DETAILED ACTION
Status of Claims
The Response filed 08/07/2026 has been acknowledged. Claims 6, 13, 19 have been amended. Claims 1-20 are currently pending and have been examined.
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 .
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 claim recites receiving information including image and text information about a premises, determining and labeling characteristics regarding premises, associating the characteristics with particular valuation trends, and providing recommendations.
The limitations of receiving, determining, labeling, and associating, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computing device”, “programmatically”, and “machine learning” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “computing device” and “programmatically” language, “receiving” and “determining” in the context of this claim encompasses a person looking over real estate listings and mentally recognizing characteristics of individual properties (for example, looking at listings in a town and seeing multiple listings are a particular style of townhome).. Similarly, the limitation of “associating” and “outputting”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “machine learning”, “associating” and “outputting” in the context of this claim encompasses the person mentally recognizing a particular style of property is common or popular and physically providing a recommendation to build/purchase a similar property. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “computing device”, and “programmatically” and “machine learning model”. The computing device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving information, performing recognition, executing instructions to recognize, store, and associate information, and outputting information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Still furthermore, although the claims recite “programmatically” and “machine learning model”, these are still considered particular instructions applied to generic processors. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The dependent claims are further directed towards the judicial exception without significantly more. The dependent claims provide limitations on the source of the information (such as claim 2), the type of information analyzed and output (such as claim 3-5), displaying the information (such as claim 6), and additional abstract steps of receiving information and performing additional analysis (such as claim 7). These are still directed towards the judicial exception as these further define the abstract elements such as further the source of information, abstract analysis, additional abstract steps, and outputting information. They are not significantly more as they do not further integrate the judicial exception into a practical application and the additional element amounts to no more than mere instructions to apply the exception using a generic computer component or extra-solution activity. The dependent claims is not patent eligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gomez et al. (US 20250182225 A1) (hereafter Gomez), in view of D’Souza et al. (US 20170032481 A1) (hereafter D’Souza).
As per claim 1:
A method comprising:
receiving, by a computing device, from a real estate database, image data associated with one or more premises and text data associated with the one or more premises; (See Gomez ¶0051, “According to some aspects, the computing device 102 may be configured to receive third party data 158 from the third party data sources 156. For example, third party data 158 may include data identified by, collected by, and/or aggregated by a third party data collector, such as a commercial entity, a government entity, a news source, an information aggregation source, an online publication, or the like. Third party data 158 may include, as non-limiting examples, the list prices of homes offered by sellers not associated with the entity (e.g., competitor home builders and/or competitor home sellers), the number of homes sold in a given time period by a home seller, plan information related to one or more sets of homes (e.g., a count of bedrooms, a count of bathrooms, a building area, a lot area, a garage plan, a floorplan, etc.), geographical information related to one or more sets of homes (e.g., including a an address, a school district, a county, a state, a type of street), and/or home listings and sales information from a commercial entity that monitors and provides regional or nation-wide home information.” Gomez discloses receiving information from real estate database regarding property listings.)
associating via a machine learning model, one or more of the premises dimensions data, premises design data, premises architectural data, or premises listing data, with one or more valuation trends; and (See Gomez ¶0082, “According to aspects, prediction engine 124 may include or correspond to a trained ML model 126. The trained ML model 126 may be specifically trained to predict performance for the home builder by comparing performance data associated with the home builder with historic performance data and with data related to demand. According to aspects, the trained ML model 126 may include or correspond to one or more neural networks. For example, the trained ML model 126 may be implemented as a long short-term memory (LSTM) model. An LSTM model is a type of recurrent neural network that is capable of learning patterns and dependencies in sequential data. Advantages of an LSTM model over other trained ML models for predictions in accordance with aspects may include better performance in identifying and predicting long-term trends in data. An LSTM model may be configured to add lag to features of the model in order to generate accurate predictions. For example, the LSTM model may identify and base predictions on the appropriate amount of lag between demand data, such as website visits over a time period or a kept showing appointments, and conversions (e.g., completed sales). These advantages are provided for illustrative purposes, and not by way of limitation. Other types of trained ML prediction models may also be configured to perform sales predictions according to aspects of the disclosure.” Gomez discloses utilizing learning machine to determine housing market trends based on collected information.)
