DETAILED ACTION
This non-final Office action is responsive to amendments filed March 4th, 2026. Claims 1, 10, and 19 have been amended. Claims 1, 7-10, and 16-21 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/04/26 has been entered.
Priority
Acknowledgment is made of applicant’s claim for priority under the benefit of United States Provisional Application Serial No. 62/886,607, filed on August 14th, 2019.
Response to Arguments
Applicant’s arguments, see page 9, filed 03/04/26, with respect to claims 1, 7-10, and 16-21 have been fully considered and are persuasive. The 35 USC 112(a) rejection of 01/20/26 has been withdrawn.
Applicant's arguments regarding claim rejections under 35 USC 101 filed 03/04/26 have been fully considered but they are not persuasive.
On pages 9-12 of the provided remarks, Applicant argues that the amended claims are directed to statutory subject matter. Following acknowledgement of the satisfied Step 1 analysis, Applicant argues on page 10 of the provided remarks, regarding Step 2A Prong One analysis, “the Examiner's Office Action characterizes the machine learning model, the feature engineering, the pre-emptive classification outputs, and the GUI-rendering elements as reciting mental processes. However, Applicant submits that this characterization does not reflect the full technical scope of the claimed limitations, and requests reconsideration as amended.” Examiner begins by asserting that the argued “machine learning model, the feature engineering, the pre-emptive classification outputs, and the GUI-rendering elements” were not included as reciting the abstract idea under Prong One analysis. Specifically, regarding the mental process abstract idea, Examiner noted the pre-processing data pertaining to at least one limited-supply product or service; identifying recurring data records related to the at least one limited-supply product or service; aggregating said data based on geographic location into dissemination areas; determining a primary feature based on the anonymized historical prices among the one or more features from a plurality of different sources; determining a location associated with the user device; determining a bid price for a user to offer for the at least one limited-supply product or service; and based on the bid price range, generating one or more matches representative of the pre-emptive classifications between the at least one limited-supply product or service and an account associated with the user, as functions of the human mind. Applicant arguments are not persuasive.
Applicant continues on to argue, “The amended claims define a machine learning model specifically configured to generate pre-emptive classifications of available or soon-to-be-available limited- supply products or services prior to their listing on a market platform user interface. That model is trained upon recurrent patterns from pre-authorized payments, e-transfers, transaction codes, and cheques as extracted from a transaction data object database. These are computational processes carried out by trained models that cannot practically be performed in the human mind. This is consistent with the USPTO's own guidance.” Examiner asserts that the generation of pre-emptive classifications of available or soon-to-be-available limited- supply products or services prior to their listing on a market platform user interface recites both Certain Methods of Organizing Human Activity Commercial Interactions in addition to Mental Processes judgment and evaluation. The argued “machine learning” and training of the model was not included within the analysis under Prong One and is therefore moot. Applicant’s arguments are not persuasive.
Citing Example 39 of the 2019 PEG, Applicant argues “the claimed feature engineering configured to determine a primary feature from anonymized historical prices across a plurality of different data sources to improve performance of the machine learning model cannot be practically performed in the human mind.” Examiner respectfully disagrees and asserts that the high-level application of “feature engineering to determine a primary feature based on the anonymized historical prices among the one or more features from a plurality of different data sources” under broadest reasonable interpretation is an evaluation of the human mind to select a primary feature amongst options. Per paragraph [0031] of the as-filed Specification, the argued feature engineering, “allows the system 100 to extract the features from raw data, including financial institution sources, rental application (e.g., Get Digs) sources and third party sources”. Examiner asserts that the extraction of features from raw data is not analogous to the recitation of specific sets of training data within Example 39 of the 2019 PEG. Applicant’s arguments are not persuasive.
Beginning on page 10 of the provided remarks, Applicant argues under Step 2A Prong Two that “the claims as amended recite a specific, ordered combination of technical elements that, taken together, constitute an improvement to how computing devices function in the domain of predictive resource matching and user interface rendering.” Examiner respectfully disagrees and asserts that the additional elements 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). Applicant’s arguments are not persuasive.
Further, Applicant argues “This is a non-trivial problem as machine estimation and prediction is required, and the claimed embodiments specifically recite a specific model that is configured for pattern recognition of specifically anonymized historical prices for determination of dynamically determined confidence intervals within dissemination areas.” Examiner respectfully disagrees and asserts the claimed “machine learning model” and “feature engineering” 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.
