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 of Claims
This Office Action is in response to the communication filed on 01/22/2026.
Claims 2, 17 and 23 have been cancelled.
Claims 1, 3-4, 16-19, 22 and 24-25 have been amended.
5 Claims 1, 3-16, 18-22 and 24-27 are currently pending and are considered below.
Continued Examination Under 37 CFR 1.114
6 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/22/2026 has been entered.
Claim Interpretation
7. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
8. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
9. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “…a network interface configured…”; “a search engine configured to:…”; and “a content modification engine configured to:…” in claim 22.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
10. 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.
11. Claims 1, 3-16, 18-22 and 24-27 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Representative claim 1, recites a method for generating custom text content responsive to a received search query, which is a statutory class (method), executed by one or more processors, a communication interface, a computing device: the method comprising:
receiving, via a communication interface from a user computing device, a search query including one or more search terms;
determining, using one or more processors and responsive to the search query, a set of search results relevant to the search query;
identifying, using one or more processors and responsive to the search query, third-party content and/or a third party relevant to the search query;
generating, using one or more processors and based on (i) the search query and (ii) the third-party content or the third party, custom text content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of search results; and
transmitting, via the communication interface to the user computing device, the custom text content, wherein generating the custom text content includes:
forming an input vector including (i) the one or more search terms of the search
query; and (ii) at least a portion of the third-party content and/or information related to
the third party.
The steps of
receiving, via a communication interface from a user computing device, a search query including one or more search terms;
determining, using one or more processors and responsive to the search query, a set of search results relevant to the search query;
identifying, using one or more processors and responsive to the search query, third-party content and/or a third party relevant to the search query;
generating, using one or more processors and based on (i) the search query and (ii) the third-party content or the third party, custom text content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of search results; and
transmitting, via the communication interface to the user computing device, the custom text content, wherein generating the custom text content includes:
forming an input vector including (i) the one or more search terms of the search
query; and (ii) at least a portion of the third-party content and/or information related to
the third party,
as drafted, is a process that, under its broadest reasonable interpretation, covers a method of organizing human activity. Given the broadest reasonable interpretation, the claim recites a method for generating custom content responsive to a received search query. The above identified method steps recite commercial interactions such as sales activities and/or tailored personalized marketing relating to transmitting the custom content to the user device.
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction such as tailored personalized marketing, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processors, a communication interface, a network interface, a search engine , a content modifier engine, a memory and a computing device. The communication interface, a network interface, a search engine , a content modifier engine, a memory and a computing device is recited at a high-level of generality (i.e., as a generic processor performing a generic computer functions of receiving a search query including one or more search terms, determining a set of search results relevant to the search query, identifying third-party content and/or a third party relevant to the search query, generating custom content relevant to the search query for presentation along with the set of search results, and transmitting the custom content.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of
one or more processors, a communication interface, a network interface, a search engine , a content modifier engine, a memory and a computing device amount to no more than mere instructions to apply the exception using generic computer components. The additional elements are similar to the additional elements found by courts to be mere instructions to apply an exception because they do no more than merely invoke computers or machinery to perform an existing process such as: a common business method or mathematical algorithm being applied on a general purpose computer (Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 US 208, 223; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334); generating a second menu from a first menu and sending the menu to the second location as performed by a generic computer components (Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44); and providing a user with tailored information like advertisements based on information known about the user such as a location, address, or personal characteristics and a time of day is a fundamental practice long prevalent in our system); In re Morsa, 809 F. App’x 913, 917 (Fed. Cir. 2020). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Thus, considered as an ordered combination, the additional elements add nothing that is not already present when the steps are considered separately. That is, one or more processors, a communication interface, a network interface, a search engine , a content modifier engine, a memory and a computing device, performing commercial interactions including: receiving a search query including one or more search terms, determining a set of search results relevant to the search query, identifying third-party content and/or a third party relevant to the search query, generating custom content relevant to the search query for presentation along with the set of search results, and transmitting the custom content, amount to mere instructions to apply the steps to a computer comprising of a processor.
