Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
The following is a Final Office action to Application Serial Number 18/647,175, filed on January 4, 2024. In response to Examiner’s Non-Final Office Action of October 16, 2025, Applicant, on January 16, 2026, amended claims 1, 7 and 13. Claims 1-18 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Regarding 35 U.S.C. § 101 rejection, the amendment has been considered and is insufficient to overcome the rejection.
Regarding 35 U.S.C. § 103 rejection, the rejection has been withdrawn.
Response to Arguments
Applicant’s arguments filed January 16, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed January 16, 2026.
On page 10-11 of the Remarks regarding 35 U.S.C. § 101, Applicant states the claims do not recite mental processes because Claim l's limitations cannot be practically performed in the human mind. In response, regarding the 35 U.S.C. § 101 rejection, Examiner finds under the broadest reasonable interpretation identifying product profile data and identifying-electronic documents associated with the computing product falls within the Abstract idea grouping of “Mental Processes” – evaluation.
On Pg. 11-12, regarding the 35 U.S.C. § 101 rejection, Applicant argues additional element has integrated the exception into a practical application. Id. Two of these considerations are (1) an improvement in the functioning of a computer, or an improvement to other technology or technical field. The present claims amount to no more than utilizing computer components as tools to perform the analysis. Examiner finds the present claims improve an existing business process of market trend prediction and there are currently no functional advancement to any technology or technological field, in order for the claim elements to be considered significantly more than the abstract idea itself. Utilizing computer structure and technology to collect, analyze and notify users of data change events are all, both individually and in combination, generic computer functions such as receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (See MPEP 2106.05(d)(II).
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- 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-18 are directed to predicting market trends of computing products.
Claim 1 recites a method for predicting market trends of computing products, Claim 7 recites a system for predicting market trends of computing products and Claim 13 recites an article of manufacture for predicting market trends of computing products, which include identifying a product profile of the computing product, including a list of a plurality of computing components associated with the computing product, wherein the list of the plurality of computing components includes, for each computing component, a plurality of features of the computing component; determining, based on the product profile of the computing product, computational capabilities of the computing product; identifying electronic documents associated with the computing product; for each electronic document: identifying elements of the electronic document, including scripts; reducing the electronic document by i) removing portions of the electronic document related to headers, footers, navigation panes, and scripts that do not expose functionality of the electronic document and ii) maintaining the elements that expose functionality of the electronic document related to HTML tags, HTML elements, and scripts related to the computing product; creating, based on the reduced electronic document, clusters of text based on a similarity of the maintained elements of HTML tags, HTML elements, and scripts; labeling, for each cluster of the clusters, the cluster based on the text associated with the HTML tags of one element of the cluster; identifying data specifications associated with the plurality of computing components, the data specifications being from labeled clusters of the electronic documents; calculating, based on the reduced electronic documents and data specifications, product sentiment, market data, and financial data results associated with the computing product; generating, using a market prediction model, market trend data associated with the computing products based on the product sentiment, market data, and financial data results associated with the computing products; and updating the model based on the generated market trend data.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Mental Processes” – evaluation. The recitation of “storage device”, “system”, “processor”, “memory media”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Mental Processes”- evaluation. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “storage device”, “system”, “processor”, “memory media”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in market analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “storage device”, “system”, “processor”, “memory media”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-6, 8-12, and 14-18 recite generating the market trend data further includes generating, using a pre-trained random forest regression model, the market trend data associated with the computing products based on the product sentiment, market data, and financial data results associated with the computing products; identifying the electronic documents associated with the computing product further includes identifying electronic documents related to reviews, blogs, videos and website data of the computing product, wherein calculating the product sentiment further includes calculating the product sentiment based on the reviews, blogs, videos and website data of the computing product; calculating the product sentiment based on a ratio of positive sentiment mentions of text of the reviews, blogs, videos and website data of the computing product to negative sentiment mentions of text of the reviews, blogs, videos and website data of the computing product; identifying the electronic documents associated with the computing product further includes identifying electronic documents related to news articles of vendors of the computing product, wherein calculating the market data further includes calculating the market data based on the new articles; identifying the electronic documents associated with the computing product further includes identifying electronic documents related to financial data of vendors of the computing product, wherein calculating the financial data results further includes calculating the financial data results based on the financial data; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 7 and 13.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-18 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 7 and Claim 13), the prior art does not teach or fairly suggest:
“… identifying-electronic documents associated with the computing product, for each electronic document: identifying elements of the electronic document, including scripts; reducing the electronic document by i) removing portions of the electronic document related to headers, footers, navigation panes, and scripts that do not expose functionality of the electronic document and ii) maintaining the elements that expose functionality of the electronic document related to HTML tags, HTML elements, and scripts related to the computing product; creating, based on the reduced electronic document, clusters of text based on a similarity of the maintained elements of HTML tags, HTML elements, and scripts; labeling, for each cluster of the clusters, the cluster based on the text associated with the HTML tags of one element of the cluster; identifying data specifications associated with the plurality of computing components, the data specifications being from labeled clusters of the electronic documents; calculating, based on the reduced electronic documents and the data specifications, product sentiment, market data, and financial data results associated with the computing product;”
Examiner finds that Hall et al., US Publication No. 20230376981A1 teaches receiving historical data for a plurality of historical products in a plurality of markets, training a predictive model to forecast at least one product performance attribute based on the historical data, receive product data associated with a particular product, generating a prediction for the particular product of the at least one product performance attribute by applying the predictive model, and perform at least one action for the particular product based on the prediction of the at least one product performance attribute (see Abstract). In particular, Hall discloses automatically identifying a product's characteristics from product-related media, such as a product description, product advertisement, or product-related writings (e.g., meeting notes, executive summaries, electronic communications, etc.). In at least one embodiment, the systems and processes predict sales data for new or planned products based on econometric data associated with the product or historical econometric data associated with similar products. In one or more embodiments, the systems and processes enable prediction of competition and/or consumer demand at product or product attribute levels (see par. 0024, 0032).
