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 .
This action is in response to the amendment filed on May 31st, 2023. The amendments are linked to the original application filed on Jun. 24th, 2026.
Response to Amendment
Regarding Claim Rejections – 35 U.S.C. 101
The applicant argues the amended claims meet the current UPSTO subject matter eligibility and request the withdrawal of the rejection under 35 U.S.C. 101. The applicant has provided arguments to support this withdrawal.
First, the applicant discusses recent updates to the MPEP reflecting the Appeals Review Panel decision in Ex Parte Desjardins. In particular the applicant notes that the claims in Desjardins, in light of their specification, recite technical improvements to computer technology and therefore required further consideration and led to eventual withdrawal of the 101 rejection. The improvements noted by the applicant in Desjardin were their proposed reduction in system complexity and streamlining of a task. The applicant argues that, similar to Desjardins, the current amended claims, in light of the specification, recite a technical improvement of streamlining group recommendations in social network workflow in a real time setting which follows a user’s submission of a post. Further, the applicant has cited sections of the specification they believe further recite a technical improvement.
In regards to Ex Parte Desjardins, the application and claims in question were directed to a multitask machine learning system and further training that system with their claimed method. Their claims and specifications recited, explicitly, improvements to a machine learning model through processing tasks and training their model accordingly. After reading their claims and specification, it would be reasonable to assume a person of ordinary skill in the art to recognize their claimed invention as an improvement. After reviewing the amended claims, specification, and remarks of this application, the examiner believes a person of ordinary skill in the art would fail to recognize a definitive improvement to technology or technical field. The claims, independent claims in particular, recite a process evaluating user generated posts in groups in an online service. Further the claims recite, briefly, training a classification model to map and classify identified topics to groups so the system can provide a user with a recommendation. The claims fail to recite how this process improves technology as classification, identification and automated prompting of a user are all well-known subjects in machine learning. For example, there is an entire field of machine learning that teaches clustering and classification, the examiner would like to point to Xu et al, “A Comprehensive Survey of Clustering Algorithms”, 2015 as an article which is dedicated to teaching conventual clustering algorithms, training methods, distance metrics and other various machine learning clustering processes. Next, the examiner would like to point to paragraphs [0065] and [0066] of the specification which recites the use of conventional clustering algorithms which further supports the claimed process of identification, evaluation, and clustering of a post in an online service is not a technical improvement to a technical field. Next, the applicant argues that their concept provides a technical improvement because it streamlines, in real time, group recommendation workflow. The examiner believes that a person of ordinary skill would not be able to recognize the concrete improvement to a technical field as the claims, in light of the specification, fail to disclose how this is an improvement generic automation of a task which a user is able perform themselves. The claims disclose a process of using post topics which are evaluated and mapped to a table. This table acts as a look up table for the system and is able to allow for real time group recommendation. The examiner would like to point to the provided specification paragraphs [0040], [0044] and [0048]. These paragraphs disclose an example case where a post is made by a user and, in real time, the system will evaluate their post and then in turn recommend a group to also post the generated post in. The applicant points to the technical requirements for real-time communication with a user. The examiner would like to point to the fact that automated agents and/or models are able to communicate with users in real time, the concept of real-time inference does not inherently provide an improves to a technical field or process. For example, many of the Large Language models, such as BERT, see Delvin et al, can evaluate and process a user’s prompt, or post, and perform various related tasks in real time. Therefore, while evaluating the claims, in light of the specification, the claimed elements do not appear to concretely recite a technical improvement outside of using conventual systems to perform a specialized task of group recommendation in an online service. The examiner believes that the claimed subject matter and specification do not provide is not a clear improvement. Accordingly, per MPEP 2106.06(b), “If the claims are a "close call" such that it is unclear whether the claims improve technology or computer functionality, a full eligibility analysis should be performed to determine eligibility. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, 1241 (Fed Cir. 2016). Only when the claims clearly improve technology or computer functionality, or otherwise have self-evident eligibility, should the streamlined analysis be used”, therefore the current claims do not qualify for a streamlined analysis and require the full eligibility analysis using the Alice/Mayo test.
Next, the applicant argues the current amended claims do not recite abstract ideas or mental concepts. The applicant argues that the current amended claim limitations cannot be performed in a human mind. In particular the applicant highlights the fact that this process is performed in real-time using a computing system. Finally, the applicant argues the claimed limitations cannot be practically performed in a human mind even if in theory a human could perform the claimed limitations.
In regards to Step 2, Prong 1, of the Alice/Mayo yest, the examiner would like to point to MPEP 2106.04(a)(2)(III)(C) which states, “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.”, the MPEP does disclose that processes performed in a computing environment using a generic computing system can recite still recite abstract ideas. The MPEP does provide limited examples of abstract ideas implemented on a generic computing system, for example MPEP, (2)(a)(III)(A) recites, “In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);”. Using this example, the amendments have been evaluated, and the examiner would like to note that the amendments made do not alter claim limitations which have been noted by the examiner to recite abstract ideas and concepts. As such, the examiner maintains prior arguments for the current claim limitations which recite abstract ideas. As stated in prior office actions, the examiner believes the claim limitations of the independent claims recite processes of, observations, i.e. “detecting additional posts …”, evaluations, i.e. “determining, by the post classifier model, a topic identifier …”, judgement, i.e. “determining a group recommendation for posting the additional post based on the topic identifier …”, and/or opinions.
Next, the applicant argues that the amendments made satisfy Step 2A, Prong 2 as discussed above. The applicant points to arguments above in regards to Ex Parte Desjardins and similarities between that case and the current application. In regards to Step 2A, Prong 2, the examiner has provided a counter argument above. Further, the examiner would like to note that the current claims have been reviewed using Step 2A, Prong 2. After reviewing the amendments and arguments, the examiner noted that the remaining claim limitations, in light of the specification fail to integrate the claimed invention into a practical application. The current amendments made merely disclose the claimed process will post a given post to a recommended group after real time user confirmation. This information, along with the remaining claims as a whole fails, to disclose improvements to technology or a technical field. As stated above, the claims are reviewed to see if the remaining limitations integrate the given inventive concept into a practical application. The claims, in light of the specification, in order to be considered a practical application must recite a technical improvement, not explicitly, but in some manner where a person of ordinary skill would recognize the improvements to technology or technical field. As stated above, the examiner believes the specification fails to concretely disclose a technical improvement to technology or computing systems and therefore the claims themselves fail to integrate the proposed improvements. Further disclosure of the inventive concepts would be needed for further prosecution.
