DETAILED ACTION
This action is in response to communications filed 11/10/2025. Claims 1-2, 5, 9, 11-12, 14-16, and 18-19 have been amended. No claims have been cancelled, nor added. Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The independent claims have been amended to incorporate additional matter not previously claimed. Particular citations to the specification were not provided to demonstrate where the support for the amendments originated. After evaluation of the originally-filed disclosure, sufficient support was found in the specification for the amended claims in at least ¶10, ¶32, and ¶34 such that it is apparent to the examiner that the applicant had possession of the claimed invention at the time of filing. No new matter has been added.
Response to Arguments
Specification
The specification has been amended in response to the objections set forth in the previous action. The amendments sufficiently overcome the objections and Examiner concurs that no new matter has been introduced by the amendments. The objections have been withdrawn.
Claim Objections
Claims 14, 15, 16, and 18 have been amended in response to the objections set forth in the previous action. The amendments sufficiently overcome the objections. The objections have been withdrawn.
Rejections under 35 U.S.C. § 112
Applicant has amended the claims to clarify the use of different models in response to the rejections set forth under 35 U.S.C. § 112.
For the issues previously noted, the amendment appears to provide clarity; however, the amendment has introduced additional ambiguities as stated in the rejections of this action under 35 U.S.C. § 112(b). Accordingly, the claims remain rejected under 35 U.S.C. § 112.
Rejections under 35 U.S.C. § 101
The claims have been amended in response to the previously set forth rejections under 35 U.S.C. § 101. The applicant argues that the claims “are actually directed to the general concept of determining potential traffic patterns within an interior physical space and determining a furnishing layout for the interior physical space, based on the potential traffic patterns”. This statement is an admission to the claims being directed towards non-statutory subject matter because determining traffic pattens and furnishing layouts are the steps considered by the examiner as abstract ideas of mental processes. A human being is capable of observing an interior physical space and subsequently making a judgment as to potential traffic patterns in that space. Likewise, a human being is capable of evaluating the determining potential traffic patterns so as to make an additional judgement of a furnishing layout for the interior physical space.
The applicant further argues that the determining potential traffic patterns, as per the amended claims 1 and 11, is performed by a first machine learning model, wherein inputs are provided to the machine learning model and outputs are received from the machine learning model. The applicant further argues that determining a furnishing layout for the interior physical space, based on the traffic patterns, as per the amended claims 1 and 11, is performed by a second machine learning model, wherein inputs are provided to the machine learning model and outputs are received from the machine learning model. Accordingly, as argued by the applicant, the claim limitations cannot be deemed to fall into the category of mental process.
Applicant’s arguments have been considered but are not persuasive. The machine learning model claimed is understood by the examiner to be a generic computing component recited at a high level of generality that is used to execute the abstract idea. The providing of input data and the receiving of output data have been identified as additional elements which are insignificant extra solution activity- particularly mere data gathering and data outputting. Under broadest reasonable interpretation and when read in light of the specification, providing input data and receiving output data encompass transmitting and receiving data over a network. These activities have been found by the courts to be well understood, routine, and conventional activities when claimed in a merely generic manner, such as in the instant claims. The courts have found that appending activities that are considered well understood, routine, and conventional activities to the judicial exception and using a computer or other machinery as a tool to perform the abstract idea are not sufficient to integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. As such, the claims continue to recite processes, except for the recitation of generic computing components in conjunction with well understood, routine, and conventional computing functions, that cover performance of the human mind.
The applicant further argues that additional elements are recited in the claims and that that additional elements provide improvements to the technical field of automating the design of furnishing layouts for interior physical spaces. While the examiner recognizes the presence of the additional elements, there are no additional elements which provide an inventive concept so as to demonstrate an improvement in the technical field, as described in the rejection of this office action. Per MPEP 2106.05(a)(II), “To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology”. The inventive concept appears to be rooted in the series of steps which can be construed as abstract ideas- that is the determination of the traffic patterns and the subsequent determination of a furniture layout using the determined traffic patterns. Per MPEP 2106.05(a), “It is important to note, the judicial exception alone cannot provide the improvement.”. The machine learning models recited in the claims are recited at such a high level of generality that they are considered general purpose computing components that apply the judicial exception. In order for the machine learning models to add significantly more to the claim, they must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. The determinations of the traffic patterns and furnishing layouts are processes that can be performed in the human mind or using assistive physical aids. The employment of the models appears to only be used so as to achieve the results more quickly and in an automatic way.
The applicant further argues that the transformation of a first model of an interior physical space into a furnishing layout for the same space is reflected in the claims. In order to determine that a transformation of an article has occurred, the nature of the article must be considered. The nature of the article being transformed is abstract in itself- that is something which can be visualized in the human mind or using assistive physical aids such as pen and paper. Per MPEP 2106.05(c), “An "article" includes a physical object or substance. The physical object or substance must be particular, meaning it can be specifically identified. "Transformation" of an article means that the "article" has changed to a different state or thing. Changing to a different state or thing usually means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which thoughts or human based actions are "changed" are not considered an eligible transformation”.
Accordingly, for the reasons stated in this response, in conjunction with the updated rejection of this office action, the claims remain rejected under 35 U.S.C. § 101.
Rejections under 35 U.S.C. § 103
Applicant has amended the independent claims in response to the rejection under 35 U.S.C. § 103 under Whitney in view of Wang. Applicant argues that the proposed combination of references fails to teach or suggest all of the claimed features of the amended claims, particularly (1) providing the first model to a first machine learning model to perform an analysis to determine potential traffic patterns and (2) providing the activity map to a second machine learning model to determine a furnishing layout.
Applicant argues that with respect to (1), Whitney discloses scoring space models which is not the same or similar as the claimed feature.
Applicant argument has been considered but is not persuasive. Whitney discloses the providing of a geometry model and space program data into a step of generating space models. The generation of space models is described as being performed by a machine learning engine, thereby indicating that a first machine learning model receives the geometry model and the space program data. The machine learning models used by the generative design computing platform are described as being configured to determine insights regarding physical spaces, thereby indicating that the machine learning model is used to perform an analysis in order to generate the space model. The space model generated by the machine learning model is further analyzed to identify predicted traffic patterns. Accordingly, all the features of (1) are disclosed by Whitney alone.
Applicant further argues that with respect to (2), Whitney discloses selecting furniture models based on scores, which is not the same or similar to the claimed feature of providing the activity map to a second machine learning model to determine a furnishing layout.
Applicant argument has been considered but is not persuasive. Whitney discloses the utilization of multiple machine learning models for analyses. Whitney further discloses that the positioning of furnishing placement can be generated in a furniture model which is part of the generated space model, generated by the machine learning model. Whitney further discloses that the machine learning models can be updated continuously using other information. Whitney is not relied upon to disclose an activity map. Wang is relied on to disclose an activity map, wherein Wang discloses the generation of an activity map indicating activity densities in each room. Wang further discloses feeding the generated activity map into a GAN (which is a machine learning model) to produce optimal floorplans. Wang suggests that these insights are important in the architectural design process. Wang further suggest that user manipulation of furniture arrangement may affect the human activity maps, thereby suggesting a relationship exists between the activity map and the placement of furniture. By integrating the activity map and optimal floor plan generation of Wang into the methodology of Whitney, the optimal floor plans of Wang could reasonably be expected to be used for subsequent optimal determination of furniture placement, such as in Whitney. Accordingly, the combination would be obvious to one having skill in the art and the combination of Whitney and Wang fully discloses the features of (2).
For the reasons disclosed in this response, in conjunction with the rejection of this action, the claims remain rejected under 35 U.S.C. § 103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “high” in claim 1 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The quantity of volume of occupant traffic is rendered indefinite by the use of the term high.
The term “few” in claim 1 is a relative term which renders the claim indefinite. The term “few” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The quantity of items of furniture is rendered indefinite by the use of the term few.
Claim 1 recites “the model” in lines 5-6 and line 11. It is unclear as to which element “the model” refers to. It appears as though the applicant intends to refer to “the first model” as “the model”, based on the first recitation. However, the second recitation creates ambiguity of the element because two models have been introduced- “a first model” and “a first machine learning model”. Examiner recommends revising the claim language to definitely recite that “the model” refers to “the first model”.
The dependent claims 2-10 incorporate the deficiencies of claim 1 and are thus rejected under the same rationale.
The term “high” in claim 11 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The quantity of volume of occupant traffic is rendered indefinite by the use of the term high.
The term “few” in claim 11 is a relative term which renders the claim indefinite. The term “few” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The quantity of items of furniture is rendered indefinite by the use of the term few.
Claim 11 recites “the model” in lines 4-5 and line 10. It is unclear as to which element “the model” refers to. It appears as though the applicant intends to refer to “the first model” as “the model”, based on the first recitation. However, the second recitation creates ambiguity of the element because two models have been introduced- “a first model” and “a machine learning model”. Examiner recommends revising the claim language to definitely recite that “the model” refers to “the first model”.
The dependent claims 12-20 incorporate the deficiencies of claim 11 and are thus rejected under the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong 1 - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2A Prong 2 - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More.
