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 . Claims 1-20 are pending.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-6 and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4 and 18 first recite “using a forecasting engine” to determine a baseline property value and later recites inputting data “into a forecasting engine”. It is unclear whether the later recited forecasting engine is the same forecasting engine previously introduced or a different forecasting engine. The rest of the claims are rejected by virtue of their dependency. Appropriate correction is required.
Additionally, claims 4-6, 11-13, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 11, and 18 recite the limitation “determine, using a forecasting engine a baseline property value for a property, wherein the property”. It is unclear whether the steps that follow are performed by the forecasting engine or are intended to further describe the property. Therefore, the scope of the claims cannot be determined with reasonable certainty. The rest of the claims are rejected by virtue of their dependency.
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 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a system (i.e., a machine), claim 8 is directed to a method (i.e., a process), and claim 15 is directed to a non-transitory machine-readable storage medium (i.e., machine or manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 USC § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea from claims 1, 8, and 15 (claim 1 being representative) is:
access a source;
determine, based on the source, a plurality of word counts, wherein each word count comprises an occurrence frequency of a word and a word sentiment associated with the word;
determine, based on the source, a plurality of word embeddings within an embedding space, wherein locations of the plurality of word embeddings within the embedding space indicate a similarity between each word of the source;
input the plurality of word embeddings and the plurality of word counts into a natural language processing model, the natural language processing model to output a source sentiment associated with the source;
determine a confidence score associated with the source sentiment, wherein the confidence score indicates a correlation between the source sentiment and the source; and
determine a trend prediction based on the source sentiment.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper, since it describes collecting information, analyzing the information, and evaluating the information to determine a sentiment, confidence score, and trend prediction. This is supported by [0034] of applicant’s specification as filed. If a claim limitation, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: A system, comprising: a computer-readable storage medium storing program instructions; and one or more processors, wherein the program instructions, when executed by the one or more processors, cause the one or more processors to.
Claim 15 recites the following additional elements: A non-transitory computer-readable medium storing specific computer- executable instructions that, when executed by a processor of a computing device, cause the computing device to.
(Examiner notes that claim 8 does not recite any additional elements to consider).
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs [0098] – [0102] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2-3, 6, 9-10, 13, 16-17, and 20 applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Claims 4, 11, and 18 include further additional elements: forecasting engine. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claims 5, 12, and 19 include further additional elements: results cache. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claims 7 and 14 include further additional elements: wherein the program instructions, when executed, further cause the one or more processors to: display the sentiment score plot and the trend prediction on a graphical user interface. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 7-10, and 14-17 are rejected under 35 U.S.C. § 103 as being unpatentable over Poreh (US 20240320225) in view of Brink (US 20160343004).
Claims 1, 8, and 15
Poreh discloses (claim 1 being representative):
(claim 1) A system, comprising: {“ Computing device 200 may include one or more processors” [0035]}
(claim 1) a computer-readable storage medium storing program instructions; and {“Memory 135 is coupled to bus 210 and typically includes computer readable media including volatile media (e.g., random access memory (RAM), cache memory, etc.), non-volatile media, removable media, and/or non-removable media.” [0035]}
(claim 1) one or more processors, wherein the program instructions, when executed by the one or more processors, cause the one or more processors to: {“Computing device 200 may include one or more processors 115 (e.g., microprocessor, controller, central processing unit (CPU), etc.), network interface 125, memory 135, a bus 210, and an Input/Output interface 220.” [0035]}
(claim 8) A method, comprising: {“A method 400 of determining values for content items in real-time (e.g., via content module 116, interface module 120, server system 110 and/or client system 114) according to an embodiment of the present invention is illustrated in FIG. 4.” [0057]}
(claim 15) A non-transitory computer-readable medium storing specific computer- executable instructions that, when executed by a processor of a computing device, cause the computing device to: {“The software of the present invention embodiments (e.g., content module 116, interface or browser module 120, etc.) may be available on a non-transitory computer useable medium.” [0131]}
access a source; {“Present invention embodiments analyze content items as well as network operations (or accesses) for the content items in order to discover, prioritize, and arrange content items for results. A content item may be any type of digital or electronic item or object (e.g., document, web page, file, data object, etc.) containing any type, or a combination of any types, of data (e.g., text, multimedia, video, audio, image, streaming data, etc.).” [0020]}
