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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 December 12, 2025 has been entered.
Notice to Applicant
The following is a Non-Final Office Action for Application Serial Number: 18/198,998, filed on May 18, 2023. In response to Examiner's Advisory Action dated November 13, 2025, Applicant on December 12, 2025, amended claims 1, 4, 8, 11 and 24, cancelled claims 5, 12, 15 and 16 and added new claims 27 and 28. Claims 1-3, 7-11, 14, and 21-28 are pending in this application and have been rejected below.
Response to Amendment
Applicant's amendments are acknowledged.
The 35 U.S.C. § 112 rejections of claims 5 and 12, are hereby withdrawn pursuant to Applicant’s canceling claims 5 and 12.
Regarding the 35 U.S.C. 101 rejection, Applicants arguments and amendments have been considered but are insufficient to overcome the rejection.
The 35 U.S.C. § 103 rejections are hereby amended pursuant to Applicants amendments to claims 1 and 8. Updated 35 U.S.C. § 103 rejections have been applied to amended claims 1-3, 7-11, 14, and 21-26. New 35 U.S.C. § 103 rejections have been applied to newly added claims 27 and 28.
Response to Arguments
Applicant's Arguments/Remarks filed December 12, 2025 (hereinafter Applicant Remarks) have been fully considered but are not persuasive. Applicant’s Remarks will be addressed herein below in the order in which they appear in the response filed December 12, 2025.
Regarding Applicant’s argument to the mental process 35 U.S.C. 101 rejection remarks from the Advisory Action (see p. 10-12, Applicant Remarks).
In response, Examiner respectfully disagrees. Examiner respectfully reminds Applicant claims are evaluated to ensure that the claim itself reflects the disclosed improvement; MPEP 21060.04(d)(1). Examiner emphasizes the pending claims do not fully reflect the methods and techniques Applicant describes. Examiner finds even in a computer environment where the claim recites how the computer processes and saves data and in what order, these limitations are still considered abstract. Claims can recite a mental process even if they are claimed as being performed on a computer; see MPEP 2106.04(a)(2)(III)(C). Examiner notes the pending 35 U.S.C. 101 rejections states the claims recite scheduling meetings based on computing optimal meeting date and time from natural language messages with limitations directed to an abstract idea based on both certain methods of organizing human activity and mental processes, not just mental processes. Additionally, Examiner finds although the amended limitations directed toward the persisting of a state of a conversation and updated conversation recites additional elements. Examiner finds, as claimed, these limitations amount to no more than insignificant extra-solution activity of collecting and delivering data because the respective limitations currently are not tied/integrated into the other limitations such that the functions could be considered more than just saying a conversation in memory to deliver to a user. Furthermore, the transformer model of a artificial intelligence chat bot is merely used as a tool to perform the parsing of messages and computing of an optimal meeting, limitations that both mimic human thought processes of observation, evaluations, judgement and opinion, that can feasibly be performed with pen and paper, where the data interpretation is perceptible in the human mind.
Regarding the 35 U.S.C. 101 rejection, Applicant argues the August 4th Memorandum (see p. 18-19).
In response, Examiner finds Applicant’s arguments are not persuasive. Examiner finds parsing meeting messages and computing an optimal meeting date and time based on meeting participate input can be done mentally by the human mind or a human using pen and paper. Both limitations are merely an act of observation and evaluation. However, Examiner finds Applicants remarks are also directed to limitations that recite additional elements (i.e. transformer model of an artificial intelligence chat bot, persisting a state of conversation, enforcement nodes, and enforcement edge nodes), and therefore were not analyzed under Step 2A-Prong One but also do not take the claim out of the certain methods of organizing human activity and mental processes groupings. Examiner maintains the claims recite an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states the amended claims here are similar to the example improvements in technology that are NOT directed to a judicial exceptions per the "2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence" published in the Federal Register on 9/17/2024 [Docket No.: PTO-P-2024-0026], hereinafter 2024 Guidance. In the guidance examples (see p. 20-21, Applicant Remarks).
The amended claims here recite the same type of improvements in computer functionality, e.g., reduction of latency and AI-based method of analyzing natural written and speech signals, among other patentable computer/technological improvements. (see p. 14-15, Applicant Remarks).
In response, Examiner respectfully disagrees. Uniloc USA, Inc. v. LG Elect. USA, Inc. was found patent eligible under 35 U.S.C. 101 because the claims at issue are directed to a patent-eligible improvement to computer functionality, namely the reduction of latency experienced by parked secondary stations in communication systems, by eliminating or reducing the delay present in conventional systems where the primary station alternates between polling and sending inquiry messages thus overcoming a problem specifically arising in the realm of computer networks. Example 48 recites a practical application of using a deep neural network (DNN) to analyze and separate speech signals from extraneous or background speech. Claim 3 of Example 48 reflects technical improvements by discussing in the disclosure details of how the DNN trained on source separation aids in the cluster assignments to correspond to the sources identified in the mixed speech signal, which are then converted into separate speech signals in the time domain to generate a sequence of words from the spectral features, thereby making individual transcription of each separated speech signal possible. Examiner finds there are no similar improvements here. The transformer model (e.g. neural network) of an artificial intelligence chat bot is currently recited in the claims as a mere tool to perform instructions of the abstract idea (i.e., the parsing of natural language messages and computing of an optimal meeting date and time).
Examiner finds Applicants arguments improves an existing business process (e.g. scheduling) and not a technology, technological field or computer related technology. Examiner notes the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015); MPEP 2106.05(f)(2). Examiner maintains the claims are directed to an abstract idea.
Regarding the 35 U.S.C. 101 rejection, Applicant states as clarified in the recent USPTO guidance, "[i]f it is determined that a claim recites a judicial exception in Step 2A, Prong One, USPTO personnel evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception, and thus is not 'directed to' the judicial exception, in Step 2A, Prong Two. See MPEP 2106.04(d) for further discussion on evaluating whether a judicial exception is integrated into a practical application of that exception in Step 2A, Prong Two." 2024 Guidance at Section III.A.2 (footnote inserted as internal citation).
