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 .
DETAILED ACTION
Status of Claims
This action is a first action on the merits in response to the application filed on 01/14/2025.
Claims 1-15 and 17-20 have been amended. Claims 21-47 have been cancelled. Therefore, Claims 1-20 are currently pending and have been examined in this application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
receiving [by a plurality of electronic interfaces], a plurality of user responses corresponding to one a plurality of users, wherein the user responses are associated with a first set of questions and a second set of questions included in one or more surveys;
classifying the plurality of users into a first group based on the user responses to the first set of questions;
classifying the of the first group into a second group based on the user responses to the second set of questions;
storing digital information indicative of an association between the second group and a plurality of engagement opportunities in an electronic database, wherein each of the plurality of engagement opportunities is configured to present electronic content for engagement by one or more of the plurality of users;
selecting, through an electronic interface, an engagement opportunity from the plurality of engagement opportunities based on the second group;
recommending, through the an electronic interfaces, the selected engagement opportunity to each of the one or more plurality of users;
initiating, through an the electronic interfaces, the selected engagement opportunity interfaces, wherein first electronic content presented in the engagement opportunity is generated based on the second group;
receiving user event responses electronically during the engagement opportunity;
inputting the received user event responses into a natural language processing model;
ranking, by the natural language processing model, the user event responses; (see at least column 4, lines 1-26, determining sentiment within a range)
generating second electronic content responsive to the ranked user event responses;
modifying the first electronic content of the engagement opportunity to include the second electronic content;
presenting the modified first electronic content through the plurality of electronic interfaces;
receiving feedback from the one or more users; and
training the natural language processing model based on the feedback from the one or more users.
The limitation under its broadest reasonable interpretation covers Certain Methods of Organizing Human Activities related to advertising, marketing or sales activities or behaviors, but for the recitation of generic computer components (e.g. a processor). For example, receiving responses for a set of questions, classifying a plurality of users into a first group, classifying users of the first group into a second group, selecting engagement opportunity, recommending the opportunity, initiating the engagement opportunity, receiving feedback, etc. involves analyzing data related to advertising, marketing or sales activities or behaviors. Accordingly, the claim recites an abstract idea of Certain Methods of Organizing Human Activity.
In addition, the claim could be seen as Mental Processes related to observation and evaluation of data.
Independent Claims 8 and 15 substantially recite the subject matter of Claim 1 and also include the abstract ideas identified above. The dependent claims encompass the same abstract ideas. For instance, Claim 2 is directed to tokenizing the plurality of user responses and TFIDF; Claims 3 is directed to ML classifying user responses (analysis); Claim 4 is directed to identifying engagement opportunities; Claim 5 is directed to determining user sentiment in real-time and generating new content (analysis); Claim 6 is directed to training nlp model based on feedback and updating weights (complex math) ( and Claim 7 is directed to ML model for classifying user responses is evaluated using exact match (analysis using complex math). Claims 9-14 and 16-20 substantially recites the subject matter of Claims 2-7 and encompass the same abstract concepts Thus, the dependent claims further limit the abstract concepts found in the independent claims.
The judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of one or more processors, a non-transitory memory, electronic interfaces and a server. Claim 15 recites the additional elements of a non-transitory computer readable medium and one or more processors. These are generic computer components recited at a high level of generality as performing generic computer functions (see Spec ¶0077).
Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer components (e.g. a processor). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (e.g. a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because it does not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above, the additional elements of a processor, a memory, a crm, etc. are considered generic computer components performing generic computer functions that amount to no more than instructions to implement the judicial exception. Mere, instructions to apply an exception using generic computer components cannot provide an inventive concept.
The dependent claims when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract. 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.
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself. Therefore, Claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 (AIA ) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-5,8, 10-12, 15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (WO 2019107669) further in view of Chen (US 11507754).
