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 .
DETAILED ACTION
Status of Claims
This action is in reply to the response filed on July 16, 2025.
Claims 1, 8, and 16 have been amended.
Claims 1-19 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 16, 2025 has been entered.
Claim Objections
Claims 1, 8, and 16 are objected to because of the following informalities: Claims 1, 8, and 16 have been amended without the proper formatting and are therefore non-compliant. Amendments to the claims filed on or after July 30, 2003 must comply with 37 CFR 1.121(c) which states: (2) When claim text with markings is required. All claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of “currently amended,” and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text. The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived. Only claims having the status of “currently amended,” or “withdrawn” if also being amended, shall include markings. If a withdrawn claim is currently amended, its status in the claim listing may be identified as “withdrawn—currently amended.” Since the amendments are minor, and for the sake of compact prosecution, the claims have been objected to rather than the Examiner issuing a notice of non-complaint amendment. Appropriate correction is required.
Claim 9 recites “wherein the generating, by the processor, a comparison metric is generated based on the scalar generated by the neural net.” which appears to be the typographical error of “network.” 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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception (i.e., an abstract idea) without significantly more.
Under Step 1 (see MPEP 2106.04(II) and 2106.04(d)) the claims fall within the statutory categories (namely, a process and an apparatus).
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
The claimed invention recites an abstract idea without significantly more. The claim(s) recite(s) a Siamese neural network having twin parallel processing pathways trained to generate a first N-dimensional vector based on a training buyer offer feature, to generate a second N-dimensional vector based on a training seller offer feature and to generate a scalar based on a distance between respective endpoints of the first and second N-dimensional vectors; receiving a buyer offer from a buyer, the buyer offer including a buyer offer feature of a property for purchasing by the buyer; receiving a seller offer from a seller, the seller offer including a seller offer feature of a property for sale by the seller; generating a comparison metric, the generating of the comparison metric based on generated scalar; selecting the buyer offer, the selecting of the buyer offer based on the comparison metric.
The steps of a Siamese neural network having twin parallel processing pathways trained to generate a first N-dimensional vector based on a training buyer offer feature, to generate a second N-dimensional vector based on a training seller offer feature and to generate a scalar based on a distance between respective endpoints of the first and second N-dimensional vectors; receiving a buyer offer from a buyer, the buyer offer including a buyer offer feature of a property for purchasing by the buyer; receiving a seller offer from a seller, the seller offer including a seller offer feature of a property for sale by the seller; generating a comparison metric, the generating of the comparison metric based on generated scalar; selecting the buyer offer, the selecting of the buyer offer based on the comparison metric is a Mathematical Concept. The steps of receiving a buyer offer from a buyer, the buyer offer including a buyer offer feature of a property for purchasing by the buyer; receiving a seller offer from a seller, the seller offer including a seller offer feature of a property for sale by the seller; generating a comparison metric, the generating of the comparison metric based on generated scalar; selecting the buyer offer, the selecting of the buyer offer based on the comparison metric is a Certain Method of Organizing Human Activity as the steps perform the commercial interaction of sales activities, specifically real estate sales.
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
The claims recite “by a processor” which does not integrate the invention into a practical application because the processor are recited such that it amounts to no more than mere instructions to apply the concept of performing the commercial interaction of sales activities, specifically real estate sales using generic computer components. See the specification at [0045]: “general-purpose processing system”. Next, the claims recitation of “a Siamese neural network” is simply only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
The combination of these additional elements is no more than mere instructions to apply the exception using a generic device. Accordingly, even in combination, these additional elements do no integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, therefore, the claims recite an abstract idea.
Under Step 2B the claims are analyzed to determine whether the claims recite additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception. The step of sending, by the processor, an indication of the selected buyer offer to the seller is insignificant extra-solution activity (i.e., the step merely sends the results to the seller).The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as discussed above, amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The limitations of “sending and receiving” are well-known, routine and conventional practices that require no more than a generic computer to perform generic computer functions. (See i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));). Also similar to above, the “a Siamese neural network” is simply only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Therefore, even when considered as a whole, the claims do not amount to significantly more.
