DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The amendment filed on 03/02/2026 has been received and fully considered.
3. Claims 1-20 are presented for examination.
Response to Arguments
4. Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive with reference to the art rejection; the rejection under 35 USC 101 has been withdrawn. Regarding applicant’s assertions that: “neither Segev nor Scott-Leikach teach or suggest at least the following elements in amended independent claim 13, or the similar recitations of amended independent claims 1 and 20, related to the "resident profile" and how it is used to select the set of candidate furnishing items for the floor plan, including: receiving, by a communication interface, a floor plan of the room and a resident profile, wherein the resident profile includes information about at least one resident of the room, including an age, a gender, and a hobby of the at least one resident, determining, by at least one processor, a functionality of the room at least partially based on the resident profile; obtain a set of candidate furnishing items for the room based on the functionality of the room or the element recited by claim 8, including "wherein the required number of bedrooms is determined based on a number of residents whose information is included in the resident profile and the ages of each resident included in the resident profile.", the Examiner respectfully disagrees and asserts that Sehev, used as the primary reference in the rejection, provide simulation of a floor plan to select particular equipment for use in the building (see title, abstract). Segev goes on to receiving, by a communication interface, a floor plan of the room (abstract, a floor plan demarcating a plurality of rooms, include accessing functional requirements associated with the rooms and accessing technical specifications associated with the functional requirements. The operations include performing floor plan analysis on the floor plan to ascertain room features associated with the functional requirements) and a resident profile ([0124], the floor plan may include textual information, such as the name or function of the rooms, the number of the room, the classification tag of the room, information regarding the dimensions of the room, or other textual information. [0209] At step 404, process 400 may include receiving a first functional requirement for at least one first room of the plurality of rooms. The first functional requirement may be applied by a user. In cases where the demarcations of rooms have been identified, the functional requirement may be applied to the entire contours of a room without input by the user being required to define the contours of the room. The first functional requirement may define expected or required performance parameters associated with the first room, it is to be note said functional requirement could include the function use of the room such as a user/occupant profile.). Scott-Leikach et al., used as a secondary reference in the rejection, provides a method for generating a furnishing plan for one or more selected items (see title, abstract), including furnishing user preferences/resident profile, wherein the resident profile includes information about at least one resident of the room, including an age, a gender, and a hobby of the at least one resident (see para [0041] The design assistant 125 receives room and preference information possibly from the client 110, and possibly in response to a series of questions posed by the design assistant 125 to the client 110. The design assistant 125 then uses the room and preference information to generate automatically a set of recommended floor plans, combinations of recommended furnishings, design and furnishing alternatives, etc. [0058]-[0060], For example, the dynamic structured query set 315 may include questions regarding rooms already existing, functional uses desired, marital status which could include gender and certain age of the couple residing in the room, number and age of children, etc. By learning about the client 110, the information-collecting module 330 will be in a better position to offer alternative uses for a room which may include the residing person’s hobby (e.g., game room, workout room, formal dining room, etc.), to offer recommended materials, whether any artwork should be attached higher (so that children cannot reach it), to offer furnishing combinations, etc.); determine an optimal placement of the furnishing items added in the placement item set using a machine learning model (see para [0062] After learning about the room dimensions and user preferences, the design engine 305 may offer the client 110 a set of alternatives uses for a space. An example module for performing this feature is the room use recommendation module 335 of FIG. 3B. For example, if the room use recommendation module 335 learns that the client 305 is married, has no kids, has a living room, family room, bedroom, guest bedroom, and one interior space scheduled for interior design, the room use recommendation module 335 may offer the client 110 options such as a game room or formal dining room. Further, if the room is sufficiently large, the room use recommendation module 335 may recommend a combined game room and exercise room. The room use recommendation module 335 can implement rule sets that generate available options given the answers to certain input variables. Alternatively, the room use recommendation module 335 can offer the client 110 to select from a wide variety of alternative room uses, e.g., a list of all reasonable room uses. Based on the room size, the room use recommendation module 335 may indicate that the client 110 can select more than one use.); and render a three-dimensional view the furnishing plan for display based on the determined optimal placement of the furnishing items added in the placement item set (see fig.5-6, para [0041], The design assistant 125 then uses the room and preference information, attribute information of various furnishings and/or designer guidelines to generate automatically a set of recommended floor plans, combinations of recommended furnishings, design and furnishing alternatives, etc. The design assistant 125 then enables the client 110 to modify the floor plans and/or furnishings combinations, to make selections, to purchase furnishings, etc. In one embodiment, the design assistant 125 automatically modifies (e.g., adjusts, narrows, adds, subtracts, etc.) the recommended floor plans, the recommended combinations of furnishings, the recommended styles and patterns, etc. based on selections made and/or preference information further indicated by the client 110. For example, if the client 110 indicates dissatisfaction of a particular item, e.g. a sofa, the design assistant 125 can discard all combinations with the item. [0062]-[0063] The room use recommendation module 335 can implement rule sets that generate available options given the answers to certain input variables. [0066], [0066] After the client 110 indicates (e.g., selects) a room use, the design engine 305 suggests floor plans. An example module for performing this feature is the floor plan generator 340 of FIG. 3B. In a rules-based embodiment, the floor plan generator 340 begins by obtaining a list of general (typical) furnishings for the intended room use. For example, if the intended use for the space is a family room, the floor plan generator 340 will consider 1-2 sofas, 1-2 love seats, 1-2 chairs, 1-2 ottomans, 1-2 lamps, 1 television, 1 entertainment center, 