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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 2026 May 19 has been entered.
Claims amended: 1-3, 5-8, 10-13, 15-18, 20, 24
Claims cancelled: 4, 9, 14, 19
Claims added: none
Claims currently pending: 1-3, 5-8, 10-13, 15-18, 20-24
Response to Arguments
Applicant, in “Remarks” and “Summary of Substance of Interview Held March 17, 2026” sections, presents opening remarks regarding the disposition of the claims, the amendments to the claims, and the content of the interview conducted. As no specific argument is raised in this/these section(s) with respect to the instant application, no rebuttal is required.
Applicant, in the “Objection to the Claims” section, argues that amendments made to claim 6 render the objection unnecessary. Examiner agrees and has withdrawn the objection.
Applicant, in the “Rejection under 35 U.S.C. § 101” section, presents opening remarks regarding the amended claim language with large sections of the claim language quoted. Substantial argument does not begin until the following section.
Applicant, in the “Step 2A/Prong One” section, argues that the shift from “advertisement” to “augmentation content” and “keyword-augmentation pairings” renders the claims away from “advertising or marketing interaction between human parties”. Examiner disagrees to this notion and points to at least paragraph 0018 of the specification that indicates that the “text augmentation system” queries a data store for “any existing associated advertisements”. As such, the “augmentation content” is advertisement content and the claims are still directed towards selecting and presenting advertisements. The claims fall neatly within the categories of certain methods of organizing human activity.
Applicant, in the “Step 2A/Prong Two” section, argues that the claims reduce “data opacity” because the augmentation of text within a conversation allows the user to better know the “relationships between conversational data and other presented content”. Examiner disagrees that the feature of presenting advertisements by highlighting a keyword within conversational text provides greatly enhanced transparency as the claim make clear that the entirety of the conversation is utilized to determine a “desired product or service”. Applicant then refers to Flook and alleges that the lack of transparency surrounding conversational data to select advertisements is a “core technical issue”. This argument is unpersuasive as the alleged problem solved by the claimed invention is not a technical issue, but rather an issue arising in the realm of advertising in general. An alleged innovative advantage within an abstract idea itself does not render a claimed invention into eligible subject matter. In the instant application, the alleged innovation is a better means of advertising. Therefore, the claims fall into certain methods of organizing human activity (advertising, marketing or sales activities or behaviors business relations), which is patent ineligible under the judicial exceptions.
Applicant, in the “Step 2B” section, refers to BASCOM and argues that the claimed “arrangement improves peer-to-peer messaging technology and cannot be reduced to generic storing, analyzing, transmitting, or displaying”. Examiner disagrees to this notion as each of the five elements noted in Applicant’s arguments does indeed fall into “storing, analyzing, transmitting, or displaying”. The use of an LLM, specified at the high level of generality found in the claims, represents little more than analyzing text content with a computer (i.e., ‘apply it’). Similarly, specifying an existing technique (i.e., meta-data analysis) for determining a score for advertisements is little more than applying an existing advertisement technique of selecting the highest predicted performing advertisements, but performed with a computer. Augmenting the conversation text (i.e., inline advertisements) was a known technique for presenting advertisements. The combination of elements does not yield anything beyond predicted results and one of ordinary skill in the art at the time the invention was filed would have readily combined the features. The additional elements, alone or in combination, do not represent significantly more than the abstract idea. Therefore, the grounds of rejection under 35 U.S.C. § 101 is herein maintained, albeit updated to reflect Applicant’s amendments to the claims.
Applicant, in the “Rejection under 35 U.S.C. § 103” section, argues that Jia does not teach the use of LLM and metadata-based analyses. This argument is moot in view of the new grounds of rejection presented herein utilizing Beier and Wikipedia. These new grounds of rejection were necessitated by Applicant’s amendments to the claims. Applicant argues that the previously applied references of Talmor and Glazier are similarly deficient with respect to newly amended claim language. However, these arguments are moot in view of the new grounds of rejection presented herein which were necessitated by Applicant’s amendments to the claims.
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 1-3, 5-8, 10-13, 15-18, 20-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claims 1-3, 5, 16-18, 20, 21, 24 are directed towards methods. Claims 6-8, 10, 22 are directed towards a system. Claims 11-13, 15, 23 are directed towards a manufacture (computer-readable medium). Thus, these claims, on their face, are directed to one of the statutory categories of 35 U.S.C. § 101.
