comparison to a stagnant generation threshold in the limitation below:
further comprising comparing the stagnant generation quantity to a stagnant generation threshold.
Zhu explicitly teaches the predetermined criteria to be comparison to a stagnant generation threshold in the limitation below:
further comprising comparing the stagnant generation quantity to a stagnant generation threshold. (0057 teaches, “It is possible that during evolution, a genetic algorithm becomes stagnant and unable to produce more fit individuals even though an optimum fitness has not been attained. This is largely because the initial population of the individuals is randomly generated. When this situation occurs, the rostering system 20 sets an extinction operator (N.sub.EXTINCT) to terminate the evolution. The extinction operator is like a biological extinction of a population that kills all but the most fit individuals. The population is then refreshed with mutated copies of such most fit individuals. The rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations.”, emphasis added to the very last line here, which teaches comparison with a specific number as the criteria.)
Accordingly, as Zeng, Howard, and Zhu are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of allowing a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations, as taught by Zhu to the genetic procedure and control system with ability to terminate the genetic procedure based on the stagnant generation quantity (the fitness improvement rate stagnates) according to predetermined criteria as taught by Zeng and Howard. The combination would have been motivated in order to take advantage of the well-known feature of specifying the criteria to compare with a threshold given by user, for example, would have helped enable achieving more flexible and optimal roster solution, as evident in Zhu, 0057-58, etc.
Regarding claim 3, Zeng and Howard teach all the elements of claim 1.
While Zeng teaches “For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” (0041),
Zeng and Howard do not explicitly disclose the predetermined criteria to be a set of conditions that comprises a stagnant generation threshold in the limitation below:
wherein terminating the genetic procedure is based on a set of conditions that comprises a stagnant generation threshold.
Zhu explicitly teaches the predetermined criteria to be a set of conditions that comprises a stagnant generation threshold in the limitation below:
wherein terminating the genetic procedure is based on a set of conditions that comprises a stagnant generation threshold. (0057 teaches, “It is possible that during evolution, a genetic algorithm becomes stagnant and unable to produce more fit individuals even though an optimum fitness has not been attained. This is largely because the initial population of the individuals is randomly generated. When this situation occurs, the rostering system 20 sets an extinction operator (N.sub.EXTINCT) to terminate the evolution. The extinction operator is like a biological extinction of a population that kills all but the most fit individuals. The population is then refreshed with mutated copies of such most fit individuals. The rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations.”, emphasis added to the very last line here, which teaches comparison with a specific number as part of a set of conditions.)
Accordingly, as Zeng, Howard, and Zhu are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of allowing a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations, as taught by Zhu to the genetic procedure and control system with ability to terminate the genetic procedure based on the stagnant generation quantity (the fitness improvement rate stagnates) according to predetermined criteria as taught by Zeng and Howard. The combination would have been motivated in order to take advantage of the well-known feature of specifying the criteria to compare with a threshold given by user, for example, would have helped enable achieving more flexible and optimal roster solution, as evident in Zhu, 0057-58, etc.
Regarding claim 4, Zeng, Howard, and Zhu teach all the elements of claim 3.
Zeng further teaches:
wherein the set of conditions further comprises a packed quantity threshold. (0037 teaches, for example, the plurality of objectives may comprise maximizing a packing density. The packing density may refer to the ratio of solid parts to empty space in the build volume 108. … 0037 teaches another example: Increased efficiency may enable the 3D-printing system 100 to print a maximum or near maximum number of parts during a single printing operation, to print a plurality of parts in a minimized or near minimized amount of time during a single printing operation, and/or to print all of a plurality of parts in a single or in a minimized number of printing operations. Here, the maximizing the ratio of solid parts to empty space in the build volume exemplifies a packed quantity threshold being in place, for example.)
Regarding claim 5, Zeng, Howard, and Zhu teach all the elements of claim 3.
Zeng further teaches:
wherein the set of conditions further comprises a fitness score threshold. (0040 teaches, in this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). This is aligned with examples in applicant specification, 0016.)
Regarding claim 6, Zeng, Howard, and Zhu teach all the elements of claim 3.
Zeng further teaches:
wherein the set of conditions further comprises a packing height threshold. (0040 teaches, in this example, an individual may be scored for each objective function— e.g., z-height for build volume. This is aligned with examples in applicant specification, 0015.)