"This analysis is performed using one or more considerations identified by the courts, such as whether the additional elements improve the functioning of a computer or another technology, whether the claim generally links the judicial exception to a particular technological environment or field of use… The considerations evaluated in Step 2A, Prong Two are discussed in MPEP 2106.04(d), subsection I, and in more detail in MPEP 2106.04(d)(1), 2106.04(d)(2), 2]06.05(a)-(c), and 2]06.05(e)-(h). Step 2A, Prong Two specifically excludes consideration of whether the additional elements represent well- understood, routine, conventional activity." 2024 Guidance at Section III.A.2 (footnote inserted as internal citation).
"AI inventions may provide a particular way to achieve a desired outcome when they claim, for example, a specific application of AI to a particular technological field (i.e., a particular solution to a problem). Example 47, claim 3, claiming a specific application of AI to the field of network intrusion detection; and Example 48, claims 2 and 3, claiming a specific application of AI to the field of speech signal processing, which are available at uspto.gov Patent Eligibility." 2024 Guidance at Section III.A.2.A (footnote inserted as internal citation).
For instance, the pending claims here are similar to the example improvements in technology that are NOT directed to a judicial exceptions.
Applicant respectfully asserts that any recited judicial exception, as a whole, is integrated into a practical application of the exception, e.g., the additional elements improve the functioning of a computer or another technology.
In response Examiner respectfully disagrees. Examiner finds the artificial intelligence limitations (i.e., transformer model) of the pending claims is similar to the ineligible subject matter disclosed in claim 2 of Example 47 disclosed in the AI-related SME examples issued in 2024. Specifically, the Step 2A- Prong Two and Step 2B analysis of claim 2 of Example 47 states, in part, all uses of the recited judicial exceptions require data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. The recitation of “using a trained ANN” in limitations (d) and (e) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained ANN” limits the identified judicial exceptions “detecting one or more anomalies in a data set using the trained ANN” and “analyzing the one or more detected anomalies using the trained ANN to generate anomaly data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Unlike claim 3 of Example 47 that provides for improved network security using the information from the detection to enhance security by taking proactive measures to remediate danger by detecting, dropping and blocking the source address associated with potentially malicious packets. Examiner finds there are no similar technological improvements here. The transformer model does not recite an improvement to the functioning of an artificial intelligence technology, computer-related technology or any technological field, thus failing to add an inventive concept to the claims. Applicant has not made any persuasive argument that would alter this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Applicant’s arguments, see pg. 22-24, filed December 12, 2025, with respect to the 35 U.S.C. 103 rejections have been fully considered. However, upon further consideration, a new ground(s) of rejection is made. Applicant’s arguments are considered moot because they are directed to newly amended subject matter and do not apply to the combination of references being used in the current rejection. Please refer to the 35 U.S.C. 103 rejection for further explanation and rationale.
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-4, 7 and 21-28 are directed towards a system, claims 8-11 and 14 are directed towards a computer-readable storage medium, both of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-4, 6-11, 14, and 21-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite scheduling meetings based on computing optimal meeting date and time from natural language messages.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receiving a natural language meeting message initiating a meeting to be scheduled, wherein the natural language meeting message includes an invite list of one or more meeting participants; sending one or more invitation messages to the one or more meeting participants, respectively, wherein the one or more invitation messages provide meeting acceptance options; receiving meeting participant input data associated with the meeting acceptance options; computing an optimal meeting date and time based on the meeting participants input data associated with the meeting acceptance options; scheduling the meeting based on the optimal meeting date and time constitutes methods based on managing relationships or interactions between people, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper. The recitation of a system comprising: logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors including a central authority and at least one enforcement edge node, host device, participants device, and transformer model of an artificial intelligence chat bot does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claim 8 recites certain method of organizing human activity for similar reasons as claim 1.
Claim 27 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receiving a natural language meeting message and indicating a meeting to be scheduled, wherein the natural language meeting message includes an invite list of one or more meeting participants; limited by the policy and configuration settings associated with the host, parse the natural language meeting message to determine the meeting to be scheduled and the one or more participants; sending one or more invitation messages to the one or more meeting participants, respectively, and as limited by the policy and configuration settings associated with the host, wherein the one or more invitation messages provide meeting acceptance options; computing an optimal meeting date and time based on the input data associated with the meeting acceptance options and as limited by the policy and configuration settings associated with each of the host and the meeting participant; and scheduling, as limited by the policy and configuration settings associated with each of the host and the meeting participant, the meeting based on the optimal meeting date and time constitutes methods based on managing relationships or interactions between people, as well as, methods based on observations, evaluations, judgements and/or opinion that can be performed by a combination of the human mind and a human using pen and paper. The recitation of a system comprising: logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors including a central authority and at least one enforcement edge node, host device, participants device and artificial intelligence chat bot does not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites persisting a state of a conversation including the natural language meeting message, utilizing the at least one enforcement node, such that the conversation may be continued by accessing the at least one enforcement edge node; receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device; and persisting an updated state of the conversation including the input data, utilizing the at least one enforcement node, such that the conversation maybe continued by accessing the at least one enforcement node, which are limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g). Additionally, claim 1 recites a system comprising: one or more processors; and logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors including a central authority and at least one enforcement edge node, host device and participants device at a high-level of generality such that they amount to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Furthermore, claim 1 recites utilizing a transformer model of at least one artificial intelligence chat bot executed at the at least one enforcement edge node, parse the natural language meeting message to determine the meeting to be scheduled and the one or more participants and utilizing the transformer model of at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the meeting participant input data associated with the meeting acceptance options. The general use of artificial intelligence techniques do not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the transformer model of the artificial intelligence chat bot disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea. Thus, the additional element do not integrate the abstract idea into practical application because it does not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The non-transitory computer-readable storage medium with program instructions executable by one or more processors, host device, participant device and transformer model of an artificial intelligence chat bot recited in claim 8 and system comprising: logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors including a central authority and at least one enforcement edge node, host device, participants device and artificial intelligence chat bot recited in claim 27 also amount to no more than mere instructions to apply the exception using a generic computer component; see MPEP 2106.05(f). Furthermore, claim 27 additionally recites retrieving, at the at least one enforcement edge node and from the central authority, policy and configuration settings associated with the host; and retrieving, at the at least one enforcement edge node and from the central authority, policy and configuration settings associated with the meeting participant, which are also limitations considered to be an insignificant extra-solution activity of collecting and delivering data; see MPEP 2106.05(g).Thus, the additional elements recited in claims 8 and 27 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including a system comprising: one or more processors; and logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors and the non-transitory computer-readable storage medium with program instructions executable by one or more processors amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The transformer model of an artificial intelligence chat bot recited in the claim is disclosed at a high-level of generality (see at least Specification [0028]; [0101]-[0102]) and does not amount to significantly more than the abstract idea Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2, 4, 9, 11, 18 and 28 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 21-26 recites limitations reciting artificial intelligence and using an artificial intelligence chat bot, respectively. The general use of an artificial intelligence technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the artificial intelligence elements disclosed in the claims are solely used as tools to perform the instructions of the abstract idea; MPEP 2106.05(f). Additionally, claims 3, 7, 10, 14 and 21-26 recite steps that further narrow the abstract idea. Therefore claims 2-4, 7, 9-11, 14, and 21-26 and 28 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-11, 14, 22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Rao DV, U.S. Publication No. 2014/0195621 [hereinafter Rao], in view of Nelson et al., U.S. Publication No. 2019/0273767 [hereinafter Nelson], and further in view of Nott et al., U.S. Publication No. 2023/0101223 [hereinafter Nott].
