DETAILED ACTION
This Office Action is in response to Applicants application filing received on July 8, 2024. Claim(s) 1-20 is/are currently pending in the instant application. The application claims priority to Indian application 202311045340 filed on July 6, 2023.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "the machine learning engine" in line 3 of the claim. There is insufficient antecedent basis for this limitation in the claim. Additionally, the claim limitations include “a machine learning engine” in line 4 of the claim. The first instance of the machine learning does not have antecedent basis in this claim.
Dependents 8-14 are rejected for the dependency on claim 7.
Claim 15 recites the limitation “the machine learning engine” on line 5 of the claim. There is insufficient antecedent basis for this limitation. Appropriate correction is required.
Claims 16-20 are rejected for their dependency on claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are directed to one of the four statutory classes of invention (e.g. process, machine, manufacture, or composition of matter). The claims include a system or “apparatus”, method or “process”, or product or “article of manufacture” and is a system and method for message handling which is a process (Step 1: YES).
The Examiner has identified independent method Claim 7 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 1 and product Claim 15. Claim 7 recites the limitations of (abstract ideas highlighted in italics and additional elements highlighted in bold)
receiving, by a message handler executable by one or more processors a message from a computing device;
passing, by the message handler, the message to the machine learning engine for analysis;
applying, by a machine learning engine executable by the one or more processors, natural language processing to the message to extract information from the message, the extracted information comprising a set of features, one or more scheduling parameters, or both;
applying, by the machine learning engine, one or more machine learning models to the extracted information to produce a set of recommendations for scheduling an event; and
generating, by the message handler, a prompt based at least in part on the set of recommendations, wherein the prompt comprises natural language text corresponding to at least a portion of the set of recommendations for scheduling the event; and
transmitting, by the message handler, the prompt to the computing device.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Certain Methods of Organizing Human Activity”. Receiving a message, applying NPL to extract information related to scheduling, applying the information to develop a recommendation, generating a response based on the recommendations, and transmitting a prompt recites managing human behavior or relationships. Accordingly, the claim recites an abstract idea. The system with a memory and one or more processors in Claim 1 is just applying generic computer components to the recited abstract limitations. The non-transitory computer-readable storage medium in Claim 15 appears to be just software. Claims 1 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as “Mental Processes”. Receiving a message, applying NPL to extract information related to scheduling, using the extracted information to develop a recommendation, generating a response based on the recommendations, recites concepts performed in the human mind and/or with pen and paper. But for the “one or more processors”, “machine learning engine and model”, and “a computing device”, the claim encompasses a person receiving a message, extracting relevant information related to a meeting, and offering one or more recommendations to schedule a meeting in response to the message using his/her mind. The mere nominal recitation of generic computer hardware does not overcome the abstract idea designation. Accordingly, the claim recites an abstract idea. The system with a memory and one or more processors in Claim 1 is just applying generic computer components to the recited abstract limitations. The non-transitory computer-readable storage medium in Claim 15 appears to be just software. Claims 1 and 15 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a memory, one or more processors, and a machine learning engine and model, and a computing device (Claim 1) a computing device, a machine learning engine and mode, and one or more processors (claim 7) and/or non-transitory computer-readable medium executing instructions by a processor (Claim 15). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1, 7, and 15 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0017, 0018, 0020, 0003] about implementation using general purpose or special purpose computing devices [0017: The one or more processors 112 may include one or more microcontrollers, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), central processing units (CPUs) and/or graphics processing units (GPUs) having one or more processing cores,
0018: The memory 114 may include random access memory (RAM) devices, read only memory (ROM) devices, erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), one or more hard disk drives (HDDs), one or more solid state drives (SSDs), flash memory devices, network accessible storage (NAS) devices, or other memory devices configured to store data in a persistent or non-persistent state.
0020: the machine learning engine 120 may include natural language processing functionality that may be used to extract features and scheduling parameters from messages exchanged between the computing device 110 and the one or more user devices 140 using the automated techniques described herein.