Regarding claim 7, Zeng, Howard, and Zhu teach all the elements of claim 3.
Zeng further teaches:
wherein the set of conditions further comprises a packing density threshold. (0037 teaches, for example, the plurality of objectives may comprise maximizing a packing density. The packing density may refer to the ratio of solid parts to empty space in the build volume 108.)
Regarding claim 8, Zeng, Howard, and Zhu teach all the elements of claim 3.
Zeng further teaches:
wherein the set of conditions is a Boolean function. (0039 teaches, in one example involving the use of a genetic process, each population may be composed of many individuals (or creatures or phenotypes). Each individual may be represented by chromosomes that encode an option for parts placement (e.g., three floating-point values for the x, y, and z coordinates of a part’s center of mass; three floating-point values for rotational angles representing the part’s orientation; and a Boolean value to indicate if the part is added to the build volume successfully). This is aligned with examples and description in applicant specification, 0032.)
Regarding claim 9, Zeng and Howard teach all the elements of claim 1.
However, Zeng does not explicitly disclose the additional limitations of claim 9.
Zhu explicitly teaches:
wherein determining the stagnant generation quantity comprises: determining whether a current chromosome score is better than the leading chromosome score; (0081 teaches, Selecting fit chromosomes according to Fk occurs at step 212. Such fit chromosomes are used to construct a mating pool. There must be a best fitness value FGi-best in each generation Gi. If FGi-best is better than FGL-best, at decision step 214, then the GA rostering engine 24 updates FGL-best by assigning FGi-best as FGL-best at step 216 and setting NNI=0. ) in a case that the current chromosome score is better than the leading chromosome score, resetting the stagnant generation quantity and setting the leading chromosome score to the current chromosome score; (As above, 0081 teaches, there must be a best fitness value FGi-best in each generation Gi. If FGi-best is better than FGL-best, at decision step 214, then the GA rostering engine 24 updates FGL-best by assigning FGi-best as FGL-best at step 216 and setting NNI=0.) and in a case that the current chromosome score is not better than the leading chromosome score, incrementing the stagnant generation quantity. (0081 teaches, otherwise if FGi-best shows no improvement, then NNI is incremented by one at step 218. In any case, the number of evolutions (i) is incremented at step 220.)
Accordingly, as Zeng, Howard, and Zhu are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of using the best fitness value among a selected pool of fit chromosomes to update the chromosome scores, or increment the stagnant generation quantity as taught by Zhu to the genetic procedure and control system with ability to terminate the genetic procedure based on the stagnant generation quantity (the fitness improvement rate stagnates) according to predetermined criteria as taught by Zeng and Howard. The combination would have been motivated in order to take advantage of the well-known feature of reproducing by being able to terminating the evolution at a time when a genetic algorithm becomes stagnant and unable to produce more fit individuals even though an optimum fitness has not been attained, which would have helped enable achieving more flexible and optimal roster solution where “The extinction operator is like a biological extinction of a population that kills all but the most fit individuals. The population is then refreshed with mutated copies of such most fit individuals. The rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations.”, as evident in Zhu, 0057-58, 0081, etc.
Regarding claim 10, Zeng teaches:
An apparatus, comprising: a memory; (0019 & 0030 teach processor and memory.) an additive manufacturing device; (See 0021-24 for instructions for 3D printing processes.) a processor coupled to the memory, and the additive manufacturing device, (0019 & 0030 teach processor and memory. 0020-24 teach 3D printing system connected thereto.) wherein the processor is to: (0030 - The apparatus 200 may also include a data store 212 on which the processor 210 may store information, such as information pertaining to the parts to be printed. The data store 212 may be volatile and/or non-volatile memory. 0032 - Figure 3 is a flow diagram illustrating an example of a method 300 for parts packing. The method 300 for parts packing may be performed by, for example, the controller 114 and/or the apparatus 200.)
