DETAILED ACTION
This is in reference to communication received 14 October 2025. Claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 are pending for examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 14 and 26 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Applicant added limitations:
reduce searching through the term matrix and reduce processor cycles used to identify text.
storing the final set of comparison properties rather than the set of comparison properties in one of memory and the database to conserve data storage space;
reduce processor cycles on comparison properties that are dissimilar from the final set of comparison properties;
which are not positively disclosed by the applicant in their application as originally filed. Applicant has not positively disclosed how one or ordinary skill in the art will reduce processor cycles without undue experimentation. Also applicant has not positively disclosed storing of final set of comparison properties to the database which will result in conserving data storage.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 are directed toward at least one judicial exception without significantly more. In accordance with the Federal Register Notice: 2019 Revised Patent Subject Matter Eligibility Guidance, (January 7, 2019), (accessible at https://www.govinfo.gov/content/pkg/FR-2019-01-07/pdf/2018-28282.pdf), the rationale for this determination is explained below:
Claim 1, representative of claims 14 and 26 is directed towards a method, which is a statutory category of invention. Although, claim 1 is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to performing valuation of a property using comparative sales information of a similar property and taking into considerations features of the property with their weighted value to derive an appraised value of the property, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components.
To determine valuation of a property, specific data of interest is recognized (e.g. type of real estate (assets), their location, content of listing agent remarks in the listing for sale) to be considered are searched from a data file containing collection of assets, and a matrix (a table, an array) of the features of a real estate to be considered to determine valuation of a real estate property is listed in the matrix. Available property data file is searched to identify one or more comparison properties with at least one feature (applicant defines features to comprise at least frontage (visible from street), type of business, equipment and design for the type of business, disability access, ease of parking (see at least 0054), exterior features like deck, porch, patio, balcony, fence, shed, gazebo, outbuilding, pool, etc. (see at least, 0128), and runs a query to identify comparative properties that comprise at least one or the query criteria (text in a remarks field, or type of property (free standing, interior-unit, end-unit, type of exterior finish, etc.). alternate spelling of a feature (e.g. a misspelled feature like “cul du sec” in addition to “cul du sec”, potentially misspelled, an alias, for example, “great” and “gr8”; “fixer-upper” and “TLC”, etc.) are also considered to as part of search query, and for features in the identified comparative properties, certain scores (value) is assigned to each of the features, performs some mathematical calculation to reflect degree of similarity to the subject property (adjusts valuation of features of the comparative property (e.g. if the property is a residential property, and it is on the main road (not as desired location for buyers), location feature value of the property is assigned a negative value, in contrast, if the property is a townhome, and it is an end unit with brick front, exterior feature values of the townhomes are assigned a positive value. After value of each feature is adjusted, mathematical calculation is performed to determine valuation of the subject property.
These limitations describe marketing/sales/advertising activities. An appraiser (or person) performs valuation of a real estate (or property) by initially determining what type of real-estate they are generating an appraisal for, identifying specific information related to the real-estate (e.g., location, type of property, lot-size, number of bedrooms and bathrooms, size of living space, etc.), identifying comparable real-estate, compare the subject property to the identified comparable real-estate, perform mathematical calculation by making adjustments to features, and determine the appraised valuation of the subject real-estate.