Referring to Claim 1, Rao teaches:
A system comprising:
one or more processors, including a central authority and at least one enforcement edge node (Rao, [0015]-[0017]; [0036]; [0039]); and
logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors and when executed operable to cause the one or more processors to perform operations comprising (Rao, [0039]; [0040]):
receiving a natural language meeting message at the at least one enforcement edge node, from a host device, and indicating a meeting to be scheduled, wherein the natural language meeting message includes an invite list of one or more meeting participants (Rao, [0018]), “such as users 112, 114, and 116, can communicate with each other via chat client applications that run on machines, such as chat clients 104, 106, and 108, respectively. The chat clients can be any type of node on network 110 with computational capability and mechanisms for communicating across the network. For example, the chat clients can include, but are not limited to: a workstation, a personal computer (PC), a laptop computer, a tablet computer, a smartphone, and/or other electronic computing devices with network connectivity. Furthermore, a chat client may couple to network 110 using wired and/or wireless connections”; (Rao, [0024]), “When a chat conversation is related to making appointments or scheduling meetings, this scheduling information can be automatically presented to chat participants. In one embodiment, with appropriate authorization, calendar-information-gathering module 208 can be configured to access multiple users' calendars and make recommendations for meeting times and/or locations based on information from all available calendars”; (Rao, [0021]), “Chat-content-monitoring module 202 is responsible for monitoring the content of a conversation. For text-based chat, a language parser can be implemented to parse the text entered by chat participants within the chat window and infer meanings of the conversation”; (Rao, [0017]), “Chat server 102 provides online chat services to multiple client machines”; (Rao, [0015]; [0012]);
utilizing a transformer model of at least one chat bot executed at the at least one enforcement edge node parse the natural language meeting message to determine the meeting to be scheduled and the one or more participants (Rao, [0024]), “…during a group chat, users A, B, and C may want to schedule a face-to-face meeting in the coming week…scheduling task can be automatically performed by intelligent chat assistant 200. More specifically, during the chat, chat-content-monitoring module 202 recognizes the request to schedule a meeting among users A, B, and C, and, in response, calendar-information-gathering module 208 accesses the calendars of all three users. In one embodiment, calendar-information-gathering module 208 compares the three calendars and selects time slots when everyone is free”; (Rao, [0033]), “Various natural language parsing techniques can be used to infer meanings of the chat content”; (Rao, [0021]), “Chat-content-monitoring module 202 is responsible for monitoring the content of a conversation. For text-based chat, a language parser can be implemented to parse the text entered by chat participants within the chat window and infer meanings of the conversation. In one embodiment, the language parser is able to adapt to and learn the writing habits of the users… monitoring the current ongoing conversation, chat-content-monitoring module 202 may also access locally or remotely cached historical data, such as previously saved conversations or previously established user behavior models, and infer meanings of the conversation using the historical data. Note that historical data is useful in inferring the meanings of phrases that may have multiple meanings”, Examiner considers the chat-content-monitoring module to teach the transformer model of a chat bot; (Rao, [0031]); and
sending one or more invitation messages to the one or more meeting participants, respectively, wherein the one or more invitation messages provide meeting acceptance options (Rao, [0024]), “during the chat, chat-content-monitoring module 202 recognizes the request to schedule a meeting among users A, B, and C, and, in response, calendar-information-gathering module 208 accesses the calendars of all three users. In one embodiment, calendar-information-gathering module 208 compares the three calendars and selects time slots when everyone is free. Intelligent chat assistant 200 then presents the selected time slots to all users to allow them to make a final decision”.
Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012), and information-gathering modules responsible for gathering information pertaining to the current conversation (see par. 0023) but Rao does not explicitly teach:
at least one artificial intelligence chat bot,
persisting a state of a conversation including the natural language meeting message, utilizing the at least one enforcement node, such that the conversation may be continued by accessing the at least one enforcement edge node;
receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device, and associated with the meeting acceptance options;
persisting an updated state of the conversation including the input data, utilizing the at least one enforcement node, such that the conversation maybe continued by accessing the at least one enforcement node;
utilizing the at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the meeting participant input data associated with the meeting acceptance options; and
scheduling the meeting based on the optimal meeting date and time.
