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 .
Allowable Subject Matter
Claim(s) 10, 17, and 19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 6-9, 11-16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Street et al. (U.S. Doc. No.7589742) in view of Zhai et al. (U.S. Pub. No. 20220203240).
Regarding claim 1, Street discloses a method for generating a graphical layout, the method comprising (col 7, “Example uses of such parameters are described below, in connection with the FIG. 8 example method for generating a random map.”): receiving an input graphical layout having a plurality of cells forming a grid structure, the plurality of cells are configured to be placed with a plurality of objects to form the graphical layout (col 6, “The map may be defined as a grid of space. FIG. 4a shows the view from FIG. 3a with a superimposed grid 401 in isometric view, while FIG. 4b shows the grid 401 from an overhead view. In grid 401, the available map area may be divided into a plurality of tiles 402, where each tile 402 serves as an elementary unit for creating the map.”; also, col 8, “When the land and water composition is known, the game program may then generate an initial grid, such as grid 401, having the required land/water makeup.”); (para 32, “calculating, for each cell, the mean values of vertical and horizontal lengths of each layout element that contains the cell and also calculates the likelihood of each layout element”; also, para 12, “The term "likelihood" is used to generically describe "possibility, likelihood, probability", and "probability of occurrence”); placing each object of the plurality of objects into the input graphical layout for forming the graphical layout, wherein placing a given object into the input graphical layout comprises (col 16, “In step 506, another looping process may begin for each object to be placed.”): selecting the given object for placement, based on a predefined placement schedule (col 17, “Accordingly, it may be desirable to sort the objects for purposes of placement in a priority order, such that higher priority objects get placed before lower priority objects. The priorities may vary depending on the particular game. For example, in an RTS in which each player starts with a town center as a base of operations, the placement of the town center may be the highest priority, and may be placed first.”); identifying one or more constrained cells from amongst the plurality of cells where at least one predefined constraint is violated if the given object is placed thereon (col 16, “Other constraints include restricting placement to a predetermined shape, such as a rectangle, pie wedge portion of a circle, etc., distances (minimum or maximum) from other objects or object classes, distances from an edge of the map, map features such as cliffs and rivers, terrain types, etc.”; also, col 17, “In step 509, the proposed location for the current object is compared with the various parameters defined in step 507.”; also, col 17, “At step 509, if the proposed location violates a parameter, the process may move to step 510, where a check may be made to determine whether the current object is a critical object.”); placement probability score for each cell of the plurality of cells with respect to each object of the plurality of objects at a first point in time, adjusting the placement probability score, for the one or more constrained cells, to be zero; adjusting the placement probability score, for each cell of constraint-free cells from amongst the plurality of cells with respect to the given object at a second point in time, based on the placement probability score for each cell of the constraint-free cells, and the adjusted placement probability score for the one or more constrained cells, wherein the given object is placed in a constraint-free cell having a highest adjusted placement probability score amongst the constraint-free cells with respect to the given object at the second point in time.