This judicial exception is not integrated into a practical application because, in particular, the claim recites using a computing device with a processor (primary device), database and computer executable instruction to user specific formulae to derive the appraised value of a specific property. Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 (SAP v. Investpic: Page 2, line 22 through Page 3, line 13) - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
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 elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits of practicing the abstract idea. Accordingly, the claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim recites the additional elements of a processor of a primary device determining valuation of a property by recognizing specific data of interest (e.g. type of real estate (assets), their location, content of listing agent remarks in the listing for sale) to be considered are searched from a data file containing collection of assets, and a matrix (a table, an array) of the features of a real estate to be considered to determine valuation of a real estate property is listed in the matrix. Said primary device accesses one or more comparison properties with at least one feature (applicant defines features to comprise at least frontage (visible from street), type of business, equipment and design for the type of business, disability access, ease of parking (see at least 0054), exterior features like deck, porch, patio, balcony, fence, shed, gazebo, outbuilding, pool, etc. (see at least, 0128), and runs a query to identify comparative properties that comprise at least one or the query criteria (text in a remarks field, or type of property (free standing, interior-unit, end-unit, type of exterior finish, etc.). alternate spelling of a feature (e.g. a misspelled feature like “cul du sec” in addition to “cul du sec”, potentially misspelled, an alias, for example, “great” and “gr8”; “fixer-upper” and “TLC”, etc.) are also considered to as part of search query, and for features in the identified comparative properties, primary device assigns certain scores (value) to each of the features, performs some mathematical calculation to reflect degree of similarity to the subject property (adjusts valuation of features of the comparative property (e.g. if the property is a residential property, and it is on the main road (not as desired location for buyers), location feature value of the property is assigned a negative value, in contrast, if the property is a townhome, and it is an end unit with brick front, exterior feature values of the townhomes are assigned a positive value. After value of each feature is adjusted, primary device performs mathematical calculation to determine valuation of the subject property, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components.
Represented claims 14 and 16, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 14), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 16).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2 – 3, 6 – 9, 11 – 12 and 15 – 18, these claims recite limitations that further define the same abstract idea of determination of defining what features will be considered to identify similar properties, what features of the identified similar properties will be considered to calculate appraised value of the property, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components.
As for dependent claims 2 – 3, 6 – 9, 11 – 12 and 15 – 18, these claims recite limitations that further define how values of different features (variables) will be assigned and used in performing mathematical calculations to determine appraised value of a specific property, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components.
As for dependent claims 2 – 3, 6 – 9, 11 – 12 and 15 – 18, these claims recite limitations that further define the same abstract idea of commercially known mathematical or statistical formulae will be used to calculate appraised value of the property, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of organizing certain methods of human activity related to advertising, marketing or sales activities or behaviors but for the recitation of generic computer components.
Therefore, dependent claims are considered patent ineligible for the reasons given above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 4, 8 – 12, 14 – 15, 19 – 21 and 26 – 27 are rejected under 35 U.S.C. 103 as being unpatentable over Sklarz et al. US Publication 2002/0087389 in view of Graboske et al. US Publication 2011/0202561, Godbee US Publication 2014/0289136 and Mathews Haynes generated Appraisal of a property labeled as Sample of an Appraisal hereinafter referred to as Haynes.
Regarding claims 1, 14 and 26, as best understood by examiner, Sklarz teaches an automated system and method for valuating a subject property (e.g. real estate), the method comprising:
at least one computer comprising at least one processor operatively connected to at least one non-transitory, computer readable medium, the at least one non-transitory computer readable medium having computer-executable instructions stored thereon (Sklarz, Fig. 1 and associated disclosure];
accessing, by the at least one processor, comparison property data from a comparison property database, wherein the comparison property data includes comparison property identifiers for a plurality of comparison properties and a plurality of features, and each comparison property identifier corresponds to at least one feature from among the plurality of features (Sklarz, The Value Your Home ("VYH") invention sources data via a data bus or data communications network (e.g., the Internet) from one or more local and/or remote sources, e.g., Multiple Listing Services ("MLS"), real property tax records, geographic information services ("GIS"), etc.) [Sklarz, 0014];
Sklarz does not explicitly teach generating a term matrix (e.g. fixer-upper, or looks-good or needs-TLC in the comments section of the listing). However, Graboske teaches system and method for providing adjustment values for keywords (terms) retrieved from a data source. Graboske teaches generating a term matrix
PNG
media_image1.png
140
392
media_image1.png
Greyscale
[Graboske, 0014].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Sklarz by adopting teachings of Graboske to improve the property value adjustment based on the condition or improvements to the property (an old and known real estate appraisal process to estimate a fair market value of a real estate).