Next, the applicant argues that the remaining limitations of the claims provide significantly more than merely limitations for implementing abstract or mental concepts. The applicant has provided examples from MPEP 2106.05 as potential consideration which would qualify the remaining claim limitations as significantly more.
In regards to Step 2B, the examiner has reviewed the current remarks, Specification and amended claims. Taking the independent claims as an example, the examiner would like to note that two remaining limitations are evaluated to see if they are provide significantly more. The first limitation recites, “training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;”. Using the BRI of this limitation, the examiner notes these limitations disclose no more than mere instructions to implement an abstract idea on a generic computing system. The limitation discloses a process of applying generic training to a classifier model on given training data. Using the BRI, generic training of machine learning models or classification models are well understood and routine. As such this limitation fails to provide significantly more.
Finally, looking at the amended claim under Step 2B, the examiner believes this limitation, alone or included with the claims as a whole, also fails to provide significantly more. Using the BRI of this limitation, “causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” The examiner believes the limitation merely discloses instructions to apply output the result of the abstract concepts using a generic computing system. The limitations disclose the automated presentation of a choice to a user to post a post in another group based on an evaluation and response from the user. This limitation fails to definitively define the inventive concept and merely provide an output for the disclosed system.
As stated above, the examiner has reviewed the amendments made to the claims, remarks, prior office actions, and prior art on record. The current amended claims have been evaluated using the Alice/Mayo eligibility test required by USPTO on all claimed subject matter. For the reasons stated above, and in previous office actions, the examiner believes the current claimed subject matter is ineligible. The examiner has noted that claims recite abstract ideas of observation, evaluations, judgment and/opinions and further analysis are needed. The remaining limitations are evaluated using steps 2A, Prong 2 and Step 2B. As stated above, and in prior office actions, the examiner believes the claims, in light of the specification, fails to recite a clear improvement to technology and/or technical field and further fail to provide significantly more than judicial exceptions. Therefore, the examiner has upheld the current rejection under 35 U.S.C. 101, see 101 rejection below.
Regarding Claim Rejections – 35 U.S.C. 103
The applicant argues that the claims have been amended and are distinctly different to the prior art on record. The applicant provides multiple supporting arguments. First the applicant argues that the prior art Miao fails to teach real-time recommendations which respond to the user making a post. The applicant argues that Miao performs background updates of groups and the recommendations and not when the user makes a post in a group as claimed. And further argues, on would not be motived to modify Miao to perform the amended claim limitations.
The examiner has reviewed the proposed amendments to the claims and the prior art on record. The prior claims recited a process of presenting addition groups to a user to after post and the examiner believes that Miao taught the presentation of a group recommendation to a user. After the submission of amendments however, the examiner notes that Miao fails to explicitly disclose the amended elements of this claim limitation. However, Miao still recites a group recommendation process in online services and is still relied upon by the examiner to teach other limitations in other claim limitations. The examiner would like to note that the system in Miao could reasonably be modified to update group recommendations after given event since, as stated in Miao pp. 3, [0025] the use of an action logger. This action logger which is able to notate different actions a user executes within the given system such as, “Users may interact with various objects on the online system 140, and information describing these interactions are stored in the action log 210. Examples of interactions with objects include commenting on posts, sharing links, and checking-in to physical locations via a mobile device, accessing content items, and any other interactions.”. As stated in the section the action logger in Miao will evaluate and process user actions such as when a user makes a post.
Next, the applicant argues the other provided art fails to cure the deficiencies of Miao and therefore the applicant argues the current claims should be considered allowable. Finally, the applicant argues that, since Miao cannot teach limitations of the independent claims and Comito fails to teach missing limitations the remaining dependent claims would also be considered allowable based on claim dependency. As stated above, The examiner recognizes that Miao is unable to teach elements of the claimed subject matter. However, as stated, a complete search is performed after each amendment by the applicants. While performing this search, the examiner noted new subject matter which, in combination of previously presented arts, are able to teach the claimed system. Therefore, the claims are not considered allowable based on dependency purely and have been reevaluated based on the remarks, amendments and specification.
After reviewing the amendments to the claims, remarks, specification and previous office actions the examiner has noted that Miao does fail to teach the amended elements of the claims. However, as noted above, Miao does still teach other key limitations of the claims, but is no longer relied upon to teach the amended limitations. Per the USPTO guidelines, after each amendment the claims are reviewed a full and thorough search is performed to ensure compliance with 35 U.S.C. 102/103. The examiner has completed this search and notes that new subject matter has been discovered, in addition to previously present arts, to teach the amended claims. Therefore, since a combination of arts are used to teach the disclosed subject matter the examiner has upheld the rejection under 35 U.S.C. 103, see 103 rejection below.
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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1 recites, "A computer-implemented method comprising:" therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate posts and groups the posts based on observed topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a table or map of groups to topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a table mapping topics to groups. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting an additional post entered by a user associated with the online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a thread or community board for further updates. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a model and evaluate data for unique topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide recommendations or opinions of a group to users based on observed comments or posts made by another user. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“determining if the user belongs to the recommended group; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to make observations and provide a judgment on where a member is part of a group or not. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to use mathematical equations and concepts to produce an outcome. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein clustering the posts further comprises: creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near realtime.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near realtime.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein determining the group recommendation further comprises: determining that the topic identifier is mapped to several group identifiers; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate topics and determine if the topic is related to groups. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“selecting the recommended group at random from the several group identifiers.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to make a judgment or opinion based on observed information. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein determining the group recommendation further comprises: determining an interest of the additional post; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a post and provide opinions or judgements about that post. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “filtering the group for being recommended based on the interest of the post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “filtering the group for being recommended based on the interest of the post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein filtering the group for being recommended further comprises: determining a percentage of posts in the group associated with the determined interest; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to evaluate a list and use known math concepts to determine a percentage of posts in a group that meets specified criteria. This claim discloses a math operation and therefore is ineligible.
