DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed on 03/13/2026 has been entered. Claims 1, 3-8, 10-11, 13-19, and 21-24 are pending in the application. In response to Applicant's amendments, Examiner withdraws the previous objections; withdraws the previous rejections under 103; and maintains the previous rejections under 112(a).
Response to Arguments
Applicant's arguments filed 03/13/2026 with respect to the rejections of claims 1, 3-8, 10-11, 13-19, and 21-24 under 112(a) for failing to comply with the enablement requirement have been fully considered, but they are not persuasive.
Regarding the rejections under 112(a) Enablement, Applicant argues “The relevant POSITA here has experience in spacecraft trajectory planning and reinforcement learning, including Q-learning, given the art cited by the Examiner (e.g., Gaskett et al., "Q-Learning in Continuous State and Action Spaces," and rocket/landing prior art). Q-learning, Q-tables, and state/action design are well-established techniques in that field” [Remarks, pg. 9].
Examiner respectfully disagrees. Gaskett et al. is directed to Q-learning, not spacecraft trajectory planning. The prior art related to landing spacecraft and Q-learning is composed of a single generic sentence in Haney (US 20220234765 A1, [0016]) and three sentences in IPCOM (IP.com, “Method and System for Recycling Space Debris with In-Space Manufacturing”, 2022). These documents provide less detail than the instant application 18491041, do not cure the deficiencies in selecting states and actions and updating the Q-table with reference to the space-to-planet trajectory, and consequently also lack enablement. The prior art does not indicate that using Q-learning or a Q-table for landing a space vehicle or delivering products from space is a “well-established technique” in this field.
Furthermore, it appears Applicant argues that given the prior art POSITA would know to combine space vehicle landing with Q-learning; i.e., it appears Applicant is indicating that the claimed inventions would be well-known in the art.
Applicant states “The amended independent claims are directed to applying Q-learning to a well-specified control problem: selecting a decoupling point and trajectory to deliver finished products manufactured in space to planetary delivery locations while optimizing aggregate transportation cost and time, across both space and ground segments” [Remarks, pg. 9]. This is a gross oversimplification of the problem. For example, to accurately account for transportation cost and time, the starting altitude of the space vehicle, the mass of the transportation vehicles and the products, and the energy/fuel capacity and cost for each vehicle and its load, among other variables, must be known. These variables do not appear to be considered by Applicant in the disclosure.
Applicant further argues “the claims are explicitly limited to finite, engineer-selected geographic states (decoupling point, landing point, intermediate ground locations) and do not require an unbounded, infinite state space” [Remarks, pg. 10].
Examiner respectfully disagrees. The claims do not recite that the state space is limited to “finite, engineer-selected geographic states,” nor does the specification. Instead, the specification discloses “the trained reinforcement learning model can be used to identify the initial state (e.g., S0), the end state (e.g., S5), one or more intermediary states and one or more actions that can be taken at any given time” [0066] and discloses a “Q-table (e.g., Q(s,a)) can be constructed for all actions that can be taken by an RL agent… [where a] most relevant action can be selected by the RL agent to traverse from the existing state to a next stable state” (emphasis added) [0071]. Clearly, it is the reinforcement learning model that determines the states and actions, not an engineer. Since “all actions” is not bounded and there is no disclosed reward estimate function or filter/assessment of actions and “stable” states, the machine learning model appears to create and update an infinite state-action space.
Furthermore, the objective of the claimed invention is finding S0 and the trajectory, so S0 is not “engineer-selected”. The specification provides no evidence that Q-learning can converge on a solution for S0 or determine proper actions for the space vehicle to transfer from orbit and safely land at the second location.
Moreover, if the claimed trajectory considerations like “environmental parameters, ground weather conditions, time needed to traverse a path from the first [decoupling] location to the second [landing] location, and respective needs of the products” [claim 7] change the optimal delivery or landing locations, then the possible and optimal states and actions—and consequently, the decoupling location and trajectory—may also change. In [0045], the specification states “The system (e.g., decoupling component 108 can identify appropriate delivery locations on Earth based on needs of products manufactured in space, and the system can identify geographical areas where a space vehicle can reach (e.g., on water, on ground, etc.).” Again, this means the states and actions are not “finite, engineer-selected” states and actions.
Applicant further argues “the specification and claims together define actions in terms of physically meaningful transitions” [Remarks, pg. 10].
While actions are stated to move a RL agent from one state (location) to another, the actions (and corresponding Q-values) as described do not account for physics—including relevant concepts and variables like velocity, acceleration, orientation of the space vehicle, mass over time, energy expenditure, conservation of momentum, etc. Therefore, the machine learning model does not appear to be able to optimize for transportation cost and time as claimed.