However, in a similar field of endeavor, Zhai discloses determining a placement probability score for each cell of the plurality of cells with respect to each object of the plurality of objects at a first point in time (para 36, “Initially, every location on a map is considered a candidate location for placement of each target prop at each possible orientation.”; also, para 98, “In an alternative embodiment, a probability is assigned to each valid location.”), adjusting the placement probability score, for the one or more constrained cells, to be zero (para 36, “based on a "no-collision" spatial rule that two objects cannot occupy the same space at the same time, the candidate (location, orientation) possibilities for a target prop are culled to remove placements in which the periphery of target prop would intersect/collide with the periphery of a map structure or with the periphery of an already-placed target prop.”; also, para 38, “After culling the placement candidates for cabinet 400 in this manner, the prop placement tool is left with a filtered set of placement candidates for cabinet 400 that satisfy the no-collision spatial rule”); adjusting the placement probability score, for each cell of constraint-free cells from amongst the plurality of cells with respect to the given object at a second point in time, based on the placement probability score for each cell of the constraint-free cells, and the adjusted placement probability score for the one or more constrained cells, wherein the given object is placed in a constraint-free cell having a highest adjusted placement probability score amongst the constraint-free cells with respect to the given object at the second point in time (para 98, “Once the placement scores for all placements of prop 1602 are determined, the placement with the highest placement score is selected as the placement for prop 1602. In an alternative embodiment, a probability is assigned to each valid location. The final placement of the prop is then determined by sampling the valid locations according to the probability distribution. For example, assume a prop has just three valid locations, A, B, and C. The algorithm might assign A a probability of 0.6, B a probability of 0.3 and C a probability of 0.1.”; also, para 92, “After generating the placement score for each candidate placement of a target prop, the prop placement tool selects the placement with the highest score as the placement for the target prop.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bean's invention of generating a graphical layout by placing objects into a grid of cells using per-cell weight values, a predefined priority placement order, and predefined placement constraints, with the features of Zhai's invention of assigning a placement probability to each valid candidate location, removing constraint-violating candidate locations from consideration, forming a probability distribution over the valid candidate locations, and selecting the candidate location having the highest placement score. Bean and Zhai are both directed to automatically placing virtual objects into the discrete locations of a game map subject to placement constraints, and both compute a per-location value used to select where each object is placed. Bean already sorts objects into a placement order and searches its per-tile values to select a tile for each object, but expresses those per-tile values as weights rather than as probabilities, does not expressly set the value of a constraint-violating cell to zero, and does not expressly select the highest-valued cell. Zhai supplies these refinements by teaching that a probability is assigned to each valid location, that constraint-violating candidate locations are culled so that only valid locations are retained for placement such that a constraint-violating cell is given no placement probability, that is, a placement probability of zero, that the retained valid locations form a probability distribution, and that the placement having the highest score is selected. A person of ordinary skill would have applied Zhai's probabilistic, constraint-masked, highest-scoring selection to Bean's grid-based map generation to obtain the predictable result of placing each object in the most suitable unconstrained cell while ensuring that a cell on which a constraint is violated, having no remaining placement probability, is never selected.
Regarding claim 2, Street as modified by Zhai discloses the method according to claim 1, placement probability score, for each cell of constraint-free cells with respect to a subsequent object to the given object at a third point in time, and wherein the subsequent object is placed in a constraint-free cell having a highest adjusted placement probability score with respect to the subsequent object at the third point in time.
However, in a similar field of endeavor, Zhai discloses further comprising adjusting the placement probability score, for each cell of constraint-free cells with respect to a subsequent object to the given object at a third point in time, and wherein the subsequent object is placed in a constraint-free cell having a highest adjusted placement probability score with respect to the subsequent object at the third point in time (para 9, “according to one embodiment, the target props are processed sequentially, and the distance transform used by each prop takes into account the existence of all already-placed props.”; para 99, “However, when generating the distance transform for prop 1604, the prop placement tool assumes that prop 1602 has been placed on the target map in the placement determined to be the final placement for prop 1602. Thus, the distance transform for 1604 will indicate that the map location(s) covered by prop 1602 are "occupied". Based on this new distance transform, the placement score for each of prop 1604's candidate placements may be determined (e.g. using formula 1702).”; also, para 92, “After generating the placement score for each candidate placement of a target prop, the prop placement tool selects the placement with the highest score as the placement for the target prop.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of placing objects one after another into the grid of cells with the features of Zhai's invention of processing target objects sequentially, recomputing each candidate cell's placement score for a subsequent object after a preceding object has been placed and its occupied cells are accounted for, and selecting the highest-scoring cell for the subsequent object. A person of ordinary skill would have made this combination because Zhai teaches that a subsequent object is processed at a successive step after the given object is placed, that the placement score for each candidate cell with respect to the subsequent object is recomputed to reflect the cells occupied by the previously placed object, and that the subsequent object is then placed in the highest-scoring cell, yielding the predictable result of iteratively placing each successive object in its most suitable unconstrained cell at each successive point in time.
