DETAILED ACTION
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 .
Status
This action is in response to the amendment filed on 2/2/2026. Claims 1-25 are pending. No claims are amended. No claims have been added. No claims have been cancelled.
Response to Arguments
Applicant's arguments filed 2/2/2026 have been fully considered but they are not persuasive. The applicant has argued “Applicant notes that the independent claims require providing feature vectors as input to a trained machine learning (ML) model comprising a plurality of initial layers, a first final layer, and a second final layer, to generate a home sales prediction that includes both a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period. This multi-layered computational architecture cannot be performed mentally. The trained ML model with its plurality of initial layers and dual final layers represents a specific computational structure that processes numerical feature vectors through multiple layers to simultaneously generate two different types of outputs. The human mind cannot maintain and process the numerous parameters and computations required by such a multi-layer model architecture, nor can it simultaneously generate dual predictions (e.g., predicted number of home sales for a time period and a predicted change in home sales) through separate computational pathways.” The examiner respectfully disagrees. Although a machine learning model with multiple layers may not be able to be performed in the human mind that is not the test. The applicant is using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. Further, the inability to mentally perform a complex mathematical computation does not remove it from the category of an abstract idea. The claims are directed to receiving data, extracting data, running a mathematical model, transmitting an instruction. The claims do not recite how the layers are structured or how they are trained. The claims do not recite which parameters are used. The machine learning model is a generic computational tool used in its ordinary capacity. The multiple layer neural networks as claimed are not an improvement to the technology. The architecture is not a technical improvement over prior machine learning approaches.
The applicant has argued “The claims also require extracting a plurality of features from the historical home sales data and the historical home demand data to generate a set of feature vectors, where the set of feature vectors comprise a numerical representation of the plurality of features. This feature extraction and vectorization process transforms raw historical data into structured numerical representations suitable for machine learning input-a computational process that cannot be performed mentally. The recitation of "a trained machine learning model" with specific structural components (plurality of initial layers, first final layer, second final layer) defines a specific computational architecture that performs simultaneous dual-output generation-a function that the human mind cannot replicate. Applicant also notes that the forecasting system relating to the homebuilder industry does not transform the claimed technological process into the alleged abstract business method, just as image recognition algorithms are not abstract ideas simply because they may be used in commercial applications.” The examiner respectfully disagrees. The claimed invention appears to involve receiving data, making a prediction, and transmitting instructions. Converting and organizing data is abstract. The fact that the process requires a computer to execute efficiently is not a technical improvement. Further, the use of historic data and using it in Machine Learning is a known feature of Machine Learning. There is nothing novel or unconventional about the use of the Machine Learning. The step of extracting data, vectorizing, and running data through a Machine Learning in a manner of apply it is directed to an abstract idea. The argument related to the various layers is not persuasive as claimed the limitations describes what the model does but not the technical details about how the steps are done. Image recognition claims that have been found eligible recite a specific technical improvement to how the recognition is performed. There is no technical improvements to the machine learning model.
The applicant has argued “Even if the claims were found to recite an abstract idea, the additional elements integrate the exception into a practical application. For example, the claims specifically require a trained ML model comprising a plurality of initial layers, a first final layer, and a second final layer, configured to generate dual outputs: a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period. This bifurcated architecture-where multiple initial layers feed into two distinct final layers that produce different types of predictions-represents a specific technological implementation. The specification describes that the ML forecasting model bifurcates the predictive modeling into two distinct outputs-a performance forecast (sales prediction) and a predicted performance change (change in sales)-using a functional application programming interface (API) to allow shared usage of the initial layers across the output layers. See Specification, [0082]. The dual-output structure provides technical advantages including at least computational efficiency through shared processing in the initial layers, dual-output capability providing both quantitative sales forecasts and qualitative directional changes in a single model execution, and improved prediction accuracy by enabling separate optimization of different prediction aspects. Improving forecasting performance for time-series data is analogous to the specific data structure improvement found patent-eligible in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016).” The examiner respectfully disagrees. Applicant’s arguments appeared to be directed to arguing the benefits of the abstract idea. However, identifying the benefits of the abstract idea is not the same thing as integrating it into a practical application. Applicant’s invention does not improve the functioning of a computer or other technology. Arguing that a model produces two outputs more efficiently that two separate models would be an improvement to the abstract idea not a technical improvement to the computing itself. Shared-layer neural network architectures are known in the machine learning field. Applicant’s invention is not analogous the claims of Enfish, specifically Enfish was directed to an improvement to the computer structure itself. Enfish improved how a computer organized and accessed data while applicant’s invention improves how accurately home sales are predicted.