0003: The user devices 140 may correspond to devices associated with users who are candidates for attending events scheduled in accordance with the techniques described herein, and may include personal computing devices, laptop computing devices, tablet computing devices, smartphones, personal digital assistants, smartwatches, and other computing devices capable of exchanging information with the computing device 110] and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 1, 7, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-6 8-14, and 16-20 further define the abstract idea that is present in their respective independent claims 1, 7, and 15 and thus correspond to Certain Methods of Organizing Human Activity and/or Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The dependent claims include steps or processes which are similar to that disclosed in MPEP 2106.05(d), (f), (g), and/or (h) which include activities and functions the courts have determined to be well-understood, routine, and conventional when claimed in a generic manner, or as insignificant extra solution activity, or as merely indicating a field of use or technological environment in which to apply the judicial exception.
For example, claims 2, 3, 8, 9, 16, and 17 correspond to elements the courts have realized as computer functions which are well-understood, routine, and conventional when they are claimed in a merely generic manner or as insignificant-extra solution activity MPEP2106.05(d)II. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);
Additionally 4, 5, 10, 11, and 18 include examples which the courts have established as mere instructions to apply an exception because they do no more than merely invoke the computer or machinery as a tool to perform an existing process (e.g. scheduling). MPEP 2106.05(f)(2) A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)
Therefore, the claims 2-6 8-14, and 16-20are directed to an abstract idea. Thus, the claims 1-20 are not patent-eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bezemer et al. U.S. Publication 2010/0153160 A1 (hereafter Bezemer).
Regarding claim 1, a memory (see at least [0140] Examples of computer readable media include for example read-only memory, random-access memory, CD-ROMs, magnetic tape and optical data storage devices.);
one or more processors communicatively coupled to the memory (see at least [0140] computer executable instructions executed by computer servers, personal computers, PDA's and/or other suitable information computing devices.);
a message handler executable by the one or more processors (see at least [0050] a client-side application 16 receives queries from users relating to scheduling of events, communicates with the server-side semantics-based application 17 to obtain query results based on a resource-utilization model,);
a machine learning engine executable by the one or more processors;
wherein the message handler is configured to:
receive a message from a computing device; and
pass the message to the machine learning engine for analysis (see at least [0061] Learners for entity extraction 352 that employing machine learning or other appropriate algorithms (e.g., natural language processing algorithms, decision trees and neural network algorithms) guide the data extractors for unstructured sources 350 to extract data and convert data to triples format, and store the converted data in the triples store 334 in the knowledge server 328.);
wherein the machine learning controller is configured to:
apply natural language processing to the message to extract information from the message, the extracted information comprising a set of features, one or more scheduling parameters, or both (see at least [0061-0062] appropriate algorithms (e.g., natural language processing algorithms, decision trees and neural network algorithms) … By using various data acquisition components, the knowledge server 328 collects, and updates in real-time, all types of meeting related information such as for example people's contact information, location and schedule, meeting room location, size and availability, voice bridge availability, other meeting facilities (e.g., interactive whiteboards, audio/video equipment, etc.), data communication servers and services, and scheduled meeting information (e.g., meeting name, participants, starting date/time, duration, booked meeting room(s) and meeting facilities, voice bridge and remote access information, participant preferences, corporate hierarchy, etc.). The resource utilization model is thereby adaptable in real-time to changes that occur with the resources, scheduling and so forth.);
apply one or more machine learning models to the extracted information to produce a set of recommendations for scheduling an event (see at least [0100] The default choices of the meeting resources are provided as recommended choices to maximize the autonomy of meeting scheduling, so that the user may set up the meeting by simply clicking the "Book This Meeting" button 762. Optionally, the user may click on buttons 758-764 to make his own selections. If, for example, all participants will come to the location Con and the user does not need to book any room in Wes, he can click the button 758 and select the option "None" from the pop-up menu. After selecting the meeting date/time and the meeting resources, the user clicks the button 762 to establish the meeting schedule. The meeting scheduler then updates all participants' schedules, and reserves the booked meeting resources at the requested date/time.); and
provide the set of recommendations for scheduling the event to the message handler, wherein the message handler generates a prompt based at least in part on the set of recommendations and transmits the prompt to the computing device (see at least [0110] The meeting scheduler also provides a checkbox 722 (see FIG. 7a) which, by default, is set to checked if the default setting is re-schedulable, or unchecked if the default setting is not-re-schedulable. Users may change the state of the checkbox 722 to override the default setting. The meeting scheduler also learns the user's preference to set up the default value of this option for the user [0055] The output of the system 10 is sent to the user interface 306 and/or other applications 310 to, for example, update schedules of user(s), reserve meeting facilities, prepare meeting resources, notify users of a meeting schedule, send reminders for an upcoming meeting, send users information related to a meeting, and/or reply to user queries with query results such as for example feasible schedules, location of a person, etc.).