outputting, based on the one or more valuation trends, one or more building recommendations or one or more purchasing recommendations. (See Gomez ¶0092, “Additionally, or alternatively, GUI 500 may include an instruction 530. Instruction 530 may include actionable steps that a home builder may undertake. Instruction 530 may be based on the predictions determined by the predictive engine 124, either on their own or as provided to the recommendation module 138. The instruction 530 may include a recommendation 532 that an entity (e.g., home builder) undertake some actionable step. Non limiting examples of the recommendation 532 may include a recommendation to raise or lower the price of a given home, a recommendation to increase or decrease a construction rate for a home, a recommendation to acquire additional property lots, a recommendation to adjust a pacing value for a given home, or a combination thereof.” Gomez discloses outputting recommendations including building recommendations. See also Gomez ¶0064, “Other selectable elements in addition to or in the alternative to those shown in FIG. 2 may be included to adjust parameters, including selectable elements for adjusting parameters such as, for example, home price ranges, dates that homes became available, floor plan details (e.g., dimensions of the home, number of bedrooms, number of bathrooms, size of lot, size of garage, and so on), which contractors are responsible for constructing the house. These and/or other parameters may also be adjusted in response to changes in data received. In some implementations, the system may automatically adjust data-based parameters as they are received. As a result of adjustments to the parameters, whether selected by a user or determined in response to newly received data, the system may then correspondingly adjust the target pace and/or historical pace. In some implementations, the system may automatically modify the GUI 200 to display the respective adjustments to the target pace and/or historical pace.” Gomez discloses the concept of making changes to particular attributes regarding a property for construction.)
Although Gomez discloses the above-enclosed invention, Gomez fails to explicitly disclose processing and labeling information using images.
However D’Souza as shown, which talks about image based property identification, teaches the concept of processing and labeling information using images.
determining, based on the image data associated with the one or more premises and the text data associated with the one or more premises, (See D’Souza ¶0048, “According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the concept of utilizing text and image information associated with listings.)
premises dimensions data associated with the one or more premises,
premises design data associated with one or more premises,
premises architectural data associated with one or more premises, and
premises listing data associated with the one or more premises;
(See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the property characteristics to include square footage, design and architectural information. See also D'Souza ¶0048, “ According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the property information to further include listing information.)
programmatically labeling, based on the premises dimensions data, premises design data, premises architectural data, and premises listing data, the image data and text data; (See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of labeling listing including particular characteristics of the property.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of D’Souza with the invention of Gomez. As shown, Gomez discloses the concept of collecting information from a plurality of sources, performing analysis on the collected information to identify trends, and providing recommendations based on the trends. D’Souza further teaches the concept of utilizing image and text processing to identify the characteristics of real estate. D’Souza teaches this concept to allow for additional information which is not explicitly stated to be considered in real estate listings including specific information for which there is not particular articulation (See D’Souza ¶0003-¶0004). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have further utilized the teachings of D’Souza with Gomez to further consider additional information regarding properties by performing analysis on the images of collected information, thereby further identifying characteristics of the other properties.
As per claim 2:
The method of claim 1, wherein the real estate database comprises a multiple listing network (MLS) database and wherein the one or more valuation trends comprise square foot values, and wherein the image data comprises one or more images captured by an image capture device. (See D’Souza ¶0016, “These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of the database to be MLS, and property characteristics including square footage.)
As per claim 3:
The method of claim 1, further comprising constructing one or more premises according to the one or more building recommendations. (See Gomez ¶0046, “The computing device 102 may be configured to receive data (e.g., home data) from a plurality of data sources and, based on insights derived from the data, to manage and control aspects of processes and systems of a home builder entity, including generating recommendations and/or instructions for agents of the home builder entity and/or for automatic performance. Several distinct aspects of this functionality are described throughout this disclosure. As an illustrative example, computing device 102 may be configured to determine a pace at which homes are constructed and sold based on data related to the homes, including construction status and availability for purchase.” Gomez discloses the concept of providing recommendations including recommending construction.)