Step 2A - Prong One: As per MPEP 2106.04, Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon. In Prong One examiners evaluate whether the claim(s) recites a judicial exception; that is, whether the claim(s) set forth or describe a law of nature, natural phenomenon, or abstract idea.
Claim 1 is presented here as a representative claim for specific analysis (The underlined claim terms here are interpreted as additional elements beyond the abstract idea.):
A computer-implemented method improving data transparency in a peer-to-peer messaging environment, comprising:
receiving, by one or more processors of a server, conversation text data from a peer-to-peer messaging platform and a plurality of previous messages associated with the conversation text data;
extracting, by the one or more processors via a large language model, semantic contextual information from the conversation text data and the plurality of previous messages;
identifying, by the one or more processors and based on the extracted semantic contextual information, a keyword within the conversation text data associated with a desired product or service and aligned with an actual intention of a user;
querying, by the one or more processors, a data store for one or more keyword-augmentation pairings associated with the identified keyword via a vector similarity algorithm that returns the one or more keyword-augmentation pairings when vector similarity scores between the identified keyword and keywords in the one or more keyword-augmentation pairings are above a similarity threshold;
selecting, by the one or more processors, augmentation content from one or more augmentation contents in the one or more keyword-augmentation pairings via weighted metadata-based analysis comprising calculating, for each keyword-augmentation pairing, a score by weighting campaign metadata values and summing the weighted campaign metadata values;
and transmitting, by the one or more processors, the identified keyword and the selected augmentation content to the peer-to-peer messaging platform, wherein the peer-to-peer messaging platform in response to the transmitting augments the conversation text data using the identified keyword and the selected augmentation content,
the augmenting comprising modifying a data structure controlling the visual presentation of the identified keyword to differentiate the keyword text from remaining text in the conversation text data, and wherein augmenting enables users of the peer-to-peer messaging platform to identify a connection between the identified keyword and the selected augmentation content.
The claims here are based on the recitation of an abstract idea (i.e. recitation other than the additional elements delineated here with underlining and further addressed per Step 2A - Prong Two and Step 2B). The claims recite the abstract idea of augmenting messages with relevant advertisements in a conversation which falls within certain methods of organizing human activity.
The phrase "certain methods of organizing human activity" applies to fundamental economic principles or practices including hedging insurance, mitigating risk; commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations; managing personal behavior or relationships or interactions between people including social activities teaching, and following rules or instructions. Refer to MPEP 2106.04(a)(2) II. A-C.
The Remaining Claims: The additional independent claims recite the additional elements of: “one or more memories” and “at least one processor” (claim 6); “non-transitory computer-readable medium”, and “at least one computing device” (claim 11). The dependent claims recite fail to recite any additional elements beyond those already identified.
The dependent claims further reiterate the same abstract idea with further embellishments: creating a hyperlink from the keyword in the message (claims 2, 7, 12, and 17); creating a pop-up or drop-down next to the keyword (claims 3, 8, 13, and 18); analyzing messages using NLP (claims 5, 10, 15, and 20); details of the determination of the threshold (claims 21-24). Therefore, the identified claims fall within the subject matter groupings of abstract ideas enumerated in MPEP 2106.04(a)(2).
Step 2A - Prong Two: As per MPEP 2106.04.II.A.2, Prong Two determines if the claim(s) recite additional elements that integrate the judicial exception into a practical application.
As for the additional elements of: “computer-implemented”, “peer-to-peer messaging platform”, “a data store”, “one or more memories”, “at least one processor”, “non-transitory computer-readable medium”, and “at least one computing device”. To be patent-eligible, the elements additional to the identified abstract idea must amount to more than "an instruction to apply the abstract idea . . . using some unspecified, generic computer" to render the claim patent-eligible. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 226 (2014). Here, Applicant's Specification broadly describes support for well-known generic computer elements: paragraphs 0077 et. seq. describe standard computer elements. It would have been readily apparent to one having ordinary skill in the art (PHOSITA) at the time the invention was filed that the additional elements represent generic computing devices. Therefore, the claims amount to no more than a mere method, system, and/or computer program product to implement the abstract idea on a generic computer system. See MPEP § 2106.05(f).