Next, Applicant states “The specification describes that the RL agent ‘can be a space vehicle, or another means by which goods manufactured in space can be transported to various locations,’ including ships, aircraft, trucks, etc., and that… arrows between states S0-S5 represent ‘paths’ from the in-space manufacturing unit to locations on Earth and between ground locations, based on parameters such as time duration, weather conditions, and need of products” [Remarks, pg. 10].
Although the disclosure presents “arrows” as “paths”, the specifics of how these paths are determined beyond the Epsilon-Greedy algorithm is not disclosed. While the specification does state that the RL agent can be a space vehicle, a ship, an aircraft, a truck, or another ground vehicle [0023], any person would understand that a ship and a truck cannot travel the same path even if they move between the same locations (states) and that a truck can drive different paths between the same two locations. Therefore, a person of ordinary skill in the art would understand that the trajectory computation should take into account the transportation means and its resulting cost and duration. However, the specification does not show or describe a way to account for transportation means, cost, or time in the trajectory determination, much less weather conditions or need of products. No evidence is provided that a person of ordinary skill in the art would reasonably understand how to combine time duration, weather conditions, and need of products into a path from an in-space manufacturing unit to locations on a planet and between ground locations. That is to say, the claim limitation “an aggregate transportation cost and transportation time for delivering the products along the trajectory are optimized” of claim 1 (and other cost, time, weather, and need-related limitations in at least claims 3, 4, and 7) is asserted but lacks enablement.
Applicant further argues “modeling actions… is straightforward and fully consistent with the specification’s examples. No undue experimentation is required to encode such transitions as actions in a Q-table” [Remarks, pg. 10].
Examiner respectfully disagrees. The claimed invention appears to be directed to a complex delivery logistics solution including 1) determining a decoupling location from an in-space manufacturing unit, 2) delivery from space to a warehouse on a planet, 3) transportation from a warehouse to an intermediate location (e.g., a distribution center or post office), and 4) delivery to the final destination. In contrast to transportation between ground locations, the modeling of the first action(s)—from the decoupling location to the landing location—is fundamentally different and more complex, at least because the RL agent moves in a significantly larger 3-dimensional space at higher speeds and in a completely different environment. For example, the orientation of the space vehicle in 3 dimensions and the angle of the orbital transfer/reentry trajectory can mean the difference between the space vehicle burning in a planet’s atmosphere, running out of fuel and crashing, and safely landing. Multiple propulsion “burns” (and thus actions; see claim 8) at precise timepoints would likely be necessary—with prior actions and their timing potentially dramatically affecting possible future actions and states, but the specification does not explain how multiple actions between two states and the timing of the actions could be determined. Also, different transportation means will have different costs and speeds for the same action. The specification does not show or describe a way to account for transportation means, cost, or time in the trajectory determination, much less weather conditions or need of products, as stated above. Therefore, significant undue experimentation would be required to accomplish accurate modeling of the actions, at least because a person of ordinary skill in the art would have to incorporate variables not considered or sufficiently explained by the specification. No evidence is provided by Applicant that shows that this complex logistics problem is “straightforward”, much less that a person of ordinary skill in the art would have the knowledge to encode such state transitions as actions in a Q-table.
Applicant further states “the specification explicitly describes the optimization objective [surface transportation cost and transportation time] and key variables” [Remarks, pg. 10] and “For a POSITA in reinforcement learning, it is routine to construct a reward function R(s,a) that increases when transportation cost and time are reduced or when delivery deadlines are satisfied, and decreases when they are not” [Remarks, pg. 11].
Simply defining a problem does not mean that the claimed invention to solve the problem does not lack enablement. As stated above, the machine learning model does not appear to be able to perform the claimed optimization, at least because the specification does not disclose how the transportation cost and time are integrated into the state-action space and estimate and reward functions. Additional variables required to solve the problem (e.g., mass of products and vehicles, velocity, etc.) are not even considered in the disclosure.
Applicant presents machine learning as a panacea that can magically solve the delivery problem without giving the specifics of how variables are integrated into the generic method of Q-learning with a Q-table. Applicant does not provide evidence that constructing the evidence and reward functions in the context of the claimed invention (particularly when the first state is in space and multiple modes of transportation are possible for a trajectory between locations) is routine.
Applicant further states “The claim does not require any particular mathematical form of R(s,a) or E(s,a)” [Remarks, pg. 11].
