DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 3/4/2026.
Claims 1, 11, and 16 have been amended and claim 20 has been added new.
Claims 1-20 are currently pending and have been examined.
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 .
Specification
The amendment filed 3/4/2026 is objected to under 35 U.S.C. 132(a) because it introduces new matter into the disclosure. 35 U.S.C. 132(a) states that no amendment shall introduce new matter into the disclosure of the invention. The added material which is not supported by the original disclosure is as follows: “first priority level of a pending task is based on one or more of a title of the pending task, communications via the UCaaS platform corresponding to the pending task, or manually entered priority information for the pending task, and wherein the second priority level of the scheduled item is based on one or more of a title of the scheduled item, a likelihood that the user will speak during the scheduled item, a likelihood that the user will attend the scheduled item, a list of participants invited to the scheduled item, multitasking histories corresponding to previous items associated with the scheduled item, communications via the UCaaS platform corresponding to the scheduled item, or manually entered priority information for the scheduled item”.
Applicant is required to cancel the new matter in the reply to this Office Action.
Response to Amendments
The previously pending 35 USC 103 rejections have been withdrawn in response to Applicant’s claim amendments. Please see below for reasoning.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims integrate the abstract idea into practical application. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that managing and monitoring meetings to make recommendations about scheduling is word for word managing how humans interact and is Organizing Human Activity. The Examiner further asserts the Applicant is merely implementing the abstract idea on a general purpose computer, which merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
Applicant argues the claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. The Applicant states that it is the combination of hardware that integrates into a practical application pointing to the UCaas platform. Applicant does not properly identify the additional elements. The Examiner asks what hardware details of the UCaas platform are recited in the claims because based on the claims the UCaas platform is just generically recited as being used, which merely adds the words apply it with the judicial exception (See MPEP 2106). The Examiner further points to Applicant’s specification Paragraph 0015 at least which states how the UCaas platform is provided by a provider, which further shows that it is merely being used as a tool for implementing the abstract idea. The Examiner further asserts that the hardware recited in the claims amounts to generic computing components for implementing the abstract idea. Applicant’s arguments are not persuasive.
The Examiner notes the “machine learning” (training/retraining) and UCaas are used generically to apply the abstract idea without limiting how the trained machine learning functions or recite any specific hardware configurations. The “machine learning” and UCaaS are described at a high level such that it amounts to using a computer with generic machine learning functionality to apply the abstract idea without any details about how the outcomes are accomplished, which merely adds the words apply it with the judicial exception (See 2024 AI SME Update and MPEP 2106). There is no improvement to the machine learning or the hardware, but rather the claims are merely using machine learning on a general purpose computer to implement the abstract idea (See MPEP 2106). Applicant’s arguments are not persuasive.
The Examiner further points to the laptop he is using to type this office action. I have the ability to participate in virtual meetings using a camera and audio using generic computer components. The audio and video is captured also using generic computer components, as claimed using hardware components of a user device. The claimed arrangement of hardware components is the arrangements of a general purpose computer (See MPEP 2106.05) and Applicant never states why or how the arrangement is unconventional. Applicant’s arguments are not persuasive.
Applicant argues the claims amount to significantly more. The Examiner respectfully disagrees. Applicant does not properly identify the additional elements in the arguments, but rather generically alleges the claims as a whole make the claims eligible without explaining how or why. The Examiner asserts that the hardware recited in the claims amounts to generic computing components for implementing the abstract idea. See the response above or rejection below for more information. Applicant’s arguments are not persuasive.
The Examiner further notes that generic use of machine learning (as claimed) merely adds the words apply it with the abstract idea because the claims are recited at such a high level of generality. Applicant does not properly identify the additional elements or point to a specific arrangement that change the way the computer functions. Applicant’s arguments are not persuasive.
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 non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1-10 are directed toward a process, claims 11-15 are directed toward a product, and claims 16-20 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward an apparatus, comprising: a memory; and a processor configured to execute instructions stored in the memory to: train a machine learning model to, using a training data set including trend data and priority data derived via user records data indicative of communications involving a user of a unified communications as a service (UCaaS) platform over multiple communication software service modalities facilitated by the UCaaS platform, generate user-specific calendar rules for the user and assert the user-specific calendar rules against a virtual calendar of the user according to a set of pending tasks of the user, wherein one or more tasks of the set of pending tasks is obtained from a third party application integrated with the UCaaS platform using an application programming interface call to the third party application, wherein the trend data corresponds to trends in user participation and non- participation across the communications and the priority data corresponds to determined importances of the communications, and wherein the virtual calendar is facilitated by a client application that enables the user to connect to the communications; facilitate, by server-side conferencing software of the UCaaS platform, a video conference between the user and one or more other users of the UCaaS platform, wherein to facilitate the video conference comprises to: receive, by the server-side conferencing software, audio and video data captured using hardware components of a user device connected to the video conference via the client application; and generate, based on the audio and video data, user participation data indicative of low participation of the user in the video conference, wherein the video conference is one of a recurring series of video conferences; cause, based on respective data of the trend data indicating that the user disabled video obtained via a camera of the hardware components during past video conferences of the recurring series and that the user remained silent during the past video conferences, the machine learning model to generate the user-specific calendar rules; cause the machine learning model to assert the user-specific calendar rules against the virtual calendar to automatically adjust the virtual calendar, wherein to automatically adjust the virtual calendar includes to: determine a first priority level for each pending task of the set of pending tasks; determine, using the user participation data, a second priority level for a scheduled item already included in the virtual calendar at a scheduled time and representing a next video conference of the recurring series of video conferences; determine that a first priority level of a pending task of the set of pending tasks is higher than the second priority level; and transmit, to the client application, data generated based on the determination that the first priority level of the pending task is higher than the second priority level to cause an automatic adjustment to the virtual calendar to remove the scheduled item and add a new event corresponding to the pending task at the scheduled time and to cause a graphical user interface output at the user device to show the automatic adjustment to the virtual calendar; and retrain the machine learning model to generate and assert the user-specific calendar rules based on whether the pending task is completed during the scheduled time (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are applying user (e.g. human) defined rules to determine priority levels of tasks and adjusting the calendar schedule accordingly and analyzing video meeting participation data to make further determinations about how to adjust the human calendars, which is managing how humans interact for commercial purposes.