Sklarz in view of Graboske teaches system and method further comprising:
generating, by at least one processor, a term matrix to determine commonly-used terms in a plurality of real-estate terms from a dictionary of the plurality of real-estate terms
PNG
media_image1.png
140
392
media_image1.png
Greyscale
[Graboske, 0014. Fig. 3 and associated disclosure];
applying, by the at least one processor, text analysis and/or text mining to text-containing data fields in a plurality of listings in a multiple listing service from a server computing device (Graboske, The data mining module 215 may search the property data for a comments or remarks field 125 (step 310) and a data matching module 218 (e.g., a processor) may extract or match keywords from the property data using a table (step 315). The table may include specific keywords to look for in the comments or remarks field 125 that are useful to adjust) [Graboske, 0014] to one of improve and optimize numeric feature scores for the plurality of listings (Graboske, method and apparatus for systematically improving valuations provided by an automated valuation model (AVM)) [Graboske, 0003], wherein the multiple listing service is a service comprising a database of properties, wherein the database comprises the plurality of listings, and wherein each of the listings in the plurality of listings represents a different comparison property from among the set of comparison properties (Graboske, At step 305, a data mining module 215 (e.g., a processor) retrieves property data from the one or more property data sources 205 and 210. As an example, the property data may be MLS listing data and the property data sources 205 and 210 may be public or private databases that include the MLS listing data for a number of residential and commercial properties. The property data retrieved may be for one or more properties) [Graboske, 0013];
Sklarz in view of Graboske does not explicitly teach to calculate numeric feature score. However, Sklarz teaches To generate a predicted sales price, the VYH trend engine takes the most recent average price, price per bed room, and price per square foot on the trend line generated for similar properties within a radius or rectilinear distance of a subject property, and applies these metrics to the subject property. Trend analysis methods are a significant improvement over comparable market analysis methods, since time adjustment factors can be used and very specific trends can be calculated [Sklarz, 0253]. Godbee teaches system and method for appraising real estate properties. Godbee teaches system and method for using features of a building to derive to valuation of a proper rent. Godbee teaches The feature factors include a rating of each of the unit's areas 23, including the kitchen, bedrooms, living room areas, and other interior spaces. The quality of features within each of the unit areas 23 is quantified based on their condition and quantity, wherein a point system is used to quantify the quality and quantity of the features. Features, such as unit size, fixtures, ceiling/wall finish, etc. are provided with a point system based on the criteria listed below. A rating system of 1.0-0.5 is used to denote the quality of each of the features in the unit [Godbee, 0048].
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Sklarz in view of Groboske by adopting teachings of Godbee to determine reasonable valuation of a real estate (an old and known real estate appraisal process to estimate a fair market value of a real estate).
Sklarz in view of Graboske and Godbee teaches system and method further comprising:
querying, by the at least one processor, in the comparison property database entries having at least one data type comprising one of continuous numeric, ranked numeric, categorical symbolic keywords, unstructured text comprising a remarks data field, and structured text and parsing data having the at least one data type to identify text related to at least one of the plurality of features using the term matrix (Graboske, The data mining module 215 may search the property data for a comments or remarks field 125 (step 310) and a data matching module 218 (e.g., a processor) may extract or match keywords from the property data using a table (step 315). The table may include specific keywords to look for in the comments or remarks field 125 that are useful to adjust) [Graboske, 0014] by performing identifying text related to at least one of the features having at least a subset of a term in the term matrix, and by identifying text related to the at least one of the features having a spelling that is within a threshold of difference from at least a subset of a term in the term matrix (Graboske, Buyers of residential and commercial real estate generally search and review MLS listings to locate particular residential and commercial properties of interest. That is, the MLS listings provide listing of a number of different properties that satisfy a predetermined criteria input by a user) [Graboske, 0011] and converting the text into a numeric feature score for each of the plurality of comparison properties [Godbee, 0048, Fig. 4 and associated disclosure] to reduce searching through the term matrix and reduce processor cycles used to identify text (Godbee, A common method of calculating rent includes using comparable rental values for similar properties in the same real estate market, and, it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database) [Godbee, 0006].
