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 .
Status of Claim(s)
Claim(s) 1-24 were previously pending and were rejected in the previous office action. Claim(s) Claim(s) 1, 6, 13, and 18 were amended. Claim(s) 2-5, 7-12, 14-17, and 19-24 were left as originally/previously presented. Claim(s) 1-24 are currently pending and have been examined.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 14, 2026, has been entered.
Response to Arguments
Claim Rejections - 35 USC § 112
Applicant’s amendments and arguments, see page 14 of Applicant’s Response, filed April 14, 2026, with respect to the rejection under 35 U.S.C. 112(a) has been fully considered and are persuasive. The 35 U.S.C. 112(a) rejection has been withdrawn.
Claim Rejections - 35 USC § 101
Applicant’s arguments, see page(s) 15-17, of Applicant’s Response, filed April 14, 2026, with respect to 35 USC § 101 rejection of Claim(s) 1-24, have been fully considered but they are not persuasive.
First, Applicant argues, on page 14-18, that the amended Independent Claim(s) 1, 6, 13, and 18, do not fall within the revised Step 2A prong one framework under certain methods of organizing human activity concepts and/or mathematical concepts. Examiner, respectively, disagrees with applicant’s arguments.
As an initial matter, courts have provided various sub groupings within organizing human activity grouping encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is also noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings, see MPEP 2106.04(a)(2)(II).
Examiner, respectfully, notes that the specific limitation(s) that fall within the subject matter groupings of the abstract idea are recited as Independent Claim(s) 1 and 6 recites “obtaining a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtaining specifications of a plurality of vehicles of plurality of vehicle types,” “training a model using a set of training data in an environment for simulating vehicle loading, wherein the set of training data is generated from a state of the environment, an observation of the environment, and a reward received form the environment, wherein the state includes numbers and types of available vehicles in the environment, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a cost of the selected vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,” “selecting from the plurality of vehicles, a vehicle for transporting or storing the load using the vehicle selection policy generated by the model,” “determining available space of the plurality of vehicles, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate vehicles from (i) matching three-dimensional (3D) coordinates of available space of the plurality of vehicles to sizes and orientations of each object in a portion of the load, and (ii) matching other specifications between the portion of the load and each candidate vehicle, wherein the candidate vehicle has space available to accommodate at least the portion of the load, and matching the 3D coordinates includes identifying and removing any matching but inaccessible space,” “wherein determining a candidate vehicle requires an amount of computational resources to computationally evaluate all possible combinations of each vehicle, sizes and orientations of the load, and other specifications associated with the load and each vehicle,” “identifying an immediate reward and a long-term consequence of taking each candidate vehicle, the long-term consequence including expected rewards represented by future expected states and expected vehicle selections,” “selecting the vehicle from the one or more candidate vehicles using the vehicle selection policy based on the immediate reward and long-term consequence of taking each candidate vehicle,” “wherein the selecting prioritizes the vehicle and the expected vehicle selections while penalizing other suboptimal vehicle selection combinations,” “wherein the model, through penalizing the other suboptimal vehicle selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each vehicle, sizes and orientations of the load,” “in response to selecting the vehicle, updating the set of training data by updating the reward, the state and the observation of the environment based on a remaining load,” “using the selected vehicle in model training of space allocation to allocate space of the selected vehicle to the load,” and “repeating steps (c)-(f) to simultaneously (i) maximize a first cumulative reward to optimize vehicle selection and (ii) maximize a second cumulative reward to optimize space allocation, wherein the first and second maximized cumulative reward indicates a vehicle selection or space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Independent Claim 13 recites “obtain a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtain a specification comprising a set of dimensions representing a plurality of spaces within the vehicle that are available for on-loading,” “train a model using a set of training data in an environment for simulating loading of the vehicle, wherein the set of training data is generated from a state of the environment, an observation of the environment, a reward received from the environment, wherein the state includes numbers and types of available vehicles in the environment, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a cost of the selected vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,“ “select, from the plurality of vehicle s, a vehicle for transporting or storing the load, using the vehicle selection policy generated by the ML model,” “determining available space of the plurality of vehicles, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate vehicles from (i) matching three-dimensional (3D) coordinates of available space of the plurality of vehicles to sizes and orientations of each object in a portion of the load, and (ii) matching other specifications between the portion of the load and each candidate vehicle, wherein the candidate vehicle has space available to accommodate at least the portion of the load, and matching the 3D coordinates includes identifying and removing