As noted in the MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are further evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-10 are directed to a system which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
perform an analysis of the model to determine potential traffic patterns within the interior space characterized by the first model; The claim limitation can be reasonably read to entail observing and evaluating the model characterizing the interior physical space to make a judgement regarding potential traffic patterns within the interior space. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
based on the analysis, generate an activity map that includes a second model characterizing the interior physical space, This claim limitation can reasonably be ready to entail making a judgement of the analysis so as to determine an activity map. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by drawing a picture of an interior physical space representation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
determine a furnishing layout for the interior physical space The claim limitation can be reasonably read to entail making a judgement regarding a furnishing layout for the interior physical space. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
one or more physical processor configured by machine-readable instructions to:- This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) because the limitation amounts to invoking computers as a tool to perform the recited mental process.
obtain a first model characterizing an interior physical space, This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
wherein the model includes characterizations of features included in the interior physical space, wherein the features include one or more walls and/or windows in the interior physical space; This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
obtain a number of occupants utilizing the interior physical space; This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
provide the first model and the number of occupants This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
as inputs to a first machine learning model configured to This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components or other machinery to perform the mental process
receive, from the first machine learning model, the potential traffic patterns within the interior physical space as determined This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
wherein individual potential traffic patterns characterize likely occupant locomotion within the interior physical space, wherein the likely occupant locomotion is defined by an area within the interior physical space where the locomotion occurs and a frequency at which the locomotion occurs This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture; This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
provide the activity map This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
as input to a second machine learning model configured to This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of computers or other machinery to perform the abstract idea
receive, from the second machine learning model, the furnishing layout for the interior physical space This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting
wherein the furnishing layout includes representations of items of furniture and positions within the interior physical space associated with the items of furniture; and This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
output the furnishing layout. This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea and merely reciting the words “apply it” or equivalent (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); generally linking the use of the judicial exception to a particular field of use or technological environment (Field of Use and Technological Environment (MPEP 2106.05(h)) ); and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
obtain a first model characterizing an interior physical space, This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
obtain a number of occupants utilizing the interior physical space; This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
provide the first model and the number of occupants This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
receive, from the first machine learning model, the potential traffic patterns within the interior physical space as determined This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
provide the activity map This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
receive, from the second machine learning model, the furnishing layout for the interior physical space This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
output the furnishing layout. This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and reciting the words “apply it” or equivalent; and generally linking the use of the judicial exception to a particular technological environment and field of use does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 11:
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
perform an analysis of the model to determine potential traffic patterns within the interior space characterized by the first model; The claim limitation can be reasonably read to entail observing and evaluating the model characterizing the interior physical space to make a judgement regarding potential traffic patterns within the interior space. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
based on the analysis, generating an activity map that includes a second model characterizing the interior physical space, This claim limitation can reasonably be ready to entail making a judgement of the analysis so as to determine an activity map. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by drawing a picture of an interior physical space representation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
determine a furnishing layout for the interior physical space The claim limitation can be reasonably read to entail making a judgement regarding a furnishing layout for the interior physical space. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
obtaining a first model characterizing an interior physical space, This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
wherein the model includes characterizations of features included in the interior physical space, wherein the features include one or more walls and/or windows in the interior physical space; This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
obtaining a number of occupants utilizing the interior physical space; This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
providing the first model and the number of occupants This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
as inputs to a machine learning model configured to This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of generic computing components or other machinery to perform the mental process
receiving, from the machine learning model, the potential traffic patterns within the interior physical space as determined, This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering.
wherein individual potential traffic patterns characterize likely occupant locomotion within the interior physical space, wherein the likely occupant locomotion is defined by an area within the interior physical space where the locomotion occurs and a frequency t which the locomotion occurs This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture; This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
providing the activity map This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering
as input to a second machine learning model configured to This claim limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of computers or other machinery to perform the abstract idea
receiving, from the second machine learning model, the furnishing layout for the interior physical space This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting
wherein the furnishing layout includes representations of items of furniture and positions within the interior physical space associated with the items of furniture; and This claim limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h))
outputting the furnishing layout. This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea and merely reciting the words “apply it” or equivalent (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); generally linking the use of the judicial exception to a particular field of use or technological environment (Field of Use and Technological Environment (MPEP 2106.05(h)) ); and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional elements do not appear to integrate the judicial exception into a practical application.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
obtaining a first model characterizing an interior physical space, This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
obtaining a number of occupants utilizing the interior physical space; This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
providing the first model and the number of occupants This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
receiving, from the machine learning model, the potential traffic patterns within the interior physical space as determined, This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
providing the activity map This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
receiving, from the second machine learning model, the furnishing layout for the interior physical space This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
outputting the furnishing layout. This claim limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. Under broadest reasonable interpretation, the limitation encompasses receiving data over a network, which the courts have recognized as a computer function that is well-understood, routine, and conventional functions when claimed in a merely generic manner.
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and reciting the words “apply it” or equivalent; and generally linking the use of the judicial exception to a particular technological environment and field of use does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Dependent claims
Claim 2
Step 1: Regarding dependent claim 2, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 2 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 3 additionally recites the limitation wherein the likely occupant
locomotion is further defined by at least one type of activity, a length of time in which the
activity is performed, and/or a number of occupants associated with the activity. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element amounts to limiting the use of the judicial exception to a particular field of use. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular field of use or technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101
Claim 3
Step 1: Regarding dependent claim 3, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 3 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 3 additionally recites the limitation wherein the potential traffic patterns are determined based on predetermined sets of human-environment interactions, wherein human environment interactions specify types of occupant behaviors that have the potential to occur in interior physical spaces. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element amounts to limiting the use of the judicial exception to a particular field of use. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular field of use or technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4
Step 1: Regarding dependent claim 4, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 4 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 4 additionally recites the limitation wherein the furnishing layout is a two-dimensional model characterizing a top-down view of the interior physical space or a three-dimensional model. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element amounts to limiting the use of the judicial exception to a particular field of use. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular field of use or technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 5 additionally recites the limitation wherein features of the interior physical space further include one or more rooms, hallways, doors, floors, and/or stairs. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element amounts to limiting the use of the judicial exception to a particular field of use. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular field of use or technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 6 additionally recites the limitation wherein outputting the furnishing layout includes presenting the furnishing layout to a user via a user interface. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting because the way in which the data is output to a user interface is recited at a high level of generality. The claim also recites the limitation wherein the user interface includes one or more user interface elements corresponding to individual items of furniture included in the furnishing layout. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element generally limits the use of the judicial exception to a particular technological environment. The courts have ruled appending insignificant extra solution activity to the judicial exception and generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: As stated previously, the element wherein outputting the furnishing layout includes presenting the furnishing layout to a user via a user interface was found to be the insignificant extra solution activity of mere data outputting. Presenting furnishing layout data to a user interface, when claimed in a merely generic manner, is well-understood, routine, and conventional activity in the art. This assertion is supported by the evidence noted in the specification which is an express statement demonstrating the well-understood, routine, and conventional nature of the additional element. The specification states “Methods for providing users with furnishing layouts are known.” in paragraph [0002]. The courts have found that limitations that amount to appending well-understood, routine, and conventional activity and generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 7 additionally recites the limitations wherein the user interface elements are selectable by the user, and wherein selection of the user interface elements facilitates the user purchasing the items of furniture corresponding to the selected user interface elements. These limitations have been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because they merely limit the use of the judicial exception to a particular technological environment of having a user interface with these features. The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional elements viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular technological environment or field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8
Step 1: Regarding dependent claim 8, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 8 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 8 additionally recites the limitation wherein presenting the furnishing layout to a user includes presenting a first-person perspective of the interior physical space including the representations of the items of furniture, wherein the presentation allows the user to navigate through the interior physical space in the first-person perspective. This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data outputting. The courts have ruled appending insignificant extra solution activity to the judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The limitation wherein presenting the furnishing layout to a user includes presenting a first-person perspective of the interior physical space including the representations of the items of furniture, wherein the presentation allows the user to navigate through the interior physical space in the first-person perspective was identified as insignificant extra solution activity, as stated previously, and requires further analysis to determine if the limitation is beyond activity that is well-understood, routine, and conventional within the art. Presenting a visual display using first-person perspective and including an interactive means for the presentation of the data is a well-understood, routine, and conventional way of presenting data to a user. This is supported by evidence noted in a publication by Wroblewski (Wroblewski, L., “Enhancing User Interaction With First Person User Interface”, September 21, 2009, SmashingMagazine.com), hereinafter referred to as Wroblewski that demonstrates the well-understood, routine, and conventional nature of the element. Wroblewski expressly states that “Not only can first person user interfaces help us move through the world, they can also help us understand it. The information that applications like Nearest Tube overlay on the World can be thought of as ‘augmenting’ our view of reality. Augmented reality applications are a popular form of first person interfaces that enhance the real world with information not visible to the naked eye. These applications present user interface elements on top of images of the real world using a camera or heads up display”. Wroblewski expressly states that first person user interfaces can help navigate the world and provides multiple examples of applications that have the functionality, indicating that it’s well-understood in the art. The explicit statement that augmented reality is a popular form of first person interfaces further supports that the functionality is well-known because of its popularity. The courts have found that limitations that amount to insignificant extra solution activity that has found to be well-understood, routine, and conventional is not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 9
Step 1: Regarding dependent claim 9, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 9 additionally recites the limitation wherein determining the potential traffic patterns is based on the number of occupants within the interior physical space, such that a first interior physical space having a first number of occupants is determined to have potential traffic patterns that are different from the first interior physical space having a second number of occupants, wherein the first number of occupants is different from the second number of occupants, which can reasonably be read to entail observing and evaluating the number of occupants within the interior physical space to inform a judgment as to determining potential traffic patterns. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 9 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10
Step 1: Regarding dependent claim 10, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 10 does not include any additional recitations of judicial exceptions.