The applicant has argued “Furthermore, the claims apply marching learning to a practical problem. For example, the claims recite transmitting an instruction corresponding to the home sales prediction to a second node of the computer network, integrating the forecasting model into an automated operational system. The system enables determining specific actionable steps for home builders, including dynamically determining pace for selling homes while accounting for home inventory and historic sales data, and enables automated responses to change in the marketplace. This represents application of machine learning to achieve a specific practical result-automated operational decision-making for homebuilder management similar to the treatment optimization found eligible in Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals International Ltd., 887 F.3d 1117 (Fed. Cir. 2018). Applicant respectfully submits that the foregoing demonstrates the patent-eligibility of claims 1-25 and respectfully requests withdrawal of the § 101 rejection.” The examiner respectfully disagrees. Transmitting instruction between nodes is a generic computer networking function, automating a business process is directed to an abstract idea, the steps of the claims are directed to a business outcome not an improvement to the technology. Applicant’s arguments that are directed to Vanda are not found persuasive. Vanda was directed to applying a specific natural low to produce a specific physical medical result. Applicant’s case is directed to applying data to produce a business forecast. The claims do not recite additional elements that impose meaningful limits on the identified abstract ideas by improving technology or applying the idea in a technically specific way. Further, applicant’s invention is not analogous to Vanda. Vanda discloses a biological relationship applicant’s invention is directed to the relationship between historical sales and futures sales to at best produce a forecast. The previous 101 rejection is maintained below.
The applicant has argued “Applicant emphasizes that a "business logic layer" in software architecture refers to the part of an application that handles business rules and user interactions, not to the computational layers of a neural network that process feature vectors through mathematical transformations. Moreover, this business logic layer is not described as part of the neural network structure of Chigogidze. The cited portions describes how users interact with the system through interview questions and behavioral analysis, not how the neural network is structured with initial layers and final layers. Accordingly, the cited portions merely describe a user interface for collecting user preferences through interview questions and not an ML model that processes feature vectors to generate dual predictions. Indeed, Chigogidze does not teach or suggest a bifurcated ML model architecture with dual final layers producing two separate types of outputs. Rather, at best, Chigogidze's system generates listing-to-user matches, not simultaneous dual forecasting outputs (predicted number of sales and predicted change in sales).” The examiner respectfully disagrees. Specifically, Chigogidze discloses providing feature vectors as input into a trained Machine learning model in paragraph 37, “given inputs X.sub.1-X.sub.n, each with corresponding weighting factors Y.sub.1-Y.sub.n, (that may have changing values based on the iterative influence of activity) the inference engine 350 will utilize the trained model 330 to generate predicted outputs Z.sub.1-Z.sub.n. Generally speaking, the weighting factors Y.sub.1-Y.sub.n may be a result of the prediction process whereby different weighting factors are determined to be more or less influential over the prediction processes. For example, initial weighting factors may be zero as there does not exist any predictive data yet—but as predictions emerge and comparisons to reality are determined, weightings of influential factors may also emerge.” In the cited portion the feature vectors are provided as input into a trained machine learning model. This can also be seen in figure 4. ¶ 25 and 31 also disclose vector generating. The prior art also discloses a neural network with multiple layers. Specifically, paragraph 34 discloses a trained model as a neural network. A neural network consist of an input layer, one or more hidden layers, and an output layer. Additionally, the prior art reference refers to machine learning frameworks that are used to implement the system in paragraph 79, which are examples of machine learning systems that use multiple layers. The reference discloses in Fig. 4 discloses multiple distinct outputs in the zi-zn values. A neural network producing multiple distinct output comprises multiple distinct output nodes or layers, which correspond to each output. Chigogidze in paragraphs 35-36, discloses that a trained model processing the inputs through a training and interface engine. The processing architecture is an example of a plurality of initial layers shared across multiple final output layers. The prior art clearly shows a model generating multiple outputs and multipole processing layers feeding into separate output nodes. Applicant’s arguments are not found persuasive. The previous 103 rejection is maintained.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-12 are directed to a system, claims 13-24 are directed to a method, and claim 25 is directed to a computer program product. Therefore, claims 1-25 are directed to patent eligible categories of invention.
