Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is a Final Office Action in response to communications received on 11/12/25.
Claims 1-5, 7-9, 11-15, and 17-19 have been amended.
Therefore, Claims 1-20 are now pending and have been addressed 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 a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-11 are directed to a method, claims 17-20 are directed to a non-transitory medium and claims 12-16 are directed to a system. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 12 and 17 recite methods for group based communication that receiving from a first user account, interaction data representing interactions between the first user account and at least one of other user accounts or channels; receiving, from third-party service provider, third-party data associated with the first user account; generating a training data set including training data associated with user accounts, a plurality of channels, and communications associated with the user accounts and the plurality of channels, providing the interaction data and third party data as an input to model; generating, first data comprising one or more representative channels and second data comprising one or more representative users; causing the first data comprising the one or more representative channels and the second data comprising the one or more representative users to be associated with profile data associated with the first user account, presenting the first data and the second data to a second user account.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) including interaction between person and computer), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as one or more processors; and one or more non-transitory computer-readable media, a group-based communication platform, a machine-learning model, training using at least one of a supervised training process or an unsupervised training process, a machine learning model, a user interface), the claims are directed to providing suggestion for channel or users in group based communication data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing suggestion. In particular, the claims only recites the additional element – one or more processors; and one or more non-transitory computer-readable media, a group-based communication platform, a machine-learning model, training using at least one of a supervised training process or an unsupervised training process, a machine learning model, a user interface. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements of using a user interface to receive and display data associated with the abstract idea, is merely an example of generally linking the abstract idea to a particular technological environment or field of use as outlined in MPEP 2106.05(h). User interfaces are recited so generally that they do not meaningfully limit the abstract idea. Further, the limitation of “training using at least one of a supervised training process or an unsupervised training process /providing input to the machine learning system; generating by the machine learning model” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In addition, limitations reciting data gathering such as “receiving from a first user account interaction data….receiving third party data associated with user account“ are insignificant pre-solution activity that merely gather data and, therefore, do not integrate the exception into a practical application for that additional reason. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en bane), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371-72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); GIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)). Also, the limitations reciting “presenting the first data and second data to a second user account.; is merely a post-solution step of presenting/displaying data output—a nominal addition to the claim that does not meaningfully limit the claim. Therefore, this limitation is an insignificant extra-solution activity. See MPEP 2106.05(g). These limitations are further analyzed under Step 2B below.
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; providing suggestion for channel or users in group based communication. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to the one or more processors; and one or more non-transitory computer-readable media, a group-based communication platform, a machine-learning model, a user interface these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0026] details “ the server(s) 102 can include one or more processors 132, computer-readable media 110, one or more communication interfaces 112, and/or input/output devices 114. [0027]The processor(s) 132 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units (CPUs), graphics processing units (GPUs), state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. [0028]The computer-readable media 110 can include volatile and nonvolatile memory and/or removable and non-removable media implemented in any type of technology for storage of data. [0044] the machine-learning model(s) 130 may include Generative Pre-trained Transformer 3 (GPT-3) model, a neural model for summarization, such as an abstractive or a generative summarization model, natural language processing, machine learning, and/or other techniques ”. These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data. As discussed in Step 2A, Prong Two above, the recitations of “receiving steps” and “presenting steps” amount to receiving or transmitting/displaying data over a network and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claims do not amount to significantly more than the abstract idea itself.
Dependent claims 2-11, 13-16, and 18-20 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as Independent claims.
Claim 2, 4 recites wherein the machine-learning model is trained based on: (i) third data that includes prior interaction data including data representing interactions between prior channels and prior user accounts; and (ii) fourth data that includes prior representative channels and representative users associated with the prior interaction data, to learn relationships between the third data and fourth data, such that the machine-learning model is configured to use the learned relationship to generate the first data and the second data upon input of the interaction data. receiving, from the machine-learning model, a confidence score associated with individual channels of the representative channels; determining an order for presenting the representative channels based on the confidence score; and presenting, via the user interface associated with the group-based communication platform, the representative channels based on the order. These limitations further define the machine learning model and model is still recited at high level of generality. . Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 3, 5-6, 10, 13-16, 18-20 recites wherein the interaction data includes at least one of: a reaction to a message; a link associated with a message; a number of replies associated with a channel; a number of views associated with a channel; or an attachment within a channel; providing a keyword or key phrase as the input to the machine-learning model; generating, by the machine-learning model and based at least in part on the input, third data representing a frequently discussed topic; and causing the third data to be associated with profile data associated with the first user account; wherein generating the first data and the second data to be associated with the first user account is based at least in part on a maximum number of representative channels and a maximum number of representative users associated with the first user account; wherein presenting the first data and the second data to the second user account is based at least in part on a permissions level associated with the first user account and the second user account; receiving, from a third user account, a request to view the profile data associated with the first user account; and presenting, via the user interface associated with the communication platform, the first data and the second data to the third user account based at least in part on the interaction data representing interactions between the first user account and the third user account. These limitations further narrow the abstract idea of independent claims by defining type of interaction data. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 7, 8-9, 11 recites receiving, from the first user account, a request to modify the profile data associated with the first user account; generating, based at least in part on the request and the interaction data, a first list of representative channels/users that are unrepresented in the profile data associated with the first user account; receiving, from the first user account, a selection of one or more representative channels/users, the selection representing a subset of representative channels from the first list of representative channels; providing the selection of the subset of representative channels from the first list of representative channels /users and the interaction data as the input to the machine-learning model; generating, by the machine-learning model and based at least in part on the input and the interaction data, a second list of representative channels; and causing display of the subset of representative channels/users on the profile data associated with the first user account; wherein the first list of representative users includes users based at least in part on one of: a number of shared channels between the user and individual users; activity level data associated with individual users associated with the shared channels; user reply data associated with individual users; or a number of keywords or key phrases used by individual users; detecting a threshold number of a keyword or key phrase associated with the first user account; or a threshold period of time elapsing; and generating, by the machine-learning model and based at least in part on the occurrence of the event, third data representing additional one or more representative channels and fourth data representing additional one or more representative users associated with the group-based communication platform; and causing the third data and the fourth data to be associated with the profile data of the first user account. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are merely used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Grant (US 2021/0026903 A1) in view of Rogynskyy (US 2020/0372016 A1)
Regarding Claims 1, 12 and 17, Grant discloses the method/system/medium, implemented at least in part by one or more computing devices of a group-based communication platform (Abstract Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. [0196] generate, match, and/or facilitate interactions with a computer through internet messaging technologies), the method comprising:
Grant discloses receiving, from a first user account associated with the group-based communication platform, interaction data representing interactions between the first user account and at least one of other user accounts or channels associated with the group-based communication platform (Fig 3 #313, 321 determine responses (interaction data) associated with message, [0051] Responses associated with the message (interaction data) may be determined at 321. For example, responses to the message by other users may include reactions (e.g., emoji, liking), clicking on a link in the message, replying to the message, downloading a file associated with the message, sharing the message from one channel to another channel, pinning the message, starring the message, and/or the like., [0092] user engagement count log reactions The number of reactions on the message); third-party data associated with the first user account ([0053] A determination may be made at 325 whether attachments are included with the message. In various implementations, attachments may include files attached to the message, links (e.g., to webpages) in the message, files from third part providers (e.g., links to G Suite files, links to Dropbox files, [0057] If there are attachments, file indexing of the files associated with the message may be facilitated at 353. For example, file contents of the associated files (e.g., attached files, linked webpages, files from third part providers) may be used to index such files to facilitate searching., [0041] the message may include data such as a message identifier, user account details, a team identifier, a channel identifier, contents (e.g., text, emojis, images, links), attachments (e.g., files), message hierarchy data (e.g., the message may be a reply to another message), third party metadata, and/or the like.);
Grant discloses generating a training data set including training data associated with user accounts of the group-based communication platform, a plurality of channels of the group-based communication platform, and communications associated with the user accounts and the plurality of channels (([0036] The MSM may utilize message metadata to generate work graphs that capture relationships between users, between users and channels (communications between user and channels), between users and topics (data associated with user accounts), between channels (a plurality of channels), between topics, and/or the like. Work graph data may be used as machine learning (ML) structure inputs for training and/or utilizing ML structures (e.g., logistic regressions, neural networks, etc.). [0081] the inputs for the ML structure may be selected from data such as message content, file content, message metadata, work graph data, other generated ML structure input data (e.g., generated via another ML structure (e.g., team-level term priority), calculated via a statistical method such as tf-idf (e.g., team-level term frequency) or BM25), search term, and/or the like [0091] At training time, the input is a list of (user, message, time) tuples with label 1 if an engagement was logged and label 0);
