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 . Claims 1-20 are pending.
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 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process), claim 10 is directed to a system (i.e., a machine), and claim 16 to a non-transitory computer-readable storage medium (i.e., a machine or manufacture). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 USC § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 10, and 16 (claim 1 being representative) is:
retrieving time-indexed data corresponding to a user identifier, the time-indexed data comprising:
(1) at least one upcoming communication event accessible to participant users associated with the user identifier, and
(2) an event feature set indicating contextual metadata associated with the at least one upcoming communication event;
identifying, using the event feature set of the at least one upcoming communication event, one or more prior communication events associated with the at least one upcoming communication event, each prior communication event comprising a stored signal of the prior communication event;
extracting, from the stored signals of the one or more prior communication events, a content signal set indicating historical event contents pertinent to the at least one upcoming communication event;
determining at least one recorded artifact representing supplementary event contents that are similar to the extracted content signal set of the one or more prior communication events;
causing to generate a natural language response using the extracted content signal set and the at least one recorded artifact, the response indicating recommended user actions during the at least one upcoming communication event; and
generating the determined at least one recorded artifact and the generated natural language response.
The abstract idea steps italicized above are directed to analyzing prior meetings, comparing information, and determining outputs, which constitutes a process that, under its broadest reasonable interpretation (BRI), could be performed mentally, including with pen and paper. This is further supported by ¶¶ [0021 – 0022] of applicant’s specification as filed. If a claim limitation, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claims are directed to managing and preparing for virtual communication events (e.g., sales meetings). This is a process that, under its BRI, covers commercial activity. This is further supported by ¶¶ [0021 – 0024] of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: computer-implemented; virtual; via an Application Programming Interface (API); audio; from a remote database; a generative machine learning model; digital; for display, at a user interface associated with the user identifier.
Claim 10 recites the following additional elements: A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to; virtual; via an Application Programming Interface (API); audio; from a remote database; a generative machine learning model; digital; for display, at a user interface associated with the user identifier.
Claim 16 recites the following additional elements: A non-transitory computer-readable storage medium comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to; virtual; via an Application Programming Interface (API); audio; from a remote database; a generative machine learning model; digital; for display, at a user interface associated with the user identifier.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in ¶¶ [0136] – [0141] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claims 2, 11, and 17, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: digital; virtual; for display, at the user interface. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 3, 12, and 18, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: audio; virtual; via a machine learning model. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 4, 13, and 19, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: via the user interface; virtual. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 5, 14, and 20, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: virtual; digital. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claim 6 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: generative machine learning model; digital; virtual. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claims 7 and 15, further narrow the abstract idea with additional steps and/or description, in addition to including additional elements: user interface; virtual; via a semantic encoder; remote database; generative machine learning model; digital. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claim 8 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: virtual; generative machine learning model. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claim 9 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: digital. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 Dotan-Cohen (US 20230014775) in view of Maurer (US 20240176960).
Claims 1, 10, and 16
Dotan-Cohen discloses:
(claim 1) A computer-implemented method for generating pre-emptive informatics for virtual communication events, the method comprising: {“The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few.” ¶ [0018]}
(claim 10) A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to: {“The computer-implemented method, the system (that includes at least one computing device having at least one processor and at least one computer readable storage medium), and/or the computer storage media as described herein may perform or be caused to perform the processes 500 or any other functionality described herein.” ¶ [0113]}
(claim 16) A non-transitory computer-readable storage medium comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to: {“For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media. The methods may be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), or a plug-in to another product, to name a few.” ¶ [0018]}
identifying, using the event feature set of the at least one upcoming virtual communication event, one or more prior virtual communication events associated with the at least one upcoming virtual communication event, each prior virtual communication event comprising a stored audio signal of the prior virtual communication event; {The system supports identifying prior events (e.g., emails, meetings) based on contextual metadata and historical user activity and associating them using contextual information. The prior events include transcripts generated from audio. ¶¶ [0043], [0045], [0048]}
extracting, from the stored audio signals of the one or more prior virtual communication events, a content signal set indicating historical event contents pertinent to the at least one upcoming virtual communication event; {The system extracts structured content including transcripts generate from audio. ¶¶ [0045], [0048], [0050]}
causing a generative machine learning model to generate a natural language response using the extracted content signal set and the at least one recorded digital artifact, the response indicating recommended user actions during the at least one upcoming virtual communication event; and {The system uses ML models to process extracted event content (e.g. transcripts and contextual data) to generate outputs such as identified tasks and user prompts. ¶¶ [0024] – [0025], [0053]}