determine, based on the source, a plurality of word counts, wherein each word count comprises an occurrence frequency of a word and a word sentiment associated with the word; {“Feature mapper 310A analyzes a content item (e.g., uploaded by a user via a client system 114) to extract features therefrom and produce a feature vector… The features may include any quantity of any types of features (e.g., keywords, topics, events, word count, word frequency, word embeddings, term frequency-inverse document frequency (tf-idf), etc.).” [0040]. “The dynamic pricing system integrates a diverse set of machine learning models tailored to specific functionalities. These models include sentiment analysis models, such as neural networks, or recurrent neural networks (RNNs), which are employed to analyze user sentiments regarding content.” [0046]}
determine, based on the source, a plurality of word embeddings within an embedding space, wherein locations of the plurality of word embeddings within the embedding space indicate a similarity between each word of the source; {“Feature mapper 310A analyzes a content item (e.g., uploaded by a user via a client system 114) to extract features therefrom and produce a feature vector… The features may include any quantity of any types of features (e.g., keywords, topics, events, word count, word frequency, word embeddings, term frequency-inverse document frequency (tf-idf), etc.).” [0040]}
input the plurality of word embeddings and the plurality of word counts into a natural language processing model, the natural language processing model to output a source sentiment associated with the source; {The system supports a vector containing word count, word frequency, and word embeddings, which is processed by classifier 320A using a ML model. NLP and neural networks are used networks are used to analyze the content, including sentiment analysis models. [0040] – [0043], [0046], [0062] – [0063]}
Poreh does not disclose, however, Brink, in a similar field of endeavor directed to sentiment analysis , teaches:
determine a confidence score associated with the source sentiment, wherein the confidence score indicates a correlation between the source sentiment and the source; and {“In some examples, the machine learning engine 112 can be tested for accuracy, and the accuracy score can be compared to a threshold. If the accuracy score does not meet the threshold, another machine learning process can be attempted. In other examples, if the accuracy score threshold is not met, the machine learning engine 112 can be provided with more examples and data in order to generate more rules and models until a sufficient accuracy score is achieved.” [0028]}
determine a trend prediction based on the source sentiment. {“In some embodiments, the system 100 can be configured to perform predictive sentiment analysis for a product or process yet to be analyzed. For example, the system 100 can determine (e.g., by machine learning, NLP, user definition, etc.) an association between multiple entities, attributes, or processes. Such associations can be used to predict sentiment of an unanalyzed entity or process based on sentiment analysis of an associated entity or process without requiring sentiment analysis of plain text data associated with the unanalyzed entity or process.” [0073]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the monitoring online activity features of Poreh to include the consumer analytics features of Brink, to improve prediction accuracy and provide users with more meaningful forecast results. (See [0073] of Brink).
Claims 2, 9, and 16
The combination of Poreh and Brink teaches the limitations set forth above. Poreh further discloses (claim 2 being representative):
wherein the source includes at least one of an article, a newspaper article, a blog article, an online publication, a print publication, a magazine, an editorial, a review, a brochure, an opinion, a press release, a post, a photo, a video, an audio file, a diagram, a column, or a feature. {“Present invention embodiments analyze content items as well as network operations (or accesses) for the content items in order to discover, prioritize, and arrange content items for results. A content item may be any type of digital or electronic item or object (e.g., document, web page, file, data object, etc.) containing any type, or a combination of any types, of data (e.g., text, multimedia, video, audio, image, streaming data, etc.).” [0020]}
Claims 3, 10, and 17
The combination of Poreh and Brink teaches the limitations set forth above. Poreh further discloses (claim 3 being representative):
wherein the trend prediction is one of a predicted property cost estimate, rental estimate, mortgage rate, inventory, demand, or a statistic. {“By leveraging information propagation and price updates, the system ensures a comprehensive understanding of market dynamics and content interactions. This approach enables the system to dynamically adjust pricing strategies in response to evolving trends and demand shifts, ensuring timely and effective decision-making.” [0021] “when a content creator uploads a new content item, a present invention embodiment performs an initial value prediction based on historical data and various factors” [0023]}
Claims 7 and 14
The combination of Poreh and Brink teaches the limitations set forth above. Brink further teaches (claim 7 being representative):
generate a sentiment score plot based on the source sentiment; and {“ For example, in the illustrated embodiment, the data manager 120 can initiate a buyer view 134 and a supplier view. The buyer view 134 can include sentiment information regarding a variety of entities or attributes associated with a buyer's role in a process or product if interest. Similarly, the supplier view 136 can include sentiment information regarding a variety of entities or attributes associated with a supplier's role in a process or product if interest. It will be appreciated that process actors 132 for which distinct views are defined are not limited to buyers and suppliers. In general, such a view generated via the data manager 120 can be tailored for any person or group of people associated with a process or product of interest.” [0055]}
display the sentiment score plot and the trend prediction on a graphical user interface. {The system supports presenting the graphical sentiment views through interface 130, where the generated sentiment graphics are displayed to a user. [0055] – [0060]}
The motivation and rationale to include the additional features of Brink is the same as set forth previously.