Step 2A Prong 2: Claim 10 additionally recites the limitation wherein the activity map is a two-dimensional top-down view characterizing the interior physical space and the determined potential traffic patterns. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)) because the element amounts to limiting the use of the judicial exception to a particular field of use. The courts have ruled generally linking the use of a judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the use of the judicial exception to a particular field of use or technological environment are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
Step 1: Regarding dependent claim 12, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 12 recites limitations that are substantially similar to that recited in claim 2 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 2 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13
Step 1: Regarding dependent claim 13, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 13 recites limitations that are substantially similar to that recited in claim 3 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 3 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14
Step 1: Regarding dependent claim 14, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 14 recites limitations that are substantially similar to that recited in claim 4 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 4 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 15
Step 1: Regarding dependent claim 15, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 15 recites limitations that are substantially similar to that recited in claim 5 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 5 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 16
Step 1: Regarding dependent claim 16, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 16 recites limitations that are substantially similar to that recited in claim 6 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 6 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 17
Step 1: Regarding dependent claim 17, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 17 recites limitations that are substantially similar to that recited in claim 7 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 7 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 18
Step 1: Regarding dependent claim 18, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 18 recites limitations that are substantially similar to that recited in claim 8 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 8 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 19
Step 1: Regarding dependent claim 19, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 19 recites limitations that are substantially similar to that recited in claim 9 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 9 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 20
Step 1: Regarding dependent claim 20, the judicial exception of independent claim 11 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1, Step 2A Prong 2 & Step 2B: Claim 120 recites limitations that are substantially similar to that recited in claim 10 but with respect to an alternative independent claim. Therefore, the claim is rejected under the same rationale as provided for claim 10 but with respect for claim 11.
This claim is not eligible subject matter under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 10-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Whitney et al (US Patent Publication No. US 20210397757 A1), hereinafter referred to as Whitney in view of Wang et al (Wang, S., Zeng, W., Chen, X., Ye, Y., Qiao, Y., Fu, C., “ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design”, November 5, 2021, Arxiv, https://doi.org/10.48550/arXiv.2111.03545), hereinafter referred to as Wang.
Regarding claim 1, Whitney discloses (except the limitations surrounded by brackets ([[..]])) A system A computing environment (system) is described as containing a generative design computing platform ((Whitney, ¶38) "For example, computing environment 100 may include a generative design computing platform 110, an internal data server 120, an external data server 130, a first designer user computing device 140, a second designer user computing device 150, a client user computing device 160, and a network 170."). configured to determine potential traffic patterns within an interior physical space The generative design computing platform identifies predicted traffic patterns in a physical space and generates a score for the space model ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters, and/or other space syntax factors)."). The physical space may include interior features, indicating an interior physical space ((Whitney, ¶48) "Interior walls data 180d may include information defining the locations of and/or other features of one or more interior walls within such buildings and/or other structures, and interior features data 180e may include information defining other interior features (e.g., windows, heating-ventilation-air-conditioning (HVAC) systems and elements, interior columns, restrooms, vertical circulation, mechanical/electrical rooms, closets, etc.)."). and determine a furnishing layout for the interior physical space based on the potential traffic patterns, the system comprising: A score is generated for each space model (based on traffic patterns as given by example stated previously in ¶75), wherein the space model contains a furniture model (See Figure 1F), thereby indicating a determination of a furniture model. The furniture model includes information regarding positioning of specific pieces of furniture at specific points in a physical space ((Whitney, ¶50) "Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space.")
one or more physical processor configured by machine-readable instructions to: A processor(s) is disclosed that executes stored instructions to control operations of the generative design computing platform ((Whitney, ¶46) "In one or more arrangements, processor(s) 111 may control operations of generative design computing platform 110. Memory(s) 112 may store instructions that, when executed by processor(s) 111, cause generative design computing platform 110 to perform one or more of the functions described herein.").
obtain a first model characterizing an interior physical space, wherein the model includes characterizations of features included in the interior physical space, wherein the features include one or more walls and/or windows in the interior physical space; A geometry model is obtained from a database ((Whitney, ¶61) " At step 206, the generative design computing platform 110 may load a first geometry model from a database storing one or more geometry models (e.g., stored in the memory 112 or at the internal data server 120)."). The geometry model includes information regarding features of various space model design elements (characterizations of features) ((Whitney, ¶61) "In loading the first geometry model, the generative design computing platform 110 may load information defining a first plurality of design rules that may be part of and/or otherwise associated with the first geometry model, such as design rules that control and/or affect the quantities, locations, sizes, and/or other features of various space model design elements, such as blocks, settings, furniture, and/or other elements (e.g., number of blocks, settings, furniture, and/or other features ; types of blocks, settings, furniture, and/or other features ; locations of blocks, settings, furniture, and/or other feature; locations of hallways; and/or other features)."). The geometry model includes rules that define ideal locations of block level features ((Whitney, ¶65) " For instance, the first geometry model may include a plurality of design rules, constraints, and/or metrics that define the ideal locations and/or other properties of block-level features."). The block level features include interior windows and walls ((Whitney, ¶48) " Referring to FIG. lC, an example block model 180 is depicted. Block model 180 may, for instance, include lot dimension data 180a, exterior walls data 180b, exterior features data 180c, interior walls data 180d, interior features data 180e, neighborhood data 180/(which may e.g., include department information, team information, group information, and/or other information), hallway data 180g (which may, e.g., include circulation data regarding hallways, aisles, corridors, stairs, elevators, and/or other areas used to access spaces in a building), and other block data 180h. Lot dimension data 180a may, for instance, include information defining one or more dimensions and/or other features of a lot or other parcel of land where one or more buildings and/or other structures may be located. Exterior walls data 180b may include information defining the locations of and/or other features of one or more exterior walls of such buildings and/or other structures, and exterior features data 180c may include information defining other exterior features (e.g., windows, landscaping, exterior columns, decorations, etc.) of such buildings and/or other structures. Interior walls data 180d may include information defining the locations of and/or other features of one or more interior walls within such buildings and/or other structures, and interior features data 180e may include information defining other interior features (e.g., windows, heating ventilation- air-conditioning (HV AC) systems and elements, interior columns, restrooms, vertical circulation, mechanical/electrical rooms, closets, etc.). ")
obtain a number of occupants utilizing the interior physical space; Space program data is received which includes receiving organization details for an organization that will occupy the physical space, and wherein the organization details include number of employees ((Whitney, ¶59) "Additionally or alternatively, in receiving the first space program data, the generative design computing platform 110 may receive organization details of an organization that does or will occupy the first physical space, such as information indicating a total number of employees of the organization, projected growth rate, organizational breakdown ( e.g., departments, teams, team compositions, relations between teams and/or departments). ")
provide the first model and the number of occupants as inputs to a first machine learning model configured to perform an analysis of the model The generative design computing platform feeds in the received space program data (which comprises the number of occupants of the space, as stated above) and the loaded geometry model into the step of generating space models (207) (See figure 2B) ((Whitney, ¶62) " At step 207, the generative design computing platform 110 may generate a first plurality of space models for the first physical space based on the first space program data and the first geometry model "). Generation of space models is described as being performed using a machine learning system/engine ((Whitney, ¶102) " FIG. 7 depicts an illustrative method for generating space models and geometry models using a machine learning system with multi-platform interfaces in accordance with one or more example embodiments. "); ((Whitney, ¶103) " At step 770, the computing platform may update a machine learning engine used to generate the geometry models and/or the space models. "). The generation of space models considers the geometry model ((Whitney, ¶102) " At step 735, the computing platform may generate one or more space models based on the one or more geometry models and the space program data. "). The machine learning models used by the generative design computing platform are described as being configured to determine insights regarding the physical spaces ((Whitney, ¶53) " different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces. ") to determine potential traffic patterns within the interior space characterized by the first model; The generated space models are assessed and quantified for features such as the identifying of predicted traffic patterns in the physical space((Whitney, ¶72) " At step 208, the generative design computing platform 110 may score the first plurality of space models based on the first geometry model "); ((Whitney, ¶75) " As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters and/or other space syntax factors). For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion). After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). ")
receive, from the first machine learning model, the potential traffic patterns within the interior physical space as determined, wherein individual potential traffic patterns characterize likely occupant locomotion within the interior physical space, A space model is assessed for predicted traffic patterns in the physical space, wherein how many turns to move from one location to another (characterization of likely occupant locomotion) is considered ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters and/or other space syntax factors). The patterns are identified and predicted as an exemplary assessment of features, indicating the receiving of such patterns ((Whitney, ¶75) " After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). The generative design computing platform 110 then may, for instance, compute an aggregate score for each space model of the first plurality of space models by computing an average of the metric scores determined for the particular space model. "). Machine learning models are described as the mechanism by which analyses of features are performed, thereby indicating that the assessment of the feature of traffic patterns is performed by a machine learning model ((Whitney, ¶53) "In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces. ") wherein the likely occupant locomotion is defined by an area within the interior physical space where the locomotion occurs and a frequency at which the locomotion occurs; Predicting traffic patterns (which include how many turns to move from one location to another as occupant locomotion) is described as considering short distances (an area within the interior physical space) at frequently visited spaces (a frequency at which the locomotion occurs) ((Whitney , ¶75) "For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion).")