determining, by the at least one processor, that the parsed data is one of missing and in need of repair and performing data quality and data cleaning on the parsed data to identify and resolve missing or out-of-range feature values and convert the data into the numeric feature score (Sklarz, Data adjustments include limiting numeric values of "outlier" transactions that would otherwise distort analysis. Data adjustments also include extrapolation and interpolation of missing data items, and limitation of outliers) [Sklarz, 0017] to reduce time converting the data into a numeric feature score and reduce processor cycles used (Sklarz, The analytic engines of the VYH invention can filter data values for a given data type by discarding abnormal, or "outlier", data values that are outside a reasonable range, or by limiting their deviation from a reference, such as a trend line, and, it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database) [Sklarz, 0221];
selecting, by the at least one processor, a set of comparison properties from the plurality of comparison properties by further selecting a plurality of search and similarity parameters, wherein the set of comparison properties comprise comparison properties most similar to the subject property based on transaction type, property type, location proximity, and time proximity, and selecting a final set of comparison properties from the set of comparison properties that comprises a subset of the set of comparison properties based on gross living area and comp modified to sale price (Sklarz,
"Comparable market analysis" predicts a sale price based on actual ("historic") sales prices of similar properties, where "similar" has a range of meanings. "Similar" can include a geographic area surrounding a subject property, or neighborhoods with similar attributes located elsewhere in the city, state, nation, or geographic radius. "Similar" can also include physical attributes of a structure, e.g., number of bedrooms, total living area, swimming pool area, number of parking spaces, etc. "Similar" can further include proximity to certain types of infrastructure, e.g., K-12 schools, universities, public parks, freeway interchanges, libraries, etc.) [Sklarz, 0007] and storing the final set of comparison properties rather than the set of comparison properties in one of memory and the database to conserve data storage space (Sklarz, The VYH server software sources such factual information, filters the information, stores the filtered data in a VYH database, and makes such information accessible to users) [Sklarz, 0052];
Sklarz in view of Godbee does not explicitly teach adjustment for features based upon features of comparison property. However, Haynes teaches that an appraisal of a subject property is generated by adjusting values of feature of the subject property based upon features in the comparable properties [Haynes, page 4].
Therefore, before the invention, it would have been obvious to one of ordinary skill in the art to modify Sklarz in view of Godbee by adopting teachings of Haynes to generate an unbiased appraisal value by giving full consideration to all comparative sold properties to derive an estimate of value (an old and known real estate appraisal process to estimate a fair market value of a real estate).
Sklarz in view of Graboske, Godbee and Haynes teaches system and method further comprising:
applying, by the at least one processor, one of a plurality of feature scoring algorithms to feature data inputs depending on feature type to calculate a numeric feature score for each of the plurality of features for both the final set of comparison properties and the subject property
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4], to reduce processor cycles on comparison properties that are dissimilar from the final set of comparison properties (it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database);
adjusting, by the at least one processor, for each comparison property in the set of comparison properties, each numeric feature score based on a difference between the numeric feature scores for the subject property and the comparison property
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4], to reduce processor cycles on comparison properties that are dissimilar from the final set of comparison properties (it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database);
calculating, by the at least one processor, a feature adjustment value for each of the plurality of features by multiplying a feature adjustment score, a feature adjustment fraction, and a comp modified sale price together for each comparable property in the set of comparison properties
PNG
media_image1.png
140
392
media_image1.png
Greyscale
[Graboske, 0014. Fig. 3 and associated disclosure], to reduce processor cycles on comparison properties that are dissimilar from the final set of comparison properties (it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database);
totaling, by the at least one processor, the feature adjustment values and the modified sale price for each comparison property in the set of comparison properties, thereby yielding an adjusted sale price for each comparison property based on a formula comprising
PNG
media_image3.png
75
214
media_image3.png
Greyscale
PNG
media_image4.png
707
960
media_image4.png
Greyscale
[Haynes, page 4]; and
where FAV(i, j) is the feature adjustment value for feature j and comp i (Haynes, see at least feature adjustments made in $ Adjustment column) [Haynes, page 4],