Regarding claim 3, Street as modified by Zhai discloses the method according to claim 2, placement probability score for each cell of the constraint-free cells with respect to the subsequent object is further based on adjusted placement probability score for the one or more constrained cells with respect to the given object at the third point time.
However, in a similar field of endeavor, Zhai discloses wherein adjusting the placement probability score for each cell of the constraint-free cells with respect to the subsequent object is further based on adjusted placement probability score for the one or more constrained cells with respect to the given object at the third point time (para 99, “However, when generating the distance transform for prop 1604, the prop placement tool assumes that prop 1602 has been placed on the target map in the placement determined to be the final placement for prop 1602. Thus, the distance transform for 1604 will indicate that the map location(s) covered by prop 1602 are "occupied". Based on this new distance transform, the placement score for each of prop 1604's candidate placements may be determined”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of placing successive objects into the map with the features of Zhai's invention of recomputing the placement score for each candidate location of a subsequent object based on a new distance transform that reflects the locations now occupied as a result of placing the preceding object. A person of ordinary skill would have made this combination because Zhai teaches that the cells made unavailable by the previously placed object must be taken into account when scoring candidate cells for the subsequent object, yielding the predictable result of scoring the subsequent object's candidate cells in light of the constrained cells produced by the previously placed object.
Regarding claim 4, Street as modified by Zhai discloses the method according to claim 3, placement probability scores for each cell of the plurality of cells with respect to the given and subsequent objects of the plurality of objects at the first, second and third points in time are performed using an Artificial intelligence model.
However, in a similar field of endeavor, Zhai discloses wherein determining and adjusting the placement probability scores for each cell of the plurality of cells with respect to the given and subsequent objects of the plurality of objects at the first, second and third points in time are performed using an Artificial intelligence model (para 32, “Techniques are described herein for facilitating the placement of props on maps by an automated prop placement tool that makes use of a trained machine learning mechanism.”; also, para 52, “the prop placement tool selects the best prop placement outcome based on a trained machine learning mechanism that is trained based on at least one training map upon which props have been placed. The placement of the props in the training maps is treated as the "ideal placement”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of determining and adjusting per-cell placement values with the features of Zhai's invention of generating placement scores using a trained machine learning mechanism that is trained on training maps reflecting an ideal placement. A person of ordinary skill would have implemented Street's per-cell placement scoring using Zhai's trained machine learning mechanism because Zhai teaches that the machine learning mechanism is trained on maps having props already placed in an ideal arrangement, yielding the predictable result of automatically generating per-cell placement scores that reproduce a human-like, ideal placement of the objects.
Regarding claim 6, Street as modified by Zhai discloses the method according to claim 1, wherein the input graphical layout is one of: an empty graphical layout devoid of objects or a partially-filled graphical layout having at least one object (Street: col 8, “When the land and water composition is known, the game program may then generate an initial grid, such as grid 401, having the required land/water makeup.”).
Regarding claim 7, Street as modified by Zhai discloses the method according to claim 1, wherein the predefined placement schedule is defined based on at least one of: an object-type, an object-size, and a structuring-plan (Street: col 17, “Accordingly, it may be desirable to sort the objects for purposes of placement in a priority order, such that higher priority objects get placed before lower priority objects. The priorities may vary depending on the particular game. For example, in an RTS in which each player starts with a town center as a base of operations, the placement of the town center may be the highest priority, and may be placed first.”).
Regarding claim 8, Street as modified by Zhai discloses the method according to claim 7, wherein objects belonging to similar and dis-similar object-types and object-sizes are selected based on the structuring-plan for generation of the graphical layout (Street: col 16, “The overall list of objects to be placed may be defined in the script, which may be chosen at the same time the user selects the basic type of map on which to play, and may also be defined based on the parameters defined in step 501.”).
Regarding claim 9, Street as modified by Zhai discloses the method according to claim 7, wherein the at least one predefined constraint that identifies the one or more constrained cells, is when a cell from amongst the plurality of cells is pre-occupied with an object or unsuitable to be placed with the given object based on the object-type, the object-size or the structuring-plan (Street: col 16, “Another parameter includes defining the types of other objects that are to be avoided by the present object. For example, the placement of a town center object may be given a parameter to avoid (or maintain a predetermined distance from) another town center, bodies of water, impassable terrain, or any other terrain features or objects that may also be placed on the map.”; also, col 15, “For example, undesired or unusable areas (e.g., impassable mountains, water features, etc.) may be omitted from the total of available tiles, and consequently may be ignored in the assignment of player areas.”).
