DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/05/2025 has been entered.
Response to Arguments
101 Rejections:
With respect to Applicant’s argument that “In particular, each of the pending claims as previously and currently amended recites computer-implemented operations to determine search results in particular manners and to present the determined search results, and as such provide, at a minimum, improvements to the functioning of a computer under Steps 2A Prong Two and/or 2B that render the pending claims
statutory subject matter…… There is nothing in the recited claim elements of the previously pending or currently pending claims that supports the contention of the claims being merely directed to 'mental processes'. Instead, the previously and currently pending claims are directed to performing particular types of computer-implemented searches in a manner that significantly decreases the use of computing resources (e.g., computing cycles and memory) during the search process, including by generating and comparing particular types of encoded building information in a particular manner as reflected in the current claim language”, examiner respectfully disagrees.
Examiner cites that Applicant’s improvement arguments do not explain how the functioning of the computer itself is improved. Rather, the argument is regarding an improved abstract idea (i.e. how to compare using math representations of graph adjacencies and vectors), but that is not any improvement to how the computer actually operates. Applicant provides a bare assertion on page 22 that this “significantly decreases the user of computing resources” but there is nothing in the specification supporting this at all. The technical improvement (i.e. improving the search process or results for building information) that the Applicant is arguing is not an improvement to the computer components or the operation of the computer. At best, this merely confines the judicial exception to the field of building information, which does not integrate the abstract idea into a practical application. Moreover, even if the specific steps are more efficient (i.e. the argument of decreased computing resources) this is insufficient to provide eligibility where the solution to the addressed problem is
essentially an improved mental process or math concept involving the comparison of information, not a change in how the employed computer-based elements operate. Therefore, the arguments are not persuasive.
With respect to Applicant’s argument that “The courts have found that improved techniques for generating and providing search results may be directed to statutory subject matter. For example, in an October 2022 decision, the U.S. Court Of Appeals for the Federal Circuit found the following, with emphasis added: ….. Weisner v. Google LLC, 2021-2228 (Fed. Cir. 2022). … For example, with respect to independent claim 1, the claims recite generating specific data structures that encode specific types of information about houses (e.g., embedding vectors, adjacency graphs) and using those specific data structures to compare a variety of types of information about houses, including types of inter-room adjacency information, determined and predicted attributes of individual rooms of the houses, etc. The other independent claims 3, 4 and 7 recite similar particular mechanisms for generating improved search results”, Examiner respectfully disagrees.
Examiner cites that the Applicant’s argument that the application is analogous to Weisner v. Google, is not persuasive. First, Weisner was an appeal of a motion to dismiss, and a different standard of review was applied than making the prima facie rejection in this application. Second, the instant claims do not recite or otherwise claim a new technique or algorithm for prioritizing search results similar to that case. The portions quoted from the decision rely on using specific gathered data of physical location history (an additional element) to improve search results. The claims of the instant application do not use location histories and the focus of the claims is not on such an improvement in computers as tools, but on abstract ideas that use computers.
Additionally, embedding vectors and adjacency graphs are not per se data structures as the Applicant argued. These are math concepts and can be done as a mental process. For instance, vector spaces are mathematical concept. As are adjacency graphs. These are not specialized computer structures, they are math-based lists and forms of information. So, the argument on page. 23 that using these ‘structures’ remove this from a mental process or creates the improvement is not persuasive either. Thus, the argument that these are technical operations that improve the functioning of the computer are bare assertions. It is not clear how the computer is improved. It is merely a comparison or search algorithm that may be improved, but that is an abstract idea. For instance, one doing this mentally by math, with the assistance of pen and paper, would have the same improvement for the comparison.
Regarding the argument based on 2106.06(b) that the claim does not need full eligibility analysis. However, MPEP 2106.06(b) explicitly indicates that full eligibility analysis can be applied stating, “If the claims are a "close call" such that it is unclear whether the claims improve technology or computer functionality, a full eligibility analysis should be performed to determine eligibility.” And the result if eligible reached should be the same as the streamlined approach. However, here it is not a “clear” improvement to technology anyways so the streamlined approach is not proper. Therefore, the arguments are not persuasive.
With respect to Applicant’s argument on pages that 24, 25, 26, 17 that “As discussed in the application as filed, such generation of embedding vectors may include using "graph representation learning ... to search for a mapping function that can map the nodes in a graph to d-dimensional vectors, such that in the learned space similar nodes in the graph have similar embeddings". …. The numbers in the embedding vector will change each time you retrain the model, even if you retrain the model with identical input.. There is simply no way for humans to practically manually generate and use such embedding vectors in their mind or using pen and paper.. Again, even if a human can mentally assess the layout and features of a house, that is not what is recited in the pending claims, which discuss using trained machine learning models to predict particular types of information that is not readily apparent from the information being analyzed. There is simply no way for humans to practically manually generate and use such predicted information in their mind or using pen and paper”, Examiner respectfully disagrees.