based on the analysis, [[generate an activity map that includes a second model characterizing the interior physical space, wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture;]] An assessment and quantification of features such as traffic patterns is subsequently used for the scoring of space models ((Whitney, ¶75) " After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). The generative design computing platform 110 then may, for instance, compute an aggregate score for each space model of the first plurality of space models by computing an average of the metric scores determined for the particular space model. ")
[[provide the activity map as input to]] a second machine learning model configured to determine a furnishing layout for the interior physical space; Multiple machine learning models can be trained from drawing models that correspond to space designs to be configured to distinguish design features, including relationships related to other space features, thereby indicating a second machine learning model configured to evaluate location of space features ((Whitney, ¶53) “Referring to FIG. 2A, at step 201, the generative design computing platform 110 may receive one or more drawing models from the internal data server 120 and/or the external data server 130, which may correspond to different space designs ( e.g., floor plans, furniture location information, best-in-class designs, or the like). For example, in receiving the one or more drawing models, the generative design computing platform 110 may receive one or more two-dimensional computer-aided design (CAD) models that may be used to train one or more machine learning models to identify design parameters and/or to distinguish between different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces."). Features of the space include features of furniture within the space including the positioning ((Whitney, ¶50) "Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space."). The machine learning engine is described as generating space models, which include furniture models ((Whitney, ¶103) "At step 770, the computing platform may update a machine learning engine used to generate the geometry models and/or the space models."); (see also Figure 1F showing the furniture model within the space model). Furniture models contain information indicating the positioning of furniture ((Whitney, ¶50) "Referring to FIG. lE, an example furniture model 184 is depicted. Furniture model 184 may, for instance, include a block model 184a, a settings model 184b, furniture identification data 184c, furniture location data 184d, and other furniture data 184e. [[…]] Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information. Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space."). Other data may be used as input to update the machine learning models ((Whitney, ¶99) "In addition, the generative design computing platform 110 may continuously update its machine learning engine 112d based on user input and/or other data received by generative design computing platform 110, so as to continuously and automatically optimize the generation of geometry models and space models.")
receive, from the second machine learning model, the furnishing layout for the interior physical space, wherein the furnishing layout includes representations of items of furniture and positions within the interior physical space associated with the items of furniture; and Space models are generated and stored, wherein the space model includes the positioning information of furniture, as stated above ((Whitney, ¶102) "At step 740, the computing platform may store the one or more space models. At step 745, the computing platform may score the one or more space models, and rank the one or more space models based on the scores."). The furniture model includes furniture identification data and furniture location data, indicating representations of items of furniture and positions within the space ((Whitney, ¶50) "Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information"); ((Whitney, Claim 10) "The computing platform of claim 9, wherein each block model of the plurality of block models indicates potential locations of different neighborhoods in the physical space, each settings model of the plurality of settings models indicates potential locations of different work settings in the physical space, and each furniture model of the plurality of furniture models indicates potential locations of different furniture items in the physical space."). The space model is generated by a machine learning engine, as stated previously, thereby indicating that the generated space model is received from the machine learning model ((Whitney, ¶86) " For instance, to the extent that a user manually refined a layout of the space model and/or manually optimized one or more parameters underlying the space model, such refinements and/or optimizations may be captured by the generative design computing platform 110 and used to retrain the machine learning engine, so that such refinements and/or optimizations may be automatically implemented by the generative design computing platform 110 when generating future space models.")
output the furnishing layout. The space model can be exported ((Whitney, ¶92) "As illustrated in greater detail below, in processing such a request, the generative design computing platform 110 may export data in various different formats, using one or more of the multi-platform interoperability features described herein. In particular, and as described above (e.g., with respect to step 207), the generative design computing platform 110 may generate each space model of a plurality of space models in a plurality of different data formats (e.g., in a CAD format, a CET format, a Revit format, a Sketch Up format, and/or one or more other formats), and this multi-format generation may expedite the process by which data may be exported in different formats."). The space model contains the furniture model, as depicted in Whitney Fig. 1F. The furniture model contains furniture location data within a physical space, as stated previously ((Whitney, ¶50) "Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information").
Whitney does not disclose generating an activity map based on the analysis used to determine potential traffic patterns; however, Wang discloses based on a simulation of theoretical traffic patterns, to generate an activity map that includes a second model characterizing the interior physical space, wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture; Human-activity maps are generated for a given interior space, where the building is characterized by a boundary ((Wang, Page 11, Col 2, ¶2-4) "Our approach can generate diverse floorplans by using different human-activity maps as guidance, which are prepared either by the automatic approach with a generative network or by the semi-automatic approach with an interactive interface. Figure 13 presents diverse floorplans produced by the automatic approach. The generative network is able to produce stochastic human-activity maps using dropouts added to the first three layers of the decoder. Here, the building boundaries are added as a reference. Note that the generated human-activity maps are subtly different, yet the floorplans are rather diverse with different room numbers and types. However, the floorplan designs may not be ideal, such as bad designs without balconies, a small master room, and two bathrooms next to each other. The semi-automatic approach can overcome the deficiency by allowing users to manipulate the human-activity maps on demand. Figure 14 presents diverse floorplans that can be derived from a single building boundary using the interactive interface (see Figure 6). For the same building boundary, the generated floorplans possess different properties, including the number of rooms, room positions and sizes, room types, and room adjacency relations, and they are different from the ground truths from RPLAN. "). The activity map depicts a heatmap corresponding to high activity density ((Wang, Page 8, Col 2, ¶2) "The output of ActFloor-GAN is a three-channel image with pixel colors indicating the room types and positions, as well as the wall positions (Figure 8(a)). Further, we leverage a post-processing module to convert the raster image into a vectorized floorplan, to make the results usable by architects. Figure 8 illustrates the process. From a raster image (Figure 8(a)), we first binarize it based on adaptive thresholding to obtain the exterior and interior walls (Figure 8(b)). The walls are, however, rather noisy and discontinuous. We further perform morphological closing operations on the results, yielding vertical and horizontal straight lines that form closing boxes. We assign semantics of room types to each room according to the predictions. Next, we find the position with the highest activity density in each room (except for the living room) to position the internal doors. Finally, the vectorized floorplan (Figure 8(d)) is generated. ")(See Figure 8)
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Wang further discloses provide the activity map as input to a machine learning model. The human activity map is provided into a deep framework ActFloor-GAN, which is a machine learning model ((Wang, Page 1, ¶Abstract) "Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. ")
Whitney is analogous to the claimed invention because it is related to the same field of interior space and furniture placement optimizations using machine learning models. Wang is analogous to the claimed invention because it is related to the same field of endeavor of interior space optimizations using machine learning models. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the utilization of activity maps as disclosed by Wang as part of the furniture layout optimization method of Whitney because some teaching, suggestion, or motivation would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Whitney suggests that a traffic pattern analysis is performed on a space model to inform furniture selection layouts but does not provide specific insight as to how this analysis is done. Wang discloses that human-activity maps provide insights into how a space is utilized which is important in the architectural design process ((Wang, Page 3, Col 2, ¶1) "This work exploits human-activity map that describes the spatial behavior of residents in an architectural space, and encodes the relationship between the environment (room layouts and furniture locations) and the residents’ activities [10]. To a certain extent, human-activity maps reveal users’ behavior intensity in buildings, helping to locate frequently used walking paths and dwell points. The information is important concerns in architectural design process [23]. "). Wang subsequently provides a mechanism by which to generate activity maps for this purpose and further discloses inputting the activity maps into a machine learning model for determining an optimal floorplan ((Wang, Page 13, Col 2, ¶2) " We presented ActFloor-GAN, a new deep framework for automated floorplan design. Unlike existing deep-learning based approaches that try to directly learn the geometric or topological properties of floorplans, we propose to tackle the problem from a new perspective, by leveraging the human-activity map as guidance for network training. The benefit of introducing the human-activity map is prominent. "). Whitney discloses that other information can be provided to update the machine learning models so as to train and configure them to determine insights and relationships of features of physical spaces ((Whitney, ¶53) "For example, in receiving the one or more drawing models, the generative design computing platform 110 may receive one or more two-dimensional computer-aided design (CAD) models that may be used to train one or more machine learning models to identify design parameters and/or to distinguish between different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces "); ((Whitney, ¶99) " In addition, the generative design computing platform 110 may continuously update its machine learning engine 112d based on user input and/or other data received by generative design computing platform 110, so as to continuously and automatically optimize the generation of geometry models and space models. "). Accordingly, because of these suggestions, it would have been obvious to one having skill in the art to combine the prior art methods in order to arrive at the claimed invention.