Step 2A Prong 1:The claim(s) recite(s) (mathematical relationships/formulas, mental process or certain methods of organizing human activity). Specifically the independent claims recite:
mental process: as drafted, the claim recites the limitations of receiving historical home sales data, receiving historical home demand data, extracting features from the home data, providing a set of features, and transmitting an instruction which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a computer network” and “a machine learning model” nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “a computer network” language, the claim encompasses the user manually receiving, extracting, providing, and sending data. The mere nominal recitation of a generic computing devices does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
(c) certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for users to forecast market data which is a method of commercial interactions (sales activities and behaviors) (see MPEP 2106.04(a)(2). Thus, the claim recites an abstract idea.
Other than reciting “a computer network,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “a computer network” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2, 4-6, 8, 12, 14, 16-18, 20, 24, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 3, 7, 9-11, 15, 19, 21-23, will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 13, and 25 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “a memory; and a processor coupled to the memory, the processor.” Claim 1 further recites the additional elements of “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” Claim 13 is a method that recites limitations performed “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” Claim 25 is a computer program product that comprises “A computer program product for machine learning-based market forecasting… the computer program product comprising: a non-transitory computer readable medium”… “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2, 4-6, 8, 12, 14, 16-18, 20, 24, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 3, 15, introduces the additional element of “where the plurality of initial layers comprises an input layer, a long short-term memory (LSTM) layer, and a dropout layer.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 7, 19, introduces the additional element of “where the second final layer is configured to perform a softmax activation function to output the predicted changes in home sales as one or more probability values associated with particular changes in home sales.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 9, 21, introduces the additional element of “where the trained ML model is configured to provide outputs of the plurality of initial layers as inputs to the first final layer and as inputs to the second final layer.” This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 10, 22, introduces the additional element of “where the historical home demand data comprises leads data, customer inquiry data, website visit data, data from a third party data source, data from a source of streaming data, data identified by web crawlers, data from a proprietary data source, publicly available data, data acquired using an application programming interface (API), or a combination thereof.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 11, 23, introduces the additional element of “where the processor is configured to perform steps comprising: training the trained machine learning model in a first phase and a second phase, the first phase comprising an early stopping phase configured to prevent overfitting from exceeding a target threshold, and the second phase comprising training the trained machine learning model on an entirety of a dataset for a determined number of epochs and at a predetermined learning rate.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 13, and 25 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, claim 1 is “a memory; and a processor coupled to the memory, the processor.” Claim 1 further recites the additional elements of “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” Claim 13 is a method that recites limitations performed “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” Claim 25 is a computer program product that comprises “A computer program product for machine learning-based market forecasting… the computer program product comprising: a non-transitory computer readable medium”… “at a first node of a computer network… at a second node of a computer network” and “a trained machine learning (ML) model.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 2, 4-6, 8, 12, 14, 16-18, 20, 24, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 3, 15, introduces the additional element of “where the plurality of initial layers comprises an input layer, a long short-term memory (LSTM) layer, and a dropout layer.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 7, 19, introduces the additional element of “where the second final layer is configured to perform a softmax activation function to output the predicted changes in home sales as one or more probability values associated with particular changes in home sales.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 9, 21, introduces the additional element of “where the trained ML model is configured to provide outputs of the plurality of initial layers as inputs to the first final layer and as inputs to the second final layer.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 10, 22, introduces the additional element of “where the historical home demand data comprises leads data, customer inquiry data, website visit data, data from a third party data source, data from a source of streaming data, data identified by web crawlers, data from a proprietary data source, publicly available data, data acquired using an application programming interface (API), or a combination thereof.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 11, 23, introduces the additional element of “where the processor is configured to perform steps comprising: training the trained machine learning model in a first phase and a second phase, the first phase comprising an early stopping phase configured to prevent overfitting from exceeding a target threshold, and the second phase comprising training the trained machine learning model on an entirety of a dataset for a determined number of epochs and at a predetermined learning rate.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1-25 are rejected under 35 USC 101.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1, 4, 9, 10, 12, 13, 16, 21, 22, 24, 25, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chigogidze (US 20230297650 A1) in view of Bleakley et al. (US 20130346151 A1).