Regarding claim 2, wherein the message handler is configured to support interactive chat sessions and e-mail communications (see at least [0143] emails and instant messaging are used by the system to notify meeting participants, meeting rooms and resources of upcoming meetings. However, those of skill in the art will appreciate that it may also be possible to use other transmission means such as for example, text messaging, voice mail, etc..
Regarding claim 3, wherein the message comprises a chat message received during an interactive chat session and the prompt comprises a reply chat message, or the message comprises an e-mail message and the prompt comprises a reply e-mail (see at least [0143] emails and instant messaging are used by the system to notify meeting participants, meeting rooms and resources of upcoming meetings. [0055] The output of the system 10 is sent to the user interface 306 and/or other applications 310 to, for example, update schedules of user(s), reserve meeting facilities, prepare meeting resources, notify users of a meeting schedule, send reminders for an upcoming meeting, send users information related to a meeting, and/or reply to user queries with query results such as for example feasible schedules, location of a person, etc.).
Regarding claim 4, wherein the message handler is configured to receive a response to the prompt, and wherein the machine learning engine is configured to:
apply the natural language processing and at least one machine learning model to the response to the prompt (see at least [0061] Data extractors for unstructured sources 350 acquire data from sources that do not organize meeting related information in a structured manner, such as for example, emails, memo, personal schedules, and documents/folders on the computer/network storage. Learners for entity extraction 352 that employing machine learning or other appropriate algorithms (e.g., natural language processing algorithms, decision trees and neural network algorithms) guide the data extractors for unstructured sources 350 to extract data and convert data to triples format, and store the converted data in the triples store 334 in the knowledge server 328. [0050] a client-side application 16 receives queries from users relating to scheduling of events, communicates with the server-side semantics-based application 17 to obtain query results based on a resource-utilization model, and provide query results to users.); and
analyze outputs of the at least one machine learning model to determine whether the response to the prompt confirms attendance of an event, and wherein the system comprises a scheduling engine configured to create a record in a database in response to detection that the response to the prompt confirms attendance of the event, wherein the record comprises information associated with at least a venue for the event, a time for the event, and attendee information for the event (see at least [0062] By using various data acquisition components, the knowledge server 328 collects, and updates in real-time, all types of meeting related information such as for example people's contact information, location and schedule, meeting room location, size and availability, voice bridge availability, other meeting facilities (e.g., interactive whiteboards, audio/video equipment, etc.), data communication servers and services, and scheduled meeting information (e.g., meeting name, participants, starting date/time, duration, booked meeting room(s) and meeting facilities, voice bridge and remote access information, participant preferences, corporate hierarchy, etc.). The resource utilization model is thereby adaptable in real-time to changes that occur with the resources, scheduling and so forth. [0122] Optionally, the meeting organizer could receive a notification that one of the attendees is ill and whether or not that attendee is necessary at the meeting. If the organizer indicates that the attendee is not necessary, the meeting will not be rescheduled. [0123] The meeting scheduler supports sending instant meeting schedule updates to handheld devices. Moreover, it updates meeting scheduling information in real-time. For example, if a meeting participant P finds that he will be late or cannot attend a meeting, he can send a notification message from his handheld device to the meeting management system. If the time between the system receiving the notification message and the meeting starting is longer than a threshold (e.g., 4 hours), the system reschedules the meeting and notifies all participants. If the time between the system receiving the notification message and the meeting starting is less than a threshold, or the meeting has started when the system receives the message, the system may reorganize the meeting agenda by moving the topics that the meeting participant P is involved with to a later time to allow the participant P to catch up with his topic. The system will notify all participants of the change of meeting schedule and/or agenda via email and/or message to participants' handheld devices.).
