DETAILED ACTION
This action is responsive to communications filed 31 August 2026.
Claims 1-20 are subject to examination.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 31 August 2026 has been entered.
Response to Arguments
Applicant’s arguments 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.
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.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS.
Regarding claim 1, Deng discloses:
A computerized meeting system configured to provide advanced data to determine the respective skills of meeting participants ([1:56-2:22] system for managing a communication session between a number of participants … system priority recommendation based on respective a participant’s familiarity to the topic, see [7:25-41] personalized user model, which is learned from the user’s historical meeting participation data and professional experience data (i.e. determining the respective skills of meeting participants)), wherein the computerized meeting system comprises:
a meeting server configured to host meetings ([9:55-15] meeting server [18:58-19:6] communication session is hosted over one or more networks by the system);
a plurality of participant devices in communication with the meeting server ([18:58-19:6] provide a service that enables users of the client computing devices 606(1) through 606(N) to participate in the communication session), wherein each participant device is assigned to a unique participant ([22:28-38] client computing device(s) may use their respective profile modules to generate participant profiles … may include one or more of an identity of a user … (e.g., a name, a unique identifier (“ID”), etc.) … register participants for the communication session (i.e. one user per device that is registered on a unique identifier are assigned to unique participants per device));
a participant skill determination engine in communication with the meeting server and with a host device that is assigned to a meeting host ([9:55-15] meeting server … include one or more analysis modules (i.e. participant skill determination engine) and one or more tools modules … remote participant devices, see [4:4-13] participant may have permissions to participate as a moderator of a meeting (i.e. meeting host, e.g. on host device));
a first database of participant attributes in communication with the participant skill determination engine ([FIG. 9] professional experience data, see [7:25-41] topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … professional experience data … database or resource that provides any information regarding a person’s career (i.e. analysis module learns from information from the database) [12:19-27] data can be stored in … professional experience data (i.e. database for skill attributes)), the first database of participant attributes includes self-identified expertise attributes ([7:25-41] professional experience data can come from social media sites, e.g. LinkedIn, or any other database or resource that provides any information regarding a person’s career (i.e. self-identified expertise attributes e.g. on LinkedIn)); and
a second database of participant participation in prior meetings ([FIG. 9] Individual Historical Meeting Data, see [7:25-41] historical meeting participation data can include any information pertaining to a previous meeting or any documents that a participant has generated or reviewed in the past [12:19-27] data can be stored in individual historical meeting data (i.e. database for participation) [12:61-67] see also [25:35-47] data store may store session data … profile data and/or other data), wherein the second database is in communication with the participant skill determination engine ([7:25-41] topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … individual historical meeting data (e.g. as above));
wherein the participant skill determination engine is provided a meeting topic by the meeting server while the current meeting is in session ([7:25-41] topic familiarity score can be determined by an analysis module … uses a topic of a meeting and compares that topic with each attendee’s personalized user model … system may analyze documentation, conversation transcripts, chat messages or any other shared information in a meeting to determine a topic of the meeting), and wherein the participant skill determination engine determines the meeting topic ([7:25-41] determine a topic of the meeting), or a change in the meeting topic, based on conversation taking place during the current meeting ([7:25-41] conversation transcripts, chat messages or any other shared information in a meeting), and based on the meeting topic queries the first database and the second database to (a) determine a name of a participant suited to address the meeting topic ([12:19-37] topic information is passed to a personalized user model to find a topic familiarity score … data from past meetings or user’s professional experience data, see [16:24-33] top recommendation [FIG. 2A] e.g. CZ), and (b) communicate the participant name to the host device ([16:24-33] top system recommended speaker, see [FIG. 2A] e.g. CZ, SME, on the display in recommendation box).
Deng does not explicitly disclose:
a second database of participant participation in a current meeting;
However, BMS discloses:
a second database of participant participation in a current meeting ([0075] one or more memory devices, databases, or enterprise storage locations as repositories … store enterprise meeting information (e.g., including meeting participants), see [0023] learn and recommend the best SME(s) to consult … may also provide suggestions to the SME(s) to handle the queries … based on … participation information);
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of BMS to have the second database also comprise participant participation in a current meeting. One of ordinary skill in the art would have been motivated to do so to learn and recommend the best SME(s) and provide suggestions to the SME(s) to handle the queries (BMS, [0023]).
Regarding claim 2, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng discloses:
wherein after the participant name is communicated to the host device ([16:24-33] top system recommended speaker, see [FIG. 2A] e.g. CZ, SME, on the display in recommendation box),
Deng does not explicitly disclose:
the host queries using the host device a unique one of the plurality of participant devices assigned to the participant suited to address the meeting topic, wherein the query regards the meeting topic.