Examiner cites that "generating…predicting” is also math concepts and/or a mental process. Applicant also provided description in the arguments describes that these vectors are created “mathematically”. Moreover, as explained in MPEP 2106.07(a)(III), “When performing the analysis at Step 2A Prong One, it is sufficient for the examiner to provide a reasoned rationale that identifies the judicial exception recited in the claim and explains why it is considered a judicial exception (e.g., that the claim limitation(s) falls within one of the abstract idea groupings). Therefore, there is no requirement for the examiner to rely on evidence, such as publications or an affidavit or declaration under 37 CFR 1.104(d)(2), to find that a claim recites a judicial exception.” Thus, the argument that there is no evidence provided as to how this can be done mentally is not persuasive as evidence is not required. The rejection itself provides a reasoned rationale why this recites a judicial exception, in that it is an evaluation or judgement which are hallmarks of mentally processes and also recites math concepts as admitted.
Additionally, “adjacency graph” can also be done mentally and math, as graphs and adjacency matrices or lists are mathematical relationships. And also, there is no requirement for evidence, just a reasoned rationale, as was provided explaining this was mental/math process.
Also, instant specification indicates these embedding vectors and graphs are math, by incorporating by reference at [0013] various academic papers. See for instance section 2.1 of "Symmetric Graph Convolution Autoencoder For Unsupervised Graph Representation Learning" by Jiwoong Park et al., 2019 International Conference On Computer Vision, August 7, 2019; Algorithm 1 in "Inductive Representation Learning On Large Graphs" by William L Hamilton et al., 31st Conference On Neural Information Processing Systems 2017, June 7, 2017; and Section 1 of "Variational Graph Auto-Encoders" by Thomas N. Kipf et al., 30th Conference On Neural Information Processing Systems 2017 (Bayesian Deep Learning Workshop), November 21, 2016 which describes “calculate embeddings z” and “adjacency matrix ” based on a math function description.
Finally, Applicant’s claim does not explain how the AI/ML functions, and are not directed to any improvement in the functioning of AI or ML nor are directed to have changed how the AI portions being claimed operated. Rather, the argument merely recite that the AI/ML is used to perform what otherwise can be done mentally or by math, but this is mere implementation on a computer. Notably, the specification appears silent as to any specific new forms of Neural Network or Machine Learning techniques itself, and appears to repeatedly just state “using trained neural networks or other trained machine learning models” which is a generic application of NNs or ML as a tool or merely tying this to the field of use of AI.
Therefore, the claims do not overcome the 101 rejection and they remain rejected under 101 rejections.
Detailed explanation is provided below in the 101 sections.
103 Rejection:
103 rejection rejections have been withdrawn based on the amendments and the arguments.
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 1-29 are rejected under 35 U.S.C. 101 because of the following reasons:
Claim 1:
At Step 1:
The claim is directed to a "computer-implemented method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“obtaining,
-‘determining,
-“determining, recites a mental process because human mind can determine whether a floor plan for the indicated house has characteristics matching at least some of the determined characteristics that are associated with the indicated house by evaluation and judgement.
-“generating,adjacency graph which has multiple nodes, represents rooms and that stores data by evaluation and judgment of data and/or with math concept/calculation.
-“and wherein the generating of the adjacency graph includes predicting,
-“generating, connectivity status for each of the multiple edges” recites a mental process because human mind can generate a mapping function an embedding vector to represent information from the adjacency graph that corresponds to attributes for the determined house by evaluation and judging the house data. Other than reciting this is done “by the computing device” and “using a graph convolutional neural network that applies representation learning” the entire “generating” is a math concept and/or mental process. The Neural Network is explicitly used just to “implement” or ‘apply’ a mapping function. Such a mapping function is a mathematical calculation or relationship, just stated in words. That is, this is a function that by math maps the nodes into a vector space, where a vector space is a mathematical representation. For instance that Fig. 2E is an example of such a generated “adjacency graph” which can be done mentally and/or with graph mathematics and includes predictions of inter-room connections such as doorways 268c, etc.