any matching but inaccessible space,” “wherein determining a candidate vehicle requires an amount of computational resources to computationally evaluate all possible combinations of each vehicle, sizes and orientations of the load, and other specifications associated with the load and each vehicle,” “identifying an immediate reward and a long-term consequence of selecting each candidate location the long-term consequence including expected rewards represented by future expected states and expected location selections,” “selecting the location from the one or more candidate vehicles using the vehicle selection policy based on the immediate reward and long-term consequence of taking each candidate vehicle,” “wherein the selecting prioritizes the vehicle and the expected vehicle selections while penalizing other suboptimal location selection combinations,” “wherein the model through penalizing the other suboptimal vehicle selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each vehicle, sizes and orientations of the load,” “in response to selecting the vehicle, updating the set of training data by updating the reward, the state, and the observation of the environment based on a remaining load,” “using the selected vehicle in model training of space allocation to allocate space of the selected vehicle to the load,” and “repeating by the steps (c)(A)-(c)(D) to simultaneously (i) maximize a first cumulative reward to optimize vehicle selection and (ii) maximize a second cumulative reward to optimize vehicle selection, wherein the first or second maximized cumulative reward indicates a vehicle selection or space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Independent Claim 18 recites “obtaining a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtaining a specification comprising a set of dimensions representing a plurality of spaces within the vehicle that are available for on-loading,” “training a model using a set of training data in an environment for simulating loading of the vehicle, wherein the set of training data is generated from a state of the environment, an observation of the environment, a reward received from the environment, wherein the state includes dimensions of a chosen enclosure, a current filled state of the vehicle and available space in the vehicle, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a change of placement spaces within the vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,“ “select a location within the plurality of spaces for placement using the space allocation policy generated by the model,” “determining available space of the plurality of spaces, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate location selection from (i) tracking the location within the plurality of spaces in the vehicle, (ii) identifying and removing any matching but inaccessible space, and (iii) constructing three-dimensional coordinates of remaining available space of the vehicle for placement of remaining enclosures based on one or more of position, orientation, and alignment of the chosen enclosure,” “wherein determining a candidate location requires an amount of computational resources to computationally evaluate all possible combinations of each location, sizes, and orientations of the load, and other specifications associated with the load and each location,” “identifying an immediate reward and a long-term consequence of selecting each candidate location the long-term consequence including expected rewards represented by future expected states and expected location selections,” “selecting the location from the one or more candidate location using the space allocation policy based on the immediate reward and long-term consequence of selecting each candidate location,” “wherein the selecting prioritizes the location and the expected location selections while penalizing other suboptimal location selection combinations,” “wherein the model through penalizing the other suboptimal location selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each location, sizes and orientations of the load,” “in response to selecting the location, updating the set of training data by updating the reward, the state, and the observation of the environment based on determining one or more position, orientation, and alignment of enclosures remaining to be loaded,” “using the selected location in model training of vehicle allocation to allocate the vehicle,” and “repeating by the steps (c)(A)-(c)(D) to simultaneously (i) maximize a first cumulative reward to optimize space selection and (ii) maximize a second cumulative reward to optimize vehicle selection, wherein the first or second maximized cumulative reward indicates a space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Similar to, Credit Acceptance Corp v, Westlake Services, where the court found that that processing a credit application between a customer and dealer, where the business relation is the relationship between the customer and the dealer during the vehicle purchase was merely a commercial transaction, which, is a form of certain methods of organizing human activity. In this case, the claim(s) are similar to a business relationship between an entity and a customer, which, the entity collects vehicle information and load information, which the entity is then able to analyze that information to select a vehicle with available space and selecting the vehicle that has the available space for the shipment load. The entity can then continue to select an available vehicle and load that is able to be stored in a vehicle until an maximized optimized cost is determined. The entity can match vehicle space to a load’s 3D coordinates, which the entity can remove the spaces that are considered to have inaccessible space thus the claims are directed to the abstract idea of a business relation such as determining and matching available vehicles for storing and transporting goods (e.g., optimizing vehicle selection and load space allocations and/or shipping and logistical management), which will be used to reduce a cost for the selected vehicle.