“determining that the group is recommended when the percentage of posts in the group is above a predetermined threshold.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and observe a list and determine if group that meets a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the additional post is detected subsequent to the user adding the post to a user feed, wherein the group recommendation is presented in response to determining that the user added the post to the user feed.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a post generated by a user and provide opinions and recommendations to the user after they post or comment in a social network. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “posting the additional post in the recommended group after the user accepts the group recommendation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “posting the additional post in the recommended group after the user accepts the group recommendation.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 11 recites, "A system comprising:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate posts and groups the posts based on observed topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a table or map of groups to topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a table mapping topics to groups. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting an additional post entered by a user associated with the online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a thread or community board for further updates. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a model and evaluate data for unique topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is Page 24 practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide recommendations or opinions of a group to users based on observed comments or posts made by another user. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the instructions further cause the one or more computer processors to perform operations comprising: determining if the user belongs to the recommended group; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to make observations and provide a judgment on where a member is part of a group or not. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to use mathematical equations and concepts to produce an outcome. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein clustering the posts further comprises: creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein clustering the posts further comprises: creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 16
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 16 recites, "A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:" therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate posts and groups the posts based on observed topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a table or map of groups to topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and generate a table mapping topics to groups. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“detecting an additional post entered by a user associated with the online service;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe a thread or community board for further updates. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to use a model and evaluate data for unique topics. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to provide recommendations or opinions of a group to users based on observed comments or posts made by another user. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 17
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“wherein the machine further performs operations comprising: determining if the user belongs to the recommended group; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to make observations and provide a judgment on where a member is part of a group or not. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 18
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites, inter alia:
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to use mathematical equations and concepts to produce an outcome. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein clustering the posts further comprises: creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein clustering the posts further comprises: creating an embedding for each post;” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 19
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 20
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A machine, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”.
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
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.
Claims 1, 3-5, 9, 11, 13-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Comito et al, (Comito et al, “Word Embedding based Clustering to Detect Topics in Social Media”, 2019, hereinafter “Comito”) in view of Napper et al, (Napper et al, “ENTITY PAGE RECOMMENDATION BASED ON POST CONTENT”, US 2017/0337638 A1, Filed 2017, hereinafter “Napper”).
Regarding claim 1, Comito discloses, “clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding. Therefore, the similarity will be the combination of two measures accounting both syntactic as well as semantics of the posts." This article discloses a clustering algorithm that is able to evaluate user's social media content. This will be able to identify different topics and interests in posts made by users in a social network.)
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” (Preliminaries, pp. 194; "To store summary information of all the posts assigned to a cluster C, we introduce the cluster centroid as a compact data structure. The centroid keeps the textual items as in the feature vector
f
v
of each social media post smp assigned to the cluster, its corresponding semantic vector
s
f
v
, together with their frequencies and their temporal evolution." The system in this article is able to generate different clusters containing similar posts and topics. The system in this article is able to map topics to clusters or groups.)
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” (Word Embedding Based Clustering, pp. 195; “While a new post
s
m
p
i
arrives at time
t
i
, the algorithm builds the representation
f
v
i
of
s
m
p
i
and its word embedding
s
f
v
i
, and computes both the semantic similarity and the syntactic one between the post and the clusters active at the time stamp
t
i
, while the inactive clusters are removed from the set of clusters. Let
C
c
be the cluster whose centroid has maximum similarity with
s
m
p
i
. If this similarity value
s
i
m
(
s
m
p
i
,
C
c
)
is lower than ϵ, a new cluster is generated from
f
v
i
and
s
f
v
i
and added to the set of clusters, otherwise the social media post in the form of
f
v
i
and
s
f
v
i
is added to
C
c
by updating the centroid.” The system in this article is able to save the created clusters and use them later. The generated clusters can be used to map different topics to clusters or groups.)
“detecting an additional post entered by a user associated with the online service;” (Word Embedding Based Clustering, pp. 195; "These steps are repeated until the algorithm receives new posts." The system in this article is able to detect when a user makes consecutive postings to a social network group.)
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding." This system is able to evaluate newly posted data and see if it aligns with an already generated cluster.)
Comito fails to explicitly disclose the following limitations:
“A computer-implemented method comprising:”
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;”
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and”
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.”
However, Napper discloses, “A computer-implemented method comprising:” (Detailed Description, pp. 7, [0154]; “FIG. 9B is a simplified schematic diagram of a computer system for executing implementations described herein. It should be appreciated that the methods described herein may be performed with a digital processing system (e.g., a conventional, general-purpose computer system). Special purpose computers, which are designed or programmed to perform only one function, may be used in the alternative. The computing device 950 includes a processor 954, which is coupled through a bus to memory 956, permanent storage 958, and Input/Output (I/O) interface 960.” This application discloses a method to be executed on a general computing system containing processors coupled to memory.)
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” (Detailed Description, pp. 6, [0108]-[0112]; “The followings operations are performed by the entity mapping system: [0109] 1. The entity to entity page mapping system takes input from a number of sources (e.g., Wikipedia) and generates a set of candidate entity to entity page mappings. [0110] 2. The candidate mappings are verified 612 for accuracy. [0111] 3. Mappings that meet the accuracy requirements are triplified and loaded into knowledge graph 608. [0112] 4. The results of the mapping verification are fed back into the entity to entity page mapping system as training data, in order to improve the accuracy of the candidate mapping generation over time.” This applicant discloses a post and entity mapping system that is able to classify text posts. The results of the inference are used to generate training data for the different models in this system.)
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” (Detailed Description, pp. 6, [0105]; “The entity page mapper 610 may utilize different sources of information (602, 604, and 606) for entity mapping. The sources may include metaweb freebase topic tables for entity data, entity page recommendation pipeline for entity page annotations and entity page metadata, data from associated Wikipedia entries, etc.” This proposed system will present the user with a group recommendation based on different sources from different sources. As stated above, this system will use topic tables to produce recommendations.)
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” (Detailed Description, pp. 4, [0062]; “In some implementations, the recommendation for following the entity is presented after the user has interacted with the content, such as by clicking on the website to read the article. In other implementations, the recommendation is presented without requiring user interaction, as the user simply viewing the content represents an expression of interest by the user, or at least by a friend of the user.” This system is able to provide a user of a social network with a recommendation after the user has interacted with content. This is used by the recommendation system to produce a result for the user.) and (Detailed Description, pp. 4, [0064]-[0065]; “FIG. 3C illustrates a hover car providing additional information about the identify entity page, according to some implementations. In some implementations, the Graphical User Interface (GUI) displays an interface for following the entity when the user hovers the mouse pointer over the title 310 of the entity or the follow-entity icon. [0065] The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” Fig. 3C discloses the use of a hover card, which can only be implemented in real time. A user is required to interact with the system and as the user hovers over an icon, a recommendation is presented to the user through a GUI in near real time.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Comito and Napper. Comito teaches a system that is able to evaluate posts from an online social network and group or cluster topics disclosed in posts. Napper teaches a recommendation system which is designed for online social networks. One of ordinary skill would have motivation to combine a system which is able to embed text and cluster topics within the text with a recommendations system able to use multiple sources to produce an entity prediction for a user of an online social network, “Implementations presented herein facilitate that users follow or subscribe to other users and other social objects (e.g., entity pages, communities) and ensure that users have interesting and engaging content in their stream. Recommendations are suggested to users of the social network to follow a specific user or other social object based on some indication that users have an interest in the user or social object. This allows the social graph in the social network to grow, improving the user experience of the system. [0036] Implementations are described below with reference to providing recommendations for following entity pages, but the same principles may be applied for following people in the social network based on the content of items viewed by the user.” (Napper, Detailed Description, pp. 2, [0035]-[0036]).