However, BMS discloses:
the host queries using the host device a unique one of the plurality of participant devices assigned to the participant suited to address the meeting topic ([0024] recommends the best SME(s), and reaches out to the SME(s) transparently with the learned response suggestions), wherein the query regards the meeting topic ([0029] reminds SME(s) about the conference meeting with at least a portion of … assigned topic).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of BMS to have the host query the participant suited to address the meeting topic with a query regarding the meeting topic. One of ordinary skill in the art would have been motivated to do so to learn and recommend the best SME(s) to consult and handle queries quickly (BMS, [0023]).
Regarding claim 3, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng discloses:
wherein the first database or the second database is resident on a backend server ([7:25-41] any other database or resource that provides any information regarding a person’s career, see [25:24-47] data store 708 includes a corpus and/or a relational database, see [FIG. 14] e.g. a device 700), the meeting server, or the participant skill determination engine.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Bell et al. (US-20160028895-A1) hereinafter Bell.
Regarding claim 5, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng-BMS do not explicitly disclose:
wherein the host utilizes the host device to initiate the participant skill determination engine to search the first database and the second database for each meeting participant.
However, Bell discloses:
wherein the host utilizes the host device to initiate the participant skill determination engine to search the first database and the second database for each meeting participant ([0026-0027] expert identification program 122 then identifies the topic being discussed in the conference call … keywords with the speech of the host or presenter (i.e. initiating an expert identification) … determines the experts … by matching a participants determined skills or subjects of expertise with the topic being discussed … skills dictionary).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Bell to have utilized the host device to initiate the participant skill determination engine to search the database for each meeting participant. One of ordinary skill in the art would have been motivated to do so to identify the topic being discussed in the conference call with the speech of the host or presenter to determine the experts (Bell, [0026-0027]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Ward (US-12106393-B1).
Regarding claim 4, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above, that further comprises:
Deng discloses:
a speech recognition processor in communication with the participant skill determination engine and configured to generate text from spoken language during the current meeting ([11:45-58] topics being discussed output of the dialog monitor … speech signals using speech recognition … to determine a topic … determine if the participant is a good candidate for a discussion topic (i.e. from the topic ascertained via speech recognition requires the engine to be in communication)), and
Deng does not explicitly disclose:
a large language module (LLM) in communication with the speech recognition processor and configured to analyze the text to determine the meeting topic provided to the skill determination engine.
However, Ward discloses:
a large language module (LLM) in communication with the speech recognition processor and configured to analyze the text to determine the meeting topic provided to the skill determination engine ([15:64-16:4] large language model (LLM) with spoken and written data may be used to understand in assist in determining speech topics, sentiment analysis, and tone and/or emotions detection).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of Ward to have utilized a LLM in communication with the speech recognition processor to analyze the text to determine the meeting topic that was provided to the skill determination engine. One of ordinary skill in the art would have been motivated to do so to determine speech topics via LLM and more (Ward, [15:64-16:4]).
Claim(s) 6, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Smith et al. (US-20090025092-A1) hereinafter Smith further in view of Golshan (US-20140019443-A1).
Regarding claim 6, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng-BMS do not explicitly disclose:
that further comprises one or more APIs in communication with the first database wherein each of the one or more APIs is configured to (a) communicate with at least one authorized private data source that includes private data related to the skill of a meeting participant, and (b) cause the private data to be transmitted to the first database, wherein private data transmitted to the first database is used by the participant skill determination engine while the current meeting is in session and based on the meeting topic to determine the participant suited to address the meeting topic.
However, Smith discloses:
that further comprises one or more APIs in communication with the first database wherein each of the one or more APIs is configured to (a) communicate with at least one authorized private data source that includes private data of a meeting participant ([0013] secure online data storage and retrieval system is provided … storing personal data provided by users … retrieve personal data … API source code interface or other secure method of transmission may be used for this purpose, see [FIG. 2]), and (b) cause the private data to be transmitted to the first database ([0015] website may transmit authenticated copies of the personal data to organizations or entities).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Smith to have one or more APIS in communication with the first database wherein each of the one or more APIs is configured to communicate with at least one authorized private data source that includes private data related to the skill of a meeting participant and transmit it to the first database. One of ordinary skill in the art would have been motivated to do so to have a secure online data storage and retrieval system (Smith, [0013]).
Deng-BMS-Smith do not explicitly disclose:
communicate with at least one authorized private data source that includes private data related to the skill of a meeting participant;
wherein private data transmitted to the first database is used by the participant skill determination engine while the current meeting is in session and based on the meeting topic to determine the participant suited to address the meeting topic.