Also, similar to, In re Maucorps, where the court found that using an algorithm for determining the optimal number of visits by a business representative to a client is merely a commercial or legal interaction, see MPEP 2106.04(a)(2)(II)(B). In this case, applicant limitations are merely commercial or legal interactions when the limitations use a machine learning model to determine an optimal vehicle and allocation of spaces to store physical objects since the determination of optimal number of vehicles and/or spaces for a transportation service using a machine learning model is merely a commercial or legal interaction thus falling within certain methods of organizing human activity. Therefore, applicant’s claims fall within at least the enumerated grouping of certain methods of organizing human activity.
Second, applicant argues, on page(s) 18-21 in applicant’s arguments, that the application is now integrated into a practical application. Examiner, respectfully, disagrees with applicant’s arguments.
As an initial matter, it is important to note that first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"), see MPEP 2106.04(d)(1). An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Here, in this the specification discloses the reinforcement learning (RL), where a machine-learning module explores different potential solutions to these two problems and, during the exploration, learns to find a feasible and potentially optimized solution in an efficient manner, i.e., without exceeding the processing and memory constraints, see applicant’s specification Paragraph 0022. This is at best an improvement to the abstract idea itself rather than a technological improvement. First, the step(s) of accomplishing this desired improvement in the specification is made in blanket conclusory manner by merely providing the machine learning module explores different potential solutions to these two problems and, during the exploration, learns to find a feasible and potentially optimized solution in an efficient manner, Paragraph 0022, thus when the specification states the improvement in a conclusory manner the examiner should not determine the claim improves technology.
Also, another important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP §2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or 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) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely obtaining, obtaining, training, generating, learning, selecting, determining, determining, evaluating, matching, matching, identifying, selecting, prioritizing, penalizing, determining, updating, and repeating, respectively, selecting available vehicles for transporting and/or storing goods using computer components that operate in their ordinary capacity (e.g., a computing system, a machine learning model, a processor, a memory, and an agent module), which are no more than “applying,” the judicial exception.
Examiner, further, notes that a recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015). In this case, applicant has merely claimed the result of accomplishing the problem when the applicant has merely provided that vehicles are selected based on determining space allocation using a ML model to reduce computational resources. At best, the benefits may be achieved by the use of a computer and/or module for making these determinations. However, there is nothing in the claim(s) or specification as to how the computer is able to reduce computational resources and how that is used to prevent a computer from running out of memory to increase memory constraints. The limitations as currently claimed are merely result-orientated thus this is the equivalent to the words of “apply it.”
Furthermore, similar to, Intellectual Ventures I LLC v. Capital One Bank, the court provided that merely “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer,” does not integrate a judicial exception into a practical application or provide an inventive concept. In this case, the judicial exception is not integrated into a practical application when model and computational efficiency is improved by training a model to learn to solve vehicle selection and space allocation, see applicant’s specification paragraph(s) 0002, 0006, and 0022, since the appending generic computer functionality merely lends to speed or efficiency to the performance of an abstract concept doesn’t meaningfully limit the claim(s) thus as a whole applicant’s limitations merely describe how to generally “apply,” the concept(s) of an existing process of determining and selecting vehicle available spaces for loading packages thus at best are mere instructions to apply the exception.
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. In this case, there is no improvement to the machine learning model, merely providing an ML model to be used in a determining optimal vehicle and load allocations is not enough to be considered significantly more.
Furthermore, applicant(s) specification nor claim amendments provide how this particular architecture of the machine learning model is improved. In fact, applicant’s specification provides a list of well-known machine-learning models such as a Q-learning model or an artificial neural network (ANN)graph neural networks, see applicant’s specification paragraph(s) 0025 and 0048, thus any algorithm can be used to achieve such functions/steps.
