DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 21-40 are pending and examined herein per Applicant’s 01/02/2026 preliminary amendment filing with the Office.
Claims 1-20 were previously canceled. Claims 21-40 are amended. No claims were withdrawn or newly added.
Response to Amendment
Applicant’s amendments to the Specification are acknowledged.
Response to Arguments
Applicant's arguments filed35 USC § 101 have been fully considered but they are not persuasive. Applicant argues:
claim 21 as presented recites using a first machine learning model trained for natural language processing to perform feature extraction against text associated with a communication channel facilitated using a unified communications as a service (UCaaS) platform to extract features from the text and using a second machine learning model trained for feature classification to predict that the features extracted using the first machine learning model correspond to an action item. Remarks p. 12.
Respectfully, the Office disagrees with Applicant’s position. The claims have been amended to recite “responsive to the first prediction and the second prediction, generating by a second machine learning model trained for feature extraction using a natural language processing engine, output representing features extracted from the first text and the second text;” with respect to the second machine learning model. It is noted that the second machine model does not expressly - second machine learning model trained for feature classification. That is to say the extracted features are not classified. Further the claim is so broadly made that it unclear what the extracted features from the second machine learning model represent in relation to the action items of the first and second prediction from the first machine learning model.
The rejection of the previous Office action is maintained as updated below.
The Office Action misapplies Step 2A, Prong 2 of the Alice/Mayo Test by clearly violating the explicit requirements for its application as set forth in the MPEP. The Office Action only evaluates the additional limitations themselves (i.e., in a vacuum, completely separate from the recited and alleged judicial exception). The Office Action does not evaluate the additional limitations or otherwise the claim as a whole in concluding that claims 21, 30, and 35 do not integrate the alleged judicial exception into a practical application. Remarks p. 13.
Respectfully, the Office disagrees with Applicant’s position. The limitations were considered in part and in the order combination. See previous Office action (mailed 10/01/2025) at at least p. 2 “rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination”, and p. 5 “the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination.”
The rejection of the previous Office action is maintained as updated below.
First, Applicant's claim limitations recite sufficient detail about what the solution embodied by the claim performs. Second, the limitations are clearly focused on a non-standard arrangement of and interaction between first and second machine learning models, trained for different purposes, for machine learning-based feature extraction and classification, which are technical concepts that are well-understood as not something for which a computer is "merely a tool to perform an existing process" (e.g., as machine learning-based feature extraction and classification are necessarily rooted in computing technology). Third, the claim limitations describe specifics that extend beyond generality to address the non-standard arrangement of and interaction between first and second machine learning models, trained for different purposes, and thus is not a mere generalization of a judicial exception. Remarks p. 15.
Respectfully, the Office disagrees with Applicant’s position. The outputs of the first model are simply inputs to the second model, the models are neither updated nor is the computing system improve by the claimed arrangement. While the models may be trained for different purposes each model is just applied to the data. As claimed the models are no more than mere instructions to implement an abstract idea or other exception on a computer.
The rejection of the previous Office action is maintained as updated below.
rejecting the limitations of claims 21, 30, and 35 as such, the Office Action uses the generally linking language as a form of boilerplate, again, devoid of information usable to understand how the limitations to which it is attached are deemed to be mere general linking. Importantly, and as noted above, the Office Action flagrantly fails to apply the Alice/Mayo Test here as required by evaluating only certain phrases and elements without any regard to the claim as a whole, as is required by the MPEP. It is convenient that these elements be considered as general linking when viewed in a vacuum, but the MPEP specifically prohibits such examination. Remarks p. 16
Respectfully, the Office disagrees with Applicant’s position. The analysis of the previous Office action (mailed 10/01/2025) follows the analysis laid out in MPEP 2106. The rejection of the previous Office action is maintained as updated below.
the extra-solution activity, the Office Action specifically alleges that the outputting limitations of claims 21, 30, and 35 describe post-solution activity per MPEP 2106.05(g). (See Office Action, pp. 4-5.) Applicant maintains its above positions without acknowledgment or admission of the allegation set forth in the Office Action. Remarks p. 16.