Regarding claim 1, Chigogidze teaches a system for machine learning-based market forecasting for homes, the system comprising: a memory; and a processor coupled to the memory, the processor configured to perform steps comprising (¶ 30, 32-34, disclose the use of machine learning. ¶ 19-21, 51, 52, 59, discloses memory and a processor);
receiving, at a first node of a computer network, historical home sales data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
receiving, at the first node of the computer network, historical home data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
extracting a plurality of features from the historical home sales data and the historical home data to generate a set of feature vectors, where the set of feature vectors comprise a numerical representation of the plurality of features extracted from the historical home sales data and the historical home data (¶ 22-26, discloses extracting data from various sources, ¶ 35, 39, discloses extracting data and creating a training data set. ¶ 30, 31, 47, discloses generating vectors);
providing the set of feature vectors as input to a trained machine learning (ML) model to generate a home sales prediction, where the trained ML model comprises a plurality of initial layers, a first final layer, and a second final layer (¶ 21-23, 34-39, disclose modeling and prediction of real estate data. ¶ 25, 29, disclose the use of logic layers. ¶ 30, 32-34, disclose the use of machine learning.);
and transmitting, to a second node of the computer network, an instruction corresponding to the home sales prediction (¶ 19, 21-23, 34-39, disclose modeling and prediction of real estate data.)
Chigogidze does not specifically teach details related to homebuilder communities and home demand data.
However, Bleakley discloses receiving, at a first node of a computer network, historical home sales data associated with a set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home sales data in real estate developments);
receiving, at the first node of the computer network, historical home demand data associated with the set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home demand data in real estate developments);
extracting a plurality of features from the historical home sales data and the historical home demand data to generate a set of feature vectors (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to extracting both historical home sale and demand data in forecasting).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform homebuilder communities and home demand data, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data features into similar systems. Further, applying homebuilder communities data and home demand data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data points when predicting future home sales for a particular group.
Chigogidze teaches a predicted change does not specifically teach where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period.
However, Bleakley discloses where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period (¶ 46-48, 50, discloses details related to forecasting real-estate data over time).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional specific data points when predicting future home sales.
Regarding claims 4, 16, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13.
Chigogidze does not teach however, Bleakley teaches where the first final layer is configured to output predicted numbers of home sales (¶ 48-50, discloses predicting and outputting a total number of home sales).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the first final layer is configured to output predicted numbers of home sales, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the first final layer is configured to output predicted numbers of home sales would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow key players to make more informed decisions and gain a competitive edge.
Regarding claims 9, 21, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13.
Chigogidze further teaches where the trained ML model is configured to provide outputs of the plurality of initial layers as inputs to the first final layer and as inputs to the second final layer (¶ 21-23, 34-39, disclose modeling and prediction of real estate data. ¶ 25, 29, disclose the use of logic layers. ¶ 30, 32-35, disclose the use of machine learning and modeling, ¶ 37, discloses generated predicted outputs).
Regarding claims 10, 22, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13.
Chigogidze further teaches where the historical home demand data comprises leads data, customer inquiry data, website visit data, data from a third party data source, data from a source of streaming data, data identified by web crawlers, data from a proprietary data source, publicly available data, data acquired using an application programming interface (API), or a combination thereof (¶ 22, 28, 40, discloses third party sources. ¶ 56). Also taught by Bleakley.
Regarding claims 12, 24, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13.
Chigogidze further teaches where the instruction comprises an instruction to raise or lower the price of a home, an instruction to increase or decrease a construction rate for a home, an instruction to acquire additional property lots, a report of the predicted number of sales, a report of the predicted change in the number of homes, or a combination thereof (¶ 15, 20-23, 38, discloses predictive real-estate listing changes. ¶ 25, 29, 34). Also taught by Bleakley.