However Nelson teaches:
at least one artificial intelligence chat bot (Nelson, [0123]), “meeting intelligence apparatus 102 is implemented by one or more computing devices configured with artificial intelligence… Meeting intelligence apparatus 102 may be replicated over multiple computing devices such that at any point in time, at least one computing device can provide meeting intelligence services”; (Nelson, [0146]; [0441]),
persisting a state of a conversation including the natural language meeting message, utilizing the at least one enforcement node, such that the conversation may be continued by accessing the at least one enforcement edge node (Nelson, [0126]), “meeting intelligence apparatus 102 is communicatively coupled to a meeting data repository… Like meeting intelligence apparatus 102, the meeting data repository may be located at different locations relative to network infrastructure 106, for example, on one or more computing devices internal or external to network infrastructure 106. The meeting data repository stores data pertaining to any number of electronic meetings, and may include data for prior electronic meetings, current electronic meetings, and future electronic meetings. Examples of data for prior, current and future electronic meetings include, without limitation… meeting participant information, meeting invitation information, meeting transcripts… Meeting data may be collected and stored by meeting intelligence apparatus 102, nodes 104A-N, or both….”; (Nelson, [0123]), “Meeting intelligence apparatus 102 may be replicated over multiple computing devices such that at any point in time, at least one computing device can provide meeting intelligence services”; (Nelson, [0125]; [0131]-[0132]; [0177]);
persisting an updated state of the conversation including the input data, utilizing the at least one enforcement node, such that the conversation maybe continued by accessing the at least one enforcement node (Nelson, [0126]), “meeting intelligence apparatus 102 is communicatively coupled to a meeting data repository… Like meeting intelligence apparatus 102, the meeting data repository may be located at different locations relative to network infrastructure 106, for example, on one or more computing devices internal or external to network infrastructure 106. The meeting data repository stores data pertaining to any number of electronic meetings, and may include data for prior electronic meetings, current electronic meetings, and future electronic meetings. Examples of data for prior, current and future electronic meetings include, without limitation… meeting participant information, meeting invitation information, meeting transcripts… Meeting data may be collected and stored by meeting intelligence apparatus 102, nodes 104A-N, or both….”; (Nelson, [0123]), “Meeting intelligence apparatus 102 may be replicated over multiple computing devices such that at any point in time, at least one computing device can provide meeting intelligence services”; (Nelson, [0125]; [0131]-[0132]; [0177]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant and gathered information pertaining to a current conversation in Rao to include the artificial intelligence and state of conversation limitations as taught by Nelson. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of an electronic meeting manager used to schedule meetings (see Nelson par. 0132).
Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012), but Rao does not explicitly teach:
receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device, and associated with the meeting acceptance options; and
utilizing the at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the meeting participant input data associated with the meeting acceptance options; and
scheduling the meeting based on the optimal meeting date and time.
However Nott teaches:
receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device, and associated with the meeting acceptance options (Nott, [0021]), “Depending on the other user's response, the other robot may transmit a reply message, either accepting, denying, or changing the calendar request. Alternatively, the other robot may peruse the other user's schedule to confirm the meeting, deny the meeting and/or suggest an alternative date/time for the meeting”; (Nott, [0072]), “the robot automatically determines a proposed date and time for the meeting, and then sends the request to the other user's email, allowing the other user to accept, deny, or modify”; (Nott, [0071]; [0073]; [0077]); and
utilizing the at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the meeting participant input data associated with the meeting acceptance options (Nott, [0005]), “a computer-implemented method for executing one or more tasks using robotic processing automation (RPA) includes assigning a workflow to a robot to monitor for one or more user actions or events via one or more triggers… The one or more relevant triggers are coded in advance or determined by machine learning (ML) or artificial intelligence (AI) to be relevant…”; (Nott, [0071]), “when the calendaring workflow is executed, the robot may populate a graphical user interface (GUI) for the user to enter calendaring information. This information may include a meeting subject line, a proposed date, and a proposed time… the robot may proceed with transmitting the request with the other user or the other user's robot without the user's involvement”; (Nott, [0051]), “Indexer server 250… stores and indexes the information logged by the robots… Messages logged by robots (e.g., using activities like log message or write line) may be sent through the logging REST endpoint(s) to indexer server 250, where they are indexed for future utilization”; (Nott, [0074]-[0075]; [0021]-[0022]); and
scheduling the meeting based on the optimal meeting date and time (Nott, [0074]-[0075]), “the robot may support automatically coordinating a meeting between the user and one or more external users…the robot constructs an email containing dates and times for the meeting, and sends the email to the user, and in some embodiments, also to the external users… The robot, using a listener module, may listen for a reply to this email and may coordinate the meeting in the background. After a predefined period of time has elapsed, or all responses have been handled or retrieved, the robot prompts the user with a calendar invite that is the best for everyone, leaving the user with the option to hit send”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao to include the participant input and optimal meeting limitations as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of performing an automated task on behalf of a user (see Nott par. 0067).
Referring to Claim 2, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao further teaches:
wherein the natural language meeting message is one or more of an email message or a text message (Rao, [0038]), “although this disclosure uses an instant messaging system as an example, the scope of the present invention is not limited to the instant messaging system. Other online chat systems may be used including, but not limited to: online chat rooms, web conferring, private messaging systems used in social networking websites, etc.”; (Rao, [0023]), “…Email-information-gathering module 206 interfaces with local or web-based email applications. In one embodiment, email-information-gathering module 206 can be configured to fetch and present one or more email messages based on the current conversation…”.
Referring to Claim 3, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches the intelligent chat assistant presents a selected time slots to all users to allow them to make a final decision (see par. 0024), but Rao does not explicitly teach:
wherein the meeting acceptance options comprise accepting a date and time indicated in the one or more invitation messages and proposing an alternative date and time.
However Nott teaches:
wherein the meeting acceptance options comprise accepting a date and time indicated in the one or more invitation messages and proposing an alternative date and time (Nott, [0021]), “Depending on the other user's response, the other robot may transmit a reply message, either accepting, denying, or changing the calendar request. Alternatively, the other robot may peruse the other user's schedule to confirm the meeting, deny the meeting and/or suggest an alternative date/time for the meeting”; (Nott, [0071]; [0077]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the decision element in Rao to include the acceptance option limitation as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of coordinating a meeting that is best of everyone (see Nott par. 0075).