Thus, claims 1, 16 and 22 are not eligible.
As for dependent claims 4, 18, 19 and 24-25, these claims recite “…wherein the input vector further includes one or more aspects of the landing page.” and “… wherein the input vector further includes one or more user characteristics.” These claims recite limitations that further define the same abstract idea in claims 2, 17 and 23, specifically with forming and processing input vector. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 5, 20 and 26, these claims recite “…wherein generating the custom text content includes: generating, responsive to the search query, all of the custom text content…” as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors and one or more memory” language in claims 1, 16 and 22. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 6, 21 and 27, these claims recite “…wherein generating the custom text content includes: determining one or more modifications to the third-party content based on one or more search terms of the search query, and at least a portion of the third-party content”; and “…modifying the third-party content based on the one or more modifications to form the custom text content …” as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors and one or more memory” language in claims 1, 16 and 22. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claim 7, this claim recites “…wherein determining the one or more modifications includes: forming an input vector including the one or more search terms of the search query, and the at least a portion of the third-party content”; and
“processing the input vector with one or more configured and trained machine learning models to determine the one or more modifications. …” as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors and one or more memory” language in claim 6. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 8-10, these claims recite limitations that further define the same abstract idea in claims 6 and 7. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claim 11, this claim recites “…further comprising: transmitting, via the communication interface to the user computing device, the set of search results, wherein the custom text content is generated before the set of search results are transmitted to the user computing device.”, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors, a communication interface, a network interface, a search engine , a content modifier engine, a memory and a computing device” language in claim 1. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claims 12-13 and 15, these claims recite limitations that further define the same abstract idea in claim 1. Therefore, they are considered patent ineligible for the reasons given above. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
As for dependent claim 14, this claim recites “…further comprising: processing landing page content with one or more configured and trained machine learning models to determine one or more aspects of the landing page, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind or using pen and paper but for the recitation of generic computer components. For example but for the “one or more processors and one or more memory” language in claim 1. The claim falls into the mental process grouping of abstract ideas. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea itself.
Claims 1, 3-16, 18-22 and 24-27 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
12. 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.
13. 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.
14. 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.
15. Claims 1, 3-16, 18-22 and 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over Gross (U.S. Pub. No. 2008/0010270) in view of Liang et al. (U.S. Patent No. 9,613,004)(hereinafter ‘Liang’).
Claims 1, 16 and 22: Gross discloses a method, a system and an apparatus for generating custom text content responsive to a received search query, (see at least figure 1 and figure 2 elements 240 and 250), the method comprising:
receiving, via a communication interface from a user computing device, a search query including one or more search terms, Gross teaches a conventional search query is received and processed (see at least paragraph 0058 and see figures 1 element 110 and figure 2 element 205);
determining, using one or more processors and responsive to the search query, a set of search results relevant to the search query, Gross teaches the search interface 300 also includes a Sponsored Search Results area 330, in which ads 331 sponsored by advertisers are presented along with the search results (see at least paragraphs 0009-0010 and see figure 1 elements 131 and 141);
identifying, using one or more processors and responsive to the search query, third-party content and/or a third party relevant to the search query, Gross teaches Interface 100 includes a number of viewable fields presented to a user for searching and visualizing query results and advertising information (see at least paragraph 0050 and see figure 1 element 130);
generating, using one or more processors and based on (i) the search query and (ii) the third-party content or the third party, custom text content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of search results, Gross teaches generating a set of search results based on the first content; wherein the search results include references to one or more separate webpages; processing content extracted from selected ones of the one or more separate webpages to identify one or more topics; and presenting at least one first advertisement in connection with the RSS feed based on the one or more topics (see at least paragraphs 0036 and 0050 and see figure 1 element 141 and figure 2 elements 210, 240 and 250); and