While applicant argues, see applicants arguments on page(s) 20-21, that the Q-learning and/or artificial neural network model(s) can iteratively refine the observation, action, and reward interactions within the environment, which recites a technological manner of implementing policy learning. Examiner, respectfully, disagrees.
The court in, Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), stated “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. Here, the Q-learning, machine learning, and/or artificial neural network model(s) are not an improvement when the models merely iteratively repeat training the observation(s), action(s), and reward(s), because merely using a Q-learning, machine-learning, and/or artificial neural network model to iteratively train using various policies is inherent to the nature of machine learning and is not, in itself, a technological improvement. Thus, applicant’s argument is not persuasive.
Third, Applicant argues on page(s) 21-22 of applicants’ arguments, that the Claims are not well-understood, routine, or conventional activity and amount to significantly more than the abstract idea. Examiner, respectfully, disagrees with applicants argument.
As an initial matter, although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: (1) Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; (2) Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): (3) Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and (4) Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d), see MPEP 2106.5(B)(II).
Examiner respectfully notes that in the Final Office Action mailed 10/14/2025 on page(s) 17-19 and 27-30, the Step 2B prong was used to analysis the previous Step 2A Prong Two additional elements that merely amounted to describing how to generally “apply,” the abstract idea in a computer environment thus Examiner carried over the identification of the additional elements and conclusions of the additional elements that were analyzed under Step 2A Prong Two, which the analysis also explained how the limitations were not an improvement to the technology. As stated above, any claim elements that were identified as insignificant extra-solution activity should be reevaluated under Step 2B for determining if they are well-understood, routine, and conventional.
Similar to, Affinity Labs v. DirecTv., the court has held that the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or 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) does not integrate a judicial exception into a practical application or provide significantly more. Here, in this case applicant’s limitations merely obtaining, obtaining, training, generating, learning, selecting, determining, determining, evaluating, matching, matching, identifying, selecting, prioritizing, penalizing, determining, updating, and repeating, respectively, selecting available vehicles for transporting and/or storing goods using computer components that operate in their ordinary capacity (e.g., a computing system, a machine learning model, a processor, a memory, and an agent module), which are no more than “applying,” the judicial exception.
It should also be noted that when making a determination whether the additional elements in a claim amount to significantly more than a judicial exception, the examiner should evaluate whether the elements define only well-understood, routine, conventional activity. In this respect, the well-understood, routine, conventional consideration overlaps with other Step 2B considerations, particularly the improvement consideration (see MPEP § 2106.05(a)), the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)), and the insignificant extra-solution activity consideration (see MPEP § 2106.05(g)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a particular element or combination of elements is well-understood, routine, conventional activity, see MPEP 2106.05(d). In this case, examiner provided why these limitations are not sufficient to show an improvement (e.g., Affinity Labs v. DirecTv; Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015); Intellectual Ventures I v. Capital One Fin. Corp.; and Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025)) and how the limitations amount to mere instructions to apply an exception, see the above analysis in the argument section(s). Thus, the claims do not provide an improvement to the vehicle selection optimization and load allocation optimization. Therefore, applicants’ argument is not persuasive.