Regarding claim 11, Street discloses a system for generating a graphical layout, the system comprising a processor configured to (col 3, “With reference to FIG. 1, the exemplary system 100 for implementing features described herein includes a general purpose-computing device in the form of a computer 110 including a processing unit 120, a system memory 130, and a system bus 121 that couples various system components including the system memory to the processing unit 120.”); receive an input graphical layout having a plurality of cells forming a grid structure, the plurality of cells are configured to be placed with a plurality of objects to form the graphical layout (col 6, “The map may be defined as a grid of space. FIG. 4a shows the view from FIG. 3a with a superimposed grid 401 in isometric view, while FIG. 4b shows the grid 401 from an overhead view. In grid 401, the available map area may be divided into a plurality of tiles 402, where each tile 402 serves as an elementary unit for creating the map.”); plurality of objects into the input graphical layout for forming the graphical layout, wherein placing a given object into the input graphical layout comprises (col 16, “In step 506, another looping process may begin for each object to be placed.”): selecting the given object for placement, based on a predefined placement schedule (col 17, “Accordingly, it may be desirable to sort the objects for purposes of placement in a priority order, such that higher priority objects get placed before lower priority objects.”); identifying one or more constrained cells from amongst the plurality of cells where at least one predefined constraint is violated if the given object is placed thereon (col 17, “Other constraints include restricting placement to a predetermined shape, such as a rectangle, pie wedge portion of a circle, etc., distances (minimum or maximum) from other objects or object classes, distances from an edge of the map, map features such as cliffs and rivers, terrain types, etc.”; also, col 17, “In step 509, the proposed location for the current object is compared with the various parameters defined in step 507.”; also, col 17, “At step 509, if the proposed location violates a parameter, the process may move to step 510, where a check may be made to determine whether the current object is a critical object.”); placement probability score for each cell of the plurality of cells with respect to each object of the plurality of objects at a first point in time, adjusting the placement probability score, for the one or more constrained cells, to be zero; adjusting the placement probability score, for each cell of constraint-free cells from amongst the plurality of cells with respect to the given object at a second point in time, based on the placement probability score for each cell of the constraint-free cells, and the adjusted placement probability score for the one or more constrained cells, wherein the given object is placed in a constraint-free cell having a highest adjusted placement probability score amongst the constraint-free cells with respect to the given object at the second point in time.
However, in a similar field of endeavor, Zhai discloses determine a placement probability score for each cell of the plurality of cells with respect to each object of the plurality of objects at a first point in time (para 36, “Initially, every location on a map is considered a candidate location for placement of each target prop at each possible orientation.”; also, para 98, “In an alternative embodiment, a probability is assigned to each valid location.”), adjusting the placement probability score, for the one or more constrained cells, to be zero (para 36, “based on a "no-collision" spatial rule that two objects cannot occupy the same space at the same time, the candidate (location, orientation) possibilities for a target prop are culled to remove placements in which the periphery of target prop would intersect/collide with the periphery of a map structure or with the periphery of an already-placed target prop.”; also, para 38, “After culling the placement candidates for cabinet 400 in this manner, the prop placement tool is left with a filtered set of placement candidates for cabinet 400 that satisfy the no-collision spatial rule”); adjusting the placement probability score, for each cell of constraint-free cells from amongst the plurality of cells with respect to the given object at a second point in time, based on the placement probability score for each cell of the constraint-free cells (para 98, “The final placement of the prop is then determined by sampling the valid locations according to the probability distribution. For example, assume a prop has just three valid locations, A, B, and C. The algorithm might assign A a probability of 0.6, B a probability of 0.3 and C a probability of 0.1.”), and the adjusted placement probability score for the one or more constrained cells, wherein the given object is placed in a constraint-free cell having a highest adjusted placement probability score amongst the constraint-free cells with respect to the given object at the second point in time (para 92, “After generating the placement score for each candidate placement of a target prop, the prop placement tool selects the placement with the highest score as the placement for the target prop.