However, Golshan discloses:
communicate with at least one authorized private data source that includes private data related to the skill of a meeting participant ([0269] processing the user’s private content (e.g. corresponds to areas of expertise of the user, see [0071]));
wherein private data transmitted to the first database is used by the participant skill determination engine while the current meeting is in session and based on the meeting topic to determine the participant suited to address the meeting topic ([0269] in each user’s matrix, the AOE (see [0071] areas of expertise (AOE)) data structure may track a rank ordered list of AOEs that correspond to the expertise of the user as determined from processing of the user’s public and private content).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Smith in view of Golshan to have the private data used by participant skill determination engine to determine the suited participant. One of ordinary skill in the art would have bene motivated to do so to determine the expertise of the user by processing both public and private content (Golshan, [0269]).
Regarding claim 8, Deng-BMS-Smith-Golshan disclose:
The computerized meeting system of claim 6, set forth above,
Deng-BMS do not explicitly disclose:
wherein participant consent is secured for accessing the participant’s data from the at least one authorized private data source.
However, Smith discloses:
wherein participant consent is secured for accessing the participant’s data from the at least one authorized private data source ([0026] initiate the process by sending to the administrator of database a request … such as an authorization letter (i.e. participant consent)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Smith to have participant consent for accessing the participant’s private data. One of ordinary skill in the art would have been motivated to do so to protect authenticated documents from being copied or forged by unauthorized end-users or third parties (Smith, [0030]).
Claim(s) 7, 10-11 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Golshan (US-20140019443-A1).
Regarding claim 7, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng-BMS do not explicitly disclose:
that further comprises a web scraping tool in communication with the first database, wherein the web scraping tool is configured to collect public data from publicly available sources, including social media and professional websites, and to transfer the public data to the first database.
However, Golshan discloses:
that further comprises a web scraping tool in communication with the first database ([0066] network system 100 includes a discovery system 130, see [0081] infused crawler (i.e. scraping tool)), wherein the web scraping tool is configured to collect public data from publicly available sources ([0066] that identifies public content elements 105 of predicted interest to a user … based on the … expertise of the authors 120), including social media and professional websites ([0116] professional networks [0120] social media site), and to transfer the public data to the first database ([0072] examines public content elements … develops an author profile for each other … identifies its author, see [0097] stored within … master profile … user profile … author profile … catalogs (i.e. database)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Golshan to have a web scraping tool in communication with the first database to collect public data including social media and professional websites. One of ordinary skill in the art would have been motivated to do so to build filters for the infused crawler to use to search the computer network for public content elements pertaining to the areas of interest (Golshan, [0081]).
Regarding claim 10, Deng-BMS disclose:
The computerized meeting system of claim 1, set forth above,
Deng-BMS do not explicitly disclose:
that further includes a processor to deduplicate data in the first database and the second database in order to eliminate redundancy to assist in obtaining accurate respective skills of meeting participants.
However, Golshan discloses:
that further includes a processor to deduplicate data in the first database and the second database in order to eliminate redundancy to assist in obtaining accurate respective skills of meeting participants ([0071] PHRI … characterize a user’s … areas of expertise (AOE), and sentiment, intentions, depth and credibility within each … AOE [0320] between the PHRIs, there may be one or more delta-examine module(s) which may interface with the rest API to assist in ensuring there is no duplication of data, see [0111] merge two author profiles into one (i.e. deduplication)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Golshan to have deduplicated duplicate data in obtaining accurate results. One of ordinary skill in the art would have been motivated to do so to ensure there is no duplication of data such as to disambiguate author identities (Golshan, [0111] [0320]).