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) 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Independent Claim(s) 1, 6, 13, and 18 recites an entity that is able to determine container types and specifications of vehicles, which the entity will then select a vehicle type and a loading location for the containers and determine a reward for the vehicle transporting the goods. The entity will then continue to process the vehicle selection and/or loads until the load is accommodated within the available space of the one or more selected vehicles. Independent Claim(s) 1, 6, 13, and 18, as a whole recites limitation(s) that are directed to an abstract idea of certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Independent Claim(s) 1 and 6 recites “obtaining a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtaining specifications of a plurality of vehicles of plurality of vehicle types,” “training a model using a set of training data in an environment for simulating vehicle loading, wherein the set of training data is generated from a state of the environment, an observation of the environment, and a reward received form the environment, wherein the state includes numbers and types of available vehicles in the environment, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a cost of the selected vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,” “selecting from the plurality of vehicles, a vehicle for transporting or storing the load using the vehicle selection policy generated by the model,” “determining available space of the plurality of vehicles, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate vehicles from (i) matching three-dimensional (3D) coordinates of available space of the plurality of vehicles to sizes and orientations of each object in a portion of the load, and (ii) matching other specifications between the portion of the load and each candidate vehicle, wherein the candidate vehicle has space available to accommodate at least the portion of the load, and matching the 3D coordinates includes identifying and removing any matching but inaccessible space,” “wherein determining a candidate vehicle requires an amount of computational resources to computationally evaluate all possible combinations of each vehicle, sizes and orientations of the load, and other specifications associated with the load and each vehicle,” “identifying an immediate reward and a long-term consequence of taking each candidate vehicle, the long-term consequence including expected rewards represented by future expected states and expected vehicle selections,” “selecting the vehicle from the one or more candidate vehicles using the vehicle selection policy based on the immediate reward and long-term consequence of taking each candidate vehicle,” “wherein the selecting prioritizes the vehicle and the expected vehicle selections while penalizing other suboptimal vehicle selection combinations,” “wherein the model, through penalizing the other suboptimal vehicle selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each vehicle, sizes and orientations of the load,” “in response to selecting the vehicle, updating the set of training data by updating the reward, the state and the observation of the environment based on a remaining load,” “using the selected vehicle in model training of space allocation to allocate space of the selected vehicle to the load,” and “repeating steps (c)-(f) to simultaneously (i) maximize a first cumulative reward to optimize vehicle selection and (ii) maximize a second cumulative reward to optimize space allocation, wherein the first and second maximized cumulative reward indicates a vehicle selection or space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Independent Claim 13 recites “obtain a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtain a specification comprising a set of dimensions representing a plurality of spaces within the vehicle that are available for on-loading,” “train a model using a set of training data in an environment for simulating loading of the vehicle, wherein the set of training data is generated from a state of the environment, an observation of the environment, a reward received from the environment, wherein the state includes numbers and types of available vehicles in the environment, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a cost of the selected vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,“ “select, from the plurality of vehicle s, a vehicle for transporting or storing the load, using the vehicle selection policy generated by the ML model,” “determining available space of the plurality of vehicles, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate vehicles from (i) matching three-dimensional (3D) coordinates of available space of the plurality of vehicles to sizes and orientations of each object in a portion of the load, and (ii) matching other specifications between the portion of the load and each candidate vehicle, wherein the candidate vehicle has space available to accommodate at least the portion of the load, and matching the 3D coordinates includes identifying and removing any matching but inaccessible space,” “wherein determining a candidate vehicle requires an amount of computational resources to computationally evaluate all possible combinations of each vehicle, sizes and orientations of the load, and other specifications associated with the load and each vehicle,” “identifying an immediate reward and a long-term consequence of selecting each candidate location the long-term consequence including expected rewards represented by future expected states and expected location selections,” “selecting the location from the one or more candidate vehicles using the vehicle selection policy based on the immediate reward and long-term consequence of taking each candidate vehicle,” “wherein the selecting prioritizes the vehicle and the expected vehicle selections while penalizing other suboptimal location selection combinations,” “wherein the model through penalizing the other suboptimal vehicle selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each vehicle, sizes and orientations of the load,” “in response to selecting the vehicle, updating the set of training data by updating the reward, the state, and the observation of the environment based on a remaining load,” “using the selected vehicle in model training of space allocation to allocate space of the selected vehicle to the load,” and “repeating by the steps (c)(A)-(c)(D) to simultaneously (i) maximize a first cumulative reward to optimize vehicle selection and (ii) maximize a second cumulative reward to optimize vehicle selection, wherein the first or second maximized cumulative reward indicates a vehicle selection or space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions).