Regarding claim 2, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination further discloses in further view of Whitney wherein the likely occupant locomotion Traffic patterns may be defined by how many turns to move from one location to another (occupant locomotion) ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters, and/or other space syntax factors)."). is further defined by at least one of a type of activity, a length of time in which the activity is performed, and/or a number of occupants associated with the activity. The scoring of predicted traffic patterns is described as balancing (defined by) various individuals (number of occupants indicated to be at least more than one by the usage of plural language) visiting portions of the physical space and experiencing chance encounters within a physical space (two types of activities), wherein the visits also include frequency (frequently) in which portions are visited (as a length of time) ((Whitney , ¶75) "For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion).")
Regarding claim 3, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination further discloses in further view of Whitney wherein the potential traffic patterns are determined based on predetermined sets of human-environment interactions, wherein human-environment interactions specify types of occupant behaviors that have the potential to occur in interior physical spaces. The predicted traffic patterns (potential traffic patterns) are described as balancing (determined based on) visited portions of locations for various individuals against individuals experiencing chance encounters (two distinct sets of human-environment interactions that specify occupant behavior) ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters and/or other space syntax factors). For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion).").
Regarding claim 4, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination in further view of Whitney discloses wherein the furnishing layout is a two-dimensional model characterizing a top-down view of the interior physical space or a three-dimensional model The exported space model (which includes the furnishing model which includes the positioning of furniture (see Whitney Fig 1E)) can be used to generate a visual rendering ((Whitney, ¶50) "Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information"); ((Whitney, ¶81) " At step 214, the generative design computing platform 110 may generate a visual rendering of the first space model"). The visual rendering of the space model may include a two dimensional or three dimensional rendering ((Whitney, ¶81) "In some instances, in generating the visual rendering of the first space model, the generative design computing platform 110 may generate a two-dimensional or three dimensional rendering of the first space model. In some instances, in generating such a rendering, the generative design computing platform 110 may use rendering software built into a drawing tool to convert blocks, settings, furniture, and/or other elements indicated in the space model into two-dimensional and/or three-dimensional objects that are viewable by a user and/or that reflect views of the space if the space model were to be implemented."). The visual rendering of the 2d model includes a top-down view, as depicted in Whitney Fig. 8:
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Regarding claim 5, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination in further view of Whitney discloses wherein features of the interior physical space further include one or more rooms, hallways, doors, floors, and/or stairs. Architectural details of a physical space (features) are received and utilized to generate the space model that characterizes the physical space ((Whitney, ¶59) "In some instances, in receiving the first space program data, the generative design computing platform 110 may receive information identifying architectural details of the first physical space, such as line drawings identifying a shell of a building corresponding to the first physical space, window locations, ceiling heights, preferred views, plannable area, elevator locations, column locations, entrances, exits, doors, and/or other space features (which may, e.g., be included in a computer-aided design file)."); ((Whitney, ¶62) "At step 207, the generative design computing platform 110 may generate a first plurality of space models for the first physical space based on the first space program data and the first geometry model."); ((Whitney, ¶48) "FIGS. lC, lD, lE, lF, and lG depict illustrative data structures for various models that may be generated, stored, and/or otherwise used in accordance with one or more example embodiments. Referring to FIG. lC, an example block model 180 is depicted. Block model 180 may, for instance, include lot dimension data 180a, exterior walls data 180b, exterior features data 180c, interior walls data 180d, interior features data 180e, neighborhood data 180/(which may e.g., include department information, team information, group information, and/or other information), hallway data 180g (which may, e.g., include circulation data regarding hallways, aisles, corridors, stairs, elevators, and/or other areas used to access spaces in a building), and other block data 180h. [[..]] Interior walls data 180d may include information defining the locations of and/or other features of one or more interior walls within such buildings and/or other structures, and interior features data 180e may include information defining other interior features (e.g., windows, heating-ventilation- air-conditioning (HV AC) systems and elements, interior columns, restrooms, vertical circulation, mechanical/electrical rooms, closets, etc.). [[..]] Hallway data 180g may include information defining the locations of various hallways, walkways, and/or other boundaries in a physical space, and other block data 180h may include information defining other features of specific areas of the physical space. In some instances, and as illustrated in greater detail below, some aspects of a block model may be defined based on input received by generative design computing platform 110, such as dimensions and/or exterior features of a lot of land or a building located on such a lot, while other aspects of a block model may be determined by generative design computing platform 110 using one or more processes described herein, such as the positioning and layout of various neighborhoods, hallways, and/or other block model features."); ((Whitney, ¶70) "In this way, the generative design computing platform 110 may place different departments throughout different floors of a given space, thereby producing a multi-floor stacking plan.")
Regarding claim 6, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination discloses in further view of Whitney wherein outputting the furnishing layout User interface data is generated (outputting) that includes space models. The space models include the furniture model that defines the layout of the furniture, as depicted in Whitney, Fig. 3. ((Whitney, ¶77) " At step 210, the generative design computing platform 110 may generate first user interface data that includes the first ranked list of space models produced at step 209. The first user interface data generated by the generative design computing platform 110 may define one or more portions of a graphical user interface, such as the user interface described in greater detail below in connection with FIG. 3."). includes presenting the furnishing layout to a user via a user interface A computing device displays a user interface which includes space model information and wherein the space model includes a furniture layout, as depicted in Whitney Fig. 3 ((Whitney, ¶78) "At step 212, the first designer user computing device 140 may display a user interface that includes at least a portion of the first ranked list of space models. For example, the first designer user computing device 140 may display a graphical user interface similar to graphical user interface 300, which is shown in FIG. 3, based on receiving the first user interface data from the generative design computing platform 110.") The user interface includes visual information indicating graphical views of space models ((Whitney, ¶78) "As seen in FIG. 3, graphical user interface 300 may include information identifying one or more different space models generated by the generative design computing platform 110, ranking information indicating the rank and/or score of one or more space models, and/or visual information indicating graphical views of one or more space models generated by the generative design computing platform 110 and/or portions thereof."); (See Whitney Figure 3 for image of furniture layout for different ranked models). wherein the user interface includes one or more user interface elements corresponding to individual items of furniture included in the furnishing layout. A user interface may be generated to display a space model to a user wherein furniture elements of a space model have associated user-selectable furniture purchase elements ((Whitney, ¶96) " Subsequently, the generative design computing platform 110 may generate and/or provide one or more user interfaces that enable a customer (e.g., an occupant of the physical space) to purchase one or more furniture elements associated with a space model and/or otherwise view and/or implement the space model. For example, at step 230, the generative design computing platform 110 may generate and send one or more commands directing client user computing device 160 to display a graphical user interface that includes a user-selectable furniture-purchase element.")
Regarding claim 7, modified Whitney in view of Wang disclose The system of claim 6, as stated previously. The proposed combination in further view of Whitney discloses wherein the user interface elements are selectable by the user, and wherein selection of the user interface elements facilitates the user purchasing the items of furniture corresponding to the selected user interface elements. The user interface is described as having user-selectable furniture-purchase elements that enable a customer to purchase furniture elements ((Whitney, ¶96) "Subsequently, the generative design computing platform 110 may generate and/or provide one or more user interfaces that enable a customer (e.g., an occupant of the physical space) to purchase one or more furniture elements associated with a space model and/or otherwise view and/or implement the space model. For example, at step 230, the generative design computing platform 110 may generate and send one or more commands directing client user computing device 160 to display a graphical user interface that includes a user-selectable furniture-purchase element."). There may be one or more user-selectable options enabling purchasing of one or more furniture items associated with the space model ((Whitney, ¶96) " As seen in FIG. 6, graphical user interface 600 may include information about a space model (e.g., metrics, scores, details associated with blocks, settings, and/or furniture, and/or other information), one or more renderings of the space model, and/or one or more user-selectable options enabling adoption of the space model and/or purchasing of one or more furniture items associated with the space model.").
Regarding claim 10, modified Whitney in view of Wang discloses The system of claim 1, as stated previously. The proposed combination discloses in further view of Wang wherein the activity map is a two-dimensional top-down view characterizing the interior physical space and the determined potential traffic patterns. The human-activity map is depicted in Figs. 3 and 4 as a two-dimensional top-down view of a floorplan of a building (characterizing an interior physical space) showing the intensity if probabilistic human activity ((Wang, Page 5, Col 2, ¶1) "Based on the connectivity graph, we adopt bi-RRT [24] to simulate the resident movements in the living room (Figure 3(d-top)) and produce a single-channel image, in which the pixel values indicate the probabilistic human activity intensity, not activity types, in the living room."). See also Wang Figure 3:
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Regarding claim 11, Whitney discloses (except the limitations surrounded by brackets ([[..]])) A method for determining potential traffic patterns within an interior physical space The generative design computing platform identifies predicted traffic patterns in a physical space and generates a score for the space model ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters, and/or other space syntax factors)."). The physical space may include interior features, indicating an interior physical space ((Whitney, ¶48) "Interior walls data 180d may include information defining the locations of and/or other features of one or more interior walls within such buildings and/or other structures, and interior features data 180e may include information defining other interior features (e.g., windows, heating-ventilation-air-conditioning (HVAC) systems and elements, interior columns, restrooms, vertical circulation, mechanical/electrical rooms, closets, etc.)."). and determining a furnishing layout for the interior physical space based on the potential traffic patterns, the method comprising: A score is generated for each space model (based on traffic patterns as given by example stated previously in ¶75), wherein the space model contains a furniture model (See Figure 1F), thereby indicating a determination of a furniture model. The furniture model includes information regarding positioning of specific pieces of furniture at specific points in a physical space ((Whitney, ¶50) "Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space.")