Claim 1:
Kim discloses:
A system comprising: a server comprising one or more processors; and a non-transitory memory, in communication with the server, storing instructions that when executed by the one or more processors, causes the one or more processors to implement a method comprising: (see at least pg. 21 para 3, CRM; see also pgs. 16-17, processor for displaying questionnaire)
receiving by a plurality of electronic interfaces, a plurality of user responses corresponding to one a plurality of users, wherein the user responses are associated with a first set of questions and a second set of questions included in one or more surveys; (see at least pgs. 3, para 8-9 – pg. 4, para 1-3, users receive a first question that can be used to classify user; see also pg. 4, para 4, apparatus determines second question for classifying a user)
classifying the plurality of users into a first group based on the user responses to the first set of questions; (see at least pg. 4, para 2, a first question for classifying a user according to a first response category; see also pg. 8, para 4)
classifying the of the first group into a second group based on the user responses to the second set of questions; (see also pg. 4, para 2-3, apparatus determines second question for classifying a user who answered negatively (first classification) for the first question, the second question forming a second classification of the user; see also pg. 10, para 3)
storing digital information indicative of an association between the second group and a plurality of engagement opportunities in an electronic database, wherein each of the plurality of engagement opportunities is configured to present electronic content for engagement by one or more of the plurality of users; (see at least pg. 8, para 2, management apparatus may search for and determine the content to send to user, which implies a plurality of content)
selecting, through an electronic interface, an engagement opportunity from the plurality of engagement opportunities based on the second group; (see at least pg. 8, para 2, management apparatus may search for and determine the content to send to user, which implies a plurality of content)
recommending, through the an electronic interfaces, the selected engagement opportunity to each of the one or more plurality of users; (see at least pg. 8, para 2, management apparatus may search for and determine the content to send to user, which implies a plurality of content)
initiating, through an the electronic interfaces, the selected engagement opportunity interfaces, wherein first electronic content presented in the engagement opportunity is generated based on the second group; (see at least pg. 8, para 2 , management apparatus may search for and determine the content to send to user, which implies a plurality of content; see also pg. 10, para 1, receive content such as advertisement and receive user responses or feedback)
receiving user event responses electronically during the engagement opportunity; (see at least pg. 8, para 2-3; see also pg. 5, para 1, receive content such as advertisement and receive user responses or feedback)
While Kim discloses the above limitations, Kim does not explicitly disclose the following limitations; however Chen does disclose:
inputting the received user event responses into a natural language processing model; (see at least column 3, lines 30-45, using NLP and other analysis tools the system analyzes each comment; see also column 9, lines 65-67-column 10, lines 1-5)
ranking, by the natural language processing model, the user event responses; (see at least column 4, lines 1-26, determining sentiment within a range)
generating second electronic content responsive to the ranked user event responses; (see at least column 5, lines 38-43, quickly react to feedback by modifying content)
modifying the first electronic content of the engagement opportunity to include the second electronic content; (see at least column 5, lines 38-43, quickly react to feedback by modifying content)
presenting the modified first electronic content through the plurality of electronic interfaces; (see at least column 5, lines 38-43, quickly react to feedback by modifying content)
receiving feedback from the one or more users; and (see at least column 2, lines 60-62, feedback for each content; see also column 1, lines 25-28, providing feedback to content)
training the natural language processing model based on the feedback from the one or more users. (see at least column 12, lines 58-64, training ML model to predict sentiment of future comments; see also column 6, lines 40-45)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the classifying target users to receive content of Kim with the feedback analysis of content of Chen by providing tool that automatically parses and analyzes feedback, and provides a meaningful summarization of the feedback to users (Spec see column 57-61).
Claim 3:
Kim and Chen disclose claim 1, Kim does not explicitly disclose the following limitation; however, Chen does disclose:
wherein a machine learning model further classifies the user event responses. (see at least column 10, lines 34-41, feedback analysis identifies one or more clusters of words from responses)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the classifying target users to receive content of Kim with the feedback analysis of content of Chen by providing tool that automatically parses and analyzes feedback, and provides a meaningful summarization of the feedback to users (Spec see column 57-61).