Grant discloses training, using at least one of a supervised training process or an unsupervised training process, a machine-learning model using the training data set ([0036] The MSM may utilize message metadata to generate work graphs that capture relationships between users, between users and channels, between users and topics, between channels and topics, between channels, between topics, and/or the like. Work graph data may be used as machine learning (ML) structure inputs for training and/or utilizing ML structures (e.g., logistic regressions, neural networks, etc.). [0080] ML approaches such as unsupervised learning, supervised learning, reinforcement learning, deep learning, and/or the like may be used to generate the ML structure (e.g., using a machine learning package such as Spark ML, TensorFlow, etc.).[0458] train, via at least one processor, the machine learning structure using at least some of the work graph data stored in the work graph data structure ), the training of the machine-learning model causing the machine-learning model to be configured to recognize and extract keywords or key phrases from the communications ([0036] Work graph data may be used as machine learning (ML) structure inputs for training and/or utilizing ML structures (e.g., logistic regressions, neural networks, etc.). The MSM may utilize message metadata and/or ML structures to rank messages, people, channels, and/or the like for a variety of applications. [0038] The MSM server may facilitate indexing message contents and/or metadata (e.g., team, channel, user, topics, responses, files, third party metadata) in message indexing 120. If the user attached a file to the message, the MSM server may facilitate indexing file contents in file indexing 122. [0039] a variety of modules to analyze MSM messages and/or other data. In one implementation, such modules may include a work graph generating process (e.g., to generate work graphs (e.g., ML structure input data such as a channel's priority for the user)), a machine learning process (e.g., to generate other ML structure input data (e.g., team-level term priority), to generate ML structures (e.g., team-level neural networks)) [0062] A ML structure generating (MLSG) component 429 may utilize MSM messages, work graph data, other ML structure input data, and/or the like to generate ML structure input data (e.g., team-level term priority), ML structures (e.g., team-level neural networks), and/or the like to facilitate ranking, Fig 4 # 433 ML structure parameters; [0044] MSM message may include data such as a message identifier, a team identifier, a channel identifier, a sending user identifier, topics (keyword or phrase), responses, contents, attachments, message hierarchy data, third party metadata, conversation primitive data, and/or the like [0050] the message may be parsed (e.g., using PHP commands) to determine topics discussed in the message. For example, hashtags in the message may indicate topics associated with the message. In another example, the message may be analyzed (e.g., by itself, with other messages in a conversation primitive) using a machine learning technique, such as topic modeling, to determine topics associated with the message. [0091] At training time, the input is a list of (user, message, time) tuples with label 1 if an engagement was logged and label 0);
Grant discloses providing, after the machine-learning model has been trained using the training data set, the interaction data and third party metadata as input to the machine-learning model ([0080]a ML structure generating (MLSG) component for the MSM. In FIG. 7, a ML structure to generate (e.g., a neural network) may be determined at 701. the ML structure may be used for ranking (e.g., for message ranking, people ranking, channel ranking, and/or the like). In another embodiment, the ML structure may be used to generate ML input data (e.g., team-level term priority) that may be used as input to another ML structure (e.g., ML structure used for ranking)., [0081] Inputs for the ML structure may be determined at 705. In one implementation, the inputs for the ML structure may be selected from data such as message content, file content, message metadata (interaction data), work graph data, [0036] The MSM may utilize message metadata to generate work graphs that capture relationships between users, between users and channels, between users and topics, between channels and topics, between channels, between topics, and/or the like. Work graph data may be used as machine learning (ML) structure inputs for training and/or utilizing ML structures (e.g., logistic regressions, neural networks, etc.). [0041] the message may include data such as a message identifier, user account details, a team identifier, a channel identifier, contents (e.g., text, emojis, images, links), attachments (e.g., files), message hierarchy data (e.g., the message may be a reply to another message), third party metadata, and/or the like);
Grant discloses generating, by the machine-learning model and based at least in part on the input, first data comprising one or more representative channels ([0082] Output for the ML structure may be determined at 709. In one implementation, the output for the ML structure may be selected from ranking outputs (e.g., message rank, person rank, channel rank), [0086] the MLSG component may be utilized to generate a highlights model. The highlights model may be utilized to assign importance scores for a given user at a given time. For example, the highlights model may be used for features such as in-channel highlights, [0100] generate channel suggestions for user) and second data comprising one or more representative users associated with the group-based communication platform ([0087] in order to measure importance (e.g., of messages), the highlights model may utilize engagements as a proxy for importance. As such, a message is considered important if the user is highly likely to engage with it., [0089] a ML structure (e.g., a logistic regression classifier) may be generated for each engagement type utilized for ranking. This classifier may be utilized to approximate the probability of a user engaging with a given message at a given time for the given engagement type. [0100]ranking request may be generated to determine relevant people, [0144] FIG. 17, screen 1701 illustrates that a user may be shown a collapsed results teaser to inform the user that experts for a user specified search term (e.g., solr) have been found (e.g., based on topic expertise scores for topic solr).);
Grant discloses causing the first data comprising the one or more representative channels ([0099] The MSM server may send a ranking response 845 to the client. The ranking response may be used to provide the highest ranked messages, files, people, channels, and/or the like to the client., [0143] Screen 1610 illustrates channel suggestions that may be provided to the user when a user joins (associate with profile/account) a channel (e.g., based on interests of other people in the channel and data regarding the user). ) and the second data comprising the one or more representative users to be associated with profile data associated with the first user account (Fig 10 #1010 teams that Myles is a member of ‘lightroom”, [0089] a ML structure (e.g., a logistic regression classifier) may be generated for each engagement type utilized for ranking. This classifier may be utilized to approximate the probability of a user engaging with a given message at a given time for the given engagement type. [0090] For each classifier, the runtime (e.g., during ranking—see the RD component and FIG. 9) input is a (user, message, time) and the runtime output is a score that should be correlated with the probability of an engagement., [0098] A ranking determining (RD) component 841 may utilize ranking data (e.g., relevant messages, relevant files, ML structure input data) to rank the relevant messages and/or files, people, channels, and/or the like using ML structure(s)., [0232]-[0233] user account table, [0241] a message indexing table includes message user, message response ); and