As per claim 4:
The method of claim 1, wherein the premises dimensions data comprises one or more of:
floor plan data, square footage data, or lot size data, and wherein the premises design data comprises one or more of: cabinetry data, finish materials data, exterior features data, and
wherein premises listing data comprises one or more of:
pricing data, geographic data, time on market data, seller data, buyer data or agent data.
(See Gomez ¶0160, “According to aspects, the multi-tiered comparison of block 1106 may include a second tier that compares a difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes to a second threshold. For example, the dimensions of the home plans may include or correspond to an area (e.g. the square footage) of the home plans, or the dimensions may include or correspond to a price per unit area (e.g., dollar per square foot) of the home plans. According to aspects, the second threshold may be a percent difference (e.g., 10%, 15%, 20%, etc.) difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes.” Gomez discloses determining property dimension and listing information including square footage and price per unit area.)
As per claim 5:
The method of claim 1, wherein associating the one or more of the premises dimensions data, premises design data, premises architectural data, or premises listing data, with one or more valuation trends comprises determining one or more changes in price from a baseline price associated with a premises. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation to change the price from a baseline price.)
As per claim 6:
The method of claim 1, wherein outputting the one or more building recommendations comprises generating one or more visual outputs indicating the one or more building recommendations. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation through a graphical interface including recommendations regarding building/construction.)
As per claim 7:
The method of claim 1, further comprising:
receiving one or more of: updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises; and
updating, based on the updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises, the one or more valuation trends.
(See Gomez ¶0058, “For example, updates to the GUI 200 based on updates to the underlying data can be performed in real-time (e.g., as new data is received, updates may be performed) or periodically, including updates performed at regular intervals (e.g., every minute, every 5 minutes, every 10 minutes, every half hour, every hour, every 6 hours, every day, or at intervals longer than, shorter than, or in between the intervals listed here).” Gomez discloses receiving and updating information including performing updated analysis based on received updated information.)
As per claim 8:
An apparatus comprising:
one or more processors; and
a memory storing processor-executable instructions that, when executed by the one or more processors, cause the apparatus to:
(See Gomez ¶0047, “While shown as a computing device 102, the same or similar functionality may be provided by other implementations, such as over a network connection to one or more additional computing devices, or to one or more client devices (e.g., client device 170), through cloud based logic 140, through a distributed computing system, or other computing methods. As shown in FIG. 1, the computing device 102 may include one or more processors 104, a memory 106, one or more input/output (I/O) devices 118, one or more communication interfaces 120, a feature extraction engine 122, a prediction engine 124, a classification engine 130, a prediction training engine 134, a classification training engine 136, and a recommendation module 138.” Gomez discloses a processor executing stored instructions.)
receive, from a real estate database, image data associated with one or more premises and text data associated with the one or more premises; (See Gomez ¶0051, “According to some aspects, the computing device 102 may be configured to receive third party data 158 from the third party data sources 156. For example, third party data 158 may include data identified by, collected by, and/or aggregated by a third party data collector, such as a commercial entity, a government entity, a news source, an information aggregation source, an online publication, or the like. Third party data 158 may include, as non-limiting examples, the list prices of homes offered by sellers not associated with the entity (e.g., competitor home builders and/or competitor home sellers), the number of homes sold in a given time period by a home seller, plan information related to one or more sets of homes (e.g., a count of bedrooms, a count of bathrooms, a building area, a lot area, a garage plan, a floorplan, etc.), geographical information related to one or more sets of homes (e.g., including a an address, a school district, a county, a state, a type of street), and/or home listings and sales information from a commercial entity that monitors and provides regional or nation-wide home information.” Gomez discloses receiving information from real estate database regarding property listings.)