Claims 4-5, 11-12, and 18-19 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Poreh and Brink in further view of Fakieh (US 20250078182).
Claims 4, 11, and 18
While the combination of Poreh and Brink teaches the limitations set forth above, it does not explicitly teach, however, Fakieh, in a similar field of endeavor directed to a decentralized protocol for tokenizing real estate appreciation, teaches (claim 4 being representative):
determine, using a forecasting engine a baseline property value for a property, wherein the property; {The system identifies a value of a real estate property using an appraisal, valuation model, or ML model. The model receives property characteristics, identifies comparable properties, and determines an estimated market value. [0049] – [0053]}
receive a request for a property value estimation for the property; {“The homeowner may request to the tokenization system a re-evaluation of the property's value at any point, such as after significant improvements or renovations (e.g., adding a pool). If the value has changed, the system could initiate a re-tokenization process.” [0126]}
access the baseline property value, weekly property data, and the source sentiment; {The system accesses an existing property valuation and market property data, including comparable property sales, pricing trends, historical behavior. Additionally, user sentiment assessment is a possible ML output. [0161] – [0162], [0193] – [0199], [0361]}
determine the property value estimation, wherein determination of the property value estimation comprises: {“One step is for the tokenization system to evaluate the property to determine its current market value. In some cases, the tokenization system employs technological methods for estimating the value of a property.” [0049]}
input, into a forecasting engine, the baseline property value, the weekly property data and the source sentiment; and {The system inputs property characteristics, historical behavior, market trends, and other relevant data into ML valuation or forecasting models. [0161], [0193] – [0197]}
adjust the baseline property value based on the weekly property data and the source sentiment to determine the property value estimation; and {The system periodically reassesses a property valuation based on comparable property sales, market conditions, external valuation feeds, and other events. The updated valuation may cause token values or quantities to be adjusted. [0193] – [0205]}
display the property value estimation. {Predictions of future token prices and asset returns are provided to tenants to support decisions, and the disclosed ML includes visual output components and UI functionality. [0288] – [0289], [0312], [0334]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Poreh and Brink to include the real estate analysis and valuation features of Fakieh, to improve property valuation accuracy using ML models that process market information and are periodically updated as new data becomes available . (See [0193] of Fakieh).
Claims 5, 12, and 19
The combination of Poreh, Brink, and Fakieh teaches the limitations set forth above. Fakieh further teaches (claim 5 being representative):
wherein the program instructions further cause the system to store the property value estimation in a results cache. {The system includes main memory, static memory, and processor cache memory for storing instructions and data. [0310] – [0311]}
The motivation and rationale to include the additional features of Fakieh is the same as set forth previously.
Claims 6, 13, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Poreh, Brink, and Fakieh in further view of Di Sciullo (US 20170083817).
Claims 6, 13, and 20
While the combination of Poreh, Brink, and Fakieh teaches the limitations set forth above, it does not explicitly teach, however, Di Sciullo, in a similar field of endeavor directed to information extraction, teaches:
wherein the program instructions, when executed, further cause the one or more processors to generate, by the forecasting engine, an interpolation of the property value estimation using cubic spline interpolation. {The system uses cubic spline interpolation on a sampled target time series as part of the predictive series correlator process. [0236], [0250] – [0254]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Poreh, Brink, and Fakieh to include the topic detection features of DiSciullo, to improve forecasting accuracy by using sentiment predictive pattern when determining a predicted value . (See [0230] of Di Sciullo).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20130018651 A1, which teaches a generative model to develop at least one topic model and at least one sentiment model for a body of text.
US 20220067102 A1, which teaches a natural language processing approach for generating domain specific reasoning-based meaning representations.
US 20250061287 A1, which teaches enhanced machine learning model accuracy through post-hoc confidence score calibration.
“Identifying Real Estate Opportunities Using Machine Learning” (NPL attached), which teaches: The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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/C.F.M./Examiner, Art Unit 3629
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