Grant discloses presenting, via a user interface associated with the group-based communication platform, the first data and the second data to a second user account.([0099] The MSM server may send a ranking response 845 to the client. The ranking response may be used to provide the highest ranked messages, files, people, channels, and/or the like to the client. For example, the client may utilize the ranking response to display results (e.g., search results, a recap of a channel, channel suggestions) to the user. See FIGS. 12-16 for examples of results that may be displayed to the user., Fig 17A #1710shows expert users & channel suggestion for user)
Grant does not specifically teach receiving, from third-party service provider, third-party data associated with the first user account; providing, after the machine-learning model has been trained using the training data set, the third party data as input to the machine learning model
Rogynskyy teaches receiving, from third-party service provider, third-party data associated with the first user account ( [0003] determining a preferred communication channel based on determining a status of a node profile using electronic activities. The method may include accessing, by the one or more processors, a plurality of electronic activities. The electronic activities may be transmitted or received via electronic accounts associated with one or more data source providers. The method may include maintaining, by one or more processors, a plurality of node profiles. The node profiles may correspond to a plurality of unique entities [0516]the data processing system 9300 can receive electronic activity from a plurality of data source providers. In some embodiments, when the data processing system 9300 receives the electronic activity, the electronic activity can label or store the electronic activity in a database in association of the data source provider that provided the electronic activity.); the training of the machine-learning model causing the machine-learning model to be configured to recognize and extract keywords or key phrases from the communications ([0333] machine learning to determine whether to filter an electronic activity. Machine learning can refer to a training set of data that includes metadata for electronic activities that are to be approved or authorized [0361] The system 200 can detect, using keywords, machine learning or natural language processing, whether the electronic activity is indicative of a legitimate business interaction based on the volume, nature, content or context of the electronic activity or based on the number of electronic activities transmitted between the user associated with the data source provider and the contact ); providing, after the machine-learning model has been trained using the training data set, the third party data as input to the machine learning model ([0230] the matching model 340 used to link electronic activities to one or more record objects can be trained using machine learning or include a plurality of heuristics. For example, as described above the features extraction engine 310 can generate a feature vector for each electronic activity., [0232] To train the machine learning models, the system can scan one or more systems of record that include manually matched electronic activity and record objects. The previous manually matched data can be used as a training set for the machine learning models., [0236] using machine learning techniques to generate matching strategies in a supervised or unsupervised learning environments.); generating by the machine learning model, first data and second data ([0687] The data processing system 9300 can identify a communication channel 2834 from the parsed electronic activities 9305. A communication channel 2834 can include a mode of communication. The mode of communication can be based on the type of the electronic activity. [0453] the system 200 can be configured to train a machine learning model to match leads and salespersons)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included receiving, from third-party service provider, third-party data associated with the first user account; providing, after the machine-learning model has been trained using the training data set, the third party data as input to the machine learning model, as disclosed by Rogynskyy in the system disclosed by Grant, for the motivation of providing a method of using training set of data that includes metadata for electronic activities and words in the electronic activities are converted into vectors so that a machine learning model can be trained based on the content of the electronic activities ([0333] Rogynskyy)
Claim 12. Grant discloses the system comprising: one or more processors; and one or more non-transitory computer-readable media ([0197] computers employ processors to process information; such processors 2603 may be referred to as central processing units (CPU). One form of processor is referred to as a microprocessor. CPUs use communicative circuits to pass binary encoded signals acting as instructions to enable various operations. These instructions may be operational and/or data instructions containing and/or referencing other instructions and data in various processor accessible and operable areas of memory 2629) storing instructions that, when executed, cause the system to perform operations comprising
Claim 17. Grant discloses the one or more non-transitory computer-readable media ([0197] memory 2629 (e.g., registers, cache memory, random access memory, etc.) storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising
Regarding Claim 2. Grant as modified by Rogynskyy teaches the method of claim 1,
Grant discloses wherein the machine-learning model is trained based on at least in part on: (i) third data that includes prior interaction data including data representing second interactions between prior channels and prior user accounts ([0081] Inputs for the ML structure may be determined at 705. In one implementation, the inputs for the ML structure may be selected from data such as message content, file content, message metadata, work graph data, [0040] The ranking data request may also specify ML structure input data to obtain for ML structure(s) (e.g., different ML structures may be used for different types (e.g., messages, people, channels) of responses to the search request) utilized for ranking (e.g., for the user's team).); and (ii) fourth data that includes prior representative channels and representative users associated with the prior interaction data, to learn relationships between the third data and fourth data ([0036] Work graph data may be used as machine learning (ML) structure inputs for training and/or utilizing ML structures (e.g., logistic regressions, neural networks, etc.). , [0039] a work graph generating process (e.g., to generate work graphs (e.g., ML structure input data such as a channel's priority for the user)), a machine learning process (e.g., to generate other ML structure input data (e.g., team-level term priority), to generate ML structures (e.g., team-level neural networks), such that the machine-learning model is configured to use the relationships to generate the first data and the second data upon the input. ([0040] utilize ML structure(s) to rank the relevant messages and/or files, people, channels, and/or the like using ML structure input data. The MSM server may send a ranking response 167 to provide the highest ranked messages, files, people, channels, and/or the like to the user.)