FAV (i, j) = FAS (i, j) * FAF(j) * P(i), where i is a dimensionless index value corresponding to a particular comparison property ranging from 1 to n (Haynes, Comparable No. 1 -- Comparable No. 3, n =3) [Haynes, page 4], j is a dimensionless index value corresponding to a particular feature ranging from 1 to m, FAS(i, j) is an input numeric feature adjustment score of a jth feature for an ith comparison property (Haynes, see at least features listed in items column) [Haynes, page 4], FAF(j) is a feature adjustment fraction for the jth feature that converts the feature adjustment score into a feature adjustment value by weighing, scaling and monetizing the feature adjustment score (Haynes, see at least adjustments for features made to each of the selected comparable properties) [Haynes, page 4], and P(i) is a comparison modified sale price for the ith comparison property that scales and monetizes the FAS (Haynes, see at least adjustable sales prices for each selected comparable property) [Haynes, page 4], and
TFAV(i) is the Total Feature Adjustment Value for comp i
PNG
media_image5.png
75
696
media_image5.png
Greyscale
(Haynes, see at least adjusted sales price in $ Adjustment column which is deemed to be summation of adjusted feature values)
applying, by the at least one processor, a weighting formula to each comparison property in the final set of comparison properties that reflects degree of similarity to the subject property based on an amount of total net adjustments, thereby reaching a final valuation of the subject property (Sklarz, the VYH comparable market analysis engine applies algorithms that weigh the key features of the subject property versus the features of the comparable properties to provide an estimated appraised value (predicted sales price) for the subject property) [Sklarz, 0223; also see Haynes page 0004 which generated appraised value of subject property based on sales comparison approach] , to reduce processor cycles on comparison properties that are dissimilar from the final set of comparison properties (it would have been obvious to one of ordinary skill in the art that if a subset of records are analyzed, it will result in using lesser processor cycles, as compared to analyze every record in the database);
Regarding claim 2, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein, in order to select the set of comparison properties, a numeric similarity score for each comparison property in the set of comparison properties is calculated, wherein the numeric similarity score is indicative of a degree of similarity and relevance of the corresponding comparison property to the subject property based on one or more characteristics
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4].
Regarding claim 3, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein, in order to calculate similarity scores to select the set of comparison properties, the plurality of search and similarity parameters is selected from the group consisting of: property type, transaction type, location proximity, time proximity, gross living area (GLA), modified sale price, and combinations thereof
PNG
media_image6.png
154
807
media_image6.png
Greyscale
[Haynes, page 4].
Regarding claims 4, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the plurality of features is selected from the group consisting of: sale price, concessions, modified sale price, number of bedrooms, number of bathrooms, flooring, fireplace, nearby amenities, contract date adjustment, current market conditions, basement features, energy, property style, remodeling, exterior finish, exterior features, parking, street traffic, gross living area (GLA), basement finished living area, basement unfinished living area, lot size, property age, days on market (DOM), quality of construction, condition, how well property shows, site landscaping, neighborhood, school quality, remarks, and combinations thereof
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4].
Regarding claims 8 and 19, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the calculation of the numeric feature score further comprises applying nominal feature scores to a plurality of aspects and/or attributes within at least one feature in the plurality of features (Godbee, The quality of features within each of the unit areas 23 is quantified based on their condition and quantity, wherein a point system is used to quantify the quality and quantity of the features) [Godbee, 0048] comprising basement features, exterior features, exterior finish, property style, flooring energy, remodeling/design, parking and street traffic
PNG
media_image7.png
101
692
media_image7.png
Greyscale
[Haynes, page 4].
Regarding claim 9, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the calculation of the numeric feature score further comprises converting a categorical symbolic data input into a nominal feature score reflective of general market desirability for at least one feature in the plurality of features (Godbee, The quality of features within each of the unit areas 23 is quantified based on their condition and quantity, wherein a point system is used to quantify the quality and quantity of the features) [Godbee, 0048].
Regarding claims 10 and 20, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, further comprising utilizing sales data to adjust for sale price differences over time between a contract date of at least one of the comparison properties in the set of comparison properties, current market conditions and current analysis date
PNG
media_image8.png
151
797
media_image8.png
Greyscale
[Haynes, page 4].