Regarding claim 3, Comito discloses, “wherein clustering the posts further comprises: creating an embedding for each post;” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” The system in this article will be able to evaluate a post from a user and embed that data in the
s
f
v
tuple.)
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” This system will use the generated tuple
s
f
v
, which contains information about the post generated by the user. This system will initially take the post data and imbed it into the system, then the system will compress the post and other information into a sim tuple. This teaches that the initial post is embedded into
s
f
v
to then use it in the smaller sim tuple.)
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” (Preliminaries, pp. 194; “The centroid of a cluster C is a tuple
C
C
=
(
c
,
t
0
,
f
v
c
,
f
f
,
s
f
v
c
)
, where c is the cluster label,
t
0
is the creation time of the cluster, tc is the time stamp of the last time a social object was added to C,
f
v
c
and
s
f
v
c
are the textual and semantic feature vectors, respectively, analogous to the ones defined for the social medial post smp, and
f
f
=
(
f
w
u
,
f
w
b
,
f
h
u
,
f
h
b
,
f
m
u
,
f
m
b
)
is the list of frequencies corresponding to
f
v
c
.” This system is able to use the clustering algorithm to further store and use the data embedded from the users posting. This information is used to generate new clusters of topics or place the post in an existing cluster.)
Regarding claim 4, Napper discloses, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near realtime.” (Detailed Description, pp. 4, [0065]; “The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” This system uses a hover card which requires the user to hover their mouse pointer over an icon to be presented with information in real time. This system requires the user to interact with the system and the system to react to the user in real or near real time.) and (Detailed Description, pp. 4, [0067]; “In some implementations, the recommendation is provided when the user returns to the social network after clicking on an article and visiting the webpage of the article. Since the user was interested in seeing the article, a recommendation for the entity associated with the article is appropriate.” Further, this system can provide a recommendation when a user returns from a given website. This would teach a system, able to monitor a user and interact with the user, live, during a web viewing session.)
Regarding claim 5, Comito discloses, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” (Algorithm
W
E
C
, pp. 195; The different clusters contain different topics posted by users. This algorithm discloses the process of evaluating social media posts from users. The set of clusters, which is interpreted as a data structure like a table or map, is saved after being generated and the set can be iterated through in Lines 14-19)
Regarding claim 9, Comito discloses, “wherein the additional post is detected subsequent to the user adding the post to a user feed,” (Word Embedding Based Clustering, pp. 195; “These steps are repeated until the algorithm receives new posts. To update a centroid
C
C
=
(
c
,
t
0
,
t
c
,
s
g
n
)
when a new post
s
m
p
=
(
i
d
,
u
,
t
,
l
,
f
v
,
s
f
v
)
is added to any cluster C, the time stamp of C is updated with the time stamp t of smp. Then all the items of each feature must be checked if already present in the centroid. Thus, the intersection between the feature vectors of smp and that of CC are computed. Then, for each feature smp.
f
v
(
i
)
of the post, if an element of this feature already appears in the feature vector of the centroid
f
v
c
(
i
)
, the corresponding frequency must be incremented by 1, otherwise, it will be added to the centroid feature and its frequency is set to 1.” The system in this article is able to monitor for additional posts made in a group. The number of clusters is designed to grow with the number of posts made.)
Comito fails to explicitly disclose the following limitations:
“wherein the group recommendation is presented in response to determining that the user added the post to the user feed.”
However, Napper discloses, “wherein the group recommendation is presented in response to determining that the user added the post to the user feed.” (Detailed Description, pp. 4, [0062]; “In some implementations, the recommendation for following the entity is presented after the user has interacted with the content, such as by clicking on the website to read the article. In other implementations, the recommendation is presented without requiring user interaction, as the user simply viewing the content represents an expression of interest by the user, or at least by a friend of the user.” This system is able to provide recommendations in real time after the user interacts with another user or group. This can include when a user or other users in a system generate a post.)
Regarding claim 11, Comito discloses, “clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding. Therefore, the similarity will be the combination of two measures accounting both syntactic as well as semantics of the posts." This article discloses a clustering algorithm that is able to evaluate user's social media content. This will be able to identify different topics and interests in posts made by users in a social network.)
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” (Preliminaries, pp. 194; "To store summary information of all the posts assigned to a cluster C, we introduce the cluster centroid as a compact data structure. The centroid keeps the textual items as in the feature vector
f
v
of each social media post smp assigned to the cluster, its corresponding semantic vector
s
f
v
, together with their frequencies and their temporal evolution." The system in this article is able to generate different clusters containing similar posts and topics. The system in this article is able to map topics to clusters or groups.)
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” (Word Embedding Based Clustering, pp. 195; “While a new post
s
m
p
i
arrives at time
t
i
, the algorithm builds the representation
f
v
i
of
s
m
p
i
and its word embedding
s
f
v
i
, and computes both the semantic similarity and the syntactic one between the post and the clusters active at the time stamp
t
i
, while the inactive clusters are removed from the set of clusters. Let
C
c
be the cluster whose centroid has maximum similarity with
s
m
p
i
. If this similarity value
s
i
m
(
s
m
p
i
,
C
c
)
is lower than ϵ, a new cluster is generated from
f
v
i
and
s
f
v
i
and added to the set of clusters, otherwise the social media post in the form of
f
v
i
and
s
f
v
i
is added to
C
c
by updating the centroid.” The system in this article is able to save the created clusters and use them later. The generated clusters can be used to map different topics to clusters or groups.)
“detecting an additional post entered by a user associated with the online service;” (Word Embedding Based Clustering, pp. 195; "These steps are repeated until the algorithm receives new posts." The system in this article is able to detect when a user makes consecutive postings to a social network group.)
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding." This system is able to evaluate newly posted data and see if it aligns with an already generated cluster.)
Comito fails to disclose the following limitations:
“a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:”
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;”
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and”
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.”