iterate a genetic procedure to generate chromosomes representing packings (Besides 0032 above, see 0038 teaches, some examples of meta-heuristic processes that may be applied for parts packing include: genetic processes, simulated annealing, ant colony optimization, particle swarm optimization, etc.; 0039 teaches example involving the use of a genetic process, each population may be composed of many individuals (or creatures or phenotypes). Each individual may represent a complete parts packing solution. Each individual may be represented by chromosomes that encode an option for parts placement (e.g., three floating-point values for the x, y, and z coordinates of a part’s center of mass; three floating-point values for rotational angles representing the part’s orientation; and a Boolean value to indicate if the part is added to the build volume successfully). The population may be initialized by random-number generators. Note, these examples align with examples provided in applicant specification, 0011-12.) wherein the packing indicates an organization of the objects in the volume; (0041-46 teach this through various examples. For instance, 0041 teaches the 3D-printing system determining a collision-free packing of 3D objects in a build envelope and then 0045-46 teach the placement, orientation, shape and number of parts in the build volume including multiple regions. Note, these examples align with description/examples provided for object packing in applicant specification, 0006-08.)
produce a user interface to display …; (0031 teaches the apparatus 200 may further include an input/output interface 214 through which the processor 210 may communicate with an external device(s) (not shown), for instance, to receive and store the information pertaining to the parts to be printed. … The input/output interface 214 may further include a network interface card and/or may also include hardware and/or machine-readable instructions to enable the processor 210 to communicate with various input and/or output devices, such as a keyboard, a mouse, a display, another computing device, etc., through which a user may input instructions into the apparatus 200.)
and determine whether to terminate the genetic procedure based on a stagnant generation quantity (0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). More specifically, 0041 teaches, “In some examples, termination of a genetic process may be determined based on a total allowed computing time and/or the rate of fitness improvement. For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” Note, the fitness improvement rate stagnating here is an example of a stagnant generation quantity.) …
While Zeng teaches a display connected to the processor/apparatus (as in 0031), and that various input and/or output devices, such as a keyboard, a mouse, a display, another computing device, etc., through which a user may input instructions into the apparatus 200 (also in 0031) and also teaches a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives and manufacturing, by the processor, the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute the … packing to organize the objects in the volume and manufacture the objects (As above, see 0040 teaches, “In this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). See other examples related to scores being used for determination of packing solutions in 0065-67. See 0021-24 for instructions for 3D printing processes.),
Zeng does not explicitly disclose the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
rank each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;]
select the packing that is represented by the chromosome that is ranked the best in satisfying the objectives;
[and manufacture the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing.
Zeng also does not explicitly teach the display data to be a tracked value of the chromosomes and also does not explicitly teach additional termination condition to be and whether a stop input from the user interface is detected.
Howard explicitly teaches the fitness measure-based ranking of chromosomes and selection for packing based on that in the following limitations:
rank each chromosome relative to other chromosomes based on [a fitness measure that indicates a degree to which each chromosome satisfies one or more objectives;] (0159-0171 teach ranking the individuals in the population by a multi-objective computational algorithm. See 0164 for ranking, for example - The population is then fitness-ranked for each objective m. Each individual is assigned a crowding distance d.sub.c per m, as the difference between the fitnesses of its immediate neighbours, normalised in the range of observed fitnesses for that objective. The first and last individuals in each front have d.sub.c=∞. The fronts F.sub.x are ranked based the summed d.sub.c of every individual in the front, and the new population is recreated to the original population size N by adding individuals from the lowest-ranked F.sub.x to the highest in turn, in order of descending d.sub.c. A generation consists of the above-described steps. These steps are repeated G times, until a satisfactory level of performance, or some computational budget, is reached.)
select the packing that is represented by the chromosome that is ranked the best in satisfying the objectives; (Besides above, see 0163-67 teach such fitness-ranking based selection of packing that best satisfies the objectives, for example, 0165 teach determining optimal packings using the above. Another example is in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
[and manufacture the objects by sending instructions to an additive manufacturing device that causes the additive manufacturing device to execute] the selected packing [to organize the objects in the volume and manufacture the objects] based on the selected packing. (As above in 0170-71 – “… this approach allows the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties. The process allows robotic manipulator to have a precise packing element configuration as desired, and thereby delivers optimal manipulator properties. Furthermore, the packing element configuration is determined by computational model, so that desirable manipulator properties can be better translated to packing element configurations to be manufactured.”.)
Accordingly, as Zeng and Howard are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of fitness-based ranking of population to determine optimal packing(s) allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, as taught by Howard to the genetic procedure and control system with ability to determine packing solutions based on multi-objective scoring for an individual into a fitness measurement as taught by Zeng. Such a combination would have enabled taking advantage of the well-known features of controlling an additive printing machine based on the packing element configuration and manipulator shape to manufacture the robot manipulator, which would have delivered an optimal manipulator function by allowing the robotic manipulator to be manufactured using 3D printing technique with a high degree of control of the resulting manipulator properties, for example, as evident in Howard, abstract, 0170-71, etc.