Regarding claim 11, Deng discloses:
A computerized method for providing advanced data to determine the respective skills of meeting participants ([1:56-2:22] system for managing a communication session between a number of participants … system priority recommendation based on respective a participant’s familiarity to the topic, see [7:25-41] personalized user model, which is learned from the user’s historical meeting participation data and professional experience data (i.e. determining the respective skills of meeting participants)), wherein the computerized method comprises the following steps:
the meeting server hosting a current meeting ([9:55-15] meeting server [18:58-19:6] communication session is hosted over one or more networks by the system);
a plurality of meeting participants joining the current meeting by using participant devices in communication with the meeting server ([18:58-19:6] provide a service that enables users of the client computing devices 606(1) through 606(N) to participate in the communication session), and wherein each participant device is assigned to a unique one of the plurality of participants ([22:28-38] client computing device(s) may use their respective profile modules to generate participant profiles … may include one or more of an identity of a user … (e.g., a name, a unique identifier (“ID”), etc.) … register participants for the communication session (i.e. one user per device that is registered on a unique identifier are assigned to unique participants per device));
a participant skill determination engine collecting attributes of each of the plurality of meeting participants from a first database ([FIG. 9] professional experience data, see [7:25-41] topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … professional experience data … database or resource that provides any information regarding a person’s career (i.e. analysis module learns from information from the database) [12:19-27] data can be stored in … professional experience data (i.e. database for skill attributes)), wherein the attributes were obtained from one or more public databases ([7:25-41] professional experience data can come from social media sites, e.g. LinkedIn, or any other database or resource that provides any information regarding a person’s career (e.g. public social media, databases, etc.)), the attributes include self-identified expertise attributes ([7:25-41] professional experience data can come from social media sites, e.g. LinkedIn, or any other database or resource that provides any information regarding a person’s career (i.e. self-identified expertise attributes e.g. on LinkedIn)); and
the participant skill determination engine collecting attributes of each of the plurality of meeting participants from a second database ([FIG. 9] Individual Historical Meeting Data, see [7:25-41] historical meeting participation data can include any information pertaining to a previous meeting or any documents that a participant has generated or reviewed in the past [12:19-27] data can be stored in individual historical meeting data (i.e. database for participation) [12:61-67] see also [25:35-47] data store may store session data … profile data and/or other data), wherein the attributes are based on participant participation in prior meetings ([7:25-41] topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … individual historical meeting data (e.g. as above));
the meeting server providing a meeting topic, and keywords and phrases related to the meeting topic, to the participant skill determination engine while the current meeting is in session ([7:25-41] topic familiarity score can be determined by an analysis module … uses a topic of a meeting and compares that topic with each attendee’s personalized user model … system may analyze documentation, conversation transcripts, chat messages or any other shared information in a meeting to determine a topic of the meeting, see [11:45-49] speech signals, natural language understanding, and conversational AI techniques (i.e. understanding key words and phrases)), and wherein the participant skill determination engine determines the meeting topic ([7:25-41] determine a topic of the meeting), or a change in the meeting topic, based on conversation taking place during the current meeting ([7:25-41] conversation transcripts, chat messages or any other shared information in a meeting), and based on the meeting topic and keywords and phrases, the participant skill determination engine determining a name of a meeting participant suited to address the meeting topic ([12:19-37] topic information is passed to a personalized user model to find a topic familiarity score … data from past meetings or user’s professional experience data, see [16:24-33] top recommendation [FIG. 2A] e.g. CZ) and communicating the participant name to a host device ([16:24-33] top system recommended speaker, see [FIG. 2A] e.g. CZ, SME, on the display in recommendation box).
Deng does not explicitly disclose:
wherein the attributes were obtained from one or more private databases,
wherein the attributes are based on participant participation in the current meeting,
However, BMS discloses:
wherein the attributes are based on participant participation in the current meeting ([0075] one or more memory devices, databases, or enterprise storage locations as repositories … store enterprise meeting information (e.g., including meeting participants), see [0023] learn and recommend the best SME(s) to consult … may also provide suggestions to the SME(s) to handle the queries … based on … participation information);
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of BMS to have the second database also comprise participant participation in a current meeting. One of ordinary skill in the art would have been motivated to do so to learn and recommend the best SME(s) and provide suggestions to the SME(s) to handle the queries (BMS, [0023]).
Deng-BMS do not explicitly disclose:
wherein the attributes were obtained from one or more private databases,
However, Golshan discloses:
wherein the attributes were obtained from one or more private databases ([0269] in each user’s matrix, the AOE (see [0071] areas of expertise (AOE)) data structure may track a rank ordered list of AOEs that correspond to the expertise of the user as determined from processing of the user’s public and private content).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Golshan to have the private data used by participant skill determination engine to determine the suited participant. One of ordinary skill in the art would have bene motivated to do so to determine the expertise of the user by processing both public and private content (Golshan, [0269]).
Regarding claim 14, Deng-BMS-Golshan disclose:
The computerized method of claim 11, set forth above, that further comprises the steps of
Deng discloses:
(a) using algorithms to analyze a categorized data to detect correlations and patterns ([12:19-60] data mining algorithms … can be used to classify user’s background data … searches the user model for a match … familiarity score), and (b) applying predictive models to assess each participant’s skill level for a topic ([12:19-60] Famscore=0.5*(NumYearExperience/10)+0.5*(NumActiveDiscussions/50) … once neural network is trained, it can be used to evaluate individual participation score, see [15:45-56] tools module maintains two threads with one constantly checking on intend-to-speak in-room and remote participants and another constantly monitoring meeting effectiveness score (i.e. predict participant’s skill level to adjust recommended speakers)).