obtaining a first model characterizing an interior physical space, wherein the model includes characterizations of features included in the interior physical space, wherein the features include one or more walls and/or windows in the interior physical space; A geometry model is obtained from a database ((Whitney, ¶61) " At step 206, the generative design computing platform 110 may load a first geometry model from a database storing one or more geometry models (e.g., stored in the memory 112 or at the internal data server 120)."). The geometry model includes information regarding features of various space model design elements (characterizations of features) ((Whitney, ¶61) "In loading the first geometry model, the generative design computing platform 110 may load information defining a first plurality of design rules that may be part of and/or otherwise associated with the first geometry model, such as design rules that control and/or affect the quantities, locations, sizes, and/or other features of various space model design elements, such as blocks, settings, furniture, and/or other elements (e.g., number of blocks, settings, furniture, and/or other features ; types of blocks, settings, furniture, and/or other features ; locations of blocks, settings, furniture, and/or other feature; locations of hallways; and/or other features)."). The geometry model includes rules that define ideal locations of block level features ((Whitney, ¶65) " For instance, the first geometry model may include a plurality of design rules, constraints, and/or metrics that define the ideal locations and/or other properties of block-level features."). The block level features include interior windows and walls ((Whitney, ¶48) " Referring to FIG. lC, an example block model 180 is depicted. Block model 180 may, for instance, include lot dimension data 180a, exterior walls data 180b, exterior features data 180c, interior walls data 180d, interior features data 180e, neighborhood data 180/(which may e.g., include department information, team information, group information, and/or other information), hallway data 180g (which may, e.g., include circulation data regarding hallways, aisles, corridors, stairs, elevators, and/or other areas used to access spaces in a building), and other block data 180h. Lot dimension data 180a may, for instance, include information defining one or more dimensions and/or other features of a lot or other parcel of land where one or more buildings and/or other structures may be located. Exterior walls data 180b may include information defining the locations of and/or other features of one or more exterior walls of such buildings and/or other structures, and exterior features data 180c may include information defining other exterior features (e.g., windows, landscaping, exterior columns, decorations, etc.) of such buildings and/or other structures. Interior walls data 180d may include information defining the locations of and/or other features of one or more interior walls within such buildings and/or other structures, and interior features data 180e may include information defining other interior features (e.g., windows, heating ventilation- air-conditioning (HV AC) systems and elements, interior columns, restrooms, vertical circulation, mechanical/electrical rooms, closets, etc.). ")
obtaining a number of occupants utilizing the interior physical space; Space program data is received which includes receiving organization details for an organization that will occupy the physical space, and wherein the organization details include number of employees ((Whitney, ¶59) "Additionally or alternatively, in receiving the first space program data, the generative design computing platform 110 may receive organization details of an organization that does or will occupy the first physical space, such as information indicating a total number of employees of the organization, projected growth rate, organizational breakdown ( e.g., departments, teams, team compositions, relations between teams and/or departments). ")
providing the first model and the number of occupants as inputs to a machine learning model configured to perform an analysis of the model The generative design computing platform feeds in the received space program data (which comprises the number of occupants of the space, as stated above) and the loaded geometry model into the step of generating space models (207) (See figure 2B) ((Whitney, ¶62) " At step 207, the generative design computing platform 110 may generate a first plurality of space models for the first physical space based on the first space program data and the first geometry model "). Generation of space models is described as being performed using a machine learning system/engine ((Whitney, ¶102) " FIG. 7 depicts an illustrative method for generating space models and geometry models using a machine learning system with multi-platform interfaces in accordance with one or more example embodiments. "); ((Whitney, ¶103) " At step 770, the computing platform may update a machine learning engine used to generate the geometry models and/or the space models. "). The generation of space models considers the geometry model ((Whitney, ¶102) " At step 735, the computing platform may generate one or more space models based on the one or more geometry models and the space program data. "). The machine learning models used by the generative design computing platform are described as being configured to determine insights regarding the physical spaces ((Whitney, ¶53) " different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces. ") to determine potential traffic patterns within the interior space characterized by the first model: The generated space models are assessed and quantified for features such as the identifying of predicted traffic patterns in the physical space((Whitney, ¶72) " At step 208, the generative design computing platform 110 may score the first plurality of space models based on the first geometry model "); ((Whitney, ¶75) " As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters and/or other space syntax factors). For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion). After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). ")
receiving, from the machine learning model, the potential traffic patterns within the interior physical space as determined, wherein individual potential traffic patterns characterize likely occupant locomotion within the interior physical space, A space model is assessed for predicted traffic patterns in the physical space, wherein how many turns to move from one location to another (characterization of likely occupant locomotion) is considered ((Whitney, ¶75) "As another example, in scoring the first plurality of space models, the generative design computing platform 110 may quantify, assess, and/or otherwise score the space syntax for a given space model by identifying predicted traffic patterns in the physical space in view of the layout of the space model (e.g., how many turns to move from one location to another location in the space, how clear are corridors in the space, how adjacent are related teams, how well does the space provide possibilities for chance encounters and/or other space syntax factors). The patterns are identified and predicted as an exemplary assessment of features, indicating the receiving of such patterns ((Whitney, ¶75) " After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). The generative design computing platform 110 then may, for instance, compute an aggregate score for each space model of the first plurality of space models by computing an average of the metric scores determined for the particular space model. "). Machine learning models are described as the mechanism by which analyses of features are performed, thereby indicating that the assessment of the feature of traffic patterns is performed by a machine learning model ((Whitney, ¶53) "In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces. ") wherein the likely occupant locomotion is defined by an area within the interior physical space where the locomotion occurs and a frequency at which the locomotion occurs; Predicting traffic patterns (which include how many turns to move from one location to another as occupant locomotion) is described as considering short distances (an area within the interior physical space) at frequently visited spaces (a frequency at which the locomotion occurs) ((Whitney , ¶75) "For example, in scoring a given space model, the generative design computing platform 110 may balance maintaining short distances between frequently visited portions of the physical space for various individuals against allowing individuals in the space to experience chance encounters (e.g., it may be desirable for everything located in the space to be conveniently accessible to people affiliated with different teams, while still allowing people affiliated with different teams to encounter someone from another team on occasion).")
based on the analysis, [[generating an activity map that includes a second model characterizing the interior physical space, wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture;]] An assessment and quantification of features such as traffic patterns is subsequently used for the scoring of space models ((Whitney, ¶75) " After quantifying and/or otherwise assessing one or more of the features described above, the generative design computing platform 110 may calculate and/or otherwise determine a score for each metric with respect to each space model of the first plurality of space models ( e.g., 1-10, or the like). The generative design computing platform 110 then may, for instance, compute an aggregate score for each space model of the first plurality of space models by computing an average of the metric scores determined for the particular space model. ")
[[providing the activity map as input to]] a second machine learning model configured to determine a furnishing layout for the interior physical space; Multiple machine learning models can be trained from drawing models that correspond to space designs to be configured to distinguish design features, including relationships related to other space features, thereby indicating a second machine learning model configured to evaluate location of space features ((Whitney, ¶53) “Referring to FIG. 2A, at step 201, the generative design computing platform 110 may receive one or more drawing models from the internal data server 120 and/or the external data server 130, which may correspond to different space designs ( e.g., floor plans, furniture location information, best-in-class designs, or the like). For example, in receiving the one or more drawing models, the generative design computing platform 110 may receive one or more two-dimensional computer-aided design (CAD) models that may be used to train one or more machine learning models to identify design parameters and/or to distinguish between different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces."). Features of the space include features of furniture within the space including the positioning ((Whitney, ¶50) "Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space."). The machine learning engine is described as generating space models, which include furniture models ((Whitney, ¶103) "At step 770, the computing platform may update a machine learning engine used to generate the geometry models and/or the space models."); (see also Figure 1F showing the furniture model within the space model). Furniture models contain information indicating the positioning of furniture ((Whitney, ¶50) "Referring to FIG. lE, an example furniture model 184 is depicted. Furniture model 184 may, for instance, include a block model 184a, a settings model 184b, furniture identification data 184c, furniture location data 184d, and other furniture data 184e. [[…]] Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information. Other furniture data 184e may include information defining other features of furniture within the physical space. In some instances, and as illustrated in greater detail below, some aspects of a furniture model may be determined by generative design computing platform 110 using one or more processes described herein, such as the inclusion of and positioning of specific pieces of furniture at specific work points within a physical space."). Other data may be used as input to update the machine learning models ((Whitney, ¶99) "In addition, the generative design computing platform 110 may continuously update its machine learning engine 112d based on user input and/or other data received by generative design computing platform 110, so as to continuously and automatically optimize the generation of geometry models and space models.")
receiving, from the second machine learning model, the furnishing layout for the interior physical space, wherein the furnishing layout includes representations of items of furniture and positions within the interior physical space associated with the items of furniture; and Space models are generated and stored, wherein the space model includes the positioning information of furniture, as stated above ((Whitney, ¶102) "At step 740, the computing platform may store the one or more space models. At step 745, the computing platform may score the one or more space models, and rank the one or more space models based on the scores."). The furniture model includes furniture identification data and furniture location data, indicating representations of items of furniture and positions within the space ((Whitney, ¶50) "Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information"); ((Whitney, Claim 10) "The computing platform of claim 9, wherein each block model of the plurality of block models indicates potential locations of different neighborhoods in the physical space, each settings model of the plurality of settings models indicates potential locations of different work settings in the physical space, and each furniture model of the plurality of furniture models indicates potential locations of different furniture items in the physical space."). The space model is generated by a machine learning engine, as stated previously, thereby indicating that the generated space model is received from the machine learning model ((Whitney, ¶86) " For instance, to the extent that a user manually refined a layout of the space model and/or manually optimized one or more parameters underlying the space model, such refinements and/or optimizations may be captured by the generative design computing platform 110 and used to retrain the machine learning engine, so that such refinements and/or optimizations may be automatically implemented by the generative design computing platform 110 when generating future space models.")
outputting the furnishing layout.