Dependent claims 2-10, 12-15 and 17-19 do not add “significantly more” to the abstract idea. Dependent claims 8 and 15 recite a Mathematical Concept (and therefore an additional abstract idea). Dependent claims 2-7, 10, 12-15 and 17-19 further recite a method of organizing human activity because they recite further limitations narrowing the abstract idea: claims 2, 3, 4, 10, 12, 17 and 18: licensing (i.e., an agreement) to receive results; claims 5, 6, 13 and 14: including additional buyers in performing the abstract idea; claim 7: selecting a result; claim 9: defining how the metric is generated; and claim: sending a result Similar to the independent claims, the dependent claims generally “apply” the concept of performing the commercial interaction of sales activities, specifically real estate sales. Even when viewed as an ordered combination, the dependent claims simply convey the abstract idea itself applied on a generic computer and are held to be ineligible under Steps 2A1/2A2/2B at least similar rationale as discussed above regarding claims 1, 11 and 16.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5-9, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Copley (US 20140289070) further in view of Stewart (US 2023/0020771)
Claim 1: Copley discloses
receiving, by the processor, a buyer offer from a buyer, the buyer offer including a buyer offer feature of a property for purchasing by the buyer; [0007] Accordingly, buyers provide reciprocal first set descriptors, such as the price range that they are willing to consider, how much square footage they desire, how many bedrooms, baths and parking spaces in a garage they desire. [0034] The algorithm first matches price points indicated by price field data entered by the seller and by the buyers upon registration. Buyers have entered a price range that they may be willing to pay for a property, and the seller has previously entered a price range that he or she may willing to accept for the offered property. By way of example, as a first step, buyers entered ceiling price value needs to be equal to or greater than the seller's entered floor price value. As an example, suppose the seller has made a price entry of $350,000 for a particular property. Buyers who have made a selection from a drop-down menu presenting price ranges in increments of $50,000, wherein a price range of $300,001-$350,000, thereby encompassing the seller's price of $350,000, are then deemed to match the first criterion. The matching finds all buyers in the registered buyer database matching this first criterion, and may then create a temporary database representing a first cohort of these buyers that match this first criterion, the first cohort being a subset of all registered buyers.
receiving, by the processor, a seller offer from a seller, the seller offer including a seller offer feature of a property for sale by the seller; [0005] The present invention is a two-tiered internet-based buyer-seller matching method that provides enhanced matches between a property owner offering a property for sale with one or more qualified potential buyers. Both the buyers and the property owner have previously registered on an internet-based interface (henceforth referred to the website or buyer-seller matching website) for the software for executing the algorithm embodying the inventive method, providing criteria for buying and selling. Sellers registering on the website may provide basic information about the offered property, for example a home, such as a sales price range, living space square footage, number of bedrooms, baths, etc. The basic information provided by the seller is a first set of descriptors that may be used in the first tier of the matching process of the inventive method.
generating, by the processor, a comparison metric, the generating of the comparison metric based on the buyer offer and the seller offer; [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection.
selecting, by the processor, the buyer offer, the selecting of the buyer offer based on the comparison metric; and sending an indication of the selected buyer offer to the seller. [0031] One of the functions of the invention is to provide an enhanced buyer-seller match, whereby a cohort of candidate buyers matched to a particular seller is provided to that seller via the inventive matching engine. The matching engine is embodied as a refined match-scoring algorithm that may be embedded as a subroutine or module of the web software. The matching engine scores buyers whose property preference profile matches a seller's property profile in by the basic property descriptors.
Copley fails to disclose using a Siamese neural network architecture having twin parallel processing pathways.
Stewart, however, teaches using a Siamese neural network architecture having twin parallel processing pathways (Siamese neural networks, Stewart [0015], [0051]-[0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a Siamese neural network, as disclosed by Stewart in the system disclosed by Copley, for the motivation of providing an improved method as value of a given real-estate property depends on a variety of factors. The decisions a seller of the real-estate property makes concerning sale of the real-estate property heavily depend on the value of the real-estate property. Sellers typically spend a great deal of resources analyzing sales of similar properties in the seller's market to estimate the value of the seller's property. Such tasks, however, are typically very time consuming and ultimately end up being inaccurate which result in sellers making poor decisions concerning the real-estate property sale (Stewart [0002]).
Claim 5: Copley and Stewart disclose a plurality of buyers. [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection.
Claims 6 and 7: Copley and Stewart disclose a first and second comparison metric. [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection. (NOTE: Since importance scores are part of the second filtering step, an importance score only on the price would achieve a second metric based on the offers.).