-“determining,
-“ determining, in part on an additional adjacency graph for the other house, wherein the at least some attributes of the other house include objective attributes about the other house that are able to be independently verified and further include one or more additional subjective attributes for the other house that are predicted by
-“determining, and include at least one indicated subjective attribute and include at least one indicated type of adjacency between at least two types of rooms and include at least one indicated type of inter-room connection between at least two types of rooms” recites a mental process because human mind can determine if the additional adjacency graph for the other houses that matches the one or more search criteria by evaluation and judgement and the limitation.
-“selecting,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“computing device” is a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“using a trained machine learning models”, “one or more first trained machine learning models”, “one or more second trained machine learning models”, “one or more third trained machine learning models”, “and using a graph convolutional neural network that applies representation learning to implement” are Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving, by the computing device, a search request that indicates an indication of one house of the multiple indicated houses and includes one or more search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“presenting, by the computing device and as part of search results for the search request, information about attributes of the determined at least one other house, to enable a determination of one or more relations to the plurality of attributes associated with the indicated house” is insignificant extra-solution activity as mere data gathering such as 'outputting data'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving, by the computing device, a search request that indicates an indication of one house of the multiple indicated houses and includes one or more search criteria” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“presenting, by the computing device and as part of search results for the search request, information about attributes of the determined at least one other house, to enable a determination of one or more relations to the plurality of attributes associated with the indicated house” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 2:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, mind can generate plurality of houses an adjacency graph that represents the house by evaluation and judgement and/or math concept/calculation.
-“learning,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the learning is based at least in part on using graph representation learning” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“and wherein the generating of the embedding vector for the indicated house is performed after the learning and includes using the learned subset of attributes for the generated embedding vector” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 3:
At Step 1:
The claim is directed to a "a method" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“obtaining,
is a mental process because human mind can obtain data about a building by observation data.
-“generating,
-“generating, multiple rooms” recites a mental process because human mind can generate an embedding vector to represent information from the adjacency graph by evaluation and judgement and/or mathematical concept. Other than reciting this is done “by the computing device” and “using a graph convolutional neural network that applies representation learning” the entire “generating” is a math concept and/or mental process. The NN is explicitly used just to “implement” or ‘apply’ a mapping function. Such a mapping function is a mathematical calculation or relationship, stated in words. That is, this is a function that by math maps the nodes into a vector space, where a vector space is a mathematical representation. For instance that Fig. 2E is an example of such a generated “adjacency graph” which can be done mentally and/or with graph mathematics and includes predictions of inter-room connections such as doorways 268c, etc.
-“determining,
-“determining,
-“selecting,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a computing device” is a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“and using a graph convolutional neural network that applies representation learning to implement” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving, by a computing device, a search request about an indicated building that is actually built and has multiple rooms” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“and presenting, by the computing device and as part of search results for the search request, information about attributes of the determined at least one other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” is insignificant extra-solution activity as mere data gathering such as 'outputting data'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving, by a computing device, a search request about an indicated building that is actually built and has multiple rooms” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“and presenting, by the computing device and as part of search results for the search request, information about attributes of the determined at least one other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" for Berkheimer support of being well-understood, routine, and conventional computer outputting of data.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4:
At Step 1:
The claim is directed to a "a system" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“generating, using at least a floor plan for the indicated building, an adjacency graph that represents the indicated building and that stores attributes associated with the indicated building, wherein the adjacency graph has multiple nodes that are each associated with one of the multiple rooms and stores information about one or more of the attributes that correspond to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and represent an inter-room adjacency in the indicated building of the associated two rooms for those two nodes” recites a mental process because human mind can generate an adjacency graph that represents the indicated house with multiple rooms and multiple edges by evaluation and judgement of data and/or by math concept. For instance Fig. 2E is an example of such a generated “adjacency graph” which can be done mentally and/or with graph mathematics and includes predictions of inter-room connections such as doorways 268c, etc.
-“and wherein the generating of the adjacency graph includes predicting, between those two rooms that includes at least one of a doorway between those two rooms, or a wall between those two rooms without an inter-room wall opening, or anon-doorway wall opening between those two rooms” recites a mental process because human mind can generate an adjacency graph that represents the indicated house by predicting a connectivity status between two rooms by evaluation and judgement of data and/or by math concept. Other than reciting this is done “by the computing device” and “using a trained ML model” the entire “generating” is a math concept and/or mental process. Note for instance that Fig. 2E is an example of such a generated “adjacency graph” which can be done mentally and/or with graph mathematics and includes predictions of inter-room connections such as doorways 268c, etc.
-“determining, from a plurality of other buildings that are actually built, at least one other building similar to the indicated building, including:” recites a mental process because human mind can determine information about an indicated building having multiple rooms by evaluation and judgement.