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of a computing system that generates a graphical layout by placing objects into a grid of cells using per-cell weight values, a predefined priority placement order, and predefined placement constraints, with the features of Zhai's invention of assigning a placement probability to each valid candidate location, removing constraint-violating candidate locations from consideration, forming a probability distribution over the valid candidate locations, and selecting the candidate location having the highest placement score. Street and Zhai are both directed to a computing system that automatically places virtual objects into the discrete locations of a game map subject to placement constraints, and both compute a per-location value used to select where each object is placed. Street already sorts objects into a placement order and searches its per-tile values to select a tile for each object, but expresses those per-tile values as weights rather than as probabilities, does not expressly set the value of a constraint-violating cell to zero, and does not expressly select the highest-valued cell. Zhai supplies these refinements by teaching that a probability is assigned to each valid location, that constraint-violating candidate locations are culled so that only valid locations are retained for placement (such that a constraint-violating cell is given no placement probability, that is, a placement probability of zero), that the retained valid locations form a probability distribution, and that the placement having the highest score is selected. A person of ordinary skill would have configured Street's processor to apply Zhai's probabilistic, constraint-masked, highest-scoring selection to Street's grid-based map generation to obtain the predictable result of placing each object in the most suitable unconstrained cell while ensuring that a cell on which a constraint is violated, having no remaining placement probability, is never selected.
Regarding claim 12, Street as modified by Zhai discloses the system according to claim 11, processor is further configured to adjust the placement probability score, for each cell of constraint-free cells with respect to a subsequent object to the given object at a third point in time, and wherein the subsequent object is placed in a constraint-free cell having a highest adjusted placement probability score with respect to the subsequent object at the third point in time.
However, in a similar field of endeavor, Zhai discloses wherein the processor is further configured to adjust the placement probability score, for each cell of constraint-free cells with respect to a subsequent object to the given object at a third point in time, and wherein the subsequent object is placed in a constraint-free cell having a highest adjusted placement probability score with respect to the subsequent object at the third point in time (para 90, “Therefore, according to one embodiment, the target props are processed sequentially, and the distance transform used by each prop takes into account the existence of all already-placed props.”, also, 99, “However, when generating the distance transform for prop 1604, the prop placement tool assumes that prop 1602 has been placed on the target map in the placement determined to be the final placement for prop 1602. Thus, the distance transform for 1604 will indicate that the map location(s) covered by prop 1602 are "occupied". Based on this new distance transform, the placement score for each of prop 1604's candidate placements may be determined (e.g. using formula 1702).”; also, para 92, “After generating the placement score for each candidate placement of a target prop, the prop placement tool selects the placement with the highest score as the placement for the target prop.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of a computing system that places objects one after another into the grid of cells with the features of Zhai's invention of processing target objects sequentially, recomputing each candidate cell's placement score for a subsequent object after a preceding object has been placed and its occupied cells are accounted for, and selecting the highest-scoring cell for the subsequent object. A person of ordinary skill would have configured Street's processor to recompute the placement scores for the subsequent object after the preceding object was placed and to select the highest-scoring cell, as taught by Zhai, because Zhai teaches that the score for a subsequent object depends on the cells occupied by the previously placed object, yielding the predictable result of iteratively placing each successive object in its most suitable unconstrained cell at each successive point in time.
Regarding claim 13, Street as modified by Zhai discloses the system according to claim 12, placement probability score for each cell of the constraint-free cells with respect to the subsequent object is further based on adjusted placement probability score for the one or more constrained cells with respect to the given object at the third point time.