Regarding claim 15, Deng-BMS-Golshan disclose:
The computerized method of claim 11, set forth above,
Deng discloses:
that further comprises the step of the meeting server and participant skill determination engine compiling a summarized overview of the current meeting ([15:45-56] tools module maintains two threads with one constantly checking on intend-to-speak in-room and remote participants and another constantly monitoring meeting effectiveness score (i.e. predict participant’s skill level to adjust recommended speakers)), in which the overview provides a rating of how accurately the participant skill determination engine participant skill level was assessed for each topic (([15:45-56] tools module maintains two threads with one constantly checking on intend-to-speak in-room and remote participants and another constantly monitoring meeting effectiveness score (i.e. predict participant’s skill level to adjust recommended speakers, e.g. rated on how accurately the skill level was assessed as an expert)).
Regarding claim 16, Deng-BMS-Golshan disclose:
The computerized method of claim 11, set forth above,
Deng discloses:
that further comprises the step of, based on input related to the meeting topic by the meeting participant selected by the participant skill determination engine as suited to address the meeting topic (([15:45-56] tools module maintains two threads with one constantly checking on intend-to-speak in-room and remote participants and another constantly monitoring meeting effectiveness score), the meeting host or the participant skill determination engine, determining a quality of the input to the meeting topic, and assigning an input score to the input (([15:45-56] tools module maintains two threads with one constantly checking on intend-to-speak in-room and remote participants and another constantly monitoring meeting effectiveness score (i.e. predict participant’s skill level to adjust recommended speakers, e.g. rated on how accurately the skill level was assessed as an expert)).
Regarding claim 17, Deng-BMS-Golshan disclose:
The computerized method of claim 16, set forth above,
Deng does not explicitly disclose:
that uses an AI learning module in communication with the participant skill determination engine, wherein the AI learning module or the host device changes an algorithm used by the participant skill determination engine to determine participant skill levels if the input score for a predetermined number of current meeting topics is beneath a preset level.
However, BMS discloses:
that uses an AI learning module in communication with the participant skill determination engine ([0075] AI-bot service may utilize one or more memory devices, databases ... data stored in various databases may be used by AWL engine to train and arrive at the best possible SME discovery ML model (i.e. predictive model to determine best SME by profile such as skills)), wherein the AI learning module or the host device changes an algorithm used by the participant skill determination engine to determine participant skill levels if the input score for a predetermined number of current meeting topics is beneath a preset level ([0147] confidence value of the suggested response is not higher than the predetermined confidence level value threshold [0151-0152] feedback may be used by the AI/ML to provide better future SME selections, see [0075] arrive at the best possible SME discovery ML model)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of BMS to have used an AI learning module to change an algorithm used by the participant skill determination engine if the input score is beneath a preset level. One of ordinary skill in the art would have been motivated to do so to use feedback by the AI/ML to provide better future SME elections and arrive at the best possible SME discovery ML model for discovering SMEs (BMS, [0075] [0151-0152]).
Regarding claim 18, Deng discloses:
A computerized meeting apparatus configured to provide advanced data to determine the respective skill of meeting participants ([1:56-2:22] system for managing a communication session between a number of participants … system priority recommendation based on respective a participant’s familiarity to the topic, see [7:25-41] personalized user model, which is learned from the user’s historical meeting participation data and professional experience data (i.e. determining the respective skills of meeting participants), see also [FIG. 14] device 700), wherein the computerized meeting apparatus comprises: (a) a meeting server ([9:55-15] meeting server); (b) a plurality of participant devices in communication with the meeting server ([18:58-19:6] provide a service that enables users of the client computing devices 606(1) through 606(N) to participate in the communication session), wherein each participant device is assigned to a unique participant ([22:28-38] client computing device(s) may use their respective profile modules to generate participant profiles … may include one or more of an identity of a user … (e.g., a name, a unique identifier (“ID”), etc.) … register participants for the communication session (i.e. one user per device that is registered on a unique identifier are assigned to unique participants per device)); (c) a participant skill determination engine in communication with the meeting server and with a host device ([9:55-15] meeting server … include one or more analysis modules (i.e. participant skill determination engine) and one or more tools modules … remote participant devices, see [4:4-13] participant may have permissions to participate as a moderator of a meeting (i.e. meeting host, e.g. on host device)); (d) a first database of participant attributes gathered from public data sources ([FIG. 9] professional experience data, see [7:25-41] topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … professional experience data can come from social media sites, e.g. LinkedIn, or any other database or resource that provides any information regarding a person’s career (e.g. public social media, databases, etc.) (i.e. analysis module learns from information from the database) [12:19-27] data can be stored in … professional experience data (i.e. database for skill attributes)); (e) a second database of participant participation in prior meetings ([FIG. 9] Individual Historical Meeting Data, see [7:25-41] historical meeting participation data can include any information pertaining to a previous meeting or any documents that a participant has generated or reviewed in the past … topic familiarity score can be determined by an analysis module … compares that topic with each attendee’s personalized user model … learned from … individual historical meeting data (e.g. as above) [12:19-27] data can be stored in individual historical meeting data (i.e. database for participation) [12:61-67] see also [25:35-47] data store may store session data … profile data and/or other data); and (f) a processor and a tangible, non-transitory memory configured to communicate with the processor, the non-transitory memory having stored instructions which, when executed by the processor, are configured to cause the computerized meeting apparatus to execute a method including the following steps:
the meeting server starting the current meeting ([18:58-19:6] communication session is hosted over one or more networks by the system);
each of the plurality of user devices connecting to the current meeting via the meeting server ([18:58-19:6] provide a service that enables users of the client computing devices 606(1) through 606(N) to participate in the communication session);
the meeting server or the host device communicating a topic of the current meeting to the participant skill determination engine while the current meeting is in session ([7:25-41] topic familiarity score can be determined by an analysis module … uses a topic of a meeting and compares that topic with each attendee’s personalized user model … system may analyze documentation, conversation transcripts, chat messages or any other shared information in a meeting to determine a topic of the meeting, see [11:45-49] speech signals, natural language understanding, and conversational AI techniques), wherein the participant skill determination engine determines the topic ([7:25-41] determine a topic of the meeting), or a change in the topic, based on conversation taking place during the current meeting ([7:25-41] conversation transcripts, chat messages or any other shared information in a meeting);
the participant skill determination engine, based on the topic, querying the first database and the second database to determine a name of a participant suited to address the topic ([12:19-37] topic information is passed to a personalized user model to find a topic familiarity score … data from past meetings or user’s professional experience data, see [16:24-33] top recommendation [FIG. 2A] e.g., CZ) and to communicate the participant name to the host device ([16:24-33] top system recommended speaker, see [FIG. 2A] e.g. CZ, SME, on the display in recommendation box); and
Deng does not explicitly disclose:
a first database of participant attributes gathered from authorized private data sources;
a second database of participant participation in a current meeting;
a host either querying the participant identified by the participant skill determination engine as suited to address the topic, or instead direct a query to a known expert on the topic in order to enhance discussion efficiency and outcome relevancy.
However, BMS discloses:
a second database of participant participation in a current meeting ([0075] one or more memory devices, databases, or enterprise storage locations as repositories … store enterprise meeting information (e.g., including meeting participants), see [0023] learn and recommend the best SME(s) to consult … may also provide suggestions to the SME(s) to handle the queries … based on … participation information);
a host either querying the participant identified by the participant skill determination engine as suited to address the topic ([0024] recommends the best SME(s), and reaches out to the SME(s) transparently with the learned response suggestions), or instead direct a query to a known expert on the topic in order to enhance discussion efficiency and outcome relevancy ([0128] option may be made available for a moderator, or other privileged participant, of the conference meeting to configure “Instant SME Consultation” … one or more SME(s) are known prior to the conference meeting).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of BMS to have the second database also comprise participant participation in a current meeting and a host querying the suitable participant or directly to a known expert. One of ordinary skill in the art would have been motivated to do so to learn and recommend the best SME(s) and provide suggestions to the SME(s) to handle the queries as well as configure for an instant SME consultation where one or more SME(s) are known (BMS, [0023] [0128]).
Deng-BMS do not explicitly disclose:
a first database of participant attributes gathered from authorized private data sources;
However, Golshan discloses:
a first database of participant attributes gathered from authorized private data sources ([0269] in each user’s matrix, the AOE (see [0071] areas of expertise (AOE)) data structure may track a rank ordered list of AOEs that correspond to the expertise of the user as determined from processing of the user’s public and private content (i.e. to access private content requires authorization to access));
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS in view of Golshan to have the private data used by participant skill determination engine to determine the suited participant. One of ordinary skill in the art would have bene motivated to do so to determine the expertise of the user by processing both public and private content (Golshan, [0269]).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Golshan (US-20140019443-A1) further in view of Scafaria (US-20200111172-A1).
Regarding claim 13, Deng-BMS-Golshan disclose:
The computerized method of claim 11, set forth above,
Deng discloses:
that further comprises the steps of using a natural language processor (NLP) in communication with the participant skill determination engine ([11:45-49] speech recognition, natural language understanding, and conversational AI techniques to determine a topic (e.g. to determine expert in topic as above)),
Deng does not explicitly disclose:
wherein the NLP identifies context and nuance of the speech of each of the plurality of meeting participants stored in the second database, in order to determine sentiment and assist in enhancing the determination of participant skill level.