associate, via a machine learning model, one or more of the premises dimensions data, premises design data, premises architectural data, or premises listing data, with one or more valuation trends; and (See Gomez ¶0082, “According to aspects, prediction engine 124 may include or correspond to a trained ML model 126. The trained ML model 126 may be specifically trained to predict performance for the home builder by comparing performance data associated with the home builder with historic performance data and with data related to demand. According to aspects, the trained ML model 126 may include or correspond to one or more neural networks. For example, the trained ML model 126 may be implemented as a long short-term memory (LSTM) model. An LSTM model is a type of recurrent neural network that is capable of learning patterns and dependencies in sequential data. Advantages of an LSTM model over other trained ML models for predictions in accordance with aspects may include better performance in identifying and predicting long-term trends in data. An LSTM model may be configured to add lag to features of the model in order to generate accurate predictions. For example, the LSTM model may identify and base predictions on the appropriate amount of lag between demand data, such as website visits over a time period or a kept showing appointments, and conversions (e.g., completed sales). These advantages are provided for illustrative purposes, and not by way of limitation. Other types of trained ML prediction models may also be configured to perform sales predictions according to aspects of the disclosure.” Gomez discloses utilizing learning machine to determine housing market trends based on collected information.)
output, based on the one or more valuation trends, one or more building recommendations or one or more purchasing recommendations. (See Gomez ¶0092, “Additionally, or alternatively, GUI 500 may include an instruction 530. Instruction 530 may include actionable steps that a home builder may undertake. Instruction 530 may be based on the predictions determined by the predictive engine 124, either on their own or as provided to the recommendation module 138. The instruction 530 may include a recommendation 532 that an entity (e.g., home builder) undertake some actionable step. Non limiting examples of the recommendation 532 may include a recommendation to raise or lower the price of a given home, a recommendation to increase or decrease a construction rate for a home, a recommendation to acquire additional property lots, a recommendation to adjust a pacing value for a given home, or a combination thereof.” Gomez discloses outputting recommendations including building recommendations. See also Gomez ¶0064, “Other selectable elements in addition to or in the alternative to those shown in FIG. 2 may be included to adjust parameters, including selectable elements for adjusting parameters such as, for example, home price ranges, dates that homes became available, floor plan details (e.g., dimensions of the home, number of bedrooms, number of bathrooms, size of lot, size of garage, and so on), which contractors are responsible for constructing the house. These and/or other parameters may also be adjusted in response to changes in data received. In some implementations, the system may automatically adjust data-based parameters as they are received. As a result of adjustments to the parameters, whether selected by a user or determined in response to newly received data, the system may then correspondingly adjust the target pace and/or historical pace. In some implementations, the system may automatically modify the GUI 200 to display the respective adjustments to the target pace and/or historical pace.” Gomez discloses the concept of making changes to particular attributes regarding a property for construction.)
Although Gomez discloses the above-enclosed invention, Gomez fails to explicitly disclose processing and labeling information using images.
However D’Souza as shown, which talks about image based property identification, teaches the concept of processing and labeling information using images.
determine, based on the image data associated with the one or more premises and the text data associated with the one or more premises, (See D’Souza ¶0048, “According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the concept of utilizing text and image information associated with listings.)
premises dimensions data associated with the one or more premises,
premises design data associated with one or more premises,
premises architectural data associated with one or more premises, and
premises listing data associated with the one or more premises;
(See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the property characteristics to include square footage, design and architectural information. See also D'Souza ¶0048, “ According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the property information to further include listing information.)
programmatically label, based on the premises dimensions data, premises design data, premises architectural data, and premises listing data, the image data and text data; (See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of labeling listing including particular characteristics of the property.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of D’Souza with the invention of Gomez. As shown, Gomez discloses the concept of collecting information from a plurality of sources, performing analysis on the collected information to identify trends, and providing recommendations based on the trends. D’Souza further teaches the concept of utilizing image and text processing to identify the characteristics of real estate. D’Souza teaches this concept to allow for additional information which is not explicitly stated to be considered in real estate listings including specific information for which there is not particular articulation (See D’Souza ¶0003-¶0004). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have further utilized the teachings of D’Souza with Gomez to further consider additional information regarding properties by performing analysis on the images of collected information, thereby further identifying characteristics of the other properties.
As per claim 9:
The apparatus of claim 8, wherein the real estate database comprises a multiple listing network (MLS) database and wherein the one or more valuation trends comprise square foot values, and wherein the image data comprises one or more images captured by an image capture device. (See D’Souza ¶0016, “These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of the database to be MLS, and property characteristics including square footage.)