However, Napper discloses, “a memory comprising instructions; and one or more computer processors, wherein the instructions, when executed by the one or more computer processors, cause the system to perform operations comprising:” (Detailed Description, pp. 7, [0154]; “FIG. 9B is a simplified schematic diagram of a computer system for executing implementations described herein. It should be appreciated that the methods described herein may be performed with a digital processing system (e.g., a conventional, general-purpose computer system). Special purpose computers, which are designed or programmed to perform only one function, may be used in the alternative. The computing device 950 includes a processor 954, which is coupled through a bus to memory 956, permanent storage 958, and Input/Output (I/O) interface 960.” This system is designed to provide users with recommendations to groups or pages using general computing systems. As stated above, the system contains processors which are coupled to memory which store computer instructions, i.e. the method presented)
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” (Detailed Description, pp. 6, [0108]-[0112]; “The followings operations are performed by the entity mapping system: [0109] 1. The entity to entity page mapping system takes input from a number of sources (e.g., Wikipedia) and generates a set of candidate entity to entity page mappings. [0110] 2. The candidate mappings are verified 612 for accuracy. [0111] 3. Mappings that meet the accuracy requirements are triplified and loaded into knowledge graph 608. [0112] 4. The results of the mapping verification are fed back into the entity to entity page mapping system as training data, in order to improve the accuracy of the candidate mapping generation over time.” This applicant discloses a post and entity mapping system that is able to classify text posts. The results of the inference are used to generate training data for the different models in this system.)
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” (Detailed Description, pp. 6, [0105]; “The entity page mapper 610 may utilize different sources of information (602, 604, and 606) for entity mapping. The sources may include metaweb freebase topic tables for entity data, entity page recommendation pipeline for entity page annotations and entity page metadata, data from associated Wikipedia entries, etc.” This proposed system will present the user with a group recommendation based on different sources from different sources. As stated above, this system will use topic tables to produce recommendations.)
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” (Detailed Description, pp. 4, [0062]; “In some implementations, the recommendation for following the entity is presented after the user has interacted with the content, such as by clicking on the website to read the article. In other implementations, the recommendation is presented without requiring user interaction, as the user simply viewing the content represents an expression of interest by the user, or at least by a friend of the user.” This system is able to provide a user of a social network with a recommendation after the user has interacted with content. This is used by the recommendation system to produce a result for the user.) and (Detailed Description, pp. 4, [0064]-[0065]; “FIG. 3C illustrates a hover car providing additional information about the identify entity page, according to some implementations. In some implementations, the Graphical User Interface (GUI) displays an interface for following the entity when the user hovers the mouse pointer over the title 310 of the entity or the follow-entity icon. [0065] The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” Fig. 3C discloses the use of a hover card, which can only be implemented in real time. A user is required to interact with the system and as the user hovers over an icon, a recommendation is presented to the user through a GUI in near real time.)
Regarding claim 13, Comito discloses, “wherein clustering the posts further comprises: creating an embedding for each post;” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” The system in this article will be able to evaluate a post from a user and embed that data in the
s
f
v
tuple.)
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” This system will use the generated tuple
s
f
v
, which contains information about the post generated by the user. This system will initially take the post data and imbed it into the system, then the system will compress the post and other information into a sim tuple. This teaches that the initial post is embedded into
s
f
v
to then use it in the smaller sim tuple.)
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” (Preliminaries, pp. 194; “The centroid of a cluster C is a tuple
C
C
=
(
c
,
t
0
,
f
v
c
,
f
f
,
s
f
v
c
)
, where c is the cluster label,
t
0
is the creation time of the cluster, tc is the time stamp of the last time a social object was added to C,
f
v
c
and
s
f
v
c
are the textual and semantic feature vectors, respectively, analogous to the ones defined for the social medial post smp, and
f
f
=
(
f
w
u
,
f
w
b
,
f
h
u
,
f
h
b
,
f
m
u
,
f
m
b
)
is the list of frequencies corresponding to
f
v
c
.” This system is able to use the clustering algorithm to further store and use the data embedded from the users posting. This information is used to generate new clusters of topics or place the post in an existing cluster.)
Regarding claim 14, Napper discloses, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” (Detailed Description, pp. 4, [0065]; “The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” This system uses a hover card which requires the user to hover their mouse pointer over an icon to be presented with information in real time. This system requires the user to interact with the system and the system to react to the user in real or near real time.) and (Detailed Description, pp. 4, [0067]; “In some implementations, the recommendation is provided when the user returns to the social network after clicking on an article and visiting the webpage of the article. Since the user was interested in seeing the article, a recommendation for the entity associated with the article is appropriate.” Further, this system can provide a recommendation when a user returns from a given website. This would teach a system, able to monitor a user and interact with the user, live, during a web viewing session.)
Regarding claim 15, Comito discloses, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” (Algorithm
W
E
C
, pp. 195; The different clusters contain different topics posted by users. This algorithm discloses the process of evaluating social media posts from users. The set of clusters, which is interpreted as a data structure like a table or map, is saved after being generated and the set can be iterated through in Lines 14-19)
Regarding claim 16, Comito discloses, “clustering posts by associating a topic identifier with each post based on text in the post, the posts having been posted in groups associated with an online service;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding. Therefore, the similarity will be the combination of two measures accounting both syntactic as well as semantics of the posts." This article discloses a clustering algorithm that is able to evaluate user's social media content. This will be able to identify different topics and interests in posts made by users in a social network.)
“mapping each of the groups to one of the topic identifiers based on topics associated with the posts;” (Preliminaries, pp. 194; "To store summary information of all the posts assigned to a cluster C, we introduce the cluster centroid as a compact data structure. The centroid keeps the textual items as in the feature vector
f
v
of each social media post smp assigned to the cluster, its corresponding semantic vector
s
f
v
, together with their frequencies and their temporal evolution." The system in this article is able to generate different clusters containing similar posts and topics. The system in this article is able to map topics to clusters or groups.)
“creating a topic-to-group table mapping each of the topic identifiers to one or more of the groups;” (Word Embedding Based Clustering, pp. 195; “While a new post
s
m
p
i
arrives at time
t
i
, the algorithm builds the representation
f
v
i
of
s
m
p
i
and its word embedding
s
f
v
i
, and computes both the semantic similarity and the syntactic one between the post and the clusters active at the time stamp
t
i
, while the inactive clusters are removed from the set of clusters. Let
C
c
be the cluster whose centroid has maximum similarity with
s
m
p
i
. If this similarity value
s
i
m
(
s
m
p
i
,
C
c
)
is lower than ϵ, a new cluster is generated from
f
v
i
and
s
f
v
i
and added to the set of clusters, otherwise the social media post in the form of
f
v
i
and
s
f
v
i
is added to
C
c
by updating the centroid.” The system in this article is able to save the created clusters and use them later. The generated clusters can be used to map different topics to clusters or groups.)
“detecting an additional post entered by a user associated with the online service;” (Word Embedding Based Clustering, pp. 195; "These steps are repeated until the algorithm receives new posts." The system in this article is able to detect when a user makes consecutive postings to a social network group.)