The claimed invention is directed to determining the decoupling location (S0) and the trajectory based in part on the values shown in Figs. 6 and 7. These values are determined at least in part by the estimate E(s,a) and reward R(s,a) functions [0075-0078], which must account for transportation cost and time to solve the optimization problem. For a person of ordinary skill in the art to understand how to make and use the claimed invention, the person would reasonably need to understand how to determine the values of the Q-table. Determining what variables are needed (see discussion above on duration, cost, weather, velocity, etc.) and how the variables are physically related to the optimization problem to properly construct the functions E and R is undue experimentation, not routine design/optimization. This is not simply parameter tuning or determination of weights or coefficients; this is finding essential undisclosed components of the invention. Therefore, a person of ordinary skill in the art would have to perform undue experimentation to determine the functions E and R.
In conclusion, the rejection of claims 1, 3-8, 10-11, 13-19, and 21-24 under 112(a) for lack of enablement is maintained. Essential elements necessary for making the machine learning model, including determining what states and actions to include in the Q-table and the functions E and R of the Q-learning equations, are not disclosed in the specification with enough information such that a person of ordinary skill in the art would know how to make and use the claimed invention. Therefore, the identifying step “using a machine learning model” still does not have sufficient enabling support for claims 1, 11, and 19, as explained in the Final Rejection of 01/14/2026. By dependency, claims 3-8, 10, 13-18, and 21-24 also do not have sufficient enabling support. In addition, the specification does not explain how the machine learning model considers or optimizes an inventory, transportation cost, transportation time, environmental parameters, ground weather conditions, need of the products, multiple modes of transportation for a given state transition, and multiple actions to transition between the first location and second location as claimed in claims 3, 4, 7, and 8.
Claim Objections
Claims 1, 3-4, 8, 10-11, 13-14, 18-19, and 21-22 are objected to because of the following informalities:
In claim 1, “identifies, using machine learning model” should read “identifies, using a machine learning model”.
Claims 1, 11, and 19 recite the limitation “a delivery location”. There is insufficient antecedent basis for this limitation. It is unclear if this delivery location is one of the “one or more delivery locations” previously recited in each claim.
In claims 1, 11, and 19, “where the Q-learning represents as respective states” should read “wherein the machine learning model represents as respective states” in view of [0024].
The end of claim 1 reads “and wherein the .” In view of similar amendments to claims 11 and 19, these words should be deleted.
Claims 3-4, 12-14, and 21-22 recite the limitation “a surface transportation cost”. Based on the specification ([0021], [0056], and [0090]), the claimed “surface transportation cost” is the same as the “aggregate transportation cost” recited in claims 1, 11, and 19. Therefore, “a surface transportation cost” should read “the aggregate transportation cost” in claims 3-4, 12-14, and 21-22.
Claims 8 and 18 recite the limitation “one or more actions”. There is insufficient antecedent basis for this limitation. It is unclear if the “one or more actions” include the “action selected by the machine learning model” or are comprised in the “set of actions” recited in claims 1 and 11, respectively.
Claim 10 recites the limitation “a third location” which is implied to be a delivery location. However, it is unclear if this third location is one of the “delivery location”, “one or more delivery locations”, or “plurality of intermediate geographic locations” recited in claim 1.
Appropriate correction is required.
Claim Interpretation
In claims 1, 11, and 19, “respective” in “where the Q-learning represents as respective states of an environment: the first location, the second location, and a plurality of intermediate geographic locations on the planetary surface” is interpreted such that each of the locations are different states in view of [0036]. The same interpretation is used for “respective independent states” in claims 6, 16, and 24.
In claims 1, 11, and 19, “intermediate” in “a plurality of intermediate geographic locations on the planetary surface” is interpreted as between the first location and the delivery location in view of [0037] and [0063]. The second location is thus an intermediate geographic location.