However, Scafaria discloses:
wherein the NLP identifies context and nuance of the speech of each of the plurality of meeting participants stored in the second database ([0029] analyze (through NLP) of the number, frequency, and quality of words and phrases … can aid in determining an individual’s expertise), in order to determine sentiment and assist in enhancing the determination of participant skill level ([0029] reply emails to Alex contain positive language when discussing “machine learning.” … likely expert in “machine learning”).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng in view of Scafaria to have NLP identify context and nuance of the speech of each of the plurality of meeting participants stored in the second database in order to determine sentiment and assist in enhancing the determination of participant skill level. One of ordinary skill in the art would have been motivated to do so to aid in determining an individual’s skill level with NLP (Scafaria, [0029]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Golshan (US-20140019443-A1) further in view of Fahrendorff et al. (US-20210056860-A1) hereinafter Fahrendorff.
Regarding claim 19, Deng-BMS-Golshan disclose:
The computerized meeting apparatus of claim 18, wherein when the stored instructions are executed by the processor, are configured to, set forth above,
Deng-BMS-Golshan do not explicitly disclose:
provide one or more topics to the participant skill determination engine prior to the start of the current meeting), and based on the one or more topics, the participation skills engine queries the first database and the second database to determine a plurality of names of participants suited to attend the current meeting and to communicate the name of the plurality of participants to the host device, wherein the host device uses plurality of names of participants to tailor invitations to the current meeting.
However, Fahrendorff discloses:
provide one or more topics to the participant skill determination engine prior to the start of the current meeting ([0208-0221] when a meeting is proposed, system refers to a database for the skillset-related keyword matches … meeting agenda, title … makes meeting participant recommendations for hosts to consider (i.e. system to obtain the meeting agenda/title and proposing information to a host from reference to a database is in a communication with the server, host, and databases)), and based on the one or more topics ([0208-0221] agenda/title as above), the participation skills engine queries the first database ([0208-0221] when a meeting is proposed, system refers to a database for the skillset-related keyword matches … meeting agenda, title … makes meeting participant recommendations for hosts to consider (i.e. system to obtain the meeting agenda/title and proposing information to a host from reference to a database is in a communication with the server, host, and databases)) and the second database ([0208-0221] when a meeting is proposed, system refers to a database for the skillset-related keyword matches … meeting agenda, title … makes meeting participant recommendations for hosts to consider (i.e. system to obtain the meeting agenda/title and proposing information to a host from reference to a database is in a communication with the server, host, and databases)) to determine a plurality of names of participants suited to attend the current meeting ([0208-0221] allowing hosts/facilitators to factor in the availability of the right experts for meetings … makes meeting participant recommendations for hosts to consider … invites participants based on the selected topic and the individual’s expertise (i.e. e.g. based on database reference for skillset-related keyword matches)) and to communicate the name of the plurality of participants to the host device ([0208] makes meeting participant recommendations for hosts to consider (i.e. for hosts to obtain the recommendations by system requires to communicate them to the host)), wherein the host device uses plurality of names of participants to tailor invitations to the current meeting ([0208-0221] invites participants based on the selected topic and the individuals expertise (e.g., as participant recommendations for hosts to consider, wherein inviting is to query the participant to join/attend)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Golshan in view of Fahrendorff to have performed the actions prior to a meeting start and utilized the databases to invite attendees to the current meeting. One of ordinary skill in the art would have been motivated to do so to make meeting participant recommendations for hosts to consider based on the selected topics and the individual’s expertise (Fahrendorff, [0208-0211]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Smith et al. (US-20090025092-A1) hereinafter Smith further in view of Golshan (US-20140019443-A1) further in view of Malfavon (US-20220022747-A1).
Regarding claim 9, Deng-BMS-Smith-Golshan disclose:
The computerized meeting system of claim 8, set forth above,
Deng-BMS-Smith-Golshan do not explicitly disclose:
that further includes a database of privacy laws in communication with the first database and the one or more APIs, and data of at least one authorized private data source is collected in accordance with privacy laws in the database of privacy laws.
However, Malfavon discloses:
that further includes a database of privacy laws in communication with the first database and the one or more APIs ([0208] privacy laws that can be stored in database 311 … TI app performs this function … to determine what personal information can and cannot be collected legally), and data of at least one authorized private data source is collected in accordance with privacy laws in the database of privacy laws ([0212] TI App collects basic personal information (i.e. in accordance to laws in [0208])).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Smith-Golshan in view of Malfavon to have a database of privacy laws in communication with the first database and the one or more APIs to collect data in accordance with privacy laws. One of ordinary skill in the art would have been motivated to do so to determine what personal information can and cannot be collected legally (Malfavon, [0208]).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Golshan (US-20140019443-A1) further in view of Hambridge et al. (US-20180330305-A1) hereinafter Hambridge.
Regarding claim 12, Deng-BMS-Golshan disclose:
The computerized method of claim 11, set forth above,
Deng-BMS-Golshan do not explicitly disclose:
that further comprises the step of providing to the participant skill determination engine to assist in determining the name of a meeting participant suited to address the topic, each meeting participant’s (a) job title, (b) education background, (c) work history, (d) hobbies, and (e) professional certifications.