Regarding claim 13, Chigogidze teaches receiving, at a first node of a computer network, historical home sales data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
receiving, at the first node of the computer network, historical home data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
extracting a plurality of features from the historical home sales data and the historical home data to generate a set of feature vectors, where the set of feature vectors comprise a numerical representation of the plurality of features extracted from the historical home sales data and the historical home data (¶ 22-26, discloses extracting data from various sources, ¶ 35, 39, discloses extracting data and creating a training data set. ¶ 30, 31, 47, discloses generating vectors);
providing the set of feature vectors as input to a trained machine learning (ML) model to generate a home sales prediction, where the trained ML model comprises a plurality of initial layers, a first final layer, and a second final layer (¶ 21-23, 34-39, disclose modeling and prediction of real estate data. ¶ 25, 29, disclose the use of logic layers. ¶ 30, 32-34, disclose the use of machine learning.);
and transmitting, to a second node of the computer network, an instruction corresponding to the home sales prediction (¶ 19, 21-23, 34-39, disclose modeling and prediction of real estate data.)
Chigogidze does not specifically teach details related to homebuilder communities and home demand data.
However, Bleakley discloses receiving, at a first node of a computer network, historical home sales data associated with a set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home sales data in real estate developments);
receiving, at the first node of the computer network, historical home demand data associated with the set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home demand data in real estate developments);
extracting a plurality of features from the historical home sales data and the historical home demand data to generate a set of feature vectors (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to extracting both historical home sale and demand data in forecasting).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform homebuilder communities and home demand data, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data features into similar systems. Further, applying homebuilder communities data and home demand data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data points when predicting future home sales for a particular group.
Chigogidze teaches a predicted change does not specifically teach where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period.
However, Bleakley discloses where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period (¶ 46-48, 50, discloses details related to forecasting real-estate data over time).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional specific data points when predicting future home sales.
Regarding claim 25, Chigogidze teaches a computer program product for machine learning-based market forecasting, the computer program product comprising (¶ 30, 32-34, disclose the use of machine learning. ¶ 50-52, 65, 76, discloses a medium);
receiving, at a first node of a computer network, historical home sales data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
receiving, at the first node of the computer network, historical home data (¶ 34-35, disclose the use of historical data, ¶ 50-51, 64, 68, 75, disclose the use of a network);
extracting a plurality of features from the historical home sales data and the historical home data to generate a set of feature vectors, where the set of feature vectors comprise a numerical representation of the plurality of features extracted from the historical home sales data and the historical home data (¶ 22-26, discloses extracting data from various sources, ¶ 35, 39, discloses extracting data and creating a training data set. ¶ 30, 31, 47, discloses generating vectors);
providing the set of feature vectors as input to a trained machine learning (ML) model to generate a home sales prediction, where the trained ML model comprises a plurality of initial layers, a first final layer, and a second final layer (¶ 21-23, 34-39, disclose modeling and prediction of real estate data. ¶ 25, 29, disclose the use of logic layers. ¶ 30, 32-34, disclose the use of machine learning.);
and transmitting, to a second node of the computer network, an instruction corresponding to the home sales prediction (¶ 19, 21-23, 34-39, disclose modeling and prediction of real estate data.)
Chigogidze does not specifically teach details related to homebuilder communities and home demand data.
However, Bleakley discloses receiving, at a first node of a computer network, historical home sales data associated with a set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home sales data in real estate developments);
receiving, at the first node of the computer network, historical home demand data associated with the set of homebuilder communities (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to historical home demand data in real estate developments);
extracting a plurality of features from the historical home sales data and the historical home demand data to generate a set of feature vectors (¶ 19, 25, 28, 37, 46-48, 53-57, discloses details related to extracting both historical home sale and demand data in forecasting).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform homebuilder communities and home demand data, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data features into similar systems. Further, applying homebuilder communities data and home demand data would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional data points when predicting future home sales for a particular group.
Chigogidze teaches a predicted change does not specifically teach where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period.