“determining, by the post classifier model, a topic identifier for the additional post based on text of the additional post;” (Preliminaries, pp. 194; "A post is assigned to a cluster if it is similar to the cluster centroid. The similarity is based both on the lexicon used in the posts and on their semantics obtained by exploiting word embedding." This system is able to evaluate newly posted data and see if it aligns with an already generated cluster.)
Comito fails to disclose the following limitations:
“A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:”
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;”
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and”
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.”
However, Napper discloses, “A non-transitory machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:” (Detailed Description, pp. 8, [0157]; “Implementations can be fabricated as computer readable code on a non-transitory computer readable storage medium. The non-transitory computer readable storage medium holds data which can be read by a computer system. Examples of the non-transitory computer readable storage medium include permanent storage 958, network attached storage (NAS), read-only memory or random-access memory in memory module 956, Compact Discs (CD), Blu-Ray™ discs, flash drives, hard drives, magnetic tapes, and other data storage devices. The non-transitory computer readable storage medium may be distributed over a network-coupled computer system so that the computer readable code is stored and executed in a distributed fashion.” This system utilizes a general computing system which contains processing systems, memory, and non-transitory memory such as permanent storage.)
“training a post classifier model with a training set comprising the text of the posts and the topic identifier associated with each post;” (Detailed Description, pp. 6, [0108]-[0112]; “The followings operations are performed by the entity mapping system: [0109] 1. The entity to entity page mapping system takes input from a number of sources (e.g., Wikipedia) and generates a set of candidate entity to entity page mappings. [0110] 2. The candidate mappings are verified 612 for accuracy. [0111] 3. Mappings that meet the accuracy requirements are triplified and loaded into knowledge graph 608. [0112] 4. The results of the mapping verification are fed back into the entity to entity page mapping system as training data, in order to improve the accuracy of the candidate mapping generation over time.” This applicant discloses a post and entity mapping system that is able to classify text posts. The results of the inference are used to generate training data for the different models in this system.)
“determining a group recommendation for posting the additional post based on the topic identifier for the additional post and the topic-to-group table; and” (Detailed Description, pp. 6, [0105]; “The entity page mapper 610 may utilize different sources of information (602, 604, and 606) for entity mapping. The sources may include metaweb freebase topic tables for entity data, entity page recommendation pipeline for entity page annotations and entity page metadata, data from associated Wikipedia entries, etc.” This proposed system will present the user with a group recommendation based on different sources from different sources. As stated above, this system will use topic tables to produce recommendations.)
“causing presentation, based on the determining, of the group recommendation to the user for posting the additional post in the recommended group in real-time within a confirmation user interface element acknowledging submission of the additional post.” (Detailed Description, pp. 4, [0062]; “In some implementations, the recommendation for following the entity is presented after the user has interacted with the content, such as by clicking on the website to read the article. In other implementations, the recommendation is presented without requiring user interaction, as the user simply viewing the content represents an expression of interest by the user, or at least by a friend of the user.” This system is able to provide a user of a social network with a recommendation after the user has interacted with content. This is used by the recommendation system to produce a result for the user.) and (Detailed Description, pp. 4, [0064]-[0065]; “FIG. 3C illustrates a hover car providing additional information about the identify entity page, according to some implementations. In some implementations, the Graphical User Interface (GUI) displays an interface for following the entity when the user hovers the mouse pointer over the title 310 of the entity or the follow-entity icon. [0065] The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” Fig. 3C discloses the use of a hover card, which can only be implemented in real time. A user is required to interact with the system and as the user hovers over an icon, a recommendation is presented to the user through a GUI in near real time.)
Regarding claim 18, Comito discloses, “wherein clustering the posts further comprises: creating an embedding for each post;” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” The system in this article will be able to evaluate a post form a user and embed that data in the
s
f
v
tuple.)
“generating reduced embeddings for the posts with a smaller dimension from the created embeddings; and” (Preliminaries, pp. 194; “A social media post smp is defined as a tuple
s
i
m
=
(
i
d
,
t
,
f
v
,
s
f
v
)
where id is the post identifier, t is the time at which the post has been published,
f
v
=
(
w
u
,
w
b
,
h
u
,
h
b
,
m
u
,
m
b
)
is a vector of textual features extracted from the post, representing words, unigram
w
u
and bigram
w
b
, hashtags, unigram
h
u
and bigram
h
b
, mentions, unigram
m
u
and bigram
m
b
,
s
f
v
=
(
e
w
u
,
e
w
b
,
e
h
u
,
e
h
b
,
e
m
u
,
e
m
b
)
is the semantic feature vector corresponding to fv.” This system will use the generated tuple
s
f
v
, which contains information about the post generated by the user. This system will initially take the post data and imbed it into the system, then the system will compress the post and other information into a sim tuple. This teaches that the initial post is embedded into
s
f
v
to then use it in the smaller sim tuple.)
“utilizing a clustering algorithm on the reduced embeddings to generate a plurality of topic identifiers.” (Preliminaries, pp. 194; “The centroid of a cluster C is a tuple
C
C
=
(
c
,
t
0
,
f
v
c
,
f
f
,
s
f
v
c
)
, where c is the cluster label,
t
0
is the creation time of the cluster, tc is the time stamp of the last time a social object was added to C,
f
v
c
and
s
f
v
c
are the textual and semantic feature vectors, respectively, analogous to the ones defined for the social medial post smp, and
f
f
=
(
f
w
u
,
f
w
b
,
f
h
u
,
f
h
b
,
f
m
u
,
f
m
b
)
is the list of frequencies corresponding to
f
v
c
.” This system is able to use the clustering algorithm to further store and use the data embedded from the users posting. This information is used to generate new clusters of topics or place the post in an existing cluster.)
Regarding claim 19, Napper discloses, “wherein determining the post classifier by the post classifier model enables generating the group recommendation in real-time or near real-time.” (Detailed Description, pp. 4, [0065]; “The interface is referred to as hover card 312 and includes information about the entity (e.g., name, title, short description, etc.) and a button 312 to start following the entity. When the user clicks on button 312, then the user is linked in the social network with the entity.” This system uses a hover card which requires the user to hover their mouse pointer over an icon to be presented with information in real time. This system requires the user to interact with the system and the system to react to the user in real or near real time.) and (Detailed Description, pp. 4, [0067]; “In some implementations, the recommendation is provided when the user returns to the social network after clicking on an article and visiting the webpage of the article. Since the user was interested in seeing the article, a recommendation for the entity associated with the article is appropriate.” Further, this system can provide a recommendation when a user returns from a given website. This would teach a system, able to monitor a user and interact with the user, live, during a web viewing session.)