According to paragraphs [0106] and [0122], the “computer readable storage medium” of claim 19 is non-transitory storage. Accordingly, claims 19 and 21-24 are directed to a statutory category of invention.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim 1 recites the generic placeholder “at least one of the computer-executable components” plus functional language “that: identifies, using machine learning model trained to optimize end-to-end delivery of products manufactured in space to one or more delivery locations on a planetary surface, a first location that is in space for decoupling a space vehicle from an in-space manufacturing unit, such that the space vehicle lands at a second location that is on the planetary surface, wherein the machine learning model learns, using Q-learning, a trajectory from the first location to a delivery location on the planetary surface associated with the products, such that the second location is within a predefined geographical proximity to the delivery location, and an aggregate transportation cost and transportation time for delivering the products along the trajectory are optimized, and where the Q-learning represents as respective states of an environment: the first location, the second location, and a plurality of intermediate geographic locations on the planetary surface; and controls decoupling of the space vehicle from the in-space manufacturing unit at the first location for landing at the second location based on an action selected by the machine learning model from a set of actions that model both space transportation of the space vehicle and ground transportation of one or more ground vehicles” without reciting sufficient structure or acts to perform the functional language. A processor that executes the at least one of the computer-executable components stored in the memory is stated in claim 1 and in paragraph [0032] of the specification. In paragraph [0034], the specification discloses “For example, decoupling component 108 can use input 120 from model 118, to identify first location 122, and decoupling component 108 can decouple the space vehicle at first location 122 based on input 120. First location 122 can be a location in space where a space vehicle can be decoupled from an in-space manufacturing unit, such that the space vehicle can land at a second location that can be on a planetary surface.” In paragraphs [0055]-[0057], the specification discloses that the decoupling component can 1) compute an orbital position of a space manufacturing module, 2) compute an altitude of an orbit of the module, 3) compute a trajectory and decoupling point upon selection of a receiving airport. However, there are no details on how these computations are performed beyond the input from the reinforcement learning model containing information about a trajectory. Additionally, the necessary steps or algorithm to identify the first location wherein “an aggregate transportation cost and transportation time for delivering the products along the trajectory are optimized” are not disclosed. Therefore, while the specification discloses the claimed function linked to a software component stored in memory and executed by a processor, the specification does not disclose the necessary steps or algorithm to perform the entire claimed function.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claims contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. In particular, the machine learning model is not described sufficiently to enable one of ordinary skill in the art to make and use the claimed invention.
Regarding the machine learning model (Q-learning with a Q-table), the specification states, “Using the input from the reinforcement learning model, an RL [reinforcement learning] agent can initialize the Q-table” in paragraph [0024]. This is a paradoxical situation where the disclosed reinforcement learning model requires the Q-table to calculate the input to the RL agent, and the input is required to initialize the Q-table.
Later, in paragraph [0071], the specification states, “A Q-table (e.g., Q(s,a)) can be constructed for all actions that can be taken by an RL agent. The Q-table can comprise n columns and m rows wherein n can be the number of actions (e.g., A0 to A5), and m can be the number of states (e.g., S0 to S5).” Because how to identify relevant states (beyond the starting state of the initial position of a space vehicle and a landing location) and actions between those states is never explained, this Q-table may have infinite entries—every possible action and every possible resulting state—and thus be computationally unusable. The problem of scale is described by Gaskett et al. in “Q-Learning in Continuous State and Action Spaces” (2001): “As the number of state and action variables increase, the size of the table used to store Q-values grows exponentially. Accurate control requires that variables be quantised finely, but as these systems fail to generalise between similar states and actions, they require [impractically] large quantities of training data. …Using a coarser representation of states leads to aliasing, functionally different situations map to the same state and are thus indistinguishable” [pg. 3].
Additionally, the difference between states should not be just position and time as indicated in [0024] and [0075] of the Specification. Considerations like the amount of fuel/propellant, mass (which changes with the amount of fuel), velocity, approach angle, vehicle orientation, heat regulation due to atmosphere (or lack of), etc. are necessary to successfully land a space vehicle on a planet. These considerations are discussed by Ealy et al. in US 20210061497 A1 and by Limotta in [0023-0030] of US 20210292011 A1.
In paragraph [0074], the specification describes “a Q-table initialized to zero (e.g., Q(s,a) = 0),” which can be updated with standard Q-learning equations (Equations 1 and 2 in [0075]-[0078]). However, the functions E (estimate) and R (reward) used in these equations are never given, nor is there useful explanation on how these functions could be derived, despite their criticality in updating the Q-table and thus having a working Q-learning model. These functions and the identification of states and actions to include in the Q-table are what distinguishes a working model tied to the physical constraints of calculating a landing trajectory of a space vehicle from generic reinforcement learning principles; without these details, the proposed model cannot properly update and converge to an optimal solution. The amount of experimentation required of a person of ordinary skill in the art to actually implement the disclosed reinforcement learning model is the same as is required upon the proposal of applying a Q-table to landing trajectory calculations; that is to say, the disclosure provides little-to-no instruction on making its proposed solution in reality.
In addition, the specification does not explain how the machine learning model considers or optimizes an inventory, transportation cost, transportation time, environmental parameters, ground weather conditions, need of the products, multiple modes of transportation for a given state transition, and multiple actions to transition between the decoupling location and landing location as recited in claims 3, 4, 7, and 8.