Regarding Claims 3, 14 and 19, Grant as modified by Rogynskyy teaches the method of claim 1,
Grant discloses wherein the interaction data includes at least one of: a reaction to a message ([0117] a message with reactions from users with high user priority with respect to the current user, [0278] a response is any of: a reaction to the metadata access control carrying message, clicking on a link in the metadata access control carrying message)) ; a link associated with a message ([0278] a response is any of: a reaction to the metadata access control carrying message, clicking on a link in the metadata access control carrying message); a number of replies associated with a channel; a number of views associated with a channel; or an attachment within a channel (Fig 18 #1801 channels and # of messages on channels).
Regarding Claim 4, Grant as modified by Rogynskyy teaches the method of claim 1, further comprising:
Grant discloses receiving, from the machine-learning model, a confidence score associated with individual channels of the one or more representative channels (Fig 15 #1510 prediction for channel and {0141] Screen 1510 illustrates ranking scores (e.g., prediction column) for various channels (e.g., channel column) for the user (e.g., username column) For example, the user's channel priority preferences and data regarding the various channels (e.g., whether the user starred a channel, data regarding messages sent in a channel, work graph data) may be used to determine ranking scores for the various channels for the user.); determining an order for presenting the representative channels based on the confidence score (Fig 15 #1510 channel presented in order /score, [0471] calculate, via at least one processor, from the perspective of each user in the set of users, a channel priority score for each of the channels in the set of channels, wherein a channel priority score from the perspective of a user for a channel is calculated based on the number of metadata access control carrying messages, in the set of metadata access control carrying messages, associated with that channel that were channel-pertinent to that user ); and presenting, via the user interface associated with the group-based communication platform, the representative channels based on the order. (Fig 15 #1510 channels based on prediction ranking scores; [0141] Screen 1510 illustrates ranking scores (e.g., prediction column) for various channels (e.g., channel column) for the user (e.g., username column) For example, the user's channel priority preferences and data regarding the various channels (e.g., whether the user starred a channel, data regarding messages sent in a channel, work graph data) may be used to determine ranking scores for the various channels for the user.)
Regarding Claims 5 and 15, Grant as modified by Rogynskyy teaches the method of claim 1, further comprising:
Grant teaches providing a keyword or key phrase as the input ([0044] MSM message may include data such as a message identifier, a team identifier, a channel identifier, a sending user identifier, topics (keyword or phrase), responses, contents, attachments, message hierarchy data, third party metadata, conversation primitive data, and/or the like [0050] the message may be parsed (e.g., using PHP commands) to determine topics discussed in the message. For example, hashtags in the message may indicate topics associated with the message. In another example, the message may be analyzed (e.g., by itself, with other messages in a conversation primitive) using a machine learning technique, such as topic modeling, to determine topics associated with the message.); generating, by the machine-learning model and based at least in part on the input, third data representing a frequently discussed topic ([0062] table 5 topic priority, [0069] user’s relationship to topic may be determined [0145] different types of results that may be shown to the user (e.g., ordered based on ranking from highest (1) to lowest (3)). In one implementation, the topic (e.g., solr) may have been discussed by an expert in a channel. Different result configurations are illustrated for cases where (1) a single expert discussed the topic in a single channel, (1a) a single expert discussed the topic in multiple channels, and (1b) multiple experts discussed the topic in a single channel.); and causing the third data to be associated with the profile data associated with the first user account. ([0069] The user's relationship to topics may be determined at 521. In one implementation, user to topic data such as how many messages the user sent regarding a topic, how many messages the user read regarding the topic, how many reactions to the user's messages regarding the topic have been received, how many times files regarding the topic that were attached to the user's messages have been downloaded by other users, how many times files regarding the topic have been downloaded by the user, and/or the like may be used to calculate a topic priority (e.g., a topic priority score) of the topic to the user. For example, a weighted average of user to topic data may be calculated for each topic (e.g., each topic discussed by the team, each topic discussed at the company), and the resulting scores normalized so that each of the topics is assigned a topic priority score) (third data))
Regarding Claims 6 and 16, Grant as modified by Rogynskyy teaches the method of claim 1,
Grant discloses wherein generating the first data and the second data to be associated with the first user account is based at least in part on a maximum number of representative channels and a maximum number of representative users associated with the first user account. ([0143] suggestions of channels (first data) to join, leave, star, and/or the like that may be generated for the user (e.g., based on data regarding the user). For example, three channels (maximum) to join may be suggested to the user., different types of results that may be shown to the user (e.g., ordered based on ranking from highest (1) to lowest (3)). [0145] In one implementation, the topic (e.g., solr) may have been discussed by an expert in a channel Different result configurations are illustrated for cases where (1) a single expert discussed the topic in a single channel, (1a) a single expert discussed the topic in multiple channels, and (1b) multiple experts discussed the topic in a single channel.)