Continuing on page 10, Applicant argues that “This is a specific recitation of a specific practical integration of machine learning”. Examiner respectfully disagrees and asserts that the claimed how the data is trained, what the model processes, and how it is processed merely describe the data analyzed by the generic machine learning method. For example, the detailed training data merely further describes the abstract idea of commercial interactions as the machine learning is being applied on monetary values. The corresponding output, as noted above further defines the abstract idea as the pre-emptive classifications and a dynamically defined confidence interval indicative of a computer-predicted actual transaction value is directed to certain methods of organizing human activity in the form of commercial interactions sales activity. As stated above the claimed “machine learning model” and “feature engineering” 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.
Citing the precedential Ex parte Desjardins ARP decision, Applicant argues on page 11 of the provided remarks, “the specification discloses that the claimed invention achieves concrete technological improvements. The machine learning model configured with feature engineering to determine a primary feature improves the performance of the model itself by identifying the most predictive feature from a plurality of different data sources. In addition, the system's ability to generate pre-emptive classifications before listing on a market platform UI represents a functional improvement over conventional systems that could only react after listings appeared.” Examiner respectfully disagrees and asserts, citing the Ex parte decision, “The Panel credited benefits including "reduced storage, reduced system complexity and streamlining, and preservation of performance attributes" as technological improvements disclosed in the specification and reflected in the claims.” Examiner asserts that the argued “identifying the most predictive feature from a plurality of different data sources” of the present claims does not present a technical improvement similar to the argued decision. Additionally, the presentation of pre-emptive classifications before listing on a market platform UI is not a technical improvement to the UI as 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.
Continuing on page 11 of the provided remarks, Applicant argues “Applicant respectfully submits that this characterization does not fully account for the specific technical architecture claimed here: a trained model operating on defined data structures (dissemination areas with confidence intervals) to produce defined outputs (pre-emptive classifications and dynamically defined confidence intervals indicative of computer-predicted actual transaction values).” Examiner respectfully disagrees and asserts, as stated above, the claimed trained machine learning model execution and outputs 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.
Applicant continues on page 11 of the provided remarks to argue, “The interplay between the API-derived location, the dissemination area framework, and the confidence interval calculation reflects a defined technical architecture.” Examiner respectfully disagrees and asserts that the claimed high-level determination of a location associated with a user device under broadest reasonable interpretation is an observation, judgment, and evaluation of the human mind. The claimed “via a location services application-programming interface (API)” 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, Applicant argues “This claimed GUI behavior is not merely "displaying data." The claims specify a particular manner of presenting information to the user: dynamically timed rendering of interface elements that are conditioned on and triggered by the predictive output of a machine learning model. The timing, content, and trigger conditions of the GUI elements are all governed by the technical pipeline. This constitutes the type of specific, technology-rooted user interface improvement that has repeatedly been found to satisfy Prong Two.” Examiner respectfully disagrees and asserts that the claimed rendering of the graphical user interface elements is recited at a high-level of generality such that the claims “each based on the identified one or more matches corresponding to the pre-emptive classifications” is not equivalent to the argued “timing, content, and trigger conditions of the GUI elements are all governed by the technical pipeline”. Examiner asserts that the claimed rendering of the graphical user interface 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.
Citing Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018), Applicant argues “As Core Wireless claimed a particular manner of summarizing and presenting information in electronic devices, including constraints on the type of data displayed, how it is accessed, and the state in which applications exist, the present claimed embodiments define a particular manner of rendering graphical user interface elements.” Examiner respectfully disagrees and asserts that the amended claims rendering of one or more graphical user interfaces is not analogous to the specific user interface display window presented in the argued decision. Additionally, as stated above, while the argued decision presented "application summary window" with its "limited list of data" accessible "directly from the menu" while applications were in an "un-launched state" constituted a "specific manner of displaying a limited set of information to the user, rather than using conventional user interface methods to display a generic index on a computer”, the amended claims merely recite “the one or more corresponding graphical user interface elements each based on the identified one or more matches corresponding to the pre-emptive classifications”. There is no “specific manner of displaying a limited set of information to the user” or particular method utilized to display in comparison to other user interfaces. Applicant’s arguments are not persuasive.