Regarding claim 5, wherein the message handler is configured to identify, based on the set of recommendations, a new candidate attendee for the event, wherein the prompt is transmitted to a second computing device corresponding to the new candidate attendee (see at least [0119] In cases when a meeting has been scheduled, but a required participant is unable to attend for some reason, the meeting scheduler automatically detects the information by, e.g., obtaining update of user schedules from Microsoft. Exchange Server.RTM., by monitoring and analyzing emails between users and/or by detecting the user's location from his/her RFID tag. The meeting scheduler then revises the meeting schedule according to the information it detects, revises the reservation of meeting resources, and notifies all meeting participants.).
Regarding claim 6, wherein the machine learning engine is configured to: validate the set of recommendations based on one or more validation criteria (see at least [0105] Based on the semantic framework and machine learning used in the meeting management system, the meeting scheduler optimizes meeting schedules in accordance with the rules set in the meeting management system and the learned pattern of user preference.); and
in response to a determination that the set of recommendations are invalid, obtaining a new set of recommendations from the one or more machine learning models, wherein the new set of recommendations are obtained from the one or more machine learning models based on the extracted information and one or more negative parameters (see at least [0123] if a meeting participant P finds that he will be late or cannot attend a meeting, he can send a notification message from his handheld device to the meeting management system. If the time between the system receiving the notification message and the meeting starting is longer than a threshold (e.g., 4 hours), the system reschedules the meeting and notifies all participants. If the time between the system receiving the notification message and the meeting starting is less than a threshold, or the meeting has started when the system receives the message, the system may reorganize the meeting agenda by moving the topics that the meeting participant P is involved with to a later time to allow the participant P to catch up with his topic. The system will notify all participants of the change of meeting schedule and/or agenda via email and/or message to participants' handheld devices.).
Claim 7 is substantially similar to claim 1 and therefore rejected under the same rationale.
Claim 8 is substantially similar to claim 2 and therefore rejected under the same rationale.
Claim 9 is substantially similar to claim 2 and therefore rejected under the same rationale.
Claim 10 is substantially similar to claim 4 and therefore rejected under the same rationale.
Claim 11 is substantially similar to claim 4 and therefore rejected under the same rationale.
Claim 12 is substantially similar to claim 5 and therefore rejected under the same rationale.
Claim 13 is substantially similar to claim 6 and therefore rejected under the same rationale.
Claim 14 is substantially similar to claim 6 and therefore rejected under the same rationale.
Claim 15 is substantially similar to claims 1 or 7 and therefore rejected under the same rationale.
Claim 16 is substantially similar to claims 2 or 8 and therefore rejected under the same rationale.
Claim 17 is substantially similar to claims 2 or 9 and therefore rejected under the same rationale.
Claim 18 is substantially similar to claim 4 and therefore rejected under the same rationale.
Claim 19 is substantially similar to claim 12 and therefore rejected under the same rationale.
Claim 20 is substantially similar to claims 13 and 14 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited prior art generally refers to meeting scheduling based on available information including associated methods and systems.
U.S. Publication 2024/0163233 A1 Enhanced chatbot response through machine learning.
U.S. Publication 2018/0083902 A1 Automated relevance analysis and prioritization of user messages for third-party action.
U.S. Publication 2017/0243307 A1 Apparatus and method for handling a message
U.S. Publication 2023/0401540 A1 Scheduling application.
U.S. Publication 2023/0237441 A1 Systems, methods, and computer program products for managing and networking schedules and calendars of events.
U.S. Publication 2020/0311579 A1 System and methods for automated tagging for scheduling events.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN C WHITE whose telephone number is (571)272-1406. The examiner can normally be reached M-F 7:30-4:00 EST.
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/DYLAN C WHITE/Primary Examiner, Art Unit 3625 November 26, 2025