2-3 end tables, 1-2 coffee tables, etc. Based on the information provided (e.g., room size, object locations, functional uses, etc.), the floor plan generator 340 will apply general designer guidelines, e.g., floor plans that maximize the use of space, do not impede ingress and egress, maximize natural light, do not impede objects, achieve the desired functional use, etc. The floor plan generator 340 will also prioritize the furnishings in the list of furnishings to determine which furnishing and how many of each furnishing can be included. For example, a small family room of about 18'.times.12' will not be able to hold more than 1 sofa, one chair, 1 television, 2 end tables, and two lamps. However, a larger family room of about 25.times.30 will be able to hold more furnishings. Based on these guidelines, the floor plan generator 340 can rate the quality of each floor plan and accordingly may list the floor plan options in order of quality. Further, when determining the floor plan, the floor plan generator 340 may also select general dimension ranges for each of the design items.), and that the combination of the cited references clearly renders obvious the limitations, contrary to applicant’s assertions.
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Segev et al. (USPG_PUB No. 2021/0073449), in view of Scott-Leikach et al. (USPG_PUB No. 2006/0101742).
6.1 In considering claims 1, 13, and 20, teaches a system for generating a furnishing plan for a room, comprising:
a communication interface configured to receive a floor plan of the room and a resident profile (see fig.4, para [0007] Embodiments consistent with the present disclosure provide systems and methods for selective simulation of equipment coverage in a floor plan. These embodiments may involve at least one processor configured to access a floor plan demarcating at least one room, receive, via a graphical user interface, information marking an area within the at least one room, wherein the marked area defines an area of interest or disinterest within the at least one room, and wherein the area of interest or disinterest covers an area less than an area of the at least on room. [0209] At step 404, process 400 may include receiving a first functional requirement for at least one first room of the plurality of rooms. The first functional requirement may be applied by a user. In cases where the demarcations of rooms have been identified, the functional requirement may be applied to the entire contours of a room without input by the user being required to define the contours of the room. The first functional requirement may define expected or required performance parameters associated with the first room, it is to be note said functional requirement could include the function use of the room such as a user/occupant profile. In some embodiments, the first functional requirement and a second functional requirement may be the different (e.g. an office space or a bedroom); and at least one processor configured to: determine a functionality of the room at least partially based on the resident profile (see fig.4, para [0007], The at least on processor may be further configured to access a functional requirement associated with an area of interest or disinterest, access technical specifications associated with the functional requirement, generatively analyze the technical specifications to define a solution that at least partially conforms to the functional requirement, and output the solution); obtain a set of candidate furnishing items for the room based on the functionality of the room (see fig.4, para [0210] At step 408, process 400 may include generatively analyzing the at least one first room in conjunction with the first functional requirement to identify a first technical specification and a first equipment placement location in order to at least partially conform to the first functional requirement. In some embodiments, the generative analysis may be an iterative process. For example, the generative analysis may include a series of simulations including one or more analyses of differing equipment placement locations, as described above with respect to FIG. 1. At step 410, process 400 may include generatively analyzing the at least one second in conjunction with the second functional requirement to identify a second technical specification and a second equipment placement location order to at least partially conform to the second functional requirement. Additional details regarding the generative analysis that may be applied to the first and second rooms are provided above); sequentially add furnishing items selected from the set of candidate furnishing items to a placement item set, wherein the furnishing items added in the placement item set collectively meet a predetermined area occupation threshold associated with the floor plan (see para [0210], analysis to identify furnishing option and placement of the furnishing items; said analysis and placement may be an iterative process. Step 410, process 400 may include generatively analyzing the at least one second in conjunction with the second functional requirement to identify a second technical specification and a second equipment placement location order to at least partially conform to the second functional requirement.[0239], In some embodiments generatively analyzing the technical specifications to define the solution may include sequentially running simulations. [0441], It is to be understood that the generative analysis may be performed on many rooms (e.g., 10's, 100's or 1000's of rooms) simultaneously, as part of the same generative process, and/or sequentially); and render a three-dimensional view the furnishing plan for display based on the determined optimal placement of the furnishing items added in the placement item set (see fig.4, para [0211] At step 412, process 400 may include outputting the first technical specification and the first equipment placement location in an associative manner with the at least one first room, i.e. a floor plan. Similarly, at step 414, process 400 may include outputting the second technical specification and the second equipment placement location in an associative manner with the at least one second room. The output may be provided in a variety of formats, as described above. In some embodiments the output may include a CAD file with blocks or other representations of the selected equipment technical specifications and locations, a BIM file containing BIM objects associated with the equipment technical specification and placement locations, an image file with the equipment placement locations overlaid on the image, i.e. a floor plan to include the selected items along with the locations. In some embodiments, the output may further include other information, including, but not limited to, a materials list, a customized setting, a programming parameter, a score evaluating the placement locations, a coverage map, a classification tag, or any other information that may be pertinent to the technical specifications or equipment placement locations). While Segev et al. does not specifically state that the term a resident profile, he provides for receiving information associated with specific room such as an office, a bedroom for a certain age of a age (para [0213]), hospital room, hotel guestroom. A sleeping area may include a bed, mattress, futon, convertible sofa bed, or other furniture suitable for sleeping (para [0321], and thus would have been obvious to a person of skilled in the art to have include a resident profile).