Claims 8 and 9: Copley and Stewart disclose as shown above with respect to claim 1. Stewart further teaches wherein the generating, by the processor, a comparison metric is performed by a Siamese neural network having twin parallel processing pathways trained to generate a first N-dimensional vector based on the 3 buyer offer feature, trained to generate a second N-dimensional vector based on the seller offer feature, and trained to generate a scalar based on a distance between respective endpoints of the first and second N-dimensional vectors (The machine-learning algorithms utilize the training data to find correlations among the identified features that affect the outcome or assessment. In some example embodiments, the training data includes labeled data, which is known data for one or more identified features and one or more outcomes, such as the values of the comps. Once the training data are collected and processed, machine learning technique training module 220 model can be built using either statistical learning or machine learning techniques. In one embodiment, regression analysis can be used to build the machine learning technique training module 220 model. Regression analysis is a statistical process for estimating the relationships among variables. There are a number of known methods to perform regression analysis, for example: linear regression or ordinary least squares regression, among others, are “parametric” in that the regression function is defined in terms of a finite number of unknown model parameters that can be estimated from training data. For a value prediction, a regression model (e.g., Equation 1) can be defined, for example, as: H≈f(X,β), (Equation 1) where “H” denotes the known values for a set of properties, “X” denotes a vector of input variables (e.g., any one of the real-estate property listing information associated with a set of comps), and “β” denotes a vector of unknown parameters to be determined or trained for the regression model. In some embodiments, there are two vectors of unknown parameters, such as in the case of jointly training the machine learning technique or model. During training, two modules of the machine learning technique training module 220 are trained jointly to estimate the value for the set of properties based on one or two vectors of unknown parameters. The modules are trained based on the same vector of unknown parameters or based on two vectors of unknown parameters. A first of the modules is trained to estimate weights for the comps and a second of the modules is trained to estimate price adjustments for the comps. Their corresponding outputs of the two modules are combined to generate a point estimate of a value for a real-estate property of interest. This value is compared with the known sale prices or values H to update the one or two vectors of unknown parameters of the machine learning technique training module 220 to estimate a value for a new real-estate property. In cases where the modules are trained jointly, the same data vector X of input variables is used to estimate one or two vectors of unknown parameters. Once β is estimated, the model can then compute H (e.g., value or price) for a new set of X values (e.g., feature vectors extracted from a new real-estate property and new set of comps). Machine learning techniques train models to accurately make predictions on data fed into the models (e.g., what was said by a user in a given utterance; whether a noun is a person, place, or thing; what the weather will be like tomorrow). During a learning phase, the models are developed against a training dataset of inputs to optimize the models to correctly predict the output for a given input. Generally, the learning phase may be supervised, semi-supervised, or unsupervised; indicating a decreasing level to which the “correct” outputs are provided in correspondence to the training inputs. In a supervised learning phase, all of the outputs are provided to the model and the model is directed to develop a general rule or algorithm that maps the input to the output. In contrast, in an unsupervised learning phase, the desired output is not provided for the inputs so that the model may develop its own rules to discover relationships within the training dataset. In a semi-supervised learning phase, an incompletely labeled training set is provided, with some of the outputs known and some unknown for the training dataset, Stewart [0038]-[0041]; dimensional vectors, Siamese networks, [0051]-[0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a Siamese neural network, as disclosed by Stewart in the system disclosed by Copley, for the motivation of providing an improved method as value of a given real-estate property depends on a variety of factors. The decisions a seller of the real-estate property makes concerning sale of the real-estate property heavily depend on the value of the real-estate property. Sellers typically spend a great deal of resources analyzing sales of similar properties in the seller's market to estimate the value of the seller's property. Such tasks, however, are typically very time consuming and ultimately end up being inaccurate which result in sellers making poor decisions concerning the real-estate property sale (Stewart [0002]).