As for the additional element(s) of: natural language processing (NLP), a vector similarity algorithm, and a large language model amounts to generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)). The ordered combination offers nothing more than employing a generic configuration of computer devices and computer functions. The claims do not amount to a practical application, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient.
Step 2B: As per MPEP 2106.05, the additional elements are analyzed, both individually and in combination, to determine whether an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.
As for the additional element(s): natural language processing (NLP): applying "machine learning" at a high level of generality represents performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012). Machine learning is well-understood, routine and conventional as exemplified in "Approaches to Machine Learning" by Langley et al. (Langley, P. and Carbonell, J.G. (1984), Approaches to machine learning. J. Am. Soc. Inf. Sci., 35: 306-316. https://doi.org/10.1002/asi.4630350509 (Year: 1984)). This is further exemplified by the statement in 0026 of the specification: “Any known or future large language models may be substituted without departing from the scope of the technology described herein”. Using a a large language model is a specific type of language model that uses a large number of parameters as detailed in the Wikipedia article: “Large language model”.
As for the additional element(s): a vector similarity algorithm: the use of similarity scores in machine learning is well-understood, routine, and conventional. Zhan et al. describes this class of algorithms as “Traditional algorithms” in section 1.1. of, “Link prediction in recommender systems based on vector similarity” found in Physica A: Statistical Mechanics and its Applications, Volume 560, 2020, 125154, ISSN 0378-4371, https://doi.org/10.1016/j.physa.2020.125154.
References of Record but not Applied in the Current Grounds of Rejection
The prior art listed below is made of record as considered pertinent to applicant's disclosure and is not relied upon in the grounds of rejection presented in this Office action. Those starred with '*' were added to this list in this Office action. Those without "*" were added in a previous Office action and are not repeated on a PTO-892 Notice of References Cited form, but are maintained herein for informational purposes only.
Doulton (Pub. #: AU 2012258326 B2) discloses a system that augments text messages with additional information that can comprise advertisements.
Hal06, in "Strange Pop-up ads on web pages with highlighted words" describes a system that inserts advertisement hyperlinks into text on webpages.
Examiner's Note on the Format of the Prior Art Rejections
The prior art rejections below contain underlined markings of the limitations (e.g. sample limitation). The underlined portions of a claim are addressed at the end of the grounds of rejection for that claim. Examiner notes that the underlining of the claim language is not a statement that the primary reference does not teach that language, but simply that said claim language is addressed at the end of the grounds of rejection for that claim.
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.
Claim(s) 1-3, 5-8, 10-13, 15-18, 20-24 is/are rejected under 35 U.S.C. § 103 as being unpatentable over
Jia (Pub. #: US 2016/0292734 A1) in view of
Wikipedia article, “Large language model”, archived 28 May 2024 in view of
Talmor et al. (Pub. #: US 2019/0043106 A1) in view of
Beier et al. (Pub. #: US 2020/0372550 A1) in view of
Glazier (Pub. #: US 2018/0293601 A1).
Claim(s) 1, 6, 11:
These claims are analogous with different representative embodiments: claim 1 is a method embodiment, claim 6 is a system embodiment, and claim 11 is a computer-readable medium embodiment. Jia teaches a computer system with computer-readable media in at least Figure 1, 0032, 0040-0044 for performing the steps:
receiving, by one or more processors of a server, conversation text data from a peer-to-peer messaging platform and a plurality of previous messages associated with the conversation text data; extracting, by the one or more processors via a large language model, semantic contextual information from the conversation text data and the plurality of previous messages;
(Jia discloses a system for adding advertising to instant messaging applications in at least 0050, 0051. Jia discloses targeting ads based on "real time conversation" in at least 0007 and 0055, using "different machine learning models including topic analysis, semantic analysis, and keywords analysis" which are forms of natural language processing in at least 0009.)
identifying, by the one or more processors and based on the extracted semantic contextual information, a keyword within the conversation text data associated with a desired product or service and aligned with an actual intention of a user;
querying, by the one or more processors, a data store for one or more keyword-augmentation pairings associated with the identified keyword
(Jia discloses determining keywords and topic words from live conversations in at least 0007 and determining ads that would be interesting to the conversant in at least 0008-0011. Jia discloses retrieving advertisements from an advertisement publisher based on keywords in at least 0008. Examiner notes that the "augmentation" is a form of advertisement.)