generating for display, at a user interface associated with the user identifier, the determined at least one recorded digital artifact and the generated natural language response. {Generated outputs are presented at user interfaces derived from processed event data, including displaying such information alongside related contextual content. ¶¶ [0075] – [0076], [0104]}
Dotan-Cohen does not disclose, however, Maurer, in a similar field of endeavor directed to techniques for transcribing and/or summarizing multimedia collaboration sessions, teaches:
retrieving, via an Application Programming Interface (API), time-indexed data corresponding to a user identifier, the time-indexed data comprising: {The system supports retrieving user data via APIs, including time-indexed metadata tied to user identifiers. ¶ [0029], [0053]}
(1) at least one upcoming virtual communication event accessible to participant users associated with the user identifier, and {Virtual communications events are accessible to participant users and are recommended to users. ¶¶ [0091], [0096]}
(2) an event feature set indicating contextual metadata associated with the at least one upcoming virtual communication event; {The ML have access to contextual metadata (e.g., virtual space data, thread data, etc.) associated with communications events. ¶ [0037]}
determining, from a remote database, at least one recorded digital artifact representing supplementary event contents that are similar to the extracted content signal set of the one or more prior virtual communication events; {Content may be accessed from a remote datastore (¶ [0041]) and such content may be selected based on embedded similarity and thresholds. ¶¶ [0141], [0144]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the monitoring and analyzing communication events features of Dotan-Cohen to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claims 2, 11, and 17
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Dotan-Cohen further discloses:
determining, from the at least one recorded digital artifact, a historical digital artifact set, each historical digital artifact representing supplementary event contents for at least one prior virtual communication event of the one or more prior virtual communication events; and {The system supports determining and outputting content regarding prior events (e.g. tasks, files, contextual data) derived from historical communications and event data. ¶¶ [0050], [0053]}
generating for display, at the user interface, a graphical timeline that arranges the one or more prior virtual communication events in chronological order, the graphical timeline comprising a visual mapping between the one or more prior virtual communication events and the determined historical digital artifact set. {An activity timeline that organizes conversations with timestamps can be generated. ¶ [0050]}
Claims 3, 12, and 18
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Dotan-Cohen further discloses:
converting the stored audio signals of the one or more prior virtual communication events into corresponding natural language transcripts, {The system supports generating transcripts from meeting activity, including audio sources. ¶ [0045]}
wherein each transcript is divided into one or more natural language segments associated with a timestamp and a speaker identifier; {Transcripts can be segmented and associated with timestamps and speaker information. ¶¶ [0050], [0059]}
grouping, via a machine learning model, the one or more natural language segments into one or more content categories, {Content is classified (e.g., identifying candidate tasks vs. non tasks using ML), i.e., grouping into categories. ¶¶ [0057]}
Maurer further teaches:
wherein member natural language segments of each category share similar type of event information; and {Grouping segments into content categories is supported by ML classification, ranking, and embedding similarity grouping of conversation content. ¶¶ [0038], [0141], [0144]}
causing the generative machine learning model to generate a response identifying at least one content signal for each of the one or more content categories, the at least one content signal indicating historical event information pertinent to the at least one upcoming virtual communication event. {The generated summary constitutes a response identifying content signals (e.g., action items, highlights) for categorized conversation content derived from prior events (i.e., pertinent to future communication contexts). ¶¶ [0038], [0144], [0185]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claims 4, 13, and 19
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Dotan-Cohen further discloses:
receiving, via the user interface, an updated time-indexed data corresponding to the user identifier, the updated time-indexed data comprising: (1) a new virtual communication event accessible to participant users associated with the user identifier, and (2) a second event feature set indicating contextual metadata associated with the new virtual communication event; {User or event data is received, including new communications, with contextual metadata (e.g., topic, participants, timing). ¶¶ [0039], [0043], [0048]}
Maurer further teaches:
determining, via comparison of the first and the second event feature sets, an event dependency score indicating shared event contents between the at least one upcoming virtual communication event and the new virtual communication event {The system supports similarity and semantic distance computations based on embeddings, producing a similarity measure. ¶¶ [0038], [0144], [0150]}
responsive to the event dependency score satisfying an alignment threshold, adding the new virtual communication event to the one or more prior virtual communication events associated with the at least one upcoming virtual communication event. {The system may apply a threshold to similarity (e.g., embedding distance) and prioritize related data. ¶¶ [0037], [0141], [0144]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claims 5, 14, and 20
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Maurer further teaches:
wherein each prior virtual communication event comprises a commentary feature set indicating participant feedback information associated with the prior virtual communication event, and wherein the method further comprises: {Each communication event (e.g., virtual communication session or meeting) includes user feedback such as messages and reactions. ¶¶ [0037], [0137], [0146]}
for each participant user of the at least one upcoming virtual communication event: identifying, from the commentary feature set, a commentary feature subset indicating participant feedback information corresponding to the participant user; {User inputs and feedback (e.g., messages, reactions) is identified by the system. ¶¶ [0038], [0137]}
accessing a stored profile representing event content preferences associated with the participant user, the stored profile comprising recorded user interactions of the participant user during the one or more prior virtual communication events; {The system may store user profiles containing past interactions and inferred preferences. ¶¶ [0038], [0042]}
generating, using the stored profile of the participant user, a priority sequence for the identified commentary feature subset; and {The system applies weights and ranks content based on user factors. ¶¶ [0038], [0141]}