Additionally, Applicant argues citing Example 37 of the 2019 PEG, “the amended claims parallel Example 37 by specifying the trigger (the predictive output of the machine learning model, combined with location data and confidence intervals), the mechanism (rendering graphical user interface elements at an estimated time before listing, based on identified matches corresponding to pre-emptive classifications), and the result (a user interface that surfaces actionable bidding opportunities to users at a predictively optimal time, before conventional listing occurs).” Examiner respectfully disagrees and asserts that the argued Example 37 provided a specific improvement over prior systems by reciting a specific manner of automatically displaying icons to the user based on usage which provides a specific improvement over prior systems, resulting in an improved user interface for electronic devices. As stated above, the claimed method does not present a “specific manner” of displaying information within a user interface. Applicant’s arguments are not persuasive.
Citing the Enfish decision, Applicant argues, “The claimed embodiments define a logical structure and process that together constitute an improvement to how the computing system operates. The claimed GUI elements are not displaying static results, and reconsideration of the claimed embodiments, as amended are requested.” Examiner respectfully disagrees and asserts, as stated above, the additional elements 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). Applicant’s arguments are not persuasive.
Regarding Step 2B analysis, Applicant argues “The ordered combination of the claimed elements reflects a non-conventional arrangement that is not merely a recitation of well-known computing functions. The claims are noted to be directed to be allowed subject matter.” Examiner asserts that the noted acknowledgement of allowable subject matter does not guarantee subject matter eligibility. 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). 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. Applicant’s arguments are not persuasive.
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, 7-10, and 16-21 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.
Step 1: Independent claims 1 (system), 10 (method), and 19 (non-transitory computer-readable medium) and dependent claims 7-9, 16-18, and 20-21, 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), claim 10 is directed to a method (i.e. process), and claim 19 is directed to a non-transitory computer-readable medium (i.e. manufacture).
Step 2A Prong 1: The independent claims recite pre-processing data pertaining to at least one limited-supply product or service by: identifying recurring data records related to the at least one limited-supply product or service in at least one financial institution data stream based on a number of the recurring data records in a predetermined threshold period and a transaction code associated with each of the recurring data records: extracting said data from the recurring data records in the at least one financial institution data stream, the extracted data comprising: a periodic transaction amount; and a geographic location associated with the limited-supply product or service; and aggregating said extracted data based on the geographic location into dissemination areas to generate aggregated data comprising anonymized historical prices of the limited-supply product or service associated with the dissemination areas, each of the dissemination areas having a dynamically determined confidence interval; using a machine learning model configured to generate pre-emptive classifications of available or soon to be available limited-supply products or services prior to the available limited-supply products being listed on a market platform user interface, ingest the aggregated data and generate through feature engineering, one or more features from the aggregated data, wherein the feature engineering is configured to determine a primary feature based on the anonymized historical prices among the one or more features from a plurality of different sources to improve performance of the machine learning model; determining, via a location services application-programming interface (API), a location associated with the user device; receiving, by at least one processor, at a demand-side user interface offering at least one limited- supply product or service, a user input regarding the limited-supply product or service in the location associated with the user device; in response to receiving the user input determining based on the one or more features, by the at least one processor, a bid price range for a user to offer for the at least one limited-supply product or service, the bid price range comprising a lower limit P1 and a higher limit P2, wherein P1 and P2 are determined based on:P1 = (CP+ IT) - CI: and P2 = (CP + IT) + CI: wherein: CP represents a current average price for the at least one limited-supply product or service in the corresponding dissemination area of the dissemination areas that corresponds to the geographic location; IT represents an increased price trend determined based on the historical prices; and Cl represents the dynamically defined confidence interval corresponding to the dissemination area for the at least one limited-supply product or service, based upon a linear regression of the primary feature, the dynamically defined confidence interval indicative of a computer predicted actual transaction value determined contemporaneously through execution of the machine learning model as triggered by tracked interactions with the demand-side user interface; causing to display, at the demand-side user interface, the bid price range; based upon a determination that the bid price range from the user input matches one or more data objects within both the corresponding dissemination area and within bounds of the dynamically defined confidence interval, automatically identify one or more matches representative of the pre-emptive classifications represented by a corresponding data object having an estimated price generated using the machine learning model trained upon recurrent patterns from at least one of pre- authorized payments, e-transfer, transaction codes, and cheques as extracted from a transaction data object database, each match of the one or more matches between a corresponding at least one limited-supply product or service and an account associated with the user that are available or soon to be available