via a vector similarity algorithm that returns the one or more keyword-augmentation pairings when vector similarity scores between the identified keyword and keywords in the one or more keyword-augmentation pairings are above a similarity threshold;
selecting, by the one or more processors, augmentation content from one or more augmentation contents in the one or more keyword-augmentation pairings via weighted metadata-based analysis comprising calculating, for each keyword-augmentation pairing, a score by weighting campaign metadata values and summing the weighted campaign metadata values;
and transmitting, by the one or more processors, the identified keyword and the selected augmentation content to the peer-to-peer messaging platform, wherein the peer-to-peer messaging platform in response to the transmitting augments the conversation text data using the identified keyword and the selected augmentation content,
(Jia discloses transmitting the advertisement to the messaging service and ultimately to the client for presentation on the GUI of the client in at least 0054, 0056, and 0061. See also Figure 4)
the augmenting comprising modifying a data structure controlling the visual presentation of the identified keyword to differentiate the keyword text from remaining text in the conversation text data, and wherein augmenting enables users of the peer-to-peer messaging platform to identify a connection between the identified keyword and the selected augmentation content.
As for, "via a large language model": Jia discloses using "different machine learning models including topic analysis, semantic analysis, and keywords analysis" which are forms of natural language processing in at least 0009. Jia does not appear to specify the user of a Large Language Model (LLM). Wikipedia teaches the use of LLM's in language understanding in at least the introductory paragraphs.
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is, in the substitution of the natural language processing taught by Jia with the language processing via an LLM taught by Wikipedia. Thus, the simple substitution of one known element for another producing predictable results would have rendered the claims obvious a person of ordinary skill in the art before the effective filing date of the claimed invention.
As for, “via a vector similarity algorithm that returns the one or more keyword-augmentation pairings when vector similarity scores between the identified keyword and keywords in the one or more keyword-augmentation pairings are above a similarity threshold;”: Jia, in view of Wikipedia, does not appear to specify the use of similarity scores above a threshold as the criteria/algorithm for selecting the keyword-advertisement pairings. However, Talmor teaches an advertisement selection process with a technique of utilizing a similarity score above a threshold to identify advertisements in at least 0046.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Wikipedia, with the alternate/specific technique of using similarity scores being above a threshold as taught by Talmor. Motivation to combine Jia, in view of Wikipedia, with Talmor derives from the desire to provide chat users with the most relevant links (Talmor: 0001-0005).
As for, "selecting, by the one or more processors, augmentation content from one or more augmentation contents in the one or more keyword-augmentation pairings via weighted metadata-based analysis comprising calculating, for each keyword-augmentation pairing, a score by weighting campaign metadata values and summing the weighted campaign metadata values;". Jia, in view of Wikipedia and Talmor, discloses retrieving advertisements from an advertisement publisher based on keywords in at least 0008. Jia, in view of Wikipedia and Talmor, does not appear to specify using a "weighted metadata-based analysis" for keyword-augmentation pairings in the selection of relevant advertisements. However, Beier teaches a technique of using weighted metadata analysis in the ranking of keywords in the selection process for advertisements in at least 0025-0028, and 0104-0120.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Talmor, with technique of weighted metadata analysis for ranking of keywords in the advertisement selection process as taught by Beier. Motivation to combine Jia, in view of Talmor, with Beier derives from the desire to present the most accurate advertisements (Beier: 0028).
As for, "the augmenting comprising modifying a data structure controlling the visual presentation of the identified keyword to differentiate the keyword text from remaining text in the conversation text data", "and wherein augmenting enables users of the peer-to-peer messaging platform to identify a connection between the identified keyword and the selected augmentation content". Jia, in view of Wikipedia, Talmor and Beier, does not appear to specify text "augmentation" (i.e., highlighting) as a form of advertisement presentation. However, Glazier teaches a technique of selecting advertisements that are displayed to users in the form of highlighted text within a conversation of the user in at least Figure 2, 0027 and 0047.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Wikipedia, Talmor and Beier, with the technique of presenting advertisements in the form of highlighted text as taught by Glazier. Motivation to combine Jia, in view of Talmor and Beier, with Glazier derives from both references pertaining to inline advertisements within text conversations and to integrate advertisements therein "naturally" (Glazier: 0007).