Claim 4:
Kim and Chen disclose claim 1. Kim further discloses:
further comprising identifying engagement opportunities to one or more users based on the second group where the one or more users are classified. (see at least pg. 5, para 1, selecting and sending a first advertisement; see also pg. 6, para, 1, selecting and sending a second advertisement; see also pg. 7, para 2, determine content or advertisement thus indicated multiple engagement opportunities)
Claim 5:
While Kim and Chen disclose claim 1, Kim does not explicitly disclose the following limitation; however, Chen does disclose:
determining, by the natural language processing model, user sentiment in real- time based on the user event responses; and (see at least column 3, lines 9-50, feedback analysis system can analyze real time feedback)
responsive to the determining, generating new content for the engagement opportunity in real-time. (see at least column 5, lines 36-44, content generator provides content in real time by viewing and interacting with graphical representation while presenting content, the content generator can quickly digest feedback and react to feedback by modifying content)
Claims 8 and 10-12 for a method and Claims 15 and 17-19 for a CRM (Chen see column 16, lines 11-15, CRM) substantially recites the subject matter of Claims 1 and 3-5 for a system (Chen see Figure 7 and associated text) and are rejected based on the same rationale.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (WO 2019107669) further in view of Chen (US 11507754) further in view of Jakobson et al. (US 2023/0075884).
Claim 2:
While Kim and Chen disclose claim 1, and Chen further discloses TFIDF (see column 10, lines 23-28), neither explicitly disclose the following limitations; however Jakobsson does disclose:
further tokenizing the plurality of user responses; and (see at least ¶0465, receive NFT feedback; see also
determining an importance of each word using a term frequency inverse document frequency model. (see at least ¶0502, TFIDF techniques used in combination with sentiment analysis)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the classifying target users to receive content of Kim and the feedback analysis of content of Chen with the NFT feedback and term frequencies of Jakobson since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 9 for a method and Claim 16 substantially recites the subject matter of Claim 2 for a system and are rejected based on the same rationale.
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (WO 2019107669) further in view of Chen (US 11507754) further in view of Gonzalez Macias et al. (US 2022/0342745).
Claim 6:
While Kim and Chen disclose claim 1, neither explicitly disclose the following limitation; however, Gonzalez Macias does disclose:
wherein training the natural language processing model based on the feedback from the users further comprises fine tuning and updating pre-trained weights of the natural language processing model. (see at least ¶0034, the machine learning model may update its configurations including weights, biases or other parameters)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the classifying target users to receive content of Kim and the feedback analysis of content using nlp of Chen with the updating of ML weights of Gonzalez Macias in order to adjust for inaccuracies or importance of parameters (Spec see ¶0034).
Claim 13 for a method and Claim 20 for a CRM substantially recites the subject matter of Claim 6 for a system and are rejected based on the same rationale.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (WO 2019107669) further in view of Chen (US 11507754) further in view of Moya et al. (US 2023/098568).
Claim 7:
While Kim and Chen discloses claim 1, neither explicitly disclose the following limitation; however
wherein performance of a machine learning model related to properly classifying the user responses is evaluated using exact match. (see at least ¶0028)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, to combine the classifying target users to receive content of Kim and the feedback analysis of content using nlp of Chen with the ML classifiers using exact match of Moya since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 for a method substantially recites the subject matter of Claim 7 for a system and is rejected based on the same rationale.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Sawicka et al. (US 2023/0351431) discloses segmenting users using a machine learning model based on transaction data. The method includes receiving survey data and historical transaction data for a first subset of users and segmenting each of the first subset of users into at least one group, where each group may be associated with at least one characteristic.
Shekhar et al. (US 2018/0213284) discloses are directed towards recommending and providing content to a user group, which includes multiple users, for the social consumption of the content.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Renae Feacher whose telephone number is 571-270-5485. The Examiner can normally be reached Monday-Friday, 9:00 am - 5:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner's supervisor, Beth Boswell can be reached at 571-272-6737.
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/Renae Feacher/
Primary Examiner, Art Unit 3625