-“determining, for each of the plurality of other buildings, a degree of similarity between the embedding vector for the indicated building and an additional embedding vector that is associated with the other building to represent at least some attributes of the other building and to represent information about additional inter-room adjacencies between at least some additional rooms of the other building; and” recites a mental process because human mind can determine other building from multiple buildings similar to indicated building by evaluation and judgement.
-“obtaining information about the indicated building that includes a generated embedding vector to represent at least a subset of a plurality of attributes associated with the indicated building from the adjacency graph representing the indicated building and to represent information about multiple inter-room adjacencies between at least some of the multiple rooms and to include information about the predicted connectivity status for each of the multiple edges, wherein the adjacency graph has multiple nodes each associated with one of the multiple rooms and storing information about one or more of the attributes corresponding to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and each represents one of the inter-room adjacencies in the indicated building of the associated rooms for those two nodes” recites a mental process because human mind can obtain information and stores information regarding a building by observation and evaluation and judgement of data.
-“selecting one or more of the plurality of other buildings that each has an associated additional embedding vector with a determined degree of similarity to the embedding vector for the indicated building that is above a determined threshold, and using the selected one or more other buildings as the determined at least one other building” recites a mental process because human mind can select a building from the multiple buildings to use as the determined other building associated with additional embedding vector for the determined other building and the embedding vector for the indicated building by evaluating and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more hardware processors of one or more computing systems” and “one or more memories with stored instructions that, when executed by at least one of the one or more hardware processors, cause the one or more computing systems to perform automated operations including at least” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“receiving a search request about an indicated building that is actually built and has multiple rooms” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“providing, as part of search results for the search request, information about attributes of the determined at least one other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” identified as insignificant extra-solution activity as data outputting.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving, by a computing device, a search request about an indicated building that is actually built and has multiple rooms” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“providing, as part of search results for the search request, information about attributes of the determined at least one other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“obtaining information about an the indicated building that includes having multiple rooms, including obtaining an embedding vector for the indicated building that is generated to represent at least a subset of a plurality of attributes associated with the indicated building and using from an adjacency graph representing the indicated building and storing the plurality of attributes to represent information about multiple inter-room adjacencies between at least some of the multiple rooms, wherein the adjacency graph has multiple nodes each associated with one of the multiple rooms” is a mental process because human mind can obtain knowledge of a building and storing the attributes to represent multiple rooms by observation and evaluation/judgement of data.
-“generating,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using a graph convolutional neural network that applies representation learning to implement” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 6:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“….generating the floor plan based at least in part on analysis of visual data of a plurality of images acquired at a plurality of acquisition locations that are associated with the building and that include multiple acquisition locations with the multiple rooms of the building and that further include one or more acquisition locations external to the building” recites a mental process because human mind can generate adjacency graph based on the visual analysis by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“client computing device”, “wherein the stored instructions include software instructions that, when executed by at least one of the one or more computing systems, cause the at least one computing system to perform” is a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“wherein the providing of the information about the attributes of the determined at least one other building includes transmitting the information about the attributes of the determined at least one other building over one or more computer networks to the client computing device” is insignificant extra-solution activity as outputting data. See MPEP 2106.05(g).
-“and wherein the automated operations further include receiving by the client computing device” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“displaying on the client computing device the provided information about the attributes of the determined at least one other building” is insignificant extra-solution activity as outputting data. See MPEP 2106.05(g).
-“and transmitting, by the client computing device and to the one or more computing systems, information from an interaction of the user with a user-selectable control on the client computing device to cause a modification of information displayed on the client computing device for the determined at least one other building” is insignificant extra-solution activity as outputting data. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the providing of the information about the attributes of the determined at least one other building includes transmitting the information about the attributes of the determined at least one other building over one or more computer networks to the client computing device” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) by at least , "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
-“and wherein the automated operations further include receiving by the client computing device” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
-“displaying on the client computing device the provided information about the attributes of the determined at least one other building” is WURC as evidence by the court cases cited in MPEP 2106.05(d)(II) by at least "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" for Berkheimer support of being well-understood, routine, and conventional computer outputting of data.
-“and transmitting, by the client computing device and to the one or more computing systems, information from an interaction of the user with a user-selectable control on the client computing device to cause a modification of information displayed on the client computing device for the determined at least one other building” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7:
At Step 1:
The claim is directed to a "a non-transitory computer-readable medium" and thus directed to a statutory category.
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining,
-“including generating,
-“determining,
-“determining,
-“selecting,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“a non-transitory computer-readable medium having stored contents that cause one or more computing systems to perform automated operations” which are all a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“using a graph convolutional neural network that applies representation learning to implement dimensionality reduction techniques” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data) and/or apply it limitation.