Claim(s) 2, 7, 12, 17:
wherein the augmenting the conversation text data comprises: embedding the selected augmentation content as a hyperlink within the identified keyword.
Jia discloses presenting hyperlinks in chat in at least 0050. Jia, in view of Wikipedia, Talmor, and Beier, does not appear to specify making the identified keyword a hyperlink advertisement. However, Glazier teaches a technique for augmenting messages in a text conversion with referrals that constitute advertisements and the referrals comprise hyperlinks according to the identified keywords in at least 0044, 0047 and Figures 1 and 2.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia with the technique of presenting advertisements as hyperlinks according to the identified keywords as taught by Glazier. Motivation to combine Jia, in view of Wikipedia, Talmor, and Beier, with Glazier derives from both references pertaining to inline advertisements within text conversations and to integrate advertisements therein "naturally" (Glazier: 0007).
Claim(s) 3, 8, 13, 18:
wherein the augmenting the conversation text data comprises: embedding the selected augmentation content inside a popup or dropdown element next to the identified keyword.
Jia discloses presenting hyperlinks in chat in at least 0050. Jia, in view of Wikipedia, Talmor, and Beier, does not appear to specify making the identified keyword a hyperlink advertisement with an embedded popup or dropdown element. However, Glazier teaches a technique for augmenting messages in a text conversion with referrals that constitute advertisements and the referrals comprise hyperlinks according to the identified keywords in at least 0044, 0047 and Figures 1 and 2. Glazier additional teaches that the advertisement may comprise a "pop-up" in at least 0064.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia with the technique of presenting advertisements as hyperlinks according to the identified keywords as taught by Glazier. Motivation to combine Jia, in view of Wikipedia, Talmor, and Beier, with Glazier derives from both references pertaining to inline advertisements within text conversations and to integrate advertisements therein "naturally" (Glazier: 0007).
Claim(s) 5, 10, 15, 20:
wherein the plurality of previous messages associated with the conversation text data comprises an entire conversation history associated with the conversation text data.
(Jia discloses targeting ads based on "real time conversation" in at least 0007 and 0055, using "different machine learning models including topic analysis, semantic analysis, and keywords analysis" which are forms of natural language processing in at least 0009.)
Claim(s) 16:
A computer-implemented method for improving data transparency in a peer-to-peer messaging environment, comprising:
(Jia teaches a computer system with computer-readable media in at least Figure 1, 0032, 0040-0044. Jia discloses a system for adding advertising to instant messaging applications in at least 0050, 0051.)
extracting, by one or more processors of a server executing on a peer-to-peer messaging platform via a large language model, semantic contextual information from conversation text data and a plurality of previous messages associated with the conversation text data;
(Jia discloses a system for adding advertising to instant messaging applications in at least 0050, 0051. Jia discloses targeting ads based on "real time conversation" in at least 0007 and 0055, using "different machine learning models including topic analysis, semantic analysis, and keywords analysis" which are forms of natural language processing in at least 0009.)
identifying, by the one or more processors and based on the extracted semantic contextual information, a keyword within conversation text data associated with a desired product or service and aligned with an actual intention of a user;
querying, by the one or more processors, a data store for one or more keyword-augmentation pairings associated with the identified keyword
(Jia discloses determining keywords and topic words from live conversations in at least 0007 and determining ads that would be interesting to the conversant in at least 0008-0011. Jia discloses retrieving advertisements from an advertisement publisher based on keywords in at least 0008. Examiner notes that the "augmentation" is a form of advertisement.)
via a vector similarity algorithm that returns the one or more keyword-augmentation pairings when vector similarity scores between the identified keyword and keywords in the one or more keyword-augmentation pairings are above a similarity threshold;
selecting, by the one or more processors, an augmentation content from one or more augmentation contents in the one or more keyword-augmentation pairings via weighted metadata-based analysis comprising calculating, for each keyword-augmentation pairing, a score by weighting campaign metadata values and summing the weighted campaign metadata values;
and augmenting, by the one or more processors, the conversation text data using the identified keyword and the selected augmentation content, the augmenting comprising modifying a data structure controlling the visual presentation of the identified keyword to differentiate the keyword text from remaining text in the conversation text data,
and wherein the augmenting enables users of the peer-to-peer messaging platform to identify a connection between the identified keyword and the selected augmentation content.