Claims 11 and 16: Copley discloses
receiving, by the processor, a buyer offer from a buyer, the buyer offer including a buyer offer feature of a property for purchasing by the buyer; [0007] Accordingly, buyers provide reciprocal first set descriptors, such as the price range that they are willing to consider, how much square footage they desire, how many bedrooms, baths and parking spaces in a garage they desire. [0034] The algorithm first matches price points indicated by price field data entered by the seller and by the buyers upon registration. Buyers have entered a price range that they may be willing to pay for a property, and the seller has previously entered a price range that he or she may willing to accept for the offered property. By way of example, as a first step, buyers entered ceiling price value needs to be equal to or greater than the seller's entered floor price value. As an example, suppose the seller has made a price entry of $350,000 for a particular property. Buyers who have made a selection from a drop-down menu presenting price ranges in increments of $50,000, wherein a price range of $300,001-$350,000, thereby encompassing the seller's price of $350,000, are then deemed to match the first criterion. The matching finds all buyers in the registered buyer database matching this first criterion, and may then create a temporary database representing a first cohort of these buyers that match this first criterion, the first cohort being a subset of all registered buyers.
receiving, by the processor, a seller offer from a seller, the seller offer including a seller offer feature of a property for sale by the seller; [0005] The present invention is a two-tiered internet-based buyer-seller matching method that provides enhanced matches between a property owner offering a property for sale with one or more qualified potential buyers. Both the buyers and the property owner have previously registered on an internet-based interface (henceforth referred to the website or buyer-seller matching website) for the software for executing the algorithm embodying the inventive method, providing criteria for buying and selling. Sellers registering on the website may provide basic information about the offered property, for example a home, such as a sales price range, living space square footage, number of bedrooms, baths, etc. The basic information provided by the seller is a first set of descriptors that may be used in the first tier of the matching process of the inventive method.
generating, by the processor, a comparison metric, the generating of the comparison metric based on the buyer offer and the seller offer; [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection.
selecting, by the processor, the buyer offer, the selecting of the buyer offer based on the comparison metric; and sending an indication of the selected buyer offer to the seller. [0031] One of the functions of the invention is to provide an enhanced buyer-seller match, whereby a cohort of candidate buyers matched to a particular seller is provided to that seller via the inventive matching engine. The matching engine is embodied as a refined match-scoring algorithm that may be embedded as a subroutine or module of the web software. The matching engine scores buyers whose property preference profile matches a seller's property profile in by the basic property descriptors.
Copley does not disclose a Siamese neural network having twin parallel processing pathways to generate a first N-dimensional vector based on a training buyer offer feature, to generate a second N- dimensional vector based on a training seller offer feature, and to generate a scalar based on a distance between respective endpoints of the first and second N dimensional vectors; or generating, by the Siamese neural network, a comparison metric, the generating of the comparison metric based on generated scalar.
Stewart, however, teaches a Siamese neural network having twin parallel processing pathways to generate a first N-dimensional vector based on a training buyer offer feature, to generate a second N- dimensional vector based on a training seller offer feature, and to generate a scalar based on a distance between respective endpoints of the first and second N dimensional vectors; or generating, by the Siamese neural network, a comparison metric, the generating of the comparison metric based on generated scalar (The machine-learning algorithms utilize the training data to find correlations among the identified features that affect the outcome or assessment. In some example embodiments, the training data includes labeled data, which is known data for one or more identified features and one or more outcomes, such as the values of the comps. Once the training data are collected and processed, machine learning technique training module 220 model can be built using either statistical learning or machine learning techniques. In one embodiment, regression analysis can be used to build the machine learning technique training module 220 model. Regression analysis is a statistical process for estimating the relationships among variables. There are a number of known methods to perform regression analysis, for example: linear regression or ordinary least squares regression, among others, are “parametric” in that the regression function is defined in terms of a finite number of unknown model parameters that can be estimated from training data. For a value prediction, a regression model (e.g., Equation 1) can be defined, for example, as: H≈f(X,β), (Equation 1) where “H” denotes the known values for a set of properties, “X” denotes a vector of input variables (e.g., any one of the real-estate property listing information associated with a set of comps), and “β” denotes a vector of unknown parameters to be determined or trained for the regression model. In some embodiments, there are two vectors of unknown parameters, such as in the case of jointly training the machine learning technique or model. During training, two modules of the machine learning technique training module 220 are trained jointly to estimate the value for the set of properties based on one or two vectors of unknown parameters. The modules are trained based on the same vector of unknown parameters