Referring to Claim 4, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches the intelligent chat assistant presents a selected time slots to all users to allow them to make a final decision (see par. 0024), but Rao does not explicitly teach:
wherein the meeting participants input data may be received in the form of email messages or text messages.
However Nott teaches:
wherein the meeting participant input data may be received in the form of email messages or text messages (Nott, [0071]), “The robot uses the information inputted by the user and transmits the request to another user's email account. For example, the robot may send a calendar request in an email format, allowing the other user to accept, deny, or modify the proposed meeting”; (Nott, [0074]-[0075]), “Upon retrieving the meeting times, the robot constructs an email containing dates and times for the meeting, and sends the email to the user, and in some embodiments, also to the external users. See FIG. 7, which is a GUI 700 illustrating an email containing possible dates and times for the user to select, according to an embodiment of the present invention. In GUI 700, the email instructs the user, and in some embodiments the external users, to respond to the email with an order of preference for dates and times… The robot, using a listener module, may listen for a reply to this email and may coordinate the meeting in the background. After a predefined period of time has elapsed, or all responses have been handled or retrieved, the robot prompts the user with a calendar invite that is the best for everyone, leaving the user with the option to hit send”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the decision element in Rao to include the input data limitation as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of coordinating a meeting that is best of everyone (see Nott par. 0075).
Referring to Claim 7, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches the intelligent chat assistant presents a selected time slots to all users to allow them to make a final decision (see par. 0024), but Rao does not explicitly teach:
wherein the computing of the optimal meeting date and time is based on one or more meeting policies concerning one or more of participants, availability, locations, services, and resources.
However Nott teaches:
wherein the computing of the optimal meeting date and time is based on one or more meeting policies concerning one or more of participants, availability, locations, services, and resources (Not, [0072]), “the robot may review user's electronic calendar to determine user availability…By accessing the database, the robot may use a ML algorithm to determine the users preferred dates/times for conducting the meeting. For example, the robot may determine that the user prefers to schedule meetings in the mornings or late afternoons”; (Nott, [0079]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the decision element in Rao to include the optimal meeting limitations as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of coordinating a meeting that is best of everyone (see Nott par. 0075).
Referring to Claim 8, Vieyra teaches:
A non-transitory computer-readable storage medium with program instructions stored thereon, the program instructions when executed by one or more processors are operable to cause the one or more processors to perform operations comprising (Vieyra, [0052]; [0083]-[0084]; [0344]):
Claim 8 disclose substantially the same subject matter as Claim 1, and is rejected using the same rationale as previously set forth.
Claim 9 disclose substantially the same subject matter as Claim 2, and is rejected using the same rationale as previously set forth.
Claim 10 disclose substantially the same subject matter as Claim 3, and is rejected using the same rationale as previously set forth.
Claim 11 disclose substantially the same subject matter as Claim 4, and is rejected using the same rationale as previously set forth.
Claim 14 disclose substantially the same subject matter as Claim 7, and is rejected using the same rationale as previously set forth.
Referring to Claim 22, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches comparing calendars and selecting time slots when everyone is free (see par. 0024), but Rao does not explicitly teach:
wherein the at least one artificial intelligence chat bot is further configured to compute the optimal meeting date and time based on the determined at least one required participant.
However Nott teaches:
wherein the at least one artificial intelligence chat bot is further configured to compute the optimal meeting date and time based on the determined at least one required participant (Nott, [0072]), “where the user did not input a proposed date and time for the proposed meeting, the robot may review user's electronic calendar to determine user availability. In some further embodiments, the robot may access a database, which includes previous meetings with the other user, other meetings with different users, and so forth. By accessing the database, the robot may use a ML algorithm to determine the users preferred dates/times for conducting the meeting. For example, the robot may determine that the user prefers to schedule meetings in the mornings or late afternoons. Using this data, the robot automatically determines a proposed date and time for the meeting, and then sends the request to the other user's email, allowing the other user to accept, deny, or modify”; (Nott, Table 1, “Internal Availability”; [0075]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the calendar availability in Rao to include the participant limitation as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of coordinating a meeting that is best of everyone (see Nott par. 0075).
Referring to Claim 24, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches the intelligent chat assistant presents a selected time slots to all users to allow them to make a final decision (see par. 0024), but Rao does not explicitly teach:
wherein the at least one artificial intelligence chat bot further computes the optimal meeting time by applying preferences for at least one of the one or more meeting participants learned from the meeting participant input data or associated profile information.
However Nott teaches:
wherein the at least one artificial intelligence chat bot further computes the optimal meeting time by applying preferences for at least one of the one or more meeting participants learned from the meeting participant input data or associated profile information (Nott, [0072]), “In some further embodiments, the robot may access a database, which includes previous meetings with the other user, other meetings with different users, and so forth. By accessing the database, the robot may use a ML algorithm to determine the users preferred dates/times for conducting the meeting. For example, the robot may determine that the user prefers to schedule meetings in the mornings or late afternoons”; (Nott, [0075]; [0079]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao to include the artificial intelligence and optimal meeting limitations as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of performing an automated task on behalf of a user (see Nott par. 0067).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Rao DV, U.S. Publication No. 2014/0195621 [hereinafter Rao], in view of Nelson et al., U.S. Publication No. 2019/0273767 [hereinafter Nelson], in view of Nott et al., U.S. Publication No. 2023/0101223 [hereinafter Nott], and further in view of Fishbeck, U.S. Publication No. 2018/0107639 [hereinafter Fishbeck].
Referring to Claim 21, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012), but Rao does not explicitly teach:
wherein the natural language meeting message indicates at least one required participant and at least one optional participant, and wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine the at least one required participant and the at least one optional participant.