transmitting, via the communication interface to the user computing device, the custom text content, Gross teaches delivering content relevant advertisements within a conventional Internet based search query interface or similar interface (see at least paragraph 0005 and see figure 1 and figure 2 element 260); wherein generating the custom text content includes:
forming an input vector including (i) one or more search terms of the search query; and (ii) at least a portion of the third-party content and/or information related to the third party, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
While Gross teaches the limitations mentioned above, Gross does not explicitly teach processing the input vector with one or more configured and trained machine learning models to determine the custom text content. However, Liang teaches a number of machine learning algorithms can be used to construct the classifiers, such as Nearest Neighbors, Decision Trees, Support Vector Machines, Maximum Entropy Models, and Rocchio's. In one embodiment, the building of category classifiers is done before new text is processed and Liang further teaches the classification model is built based on manually extracted ground truth (i.e., facts that are known). For example, to construct a classification model, the text in a given document is processed and candidate entities are presented to a human annotator. The annotator then assigns a class to each candidate entity ("RELEVANT" or "NON RELEVANT"). This classification, together with feature vectors that represent the training data (candidate entries) are stored in a machine readable format. Based on this stored data, a classification model is built using statistical and machine learning techniques. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Gross to modify to include the teaching of Liang for recognizing and disambiguating entities in arbitrary text using natural language processing (see at least column 2 lines 8-24).
Claims 3, 18 and 24: Gross in view of Liang disclose the method according to claim 1, Liang further teaches wherein the input vector further includes one or more aspects of the landing page, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Claims 4, 19 and 25: Gross in view of Liang disclose the method according to claim 1, Liang further teaches wherein the input vector further includes one or more user characteristics, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Claims 5, 20 and 26: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches wherein generating the custom content includes:
generating, responsive to the search query, all of the custom text content, Gross teaches generating a set of search results based on the first content; wherein the search results include references to one or more separate webpages; processing content extracted from selected ones of the one or more separate webpages to identify one or more topics; and presenting at least one first advertisement in connection with the RSS feed based on the one or more topics (see at least paragraphs 0036 and 0050 and see figure 1 element 141 and figure 2 elements 210, 240 and 250).
Claims 6, 21 and 27: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches wherein generating the custom text content includes:
Gross in view of Liang disclose the method according to claim 1, and Gross further teaches determining one or more modifications to the third-party content based on one or more search terms of the search query, and at least a portion of the third-party content, Gross the pseudo pages could be updated by the collective or the search engine operator at regular, predetermined intervals to maintain their relevance and freshness (see at least paragraphs 0061 and 0071); and
modifying the third-party content based on the one or more modifications to form the custom text content, Gross teaches the pseudo pages could be updated by the collective or the search engine operator at regular, predetermined intervals to maintain their relevance and freshness and Gross further teaches in the end result of step 235, a second set of one or more organic ads is determined therefore based on the aggregate content calculated for the synthetic/aggregate search page. At step 240 these second set of ads arc combined with any of the first set of ads generated during step 230 to form a final set of organic ad result which will be presented within search interface 100 (see at least paragraphs 0061 and 0071).
Claim 7: Gross in view of Liang disclose the method according to claim 6, and Gross further teaches wherein determining the one or more modifications includes:
forming an input vector including the one or more search terms of the search query, and the at least a portion of the third-party content, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Liang teaches processing the input vector with one or more configured and trained machine learning models to determine the one or more modifications. Liang teaches a number of machine learning algorithms can be used to construct the classifiers, such as Nearest Neighbors, Decision Trees, Support Vector Machines, Maximum Entropy Models, and Rocchio's. In one embodiment, the building of category classifiers is done before new text is processed and Liang further teaches the classification model is built based on manually extracted ground truth (i.e., facts that are known). For example, to construct a classification model, the text in a given document is processed and candidate entities are presented to a human annotator. The annotator then assigns a class to each candidate entity ("RELEVANT" or "NON RELEVANT"). This classification, together with feature vectors that represent the training data (candidate entries) are stored in a machine readable format. Based on this stored data, a classification model is built using statistical and machine learning techniques. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Gross to modify to include the teaching of Liang for recognizing and disambiguating entities in arbitrary text using natural language processing (see at least column 2 lines 8-24).