However, Bleakley discloses where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period (¶ 46-48, 50, discloses details related to forecasting real-estate data over time).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period, as taught/suggested by Bleakley. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed to real-estate data management systems. One of ordinary skill in the art would have recognized that applying the known technique of Bleakley would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Bleakley to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the home sales prediction includes a predicted number of home sales for a time period and a predicted change in home sales from a previous time period to the time period would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow additional specific data points when predicting future home sales.
Claim(s) 2, 5, 6, 14, 17, 18, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chigogidze (US 20230297650 A1) in view of Bleakley et al. (US 20130346151 A1) in further view of Beharie et al. (US 20230419345 A1).
Regarding claims 2, 14, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13, including predicted home sales. The combination does not specifically teach where the predicted change comprises an increase in home sales, a decrease in home sales, or no change in home sales.
However, Beharie teaches where the predicted change comprises an increase in sales, a decrease in sales, or no change in sales (¶ 13, 33, 484, disclose an increase, decrease, or a neutral value for a sales volume.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the predicted change comprises an increase in home sales, a decrease in home sales, or no change in home sales, as taught/suggested by Beharie. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Beharie would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Beharie to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the predicted change comprises an increase in home sales, a decrease in home sales, or no change in home sales would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow an output of a specific data point.
Regarding claims 5, 17, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13. The combination does not specifically teach where the first final layer is configured to optimize a mean squared error (MSE) loss function.
However, Beharie teaches where the first final layer is configured to optimize a mean squared error (MSE) loss function (¶ 457, 397, 407, disclose the validating including a generation of a root mean square error).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the first final layer is configured to optimize a mean squared error (MSE) loss function, as taught/suggested by Beharie. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Beharie would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Beharie to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such MSE features into similar systems. Further, applying where the first final layer is configured to optimize a mean squared error (MSE) loss function would have been recognized by those of ordinary skill in the art as resulting in an improved system that minimizes large prediction errors and ensuring a model avoids mistakes.
Regarding claims 6, 18, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13, including predicted home sales. The combination does not specifically teach where the predicted change comprises an increase in home sales, a decrease in home sales, or no change in home sales.
However, Beharie teaches where the second final layer is configured to output predicted changes in sales (¶ 13, 33, 484, disclose an increase, decrease, or a neutral value for a sales volume.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the second final layer is configured to output predicted changes in sales, as taught/suggested by Beharie. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Beharie would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Beharie to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the second final layer is configured to output predicted changes in sales would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow an output of a specific data point.
Claim(s) 3, 11, 15, 23, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chigogidze (US 20230297650 A1) in view of Bleakley et al. (US 20130346151 A1) in further view of Ravindran et al. (US 10002322 B1).
Regarding claims 3 and 15, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13. The combination teaches an input layer but not a dropout or an LSTM layer.
However, Ravindran teaches where the plurality of initial layers comprises an input layer, a long short-term memory (LSTM) layer, and a dropout layer (col. 4, line 55 – col. 5, line 30, discloses an input layer, an output later, and a hidden layer. Col. 9, line 43 – col. 10, line 25, discloses an input layer, a LSTM layer, and a drop out layer.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the plurality of initial layers comprises an input layer, a long short-term memory (LSTM) layer, and a dropout layer, as taught/suggested by Ravindran. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Ravindran would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ravindran to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such layer features into similar systems. Further, applying where the plurality of initial layers comprises an input layer, a long short-term memory (LSTM) layer, and a dropout layer would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for increased functionality as the input layer ensures the sequential data is handled correctly, with the LSTM layer capturing temporal patterns and the dropout layer preventing the network from becoming too specialized to the training data.
Regarding claims 11 and 23, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13. The combination does not specifically teach where the processor is configured to perform steps comprising: training the trained machine learning model in a first phase and a second phase, the first phase comprising an early stopping phase configured to prevent overfitting from exceeding a target threshold, and the second phase comprising training the trained machine learning model on an entirety of a dataset for a determined number of epochs and at a predetermined learning rate.