Respectfully, the Office disagrees with Applicant’s position. MPEP provides under the Alice/Mayo analysis, “Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application” and “(1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application”, See MPEP 2106.
The only additional element found in the claim was “outputting data indicative of the action item to a software service of the unified communications as a service platform.” (claim 21). The instant specification provides, “output information may be displayed on a dashboard, in push notifications, or within other UI aspects of a personal device, thus providing notification or task planning for personal assistance.” (Spec. [18]). The MPEP makes clear that returning the results of the analysis is neither significantly more or a practical application. See for example “[the] term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity . . . post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.” See MPEP 2106.05(g).
Interpreting the additional element in light of the instant specification, the element found to be the additional elements when considered individually and as part of the ordered combination are found to be insignificant extra-solution activity to the judicial exception that cannot transform the abstract idea in to patent eligible subject matter by incorporating the abstract idea into significantly more or a practical application.
For the reasons given above the rejection of the previous Office action is maintained as updated below.
The bare language presented in the Office Action, being completely devoid of anything useful for identifying specifically why the Examiner believes these aspects of Applicant's claims are not relevant under Step 2B of the Alice/Mayo Test, suggests a failure to consider these technical aspects of claims 21, 30, and 35, given that all that is presented in boilerplate language that was apparently simply copied into the Office Action from elsewhere. Remarks p. 17.
Respectfully, the Office disagrees with Applicant’s position. The claim as a whole was considered under Step 2B, see p. 5-7 of the previous Office action (mailed 10/02/2025).
For the reasons given above the rejection of the previous Office action is maintained as updated below.
The machine learning model 410 may be trained to classify strings as either concerning an action item or not concerning an action item." (I[0064].) "The system 400 includes a second machine learning model 420 for extracting action item data from strings that have been classified as concerning action items. The action item data may describe a task to be completed and/or enable the association of an action item with one or more users (e.g., a task owner). The action item data may be determined based on the contents of an action item string." (I[0065].) Remarks p. 19.
Respectfully the Office disagrees with Applicant’s position and notes that while the claim uses a second machine learning model it does not use the second model to “describe a task to be completed and/or enable the association of an action item with one or more users”. As claimed it “generating by a second machine learning model trained for feature extraction using a natural language processing engine, output representing features extracted from the first text and the second text” see claim 21. The limitation is made so broadly that it could outputs other than a task to be completed and/or enable the association of an action item with one or more users.
For the reasons given above the rejection of the previous Office action is maintained as updated below.
USPTO Director John A. Squires strongly urges caution toward examiners in how they choose to apply 35 U.S.C. § 101 to artificial intelligence innovations while also emphasizing that the "traditional and appropriate tools to limit patent protection to its proper scope" are 35 U.S.C. §§ 102, 103, and 112 and not 35 U.S.C. § 101. These are important considerations owing when considering whether and how to reject claims under 35 U.S.C. § 101 moving forward.
Respectfully the Office disagrees with Applicant’s position. The USPTO Memorandum, “Reminders on subject matter eligibility of claims under 35 U.S.C. 101” (08/04/2025) provides, “Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the flowchart provided in MPEP 2106, subsection III.3 It is essential that the broadest reasonable interpretation (BRI) of the claim be established prior to examining a claim for eligibility.” Since publication this Memoranda the Court ruled in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) further explaining when the model itself is improved and when it is not.
“In Recentive, the Federal Circuit determined that the claims at issue, which recite using machine leaning to generate network maps and schedules for television broadcasts and live events, are directed to “the abstract idea of using a generic machine leaning technique in a particular environment, with no inventive concept.” Recentive Analytics, 134 F.4th at 1207–08. In doing so, the court noted that the claims rely on generic machine learning technology in carrying out the claimed methods for generating event schedules and network maps, i.e., that the claims do not require that any particular machine learning technique (e.g., a regression, a neural network, a decision tree) be used. Id. at 1212. And the court reasoned that “[t]he requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement” at least because they are “incident to the very nature of machine learning.” Id.” (Quoting from PTAB Appeal 2025-003304 at p. 27).
In the instant claims, the method simply applies two different machine learning models without an improvement to either the model or the computer. The claimed models are simply applied in a new data environments without more or a practical application when the limitations are considered individually and in the ordered combination.