Regarding Claim 7, Grant as modified by Rogynskyy teaches the method of claim 1, further comprising:
receiving, from the first user account, a request to modify the profile data associated with the first user account ([0093] a client 802 may send a ranking request 821 to a MSM server 806. For example, the ranking request may be a search request that includes a search term (e.g., “patents”) specified by a user. In one implementation, the ranking request may include data such as a request identifier, a user identifier (e.g., to facilitate access control), a ranking type (e.g., search for messages, people, channels; recap a channel), a ranking filter), [0232]account update, [0130] a member of any of the teams (e.g., anyone in the company) may find and/or join global channels. For example, members of both Accounts team and Mobile team may join the “food-n-drink, SF” channel.(modify profile data));
generating, based at least in part on the request and the interaction data, a first list of representative channels that are unrepresented in the profile data associated with the first user account ([0094] a channel suggestion request (e.g., suggesting channels to join, leave, star, and/or the like) periodically generated by the MSM server for the user. In another example, the ranking request may be a contextual search for key phrases in a channel to augment the user's reading experience with relevant messages and/or files. [0098] (RD) component 841 may utilize ranking data (e.g., relevant messages, relevant files, ML structure input data) to rank the relevant messages and/or files, people, channels, and/or the like using ML structure(s) [0099] The ranking response may be used to provide the highest ranked messages, files, people, channels, and/or the like to the client., [0100] generating channel suggestions for user, [0101]suggesting channels for the user to join, Fog 11 # global channels 1101, [0131]) Screen 1110 illustrates select team channels. In one embodiment, access (e.g., ability to find and/or join) to select team channels may be restricted to members of specified teams;
receiving, from the first user account, a selection of one or more second representative channels, the selection representing a subset of representative channels from the first list of representative channels ([0130] a member of any of the teams (e.g., anyone in the company) may find and/or join global channels. For example, members of both Accounts team and Mobile team may join the “food-n-drink, SF” channel. [0139] if the user joins a new channel that already has a lot of messages, the user may wish to catch up on the 20 most important messages sent in this channel in the last six month. [0048] MSM users may join channels (e.g., chat rooms that they find interesting). [0128] the user may request access to Lemon Croutons team 1005, and, if the user meets criteria for joining the team, the user may be allowed to join the team, [0153] a feedback mechanism that may be utilized by a user to provide feedback regarding a message included in the briefing. Screen 1820 illustrates that when the user chooses to give positive or negative feedback, a second set of optional choices may be shown to obtain additional feedback (e.g., to help improve a ML model));
providing the selection of the subset of representative channels from the first list of representative channels and the interaction data as the input ([0153] a feedback mechanism that may be utilized by a user to provide feedback regarding a message included in the briefing. Screen 1820 illustrates that when the user chooses to give positive or negative feedback, a second set of optional choices may be shown to obtain additional feedback (e.g., to help improve a ML model, [0081] Inputs for the ML structure may be determined at 705. In one implementation, the inputs for the ML structure may be selected from data such as message content, file content, message metadata, work graph data).;
generating, by the machine-learning model and based at least in part on the input and the interaction data, a second list of representative channels ([0128] a ranking request may be used to determine teams to recommend for the user to join (e.g., utilizing the user's data such as the user's job title, geographic location, and/or the like). The user may also browse 1007 teams in the user's company that the user may join.; and
causing display of the subset of representative channels on the profile data associated with the first user account. (Fig 19 displaying starred channel, Fig 20 #3 show channels, ask for 5 most/least important, [0148] Tier 1: Take the top X % of starred channels based on channel priority. Select the highest scoring messages, and order the channels by score)
Regarding Claim 8. Grant as modified by Rogynskyy teaches the method of claim 1, further comprising:
receiving, from the first user account, a request to modify the profile data associated with the first user account ([0093] a client 802 may send a ranking request 821 to a MSM server 806. For example, the ranking request may be a search request that includes a search term (e.g., “patents”) specified by a user. In one implementation, the ranking request may include data such as a request identifier, a user identifier (e.g., to facilitate access control), a ranking type (e.g., search for messages, people, channels; recap a channel), a ranking filter), [0232]account update, [0130] a member of any of the teams (e.g., anyone in the company) may find and/or join global channels. For example, members of both Accounts team and Mobile team may join the “food-n-drink, SF” channel.(modify profile data));