However Fishbeck teaches:
wherein the natural language meeting message indicates at least one required participant and at least one optional participant, and wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine the at least one required participant and the at least one optional participant (Fishbeck, [0054]), “the action item may be created by a user to specify that a meeting to discuss kitchen appliances for a kitchen of a remodeled home should take place. If the conversation subsequently takes place and includes discussions regarding the required or optional attendees for such a meeting, the context subsystem 118 may generate a calendar invite for the meeting and add the meeting to the project based on the conversation. The generated calendar invite may, for instance, include the required or optional attendees based on the context subsystem 118 detecting such discussion during the conversation…”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao to include the natural language and participant limitations as taught by Fishbeck. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of automatically initiating meeting invitations based on context sources (see Fishbeck par. 0050).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Rao DV, U.S. Publication No. 2014/0195621 [hereinafter Rao], in view of Nelson et al., U.S. Publication No. 2019/0273767 [hereinafter Nelson], in view of Nott et al., U.S. Publication No. 2023/0101223 [hereinafter Nott], and further in view of Hashimoto, U.S. Publication No. 2021/0350333 [hereinafter Hashimoto].
Referring to Claim 23, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012) and the intelligent chat assistant presents a selected time slots to all users to allow them to make a final decision (see par. 0024), but Rao does not explicitly teach:
wherein the at least one artificial intelligence chat bot is further configured to determine a time zone associated with the meeting from profile information associated with a user generating the natural language meeting message or by parsing the natural language meeting message, and wherein at least one of the one or more meeting acceptance options or computing the optimal meeting time is based on the determined time zone.
However Hashimoto teaches:
wherein the at least one artificial intelligence chat bot is further configured to determine a time zone associated with the meeting from profile information associated with a user generating the natural language meeting message or by parsing the natural language meeting message, and wherein at least one of the one or more meeting acceptance options or computing the optimal meeting time is based on the determined time zone (Hashimoto, [0249]), “When there is a plurality of invited members, the reservation management server 20 transmits the one or more available schedules common to the plurality of invited members to the bot server 80. The available schedule is a schedule generated based on the meeting time, among one or more time zones in each user schedule available for each user”; (Hashimoto, [0039]; [0063]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant and decision element in Rao to include the time zone limitation as taught by Hashimoto. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of notifying the invited members of the possible dates and times for the schedule by e-mail is a burden on the organizer (see Hashimoto par. 0036).
Claims 25 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Rao DV, U.S. Publication No. 2014/0195621 [hereinafter Rao], in view of Nelson et al., U.S. Publication No. 2019/0273767 [hereinafter Nelson], in view of Nott et al., U.S. Publication No. 2023/0101223 [hereinafter Nott], and further in view of Donofrio et al., U.S. Publication No. 2019/0043020 [hereinafter Donofrio].
Referring to Claim 25, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012, the intelligent chat assistant can parse text entered by the users to extract meaningful keywords (see par. 0014) and Nott teaches a digital assistant performed using artificial intelligence (see par. 0017), but the combination of Rao in view of Nott does not explicitly teach:
wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine one or more resources required for the meeting, and wherein at least one of the one or more meeting acceptance options or computing the optimal meeting time is based on an availability of the determined one or more resources required for the meeting.
However Donofrio teaches:
wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine one or more resources required for the meeting, and wherein at least one of the one or more meeting acceptance options or computing the optimal meeting time is based on an availability of the determined one or more resources required for the meeting (Donofrio, [0027]), “…keyword matching could be used to search for words of months, such as January or February. After finding a matching month, the text in the vicinity of the month may be retrieved and analyzed with the pattern matching to identify the time of the deposition. One such pattern may be “MONTH followed by ##, ####,” where #s represent some number. Another pattern to identify a date may be “##/##/####.” Similarly, identifying a meeting time may also utilize a pattern, such as by identifying a colon with one- or two-digit numerals on both sides of the colon. Such a pattern may readily recognize a time stamp, such as 9:30 a.m., to identify a meeting-related timestamp from a deposition notice. In some instances, identifying words such as “video” or “conference call” may indicate the need for a certain type of resource, such as a camera, court reporter and/or videographer”; (Donofrio, [0032]), “a technological resource may be video recording equipment or a conference call facility. Similarly, when the system determines that video recording equipment is needed, the system may automatically determine availability of the equipment based on data in one or more data stores and propose a suitable meeting time and site”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao and artificial intelligence and optimal meeting in Nott to include the resource limitations as taught by Donofrio. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of organizing a meeting with necessary resources (see Donofrio par. 0018).
Referring to Claim 26, Rao in view of Nelson in view of Nott teaches the system of claim 1. Rao teaches the intelligent chat assistant can parse text entered by the users to extract meaningful keywords (see par. 0014) and Nott teaches a digital assistant performed using artificial intelligence (see par. 0017), but the combination of Rao in view of Nott does not explicitly teach:
wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine a type of service indicated, and wherein the operations further comprise: identifying at least one additional meeting participant based on a tag associated with each of the determined type of service and the at least one additional meeting participant.
However Hashimoto teaches:
wherein the at least one artificial intelligence chat bot is further configured to parse the natural language meeting message to determine a type of service indicated, and wherein the operations further comprise: identifying at least one additional meeting participant based on a tag associated with each of the determined type of service and the at least one additional meeting participant (Donofrio, [0026]-[0027]), “…identify additional meeting-related information from the OCR result, such as one or more additional participants to the meeting. For example, the identity of a deposed witness, particular court information, case docket number, deposition site, notice date, noticing attorney, and counsel for plaintiff or defendant may be identified, among other fields. The meeting-related information may be obtained from keyword analysis, pattern matching, and/or natural language processing of the text data in the OCR result… keyword matching could be used to search for words of months, such as January or February. After finding a matching month, the text in the vicinity of the month may be retrieved and analyzed with the pattern matching to identify the time of the deposition. One such pattern may be “MONTH followed by ##, ####,” where #s represent some number. Another pattern to identify a date may be “##/##/####.” Similarly, identifying a meeting time may also utilize a pattern, such as by identifying a colon with one- or two-digit numerals on both sides of the colon. Such a pattern may readily recognize a time stamp, such as 9:30 a.m., to identify a meeting-related timestamp from a deposition notice. In some instances, identifying words such as “video” or “conference call” may indicate the need for a certain type of resource, such as a camera, court reporter and/or videographer”; (Donofrio, [0030]), “the system may proceed to identify additional meeting-related information. One type of such information is resources, including both human resources and technological resources…This name recognition and language association in combination with a keyword appearance in the OCR results such as “interpreter” (without necessarily explicitly identifying the language of the interpreter in the OCR results) may enable the system to narrow the specific resources to assign to a meeting (e.g., from interpreter to Korean interpreter) that were initially identified from a keyword in the text”
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao and artificial intelligence and optimal meeting in Nott to include the resource limitations as taught by Donofrio. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of organizing a meeting with necessary resources (see Donofrio par. 0018).
