DETAILED ACTION
This final Office action is responsive to amendments filed December 17th, 2025. Claims 1, 5, 11, 13, and 19 have been amended. Claims 21-24 have been added. Claims 1-6, 8-11,13-14, 16-19, and 21-24 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments regarding claim rejections under 35 USC 101 filed 12/17/25 have been fully considered but they are not persuasive.
On pages 14-16 of the provided remarks, Applicant argues that the amended claims present statutory subject matter. Beginning on pages 14-15 of the provided remarks, Applicant argues “claims 1, 5, and 13 provide a technical solution for configuring graphical user interfaces (GUIs) to present time-sensitive information associated with real estate listings in an easily identifiable manner.” Examiner respectfully disagrees and asserts that the argued technical solution to real estate systems is not an improvement as the provision of real estate information, because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Additionally, Applicant is reminded that in most cases, “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“[M]erely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.”); Alice, 573 U.S. at 223 (“Thus, if a patent’s recitation of a computer amounts to a mere instruction to implement an abstract idea on a computer, that addition cannot impart patent eligibility.”). Applicant’s arguments are not persuasive.
Continuing on page 15 of the provided remarks, Applicant argues, citing paragraphs [0010-11] of the provided Specification, “these drawbacks are overcome by developing a system that uses a machine learning model to determine whether a real estate property will sell soon based on a region in which the property is located, how many days the real estate listing has been listed, home attributes of the property, or user interaction data with an associated real estate listing”. Examiner respectfully disagrees and asserts that the present claiming of the use of the machine learning model is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
Further on pages 15-16 of the provided remarks, Applicant argues claims 1, 5, and 13 reflect the technical improvements disclosed in the specification. Specifically, Applicant argues, “Claims 1, 5, and 13 recite the details of updating training data, specifically a days-sold value of a real estate listing used in the training data, and using the selling soon prediction generated by the machine learning model to produce a graphical user interface (GUI) displaying the selling soon prediction.” Examiner respectfully disagrees and asserts that the present claims recite the argued “updating” is recited with a high-level of generality such that the update is not associated with the training data. The claim merely recites “updating the days-sold value based on an update to an attribute of the set of home attributes”. This updating could be performed as a mere observation, judgment, and evaluation of the human mind and also directly relates to the marketing strategy/sales activity associated with the home. As a result, this amended limitation recites the abstract idea of both Certain Methods of Organizing Human Activity as well as Mental Process. Additionally, this limitation is merely performed “by the processor” which would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Further, regarding the production of the GUI, this steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Applicant’s arguments are not persuasive.
Regarding newly added claims 21-24, Applicant argues on pages 19-20 of the provided remarks that the claims present statutory subject matter. Beginning with claims 21-23, Applicant argues on page 19, “under Step 2A Prong Two, claims 21-23 recite a technical solution for improving the accuracy of machine learning models when the training data includes data that may not be “good data”.” Examiner respectfully disagrees and begins by asserting that improving the accuracy of training data that may not be “good data” is not a technical problem. Additionally, the claimed limitations within claims 21-23 are not technical in nature as the described “determining, for each real estate listing stored in the remote data store, the user interaction values by monitoring data requests from a plurality of client devices” and “comparing the first days-on-market value to a predetermined minimum days-on-market value, wherein the first selling soon prediction is generated based on the user interaction data associated with the first subject real estate listing in response to the first days-on-market value being greater than or equal to the predetermined minimum days- on-market value” are observations, judgments, and evaluations of the human mind. The claimed “generating the training data” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). Applicant’s arguments are not persuasive.
Regarding claim 24, Applicant argues on page 20 of the provided remarks, “claim 24 recites a technical solution for improving the efficiency of determining selling soon predictions for subject real estate listings. By determining the geographic region associated with a subject real estate listing based on Natural Language Processing of a description associated with the subject real estate listing, the geographic region of each subject real estate listing does not have to be stored in a database, reducing the amount of database memory needed to determine a selling soon prediction. Additionally, it allows for a selling soon prediction to be made for newly added or identified subject real estate listings that may not have been pre-processed to determine a geographic region for the subject real estate listing.” Examiner respectfully disagrees and asserts that the claim recites “wherein the first subject real estate listing is determined to be associated with the first geographic region based on Natural Language Processing of a description associated with the first subject real estate listing”. While Applicant argues this “allows for a selling soon prediction to be made for newly added or identified subject real estate listings that may not have been pre-processed to determine a geographic region for the subject real estate listing” the current claim does not relate the determination of the first subject real estate listing to generation of the sell soon prediction. Amended independent claim 1 simply recites, “accessing the remote data store to obtain a first subject real estate listing of a first home currently listed for sale in a first geographic region of the plurality of geographic regions” and “generating, using a machine learning model associated with the first geographic region, a first selling soon prediction indicating a number of days in which the first subject real estate listing is expected to sell”. It is unclear how the application of natural language processing on a first subject real estate listing improves the generation of the sell soon prediction as the claim merely recites accessing the information and inputting it into a generated model. Per MPEP 2106.05(a) “After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification.” Therefore, present claim is not deemed to present an improvement to technology. The 35 USC 101 rejections is maintained. Applicant’s arguments are not persuasive.