Nonetheless, Scott-Leikach et al. teaches a method for generating a furnishing plan for one or more selected items (see title, abstract), including furnishing user preferences/resident profile, wherein the resident profile includes information about at least one resident of the room, including an age, a gender, and a hobby of the at least one resident (see para [0041] The server 105 includes a design assistant 125 and a furnishings database 130. The design assistant 125 receives room and preference information possibly from the client 110, and possibly in response to a series of questions posed by the design assistant 125 to the client 110. The design assistant 125 then uses the room and preference information, attribute information of various furnishings and/or designer guidelines to generate automatically a set of recommended floor plans, combinations of recommended furnishings, design and furnishing alternatives, etc. [0058]-[0060], the user preferences for the space may also be asked of the client 110. For example, the dynamic structured query set 315 may include questions regarding rooms already existing, functional uses desired, marital status which could include gender and certain age of the couple residing in the room, number and age of children, etc. By learning about the client 110, the information-collecting module 330 will be in a better position to offer alternative uses for a room which may include the residing person’s hobby (e.g., game room, workout room, formal dining room, etc.), to offer recommended materials, whether any artwork should be attached higher (so that children cannot reach it), to offer furnishing combinations, etc.); determine an optimal placement of the furnishing items added in the placement item set using a machine learning model (see para [0062] After learning about the room dimensions and user preferences, the design engine 305 may offer the client 110 a set of alternatives uses for a space. An example module for performing this feature is the room use recommendation module 335 of FIG. 3B. For example, if the room use recommendation module 335 learns that the client 305 is married, has no kids, has a living room, family room, bedroom, guest bedroom, and one interior space scheduled for interior design, the room use recommendation module 335 may offer the client 110 options such as a game room or formal dining room. Further, if the room is sufficiently large, the room use recommendation module 335 may recommend a combined game room and exercise room. The room use recommendation module 335 can implement rule sets that generate available options given the answers to certain input variables. Alternatively, the room use recommendation module 335 can offer the client 110 to select from a wide variety of alternative room uses, e.g., a list of all reasonable room uses. Based on the room size, the room use recommendation module 335 may indicate that the client 110 can select more than one use.); and render a three-dimensional view the furnishing plan for display based on the determined optimal placement of the furnishing items added in the placement item set (see para [0041], The design assistant 125 then uses the room and preference information, attribute information of various furnishings and/or designer guidelines to generate automatically a set of recommended floor plans, combinations of recommended furnishings, design and furnishing alternatives, etc. The design assistant 125 then enables the client 110 to modify the floor plans and/or furnishings combinations, to make selections, to purchase furnishings, etc. In one embodiment, the design assistant 125 automatically modifies (e.g., adjusts, narrows, adds, subtracts, etc.) the recommended floor plans, the recommended combinations of furnishings, the recommended styles and patterns, etc. based on selections made and/or preference information further indicated by the client 110. For example, if the client 110 indicates dissatisfaction of a particular item, e.g. a sofa, the design assistant 125 can discard all combinations with the item. [0062]-[0063] The room use recommendation module 335 can implement rule sets that generate available options given the answers to certain input variables. [0066], [0066] After the client 110 indicates (e.g., selects) a room use, the design engine 305 suggests floor plans. An example module for performing this feature is the floor plan generator 340 of FIG. 3B. In a rules-based embodiment, the floor plan generator 340 begins by obtaining a list of general (typical) furnishings for the intended room use. For example, if the intended use for the space is a family room, the floor plan generator 340 will consider 1-2 sofas, 1-2 love seats, 1-2 chairs, 1-2 ottomans, 1-2 lamps, 1 television, 1 entertainment center, 2-3 end tables, 1-2 coffee tables, etc. Based on the information provided (e.g., room size, object locations, functional uses, etc.), the floor plan generator 340 will apply general designer guidelines, e.g., floor plans that maximize the use of space, do not impede ingress and egress, maximize natural light, do not impede objects, achieve the desired functional use, etc. The floor plan generator 340 will also prioritize the furnishings in the list of furnishings to determine which furnishing and how many of each furnishing can be included. For example, a small family room of about 18'.times.12' will not be able to hold more than 1 sofa, one chair, 1 television, 2 end tables, and two lamps. However, a larger family room of about 25.times.30 will be able to hold more furnishings. Based on these guidelines, the floor plan generator 340 can rate the quality of each floor plan and accordingly may list the floor plan options in order of quality. Further, when determining the floor plan, the floor plan generator 340 may also select general dimension ranges for each of the design items.).