The space model can be exported ((Whitney, ¶92) "As illustrated in greater detail below, in processing such a request, the generative design computing platform 110 may export data in various different formats, using one or more of the multi-platform interoperability features described herein. In particular, and as described above (e.g., with respect to step 207), the generative design computing platform 110 may generate each space model of a plurality of space models in a plurality of different data formats (e.g., in a CAD format, a CET format, a Revit format, a Sketch Up format, and/or one or more other formats), and this multi-format generation may expedite the process by which data may be exported in different formats."). The space model contains the furniture model, as depicted in Whitney Fig. 1F. The furniture model contains furniture location data within a physical space, as stated previously ((Whitney, ¶50) "Furniture location data 184d may include information defining the locations of one or more specific pieces of furniture within a physical space, such as identifiers indicating positioning of desks, chairs, and/or other furniture components at specific work points, coordinates indicating positioning of each piece of furniture within the physical space, and/or other location information").
Whitney does not disclose generating an activity map based on the analysis used to determine potential traffic patterns; however, Wang discloses based on a simulation of theoretical traffic patterns, to generating an activity map that includes a second model characterizing the interior physical space, wherein the activity map includes representations of the potential traffic patterns, and wherein the representations include one or more areas of the interior physical space that correspond to either a high volume of occupant traffic and/or a requirement of few items of furniture; Human-activity maps are generated for a given interior space, where the building is characterized by a boundary ((Wang, Page 11, Col 2, ¶2-4) "Our approach can generate diverse floorplans by using different human-activity maps as guidance, which are prepared either by the automatic approach with a generative network or by the semi-automatic approach with an interactive interface. Figure 13 presents diverse floorplans produced by the automatic approach. The generative network is able to produce stochastic human-activity maps using dropouts added to the first three layers of the decoder. Here, the building boundaries are added as a reference. Note that the generated human-activity maps are subtly different, yet the floorplans are rather diverse with different room numbers and types. However, the floorplan designs may not be ideal, such as bad designs without balconies, a small master room, and two bathrooms next to each other. The semi-automatic approach can overcome the deficiency by allowing users to manipulate the human-activity maps on demand. Figure 14 presents diverse floorplans that can be derived from a single building boundary using the interactive interface (see Figure 6). For the same building boundary, the generated floorplans possess different properties, including the number of rooms, room positions and sizes, room types, and room adjacency relations, and they are different from the ground truths from RPLAN. "). The activity map depicts a heatmap corresponding to high activity density ((Wang, Page 8, Col 2, ¶2) "The output of ActFloor-GAN is a three-channel image with pixel colors indicating the room types and positions, as well as the wall positions (Figure 8(a)). Further, we leverage a post-processing module to convert the raster image into a vectorized floorplan, to make the results usable by architects. Figure 8 illustrates the process. From a raster image (Figure 8(a)), we first binarize it based on adaptive thresholding to obtain the exterior and interior walls (Figure 8(b)). The walls are, however, rather noisy and discontinuous. We further perform morphological closing operations on the results, yielding vertical and horizontal straight lines that form closing boxes. We assign semantics of room types to each room according to the predictions. Next, we find the position with the highest activity density in each room (except for the living room) to position the internal doors. Finally, the vectorized floorplan (Figure 8(d)) is generated. ")(See Figure 8)
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Wang further discloses providing the activity map as input to a machine learning model. The human activity map is provided into a deep framework ActFloor-GAN, which is a machine learning model ((Wang, Page 1, ¶Abstract) "Second, we feed the human-activity map into our deep framework ActFloor-GAN to guide a pixel-wise prediction of room types. ")
Whitney is analogous to the claimed invention because it is related to the same field of interior space and furniture placement optimizations using machine learning models. Wang is analogous to the claimed invention because it is related to the same field of endeavor of interior space optimizations using machine learning models. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have incorporated the utilization of activity maps as disclosed by Wang as part of the furniture layout optimization method of Whitney because some teaching, suggestion, or motivation would have led one having ordinary skill in the art to do so in order to arrive at the claimed invention. Whitney suggests that a traffic pattern analysis is performed on a space model to inform furniture selection layouts but does not provide specific insight as to how this analysis is done. Wang discloses that human-activity maps provide insights into how a space is utilized which is important in the architectural design process ((Wang, Page 3, Col 2, ¶1) "This work exploits human-activity map that describes the spatial behavior of residents in an architectural space, and encodes the relationship between the environment (room layouts and furniture locations) and the residents’ activities [10]. To a certain extent, human-activity maps reveal users’ behavior intensity in buildings, helping to locate frequently used walking paths and dwell points. The information is important concerns in architectural design process [23]. "). Wang subsequently provides a mechanism by which to generate activity maps for this purpose and further discloses inputting the activity maps into a machine learning model for determining an optimal floorplan ((Wang, Page 13, Col 2, ¶2) " We presented ActFloor-GAN, a new deep framework for automated floorplan design. Unlike existing deep-learning based approaches that try to directly learn the geometric or topological properties of floorplans, we propose to tackle the problem from a new perspective, by leveraging the human-activity map as guidance for network training. The benefit of introducing the human-activity map is prominent. "). Whitney discloses that other information can be provided to update the machine learning models so as to train and configure them to determine insights and relationships of features of physical spaces ((Whitney, ¶53) "For example, in receiving the one or more drawing models, the generative design computing platform 110 may receive one or more two-dimensional computer-aided design (CAD) models that may be used to train one or more machine learning models to identify design parameters and/or to distinguish between different design parameters. In some instances, in receiving the one or more drawing models, the generative design computing platform 110 may receive a quantity of drawing models that is satisfactory and/or sufficient to train the one or more machine learning models to distinguish between different room types (e.g., meeting rooms, offices, common spaces, or the like) and/or other design features. This training may, for instance, configure and/or cause the generative design computing platform 110 to determine insights and/or relationships relating to square footage, adjacency (which may, e.g., define and/or indicate the proximity and/or location of various departments, settings, rooms, and/or other space features), and/or other typical and/or preferred features of physical spaces and/or relationships of features of physical spaces "); ((Whitney, ¶99) " In addition, the generative design computing platform 110 may continuously update its machine learning engine 112d based on user input and/or other data received by generative design computing platform 110, so as to continuously and automatically optimize the generation of geometry models and space models. "). Accordingly, because of these suggestions, it would have been obvious to one having skill in the art to combine the prior art methods in order to arrive at the claimed invention.
Regarding claim 12, the limitations The method of claim 11, wherein the likely occupant locomotion is further defined by at least one of a type of activity, a length of time in which the activity is performed, and/or a number of occupants associated with the activity are substantially similar to the recited in claim 2 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 13, the limitations The method of claim 11, wherein the potential traffic patterns are determined based on predetermined sets of human-environment interactions, wherein human-environment interactions specify types of occupant behaviors that have the potential to occur in interior physical spaces are substantially similar to the recited in claim 3 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 14, the limitations The method of claim 11, wherein the furnishing layout is a two-dimensional model characterizing a top-down view of the interior physical space or a three-dimensional model are substantially similar to the recited in claim 4 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 15, the limitations The method of claim 11,wherein features of the interior physical space further include one or more rooms, hallways, doors, floors, and/or stairs. are substantially similar to the recited in claim 5 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 16, the limitations The method of claim 11, wherein outputting the furnishing layout includes presenting the furnishing layout to a user via a user interface, wherein the user interface includes one or more user interface elements corresponding to individual items of furniture included in the furnishing layout are substantially similar to the recited in claim 6 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 17, the limitations The method of claim 16, wherein the user interface elements are selectable by the user, and wherein selection of the user interface elements facilitates the user purchasing the items of furniture corresponding to the selected user interface elements are substantially similar to the recited in claim 7 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Regarding claim 20, the limitations The method of claim 11, wherein the activity map is a two-dimensional top-down view characterizing the interior physical space and the determined potential traffic patterns are substantially similar to the recited in claim 10 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Whitney in view of Wang as applied to claims 6 and 16 above, and further in view of Vadakkeveedu (US Patent Publication No. US 20190311538 A1), hereinafter referred to as Vadakkeveedu.
Regarding claim 8, modified Whitney in view of Wang discloses The system of claim 6, as stated previously. The proposed combination in further view of Whitney teaches (except the limitations surrounded by brackets ([[…]]) wherein presenting the furnishing layout to a user includes presenting a [[first-person perspective]] of the interior physical space including the representations of the items of furniture, A user interface displays a furniture layout, which includes a physical space and representations of items of furniture, as depicted in Whitney Fig. 8 ((Whitney, ¶9) "The computing platform may send, via the communication interface and to the first user computing device, the visual rendering of the first space model, which may cause the first user computing device to display a user interface comprising at least a portion of the visual rendering of the first space model.") [[wherein the presentation allows the user to navigate through the interior physical space in the first-person perspective.]]