-“providing, by the one or more computing systems and as part of search results for the indicated building, information about attributes of the determined other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” identified as insignificant extra-solution activity as data outputting.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“providing, by the one or more computing systems and as part of search results for the indicated building, information about attributes of the determined other building, to enable a determination of one or more relations to the plurality of attributes associated with the indicated building” is WURC see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“obtaining,
-“generating, such a generated “adjacency graph” which can be done mentally and/or with graph mathematics and includes predictions of inter-room connections such as doorways 268c, etc
-“wherein the generating of the embedding vector includes implementing a mapping function that maps the multiple nodes of the adjacency graph to a space with d-dimensional vectors in such a manner that similar adjacency graphs have similar embeddings in the space” recites a mental process because human mind can generate a adjacency graph by evaluation and judgement and/or a mathematical calculation.
Claim 9:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the selecting of the one other building includes using a similarity distance as the measure of difference to measure a degree of similarity for that other building between the associated additional embedding vector for that other building and the embedding vector for the indicated building, and further includes selecting the other building based each of the one or more other buildings being above a defined threshold” recites a mental process because human mind can select buildings by measure a difference to measure a degree of similarity that are above a threshold with other buildings by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the stored contents include software instructions that, when executed by at least one of the one or more computing systems, cause the at least one computing system to perform further automated operations including obtaining the plurality of images, wherein the plurality of images further include one or more images acquired at one or more acquisition locations external to the building” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“and wherein the providing of the information about the attributes of the determined other building includes transmitting the information about the attributes of the determined other building over one or more computer networks to at least one client computing device for display” is insignificant extra-solution activity as data outputting See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the stored contents include software instructions that, when executed by at least one of the one or more computing systems, cause the at least one computing system to perform further automated operations including obtaining the plurality of images, wherein the plurality of images further include one or more images acquired at one or more acquisition locations external to the building” is WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" .
-“and wherein the providing of the information about the attributes of the determined other building includes transmitting the information about the attributes of the determined other building over one or more computer networks to at least one client computing device for display” is WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“and identifying the indicated building based at least in part on the one or more search criteria” recites a mental process because human mind can identify the indicated building based at least in part of the one or more search criteria by evaluation and judgment.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the automated operations further include receiving, by the one or more computing systems, one or more search criteria” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“wherein the providing of the information about the attributes of the determined other building includes providing the search results for presentation the search results including that include the determined other building” is insignificant extra-solution activity as data outputting. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the automated operations further include receiving, by the one or more computing systems, one or more search criteria” is WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" .
-“wherein the providing of the information about the attributes of the determined other building includes providing the search results for presentation the search results including that include the determined other building” is WURC, see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9" for Berkheimer support of being well-understood, routine, and conventional computer outputting of data.
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11:
At Step 2A, Prong Two:
The claim recites the following additional elements:
“wherein the one or more search criteria include one or more criteria that are based on an inter- room adjacency of at least two types of rooms, wherein the embedding vector includes information about adjacencies of the multiple rooms in the indicated building, and wherein the additional embedding vector for the determined other building represents information about adjacencies of rooms in that other building, and the determined measure of difference for that additional embedding vector for the determined other building to the embedding vector for the indicated building is based at least in part on the inter-room adjacency pairs adjacencies of the multiple rooms in the indicated building and the adjacencies additional inter-room adjacency pairs of the additional rooms in that other building” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 12:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the one or more search criteria include one or more criteria that are based on visual attributes of a building interior, wherein the embedding vector includes information about visual attributes of an interior of the indicated building, and wherein the additional embedding vector for the determined other building represents information about additional visual attributes of an interior of that other building, and the determined measure of difference for that additional embedding vector for the determined other building to the embedding vector for the indicated building is based at least in part on the visual attributes of the interior of the indicated building and the additional visual attributes of the interior of that other building” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 13:
At Step 2A, Prong Two:
The claim recites the following additional elements:
- “wherein the one or more search criteria include one or more criteria that are based on one or more types of exterior views from a building, wherein the embedding vector includes information about views from the indicated building to its surroundings, and wherein the additional embedding vector for the determined other building represents information about additional views from that other building to its surroundings, and the determined measure of difference for that additional embedding vector for the determined other building to the embedding vector for the indicated building is based at least in part on the views from the indicated building to its surroundings and the additional views from that other building to its surroundings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 14:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining information about the attributes of the determined other building that is personalized to the user” recites a mental process because human mind can determine information about the attributes of the determined other building that is personalized to the user by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the automated operations further include receiving, by the one or more computing systems, information about the indicated building being associated with a user, wherein the determining of the at least one other building is performed in response to the receiving of the information and includes” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