As per claim 10:
The apparatus of claim 8, wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to construct one or more premises according to the one or more building recommendations. (See Gomez ¶0046, “The computing device 102 may be configured to receive data (e.g., home data) from a plurality of data sources and, based on insights derived from the data, to manage and control aspects of processes and systems of a home builder entity, including generating recommendations and/or instructions for agents of the home builder entity and/or for automatic performance. Several distinct aspects of this functionality are described throughout this disclosure. As an illustrative example, computing device 102 may be configured to determine a pace at which homes are constructed and sold based on data related to the homes, including construction status and availability for purchase.” Gomez discloses the concept of providing recommendations including recommending construction.)
As per claim 11:
The apparatus of claim 8, wherein the premises dimensions data comprises one or more of:
floor plan data, square footage data, or lot size data, and wherein the premises design data comprises one or more of: cabinetry data, finish materials data, exterior features data, and
wherein premises listing data comprises one or more of: pricing data, geographic data, time on market data, seller data, buyer data or agent data.
(See Gomez ¶0160, “According to aspects, the multi-tiered comparison of block 1106 may include a second tier that compares a difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes to a second threshold. For example, the dimensions of the home plans may include or correspond to an area (e.g. the square footage) of the home plans, or the dimensions may include or correspond to a price per unit area (e.g., dollar per square foot) of the home plans. According to aspects, the second threshold may be a percent difference (e.g., 10%, 15%, 20%, etc.) difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes.” Gomez discloses determining property dimension and listing information including square footage and price per unit area.)
As per claim 12:
The apparatus of claim 8, wherein the processor-executable instructions, that, when executed by the one or more processors, cause the one or more processors to associate the one or more of the premises dimensions data, premises design data, premises architectural data, or premises listing data, with one or more valuation trends further cause the one or more processors to determining one or more changes in price from a baseline price associated with a premises. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation to change the price from a baseline price.)
As per claim 13:
The apparatus of claim 8, wherein the processor-executable instructions, that, when executed by the one or more processors, cause the one or more processors to output the one or more building recommendations, further cause the one or more processors to generate one or more visual outputs indicating the one or more building recommendations. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation through a graphical interface including recommendations regarding building/construction.)
As per claim 14:
The apparatus of claim 8, wherein the processor-executable instructions, when executed by the one or more processors, further cause the one or more processors to:
receive one or more of: updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises; and
update, based on the updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises, the one or more valuation trends.
(See Gomez ¶0058, “For example, updates to the GUI 200 based on updates to the underlying data can be performed in real-time (e.g., as new data is received, updates may be performed) or periodically, including updates performed at regular intervals (e.g., every minute, every 5 minutes, every 10 minutes, every half hour, every hour, every 6 hours, every day, or at intervals longer than, shorter than, or in between the intervals listed here).” Gomez discloses receiving and updating information including performing updated analysis based on received updated information.)
As per claim 15:
One or more non-transitory computer-readable media storing processor-executable instructions thereon, which, when executed by at least one processor cause the at least one processor to: (See Gomez ¶0047, “While shown as a computing device 102, the same or similar functionality may be provided by other implementations, such as over a network connection to one or more additional computing devices, or to one or more client devices (e.g., client device 170), through cloud based logic 140, through a distributed computing system, or other computing methods. As shown in FIG. 1, the computing device 102 may include one or more processors 104, a memory 106, one or more input/output (I/O) devices 118, one or more communication interfaces 120, a feature extraction engine 122, a prediction engine 124, a classification engine 130, a prediction training engine 134, a classification training engine 136, and a recommendation module 138.” Gomez discloses a processor executing stored instructions.)
receive, from a real estate database, image data associated with one or more premises and text data associated with the one or more premises; (See Gomez ¶0051, “According to some aspects, the computing device 102 may be configured to receive third party data 158 from the third party data sources 156. For example, third party data 158 may include data identified by, collected by, and/or aggregated by a third party data collector, such as a commercial entity, a government entity, a news source, an information aggregation source, an online publication, or the like. Third party data 158 may include, as non-limiting examples, the list prices of homes offered by sellers not associated with the entity (e.g., competitor home builders and/or competitor home sellers), the number of homes sold in a given time period by a home seller, plan information related to one or more sets of homes (e.g., a count of bedrooms, a count of bathrooms, a building area, a lot area, a garage plan, a floorplan, etc.), geographical information related to one or more sets of homes (e.g., including a an address, a school district, a county, a state, a type of street), and/or home listings and sales information from a commercial entity that monitors and provides regional or nation-wide home information.” Gomez discloses receiving information from real estate database regarding property listings.)