Regarding claim 20, Comito discloses, “wherein determining the group recommendation further comprises: accessing the topic-to-group table to determine entries with the topic identifier.” (Algorithm
W
E
C
, pp. 195; The different clusters contain different topics posted by users. This algorithm discloses the process of evaluating social media posts from users. The set of clusters, which is interpreted as a data structure like a table or map, is saved after being generated and the set can be iterated through in Lines 14-19)
Claims 2, 6-8, 12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Comito and Napper in view of Miao et al, (Miao et al, “RECOMMENDATIONS FOR ONLINE SYSTEM GROUPS”, US 2018/0322122 A1, Filed 2017, hereinafter “Miao”).
Regarding claim 2, Miao discloses, “determining if the user belongs to the recommended group; and” (System Architecture, pp. 3, [0031]; "The group store 230 stores objects that each represents a group on the online system 140. Groups include one or more users and can have one or more characteristics." And "A user becomes included in a group after the user joins the group, and a single user can join a plurality of groups. The plurality of groups that a particular user has joined is referred to as "the user's groups," "the user's associated groups," or "groups connected to the user." This system has a "group store" which contains information about the groups and users in the network. This store is able to evaluate and determine if a user is designed as part of that group or not a member)
“causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” (System Architecture, pp. 7, [0059]; "The group recommendation module 240 selects 308 one or more candidate groups to be displayed to the user. For example, the group recommendation module 240 selects every candidate group above a threshold position in the ranking. After selecting 308 the candidate groups, the group recommendation module 240 sends 310 recommendations to the target user to join the selected groups." To generate recommendations the system will rely on data from the different modules including the "group store". This group store contains information about the users including what groups they have already joined. When evaluating the recommendation score used to rank the groups, the group recommendation module will use data from the different modules including the group store.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Comito, Napper, and Miao. Comito teaches a system that is able to evaluate posts from an online social network and group or cluster topics disclosed in posts. Napper teaches a recommendation system which is designed for online social networks. Miao teaches a system that is able to make group recommendations to a user on an online social network. One of ordinary skill would have motivation to combine a text or post classification and clustering system which is able to evaluate text and identify different topics, with systems able to use clustered text topics and use them to produce recommendations in online social networks, , “To increase user engagement with the online system in general and with groups in particular, the online system may provide a feature that generates group recommendations for users. One way to implement a group recommendation feature is for the online system to generate a score between a target user and each group maintained by the online system, where the score represents the likelihood that the target user will join the group if presented with a recommendation to join the group. The online system can then display recommendations for the groups that received the highest scores. [0014] This method of providing group recommendations is infeasible for online systems with large numbers of groups and users. For example, a popular online system may have tens or hundreds of millions of groups, and generating a score for every group require an impractical amount of computing power, especially when the process is repeated for many users of the online system.” (Miao, Detailed Description, pp. 1, [0013]-[0014]).
Regarding claim 6, Miao discloses, “wherein determining the group recommendation further comprises: determining that the topic identifier is mapped to several group identifiers; and” (System Architecture, pp. 4, [0036]; “To identify topics associated with a group, the topic extraction engine 235 identifies content items associated with the group based on information included in the group store 230 and determines topics associated with content items associated with the group based on anchor terms included in the content items as described above.” The system in this application is able to use a topic extraction engine able to evaluate text and posts. This will be able to associate different topics to different groups based on information.)
“selecting the recommended group at random from the several group identifiers.” (System Architecture, pp. 6, [0049]; “The group recommendation module 240 can implement several different types of sourcing rules, and the set of sourcing rules 404 that the group recommendation module 240 applies to identify the plurality of candidate groups 406 can include any combination of one or more types of sourcing rules.” The group recommendation module has the ability to alter groups being recommended using different sourcing rules. This system would be able add more sourcing rules as stated and combine rules.) And Miao (System Architecture, pp. 7, [0058]; “In some embodiments, the group recommendation module 240 modifies the ranking of the candidate groups based on one or more diversity rules. A diversity rule prevents candidate groups having a common characteristic from appearing in consecutive positions in the ranking, which allows for a more diverse set of groups to be selected 308 and recommended 310 to the user.” Further the system in this application is able to use diversity rules to recommend groups. This teaches that the system can alter and change the output depending on sourcing rules and diversity rules. In combination this can generate recommendations of different dimensions and requirements, and an ordinary skilled person of the art would be able to implement a rule similar to selecting at random.)
Regarding claim 7, Comito discloses, “wherein determining the group recommendation further comprises: determining an interest of the additional post; and” (Word Embedding Based Clustering, pp. 195; “These steps are repeated until the algorithm receives new posts. To update a centroid
C
C
=
(
c
,
t
0
,
t
c
,
s
g
n
)
when a new post
s
m
p
=
(
i
d
,
u
,
t
,
l
,
f
v
,
s
f
v
)
is added to any cluster C, the time stamp of C is updated with the time stamp t of smp. Then all the items of each feature must be checked if already present in the centroid. Thus, the intersection between the feature vectors of smp and that of CC are computed. Then, for each feature smp.
f
v
(
i
)
of the post, if an element of this feature already appears in the feature vector of the centroid
f
v
c
(
i
)
, the corresponding frequency must be incremented by 1, otherwise, it will be added to the centroid feature, and its frequency is set to 1.” The system in this article is able to evaluate additional posts. After a post is made, the system will attempt to identify the topics disclosed in the post using a clustering method.)
Comito fails to explicitly disclose the following limitations:
“filtering the group for being recommended based on the interest of the post.”
However, Miao discloses, “filtering the group for being recommended based on the interest of the post.” (System Architecture, pp. 7, [0059]; “The group recommendation module 240 selects 308 one or more candidate groups to be displayed to the user. For example, the group recommendation module 240 selects every candidate group above a threshold position in the ranking. After selecting 308 the candidate groups, the group recommendation module 240 sends 310 recommendations to the target user to join the selected groups.” The group recommendation module is able to apply rules to the recommended groups including sourcing and diversity rules. This process also uses a threshold that is required in a ranked structure. This teaches many different forms of filtering techniques.)