For the reasons given above the rejection of the previous Office action is maintained as updated below.
Applicant's arguments filed35 USC § 102 are to newly added claim limitations that are fully addressed in the updated rejection.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 21-29 are to a method (process), claims 30-34 are to a non-transitory computer readable medium (manufacture), and claims 35-40 are to a system (machine).
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of mental processes. Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
21. (New) A method, comprising:
generating, by a first machine learning model trained for feature classification of contents of multiple communication channels of unified communications as a service platform, a first prediction that first text within a first communication channel of the communication channels is classified as an action item and a second prediction that second text within a second communication channel of the communication channels is classified as the action item;
responsive to the first prediction and the second prediction, generating by a second machine learning model trained for feature extraction using a natural language processing engine, output representing features extracted from the first text and the second text; and
presenting the output within a graphical user interface of a software service of the unified communications as a service platform.
The claims are directed towards extracting information (text associated with a communication channel), analyzing that information (generates prediction) and outputting the results of the analysis (outputs the predictions to the second model). See Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) and Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014) at MPEP 2106.04(a)(2), subsection III. It is further noted that the system uses two models, but the second model also simply receives information (first/second prediction data), analyzes that information (extracts features) and outputs the results of the analysis (outputs features extractions). The Office finds given the known information (text from a communication channel) a person using his mental ability to evaluate could make a judgement or form an opinion predicting or determining that text from a communication channel should be classified as an action item. Further using his mental ability to perceive known information (text classified as action items) could make a judgement to extract features from the text.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application; when the limitations are considered individually and in the ordered combination. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The outputting elements are determined to be extra-solution/post-solution activity – outputting presenting elements.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
Further as claimed the trained models, only recite the outcome of the analysis. The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application. Where the claim fails to recite details of how a solution to a problem is accomplished, or the claim covers a particular solution to a problem or a particular way to achieve a desired outcome. See USPTO August 4, 2025 Memorandum “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. Further, the claimed machine learning models are mere instruction to implement the abstract idea on a computer. Where MPEP 2106.05(f) provides, “claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").”
The hardware components are found to be general purpose and generic, see Specification [24]. The specification also provides “One type of system which addresses problems such as these includes a unified communications as a service (UCaaS) platform” Specification [2]. It is noted that Applicant does not claim to have invented UCaaS – the specification discloses it is a known system used to address the type of problem that Applicant seeks to provide an improvement to.
Where MPEP 2106.05(d)(I)(2) provides, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial 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, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 21-40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dotan-Cohen et al (US 2018/0083908 A1).
Claims 21, 30, and 35
Dotan-Cohen teaches a method, comprising (Dotan-Cohen [16] “systems, methods, and computer storage media for, among other things, analyzing and tracking user messages, such as emails, with respect to users for determining notifications or other actions to take with respect to the user messages.”):
generating, by a first machine learning model trained for feature classification of contents of multiple communication channels of unified communications as a service platform, a first prediction that first text within a first communication channel of the communication channels is classified as an action item and a second prediction that second text within a second communication channel of the communication channels is classified as the action item; (Dotan-Cohen abstract “a method includes extracting completion criteria of an action item and parameters of the completion criteria from a message portion of a user message between users”, [39] “interaction data may be received from a plurality of user devices (such as user devices 102a and 102b through 102n of FIG. 1) associated with a user or in some instances, associated with multiple users . . . interaction data may be received from a plurality of user accounts, such as social media accounts, email accounts, computer login accounts, and/or computer messaging accounts”, where interaction data is the equivalent of the claimed first/second data the user account types are the equivalent of the claimed first/second communication channels [48] “user routine model 235 may be a machine-learned, probabilistic inference model configured to determine routine-related inferences by evaluating data associated with currently-sensed and/or historically-sensed interaction data”, [77] “routine-related logic includes one or more of the following probabilistic rule types: prediction rules, ranking rules, clustering rules, or classifying rules” [7] “action items can be grouped together based on shared parameters of completion criteria”, where grouping is a form of classification, and [19] “action items determined from user messages between users. In some respects, user messages, such as emails, which are sent by a user and received by the user are analyzed and tracked. Action items, completion criteria of the action items, and parameters of the completion criteria are extracted from at least the message portion of the user messages”);
responsive to the first prediction and the second prediction, generating by a second machine learning model trained for feature extraction using a natural language processing engine, output representing features extracted from the first text and the second text (Dotan-Cohen abstract “extracting completion criteria of an action item and parameters of the completion criteria from a message portion of a user message between users” where criteria is the equivalent of the claimed features and [72] and [121] “amount of time used as a timing attribute of an action item can be machine learned, such as using routine model engine 240. For example, inference engine 350C can learn timings based on when the user typically performs or completes the task associated with the action item and/or prefers to perform or complete the task associated with the action item.”); and
presenting the output within a graphical user interface of a software service of the unified communications as a service platform (Dotan-Cohen [87] “Action manager 366B generates and updates the actions, such as notifications to parties regarding action items, which can be about sent or received user messages” and [88] “ Action router 366D works in conjunction with presentation component 398 to present and display notifications provided by action item engine 366 to users.” Where the display is the equivalent of the claimed graphical user interface).