Independent Claim 18 recites “obtaining a specification of a load comprising a plurality of enclosures of a plurality of enclosure types,” “obtaining a specification comprising a set of dimensions representing a plurality of spaces within the vehicle that are available for on-loading,” “training a model using a set of training data in an environment for simulating loading of the vehicle, wherein the set of training data is generated from a state of the environment, an observation of the environment, a reward received from the environment, wherein the state includes dimensions of a chosen enclosure, a current filled state of the vehicle and available space in the vehicle, the observation includes numbers and types of enclosures remaining to be loaded, and the reward is related to a change of placement spaces within the vehicle,” “wherein the model performs learning to generate a vehicle selection policy and a space allocation policy,“ “select a location within the plurality of spaces for placement using the space allocation policy generated by the model,” “determining available space of the plurality of spaces, each available space including one or more discontiguous volumes and associated weight capacity,” “determining one or more candidate location selection from (i) tracking the location within the plurality of spaces in the vehicle, (ii) identifying and removing any matching but inaccessible space, and (iii) constructing three-dimensional coordinates of remaining available space of the vehicle for placement of remaining enclosures based on one or more of position, orientation, and alignment of the chosen enclosure,” “wherein determining a candidate location requires an amount of computational resources to computationally evaluate all possible combinations of each location, sizes, and orientations of the load, and other specifications associated with the load and each location,” “identifying an immediate reward and a long-term consequence of selecting each candidate location the long-term consequence including expected rewards represented by future expected states and expected location selections,” “selecting the location from the one or more candidate location using the space allocation policy based on the immediate reward and long-term consequence of selecting each candidate location,” “wherein the selecting prioritizes the location and the expected location selections while penalizing other suboptimal location selection combinations,” “wherein the model through penalizing the other suboptimal location selection combinations using reward-guided learning, requires evaluation of fewer than all possible combinations of each location, sizes and orientations of the load,” “in response to selecting the location, updating the set of training data by updating the reward, the state, and the observation of the environment based on determining one or more position, orientation, and alignment of enclosures remaining to be loaded,” “using the selected location in model training of vehicle allocation to allocate the vehicle,” and “repeating by the steps (c)(A)-(c)(D) to simultaneously (i) maximize a first cumulative reward to optimize space selection and (ii) maximize a second cumulative reward to optimize vehicle selection, wherein the first or second maximized cumulative reward indicates a space allocation achieved in the environment with arbitrary states, observations, and rewards,” step(s)/function(s) are merely certain methods of organizing human activity: fundamental economic practices or principles, commercial or legal interactions (e.g., business relations) and/or managing personal behavior or relationships or interactions between people (e.g., including social activities and/or following rules or instructions). For instance, in this case, Independent Claim(s) 1, 6, 13, and 18, are similar to an entity that is able to match vehicle types to container types and/or containers to locations within a vehicle, which the entity will then load the vehicle with the containers for a reward. The mere recitation of generic computer components (Claim(s) 1 and 6: a computing system and an a machine learning model; and Claim(s) 13 and 18: a processor, a memory, an agent module, and an machine learning model). Therefore, Independent Claim(s) 1, 6, 13, and 18 recites the above abstract idea(s).
Step 2A Prong 2: This judicial exception is not integrated into a practical application because the claims as a whole describes how to generally “apply,” the concept(s) of “obtaining,” “obtaining,” “training,” “generating,” “learning,” “selecting,” “determining,” “determining,” “evaluating,“ “matching,” “matching,” “identifying,” selecting,” “prioritizing,” “penalizing,” “determining,” “updating,” and “repeating,” respectively, information in a computer environment, respectively. The limitations that amount to “apply it,” are as follows (Claim(s) 1 and 6: a computing system and an a machine learning model; and Claim(s) 13 and 18: a processor, a memory, an agent module, and an machine learning model). Examiner, notes that the computing system, machine learning model, processor, memory, and agent module, respectively, are recited so generically that they represent no more than mere instructions to apply the judicial exception on a computer.
Similar to, Affinity Labs v. DirecTv., the court has held that certain additional elements are not integrated into a practical application or provide significantly more when the additional elements merely use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or 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) thus they do no more than merely invoke computers or machinery as a tool to perform an existing process, which, amounts to no more than “applying,” the judicial exception. Here, the above additional elements are not integrated into a practical application or provide significantly more when they are merely obtaining, obtaining, selecting, receiving, training, constructing, tracking, and repeating, loading containers onto a delivery vehicle which is no more than merely invoking computers or machinery as a tool to perform an existing process (e.g., determining vehicles to load containers) thus merely “applying,” the judicial exception.