associate, via a machine learning model, one or more of the premises dimensions data, premises design data, premises architectural data, or premises listing data, with one or more valuation trends; and (See Gomez ¶0082, “According to aspects, prediction engine 124 may include or correspond to a trained ML model 126. The trained ML model 126 may be specifically trained to predict performance for the home builder by comparing performance data associated with the home builder with historic performance data and with data related to demand. According to aspects, the trained ML model 126 may include or correspond to one or more neural networks. For example, the trained ML model 126 may be implemented as a long short-term memory (LSTM) model. An LSTM model is a type of recurrent neural network that is capable of learning patterns and dependencies in sequential data. Advantages of an LSTM model over other trained ML models for predictions in accordance with aspects may include better performance in identifying and predicting long-term trends in data. An LSTM model may be configured to add lag to features of the model in order to generate accurate predictions. For example, the LSTM model may identify and base predictions on the appropriate amount of lag between demand data, such as website visits over a time period or a kept showing appointments, and conversions (e.g., completed sales). These advantages are provided for illustrative purposes, and not by way of limitation. Other types of trained ML prediction models may also be configured to perform sales predictions according to aspects of the disclosure.” Gomez discloses utilizing learning machine to determine housing market trends based on collected information.)
output, based on the one or more valuation trends, one or more building recommendations or one or more purchasing recommendations. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation through a graphical interface.)
Although Gomez discloses the above-enclosed invention, Gomez fails to explicitly disclose processing and labeling information using images.
However D’Souza as shown, which talks about image based property identification, teaches the concept of processing and labeling information using images.
determine, based on the image data associated with the one or more premises and the text data associated with the one or more premises, (See D’Souza ¶0048, “According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the concept of utilizing text and image information associated with listings.)
premises dimensions data associated with the one or more premises, premises design data associated with one or more premises, premises architectural data associated with one or more premises, and premises listing data associated with the one or more premises; (See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the property characteristics to include square footage, design and architectural information. See also D'Souza ¶0048, “ According to further embodiment, there is provided a method for determining a potential home for purchase by a user, comprising: collecting user image data 111, the user image data including images which reference housing in the image, in any associated text, and/or in any metadata 112 associated with the image; analyzing the user image data 11 against a list of weighted home attributes, the home attributes including one or more of price, location, size, type, amenities, and aesthetics; retrieving one or more sample images associated with each home attribute; sending the user two sample images, each associated with a different home attribute, and requesting an indication of user preference; receiving the indication of user preference and adjusting the weighting of the home attributes accordingly; generating a recommended home profile containing the weighted home attributes; searching a home availability database (e.g., a MLS database 170) for an available home matching the recommended home profile; and, sending information pertaining to the available home to the user.” D’Souza teaches the property information to further include listing information.)
programmatically label, based on the premises dimensions data, premises design data, premises architectural data, and premises listing data, the image data and text data; (See D’Souza ¶0016, “According to one embodiment, the user's image data is analyzed to identify “aesthetic” property features or attributes such as exterior siding, doors, balconies, floors, color, island kitchens, etc. These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of labeling listing including particular characteristics of the property.)
Therefore it would have been obvious to one of ordinary skill in the art at the time of filing to have utilized the teachings of D’Souza with the invention of Gomez. As shown, Gomez discloses the concept of collecting information from a plurality of sources, performing analysis on the collected information to identify trends, and providing recommendations based on the trends. D’Souza further teaches the concept of utilizing image and text processing to identify the characteristics of real estate. D’Souza teaches this concept to allow for additional information which is not explicitly stated to be considered in real estate listings including specific information for which there is not particular articulation (See D’Souza ¶0003-¶0004). Thus it would have been obvious to one of ordinary skill in the art at the time of filing to have further utilized the teachings of D’Souza with Gomez to further consider additional information regarding properties by performing analysis on the images of collected information, thereby further identifying characteristics of the other properties.