However, in a similar field of endeavor, Zhai discloses wherein adjusting the placement probability score for each cell of the constraint-free cells with respect to the subsequent object is further based on adjusted placement probability score for the one or more constrained cells with respect to the given object at the third point time (para 99, “However, when generating the distance transform for prop 1604, the prop placement tool assumes that prop 1602 has been placed on the target map in the placement determined to be the final placement for prop 1602. Thus, the distance transform for 1604 will indicate that the map location(s) covered by prop 1602 are "occupied". Based on this new distance transform, the placement score for each of prop 1604's candidate placements may be determined (e.g. using formula 1702).”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention of placing successive objects into the map with the features of Zhai's invention of recomputing the placement score for each candidate location of a subsequent object based on a new distance transform that reflects the locations now occupied as a result of placing the preceding object. A person of ordinary skill would have made this combination because Zhai teaches that the cells made unavailable by the previously placed object must be taken into account when scoring candidate cells for the subsequent object, yielding the predictable result of scoring the subsequent object's candidate cells in light of the constrained cells produced by the previously placed object.
Regarding claim 14, Street as modified by Zhai discloses the system according to claim 11, wherein the predefined placement schedule is defined based on at least one of: an object-type, an object-size, and a structuring- plan (Street: col 17, “Accordingly, it may be desirable to sort the objects for purposes of placement in a priority order, such that higher priority objects get placed before lower priority objects. The priorities may vary depending on the particular game. For example, in an RTS in which each player starts with a town center as a base of operations, the placement of the town center may be the highest priority, and may be placed first.”).
Regarding claim 15, Street as modified by Zhai discloses the system according to claim 14, wherein the at least one predefined constraint that identifies the one or more constrained cells, is when a cell from amongst the plurality of cells is pre-occupied with an object or unsuitable to be placed with the given object based on the object-type, the object-size or the structuring-plan (Street: col 16, “Another parameter includes defining the types of other objects that are to be avoided by the present object. For example, the placement of a town center object may be given a parameter to avoid (or maintain a predetermined distance from) another town center, bodies of water, impassable terrain, or any other terrain features or objects that may also be placed on the map.”; also, col 15, “For example, undesired or unusable areas (e.g., impassable mountains, water features, etc.) may be omitted from the total of available tiles, and consequently may be ignored in the assignment of player areas.”).
Regarding claim 16, Street as modified by Zhai discloses the method according to claim 9, wherein the at least one predefined constraint comprises a rule that an object of a predefined object-type cannot be placed in a cell belonging to a boundary region of the input graphical layout (col 7, “For example, one script may be created for a random map in a mountainous region, and the relevant parameters for the generation below may be predetermined to result in such a map (e.g., higher weights may be given to mountain terrain textures, default elevations may be increased, etc.). The use of these parameters and the generation of the map will be discussed below.”; also, col 16, “Other constraints include restricting placement to a predetermined shape, such as a rectangle, pie wedge portion of a circle, etc., distances (minimum or maximum) from other objects or object classes, distances from an edge of the map, map features such as cliffs and rivers, terrain types, etc.”).
Regarding claim 18, Street as modified by Zhai discloses the method according to claim 7, wherein selecting the given object for placement based on the predefined placement schedule comprises selecting an object having a first priority in the schedule to be placed before other objects having a lower priority (Street: col 17,” Accordingly, it may be desirable to sort the objects for purposes of placement in a priority order, such that higher priority objects get placed before lower priority objects. The priorities may vary depending on the particular game. For example, in an RTS in which each player starts with a town center as a base of operations, the placement of the town center may be the highest priority, and may be placed first.”).
Regarding claim 20, Street as modified by Zhai discloses the system according to claim 15, wherein the at least one predefined constraint comprises a rule that an object of a predefined object-type cannot be placed in a cell belonging to a boundary region of the input graphical layout (col 7, “For example, one script may be created for a random map in a mountainous region, and the relevant parameters for the generation below may be predetermined to result in such a map (e.g., higher weights may be given to mountain terrain textures, default elevations may be increased, etc.). The use of these parameters and the generation of the map will be discussed below.”; also, col 16, “Other constraints include restricting placement to a predetermined shape, such as a rectangle, pie wedge portion of a circle, etc., distances (minimum or maximum) from other objects or object classes, distances from an edge of the map, map features such as cliffs and rivers, terrain types, etc.”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Street et al. (U.S. Doc. No.7589742) in view of Zhai et al. (U.S. Pub. No. 20220203240), further in view of Inoue et al. (arXiv:2303.08137v1).