Claims 27 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Rao DV, U.S. Publication No. 2014/0195621 [hereinafter Rao], and further in view of Nott et al., U.S. Publication No. 2023/0101223 [hereinafter Nott].
Referring to Claim 27, Rao teaches:
A system comprising:
one or more processors, including a central authority and at least one enforcement edge node (Rao, [0015]-[0017]; [0036]; [0039]); and
logic encoded in one or more non-transitory computer-readable storage media for execution by the one or more processors and when executed operable to cause the one or more processors to perform operations comprising (Rao, [0039]; [0040]):
receiving a natural language meeting message at the at least one enforcement edge node, from a host device, and indicating a meeting to be scheduled, wherein the natural language meeting message includes an invite list of one or more meeting participants (Rao, [0018]), “such as users 112, 114, and 116, can communicate with each other via chat client applications that run on machines, such as chat clients 104, 106, and 108, respectively. The chat clients can be any type of node on network 110 with computational capability and mechanisms for communicating across the network. For example, the chat clients can include, but are not limited to: a workstation, a personal computer (PC), a laptop computer, a tablet computer, a smartphone, and/or other electronic computing devices with network connectivity. Furthermore, a chat client may couple to network 110 using wired and/or wireless connections”; (Rao, [0024]), “When a chat conversation is related to making appointments or scheduling meetings, this scheduling information can be automatically presented to chat participants. In one embodiment, with appropriate authorization, calendar-information-gathering module 208 can be configured to access multiple users' calendars and make recommendations for meeting times and/or locations based on information from all available calendars”; (Rao, [0021]), “Chat-content-monitoring module 202 is responsible for monitoring the content of a conversation. For text-based chat, a language parser can be implemented to parse the text entered by chat participants within the chat window and infer meanings of the conversation”; (Rao, [0017]), “Chat server 102 provides online chat services to multiple client machines”; (Rao, [0015]; [0012]);
retrieving, at the at least one enforcement edge node and from the central authority, policy and configuration settings associated with the host (Rao, [0034]), “intelligent chat assistant 200 determines whether the chat participants (local, remote, or both) are authorized to view the additional data (operation 406). If not, the system continues to monitor the chat content. If at least one chat participant is authorized to view the additional data, intelligent chat assistant 200 automatically obtains the additional data from various local or remote data sources (operation 408)”; (Rao, [0022]), “Note that intelligent chat assistant 200 is able to access local and remote information that the user may want to keep private…However, the user may want to control what type of information can be used by intelligent chat assistant 200 in assisting the conversation. Privacy-control module 204 is responsible for privacy control of personal information released to other chat participants by intelligent chat assistant 200…Various types of techniques can be used to control privacy. In one embodiment, privacy-control module 204 can use a rule-based system that allows a user to specify privacy rules”; (Rao, [0033]), “intelligent chat assistant 200 can also use a database of rules to determine whether certain chat content can trigger the retrieval of additional data”; (Rao, [0036]-[0037]);
utilizing at least one artificial intelligence chat bot executed at the at least one enforcement node and as limited by the policy and configuration settings associated with the host, parse the natural language meeting message to determine the meeting to be scheduled and the one or more participants (Rao, [0033]), “Various natural language parsing techniques can be used to infer meanings of the chat content. Additionally, historical data can also be used to assist in inferring meanings of the chat content. In one embodiment, intelligent chat assistant 200 can also use a database of rules to determine whether certain chat content can trigger the retrieval of additional data”; (Rao, [0024]), “…during a group chat, users A, B, and C may want to schedule a face-to-face meeting in the coming week…scheduling task can be automatically performed by intelligent chat assistant 200. More specifically, during the chat, chat-content-monitoring module 202 recognizes the request to schedule a meeting among users A, B, and C, and, in response, calendar-information-gathering module 208 accesses the calendars of all three users. In one embodiment, calendar-information-gathering module 208 compares the three calendars and selects time slots when everyone is free”; (Rao, [0021]), “Chat-content-monitoring module 202 is responsible for monitoring the content of a conversation. For text-based chat, a language parser can be implemented to parse the text entered by chat participants within the chat window and infer meanings of the conversation. In one embodiment, the language parser is able to adapt to and learn the writing habits of the users… monitoring the current ongoing conversation, chat-content-monitoring module 202 may also access locally or remotely cached historical data, such as previously saved conversations or previously established user behavior models, and infer meanings of the conversation using the historical data. Note that historical data is useful in inferring the meanings of phrases that may have multiple meanings”;
sending one or more invitation messages to the one or more meeting participants, respectively, and as limited by the policy and configuration settings associated with the host, wherein the one or more invitation messages provide meeting acceptance options (Rao, [0024]), “during the chat, chat-content-monitoring module 202 recognizes the request to schedule a meeting among users A, B, and C, and, in response, calendar-information-gathering module 208 accesses the calendars of all three users. In one embodiment, calendar-information-gathering module 208 compares the three calendars and selects time slots when everyone is free. Intelligent chat assistant 200 then presents the selected time slots to all users to allow them to make a final decision”; (Rao, [0022]).