Segev et al. and Scott-Leikach et al. are analogous art because they are from the same of endeavor and that the model analyzes by Scott-Leikach et al. is similar to that of Segev et al. Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.2 Regarding claims 2, 14, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein each candidate furnishing item is associated with a priority at least partially based on the resident profile (see Segev et al. para [0165], For example, the disclosed embodiments may include receiving a third functional requirement for the at least one first room, and generatively analyzing the at least one first room may further include using the third functional requirement to identify the first technical specification and first equipment placement location. In instances where the functional requirements conflict with each other, the generative analysis may include selecting which functional requirement to prioritize. For example, the generative analysis may include prioritizing a more stringent requirement, prioritizing based on a building hierarchy of building, levels, zones, and rooms in which smaller constituent groups such as rooms prioritize over large groups such as zones or levels, prioritizing functional requirements based on a predefined priority ranking, prioritizing user-defined functional requirements over default requirements, prioritizing based on codes, standards or regulations, or any other basis of prioritizing. Further Scott-Leikach et al. [0093]), wherein the furnishing items are selected from the set of candidate furnishing items and sequentially added to the placement item set according to their priorities (see Segev et al. para [0164]-[0165], For example, the disclosed embodiments may include receiving a third functional requirement for the at least one first room, and generatively analyzing the at least one first room may further include using the third functional requirement to identify the first technical specification and first equipment placement location. In instances where the functional requirements conflict with each other, the generative analysis may include selecting which functional requirement to prioritize. …, the generative analysis may include prioritizing a more stringent requirement, prioritizing based on a building hierarchy of building, levels, zones, and rooms in which smaller constituent groups such as rooms prioritize over large groups such as zones or levels, prioritizing functional requirements based on a predefined priority ranking, prioritizing user-defined functional requirements over default requirements, prioritizing based on codes, standards or regulations, or any other basis of prioritizing, [0441], It is to be understood that the generative analysis may be performed on many rooms (e.g., 10's, 100's or 1000's of rooms) simultaneously, as part of the same generative process, and/or sequentially. Further see Scott-Leikach et al. [0093]). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.3 As per claims 3 and 15, the combined teachings of Segev et al. and Scott-Leikach et al. teach the step to: select a next furnishing item from the set of candidate furnishing items (see Segev et al. para [0108] The disclosed methods and systems are directed to provided methods of selecting equipment for use in buildings. In large building projects for example, thousands of pieces of equipment might need to be specified, placed, oriented and adjusted. In some instances, there is an interrelationship between pieces of equipment, leading to a preferred solution that includes interdependencies. And once equipment is selected and placed, there is sometimes a need to change the selection or placement, which can have a cascading effect. Embodiments of this disclosure enable both generative analysis of floor plans to select, place and adjust equipment, and further enable movement of equipment in the floor plans after placement, along with associated recalculations to arrive at a preferred solution. [0109] Embodiments of the present disclosure enable design constraints to be achieved must more accurately than conventional approaches and in much shorter time. In addition, if changes are needed after an equipment selection is already made, disclosed embodiments may enable rapid adjustment unattainable with conventional approaches); calculate a total area occupied by the next furnishing item along with existing furnishing items previously added in the placement item set (see Segev et al. para [0120], A processor may run a calculation or series of calculations to suggest equipment and equipment placements in rooms, or use a machine learning model to suggest equipment and equipment placements models in rooms. [0679] Disclosed embodiments may further include calculating an area of the 2D floor plan. Calculating an area of the 2D floor plan may include estimating a two-dimensional areal extent or size of a room, corridor, area of interest, area of disinterest, or other space within the floor plan. An area calculation may be approximate, rather than exact. A calculation may include summing pixels contained within boundaries of the area, using an equation (e.g., length times width for rectangular or square spaces, one half base time height for triangular areas). A calculation may include summing estimates of sub-areas of an area (e.g., a sum of a rectangular sub-area plus a semi-circular sub-area). Calculating an area may be based on boundaries of a room. Calculating an area may include subtracting enclosed spaces (e.g., subtracting the area of a stairway for a room enclosing the stairway). More generally, calculating an area of a 2D floor plan may include using any suitable method of geometric or image analysis to estimate the area.); and determine whether adding the next furnishing item meets the predetermined area occupation threshold based on the total occupied area and an effective area of the room determined based on the floor plan (see Segev et al. para [0099], [0217] In accordance with the present disclosure, systems, methods, and computer readable media may be provided for selective simulation of equipment coverage in a floor plan. Selective simulation of equipment coverage may refer to a process of selecting and locating equipment to identify whether a coverage area of the equipment at least partly meets a functional requirement. Equipment coverage may include an area that equipment can illuminate. Further see Yang et al. para [0076]-[0077]. Further see Scott-Leikach et al. [0063]). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.4 With regards to claims 4 and 16, the combined teachings of Segev et al. and Scott-Leikach et al. teach the step to: select a substitute furnishing item of the next furnishing item, wherein the substitute furnishing item is of a same furniture type as the next furnishing item but occupies a smaller area than the next furnishing item (see Scott-Leikach et al. para 0072] (5) Enabling Substitutions. [0073] Each piece and/or attribute may be substituted for other pieces and/or attributes. An example module that enables this feature is the substitution module 350 of FIG. 3B. Alternatively, the substitution module 350 (in coordination with the furnishing combination generator 345) may determine that 2 of the 4 lamps do not match the coffee table that matches the sofa, and accordingly will offer only the 2 lamps as alternatives. Many other search and substitution permutations are possible. FIG. 6B illustrates a set of 6 coordinating fabric choices (attributes) from which the client 110 can select. [0074] In a different embodiment, the substitution module 350 may enable the client 110 to select any alternative or from various alternatives, some of which may not coordinate well. At times, a client 110 may request a substitution for a design item that does not coordinate with other pieces, e.g., the client 110 may select a traditional lamp with a modern sofa.); calculate a total area occupied by the substitute furnishing item along with existing furnishing items previously added in the placement item set (see Segev et al. para [0120], A processor may run a calculation or series of calculations to suggest equipment and equipment placements in rooms, or use a machine learning model to suggest equipment and equipment placements models in rooms. [0679] Disclosed embodiments may further include calculating an area of the 2D floor plan. Calculating an area of the 2D floor plan may include estimating a two-dimensional areal extent or size of a room, corridor, area of interest, area of disinterest, or other space within the floor plan. An area calculation may be approximate, rather than exact. A calculation may include summing pixels contained within boundaries of the area, using an equation (e.g., length times width for rectangular or square spaces, one half base time height for triangular areas). A calculation may include summing estimates of sub-areas of an area (e.g., a sum of a rectangular sub-area plus a semi-circular sub-area). Calculating an area may be based on boundaries of a room. Calculating an area may include subtracting enclosed spaces (e.g., subtracting the area of a stairway for a room enclosing the stairway). .., generally, calculating an area of a 2D floor plan may include using any suitable method of geometric or image analysis to estimate the area); and determine whether adding the substitute furnishing item meets the predetermined area occupation threshold based on the total occupied area and the effective area of the room (see Segev et al. para [0099], [0217] In accordance with the present disclosure, systems, methods, and computer readable media may be provided for selective simulation of equipment coverage in a floor plan. Selective simulation of equipment coverage may refer to a process of selecting and locating equipment to identify whether a coverage area of the equipment at least partly meets a functional requirement. Equipment coverage may include an area that equipment can illuminate. Further see Yang et al. para [0076]-[0077]). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.5 As per claim 5, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein to calculate the total area, the at least one processor is further configured to: calculate an occupied area of the next furnishing item based on dimensions of the next furnishing item and allowances associated with the next furnishing item (see Segev et al. para [0120], A processor may run a calculation or series of calculations to suggest equipment and equipment placements in rooms, or use a machine learning model to suggest equipment and equipment placements models in rooms. [0679] Disclosed embodiments may further include calculating an area of the 2D floor plan. Calculating an area of the 2D floor plan may include estimating a two-dimensional areal extent or size of a room, corridor, area of interest, area of disinterest, or other space within the floor plan. An area calculation may be approximate, rather than exact. A calculation may include summing pixels contained within boundaries of the area, using an equation (e.g., length times width for rectangular or square spaces, one half base time height for triangular areas). A calculation may include summing estimates of sub-areas of an area (e.g., a sum of a rectangular sub-area plus a semi-circular sub-area). Calculating an area may be based on boundaries of a room. Calculating an area may include subtracting enclosed spaces (e.g., subtracting the area of a stairway for a room enclosing the stairway; calculating an area of a 2D floor plan may include using any suitable method of geometric or image analysis to estimate the area, further [0298]); and add the occupied area of the next furnishing item with a total area of the existing furnishing items (see Segev et al. para [0679], A calculation may include summing estimates of sub-areas of an area (e.g., a sum of a rectangular sub-area plus a semi-circular sub-area). Calculating an area may be based on boundaries of a room. [0298] Generative analysis may include performing an analysis of available space for an outputted solution. Performing an analysis of available space for the outputted solution may refer to determining an area and/or volume of the defined area of interest or disinterest through any of image analysis, semantic analysis, geometric analysis, and/or any combination thereof. For example, the analysis may identify space available for or associated with an installation of equipment based on a determined equipment placement location. Embodiments may include identifying an equipment placement location and a maximize areal extent of the equipment that may be sited at that location without intruding on other objects in a floor plan (e.g., a maximum couch size). Additionally or alternatively, an analysis of available space may indicate a distance between a selected equipment placement location and other objects in a floor plan (e.g., other equipment, architectural features, etc.). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.6 Regarding claims 6 and 17, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein the room is a part of a property (see Segev et al. para [0123] The floor plan may be associated with residential, commercial, or public buildings (e.g., offices, homes, schools, museums, transportation stations/airports, medical facilities, or other public structures), or any other structure. For example, the floor plan may depict a physical layout of one or more rooms in the building (e.g., a single room in a building, a suite of rooms, a whole floor of rooms, or an entire building of rooms. Further Yang et al. fig. 1, para [0103]), wherein to determine the functionality of the room based on the resident profile, the at least one processor is further configured to: determine a required number of bedrooms based on the resident profile (see Leikach et al. et al. para [0058-0060], Segev et al. para [0138], In some examples, accessing a floor plan may include loading a floor plan in memory based on user inputs defining the floor plan (e.g., based on a scan of a floor plan or a drawing of a floor plan via a graphical user interface). A floor plan may be accessed based on a search of floor plans using search criteria. For example, the at least one processor may identify a floor plan using a Boolean search method of textual data associated with a floor plan, such as identifiers of the floor plan. Alternatively or additionally, accessing a floor plan may include identifying a floor plan which satisfies a minimum or maximum size, has a specific number of rooms, has a date of creation that meets particular criteria, has a room of a particular type, is associated with a functional requirement, or which satisfies any other search condition); determine that the room is a bedroom when current number of bedrooms in the property does not meet the required number of bedrooms (see Leikach et al. et al. para [0063] [0063] The room use recommendation module 335 can rate the possible uses of the space. That way, when the room use recommendation module 335 offers alternatives, it can offer them in order of room use score. Room use score can be based on rooms currently in the home, e.g., a second guest room may not rate as highly as an exercise room for a single active person. Room use score can be also based on room attributes, such as whether the size of the room meets certain standards for its selected purpose. For example, a room of 10'.times.8' will likely be too tight for a master bedroom but may be ideal for a game room. Further, room use score can be based on general designer guidelines. One skilled in the art will recognize that room use scoring can be based on a vast number of variables.); and determine that the room is a non-bedroom when the current number of bedrooms in the property meets the required number of bedrooms (see Segev et al. para [0321] In some embodiments, floor plans may demarcate a plurality of spaces. Spaces within a floor plan may indicate rooms other areas within a building or structure. For example, a space may be an office, corridor, conference room, sleeping area, kitchen, balcony, staircase, or other area of a building or structure. An office may be a single room or plurality of rooms used for work. For example, an office may include a desk, chair, and computer. An office can be a private office for a single individual, a shared office for many individuals, or an open plan office. A corridor may be a passageway in a building from with openings such as doors leading into rooms. Corridors may be of various shapes, such as a narrow shape. For example, a corridor may be a long or short a hallway that connects rooms. A sleeping area may be an area where people sleep, including but not limited to, a bedroom, hospital room, hotel guestroom. A sleeping area may include a bed, mattress, futon, convertible sofa bed, or other furniture suitable for sleeping. Further Yang et al. para [0029]). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.7 As per claim 7, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein when the room is determined to be a non-bedroom, the at least one processor is further configured to: select the functionality of the room from a list of functionalities arranged in a predetermined order (see Segev et al. fig.4, 19 (1903), para [0321] In some embodiments, floor plans may demarcate a plurality of spaces. Spaces within a floor plan may indicate rooms other areas within a building or structure. For example, a space may be an office, corridor, conference room, sleeping area, kitchen, balcony, staircase, or other area of a building or structure. An office may be a single room or plurality of rooms used for work. For example, an office may include a desk, chair, and computer. An office can be a private office for a single individual, a shared office for many individuals, or an open plan office). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.8 As per claim 8, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein the required number of bedrooms is determined based on a a number of residents whose information is included in the resident profile and the ages of each resident included in the resident profile (see Segev et al. para [0138], Alternatively or additionally, accessing a floor plan may include identifying a floor plan which satisfies a minimum or maximum size, has a specific number of rooms, has a date of creation that meets particular criteria, has a room of a particular type, is associated with a functional requirement, or which satisfies any other search condition. Further Scott-Leikach et al. [0060] Questions indirectly related to the user preferences for the space may also be asked of the client 110. For example, the dynamic structured query set 315 may include questions regarding rooms already existing, functional uses desired, marital status, number and age of children, etc. By learning about the client 110, the information-collecting module 330 will be in a better position to offer alternative uses for a room (e.g., game room, workout room, formal dining room, etc.), to offer recommended materials, whether any artwork should be attached higher (so that children cannot reach it), to offer furnishing combinations, etc. Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.9 With regards to claim 9, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein to obtain a set of candidate furnishing items for the room, the at least one processor is further configured to: determine an occupant of the room (see Scott-Leikach et al. para [0060] Questions indirectly related to the user preferences for the space may also be asked of the client 110. For example, the dynamic structured query set 315 may include questions regarding rooms already existing, functional uses desired, marital status, number and age of children, etc. By learning about the client 110, the information-collecting module 330 will be in a better position to offer alternative uses for a room (e.g., game room, workout room, formal dining room, etc.), to offer recommended materials); determine characteristics and preferences of the occupant based on the resident profile (see Segev et al. [0178] In some embodiments of the present disclosure, the generative analysis may account for other variables, such as user inputs, when determining the equipment placement locations and technical specifications. The user inputs may include any constraints or other parameters in addition to the functional requirements that may inform the generative analysis results. In some embodiments, these inputs may be enforced strictly, in which the generative analysis will not consider solutions that do not satisfy the user inputs. In other embodiments, the inputs may be preferences, and the generative analysis may weigh solutions that satisfy the inputs more favorably than those that do not. In some embodiments, the user may define a preferred area for equipment placement, and identifying the first technical specification and first equipment placement location may be based on the preferred area. [0501], Receiving instruction to vary parameters associated with the equipment in a solution allows for customization of a solution based on user preferences while additionally providing improvements in efficiency, speed, accuracy, level of detail, design capabilities, and project scale when selecting equipment for use in buildings.); and assign priorities to the set of candidate furnishing items based on the characteristics and preferences of the occupant (see Segev et al. para [0165], the generative analysis may identify technical specifications and equipment placement locations based on multiple functional requirements. For example, the disclosed embodiments may include receiving a third functional requirement for the at least one first room, and generatively analyzing the at least one first room may further include using the third functional requirement to identify the first technical specification and first equipment placement location. In instances where the functional requirements conflict with each other, the generative analysis may include selecting which functional requirement to prioritize. For example, the generative analysis may include prioritizing a more stringent requirement, prioritizing based on a building hierarchy of building, levels, zones, and rooms in which smaller constituent groups such as rooms prioritize over large groups such as zones or levels, prioritizing functional requirements based on a predefined priority ranking, prioritizing user-defined functional requirements over default requirements, prioritizing based on codes, standards or regulations, or any other basis of prioritizing.). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.10 As per claims 10 and 18, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein the at least one processor is further configured to: determine that the furnishing plan does not meet one or more placement criteria and adjust at least one furnishing item in the placement item set until the furnishing plan meets the one or more placement criteria (see Segev et al. fig. 19, para [0726] In accordance with the present disclosure, systems, methods, and computer readable media may be provided for selecting equipment models and optimizing placement of equipment. Selecting equipment models may refer to a process of choosing and locating equipment in a floor plan in order to identify an equipment model that at least partially conforms to a functional requirement. Optimizing placement of equipment may include adjusting the location of equipment in order to increase conformity with a functional requirement. [0733], [0743], further see Yang et al. [0099] After the structures for remodeling are identified, the remodeling plan may be generated according to the remodeling information. In some embodiments, the remodeling plan may include an adjusted floor plan that reflects the remodeled property); and generate a new furnishing plan based on the adjusted placement item set (see Scott-Leikach et al. para [0041] The design assistant 125 then uses the room and preference information, attribute information of various furnishings and/or designer guidelines to generate automatically a set of recommended floor plans, combinations of recommended furnishings, design and furnishing alternatives, etc. The design assistant 125 then enables the client 110 to modify the floor plans and/or furnishings combinations, to make selections, to purchase furnishings, etc. In one embodiment, the design assistant 125 automatically modifies (e.g., adjusts, narrows, adds, subtracts, etc.) the recommended floor plans, the recommended combinations of furnishings, the recommended styles and patterns, etc. based on selections made and/or preference information further indicated by the client 110.). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.11 Regarding claims 11 and 19, the combined teachings of Segev et al. and Scott-Leikach et al. teach that wherein to adjust the at least one furnishing item in the placement item set, the at least one processor is further configured to: traverse the furnishing items in the placement item set in a reverse order (see Scott-Leikach et al. para [0074], At times, a client 110 may request a substitution for a design item that does not coordinate with other pieces, e.g., At times, a client 110 may modify an item such that it no longer coordinates with the given space, .., the substitution module 350 may politely inform the user via the user interface 310 that such modification would not be recommended and may provide reasoning…., and may inform the client 110 that the traditional lamp will detract from the modern feel of his home. In another embodiment, however, the substitution module 350 will allow the client 110 to make any substitution requested, regardless of coordination, thereby traversing the item in place, since the customer is always right. [0075] (6) Updating Furnishings Selections Based on Client Activity. [0076] As the client 110 makes selections and substitutions, the design engine 305 may update the available combinations and recommended substitutions. An example module for performing this feature is the results controller 355 of FIG. 3B. For example, if the client 110 selects a particular sofa, fabric and color, the results controller 355 may discard all design items that do not coordinate with this particular sofa, fabric and color. This technique would assist the client 110 by narrowing the scope of his search. [0077] The database manager 320 enables the designer to add additional furnishings to the furnishings database 130, to modify or enter attribute information 205, to modify or enter grouping information 210, to remove furnishings from the furnishings database, etc.); and replace at least one furnishing item with a respective substitute furnishing item or delete the furnishing item until the furnishing plan meets the one or more placement criteria (see Scott-Leikach et al. para [0075] (6) Updating Furnishings Selections Based On Client Activity. [0076] As the client 110 makes selections and substitutions, the design engine 305 may update the available combinations and recommended substitutions. An example module for performing this feature is the results controller 355 of FIG. 3B. For example, if the client 110 selects a particular sofa, fabric and color, the results controller 355 may discard all design items that do not coordinate with this particular sofa, fabric and color. This technique would assist the client 110 by narrowing the scope of his search. [0077] The database manager 320 enables the designer to add additional furnishings to the furnishings database 130, to modify or enter attribute information 205, to modify or enter grouping information 210, to remove furnishings from the furnishings database, etc., further Segev et al. para [0378]). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
6.12 As per claim 12, the combined teachings of Segev et al. and Scott-Leikach et al. teach that where to determine that the furnishing plan does not meet one or more placement criteria, the at least one processor is further configured to: calculate a matching score between the furnishing plan and the floor plan (see Segevet al. para [0185], Some embodiments may further include displaying a score evaluating the equipment placement location. In this context a score may indicate a numeric appraisal of the performance of one or more pieces of equipment at a certain location. The score may indicate a degree of conformance to one or more functional requirements, such as an amount of coverage of the equipment, a number of functional requirements that are conformed to, or other means of evaluating conformance to the functional requirements. The score may also take into account other factors, such as whether user-defined constraints were met (e.g., preferred placement areas, etc.), equipment cost, equipment availability, equipment size, equipment noise, energy consumption, or other parameters that may indicate the desirability of the equipment placement location. The score may be represented as a number on a scale (e.g., a score from 1-100, a score from 1-10, etc. [0378], The series of simulations may be run iteratively until a solution conforms to one or more functional requirements, for example. Simulations may be associated with scores reflecting a degree of conformance to functional requirements, and generative analysis may be performed until a simulation score reaches a predetermined threshold. The series of simulations may be generated based on user input indicating a number of simulations, a desired simulation performance score), a text-based grading scale (e.g., B+, Excellent, etc.), a percentage (e.g., percentage of conformance, percentage of coverage, etc.), or any other representation of how well the equipment is placed.); and compare the matching score with a predetermined threshold (see Segev et al. para [0057], The output may be an electronic file, database, and/or index containing information such as placement information and/or technical specifications. The comparison may include an interactive list of auxiliary equipment technical specifications illustrating the differences and similarities among one or more pieces of auxiliary equipment. [0778], In some embodiments, the confidence levels may be compared to a confidence threshold to determine appropriate actions. As used herein, a confidence threshold may refer to a minimum confidence level associated with a determined semantic designation (or maximum confidence level, depending on how the value is defined). For example, the semantic enrichment process may not display semantic designations with a confidence rating is below 60%, or any other suitable threshold.). Therefore, it would have been obvious at the time of filing of the applicant’s invention to combine the method of Scott-Leikach et al. with that of Segev et al. because Scott-Leikach et al. teaches a model that maximizes efficiency (para [0082]).
Conclusion
7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
7.1 Cini (USPG_PUB No. 2020/0242849) teaches a system for rendering and modifying three-dimensional models for interior design includes a modeling device configured to receive a current design of an interior space, generate a data structure representing the interior space, by populating a plurality of attributes of the data structure and generating a first three-dimensional model of a first portion of the interior space based on the current design.
8 Claims 1-20 are rejected and THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDRE PIERRE-LOUIS whose telephone number is (571)272-8636. The examiner can normally be reached M-F 9:00 AM-5:00 PM.
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/ANDRE PIERRE LOUIS/Primary Patent Examiner, Art Unit 2187 May 12, 2026
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