Applicant’s arguments, see pages 17-20, filed 12/17/25, with respect to claims 1-6, 8-11, 13-14, 16-19, and 21-24 have been fully considered and are persuasive. The 35 USC 103 rejection of 09/17/25 has been withdrawn.
Claim Objections
Claims 1 and 13 are objected to because of the following informalities: the limitation beginning "generating, using a machine learning model" contains a . Appropriate correction is required.
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 1-6, 8-11, 13-14, 16-19, and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
Claims 1-4, 21, and 24
Step 1: Independent claim 1 (system), and dependent claims 2-4, 21, and 24, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 1 is directed to a system (i.e. machine).
Step 2A Prong 1: The independent claim recites improving real estate-related user interfaces by providing visual indications of time sensitive real estate listing information on a graphical user interface (GUI), the system comprising: at least one processor; at least one remote data store storing real estate listings from a plurality of geographic regions, wherein each real estate listing is associated with (i) a geographic region of the plurality of geographic regions, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; and at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising: for each geographic region of the plurality of geographic regions: generating training data for a machine learning model specific to the geographic region, wherein generating the training data includes: accessing the remote data store to obtain a set of real estate listings of homes sold in the geographic region; updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, wherein the second days-sold value is less than the first days-sold value; extracting, from each real estate listing of the set of real estate listings, the days-sold value and the set of home attributes, to generate the training data; and training the machine learning model using the training data, wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically; accessing the remote data store to obtain a first subject real estate listing of a home currently listed for sale in a first geographic region of the plurality of geographic and a second subject real estate listing of a second home currently listed for sale in the first geographic region, wherein the first subject real estate listing is further associated with a days-on- market value and the second subject real estate listing is further associated with a second days-on-market value; generating, using a machine learning model associated with the first geographic region, a first selling soon prediction indicating a number of days in which the first subject real estate listing is expected to sell and a second selling soon prediction indicating a number of days in which the second subject real estate listing is expected to sell; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating a first & second selling soon prediction indicating a number of days in which the subject real estate listing is expected to sell, which is commercial interactions in the form of advertising and sales activity. The Applicant’s claimed limitations are generating a first & second selling soon prediction indicating a number of days in which the subject real estate listing is expected to sell, which recite the abstract idea of Certain Methods of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are updating, for a real estate listing in the set of real estate listings, the days-sold values based on an update to an attribute of the set of home attributes for the real estate listing; extracting, from each real estate listing of the set of real estate listings, the days-sold value and the set of home attributes; and generating a first & second selling soon prediction indicating a number of days in which the subject real estate listing is expected to sell, which are human functions of the mind in the form of observation, judgment, and evaluation. The Applicant’s claimed limitations are updating data from real estate listings, extracting data from real estate listings, and generating a first & second selling soon prediction indicating a number of days in which the subject real estate listing is expected to sell, which recite the abstract idea of Mental Process.
In addition, dependent claims 2-4 and 21 further narrow the abstract idea and recite further defining the extraction of data by generating a distribution indicating days-sold values versus real estate listing counts, using the days-sold values from the set of real estate listings; determining a set of threshold days-sold values; selecting, based on a user input, a first quartile value; determining, based on the first quartile value, a respective second days-sold value; for each real-estate listing: comparing the days-sold value to the respective second days-sold value corresponding to the first quartile value; labeling the respective real-estate listing as a positive or negative example; and generating the sell soon prediction. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as advertising and sales activity as well as mental process evaluations. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas. Dependent claims 21 and 24 will be discussed in Prong 2 analysis below.