Rao teaches an intelligent chat system that provides an intelligent chat assistant that runs in the background, monitors the conversation, and provides assistance, such as calendar lookup and making recommendations, to the users based on the content of the conversation (see par. 0012), and a rule-based system that allows a user to specify privacy rules and user-adjustable privacy settings (see par. 0022), but Rao does not explicitly teach:
utilizing at least one artificial intelligence chat bot;
receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device, and associated with the meeting acceptance options;
retrieving, at the at least one enforcement edge node and from the central authority, policy and configuration settings associated with the meeting participant;
utilizing the at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the input data associated with the meeting acceptance options and as limited by the policy and configuration settings associated with each of the host and the meeting participant; and
scheduling, as limited by the policy and configuration settings associated with each of the host and the meeting participant, the meeting based on the optimal meeting date and time.
However Nott teaches:
utilizing at least one artificial intelligence chat bot (Nott, [0017]), “the digital assistant may perform using artificial intelligence (AI) technology scheduling”; (Nott, [0072]; [0075]);
receiving meeting participant input data at the at least one enforcement edge node, from a meeting participant device, and associated with the meeting acceptance options (Nott, [0021]), “Depending on the other user's response, the other robot may transmit a reply message, either accepting, denying, or changing the calendar request. Alternatively, the other robot may peruse the other user's schedule to confirm the meeting, deny the meeting and/or suggest an alternative date/time for the meeting”; (Nott, [0072]), “the robot automatically determines a proposed date and time for the meeting, and then sends the request to the other user's email, allowing the other user to accept, deny, or modify”; (Nott, [0071]; [0073]; [0077]);
retrieving, at the at least one enforcement edge node and from the central authority, policy and configuration settings associated with the meeting participant (Nott, [0072]), “the robot may review user's electronic calendar to determine user availability. In some further embodiments, the robot may access a database, which includes previous meetings with the other user, other meetings with different users, and so forth. By accessing the database, the robot may use a ML algorithm to determine the users preferred dates/times for conducting the meeting. For example, the robot may determine that the user prefers to schedule meetings in the mornings or late afternoons”; (Nott, [0074]), “the robot may support automatically coordinating a meeting between the user and one or more external users… the robot retrieves meeting times that are available for each external user. Upon retrieving the meeting times, the robot constructs an email containing dates and times for the meeting, and sends the email to the user, and in some embodiments, also to the external users. See FIG. 7, which is a GUI 700 illustrating an email containing possible dates and times for the user to select… the email instructs the user, and in some embodiments the external users, to respond to the email with an order of preference for dates and times”;
utilizing the at least one artificial intelligence chat bot executed at the at least one enforcement edge node, computing an optimal meeting date and time based on the input data associated with the meeting acceptance options and as limited by the policy and configuration settings associated with each of the host and the meeting participant (Nott, [0005]), “a computer-implemented method for executing one or more tasks using robotic processing automation (RPA) includes assigning a workflow to a robot to monitor for one or more user actions or events via one or more triggers… The one or more relevant triggers are coded in advance or determined by machine learning (ML) or artificial intelligence (AI) to be relevant…”; (Nott, [0071]), “when the calendaring workflow is executed, the robot may populate a graphical user interface (GUI) for the user to enter calendaring information. This information may include a meeting subject line, a proposed date, and a proposed time… the robot may proceed with transmitting the request with the other user or the other user's robot without the user's involvement”; (Nott, [0051]), “Indexer server 250… stores and indexes the information logged by the robots… Messages logged by robots (e.g., using activities like log message or write line) may be sent through the logging REST endpoint(s) to indexer server 250, where they are indexed for future utilization”; (Nott, [0074]-[0075]; [0021]-[0022]); and
scheduling, as limited by the policy and configuration settings associated with each of the host and the meeting participant, the meeting based on the optimal meeting date and time (Nott, [0074]-[0075]), “the robot may support automatically coordinating a meeting between the user and one or more external users…the robot constructs an email containing dates and times for the meeting, and sends the email to the user, and in some embodiments, also to the external users… The robot, using a listener module, may listen for a reply to this email and may coordinate the meeting in the background. After a predefined period of time has elapsed, or all responses have been handled or retrieved, the robot prompts the user with a calendar invite that is the best for everyone, leaving the user with the option to hit send”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the intelligent chat assistant in Rao to include the participant input and optimal meeting limitations as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of performing an automated task on behalf of a user (see Nott par. 0067).
Referring to Claim 28, Rao in view of Nott teaches the system of Claim 27. Rao teaches a network that may correspond to any type of wired or wireless networks capable of coupling computing nodes (e.g., chat server and chat clients) (see par. 0016, 0018), but Rao does not explicitly teach:
wherein the policy and configuration settings of at least one of the host or the meeting participant include integrated internet security policy and configuration settings.
However Nott teaches:
wherein the policy and configuration settings of at least one of the host or the meeting participant include integrated internet security policy and configuration settings (Nott, [0042]), “Having components of robots 130 split as explained above helps developers, support users, and computing systems more easily run, identify, and track what each component is executing. Special behaviors may be configured per component this way, such as setting up different firewall rules for the executor and the service. The executor may always be aware of DPI settings per monitor in some embodiments”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the network in Rao to include security limitations as taught by Nott. The motivation for doing this would have been to improve the method of monitoring chat content, extracting useful information from internal or external data sources based on the chat content, and presenting that useful information to the chat participants in Rao (see par. 0014) to efficiently include the results of workflows may be executed at any DPI, regardless of the configuration of the computing system (see Nott par. 0042).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zurko et al. (US 20040128181 A1) – A method and system for scheduling a meeting. The method comprises the steps of receiving a request from a participant in an instant message session to schedule a meeting; and running a natural language processing tool to determine meeting participants and available times, from a record of the message session. A calendaring and scheduling application is run to accept the meeting participants and available times, consult calendars of the meeting participants, and schedule the meeting. A notification is sent to the participants in the instant message session of the meeting schedule, and the meeting schedule is added to the calendars of the meeting participants.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624