Regarding claim 8, Comito discloses, “wherein filtering the group for being recommended further comprises: determining a percentage of posts in the group associated with the determined interest; and” (Word Embedding Based Clustering, pp. 195; “These steps are repeated until the algorithm receives new posts. To update a centroid
C
C
=
(
c
,
t
0
,
t
c
,
s
g
n
)
when a new post
s
m
p
=
(
i
d
,
u
,
t
,
l
,
f
v
,
s
f
v
)
is added to any cluster C, the time stamp of C is updated with the time stamp t of smp. Then all the items of each feature must be checked if already present in the centroid. Thus, the intersection between the feature vectors of smp and that of CC are computed. Then, for each feature smp.
f
v
(
i
)
of the post, if an element of this feature already appears in the feature vector of the centroid
f
v
c
(
i
)
, the corresponding frequency must be incremented by 1, otherwise, it will be added to the centroid feature and its frequency is set to 1.” The system in this article is able to cluster topics based on different elements of a post. This system is able to keep track of the number of topics in a cluster using a frequency counter. This would teach the number of topics is associated to a number of groups or clusters.)
Comito fails to explicitly disclose the following limitations:
“determining that the group is recommended when the percentage of posts in the group is above a predetermined threshold.”
However, Miao discloses, “determining that the group is recommended when the percentage of posts in the group is above a predetermined threshold.” (Recommending Groups to a User, pp. 6, [0053]; “A third type of sourcing rule identifies a group as a candidate group if the group is especially active or popular. The level of activity in a group can be quantified by computing an activity score based on the number of actions associated with the group that were taken in a preceding time period (e.g., the preceding 24 hours, the preceding 7 days). Actions associated with the group can include, for example, the posting of a content item to the group and an action taken toward a content item posted to the group (e.g., adding a comment to the content item, expressing a preference for the content item, or sharing the content item).” This system is able to recommend popular or active groups. An active group would contain members making regular comments on certain subjects. The teaches a system able to recommend groups based a popularity threshold using an activity score over a period of time.)
Regarding claim 12, Miao discloses, “wherein the instructions further cause the one or more computer processors to perform operations comprising: determining if the user belongs to the recommended group; and” (System Architecture, pp. 3, [0031]; "The group store 230 stores objects that each represents a group on the online system 140. Groups include one or more users and can have one or more characteristics." And "A user becomes included in a group after the user joins the group, and a single user can join a plurality of groups. The plurality of groups that a particular user has joined is referred to as "the user's groups," "the user's associated groups," or "groups connected to the user." This system has a "group store" which contains information about the groups and users in the network. This store is able to evaluate and determine if a user is designed as part of that group or not a member)
“causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” (System Architecture, pp. 7, [0059]; "The group recommendation module 240 selects 308 one or more candidate groups to be displayed to the user. For example, the group recommendation module 240 selects every candidate group above a threshold position in the ranking. After selecting 308 the candidate groups, the group recommendation module 240 sends 310 recommendations to the target user to join the selected groups." To generate recommendations the system will rely on data from the different modules including the "group store". This group store contains information about the users including what groups they have already joined. When evaluating the recommendation score used to rank the groups, the group recommendation module will use data from the different modules including the group store.)
Regarding claim 17, Miao discloses, “wherein the machine further performs operations comprising: determining if the user belongs to the recommended group; and” (System Architecture, pp. 3, [0031]; "The group store 230 stores objects that each represents a group on the online system 140. Groups include one or more users and can have one or more characteristics." And "A user becomes included in a group after the user joins the group, and a single user can join a plurality of groups. The plurality of groups that a particular user has joined is referred to as "the user's groups," "the user's associated groups," or "groups connected to the user." This system has a "group store" which contains information about the groups and users in the network. This store is able to evaluate and determine if a user is designed as part of that group or not a member)
“causing presentation of a recommendation to the user to join the group when the user does not belong to the group.” (System Architecture, pp. 7, [0059]; "The group recommendation module 240 selects 308 one or more candidate groups to be displayed to the user. For example, the group recommendation module 240 selects every candidate group above a threshold position in the ranking. After selecting 308 the candidate groups, the group recommendation module 240 sends 310 recommendations to the target user to join the selected groups." To generate recommendations the system will rely on data from the different modules including the "group store". This group store contains information about the users including what groups they have already joined. When evaluating the recommendation score used to rank the groups, the group recommendation module will use data from the different modules including the group store.)
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Comito and Napper in view of Lambert et al, (Lambert et al, “Managing Digital Messages Across a Plurality of Social Networking Groups”, US 2018/0131660 A1, Filed, 2016, hereinafter “Lambert”).
Regarding claim 10, Lambert discloses, “posting the additional post in the recommended group after the user accepts the group recommendation.” (Detailed Description, pp. 14, [0137]; "Based on user interaction with the modification elements 442b-442d the digital multi-group messaging system 100 can modify a digital message posted across a plurality of social networking groups. For example, upon user interaction with the edit post element 442b, the digital multi-group messaging system 100 can receive modifications to digital message content (e.g., a change to a digital image, digital video, or digital text). Moreover, the digital multi-group messaging system 100 can automatically post the modified digital message to a plurality of social networking groups (e.g., the "West Town Camping Market" social networking group, the "North Cali OutdoorTraders" social networking group, and the "San Francisco Tent Mart" social networking group)." The system in this application is able to automatically send messages to joined groups. It can also alter the message depending on the group it is posting in or to.) And (Detailed Description, pp. 15, [00146]; "Similarly, the digital multigroup messaging system 100 can manage private social networking groups that require a user to obtain permission from an administrator before posting joining the group and posting on the social networking group page." The proposed system in this application is able to recognize when the user needs to approve prior to posting or commenting in the group. After this achieved the system can automatically post in the group.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Comito, Napper, and Lambert. Comito teaches a system that is able to evaluate posts from an online social network and group or cluster topics disclosed in posts. Napper teaches a recommendation system which is designed for online social networks. Lambert teaches a system that is able to handle message automation of a user in an online social network. One of ordinary skill would have motivation to combine a group recommendation system that is able to send group recommendations to users based on topics and a system that is able to group or cluster topics from posts generated by a user with a system that is able to handle automated messages in an on line social network, "In addition, the disclosed systems and methods can further improve efficiency of various computing devices utilized to manage digital messages across social networking groups. Indeed, the disclosed systems and methods can store a digital message and post the digital message in multiple social networking group pages by reference to the stored digital message. In this manner, the disclosed systems and methods can reduce duplicative, unnecessary storage and processing of multiple copies of a digital message." (Summary, pp. 1, [0008])
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure:
“BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding” (Delvin et al, 2019) teaches a machine model that is able to process user request in real time and provide a response. This article teaches real time text processing of a user and generation of response or actions based on the text processing.
“A Comprehensive Survey of Clustering Algorithms” (Xu et al, 2015) teaches different cluster algorithms commonly used and compares the different models.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL MICHAEL GALVIN-SIEBENALER whose telephone number is (571)272-1257. The examiner can normally be reached Monday - Friday 8AM to 5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at (571) 270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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