Also, see a recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
Furthermore, similar to, Intellectual Ventures I LLC v. Capital One Bank, the court provided that merely “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer,” does not integrate a judicial exception into a practical application or provide an inventive concept. In this case, the judicial exception is not integrated into a practical application when model and computational efficiency is improved by training a model to learn to solve vehicle selection and space allocation, see applicant’s specification paragraph(s) 0002, 0006, and 0022, since the appending generic computer functionality merely lends to speed or efficiency to the performance of an abstract concept doesn’t meaningfully limit the claim(s) thus as a whole applicant’s limitations merely describe how to generally “apply,” the concept(s) of an existing process of determining and selecting vehicle available spaces for loading packages thus at best are mere instructions to apply the exception.
Also, see Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025). In that case, the court provided "[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025) (slip op. at 18). The court also stated "[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment." Recentive Analytics, Inc. v. Fox. Corp., Fed Cir. No. 2023-2437 (Apr. 18, 2025), slip op. at 13. The court in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), also stated “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement” because “[i|terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning.” Id. at 1212. Each of the above limitations simply implement an abstract idea that is no more than mere instructions to apply the exception using a generic computer component, which, is not a practical application of the abstract idea. Therefore, when viewed in combination these additional elements do not integrate the recited judicial exception into a practical application and the claims are directed to the above abstract idea(s).
Step 2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted previously, the claims as a whole merely describe a field-of-use, generally linking, and how to generally “apply it,” to the abstract idea in a computer environment. Thus, even when viewed as a whole, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are ineligible
Claim(s) 2-3, 7-11, 14-15, and 19-23: The various metrics of Dependent Claim(s) 2-3, 7-11, 14-15, and 19-23 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to Independent Claim(s) 1, 6, 13, and 18 these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than an abstract idea.
Claim(s) 4 and 16: The additional limitation of “constructing,” “choosing,” “selecting,” and “selecting,” are further directed to a certain method of organizing human activity, as described in Claim(s) 1 and 13. The recitation(s) of “constructing a set of actions for a plurality of states of the environment,” “wherein the set of actions includes at least one of choosing different types of vehicles,” and “selecting two or more vehicles of the particular type, and using a combination of the different types of vehicles,” step(s)/function(s) falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely constructing, choosing, and selecting, information which is no more than “applying,” the judicial exception. Therefore, for the reasons described above with respect to Claim(s) 4 and 16 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
Claim(s) 5, 12, 17, and 24: The additional limitation of “training,” selecting,” and “updating,” are further directed to a certain method of organizing human activity as described in Claims 1, 6, 13, and 18. The Q-learning module and artificial neural network (ANN), respectively, are recited so generically that it represents no more than mere instructions to apply the judicial exception on a computer. The recitation(s) of “at least training, selecting, and updating steps in (c)-(e) are implemented through a module or network,” step(s)/function(s) falls within the enumerated grouping certain methods of organizing human activity. Similar to, Affinity Labs v. DirecTv, the court has held that task to receive, store, or transmit data are additional elements that amount to no more than “applying,” the judicial exception. (MPEP 2106.05(f)). Here, the above additional elements merely training, selecting, and updating, information which is no more than “applying,” the judicial exception. Also, See, a commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or 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) does not integrate a judicial exception into a practical application or provide significantly more, Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016); MPEP 2106.05(f). Therefore, for the reasons described above with respect to Claim(s) 5, 12, 17, and 24 the judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
The dependent claim(s) 2-5, 7-12, 14-17, and 19-24 above do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) in the dependent claim(s) above are no more than mere instructions to apply the exception using generic computer component(s), which, doesn’t provide an inventive concept. Therefore, Claim(s) 1-24 are not patent eligible.
Conclusion
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/B.A.H./Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
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