or based on two vectors of unknown parameters. A first of the modules is trained to estimate weights for the comps and a second of the modules is trained to estimate price adjustments for the comps. Their corresponding outputs of the two modules are combined to generate a point estimate of a value for a real-estate property of interest. This value is compared with the known sale prices or values H to update the one or two vectors of unknown parameters of the machine learning technique training module 220 to estimate a value for a new real-estate property. In cases where the modules are trained jointly, the same data vector X of input variables is used to estimate one or two vectors of unknown parameters. Once β is estimated, the model can then compute H (e.g., value or price) for a new set of X values (e.g., feature vectors extracted from a new real-estate property and new set of comps). Machine learning techniques train models to accurately make predictions on data fed into the models (e.g., what was said by a user in a given utterance; whether a noun is a person, place, or thing; what the weather will be like tomorrow). During a learning phase, the models are developed against a training dataset of inputs to optimize the models to correctly predict the output for a given input. Generally, the learning phase may be supervised, semi-supervised, or unsupervised; indicating a decreasing level to which the “correct” outputs are provided in correspondence to the training inputs. In a supervised learning phase, all of the outputs are provided to the model and the model is directed to develop a general rule or algorithm that maps the input to the output. In contrast, in an unsupervised learning phase, the desired output is not provided for the inputs so that the model may develop its own rules to discover relationships within the training dataset. In a semi-supervised learning phase, an incompletely labeled training set is provided, with some of the outputs known and some unknown for the training dataset, Stewart [0038]-[0041]; dimensional vectors, Siamese networks, [0051]-[0053]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included a Siamese neural network, as disclosed by Stewart in the system disclosed by Copley, for the motivation of providing an improved method as value of a given real-estate property depends on a variety of factors. The decisions a seller of the real-estate property makes concerning sale of the real-estate property heavily depend on the value of the real-estate property. Sellers typically spend a great deal of resources analyzing sales of similar properties in the seller's market to estimate the value of the seller's property. Such tasks, however, are typically very time consuming and ultimately end up being inaccurate which result in sellers making poor decisions concerning the real-estate property sale (Stewart [0002]).
Claims 2-4, 10, 12-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Copley in view of Stewart in view of Ironclad (“What is a Clickwrap Agreement?” retrieved from the Internet Archive at https://ironcladapp.com/journal/contract-management/what-is-a-clickwrap-agreement/) in view of Goodrich et al. (US 2007/0043770).
Claims 2, 3 and 4: Copley discloses that sellers and buyers must register with the service. [0005] The present invention is a two-tiered internet-based buyer-seller matching method that provides enhanced matches between a property owner offering a property for sale with one or more qualified potential buyers. Both the buyers and the property owner have previously registered on an internet-based interface (henceforth referred to the website or buyer-seller matching website) for the software for executing the algorithm embodying the inventive method, providing criteria for buying and selling. Sellers registering on the website may provide basic information about the offered property, for example a home, such as a sales price range, living space square footage, number of bedrooms, baths, etc. The basic information provided by the seller is a first set of descriptors that may be used in the first tier of the matching process of the inventive method.
Copley further discloses that buyers may either be named or anonymous. [0027] A buyer logging on to the buyer-seller matching website may browse the database of unlisted homes and come upon a particular home, that, according to the statistics included with the listing, may be of interest to the buyer. The buyer may have one or more ways to express interest in the unlisted property. As an example, the buyer may want to contact the homeowner immediately. In such a case, the buyer can generate a tag that will direct the website software to automatically generate a letter addressed to the homeowner, informing the homeowner that there the buyer expresses interest in making contact explore the homeowner's interest in selling the home, and may include the buyer's contact information, or the buyer may remain anonymous, depending on the buyer's level of interest. The letter may also invite the homeowner to register with the website.
The combination of Copley and Stewart fail to disclose a click-through license.
Ironclad, however, discloses “A clickwrap (also known as click-accept, click-to-sign, or clickthrough) agreement is an online agreement that users agree to by clicking a button or checking a box that says “I agree.” The act of signing via an electronic signature is replaced with the act of clicking. Related agreement types include sign-in-wraps (where clicking “register” or “sign-in” constitutes acceptance to the terms) or browsewraps (where using the site indicates acceptance of terms). Clickwrap agreements are the best way for businesses to limit their risk without impacting conversion or customer experience. Companies add clickwrap agreements to sign up pages, checkout flows, and login pages.” (page 1; para. 1 and 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included using a click-through licensing agreement, as disclosed by Ironclad in the system disclosed by Stewart and Copley, for the motivation of providing a method of creating an audit trail, confirming that a user “actively assented” to an agreement through an action, such as clicking a button. (Ironclad; pg. 2; para. 2).