With respect to claims 30 and 35 that recite substantially similar limitations as those rejected above, these claims are rejected for the same reasoning given above. Dotan-Cohen further teaches the additional claimed elements of the claims:
Dotan-Cohen teaches a non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations comprising (Dotan-Cohen [151] “Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device 600. Computer storage media does not comprise signals per se.”).
Dotan-Cohen teaches a system, comprising (Dotan-Cohen [16] “systems, methods, and computer storage media for, among other things, analyzing and tracking user messages, such as emails, with respect to users for determining notifications or other actions to take with respect to the user messages.”):
one or more memories (Dotan-Cohen [152] “Memory 612 includes computer storage media in the form of volatile and/or nonvolatile memory”); and
one or more processors configured to execute instructions stored in the one or more memories to (Dotan-Cohen [152] “Computing device 600 includes one or more processors 614 that read data from various entities such as memory 612 or I/O components”):
Claims 22 and 36
Dotan-Cohen teaches all the limitations of the method of claim 21, comprising:
pre-processing the first text to remove one or more stop words from the first text, wherein the first prediction is generated based on the pre-processed first text (Dotan-Cohen [75]).
Claim 23
Dotan-Cohen teaches all the limitations of the method of claim 21, comprising:
post-processing the first prediction by mapping the first prediction to a binary classification of the first text (Dotan-Cohen [77] and [79]).
Claims 24 and 38
Dotan-Cohen teaches all the limitations of the method of claim 21, comprising:
identifying named entities within the features (Dotan-Cohen [109] and [119]);
identifying parameters of the action item based on the named entities (Dotan-Cohen [106-108]); and
associating the parameters with the output (Dotan-Cohen [112]).
Claims 25
Dotan-Cohen teaches all the limitations of the method of claim 21, comprising:
obtaining, using speech recognition software of the unified communications as a service platform, a voice input indicating a request to process the first text, wherein the first prediction is generated in response to the voice input (Dotan-Cohen [42] and [84]).
Claim 26
Dotan-Cohen teaches all the limitations of the method of claim 21, wherein presenting the output within the graphical user interface of the software service of the unified communications as a service platform comprises: adding data indicative of the action item to a task data structure (Dotan-Cohen [106]).
Claims 27
Dotan-Cohen teaches all the limitations of the method of claim 21, wherein the first communication channel corresponds to a conference and the second communication channel corresponds to a message (Dotan-Cohen [16-17]).
Claims 28
Dotan-Cohen teaches all the limitations of the method of claim 21, wherein the first text and the second text both include content from a single user of the unified communications as a service platform (Dotan-Cohen [89]).
Claims 29
Dotan-Cohen teaches all the limitations of the method of claim 21, wherein the first text and the second text include content from multiple users of the unified communications as a service platform (Dotan-Cohen [99] and [105]).
Claim 31
Dotan-Cohen teaches all the limitations of the non-transitory computer readable medium of claim 30, the operations comprising:
pre-processing the first text to remove one or more stop words from the first text before generating the first prediction (Dotan-Cohen [75]); and
post-processing the first prediction by mapping the first prediction to a binary classification (Dotan-Cohen [96]).