Regarding claim 11, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, further comprising adjusting the valuation of a comparable property based on current market conditions by considering factors selected from the group consisting of: DOM, contract price/listing price, mortgage rates, sale listings, recent sales, short sales of other properties near the subject property, percent rentals, and combinations thereof
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4].
Regarding claim 12, Sklarz in view of Graboske, Godbee and Haynes teaches system and method further comprising storing at least one of the plurality of features, the numeric feature scores, and the feature adjustment values (Godbee, The method compiles and stores the data related to the home for later retrieval and access, allowing users to view and see the attributes of the property both visually and textually) [Godbee, 0012].
Regarding claims 15, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the operations further comprise
calculating a similarity score for each comparison property in a plurality of potential comparison properties, wherein the score is indicative of a degree of similarity of the corresponding comparison property and the subject property based on one or more characteristics
PNG
media_image2.png
169
832
media_image2.png
Greyscale
[Haynes, page 4], and
applying the similarity scores to select the set of comparison properties from the plurality of potential comparison properties
PNG
media_image6.png
154
807
media_image6.png
Greyscale
[Haynes, page 4].
Regarding claim 21, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, further comprising adjusting valuation of the subject property based on current market conditions by considering factors selected from the group consisting of: days on market, contract price/listing price, recent sale price trends of other properties near the subject property, seasonality, demand, inventory, regional/local average housing sale prices, percent of real estate owned by banks, percent of short sales, and percent of rentals
PNG
media_image8.png
151
797
media_image8.png
Greyscale
[Haynes, page 4].
Regarding claim 27, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the one or more characteristics comprise gross living area (GLA) and Modified Sale Price
PNG
media_image9.png
122
963
media_image9.png
Greyscale
[Hanes, page 4].
Claims 5, 16 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Sklarz et al. US Publication 2002/0087389 in view of Graboske et al. US Publication 2011/0202561, Godbee US Publication 2014/0289136, Mathews Haynes generated Appraisal of a property labeled as Sample of an Appraisal hereinafter referred to as Haynesm and Vaibhav Haswani published article “Learning Rate Decay and methods in Deep Learning”.
Regarding claims 5 and 16, Sklarz in view of Graboske, Godbee and Haynes does not explicitly teach applying one or more exponential decay formulas. However, Haswani teaches that learning rate decay is a technique for training modern neural networks. It starts training the network with a large learning rate and then slowly reducing/decaying it until the local minima is obtained. It is empirically observed to help both optimization and generalization.
Therefore, at the time of filing, it would have been obvious to one or ordinary skill in the art to modify Sklarz in view of Graboske, Godbee and Haynes by adopting teachings of Haswani and provide exponential decay function (formula) to assign depreciated value to a feature of a real estate. For example, roof is old and nearing end of its life, and/or appliance(s) are older than their average service life.
Sklarz in view of Graboske, Godbee, Haynes and Haswani teaches system and method, wherein the calculation of the numeric feature score further comprises applying either an exponential decay formula or, alternatively, a polynomial regression formula to calculate value for at least some features in the plurality of features, the at least some features comprising GLA, Basement Finished, Basement Unfinished, Lot Size, Property Age, and DOM
PNG
media_image10.png
331
635
media_image10.png
Greyscale
[Haswani, page 4].
Regarding claim 28, Sklarz in view of Graboske, Godbee and Haynes teaches system and method, wherein the at least some features in the plurality of features comprise gross living area (GLA), basement finished living area, basement unfinished living area, lot size, and property age
PNG
media_image9.png
122
963
media_image9.png
Greyscale
[Hanes, page 4].
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sklarz et al. US Publication 2002/0087389 in view of Graboske et al. US Publication 2011/0202561, Godbee US Publication 2014/0289136 and Mathews Haynes generated Appraisal of a property labeled as Sample of an Appraisal hereinafter referred to as Haynes and Saul McLeod published article “Likert scale definition, examples and analysis”.