The combination of Copley, Stewart and Ironclad fail to explicitly disclose two distinct phases of buyer data sharing (i.e., buyer offer feature and buyer’s identity).
Goodrich, however, discloses [0026] From terminal A2 (FIG. 3C), the method executes the steps between continuation terminals E-F to obtain a list of buyers whose property search features substantially match the features of the seller's property. See block 326. The method then proceeds to decision block 328, where a test is performed to determine whether the seller has subscribed to obtain buyers' identities. If the answer to the test at decision block 328 is no, the method proceeds to another continuation terminal ("terminal A4"). Otherwise, if the answer to the test at decision block 328 is yes, the method proceeds to another continuation terminal ("terminal A3"). [0027] From terminal A3 (FIG. 3D), the method proceeds to block 330 where the method proceeds to review buyers' identities whose property search profiles are potential matches with the seller's property. The method then continues to another continuation terminal ("terminal A5"). From terminal A4 (FIG. 3D), the method proceeds to block 332 where the method conceals buyers' identities in subsequent steps. The method continues to terminal A5 (FIG. 3D), and proceeds to block 334 where the method provides to the seller a scoring of the extent of the match for various buyers (similar to a display that buyers receive regarding a subject property). At block 336, the method provides to the seller the number of buyers who are likely to be interested in the seller's property. The method also presents a list of buyers who are pre-qualified for financing. See block 338. At block 340, the method also presents a list of buyers who will not require financing. The method then continues to another continuation terminal ("terminal A6"). (See also Fig. 3C and 3D).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included disclosing to the seller a buyer offer feature and buyer’s identity, as disclosed by Goodrich in the system disclosed by Ironclad, Stewart, and Copley, for the motivation of providing a method of collecting subscription fees. (Goodrich; [0028] and [0029]).
Claims 10, 12, 17, 18 and 19: Copley discloses that sellers and buyers must register with the service. [0005] The present invention is a two-tiered internet-based buyer-seller matching method that provides enhanced matches between a property owner offering a property for sale with one or more qualified potential buyers. Both the buyers and the property owner have previously registered on an internet-based interface (henceforth referred to the website or buyer-seller matching website) for the software for executing the algorithm embodying the inventive method, providing criteria for buying and selling. Sellers registering on the website may provide basic information about the offered property, for example a home, such as a sales price range, living space square footage, number of bedrooms, baths, etc. The basic information provided by the seller is a first set of descriptors that may be used in the first tier of the matching process of the inventive method.
Copley further discloses that buyers may either be named or anonymous. [0027] A buyer logging on to the buyer-seller matching website may browse the database of unlisted homes and come upon a particular home, that, according to the statistics included with the listing, may be of interest to the buyer. The buyer may have one or more ways to express interest in the unlisted property. As an example, the buyer may want to contact the homeowner immediately. In such a case, the buyer can generate a tag that will direct the website software to automatically generate a letter addressed to the homeowner, informing the homeowner that there the buyer expresses interest in making contact explore the homeowner's interest in selling the home, and may include the buyer's contact information, or the buyer may remain anonymous, depending on the buyer's level of interest. The letter may also invite the homeowner to register with the website.
The combination of Copley and Stewart fails to disclose a click-through license.
Ironclad, however, discloses “A clickwrap (also known as click-accept, click-to-sign, or clickthrough) agreement is an online agreement that users agree to by clicking a button or checking a box that says “I agree.” The act of signing via an electronic signature is replaced with the act of clicking. Related agreement types include sign-in-wraps (where clicking “register” or “sign-in” constitutes acceptance to the terms) or browsewraps (where using the site indicates acceptance of terms). Clickwrap agreements are the best way for businesses to limit their risk without impacting conversion or customer experience. Companies add clickwrap agreements to sign up pages, checkout flows, and login pages.” (page 1; para. 1 and 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included using a click-through licensing agreement, as disclosed by Ironclad in the system disclosed by Stewart and Copley, for the motivation of providing a method of creating an audit trail, confirming that a user “actively assented” to an agreement through an action, such as clicking a button. (Ironclad; pg. 2; para. 2).