limited-supply products or services prior to the available limited-supply products being listed on the market platform user interface; and render one or more corresponding graphical user interface elements on one or both of the demand-side user interface and the supply-side user interface, the one or more corresponding graphical user interface elements each based on the identified one or more matches corresponding to the pre-emptive classifications (Certain Methods of Organizing Human Activity & Mental Process & Mathematical Concepts), 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 offering at least one limited-supply product or service to a user and determining a bid price for a user to offer for the at least one limited-supply product or service and then based on the bid price range, generating one or more matches representative of the pre-emptive classifications between the at least one limited-supply product or service and an account associated with the user, which is commercial interactions in the form of sales activities. The Applicant’s claimed limitations are matching users to determined bids of limited-supply products or services, which recites the abstract idea 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 pre-processing data pertaining to at least one limited-supply product or service; identifying recurring data records related to the at least one limited-supply product or service; aggregating said data based on geographic location into dissemination areas; determining a primary feature based on the anonymized historical prices among the one or more features from a plurality of different sources; determining a location associated with the user device; determining a bid price for a user to offer for the at least one limited-supply product or service; and based on the determination that the bid price range from the user input matches one or more data objects within both the corresponding dissemination area and within bounds of the dynamically defined confidence interval, identifying one or more matches representative of the pre-emptive classifications represented by a corresponding data object having an estimated price, each match of the one or more matches between a corresponding at least one limited-supply product or service and an account associated with the user that are available or soon to be available limited-supply products or services prior to the available limited-supply products being listed on the market platform user interface, which is a function of the human mind in the form of observation, judgement, and evaluation. Additionally, the above limitation could be performed using pen & paper. The Applicant’s claimed limitations are determining bids and matching users to limited-supply products or services, which recites the abstract idea of Mental Process.
The steps/functions disclosed above and in the independent claims are recite the abstract idea of Mathematical Concepts because the claimed limitations are determining a bid price range comprising a lower limit P1 and a higher limit P2 as well as defining a confidence interval based upon linear regression of the primary feature, which is a Mathematical Concepts specifically Mathematical Calculations and Mathematical Equations. The Applicant’s claimed limitations are determining bids prices through the use of addition as well as determining confidence intervals using linear regression, which recites the abstract idea of Mathematical Concepts.
Dependent claims 7 and 16 recites the confidence interval comprises a Z-value factored by a standard deviation of historical rental prices (Mathematical Concepts), which are considered to be abstract ideas (See PEG 2019 and MPEP 2106.05). The steps/functions disclosed above and in the independent claims recite the abstract idea of Mathematical Concepts because the claimed limitations are determining a bid price by plus/less a confidence interval; and determining a confidence interval by a Z-score factored by a standard deviation, which are Mathematical Concepts specifically Mathematical Calculations. The Applicant’s claimed limitations are determining bids prices and confidence intervals, which recite the abstract idea of Mathematical Concepts.
Dependent claims 8-9 and 17-18 recite further narrowing the abstract idea and further define the registration of a user and the registration of user interest in a rental property. 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 and legal interactions such as sales activities. Accordingly, these claim elements do not serve to confer subject matter eligibility to the claims since they recite abstract ideas.
Dependent claims 20-21 further narrow the abstract idea and further defining the aggregation of additional data with the aggregated data based on geographic location into the dissemination areas. These processes are similar to the abstract idea noted in the independent claims because they further the limitations of the independent claims which recite mental processes. 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 “extracting said data from the recurring data records in the at least one financial institution data stream, the extracted data comprising: a periodic transaction amount; and a geographic location associated with the limited-supply product or service; receiving, by at least one processor, at a demand-side user interface offering at least one limited- supply product or service, a user input regarding the limited-supply product or service in the location associated with the user device; causing to display, at the demand-side user interface, the bid price range; as extracted from a transaction data object database; render one or more corresponding graphical user interface elements on one or both of the demand-side user interface and the supply-side user interface, the one or more corresponding graphical user interface elements each based on the identified one or more matches corresponding to the pre-emptive classifications” 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 comprising at least one processor and a memory storing instructions which when executed by the at least one processor configure the at least one processor to; a market platform user interface; transaction data object database; a demand-side user interface; computer; a location services application-programming interface (API); a user device; A non-transitory computer-readable medium having instructions thereon which, when executed by a 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).