-“wherein the providing of the information about the attributes of the determined other building includes presenting to the user the information about the attributes of the determined other building” is insignificant extra-solution activity as mere data outputting. See MPEP 2106.05(g)
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the automated operations further include receiving, by the one or more computing systems, information about the indicated building being associated with a user, wherein the determining of the at least one other building is performed in response to the receiving of the information and includes” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
-“and wherein the providing of the information about the attributes of the determined other building includes presenting to the user the information about the attributes of the determined other building” is WURC and/or "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 15:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“determining, by the one or more computing systems, an expected assessment of at least one of condition or quality or value of the indicated building based at least in part on assessments of the multiple other buildings” recites a mental process because human mind can determine an expected assessment of the condition or quality or value of the building by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the determined other building includes multiple other buildings” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“and wherein the providing of the information about the attributes of the determined other building, and providing information about the determined expected assessment” is insignificant extra-solution activity as data outputting, see MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“and wherein the providing of the information about the attributes of the determined other building, and providing information about the determined expected assessment” is WURC, see MPEP 2106.05(d)(II) "iv. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-9".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 16:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“automatically learning,
“-and generating the embedding vector to encode information about some attributes of the indicated building” recites a mental process because human mind can generate embedding vector to encode by evaluation and judgement and/or a mathematical calculation.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“using graph representation learning”, Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 17:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
“generating of the embedding vector for the indicated building includes incorporating information in the embedding vector about, for each of the multiple rooms, at least one attribute that corresponds to the room and about information about adjacencies the inter-room adjacency pairs of the multiple rooms that are adjacent to each other in the indicated building” recites a mental process because human mind can generate embedding vector by evaluation and judgement and/or mathematical calculation.
Claim 18:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the embedding vector further includes incorporating,
Claim 19:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the embedding vector further includes incorporating,
Claim 20:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the embedding vector further includes incorporating, mental process because human mind can incorporate data in the embedding vector by evaluation and judgement and/or mathematical important.
Claim 21:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the automated operations further include predicting,
-“wherein the generating of the embedding vector further includes incorporating,
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more trained machine learning models”, “wherein the plurality of attributes associated with the indicated building are objective attributes that are independently verifiable” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
- “automated operations” , “one or more computing systems” is a high-level recitation of a generic computer components and represent mere instructions to apply the judicial exception on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
-“by supplying information about the indicated building to one or more…models” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
- “receiving output indicating the one or more additional subjective attributes” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“by supplying information about the indicated building to one or more…models” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
- “receiving output indicating the one or more additional subjective attributes” is WURC as evidenced by the court cases cited in MPEP 2106.05(d)(II) by at least "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" and "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, … OIP Techs., 788 F.3d at 1363."
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 22:
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the one or more additional subjective attributes include at least one of an atypical floor plan that differs from typical floor plans, or an open floor plan, or an accessible floor plan, or a non-standard floor plan” is generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 23:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the automated operations further include predicting,
-“and wherein the generating of the embedding vector further includes incorporating, by the one or more computing systems, information in the embedding vector about the room types of the multiple rooms” recites a mental process because human mind can generate embedding vectors by incorporating data by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more trained machine learning models” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“by supplying information about the indicated building to one or more Machine learning Models” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“receiving output indicating the room types of the multiple room” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“receiving output indicating the room types of the multiple room” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g) is WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)".
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 24:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the predicting of the room types of the multiple rooms includes using,
Claim 25:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the automated operations further include predicting, by the one or more computing systems, and for each inter-room adjacency pair of two rooms of the indicated building, a connectivity status of whether the two rooms are connected via an inter-room wall opening recites a mental process because human mind can predict connectivity status by evaluation and judgment.
-“wherein the generating of the embedding vector further includes incorporating, by the one or more computing systems, information in the embedding vector about the connectivity status for each of the edges” recites a mental process because human mind can generate embedding vectors by incorporating data by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“one or more trained machine learning models” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“by supplying information about the indicated building to one or more Machine learning Models” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
-“and receiving output indicating the connectivity status for each of the edges” is insignificant extra-solution activity as mere data gathering such as 'obtaining information'. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
-The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“and receiving output indicating the connectivity status for each of the edges” WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" .