However, Zeng and Howard also do not explicitly teach the display data to be a tracked value of the chromosomes and also does not explicitly teach additional termination condition to be and whether a stop input from the user interface is detected.
Zhu explicitly teaches the display data to be a tracked value of the chromosomes. (0036 - processing user input information by the rostering system to derive a roster associated with one or more individuals. 0087 also teaches users with different access rights that can access the rostering system. 0006 teaches, in a rostering system based upon genetic algorithms, solution of a problem is encoded in a chromosome.) and also teaches additional termination condition to be and whether a stop input from the user interface is detected. (0057 teaches the rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations.)
Accordingly, as Zeng, Howard, and Zhu are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of allowing a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations via having access to the roster system, as taught by Zhu to the genetic procedure and control system with ability to terminate the genetic procedure based on the stagnant generation quantity (the fitness improvement rate stagnates) according to predetermined criteria that also has a display as taught by Zeng and Howard. The combination would have been motivated in order to take advantage of the well-known feature of specifying the criteria to compare with a threshold given by user, for example, would have helped enable achieving more flexible and optimal roster solution, as evident in Zhu, 0006, 0057-58, etc.
Regarding claim 11, Zeng, Howard and Zhu teach all the elements of claim 10.
Zhu further teaches:
wherein the user interface comprises a stop control and a plot of the tracked value. (Fig. 8-9 and 0085 teach “FIG. 8 is a graph showing fitness improvements of the roster 28 during the GA evolution step 104 of dynamic shifting in the method 100. FIG. 9 is a graph showing fitness improvements of the roster 28 during the fine-tuning step 110 of swapping in the method 100.)
Motivation to combine the teachings would have been similar to the reasons stated above. In addition, this combination would have allowed users access the roster and get visuals and data, input termination specification by observing, etc. as in Zhu, 0006, 0057-58, 0085, etc.
Regarding claim 12, Zeng, Howard and Zhu teach all the elements of claim 11.
Zhu further teaches:
wherein the user interface further includes a query interval control, a generation range control, and a chromosome identifier selection control. (0057 teaches the rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations. Fig. 8-9 and 0085 teach “FIG. 8 is a graph showing fitness improvements of the roster 28 during the GA evolution step 104 of dynamic shifting in the method 100. FIG. 9 is a graph showing fitness improvements of the roster 28 during the fine-tuning step 110 of swapping in the method 100. Note the “No of generations” and “No of Generations (Fine-tuning)” axes for the intervals; For chromosome identifier selection control, see Fig. 6, 0072 - intermediate shift list matrix Ωbest. … with a ‘Yes’, the method proceeds to provide the roster 28 at step 116. The roster 28 is provided as a shift list matrix as shown in FIG. 1. 0076 along with Fig. 7 teach dynamic adjustment of the shift list. Note, these are all aligned with the examples in applicant specification, 0062-64.)
Motivation to combine the teachings would have been similar to the reasons stated above. In addition, this combination would have allowed users access the roster and get dynamic visuals and data, input termination specification by observing, etc. as in Zhu, 0006, 0057-58, 0085, etc.
Regarding claim 15, Zeng and Howard teach all the elements of claim 14.
Zeng further teaches:
wherein the logic rule comprises a packed quantity threshold, (0037 teaches, for example, the plurality of objectives may comprise maximizing a packing density. The packing density may refer to the ratio of solid parts to empty space in the build volume 108. … 0037 teaches another example: Increased efficiency may enable the 3D-printing system 100 to print a maximum or near maximum number of parts during a single printing operation, to print a plurality of parts in a minimized or near minimized amount of time during a single printing operation, and/or to print all of a plurality of parts in a single or in a minimized number of printing operations. Here, the maximizing the ratio of solid parts to empty space in the build volume exemplifies a packed quantity threshold being in place, for example.) a fitness score threshold, (0040 teaches, in this example, an individual may be scored for each objective function— e.g., z-height for build volume, number of undesirable thermal regions, etc. In one configuration, preferences may be provided among all objectives. For instance, objectives may be assigned relative weights, which may enable assembly of all scores into a single fitness value. The genetic process may rely on the fitness value to determine which individuals are preserved in future generations (e.g., survivability). This is aligned with examples in applicant specification, 0016.) …
While Zeng teaches “For example, when the computing time exceeds the allowed computing time and/or the fitness improvement rate stagnates according to predetermined criteria, the process may terminate and the current best solution may be outputted.” (0041),
Zeng and Howard do not explicitly disclose “and a stagnant generation threshold”.