The combination of Copley, Stewart and Ironclad fails to explicitly disclose two distinct phases of buyer data sharing (i.e., buyer offer feature and buyer’s identity).
Goodrich, however, discloses [0026] From terminal A2 (FIG. 3C), the method executes the steps between continuation terminals E-F to obtain a list of buyers whose property search features substantially match the features of the seller's property. See block 326. The method then proceeds to decision block 328, where a test is performed to determine whether the seller has subscribed to obtain buyers' identities. If the answer to the test at decision block 328 is no, the method proceeds to another continuation terminal ("terminal A4"). Otherwise, if the answer to the test at decision block 328 is yes, the method proceeds to another continuation terminal ("terminal A3"). [0027] From terminal A3 (FIG. 3D), the method proceeds to block 330 where the method proceeds to review buyers' identities whose property search profiles are potential matches with the seller's property. The method then continues to another continuation terminal ("terminal A5"). From terminal A4 (FIG. 3D), the method proceeds to block 332 where the method conceals buyers' identities in subsequent steps. The method continues to terminal A5 (FIG. 3D), and proceeds to block 334 where the method provides to the seller a scoring of the extent of the match for various buyers (similar to a display that buyers receive regarding a subject property). At block 336, the method provides to the seller the number of buyers who are likely to be interested in the seller's property. The method also presents a list of buyers who are pre-qualified for financing. See block 338. At block 340, the method also presents a list of buyers who will not require financing. The method then continues to another continuation terminal ("terminal A6"). (See also Fig. 3C and 3D).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included disclosing to the seller a buyer offer feature and buyer’s identity, as disclosed by Goodrich in the system disclosed by Ironclad, Stewart, and Copley, for the motivation of providing a method of collecting subscription fees. (Goodrich; [0028] and [0029]).
Claim 13: Copley, Stewart, Ironclad, and Goodrich disclose as shown above with respect to claim 12. Copley further discloses a plurality of buyers. [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection.
Claim 14: Copley, Stewart, Ironclad, and Goodrich disclose as shown above with respect to claim 13. Copley further discloses a first and second comparison metric. [0008] Accordingly, the inventive two-tiered buyer-seller matching method comprises two levels of filtration for providing a list of qualified buyers to a property owner. After a first filtration step based on matching the first set of search criteria inputs by the inventive algorithm, a cohort of buyers is selected from the plurality of all buyers registered on the website. Subsequently, a second filtration step is executed. In the second filtration step, the inventive algorithm refines the matched buyers from the cohort of buyers selected in the first filtering step, whereby the matched buyers are now ranked according to the importance scores that they placed on the property attributes that were common with the seller's selection. (NOTE: Since importance scores are part of the second filtering step, an importance score only on the price would achieve a second metric based on the offers.).
Claim 15: Copley, Stewart, Ironclad, and Goodrich disclose as shown above with respect to claim 14. Copley further discloses using a ranking to adjust a score (i.e., a scalar). Abstract: An inventive two-tiered buyer-seller matching method directed to property transactions is disclosed herein. The inventive method matches buyers that are recruited on an internet website to list criteria for desired real estate properties. Sellers of property are similarly recruited to list the property. In a first matching tier, a cohort of matched buyers is chosen by matching basic property descriptors entered by buyers and sellers. As a further refinement to the first tier of the matching process, a second tier function of the inventive method ranks individual buyers in the cohort by the overall importance of the selected property features that are common to that buyer's choices and to the seller. A list comprising the cohort of buyers ordered by the ranking score is then presented to the seller. In this manner, the seller is provided with a knowledge of which buyers would potentially be most interested in the property for sale, and is thereby provided with a basis upon which one or more of high potential buyers may be contacted with an offer to sell.
Response to Arguments
Applicant’s remarks, filed July 16, 2025, have been considered.
Applicants argue that the 35 U.S.C. 101 rejection under the Alice Corp. vs. CLS Bank Int’l be withdrawn; however the Examiner respectfully disagrees. As an initial note, the arguments are not compliant under 37 CFR 1.111(b) as they amount to a mere allegation of patent eligibility. The claims recitation of the “a Siamese neural network” only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). Here, Applicant specifically relies upon the “Siamese neural network” however this element does not direct the abstract idea to a