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Claim 26:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the predicting, for each inter-room adjacency pair of in the indicated building between two rooms of the indicated building, of the connectivity status includes at least one of predicting a wall between the two rooms without an inter-room wall opening or predicting a doorway between the two rooms or predicting a non-doorway wall opening between the two rooms, and wherein the incorporated information in the embedding vector includes information about the at least one of the predicted wall or the predicted doorway or other predicted non-doorway wall opening” recites a mental process because human mind can predict data by evaluation and judgement.
Claim 27:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the adjacency information for the indicated building includes an the adjacency graph, the adjacency graph further having multiple edges between the multiple nodes that are each between two nodes and represent one of the inter-room adjacency pairs for the associated rooms for those two nodes, wherein the multiple edges of the adjacency graph include one or more connectivity edges that each represents that two rooms whose adjacency is represented by the connectivity edge are connected in the indicated building via a doorway or a non-doorway wall opening, wherein the one or more connectivity edges each further stores information about characteristics of the doorway or the non-doorway wall opening for that connectivity edge, and wherein the generating of the embedding vector further includes incorporating, by the computing device, information in the embedding vector about characteristics of the doorway or the non-doorway wall opening for each of the one or more connectivity edges” recites a mental process because human mind can generate embedding vectors by incorporating data by evaluation and judgment and/or mathematical concept/calculation.
Claim 28:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“wherein the generating of the embedding vector for the indicated building includes incorporating information in the embedding vector about the at least one of the exterior area or the external view for each of the one or more additional nodes” recites a mental process because human mind can incorporate data by evaluation and judgment and/or mathematical concept/calculation.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“further has multiple edges between the multiple nodes that are each between two nodes and represent one of the inter-room adjacency pairs for the associated rooms for those two nodes and that further has one or more additional nodes that each corresponds to at least one of an exterior area outside of the indicated building or an external view from an interior of the building to an exterior of the indicated building and that further has at least one additional edge for each of the one or more additional nodes that connects that additional node to another node of the adjacency graph” is Generally linking the use of the judicial exception to a particular technological environment or field of use by limiting it to a particular data source or type. See MPEP § 2106.05(h) and Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
Claim 29:
At Step 2A, Prong One:
The claim recites the following limitations directed to an abstract idea:
-“obtaining a further embedding vector for each of the one or more additional indicated buildings, wherein the determining of the measure of difference is further performed for each of the one or more additional indicated buildings between the further embedding vector for that additional indicated building and the additional embedding vectors for each of the plurality of other buildings” is a mental process because human mind can obtain embedding vector for each of the additional buildings by observation of data.
-“wherein the determining of the measure of difference is further performed for each of the one or more additional indicated buildings between the further embedding vector for that additional indicated building and the additional embedding vectors for each of the plurality of other buildings” recites a mental process because human mind can determine the measure of difference by evaluation and judgement and/or mathematical calculation/concept.
-“wherein the selecting of the one or more other buildings is further based on the determined measures of difference between the associated additional embedding vector for each of the one or more other buildings and the further embedding vectors for each of the one or more additional indicated buildings, such that selection of the one or more other buildings is based on aggregate differences for the embedding vector of the indicated building and the further embedding vectors for the additional indicated buildings to the associated additional embedding vector for each of the one or more other buildings” recites a mental process because human mind can select data by evaluation and judgement.
At Step 2A, Prong Two:
The claim recites the following additional elements:
-“wherein the automated operations further include receiving information about multiple buildings that include the indicated building and one or more additional indicated buildings” is insignificant extra-solution activity as outputting data. See MPEP 2106.05(g).
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
At Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
-“wherein the automated operations further include receiving information about multiple buildings that include the indicated building and one or more additional indicated buildings” is WURC see MPEP 2106.05(d)(II) "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)" .
Accordingly, at step 2B, these additional elements, both individually and in combination, do not amount to significantly more than the judicial exception. See MPEP § 2106.05. Therefore, the claim is not eligible subject matter under 35 U.S.C. 101.
Prior arts considerations
101 rejections are cited for claims 1-29. Prior arts are not cited for claims 1-29.
Prior arts of record Hu teaches embedding vectors and adjacency graphs for rooms with nodes in page 11, 1st and 2nd col. Pal teaches comparing embedding vectors with measure of difference in col. 10, lines 5-15; Saad teaches comparing embedding vectors with a threshold in para. [0034].