Step 2A Prong 2: In this application, the above “providing visual indications of time sensitive real estate listing information; storing real estate listings, wherein each real estate listing is associated with (i) a geographic region of the plurality of geographic regions, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; accessing the remote data store to obtain a set of real estate listings of homes sold in a first geographic region of the plurality of geographic regions; accessing the remote data store to obtain a first subject real estate listing of a home currently listed for sale in the first geographic region, wherein the first subject real estate listing is further associated with a days-on- market value; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “A system; real estate-related user interfaces; a graphical user interface (GUI); at least one processor; at least one remote data store; at least one memory coupled to the at least one processor” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). In addition, dependent claims 2-4 and 21 further narrow the abstract idea.
Independent claim 1 and dependent claim 21 recites the following limitation, “generating training data for a machine learning model; training the machine learning model using the training data, wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically; apply the machine learning model; and generating the training data further includes: determining, for each real estate listing stored in the remote data store, the user interaction values by monitoring data requests from a plurality of client devices, wherein the user interaction values for each real estate listing of the set of real estate listings is extracted to generate the training data”. The “generating training data for a machine learning model; training the machine learning model using the training data; apply the machine learning model” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. The amended limitations regarding “assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model; determining, for each real estate listing stored in the remote data store, the user interaction values by monitoring data requests from a plurality of client devices” are recited with such a high-level of generality such that the functions are observations, judgments, and evaluations of the human mind. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Dependent claim 24 recites “wherein the first subject real estate listing is determined to be associated with the first geographic region based on Natural Language Processing of a description associated with the first subject real estate listing”. The “based on Natural Language Processing” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
The claimed “A system; real estate-related user interfaces; a graphical user interface (GUI); at least one processor; at least one remote data store; at least one memory coupled to the at least one processor” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, system claims 1-4, 21, and 24 recite “A system; real estate-related user interfaces; a graphical user interface (GUI); at least one processor; at least one remote data store; at least one memory coupled to the at least one processor”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraph 0017 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “providing visual indications of time sensitive real estate listing information; storing real estate listings, wherein each real estate listing is associated with (i) a geographic region of the plurality of geographic regions, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; accessing the remote data store to obtain a set of real estate listings of homes sold in a first geographic region of the plurality of geographic regions; accessing the remote data store to obtain a first subject real estate listing of a home currently listed for sale in the first geographic region, wherein the first subject real estate listing is further associated with a days-on- market value; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim.
With respect to reliance on “natural language processing (NLP)” to determine the first subject real estate listing, this activity is recognized as well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Morsa, US 2006/0085408 (paragraph 0144: well -known-to-the-arts natural language processing (NLP) (computational linguistics) or some other method as is well known to the arts may be used). See also, Szabo, US Pat. No. 5,966,126 (col. 6, lines 57-62 and col. 28, lines 16-19: e.g., definitions may be produced in known manner, such as by explicit definition, or through use of assistive technologies, such as natural language translators; user defines a search using prior known techniques, such as natural language searching.
In addition, claims 2-4 and 21 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 5-6, 8-11, 13-14, 16-19, and 22-23
Step 1: Independent claims 5 (method), 13 (non-transitory computer-readable media), and dependent claims 6, 8-11, 14, 16-19, and 22-23, respectively, fall within at least one of the four statutory categories of 35 U.S.C. 101: (i) process; (ii) machine; (iii) manufacture; or (iv) composition of matter. Claim 5 is directed to a method (i.e. process) and claim 13 is directed to a non-transitory computer-readable medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite improving real estate-related user interfaces by providing visual indications of time sensitive real estate listing information on a graphical user interface (GUI), comprising: for each geographic region of a plurality of geographic regions: generating training data for a machine learning model, wherein generating the training data includes: accessing a remote data store to obtain a set of real estate listings, wherein each real estate listing is associated with (i) the geographic region, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, wherein the second days-sold value is less than the first days-sold value; extracting, from each real estate listing of the set of real estate listings, the days-sold value and the set of home attributes, to generate the training data; training the machine learning model using the training data, wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model: generating a predicted days-sold value for a training real estate listing in the training data determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing: and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically; accessing the remote data store to obtain a first subject real estate listing and a second subject real estate listing, wherein the first subject real estate listing is associated with a first days-on-market value, a first set of home attributes, and a first geographic region of the plurality of geographic regions, and wherein the second subject real estate listing is associated with a second days-on-market value, a second set of home attributes, and the first geographic region; generating, using the machine learning model associated with the first geographic region, a first selling soon prediction indicating a number of days in which the first subject real estate listing is expected to sell and a second selling soon prediction indicating a number of days in which the second subject real estate listing is expected to sell; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon (Certain Method of Organizing Human Activity & Mental Process), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). [Examiner notes the underlined limitations above recite the abstract idea].