Claims 8: Gross in view of Liang disclose the method according to claim 7, and Liang further teaches wherein the input vector further includes one or more aspects of the landing page, and/or one or more user characteristics, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Claims 9: Gross in view of Liang disclose the method according to claim 6, and Gross further teaches wherein the one or more modifications include at least one of an inserted word, an inserted phrase, a deleted word, a deleted phrase, a replacement word, a replacement phrase, a modified word, or a modified phrase, Gross further teaches in the end result of step 235, a second set of one or more organic ads is determined therefore based on the aggregate content calculated for the synthetic/aggregate search page. At step 240 these second set of ads arc combined with any of the first set of ads generated during step 230 to form a final set of organic ad result which will be presented within search interface 100 (see at least paragraph 0071).
Claim 10: Gross in view of Liang disclose the method according to claim 6, and Gross further teaches wherein the one or more modifications include a rewrite of one or more portions of the identified third-party content, Gross further teaches in the end result of step 235, a second set of one or more organic ads is determined therefore based on the aggregate content calculated for the synthetic/aggregate search page. At step 240 these second set of ads arc combined with any of the first set of ads generated during step 230 to form a final set of organic ad result which will be presented within search interface 100 (see at least paragraph 0071).
Claim11: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches further comprising:
transmitting, via the communication interface to the user computing device, the set of search results, wherein the custom text content is generated before the set of search results are transmitted to the user computing device, Gross teaches delivering content relevant advertisements within a conventional Internet based search query interface or similar interface (see at least paragraph 0005 and see figure 1 and figure 2 element 260).
Claim 12: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches wherein generating the custom content includes:
including in the custom text content substantially only words from the third-party content or the landing page, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Claim 13: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches wherein the custom text content comprises a customized search advertisement, Gross teaches generating a set of search results based on the first content; wherein the search results include references to one or more separate webpages; processing content extracted from selected ones of the one or more separate webpages to identify one or more topics; and presenting at least one first advertisement in connection with the RSS feed based on the one or more topics (see at least paragraphs 0036 and 0050 and see figure 1 element 141 and figure 2 elements 210, 240 and 250).
Claim 14: Gross in view of Liang disclose the method according to claim 1, and Liang further teaches further comprising:
processing landing page content with one or more configured and trained machine learning models to determine one or more aspects of the landing page, Liang teaches a number of machine learning algorithms can be used to construct the classifiers, such as Nearest Neighbors, Decision Trees, Support Vector Machines, Maximum Entropy Models, and Rocchio's. In one embodiment, the building of category classifiers is done before new text is processed and Liang further teaches the classification model is built based on manually extracted ground truth (i.e., facts that are known). For example, to construct a classification model, the text in a given document is processed and candidate entities are presented to a human annotator. The annotator then assigns a class to each candidate entity ("RELEVANT" or "NON RELEVANT"). This classification, together with feature vectors that represent the training data (candidate entries) are stored in a machine readable format. Based on this stored data, a classification model is built using statistical and machine learning techniques. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention for Gross to modify to include the teaching of Liang for recognizing and disambiguating entities in arbitrary text using natural language processing (see at least column 2 lines 8-24).
Claim 15: Gross in view of Liang disclose the method according to claim 1, and Gross further teaches wherein one or more aspects of the landing page were determined prior to receipt of the search query, Gross teaches the collective content is preferably based on a term vector computed across the selected set of documents, but may be constituted by other known means (see at least paragraphs 0027 and 0063-0065).
Response to Arguments
16. Applicant's arguments filed on 01/22/2026, with respect to the rejection of claims 1, 3-16, 18-22 and 24-27 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
17. Applicant argued that “…A. Amended claim 1 at least provides an improvement to the functioning of a computer by preserving computational and storage resources.