Zhu explicitly teaches “and a stagnant generation threshold”. (0057 teaches, “It is possible that during evolution, a genetic algorithm becomes stagnant and unable to produce more fit individuals even though an optimum fitness has not been attained. This is largely because the initial population of the individuals is randomly generated. When this situation occurs, the rostering system 20 sets an extinction operator (N.sub.EXTINCT) to terminate the evolution. The extinction operator is like a biological extinction of a population that kills all but the most fit individuals. The population is then refreshed with mutated copies of such most fit individuals. The rostering system 20 allows a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations.”, emphasis added to the very last line here, which teaches comparison with a specific number as part of a set of conditions.)
Accordingly, as Zeng, Howard, and Zhu are directed to various genetic algorithms and control technologies, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have specifically added the feature of utilizing the well-known technology of allowing a user to specify a number of generations after which extinction occurs if there is no improvement in fitness after that number of generations, as taught by Zhu to the genetic procedure and control system with ability to terminate the genetic procedure based on the stagnant generation quantity (the fitness improvement rate stagnates) according to predetermined criteria as taught by Zeng and Howard. The combination would have been motivated in order to take advantage of the well-known feature of specifying the criteria to compare with a threshold given by user, for example, would have helped enable achieving more flexible and optimal roster solution, as evident in Zhu, 0057-58, etc.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009,158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005) (reference disclosing optional inclusion of a particular component teaches compositions that both do and do not contain that component); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Response to arguments
Applicant's remarks and arguments filed 12/08/2025 have been fully considered. See below for further details.
The amendment made to the disclosure (amended title) filed 12/08/2025 is accepted and acknowledged by the examiner for this stage of prosecution, hence the objection to specification has been overcome and removed from above.
Based on the amendment, the claim objections have been overcome and thus removed from above.
Applicant argues in remarks (p8-9) focusing on the amended additional claim limitations of claim 1 for the 35 U.S.C. 101 rejections. The rejections have been updated accordingly, necessitated by amendment.
Applicant’s arguments regarding the prior art rejection are fully considered.
Applicant argues regarding claim 1, as amended, in applicant remarks (especially in page 10-11) regarding the amended claim limitations. The above amendment has changed the overall scope of the claim language and thus has overcome the prior art rejections.
Accordingly, new grounds of rejection introducing a new art, Howard, has been provided above, necessitated by amendment. See above for details.
Claims 10 and 13 have been updated with new grounds of rejection accordingly as well. All the citations and rejections for the dependent claims have been updated and further clarified accordingly.
Accordingly, claims 1-15 are not patentable over prior arts.
Suggestions:
In order to move the prosecution forward, examiner recommends applicant to provide further claim amendments with inventive features that may help overcome the current rejection based on further search and consideration.
Pertinent Art(s)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Goel; Tushar et al. (US 20090319453 A1) relates to a sampling strategy using genetic algorithms (GA) in engineering design optimization. A product is to design and optimize with a set of design variables, objectives and constraints. A suitable number of design of experiments (DOE) samples is then identified such that each point represents a particular or unique combination of design variables. The sample selection strategy is based on genetic algorithms. Computer-aided engineering (CAE) analysis or analyses (e.g., finite element analysis, finite difference analysis, mesh-free analysis, etc.) is/are performed for each of the samples during the GA based sample selection procedure. A meta-model is created to approximate the CAE analysis results at all of the DOE samples. Once the meta-model is satisfactory (e.g., accuracy within a tolerance), an optimized "best" design can be found by using the meta-model as function evaluator for the optimization method. Finally, a CAE analysis is performed to verify the optimized "best" design.
Conclusion
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 MARZIA T MONTY whose telephone number is (571)272-5441. The examiner can normally be reached on T-F: 11am -5pm (approximately). 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.
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/MARZIA T MONTY/Examiner, Art Unit 2117
/ROBERT E FENNEMA/Supervisory Patent Examiner, Art Unit 2117