Independent claims 1, 10, and 19 recite the following limitation, “using a machine learning model”, “generate through feature engineering, one or more features from the aggregated data”, “determined contemporaneously through execution of the machine learning model”, and “using the machine learning model trained upon recurrent patterns from at least one of pre-authorized payments, e-transfer, transaction codes, and cheques”. The “machine learning model” and “feature engineering” 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).
In addition, dependent claims 7-9, 16-18, and 20-21 further narrow the abstract idea and dependent claims 8-9, 17-18, and 20-21 additionally recite “receiving information regarding an available limited-supply product or service”, “receiving information regarding an available rental property in the location area”, “sending a notification to the user of the bid price for the available rental property in the location area”, “receive a notification from the user of a move from the location associated with the limited-supply product or service”, “receive financial details pertaining to the move”, and “receive additional data pertaining to the move” which do 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 the claimed “at least one processor” which do 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 comprising at least one processor and a memory storing instructions which when executed by the at least one processor configure the at least one processor to; a market platform user interface; transaction data object database; a demand-side user interface; computer; a location services application-programming interface (API); a user device; A non-transitory computer-readable medium having instructions thereon which, when executed by a 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, method claims 10, 16-18, and 21; System claims 1, 7-9, and 20; and non-transitory computer-readable medium claim 19 recite “A system comprising at least one processor and a memory storing instructions which when executed by the at least one processor configure the at least one processor to; a market platform user interface; transaction data object database; a demand-side user interface; computer; a location services application-programming interface (API); a user device; A non-transitory computer-readable medium having instructions thereon which, when executed by a 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 Paragraphs 0045-0049 and Figures 1 and 25. 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 “extracting said data from the recurring data records in the at least one financial institution data stream, the extracted data comprising: a periodic transaction amount; and a geographic location associated with the limited-supply product or service; receiving, by at least one processor, at a demand-side user interface offering at least one limited- supply product or service, a user input regarding the limited-supply product or service in the location associated with the user device; causing to display, at the demand-side user interface, the bid price range; as extracted from a transaction data object database; render one or more corresponding graphical user interface elements on one or both of the demand-side user interface and the supply-side user interface, the one or more corresponding graphical user interface elements each based on the identified one or more matches corresponding to the pre-emptive classifications” 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.
Additionally, when “feature engineering” 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, Mestha et al., US 2018/0186234 A1, noting in paragraph [0031] that “A well-known feature engineering methodology is incorporated to extract well-correlated features,”. See also, Gu et al., US 2019/0180469 A1, noting in paragraph [0050] that “problem-specific tracker-engineering or feature-engineering, that are required in conventional techniques”. Accordingly, the use of feature engineering to extract features does not add significantly more to the claim.
In addition, claims 7-9, 16-18, and 20-21 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. Similarly, claims 8-9, 17-18, and 20-21 additionally recite “receiving information regarding an available limited-supply product or service”, “receiving information regarding an available rental property in the location area”, “sending a notification to the user of the bid price for the available rental property in the location area”, “receive a notification from the user of a move from the location associated with the limited-supply product or service”, “receive financial details pertaining to the move”, and “receive additional data pertaining to the move” which do not account for additional elements that amount to 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 and the claimed “at least one processor” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). 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
Claims 1, 7-10, and 16-21 are allowable over prior art but have other pending rejections as indicated above. The prior art of record that was found and cited comprised the following reference(s):
Alizadeh (U.S 2016/0379299 A1) – directed to a system for brokering available rental properties;
Vasta (U.S 2020/0342524 A1) – directed to a system and method for producing a bid price for a service or good;
Takenaka (U.S 2017/0140483 A1) – directed to an information processing system for users engaged in real estate transactions;
Gosh (U.S 2015/0294401 A1) – directed to a method for determining actual pricing data for rental properties;
Dozier (U.S 2019/0213766 A1) – directed to computer assisted valuation modeling;
Wang (U.S 2018/0253780 A1) – directed to smart matching for real estate transactions utilizing a machine learning model;
Packes (U.S 2015/0242747 A1) – directed to real estate evaluating platforms utilizing machine learning and pattern recognition;
Wagner (U.S 2016/0225069 A1) – directed to techniques associated with bidding on properties for purchase, rent or lease from owners of properties;
Lee (CN 106062811A) – directed to a revenue management system;
Berg (US 2017/0337648 A1) – directed to generating panel objects based on content items relating to rental properties;
Lou (CN 1760905 A1) – directed to an electric competitive bidding system and method;
Blume (WO 2014/137510 A1) – directed to predicting a rent amount of a subject property;
Pimentel (‘An evaluation of the bid price and nested network revenue management allocation methods’) – directed to comparing revenue generation capabilities of the bid price allocation method and the nested network methods in hotel revenue management;
Conway (‘Artificial intelligence and machine learning: Current applications in real estate’) – directed to defining machine learning and artificial intelligence for the investor and real estate audience, examine the ways in which these new analytic, predictive, and automating technologies are being used in the real estate industry, and postulate potential future applications and associated challenges.