First, Applicant respectfully contends that amended claim 1 is allowable under 35 U.S.C. § 101 at least because amended claim 1 recites significantly more than any alleged judicial exception. Applicant respectfully contends that amended claim 1 recites significantly more than any alleged judicial exception because amended claim 1 provides an improvement to the functioning of a computer, an/or an improvement to another technology or technical field.
Notably, amended claim 1 recites, in part:
receiving, via a communication interface from a user computing device, a search query including one or more search terms;
determining, using one or more processors and responsive to the
search query, a set of search results relevant to the search query;
identifying, using the one or more processors and responsive to the
search query, third-party content and/or a third party relevant to the search
query;
generating, using the one or more processors and based on (i) the
search query and (ii) the third-party content or the third party, custom text content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of search results; and
transmitting, via the communication interface to the user computing
device, the custom text content.
(Emphasis added).
The Present Application describes two potential approaches to generate custom content. In one of the approaches, which contrasts with the method recited in claim 1, the system may "generate a relatively large amount of custom content similar to the content 124 offline for a large number of possible search queries, the number of possible combinations requires a large amount of storage space and a significant amount of computational resources." Present application at para. [0030] (emphasis added). As such, under that approach, the system may need to "consider and score a larger number of potentially relevant ads in real time, which results in longer processing time…." Id. (emphasis added). Remarks pages 8-9
17. Examiner notes that claim 1 does not recite or suggest what is being argued such as “…generate a relatively large amount of custom content similar to the content 124 offline for a large number of possible search queries, the number of possible combinations requires a large amount of storage space and a significant amount of computational resources…” The argument is not persuasive.
18. Applicant's arguments filed 01/22/2026 with respect to the rejection of claims 1, 3-16, 18-22 and 24-27 under 35 U.S.C. 102/103(a) have been fully considered but they are not persuasive.
19. Applicant argued that “…Gross fails to disclose “…generating, using one or more processors and based on (i) the search query and (ii) the third-party content or the third party, custom text content relevant to the search query and related to a landing page associated with the third-party content or the third party, for presentation along with the set of search results…” Remarks pages 12-14
20. Examiner notes that Gross teaches generating a set of search results based on the first content; wherein the search results include references to one or more separate webpages; processing content extracted from selected ones of the one or more separate webpages to identify one or more topics; and presenting at least one first advertisement in connection with the RSS feed based on the one or more topics (see at least paragraphs 0036 and 0050 and see figure 1 element 141 and figure 2 elements 210, 240 and 250).
Conclusion
21. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
22. Dhillon et al. (U.S. Patent No. 8,954,469) discloses techniques for creating, managing, and using query templates to facilitate the execution of relationship queries are provided. Example embodiments provide a Query Template System "QTS", which enables users, a system, program code, or other people or code to define search tips (i.e., predefined searches) through the generation of query templates that can be used by other users or code, to perform relationship searches using IQL. In one embodiment, the QTS includes a QT editor, a QT dispatcher, a QT creation and index management system, and one or more QT data repositories and indexes. These components cooperate to create and maintain query templates and to search for and retrieve matching query templates (see at least the Abstract).
23. Cook,Jr. et al (U.S. Patent No. 8,473,470) discloses a software program and associated web-based portal is provided for industry-specific product comparison. The program and an associated web portal allows the user the ability to search multiple manufacturers' catalogs and to enter a query based upon customized search criteria. Query results are returned of products that satisfy the user's search criteria. The query is made available to manufacturers whose products are identified in the query results and a communication link is provided whereby such manufacturers can contact the user to discuss the product identified in the search. The user can respond using the message board associated with the web portal. The program and portal can also integrate updates to pump manufacturers' catalogs and can also produce best-fit solutions for users' design criteria (see at least the Abstract).
24. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARILYN G MACASIANO whose telephone number is (571)270-5205. The examiner can normally be reached Monday-Friday 12:00-9:00 pm.
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/MARILYN G MACASIANO/Primary Examiner, Art Unit 3622 02/07/2026