However, Hambridge discloses:
that further comprises the step of providing to the participant skill determination engine to assist in determining the name of a meeting participant suited to address the topic ([0023] access a variety of information … candidate pool … user profile [0033] can select, in real time, at least one candidate from the candidate pool to assign a task of resolving the issue), each meeting participant’s (a) job title ([0027] candidate’s work location (i.e. job title, e.g. work name)), (b) education background ([0027] candidate’s skill sets and/or certifications (i.e. requires an education background to retrieve the certification)), (c) work history ([0027] historical information related to how the candidate has responded to past on-call notifications/working patterns of the candidate), (d) hobbies ([0048] infer an activity of the user to be recreation based on previous patterns of behavior of the candidate), and (e) professional certifications ([0027] candidate’s skill sets and/or certifications).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Golshan in view of Hambridge to have provided a job title, education background, work history, hobbies and professional certifications to the engine to assist in determining a participant suited to address the topic. One of ordinary skill in the art would have been motivated to do so to ensure that the candidates that are selected are not only available to immediately respond to the issue, but also well qualified to resolve the issue (Hambridge, [0009]).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US-11770425-B2) hereinafter Deng in view of B M S et al. (US-20210390144-A1) hereinafter BMS further in view of Golshan (US-20140019443-A1) further in view of Hambridge et al. (US-20180330305-A1) hereinafter Hambridge further in view of Tabrizi et al. (US-9735973-B2) hereinafter Tabrizi.
Regarding claim 20, Deng-BMS-Golshan disclose:
The computerized meeting apparatus of claim 18, set forth above,
Deng-BMS-Golshan do not explicitly disclose:
wherein the participant attributes comprise (a) education, (b) title, (c) type of work experience, (d) length of work experience, (e) current job title, (f) past job titles, (g) hobbies, (h) presentations, (i) awards, and (j) training courses taken.
However, Hambridge discloses:
wherein the participant attributes comprise (a) education ([0027] candidate’s skill sets and/or certifications (i.e. requires an education background to retrieve the certification)), (b) title ([0027] candidate’s work location (i.e. job title, e.g. work name)), (c) type of work experience ([0027] historical information related to how the candidate has responded to past on-call notifications/working patterns of the candidate), (d) length of work experience ([0027] historical information related to how the candidate has responded to past on-call notifications/working patterns of the candidate), (e) current job title ([0027] historical information related to how the candidate has responded to past on-call notifications/working patterns of the candidate … work location (i.e. job title as above, e.g. work name)), (f) past job titles ([0027] historical information related to how the candidate has responded to past on-call notifications/working patterns of the candidate … work location (i.e. job title as above, e.g. work name)), (g) hobbies ([0048] infer an activity of the user to be recreation based on previous patterns of behavior of the candidate), and (j) training courses taken ([0027] candidate’s skill sets and/or certifications (i.e. requires an education background to retrieve the certification, e.g. training courses)).
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Golshan in view of Hambridge to have attributes of education, title, type of work experience, length of work experience, current job title, past job titles, hobbies and training courses. One of ordinary skill in the art would have been motivated to do so to ensure that the candidates that are selected are not only available to immediately respond to the issue, but also well qualified to resolve the issue (Hambridge, [0009]).
Deng-BMS-Golshan-Hambridge do not explicitly disclose:
participant attributes comprise (h) presentations, (i) awards,
However, Tabrizi discloses:
participant attributes comprise (h) presentations ([18:27-63] number and/or character of publications … track record of participating in public discourse), (i) awards ([18:27-63] expert’s awards),
It would have been obvious to one of ordinary skill in the pertinent art before the effective filing date of the claimed invention to modify the invention of Deng-BMS-Golshan-Hambridge in view of Tabrizi to have participant attributes comprise presentations and awards. One of ordinary skill in the art would have been motivated to do so to determine one or more best-rated experts using a variety of factors (Tabrizi, [18:27-63]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bastide et al. (US-11049050-B2) Proactive Communication Channel Controller In A Collaborative Environment;
Spaulding et al. (US-10102290-B2) Methods For Identifying, Ranking, And Displaying Subject Matter Experts On Social Networks;
Bolshinsky et al. (US-20170132519-A1) Automatic Suggestion Of Experts For Electronic Discussions;
Yarnell (US-20180262619-A1) SYSTEM FOR PROVIDING REMOTE EXPERTISE.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alex Tran whose telephone number is (571)272-8173. The examiner can normally be reached Monday-Friday 10AM-6PM ET.
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/Alex Tran/Primary Examiner, Art Unit 2453
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