The proposed combination in further view of Whitney does not disclose; however Vadakkeveedu discloses first-person perspective A method and system is disclosed for providing real-time mapping of products to an architectural design in a virtual reality representation, wherein it is understood that the virtual reality representation is equivalent to a first-person perspective ((Vadakkeveedu, ¶19) "FIG. 1 illustrates a general overview of a system 100 for real-time mapping of products to an architectural design in a virtual reality representation, in accordance with various embodiments of the present disclosure."); ((Vadakkeveedu, ¶20) "In general, virtual reality refers to computer technology which uses tools to generate realistic images, sounds, and other sensations. Further, the sensation simulates a person's physical presence in a virtual or imaginary environment. The tools include but may not be limited to virtual reality headsets or multi-projected environments. The person using virtual reality equipment is able to look around the artificial world.")
wherein the presentation allows the user to navigate through the interior physical space in the first-person perspective. The visualization system allows the user to navigate inside the virtual reality representation, which is equivalent to first-person perspective, as stated previously ((Vadakkeveedu, ¶46) "The facility visualization system 110 allows the user 102 to navigate and interact inside the virtual reality representation. The user 102 is allowed to navigate and interact for customizing the one or more facility material products associated with the facility. The customization is done by utilizing the one or more shortlist facility material products displayed on the plurality of virtual reality devices 106 in the virtual reality representation.")
Vadakkeveedu is analogous because they it is related to the same field of endeavor of visualizing architectural designs. Whitney discloses a furniture layout visualization that is a top-down view of a 2-dimensional model and alternately discloses a 3-dimensional rendering of a model but does not disclose the perspective in which the 3-dimensional model is viewed. Wang discloses a 2-dimensional top-down visualization of building layouts and furniture arrangements. Vadakkeveedu discloses a system and method for visualizing a facility, wherein the facility material products contained in the visualization include furniture and the corresponding placement. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination of Whitney and Wang to incorporate the virtual reality visualization with navigation capabilities as taught by Vadakkeveedu because some teaching, suggestion, or motivation in the prior art would have led one of ordinary skill to modify the prior art reference to arrive at the claimed invention. Vadakkeveedu discloses that a blueprint of a facility does not allow for an immersive experience that allows designers a clear understanding about a look and feel of a facility but that the virtual reality representation with navigation capabilities enables users to obtain a clear understanding about the look and feel of a facility ((Vadakkeveedu, ¶2) " The blueprint may be designed in a plurality of methods. The methods include a 2-dimensional image, a 2-dimensional model, a 3-dimensional model and the like. Further, interior decoration requires selection of material and color of finishing material by a user. The user needs to visualize design of construction material on the blueprint in mind and customize design in visualizations. However, this does not provide the user with a clear picture of the look and feel of the facility in mind. In addition, the blueprint only provides a rough idea about the architecture of the facility. Also, the blueprint does not provide us with real-time cost estimation and customization of the facility. In addition, the blueprint does not provide us with the ability to view and feel architecture of the facility as if the architecture is already constructed. The blueprint does not allow us to immerse inside architecture of the facility in an interactive way. Sometimes, the facility is not constructed according to requirements of the user even when the facility is constructed based on the blueprint of the facility. This situation occurs because the blueprint does not provide us with a clear understanding about look and feel of the facility in an immersive and interactive way. Also, the blueprint cannot provide real-time cost estimation of construction of the facility in an interactive way. There is a constant need for a system to view and customize blueprint of the facility in an immersive and interactive way in real time."). Therefore, it would have been obvious to one having ordinary skill in the art to modify the proposed combination further in view of Vadakkeveedu to achieve the benefits noted.
Regarding claim 18, the limitations The method of claim 16,wherein presenting the furnishing layout to a user includes presenting a first-person perspective of the interior physical space including the representations of the items of furniture, wherein the presentation allows the user to navigate through the interior physical space in the first-person perspective. are substantially similar to the recited in claim 8 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Whitney in view of Wang as applied to claims 1 and 19 above, and further in view of Nassar (Nassar, K., “A model for assessing occupant flow in building spaces”, 2010, Automation in Construction, Vol. 19, pp 1027-1036), hereinafter referred to as Nassar.
Regarding claim 9, the proposed combination discloses The system of claim 1, as stated previously. The proposed combination in further view of Nassar discloses wherein determining the potential traffic patterns is based on the number of occupants within the interior physical space, A model is used to model occupant movement in building corridors ((Nassar, Page 1032, Col 1, ¶1) " The model represents a simple yet robust model of occupant movement in building corridors and may be taken further to optimize corridor design in terms of layout of spaces on the corridor as well as its width."). Occupant flow density is estimated using the model based on various inputs ((Nassar, Page 1027, Col. 2, ¶1) "The proposed formula is able to estimate the OFD based on various inputs, including arrival rates, service rates of the activities on the corridor, dimensions of the corridor as well as spacing of the activities on the corridor. Real-life data is collected and analyzed for various space morphologies to validate the results of the queuing model"). One of the inputs of the occupancy flow density model is density, which is defined as occupants in a given space ((Nassar, Page 1029, Col. 2, ¶2) " Other model parameters are the effective width of the space, the density, the occupant flow density (OFD) and the calculated occupant flow. Density here is the measurement of the degree of crowdedness and is expressed in persons per unit area. Occupant flow density, Fs, is the flow of occupants past a point in the space per unit of time per unit of effective width, We. OFD is expressed in persons/s/m of effective width. Calculated flow is the flow rate of persons passing a particular point and is measured in person/s.") such that a first interior physical space A corridor width is given for multiple samples, indicating a first interior physical space when the corridor width is the same, as depicted in Nassar Fig. 7. having a first number of occupants The arrival rate is described as the number of people arriving per minute, indicating differing numbers of occupants for different samples, as depicted in Nassar Fig. 7. is determined to have potential traffic patterns The occupancy flow density is determined for a given arrival rate at a given corridor width, as depicted in Nassar Fig. 7that are different from the first interior physical space having a second number of occupants, The occupancy flow density is determined to be different for a single given corridor width when the arrival rate is varied to include a different number of occupants per minute, as depicted in Nassar Fig. 7 wherein the first number of occupants is different from the second number of occupants. Six exemplary cases of different arrival rates for given corridors are given, thereby indicating that the number of occupants is different, as depicted in Nassar Fig. 7.
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Nassar is analogous art because it is related to the same field of endeavor of building layout design optimizations. Whitney discloses a building layout optimization method that accounts for predicted traffic patterns in the optimization process and also accounts for the number of occupants in the optimization process but does not explicitly disclose how differing numbers of occupants affects the prediction of traffic patterns in the same physical space. Wang discloses a method of generating and visualizing traffic patterns in a space using simulations but does not explicitly disclose the number of occupants as part of the simulation. Nassar discloses a method for modeling occupant flow in a building to predict level of service in building spaces so as to inform design choices for the building, wherein the density of occupants is accounted for in predicting the flow and different densities are observed in the same physical space so as to demonstrate how density of occupants influences occupant flow within the space. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have further modified the proposed combination of Whitney in view of Wang with the teachings of Nassar because some teaching, suggestion, or motivation in the prior art would have led one of ordinary skill to make the modification to arrive at the claimed invention. Nassar suggests that occupant flow is fundamentally affected by the crowd density and the module (square meter per person) ((Nassar, Page 1028, Col 2, ¶5) "To summarize the information given above, there are two fundamental relationships related to occupant flow. The first is between the crowd density and the mean walking speed and the second is between the module (squared-meter/occupant) and flow rate."). Nassar further provides a motivation that efficient occupant flow is a desirable metric to achieve in the building design process ((Nassar, Page 1027, ¶Abstract) " Therefore, there is a need for a simple and quick analysis method to aid in the sizing and design of building spaces during the early design stages so that these spaces can accommodate occupant flow efficiently and safely. This paper presents a method to evaluate the LOS of occupants in dynamic buildings spaces without the need for building and running detailed simulations, so that designers can understand how well a particular space accommodates occupants' movements and activities early on in the design phase."). Therefore, one having skill in the art and having the teachings of Whitney, Wang, and Nassar before them, would be obviously compelled to integrate the teachings of Nassar into the proposed combination of Whitney and Wang to account for the fundamental parameter of occupant density when modeling the flow of individuals for achieving desirable outcomes in the building design process. Such a combination of the arts would yield a physics-informed machine learning model to account for the physics-based modeling disclosed by Nassar into the machine learning modeling disclosed by Whitney.
Regarding claim 19, the limitations The method of claim 11, wherein determining the potential traffic patterns is based on the number of occupants within the interior physical space, such that a first interior physical space having a first number of occupants is determined to have potential traffic patterns that are different from the first interior physical space having a second number of occupants, wherein the first number of occupants is different from the second number of occupants. are substantially similar to the recited in claim 9 but with respect to independent claim 11 and therefore the limitations are rejected under the same rationale.
Conclusion
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/E.G.L./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187