determining, using the identified commentary feature subset and the priority sequence, one or more recorded digital artifacts representing supplementary event contents pertinent to the participant user for the at least one upcoming virtual communication event. {The system supports selecting relevant outputs (e.g., documents, files, links) based on user context and ranked data. ¶¶ [0037], [0141], [0144]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claim 6
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Maurer further teaches:
causing the generative machine learning model to generate, using the determined one or more recorded digital artifacts and the stored profile of the participant user, a response indicating at least one personalized user action of the participant user for the upcoming virtual communication event. {The system supports generating outputs specific to users (e.g., summaries, tasks) using ML models based on contextual data. ¶¶ [0037] – [0038], [0141],[0158]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claims 7 and 15
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Maurer further teaches:
receiving, from the user interface, a user query for information associated with a virtual communication event, the user query comprising a content feature set indicating contextual attributes associated with the user query; {The system may receive user queries with associated contextual metadata. ¶¶ [0065], [0168]}
generating, using the content feature set of the received user query, a modified user query comprising a content feature subset that share content similarities to an event feature set of the virtual communication event; {The system supports processing query content via semantic comparison (i.e., similarity). ¶¶ [0144], [0150]}
determining, via a semantic encoder, an embedded identifier for the modified user query based on the content feature subset; {“The ML model(s) 142 can classify text based on comparing embeddings associated with text from the meeting with embeddings (e.g., intermediate output data of the ML model(s) 142 such as a vector encoding a portion of data) associated with the channel to determine relevancy data.” ¶ [0144]}
identifying, from the remote database, a digital artifact set representing supplemental event contents of prior virtual communication events, each digital artifact comprising an embedded content identifier that satisfies a similarity threshold in comparison with the embedded identifier for the modified user query; {The system allows for selecting content (e.g., files, links) based on embedding similarity and thresholds. ¶¶ [0141], [0144]}
accessing a stored profile representing event content preferences associated with the user identifier; {The system stores user profiles with preferences and historical interactions. ¶¶ [0038], [0042]}
generating, using the stored profile of the user identifier, a priority sequence for the identified digital artifacts of the digital artifact set; {Weights and ranks are applied to communications and topics. ¶¶ [0038], [0141]}
causing the generative machine learning model to generate a response to the modified user query using the identified digital artifact set and the generated priority sequence for digital artifacts of the digital artifact set; and {The ML model generates outputs based on elected and prioritized content. ¶¶ [0037], [0158]}
generating for display, at the user interface, the response to the modified user query. {“[C]ausing display of the teleconferencing meeting summary. In at least some examples, causing display of the teleconferencing meeting summary may be initiated by a user request.” ¶ [0153]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claim 8
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Maurer further teaches:
wherein the user query for content information is received during a real-time virtual communication event, and wherein the generative machine learning model is caused to generate a real-time response to the modified user query. {The system supports receiving user inputs (e.g., messages or text) during real-time virtual communication events (¶¶ [0033], [0146]) and generating outputs in real-time or near real-time using ML models during the session. ¶¶ [0158], [0167]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Dotan-Cohen and Maurer to include the ML transcription and analysis of virtual communications features of Maurer, to improve the relevance and usefulness of generated summaries by leveraging contextual data and user interactions to identify and prioritize meaningful content within virtual communications. (See ¶ [0037] of Maurer).
Claim 9
The combination of Dotan-Cohen and Maurer teaches the limitations set forth above. Dotan-Cohen further discloses:
wherein the recommended user actions of the generated natural language response include presentation of content information embedded in the at least one recorded digital artifact, participation in educational resources, proposal of enterprise activity, or a combination thereof. {The system supports generating outputs that include presentation of content derived from stored event data (e.g., files, contextual information) and prompting users to perform actions. ¶¶ [0053], [0075], [0104]}
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure (additional pertinent references can be found on attached form PTO-892):
US 20170316383 A1, which teaches: A meeting request is received that specifies a first participant account and a proposed topic for a meeting. A database is traversed to determine one or more second participant accounts for the meeting that are linked to the proposed topic. The meeting is scheduled with the first participant account and the one or more second participant accounts. The database contains data for a plurality of accounts (including the first participant account and the one or more second participant account) associated with entities, a plurality of events involving the entities, a plurality of prior topics derived from the events, and a plurality of links between the accounts, events and prior topics.
US 20230306445 A1, which teaches: In some implementations, a device may obtain historical information associated with user engagement with one or more historical communications associated with a user account. The device may train a machine learning model, using the historical information, to predict at least one of preferred communication channels, preferred communication timings, or preferred communication content associated with the user account. The device may determine that a communication associated with the user account is to be transmitted. The device may obtain, from the machine learning model and by the device, recommendation information including at least one of a recommended timing, a recommended communication channel, or a recommended content of the communication based on providing information associated with the user account to the machine learning model. The device may generate the communication according to the recommendation information.
“Learning about meetings” (NPL attached), which teaches: Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim in this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: (i) it is possible to automatically detect when during the meeting a key decision is taking place, from analyzing only the local dialogue acts, (ii) there are common patterns in the way social dialogue acts are interspersed throughout a meeting, (iii) at the time key decisions are made, the amount of time left in the meeting can be predicted from the amount of time that has passed, (iv) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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