The steps/functions disclosed above and in the independent claims recite the abstract idea of Certain Methods of Organizing Human Activity because the claimed limitations are generating a first & second selling soon prediction indicating a number of days in which the first & second subject real estate listing is expected to sell, which is commercial interactions in the form of advertising and sales activity. The Applicant’s claimed limitations are generating a first & second selling soon prediction indicating a number of days in which the first & second subject real estate listing is expected to sell, which recite the abstract idea of Certain Methods of Organizing Human Activity.
The steps/functions disclosed above and in the independent claims recite the abstract idea of Mental Process because the claimed limitations are determining a geographic region to which a subject real estate listing is associated with; updating, for a real estate listing in the set of real estate listings, the days-sold values based on an update to an attribute of the set of home attributes for the real estate listing; extracting, from each real estate listing of the set of real estate listings, the days-sold value and the set of home attributes; and generating a selling soon prediction indicating a number of days in which the subject real estate listing is expected to sell, which are human functions of the mind in the form of observation, judgement, and evaluation. The Applicant’s claimed limitations are determining a geographic region for a real estate listing; updating data from real estate listings; extracting data from real estate listings; and generating a first & second selling soon prediction indicating a number of days in which the first & second subject real estate listing is expected to sell, which recite the abstract idea of Mental Process.
In addition, dependent claims 6, 8-11, 14, 16-19, and 22-23 further narrow the abstract idea and recite further defining the first days-on-market value; user interaction information associated with each real estate listing; the prediction of the day-of sale value; and generating the first selling soon prediction. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite a certain method of organizing human activity which include commercial interactions such as advertising and sales activity. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Step 2A Prong 2: In this application, the above “providing visual indications of time sensitive real estate listing information; accessing a remote data store to obtain a set of real estate listings, wherein each real estate listing is associated with (i) the geographic region, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; accessing the remote data store to obtain a first subject real estate listing and the second subject real estate listing, wherein the first subject real estate listing is further associated with a first days-on-market value, a first set of home attributes, and a first geographic region of the plurality of geographic regions, and wherein the second subject real estate listing is associated with a second days-on-market value and a second set of home attributes, and the first geographic region; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon” steps/functions of the independent claims would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the claimed “real estate-related user interfaces; a graphical user interface (GUI); a remote data store; One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors” would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05).
Independent claims 5 and 13 recite the following limitation, “generating training data for a machine learning model”; “training the machine learning model based on the training data, wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model: generating a predicted days-sold value for a training real estate listing in the training data determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing: and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically; and “applying the machine learning model”. Dependent claims 11 and 19 additionally recite “applying the machine learning model to generate the day-of-sale value corresponding to the first subject real estate listing”. The “generating training data for a machine learning model”; “training the machine learning model based on the training data; and “applying the machine learning model” is recited so generically (no details whatsoever are provided other than that they are general purpose computing components) that it represents no more than mere instructions to apply the judicial exception on a computer. The amended limitations regarding “assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model” are recited with such a high-level of generality such that the functions are observations, judgments, and evaluations of the human mind. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. These limitations would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See PEG 2019 and MPEP 2106.05). In addition, dependent claims 6, 8-11, 14, and 16-19 further narrow the abstract idea.