The prior art made of record discloses pre-processing data pertaining to at least one limited-supply product or service by: identifying recurring data records related to the at least one limited-supply product or service in at least one financial institution data stream based on a number of the recurring data records in a predetermined threshold period and a transaction code associated with each of the recurring data records: extracting said data from the recurring data records in the at least one financial institution data stream, the extracted data comprising: a periodic transaction amount; and a geographic location associated with the limited-supply product or service; and aggregating said extracted data based on the geographic location into dissemination areas to generate aggregated data comprising anonymized historical prices of the limited-supply product or service associated with the dissemination areas, each of the dissemination areas having a dynamically determined confidence interval; using a machine learning model configured to generate pre-emptive classifications of available or soon to be available limited-supply products or services prior to the available limited-supply products being listed on a market platform user interface, ingesting the aggregated data and generating, through feature engineering, one or more features from the aggregated data, wherein the feature engineering is configured to determine a primary feature based on the historical prices among the one or more features from a plurality of different data sources to improve performance of the machine learning model; determining, via a location services application-programming interface (API), a location associated with a user device; receiving, by at least one processor, at a demand-side user interface offering at least one limited- supply product or service, a user input regarding the limited-supply product or service in the geographic location; in response to receiving the user input, determining based on the one or more features, by the at least one processor, a bid price range for a user to offer for the at least one limited-supply product or service; causing to display, at the demand-side user interface, the bid price range; based upon a determination that the bid price range from the user input matches one or more data objects within both the corresponding dissemination area and within bounds of the dynamically defined confidence interval, automatically identify one or more matches representative of the pre-emptive classifications represented by a corresponding data object having an estimated price generated using the machine learning model trained upon recurrent patterns from at least one of pre-authorized payments. e-transfer. transaction codes. and cheques as extracted from a transaction data object database. each match of the one or more matches between a corresponding at least one limited-supply product or service and an account associated with the user that are available or soon to be available limited-supply products or services prior to the available limited-supply products being listed on the market platform user interface; and render one or more corresponding graphical user interface elements on one or both of the demand-side user interface and the supply-side user interface, the one or more corresponding graphical user interface elements each based on the identified one or more matches corresponding to the pre-emptive classifications. However, the prior art does not specifically disclose the sequence of steps as recited in the claims: wherein the feature engineering is configured to determine a primary feature based on the anonymized historical prices among the one or more features from a plurality of different data sources to improve performance of the machine learning model; the bid price range comprising a lower limit P1 and a higher limit P2, wherein P1 and P2 are determined based on: P1 = (CP+ IT) – CI; and P2 = (CP + IT) + CI; wherein: CP represents a current average price for the at least one limited-supply product or service in the corresponding dissemination area of the dissemination areas that corresponds to the geographic location; IT represents an increased price trend dissemination area for the at least one limited-supply product or service, based upon a linear regression of the primary feature, the dynamically defined confidence interval indicative of a computer predicted actual transaction value determined contemporaneously through execution of the machine learning model as triggered by tracked interactions with the demand-side user interface and the user input.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chegut, Andrea, Piet Eichholtz, and Nils Kok. "Supply, demand and the value of green buildings." Urban studies 51.1 (2014): 22-43.
DOCUMENT ID
INVENTOR(S)
TITLE
US 2004/0158515 A1
Schoen, Neil C.
Home Asset Value Enhancement Notes (HAVENs)
CN110119959A
Hu, Yan
A Price Prediction System
WO2018043858A1
Koh, Jae-Ho
DEVICE FOR CALCULATING SALE PRICE OF RENTAL ITEM AND METHOD USING SAME
US 2015/0206229 A1
Kang et al.
APPARATUS AND METHOD FOR RENTING VEHICLE USING REVERSE AUCTION METHOD
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/KRISTIN E GAVIN/Primary Examiner, Art Unit 3624
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