However, Ravindran teaches where the processor is configured to perform steps comprising: training the trained machine learning model in a first phase and a second phase, the first phase comprising an early stopping phase configured to prevent overfitting from exceeding a target threshold, and the second phase comprising training the trained machine learning model on an entirety of a dataset for a determined number of epochs and at a predetermined learning rate (col. 1, line 29 – col. 2, line 18, discloses deep learning machine learning models. Col. 10, lines 31-45, col. 10, line 65 – col. 11, line 45 discloses learning rates, number of learning epochs, limits and a learning rate).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform the specifics of training the trained machine learning model in a first phase and a second phase as claimed and as taught/suggested by Ravindran. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Ravindran would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ravindran to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such training features into similar systems. Further, applying the specifics of training the trained machine learning model in a first phase and a second phase as claimed would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the model to iteratively learn, refine its parameters, and minimize loss over multiple passes through the data.
Claim(s) 7, 8, 19, 20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chigogidze (US 20230297650 A1) in view of Bleakley et al. (US 20130346151 A1) in further view of Beharie et al. (US 20230419345 A1) in further view of Jayne et al. (US 20210110439 A1).
Regarding claims 7, 19, the combination of Chigogidze, Bleakley. Beharie teaches the limitations of claims 6 and 18. The combination also teaches predicted changes in home sales as one or more probability values associated with particular changes in home sales. The combination does not teach softmax.
However, Jayne teaches where the second final layer is configured to perform a softmax activation function to output the predicted changes (¶ 63, 65, 68-71, 75, 95, discloses the use of softmax layers)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the second final layer is configured to perform a softmax activation function to output the predicted changes in home sales as one or more probability values associated with particular changes in home sales, as taught/suggested by Jayne. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Jayne would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Jayne to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such softmax features into similar systems. Further, applying where the second final layer is configured to perform a softmax activation function to output the predicted changes in home sales as one or more probability values associated with particular changes in home sales would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for making data interpretable as probabilities for each class that sum to one.
Regarding claims 8, 20, the combination of Chigogidze and Bleakley teaches the limitations of claims 1 and 13, including predicted home sales.
Chigogidze discloses where the second final layer comprises a dense layer configured to determine a first probability that the predicted changes in home sales for the time period a second probability that the predicted changes in home sales for the time period and a third probability that the predicted changes in home sales for the time period, and where the second final layer is further configured to output a highest probability of the first probability, the second probability, and the third probability (¶ 21-23, 34-39, disclose modeling and prediction of real estate data.);
The combination does not specifically teach where the predicted change comprises an increase in home sales, a decrease in home sales, or no change in home sales.
However, Beharie teaches where the second final layer comprises a dense layer configured to determine a first probability that the predicted changes in sales for the time period is an increase in sales from the previous time period, a second probability that the predicted changes in sales for the time period is a decrease in sales from the previous time period, and a third probability that the predicted changes in sales for the time period do not change from the previous time period, and where the second final layer is further configured to output a highest probability of the first probability, the second probability, and the third probability. (¶ 13, 33, 389, 434, 484, disclose an increase, decrease, or a neutral value for a sales volume.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Chigogidze to include/perform where the second final layer is configured to output predicted changes in sales, as taught/suggested by Beharie. This known technique is applicable to the system of Chigogidze as they both share characteristics and capabilities, namely, they are directed methods of predictive management systems. One of ordinary skill in the art would have recognized that applying the known technique of Beharie would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Beharie to the teachings of Chigogidze would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such prediction features into similar systems. Further, applying where the second final layer is configured to output predicted changes in sales would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow an output of a specific data point.
Other pertinent prior art includes Bolen (US 20150269264 A1) which discloses an interactive real estate system comprises a standardization engine communicatively coupled with a plurality of MLS databases. Guillo et al. (US 20240005348 A1) which discloses performing a valuation of a new property using Machine Learning Algorithms. Said et al. (US 20200134677 A1) which discloses the right value for a given real-estate property is a key measure used by various entities (e.g., buyers, sellers, financial institutions, and other interested parties) to perform various real-estate transactions (e.g., a purchase and sale transaction, a mortgage transaction, etc.).
Conclusion
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 JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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, Brian Epstein can be reached at (571) 270 5389. 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.
JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
Prosecution Insights