Regarding claim 5, Street as modified by Zhai discloses the method according to claim 4, Artificial intelligence model is a neural network model based on an encoder decoder Vision Transformer model discloses operable based on a discrete diffusion process for determining and adjusting the placement probability scores.
However, in a similar field of endeavor, Inoue discloses (abstract, “In this work, we try to solve a broad range of lay out generation tasks in a single model that is based on dis crete state-space diffusion models”; also, abstract,“; also, sec 3.2, “Similarly to D3PM[3], we parameterize pθ by a Transformer encoder [45], which processes an ordered 1D sequence.”; also, sec 1, “LayoutDM is trained to gradually generate a complete layout from a blank state in discrete state space.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Street's invention as combined with Zhai's trained machine learning mechanism for generating per-cell placement scores, with the features of Inoue's invention of a trained neural network parameterized by a Transformer that performs layout generation based on a discrete state-space diffusion process. A person of ordinary skill implementing the trained machine learning mechanism of Zhai to generate the per-cell placement scores of Street would have selected Inoue's transformer-based neural network operating on a discrete diffusion process, because Inoue teaches that such a model naturally handles structured layout data in a discrete representation and progressively infers a complete layout from a blank state, which is the same discrete, grid-based, progressive object placement performed by Street and Zhai, yielding the predictable result of generating the placement scores with a trained transformer-based discrete diffusion model. The further recitation of an encoder-decoder Vision Transformer is a selection among art-recognized transformer architectures; Inoue teaches parameterizing the discrete-diffusion model by a Transformer that processes the layout representation, and configuring that Transformer as an encoder-decoder Vision Transformer is an obvious choice among known transformer architectures that yields the predictable result of determining and adjusting the placement probability scores.
Response to Arguments
Applicant’s arguments, filed 05/20/2026 have been fully considered.
The rejection of claims 1-3 and 6-10 under 35 U.S.C. 102(a)(1) as anticipated by Yamada (U.S. Pub. No. 20040255245), and the rejection of claims 4-5 and 11-15 under 35 U.S.C. 103 over Yamada in view of Yu (U.S Pub. No. 20240104809), are withdrawn. Upon further consideration and an updated search, new grounds of rejection under 35 U.S.C. 103 over Street (U.S. Doc. No. 7589742) in view of Zhai (U.S. Pub. No. 20220203240), and further in view of Inoue (arXiv:2303.08137) for claim 5, are set forth above. Applicant's arguments directed to Yamada and Yu are therefore moot with respect to the claims rejected on the new grounds.
Applicant's argument that Yamada does not disclose dynamically adjusting the placement probability score after an object has been placed has been fully considered and is persuasive with respect to Yamada. However, the newly applied Zhai reference teaches this feature, as set forth above, in that Zhai recomputes the placement score for each candidate cell after a preceding object has been placed and its occupied cells are accounted for, and selects the cell having the highest score. Claim 1 therefore stands rejected on the new grounds.
Applicant's argument regarding claim 10, that the prior art does not disclose determining the constraint-free cell having the highest adjusted placement probability score by normalizing the placement probability score of the constraint-free cells such that their cumulative sum equals one, has been fully considered and is persuasive. A further search did not identify prior art teaching or suggesting this feature. Accordingly, claim 10, and claims 17 and 19 which recite commensurate subject matter, are indicated as containing allowable subject matter, as set forth in the Allowable Subject Matter section above.
Applicant's argument that the prior rejection of claims 11-15 was based on a factual error, because Yamada already discloses a processor (Yamada, CPU 30), has been fully considered and is persuasive. The prior rejection relying on Yu to supply a processor is withdrawn. Claims 11-15 are rejected on the new grounds over Street in view of Zhai, consistent with the treatment of their method counterparts.
Conclusion
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/JAI W LI/Junior Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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