Unnikrishnan, US Publication No. 20250182181A1 teaches catalog content and customer content associated with a product type of a product is obtained. Thereafter, candidate attributes for the product are identified from the catalog and customer content associated with the product type. A model prompt is generated to be input into a large language model. The model prompt includes the candidate attributes, or a portion thereof, a product profile associated with the product, and an instruction to generate a recommendation for a new attribute to associate with the product based on the product profile and the candidate attributes. An attribute recommendation is generated, via the large language model, that recommends the new attribute to include in the product profile. Such an attribute recommendation is provided as a recommendation to include in the product profile associated with the product. (see Abstract).
Newly Cited Art
Ashkenazi et al. (US Publication No. 20120101979A1) teaches Systems and methods for extracting information from structured documents are provided. The systems and methods relate to selecting a centroid document from a group of structured documents, selecting a subset of the group of structured documents in order to form a cluster of the subset of documents about the centroid document. The selecting the subset is preferably based on the relative similarity between each of the selected subset and the centroid document. Then, systems and methods according to the invention include marking a data element on the centroid document. The systems and elements also include identifying a data element on each of the subset of documents, the data element that corresponds to the marked data element on the centroid document. Finally, data may be extracted from the subset of documents based on the identifying step. (see Abstract). In particular, Ashkenazi discloses based on the respective similarity between the pages, the pages are then clustered about a number of centroids, each centroid representing an arbitrarily-selected or systematically-selected central document of the cluster. Each cluster member is aligned about its respective centroid to be included in a particular cluster. A pre-determined threshold is used to determine whether the cluster member is sufficiently similar to the centroid. When a group of cluster members is determined to be sufficiently close to a given centroid, the system or method then identifies desired data elements in each centroid. Thereafter, in a step that may also be based on the previously used alignment algorithm, the data from the aligned element in each cluster member that corresponds to the identified data element in the centroid may be identified, extracted and aggregated in some suitable fashion. (see par. 0013).
Chauhan et al. (US Publication No. 20250061308A1) teaches Systems and methods for using machine learning to extract data from electronic communications are disclosed. According to certain aspects, a machine learning model is trained on a set of tasks using a set of training data. An electronic communication indicating a purchase of a product and/or service is processed to generate augmented text that is input into the machine learning model. After analyzing the augmented text, the machine learning model outputs a set of predicted values for a set of defined categories, which an entity may use for various purposes such as to apply digital rewards to user accounts.. (see Abstract). In particular Chauhan teaches In performing the tag processing, the server computer 215 may further generate an augmented text input that preserves a set of specific HTML tags. In particular, the server computer 215 may initially remove any unnecessary information such as CSS styling from the electronic communication, after which document-style formatting may remain. Additionally, the server computer 215 may replace some HTML tags and remove other HTML tags. For example, the server computer 215 may identify certain tags as being replaceable (e.g., <tr>, <br>, <div>, <td>, <span>, and/or others), and certain tags as being removable (e.g., <script>, <style>, <comment>, <area>, <link>, <meta>, <img>, and/or others).(par 0064-0065)
Although Hall, Unnikrishnan, Ashkenazi and Chauhan teach analysis elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the removal , clustering and modelling elements..
The dependent claims 2-6, 8-12 and 14-18 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1, claim 7 and claim 13 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Publication No. 20230230110A1 to Alpert et al.- Abstract-“ Predicting trends may include obtaining trend data from one or more sources, extracting a plurality of trends from the trend data, and producing permutations combining terms or concepts appearing in the plurality of trends to create trend candidates. A first term from a first trend or concept in the plurality of trends may be combined with a second term or concept from a second trend in the plurality of trends.”
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 extension fee 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 Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/Examiner, Art Unit 3624
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