Claims 32
Dotan-Cohen teaches all the limitations of the non-transitory computer readable medium of claim 30, the operations comprising: associating parameters identified based on named entities within the features with the data indicative of the action item (Dotan-Cohen [40] and [91]).
Claim 33
Dotan-Cohen teaches all the limitations of the non-transitory computer readable medium of claim 30, wherein generating the first prediction and the second prediction comprises: generating, by the first machine learning model, the first prediction and the second prediction in response to a voice input obtained using speech recognition software of the unified communications as a service platform (Dotan-Cohen [42] and [153]).
Claims 34
Dotan-Cohen teaches all the limitations of the non-transitory computer readable medium of claim 30, wherein the first communication channel corresponds to a conference transcript and the second communication channel corresponds to one of a voicemail transcript, a chat log, or an email. (Dotan-Cohen [84] where the limitation is made in the alternative only one element needs to be present in the art).
Claims 37
Dotan-Cohen teaches all the limitations of the system of claim 35, wherein the first prediction is mapped to a binary classification before the generation of the output (Dotan-Cohen [96]).
Claims 39
Dotan-Cohen teaches all the limitations of the system of claim 35, wherein the feature extraction is performed first prediction and the second prediction are generated in response to a voice input prompt obtained via the unified communications as a service platform (Dotan-Cohen [42] and [153]).
Claims 40
The system of claim 35, wherein the first communication channel corresponds to one or both of a conference or a transcript and the second communication channel corresponds to a message (Dotan-Cohen [5] and [94] where the claim is made in the alternative only one element needs to be found in the art).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chan et al (US 2025/0086895 A1) teaches an operations may include summarizing data within the virtual space, generating one or more action items (e.g., creating a to-do list or task list) based on the data within the virtual space, simplifying data associated with the virtual space, providing added detail to the data within the virtual space, fixing spelling and/or grammatical errors associated with data within the virtual space, converting data (or text) to a graphical identifier (e.g., emoji), and/or changing (or modifying) the tone (e.g., formal, casual, fun, confident, etc.) of text within the virtual space.
Muralidharan et al (US 2023/0024040 A1) teaches process the content segmentation using an action item classification machine learning model to determine an action item presence prediction for the content segmentation unit; determine, based on each action item presence prediction, a candidate action item subset of the plurality of content segmentation units; for each content segmentation unit in the candidate action item subset, process the content segmentation unit using an action item extraction machine learning model to generate an action item set for the content segmentation unit.
Siohan et al (US 2023/0186198 A1) teaches the action item extractor 252 executing an action item classification model 255 that is capable of leveraging the context of other dialog acts 204 in the transcript 202 when determining whether the corresponding dialog act 204 includes the action item 254. For instance, the action item classification model 254 may receive a respective sequence of dialog acts 204 that includes the corresponding dialog and generate a classification output that classifies the corresponding dialog act as either including the action item 254 or not including the action item 254. Here, the extractor 252 may identify each dialog act 204 classified by the classification model 255 as including an action item as a respective one of the plurality of action items 254 extracted from the transcript.
Muralidharan et al (US 2024/0370655 A1) teaches apparatus reduces burden by accurately and concisely summarizing an entire document's action items in a succinct manner, time for a user to read a document, and operational load on a document collaboration server system, improves training efficiency of the action item classification machine learning models and the action item extraction machine learning models, facilitates reliable and efficient analysis of the action items from the content data by end users, and reduces computational time takes for a machine learning model to generate the action item log from the content data of the document and creation of less accurate and less concise action item logs.
Kim (US 2021/0103811 A1) teaches apparatus 10 for suggesting an action item may use the computational function of the server 300, but perform the speech recognition, the keyword extraction, and the analysis and suggestion of the action item based on the deep neural network using the AI acceleration chipset 182 specially manufactured for the computation of the artificial intelligence algorithms among the processor.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FOLASHADE ANDERSON whose telephone number is (571)270-3331. The examiner can normally be reached Monday to Thursday 12:00 P.M. to 6:00 P.M. CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623
Prosecution Insights