The claimed “real estate-related user interfaces; a graphical user interface (GUI); a remote data store; One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors” are recited so generically (no details whatsoever are provided other than that they are general purpose computing components and regular office supplies) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computers and other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology (See PEG 2019).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05 and PEG 2019). Further, method claims 5-6, 8-11, and 22; and non-transitory computer-readable media claims 13-14, 16-19, and 23 recite “real estate-related user interfaces; a graphical user interface (GUI); a remote data store; One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraph 0017 and Figures 1-2. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the above “providing visual indications of time sensitive real estate listing information; accessing a remote data store to obtain a set of real estate listings, wherein each real estate listing is associated with (i) the geographic region, (ii) a days-sold value, and (iii) a set of home attributes, wherein the days-sold value includes a time value from a time at which the real estate listing was posted as available on a market to a time at which the real estate listing became unavailable on the market; accessing the remote data store to obtain a first subject real estate listing and the second subject real estate listing, wherein the first subject real estate listing is further associated with a first days-on-market value, a first set of home attributes, and a first geographic region of the plurality of geographic regions, and wherein the second subject real estate listing is associated with a second days-on-market value and a second set of home attributes, and the first geographic region; configuring a GUI displaying information of the first subject real estate listing and the second subject real estate listing, wherein, based on the first selling soon prediction indicating that a home sale associated with the first subject real estate listing is predicted to be imminent, the GUI is configured to present the first selling soon prediction overlaid on an image of the first subject real estate listing, and wherein, based on the second selling soon prediction indicating that a home sale associated with the second subject real estate listing is predicted to be imminent, the GUI is configured to present an image of the second subject real estate listing without the second selling soon prediction overlaid thereon” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Next, when the “machine learning” is evaluated as an additional element, this feature is recited at a high level of generality and encompasses well-understood, routine, and conventional prior art activity. See, e.g., Balsiger et al., US 2012/0054642, noting in paragraph [0077] that “Machine learning is well known to those skilled in the art.” See also, Djordjevic et al. US 2013/0018651, noting in paragraph [0019] that “As known in the art, a generative model can be used in machine learning to model observed data directly.” See also, Bauer et al., US 2017/0147941, noting at paragraph [0002] that “Problems of understanding the behavior or decisions made by machine learning models have been recognized in the conventional art and various techniques have been developed to provide solutions.” Accordingly, the use of machine learning to generate a learning model does not add significantly more to the claim.
In addition, claims 6, 8-11, 14, 16-19, and 22-23 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter: Claims 1, 5, and 13 recite a combination of claim limitations that, as drafted, under considerations of the broadest reasonable interpretation of the claimed invention, are rendered neither obvious nor anticipated by the available field of prior art. The prior art of the record fails to explicitly teach, disclose, or suggest the combination of claim limitations, including at least: generating the training data includes: updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, where the second days-sold value is less than the first days-sold value; wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically. The closest prior art of the record discloses:
Lundgren (U.S 2020/0034861 A1) discloses automatically projecting a number of days to pending for a real-estate property using trained machine learning technique is applied to new real-estate property activities associated with a new real-estate property to predict a length of time between a first time the new real-estate property was listed and a second time when the new real-estate property will be sold. However, Lundgren does not explicitly teach or disclose generating the training data includes: updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, where the second days-sold value is less than the first days-sold value; wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically.
Liu (U.S 2021/0118074 A1) discloses a simple and secure user experience by bundling user interactions involved in a real estate transaction event into a fewer number of steps using a machine learning engine to train the one or more machine learning models. However, Liu does not explicitly teach or disclose generating the training data includes: updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, where the second days-sold value is less than the first days-sold value; wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically.
Shenoy (U.S 11,069,010 B1) discloses a unique consumer facing property search portal that provides users the ability to key in critical home search criteria and constraints concurrently, including: commute time, school preferences, lifestyle choices, noise tolerance, and others. However, Shenoy does not explicitly teach or disclose generating the training data includes: updating, for a real estate listing in the set of real estate listings, the days-sold value from a first days-sold value to a second days-sold value based on an update to an attribute of the set of home attributes for the real estate listing, where the second days-sold value is less than the first days-sold value; wherein training the machine learning model includes: assigning values to a set of model parameters of the machine learning model; generating a predicted days-sold value for a training real estate listing in the training data; determining an error measure for the machine learning model based on a difference between the predicted days-sold value and the days-sold value associated with the training real estate listing; and adjusting the values of the set of model parameters based on the error measure associated with the machine learning model, wherein the machine learning model is trained periodically.
Therefore, the combination of claim limitations, when considered in view of the available field of prior art, are rendered neither obvious nor anticipated.
However, the present claims are not in condition for allowance because the claims are rejected under 35 USC 101, as set forth in the current office action. Therefore, the claims are not condition for allowance at this time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kostic, Zona, and Aleksandar Jevremovic. "What image features boost housing market predictions?." IEEE Transactions on Multimedia 22.7 (2020): 1904-1916.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2002/0065739 A1
Florance et al.
System And Method For Collection, Distribution, And Use Of Information In Connection With Commercial Real Estate
US 2022/0261932 A1
Keller et al.
DYNAMIC REAL ESTATE TICKER SYSTEM, METHODS, AND APPARATUS
KR102149683B1
Noh Seung Chul
Method for predicting days on market of real estate and apparatus thereof
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.
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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