generating, based at least in part on the request and the interaction data, a first list of representative users that are unrepresented in the profile data associated with the first user account, representative users included in the first list of representative users representing users the first user account is most likely to interact with ([0087] in order to measure importance (e.g., of messages), the highlights model may utilize engagements as a proxy for importance. As such, a message is considered important if the user is highly likely to engage with it., [0089] a ML structure (e.g., a logistic regression classifier) may be generated for each engagement type utilized for ranking. This classifier may be utilized to approximate the probability of a user engaging with a given message at a given time for the given engagement type. [0098] (RD) component 841 may utilize ranking data (e.g., relevant messages, relevant files, ML structure input data) to rank the relevant messages and/or files, people, channels, and/or the like using ML structure(s) [0099] The ranking response may be used to provide the highest ranked messages, files, people, channels, and/or the like to the client., Fig 17A shows result for expert suggestion, [0108] a search for experts (e.g., triggered based on a heuristic or a ML classifier) may utilize tf-idf, BM25, and/or the like techniques to rank users as experts with regard to a given topic, [0144] FIG. 17, screen 1701 illustrates that a user may be shown a collapsed results teaser to inform the user that experts for a user specified search term (e.g., solr) have been found (e.g., based on topic expertise scores for topic solr).);
receiving, from the first user account, a selection of one or more representative users, the selection representing a subset of the representative users from the first list of representative users ([0144] FIG. 17, screen 1701 illustrates that a user may be shown a collapsed results teaser to inform the user that experts for a user specified search term (e.g., solr) have been found (e.g., based on topic expertise scores for topic solr). The user may click on the results teaser to view results., [0153] a feedback mechanism that may be utilized by a user to provide feedback regarding a message included in the briefing. Screen 1820 illustrates that when the user chooses to give positive or negative feedback, a second set of optional choices may be shown to obtain additional feedback (e.g., to help improve a ML model));
providing the selection of the subset of representative users from the first list of representative users and the interaction data as the input to the machine-learning model ([0153] a feedback mechanism that may be utilized by a user to provide feedback regarding a message included in the briefing. Screen 1820 illustrates that when the user chooses to give positive or negative feedback, a second set of optional choices may be shown to obtain additional feedback (e.g., to help improve a ML model, [0081] Inputs for the ML structure may be determined at 705. In one implementation, the inputs for the ML structure may be selected from data such as message content, file content, message metadata, work graph data).;
generating, by the machine-learning model and based at least in part on the input and the interaction data, a second list of representative users ([0145] Different result configurations are illustrated for cases where (1) a single expert discussed the topic in a single channel, (1a) a single expert discussed the topic in multiple channels, and (1b) multiple experts discussed the topic in a single channel. For example, clicking on an expert may take the user to the relevant channel, where the user may ask a question and discuss the answer. ).; and
causing display of the subset of representative users on the profile data associated with the first user account. (Fig 17A #1701 show results of expert, [0146] Fig 17B Screen 1720 illustrates an example of results that may be shown to the user)
Regarding Claims 9 and 18, Grant as modified by Rogynskyy teaches the method of claim 8 and medium of claim 17,
Grant discloses wherein the first list of representative users includes users based at least in part on one of: a number of shared channels between the user and individual users ([0067] how many channels the user and another user joined in common); activity level data associated with individual users associated with the shared channels ([0037] A message server 106 may obtain the message and send a content MSM message 135 to other users, who are authorized recipients of the message (e.g., other users on the user's team who joined the channel); user reply data associated with individual users; or a number of second keywords or second key phrases used by individual users ([0244] A work graphs table 2619m includes fields such as, but not limited to: workGraphID, workGraphTeamID, workGraphUserData, workGraphChannelData (channel shared between users), workGraphTopicData, and/or the like);.