Regarding claims 6 and 17, Sklarz in view of Graboske, Godbee and Haynes does not explicitly teach using commercially known Likert Scale (a multipoint scale) to grade subjective judgement. However, McLeod teaches that Likert Scale (a multipoint scale) is used to allow individuals to express how much they agree or disagree with a particular statement (feature), and allows respondents to indicate their positive-to-negative strength of agreement regarding the statement (feature) [McLeod, page 1].
Therefore, before the invention, it would have been obvious to one of ordinary skill in the art to modify Sklarz in view of Godbee and Haynes and gather strength of opinion using a multipoint scale to determine a degree of opinion.
Sklarz in view of Graboske, Godbee, Haynes and McLeod teaches system and method, wherein the calculation of the numeric feature score further comprises applying particular Likert scale measurements from one to seven (a multipoint scale) that measure both feature quality and quantity to grade subjective judgments as numeric rankings for at least some features in the plurality of features, the at least some features (e.g. features important to a user like a buyer) comprising parking, street traffic, quality of construction, condition, how well property shows, site landscaping, nearby amenities, and neighborhood (McLeod, Likert Scale (a multipoint scale) is used to allow individuals to express how much they agree or disagree with a particular statement (feature), and allows respondents to indicate their positive-to-negative strength of agreement regarding the statement (feature)) [McLeod, page 1].
Response to Amendment
The Declaration filed under 37 CFR 1.132 filed 14 October 2025 is insufficient to overcome the rejection of claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 based upon 35 U.S.C. 112(a); lack of utility and/or inoperativeness under 35 U.S.C. 101; a specific reference applied under 35 U.S.C. 103 as set forth in the last Office action because: Patent Trial And Appeal Board in their decision mailed 15 August 2025 informed the applicant that Examiner has not erred in rejection claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28.
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the invention produces an objective method of property valuation that is unexpectedly better than, and superior to, current methods and results in improved computer performance and functionality. Accordingly, one of ordinary skill in the art would not simply be able to arrive at the claimed invention via solely mental steps, since existing valuation methods used by such skilled artisans are inherently subjective (e.g., drawn from each individual's subjective experience rather than from objective calculations, as in the Application), is acknowledged and considered.
However, upon further consideration, it is deemed that the pending claimed invention is not eligible for patent under 35 USC 101, and Patent Trial And Appeal Board in their decision mailed 15 August 2025 informed the applicant that Examiner has not erred in rejection claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 to determine eligibility of claimed invention under 35 USC 101.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because their claimed invention produces up to 99% accurate results when compared to the adjusted subject sale price, and such accuracy is higher than that achieved via existing approaches Zillow, as shown below, has an accuracy of, at best, under 98%, is acknowledged and considered.
However, it is deemed that their applicant is arguing that their subjective opinion that appraisal value generated by their mathematical formula(e) is 1% better than Zillow. Upon further consideration, Rejection under 35 USC 101 is maintained. Also, in their decision mailed 15 August 2025, Patent Trial And Appeal Board informed the applicant that Examiner has not erred in rejection claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 to determine eligibility of claimed invention under 35 USC 101.
Applicant's argument provided sample of Appraisal for 216 Lower Count Dr. by Appraiser Gus E. Taci has many serious errors because it differed by the appraised value of the property calculated by subjective opinion of the applicant is acknowledged and considered.
It is deemed that applicant is arguing that only their subjective way of generating appraised value of a real-estate produces real value of the real-estate (applicant’s opinion), therefore claimed invention is eligible for patent. However, applicant’s arguments are not perusable. Also, in their decision mailed 15 August 2025, Patent Trial and Appeal Board informed the applicant that Examiner has not erred in rejection claims 1 – 6, 8 – 12, 14 – 17, 19 – 21 and 26 – 28 to determine eligibility of claimed invention under 35 USC 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stewart US Publication 2022/0084079 teaches system and method for automatically determining property value.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Thu 05:30a - 03:00p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached on 571.270.7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NARESH VIG/Primary Examiner, Art Unit 3622
March 20, 2026