As for, "via a large language model": Jia discloses using "different machine learning models including topic analysis, semantic analysis, and keywords analysis" which are forms of natural language processing in at least 0009. Jia does not appear to specify the user of a Large Language Model (LLM). Wikipedia teaches the use of LLM's in language understanding in at least the introductory paragraphs.
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is, in the substitution of the natural language processing taught by Jia with the language processing via an LLM taught by Wikipedia. Thus, the simple substitution of one known element for another producing predictable results would have rendered the claims obvious a person of ordinary skill in the art before the effective filing date of the claimed invention.
As for, “via a vector similarity algorithm that returns the one or more keyword-augmentation pairings when vector similarity scores between the identified keyword and keywords in the one or more keyword-augmentation pairings are above a similarity threshold;”: Jia, in view of Wikipedia, does not appear to specify the use of similarity scores above a threshold as the criteria/algorithm for selecting the keyword-advertisement pairings. However, Talmor teaches an advertisement selection process with a technique of utilizing a similarity score above a threshold to identify advertisements in at least 0046.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Wikipedia, with the alternate/specific technique of using similarity scores being above a threshold as taught by Talmor. Motivation to combine Jia, in view of Wikipedia, with Talmor derives from the desire to provide chat users with the most relevant links (Talmor: 0001-0005).
As for, "selecting, by the one or more processors, augmentation content from one or more augmentation contents in the one or more keyword-augmentation pairings via weighted metadata-based analysis comprising calculating, for each keyword-augmentation pairing, a score by weighting campaign metadata values and summing the weighted campaign metadata values;". Jia, in view of Wikipedia and Talmor, discloses retrieving advertisements from an advertisement publisher based on keywords in at least 0008. Jia, in view of Wikipedia and Talmor, does not appear to specify using a "weighted metadata-based analysis" for keyword-augmentation pairings in the selection of relevant advertisements. However, Beier teaches a technique of using weighted metadata analysis in the ranking of keywords in the selection process for advertisements in at least 0025-0028, and 0104-0120.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Talmor, with technique of weighted metadata analysis for ranking of keywords in the advertisement selection process as taught by Beier. Motivation to combine Jia, in view of Talmor, with Beier derives from the desire to present the most accurate advertisements (Beier: 0028).
As for, "the augmenting comprising modifying a data structure controlling the visual presentation of the identified keyword to differentiate the keyword text from remaining text in the conversation text data", "and wherein augmenting enables users of the peer-to-peer messaging platform to identify a connection between the identified keyword and the selected augmentation content". Jia, in view of Wikipedia, Talmor and Beier, does not appear to specify text "augmentation" (i.e., highlighting) as a form of advertisement presentation. However, Glazier teaches a technique of selecting advertisements that are displayed to users in the form of highlighted text within a conversation of the user in at least Figure 2, 0027 and 0047.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Wikipedia, Talmor and Beier, with the technique of presenting advertisements in the form of highlighted text as taught by Glazier. Motivation to combine Jia, in view of Talmor and Beier, with Glazier derives from both references pertaining to inline advertisements within text conversations and to integrate advertisements therein "naturally" (Glazier: 0007).
Claim(s) 21, 22, 23, 24:
wherein the similarity threshold is configured by the peer-to-peer messaging platform.
As for, “wherein the similarity threshold is configured by the peer-to-peer messaging platform”: Jia, in view of Wikipedia, Glazier, and Beier, does not appear to specify the use of similarity scores above a threshold as the criteria/algorithm for selecting the keyword-advertisement pairings. However, Talmor teaches an advertisement selection process with a technique of utilizing a similarity score above a threshold to identify advertisements in at least 0046. Talmor further teaches adjusting the similarity score threshold via the networking system in at least 0076.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the text conversation with inline advertisements system of Jia, in view of Wikipedia, Glazier, and Beier, with the alternate/specific technique of using similarity scores being above a threshold as taught by Talmor. Motivation to combine Jia, in view of Wikipedia, Glazier, and Beier, with Talmor derives from the desire to provide chat users with the most relevant links (Talmor: 0001-0005).
Conclusion
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/S.S/Examiner, Art Unit 3621 /WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
Prosecution Insights