Regarding Claims 10 and 20, Grant as modified by Rogynskyy teaches the method of claim 1 and medium of claim 17,
Grant discloses wherein presenting the first data and the second data to the second user account is based at least in part on a permissions level associated with the first user account and the second user account. ([0093] a client 802 may send a ranking request 821 to a MSM server 806. For example, the ranking request may be a search request that includes a search term (e.g., “patents”) specified by a user. In one implementation, the ranking request may include data such as a request identifier, a user identifier (e.g., to facilitate access control) (permissions), a ranking type (e.g., search for messages, people, channels; recap a channel), a ranking filter (e.g., search results should include messages and exclude people and channels), [0095] searching for message indexing 810 to filter over (e.g., search term, team and/or company identifier associated with the user. Table 15 access control – data accessible to team, [0232]account access privilege, account restrictions, [0233] user preferences, user restrictions, [0269] the message access control data includes group level access control data and channel level access control data (permission))
Regarding claim 11. Grant as modified by Rogynskyy teaches the method of claim 1, further comprising: determining an occurrence of an event associated with the group-based communication platform, wherein the event comprises at least one of:
receiving, from the first user account, a request to modify the profile data associated with the first user account ([0093] a client 802 may send a ranking request 821 to a MSM server 806. For example, the ranking request may be a search request that includes a search term (e.g., “patents”) specified by a user. In one implementation, the ranking request may include data such as a request identifier, a user identifier (e.g., to facilitate access control), a ranking type (e.g., search for messages, people, channels; recap a channel), a ranking filter), [0232]account update, [0130] a member of any of the teams (e.g., anyone in the company) may find and/or join global channels. For example, members of both Accounts team and Mobile team may join the “food-n-drink, SF” channel.(modify profile data));
detecting a threshold number of a keyword or key phrase associated with the first user account ([0108] a search for experts (e.g., triggered based on a heuristic or a ML classifier) may utilize tf-idf, BM25, and/or the like techniques to rank users as experts with regard to a given topic (e.g., specified in the user's search query). [0109]Highest ranked applicable data items to use may be determined at 937. In one implementation, a threshold number of highest ranked applicable data items may be specified (e.g., use up to 10 most relevant messages). In another implementation, a threshold ranking score may be specified (e.g., use messages with message rank score of at least 0.7 out of 1).; or a threshold period of time elapsing; and
generating, by the machine-learning model and based at least in part on the occurrence of the event, third data representing additional one or more representative channels ([0100] generating channel suggestions for user, [0101]suggesting channels for the user to join, Fog 11 # global channels 1101) and fourth data representing additional one or more representative users associated with the group-based communication platform (Fig 17C shows additional expert data for topic [0146]Screen 1730 illustrates various actions that may be taken in response to the user clicking on various elements of the results GUI.); and
causing the third data and the fourth data to be associated with the profile data of the first user account. ([0040] utilize ML structure(s) to rank the relevant messages and/or files, people, channels, and/or the like using ML structure input data. The MSM server may send a ranking response 167 to provide the highest ranked messages, files, people, channels, and/or the like to the user. Fig 19 displaying starred channel, Fig 20 #3 show channels, ask for 5 most/least important, [0148] Tier 1: Take the top X % of starred channels based on channel priority. Select the highest scoring messages, and order the channels by score, [0146]Screen 1730 illustrates various actions that may be taken in response to the user clicking on various elements of the results GUI.)
Regarding Claim 13, Grant as modified by Rogynskyy teaches the system of claim 12, the operations further comprising:
Grant discloses receiving, from a third user account, a request to view the profile data associated with the first user account ([0093] a client 802 may send a ranking request 821 to a MSM server 806. For example, the ranking request may be a search request that includes a search term (e.g., “patents”) specified by a user. In one implementation, the ranking request may include data such as a request identifier, a user identifier (e.g., to facilitate access control), a ranking type (e.g., search for messages, people, channels; recap a channel), a ranking filter (e.g., search results should include messages and exclude people and channels), [0144] a collapsed results teaser to inform the user that experts for a user specified search term (e.g., solr) have been found (e.g., based on topic expertise scores for topic solr. The user may click on the results teaser to view results. Fig 17 #1730 user can click profile f expert Noah Weiss (profile data)); and presenting, via the user interface associated with the communication platform, the first data and the second data to the third user account based at least in part on the interaction data representing interactions between the first user account and the third user account. (Fig 17 #1730 and [0143] channel suggestions that may be provided to the user when a user joins a channel (e.g., based on interests of other people in the channel and data regarding the user). [0144] a collapsed results teaser to inform the user that experts for a user specified search term (e.g., solr) have been found (e.g., based on topic expertise scores for topic solr). In one implementation, the user may click on the results teaser to view results.)
Response to Arguments
Applicant's arguments filed 11/12/25 have been fully considered but they are not persuasive.
Regarding 101 rejection, new limitations have been considered in claim rejection above. This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving data, analyzing it, and providing suggestion. In particular, the claims only recites the additional element – one or more processors; and one or more non-transitory computer-readable media, a group-based communication platform, a machine-learning model, training using at least one of a supervised training process or an unsupervised training process, a machine learning model, a user interface. The additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The additional elements of using a user interface to receive and display data associated with the abstract idea, is merely an example of generally linking the abstract idea to a particular technological environment or field of use as outlined in MPEP 2106.05(h). User interfaces are recited so generally that they do not meaningfully limit the abstract idea. Further, the limitation of “training using at least one of a supervised training process or an unsupervised training process /providing input to the machine learning system; generating by the machine learning model” is simply application of a computer model, itself an abstract idea. Furthermore, such training and applying of a model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea
Applicant’s arguments with respect to claims rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. New limitations have been considered in rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Baurassa (US 2020/0036783) discloses generate and provide digests of relevant group-based communications transmitted between a plurality of client devices and a group-based communication system. The suggested topics, users, or group-based communication channels are determined based on the likelihood that the user will follow the suggestion and/or a measure of importance of content on the group-based communication system. ([0034])
Kannan (US 10089639) discloses a user profile is creates, and personalization is provided, by compiling interaction data. The interaction data is compiled to generate a value index or score from a user model. Parameterized data is used to build tools which help decide an engagement strategy and modes of engagement with a user.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626