As per claim 16:
The one or more non-transitory computer-readable media of claim 15, wherein the real estate database comprises a multiple listing network (MLS) database and wherein the one or more valuation trends comprise square foot values, and wherein the image data comprises one or more images captured by an image capture device. (See D’Souza ¶0016, “These aesthetic property attributes are in contrast to the “functional” property features or attributes such as number of bedrooms, number of bathrooms, number of floors, square footage, etc., that are typically included in available property databases (e.g., MLS™ listings, Google™ Street View, etc.). Thus, as used herein, aesthetic property attributes pertain to the “look” of a house while functional property attributes pertain to the “specifications” of the house. The use of aesthetic property attributes allows for houses having the same or similar functional property attributes to be distinguished.” D’Souza teaches the concept of the database to be MLS, and property characteristics including square footage.)
As per claim 17:
The one or more non-transitory computer-readable media of claim 15, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to construct one or more premises according to the one or more building recommendations. (See Gomez ¶0046, “The computing device 102 may be configured to receive data (e.g., home data) from a plurality of data sources and, based on insights derived from the data, to manage and control aspects of processes and systems of a home builder entity, including generating recommendations and/or instructions for agents of the home builder entity and/or for automatic performance. Several distinct aspects of this functionality are described throughout this disclosure. As an illustrative example, computing device 102 may be configured to determine a pace at which homes are constructed and sold based on data related to the homes, including construction status and availability for purchase.” Gomez discloses the concept of providing recommendations including recommending construction.)
As per claim 18:
The one or more non-transitory computer-readable media of claim 15, wherein the premises dimensions data comprises one or more of: floor plan data, square footage data, or lot size data, and wherein the premises design data comprises one or more of: cabinetry data, finish materials data, exterior features data, and wherein premises listing data comprises one or more of: pricing data, geographic data, time on market data, seller data, buyer data or agent data. (See Gomez ¶0160, “According to aspects, the multi-tiered comparison of block 1106 may include a second tier that compares a difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes to a second threshold. For example, the dimensions of the home plans may include or correspond to an area (e.g. the square footage) of the home plans, or the dimensions may include or correspond to a price per unit area (e.g., dollar per square foot) of the home plans. According to aspects, the second threshold may be a percent difference (e.g., 10%, 15%, 20%, etc.) difference between the dimensions of the home plans of the one or more selected homes and the dimensions of the home plans of the subset of the second set of homes.” Gomez discloses determining property dimension and listing information including square footage and price per unit area.)
As per claim 19:
The one or more non-transitory computer-readable media of The one or more non-transitory computer-readable media of wherein the processor-executable instructions, that, when executed by the at least one processor, cause the at least one processor to output the one or more building recommendations, further cause the at least one processor to generate one or more visual outputs indicating the one or more building recommendations. (See Gomez ¶0115, “GUI 700 may include an instruction 730. Instruction 730 may include a recommendation 732. Recommendation 732 may instruct a home builder to undertake an actionable step. For example, recommendation 732 may include an instruction to raise or lower a price of the target home, an instruction to make the price of the target home equal to a weighted average of the prices of the comparable homes, an instruction to adjust an advertising effort for the target home, an instruction to increase or decrease a construction rate for the target home, a report of a recommended target price, a report of the prices of the one or more related homes, a report of a range of prices about the target price, or a combination thereof. Instruction 730 may include a comparable feature highlight 734.” Gomez discloses providing recommendation through a graphical interface including recommendations regarding building/construction.)
As per claim 20:
The one or more non-transitory computer-readable media of The one or more non-transitory computer-readable media of wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to: receive one or more of: updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises; and update, based on the updated premises dimensions data associated with the one or more premises, updated premises design data associated with the one or more premises, updated premises architectural data associated with the one or more premises, or updated premises listing data associated with the one or more premises, the one or more valuation trends. (See Gomez ¶0058, “For example, updates to the GUI 200 based on updates to the underlying data can be performed in real-time (e.g., as new data is received, updates may be performed) or periodically, including updates performed at regular intervals (e.g., every minute, every 5 minutes, every 10 minutes, every half hour, every hour, every 6 hours, every day, or at intervals longer than, shorter than, or in between the intervals listed here).” Gomez discloses receiving and updating information including performing updated analysis based on received updated information.)