The claimed invention appears to be directed to a complex delivery logistics solution including 1) determining a decoupling location for a space vehicle from an in-space manufacturing unit, 2) delivery from space to a warehouse on a planet by the space vehicle, 3) transportation from a warehouse to an intermediate location (e.g., a distribution center or post office), and 4) delivery to the final destination, by a reinforcement learning model using a Q-table. The claims and the disclosure do not provide sufficient information for a person of ordinary skill in the art to make and use the claimed invention. The amount of direction provided by the inventor is low. The prior art does not indicate that using Q-learning or a Q-table for landing a space vehicle or delivering products from space is well-known in this field. Gaskett et al. is directed to Q-learning, not spacecraft trajectory planning. The prior art related to landing spacecraft and Q-learning is composed of a single generic sentence in Haney and three sentences in IPCOM. These documents provide less detail than the instant application 18491041, do not cure the deficiencies in selecting states and actions and updating the Q-table with reference to the space-to-planet trajectory, and consequently also lack enablement. As a result, the level of predictability in the art is low, and there are no known working examples.
Since using the reinforcement learning model is required in each of the independent claims, and by dependency also required in all of the dependent claims, the incompletely disclosed Q-learning model does not provide sufficient enabling support for claims 1, 3-8, 10-11, 13-19, and 21-24.
Claims 1, 3-8, and 10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, the claim limitation “at least one of the computer-executable components that: identifies, using machine learning model trained to optimize end-to-end delivery of products manufactured in space to one or more delivery locations on a planetary surface, a first location that is in space for decoupling a space vehicle from an in-space manufacturing unit, such that the space vehicle lands at a second location that is on the planetary surface, wherein the machine learning model learns, using Q-learning, a trajectory from the first location to a delivery location on the planetary surface associated with the products, such that the second location is within a predefined geographical proximity to the delivery location, and an aggregate transportation cost and transportation time for delivering the products along the trajectory are optimized, and where the Q-learning represents as respective states of an environment: the first location, the second location, and a plurality of intermediate geographic locations on the planetary surface; and controls decoupling of the space vehicle from the in-space manufacturing unit at the first location for landing at the second location based on an action selected by the machine learning model from a set of actions that model both space transportation of the space vehicle and ground transportation of one or more ground vehicles” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification merely recites the function and does not identify specific steps or an algorithm sufficient to perform the function, as described above in the Claim Interpretation Section. Therefore, the claim lacks an adequate written description as required by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because an indefinite, unbounded functional limitation would cover all ways of performing a function and indicate that the inventor has not provided sufficient disclosure to show possession of the invention. See MPEP 2163.03 and 2181.
This rejection can be overcome by amending “at least one of the computer-executable components” to one of “instructions”, “code”, or “a program” in claim 1.
Claims 3-8 and 10 are rejected for depending upon the rejected independent claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-8, 10-11, 13-19, and 21-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 11, and 19 recite the limitation “where the Q-learning represents as respective states of an environment: the first location, the second location, and a plurality of intermediate geographic locations on the planetary surface”. It is not clear what the “environment” is. The first location is in a space environment, while the second location and the plurality of intermediate geographic locations are on the planetary surface. The specification also refers to a “computing environment” including a computer and networked systems in [0026], but this does not appear to be the claimed environment. Therefore, the unknown environment renders the claims indefinite.
Claims 3-8, 10, 13-18, and 21-24 are rejected for depending upon one of the rejected claims 1, 11, and 19.
Regarding claim 1, the claim limitation “at least one of the computer-executable components that: identifies, using machine learning model trained to optimize end-to-end delivery of products manufactured in space to one or more delivery locations on a planetary surface, a first location that is in space for decoupling a space vehicle from an in-space manufacturing unit, such that the space vehicle lands at a second location that is on the planetary surface, wherein the machine learning model learns, using Q-learning, a trajectory from the first location to a delivery location on the planetary surface associated with the products, such that the second location is within a predefined geographical proximity to the delivery location, and an aggregate transportation cost and transportation time for delivering the products along the trajectory are optimized, and where the Q-learning represents as respective states of an environment: the first location, the second location, and a plurality of intermediate geographic locations on the planetary surface; and controls decoupling of the space vehicle from the in-space manufacturing unit at the first location for landing at the second location based on an action selected by the machine learning model from a set of actions that model both space transportation of the space vehicle and ground transportation of one or more ground vehicles” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification merely recites the function and does not identify specific steps or an algorithm sufficient to perform the function, as described above in the Claim Interpretation Section. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
This rejection can be overcome by amending “at least one of the computer-executable components” to one of “instructions”, “code”, or “a program” in claim 1.
Claims 3-8 and 10 are rejected for depending upon the rejected independent claim 1.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Conclusion
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/MOYA LY/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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