Prior arts of record in combination do not explicitly teach “generating, by the computing device, and for the one indicated house by using at least the floor plan of the one indicated house, an adjacency graph that represents the one indicated house and that stores attributes associated with the one indicated house including at least one subjective attribute determined for the one indicated house, wherein the adjacency graph has multiple nodes that are each associated with one of multiple rooms of the one indicated house and stores information about one or more of the attributes that correspond to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and represent an adjacency in the one indicated house of the associated rooms for those two nodes, and wherein the generating of the adjacency graph includes predicting, using a trained machine learning model and for each of the multiple edges representing an inter-room adjacency in the indicated building between two rooms, a connectivity status between those two rooms that includes at least one of a doorway between those two rooms, or a wall between those two rooms without an inter-room wall opening, or a non-doorway wall opening between those two rooms; generating, by the computing device; and using a graph convolutional neural network that applies representation learning to implement a mapping function that maps the multiple nodes of the adjacency graph to a concise format in a space with d-dimensional vectors in such a manner that similar adjacency graphs have similar embeddings in the space, an embedding vector to in that format that concisely represents information from the adjacency graph that corresponding to a subset of a plurality of attributes of the indicated house including the at least one subjective attribute determined for the one indicated house and including information about the predicted connectivity status for each of the multiple edges” as recited in claim 1, “generating, by the computing device, and using at least the floor plan, an adjacency graph that represents the indicated building and that stores attributes associated with the indicated building, wherein the adjacency graph has multiple nodes that are each associated with one of the multiple rooms and stores information about one or more of the attributes that correspond to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and represent an inter-room adjacency in the indicated building of the associated rooms for those two nodes; generating, by the computing device, and using a graph convolutional neural network that applies representation learning to implement dimensionality reduction techniques that map the multiple nodes of the adjacency graph to a concise format, an embedding vector to in that format that concisely represents information from the adjacency graph that correspondings to a subset of a plurality of attributes of the indicated building and that includes information from two or more edges of the multiple edges about multiple inter-room adjacencies that are each between two rooms of the multiple rooms; determining, by the computing device, and from a plurality of other buildings that are actually built, at least one other building similar to the indicated building, including: determining, by the computing device, and for each of the plurality of other buildings, a degree of similarity between the generated embedding vector for the indicated building and an additional embedding vector that is associated with the other building to represent at least some attributes of the other building and includes information about additional inter-room adjacencies between rooms of the other building”; “generating, using at least a floor plan for the indicated building, an adjacency graph that represents the indicated building and that stores attributes associated with the indicated building, wherein the adjacency graph has multiple nodes that are each associated with one of the multiple rooms and stores information about one or more of the attributes that correspond to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and represent an inter-room adjacency in the indicated building of the associated two rooms for those two nodes, and wherein the generating of the adjacency graph includes predicting, using a trained machine learning model and for each of the multiple edges representing an inter-room adjacency in the indicated building between two rooms, a connectivity status between those two rooms that includes at least one of a doorway between those two rooms, or a wall between those two rooms without an inter-room wall opening, or anon-doorway wall opening between those two rooms; obtaining information about the indicated building that includes an a generated embedding vector generated to represent at least a subset of a plurality of attributes associated with the indicated building from an the adjacency graph representing the indicated building and to represent information about multiple inter-room adjacencies between at least some of the multiple rooms and to include information about the predicted connectivity status for each of the multiple edges, wherein the adjacency graph has multiple nodes each associated with one of the multiple rooms and storing information about one or more of the attributes corresponding to the associated room, and wherein the adjacency graph further has multiple edges between the multiple nodes that are each between two nodes and each represents one of the inter-room adjacencies in the indicated building of the associated rooms for those two nodes” in claim 4 and “determining, by the one or more computing systems and using an adjacency graph having multiple nodes each associated with one of multiple rooms of an indicated building that is actually built and storing a plurality of attributes associated with the indicated building, information about an the indicated building that is actually built and has multiple rooms, including obtaining generating, by the computing device and using a graph convolutional neural network that applies representation learning to implement dimensionality reduction techniques that map the multiple nodes of the adjacency graph to a concise format, an embedding vector for the indicated building in that format that concisely that is generated to represents at least a subset of a-the plurality of attributes associated with the indicated building and to represent adjacency information for the indicated building including at least one attribute for each of the multiple rooms and further including indications of inter-room adjacency pairs of the multiple rooms that are adjacent to each other in the indicated building; determining, by the one or more computing systems and from a plurality of other buildings, other building corresponding to the indicated building, including: determining, by the one or more computing systems and for each of the plurality of other buildings, a measure of a difference between the embedding vector for the indicated building and an additional embedding vector that is associated with the other building to represent at least some attributes of the other building and to represent additional inter-room adjacency pairs of additional rooms in the other building” in claim 7.
Conclusion
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/FATIMA P MINA/Examiner, Art Unit 2159
/ANN J LO/Supervisory Patent Examiner, Art Unit 2159