Response to Arguments
Applicant's arguments filed 08/07/2026 have been fully considered but they are not persuasive.
In response to the Applicant’s arguments as directed towards the 35 U.S.C. 101 rejection, the Examiner respectfully disagrees. The Applicant first asserts the claimed invention does not recite a mental process as the claims recite limitations such as programmatic labeling and machine learning. The Examiner notes as discussed in MPEP 2106.04(a)(2) and Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014), the concept of extracting and organizing visual information into a computer recognizable format is still directed towards a mental process. The Examiner further notes as discussed in the July 2024 Subject Matter Eligibility Update, the recitation of utilizing machine learning is still considered abstract as demonstrated in example 47 claim 2. Furthermore, although the specification further defines the programmatic labeling, as currently claimed, the labelling is recited at a high level of generality. The Applicant further asserts the claimed invention is integrated into a practical application as the claimed invention utilizes machine learning to provide recommendations and employes advance computer vision to determine features of properties. The Examiner notes as discussed above, although the claims recite the utilization/implementation of these technologies, the recitation is at a high level of generality such that it is merely applying known technologies in well-understood, routine, and conventional manner. As discussed in MPEP 2106.05(a) and 2106.05(d), concepts such as utilizing computer vision and computer processing to speed up processing of information are both well-understood, routine, and conventional. The Applicant further asserts the claimed invention is significantly more as the ordered combination is not well-understood, routine, or conventional. The Examiner notes as discussed in MPEP 2106.04(a)(2), the utilization of computers as a tool to perform mental process including utilizing well-understood, routine, and conventional computer technologies, are still considered abstract. As such, the Examiner asserts the claimed invention is directed towards a judicial exception without significantly more and the rejection has been maintained.
In response to the Applicant’s arguments as directed towards the combination of Gomez and D’Souza, the Examiner respectfully disagrees. In response to the Applicant’s argument as directed towards the D’Souza reference, the Examiner notes that although paragraph 0016 does discuss where attributes are available, D’Souza does not explicitly discuss extracting attribute information from MLS listings. Instead, as discussed in paragraph 0018-0019, D’Souza teaches the concept of utilizing images to identify attributes associated with real estate through computer vision. Although D’Souza fails to explicitly recite a step of labeling, D’Souza does teach that attributes determined based on images and associating those attributes with the respective image (such as identifying double doors in an image as discussed in paragraph 0032). To further clarify the rejection, Gomez discloses the concept of providing real estate recommendations using machine learning and analysis of the attributes/characteristics. D’Souza further teaches the concept of identifying real estate attributes based on computer vision. D’Souza teaches this concept to utilize computer vision to automatically determine real estate attributes including information which maybe not to explicitly stated or missing articulation. Thus the invention of Gomez is modified to further determine real estate attributes using the teachings of D’Souza using computer vision including identifying attributes as taught by D’Souza. As such, the Examiner asserts the claims are obvious over the combination of Gomez and D’Souza and the rejection has been maintained.
All rejections made towards the dependent claims are maintained due to the lack of a reply by the applicant in regards to distinctly and specifically point out the supposed errors in the Examiner’s action in the prior Office Action (37 CFR 1.111). The Examiner asserts that the applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over Gomez in view of D'Souza.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al-Sharieh et al. (US 20230384924 A1), which talks about generating property landing pages including utilizing captured images to determine attributes of the property.
Khosravan et al. (US 11830135 B1), which talks about generating building interior descriptions including utilizing image information.
Poursaeed, Omid, Tomáš Matera, and Serge Belongie. "Vision-based real estate price estimation." Machine Vision and Applications 29.4 (2018): 667-676. (Year: 2017)
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT M CAO whose telephone number is (571)270-5598. The examiner can normally be reached Monday - Friday 11-7.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ILANA SPAR can be reached at (571) 270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VINCENT M CAO/ Primary Examiner, Art Unit 3622