Dependent claims 2-10, 12-15, and 17-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “obtained from a third party application integrated with the UCaaS platform using an application programming interface call to the third party application; obtained via a camera of the hardware components; a graphical user interface output at the user device (claims 1, 11, and 16)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data and displaying data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the Independent claims further recite “an apparatus, comprising: a memory; and a processor configured to execute instructions stored in the memory to: train a machine learning model to, involving a user of a unified communications as a service (UCaaS) platform; over multiple communication software service modalities facilitated by the UCaaS platform, and wherein the virtual calendar is facilitated by a client application that enables the user to connect to the communications; facilitate, by server-side conferencing software of the UCaaS platform, a video conference between the user and one or more other users of the UCaaS platform, by the server-side conferencing software, audio and video data captured using hardware components of a user device connected to the video conference via the client application; and retrain the machine learning model (claims 1 and 16)”; “non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations; a third party application integrated with the UCaaS platform using an application programming interface; a camera of the hardware components; a graphical user interface (claim 13)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
The Examiner notes the “machine learning” (training/retraining) is used generically to apply the abstract idea without limiting how the trained machine learning functions. The “machine learning” is described at a high level such that it amounts to using a computer with generic machine learning functionality to apply the abstract idea without any details about how the outcomes are accomplished, which merely adds the words apply it with the judicial exception (See 2024 AI SME Update and MPEP 2106).
In addition, dependent claims 2-10, 12-15, and 17-20 further narrow the abstract idea and dependent claims 2, 5, 17, and 20 additionally recite “email client (claims 2, 17, and 20); third party application (claim 5); application programming interface (claim 5)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106). Further, method; System; and Product Independent claims 1-20 recite “an apparatus, comprising: a memory; and a processor configured to execute instructions stored in the memory to: train a machine learning model to, involving a user of a unified communications as a service (UCaaS) platform; over multiple communication software service modalities facilitated by the UCaaS platform, and wherein the virtual calendar is facilitated by a client application that enables the user to connect to the communications; facilitate, by server-side conferencing software of the UCaaS platform, a video conference between the user and one or more other users of the UCaaS platform, by the server-side conferencing software, audio and video data captured using hardware components of a user device connected to the video conference via the client application; and retrain the machine learning model (claims 1 and 16)”; “non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations; a third party application integrated with the UCaaS platform using an application programming interface; a camera of the hardware components; a graphical user interface (claim 13)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0101-0116 and Figures 1-4. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the independent claims recite “obtained from a third party application integrated with the UCaaS platform using an application programming interface call to the third party application; obtained via a camera of the hardware components; a graphical user interface output at the user device (claims 1, 11, and 16)”, which would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-10, 12-15, and 17-20 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, dependent claims 2, 5, 17, and 20 additionally recite “email client (claims 2, 17, and 20); third party application (claim 5); application programming interface (claim 5)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Allowable over 35 USC 103
Claims 1-20 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Independent claims 1, 11, and 16 disclose a system, product, and method for applying user (e.g. human) defined rules to determine priority levels of tasks and adjusting the calendar schedule accordingly and analyzing video meeting participation data to make further determinations about how to adjust the human calendars by determining based on audio and video data if a human user is actually participating in a meeting.
Regarding a possible 103 rejection: The closest prior art of record is:
Gillam (US 2024/0086859 A1) – which discloses optimizing resource allocation for scheduling purposes.
Evans et al. (US 10,572,859 B1) – which discloses an intelligent meeting and conferencing system.
Rodden et al. (US 2019/0334907 A1) – which discloses scheduling for resource optimization.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 1, 11, and 16, such as applying user (e.g. human) defined rules to determine priority levels of tasks and adjusting the calendar schedule accordingly and analyzing video meeting participation data to make further determinations about how to adjust the human calendars by determining based on audio and video data if a human user is actually participating in a meeting. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “an apparatus, comprising: a memory; and a processor configured to execute instructions stored in the memory to: train a machine learning model to, using a training data set including trend data and priority data derived via user records data indicative of communications involving a user of a unified communications as a service (UCaaS) platform over multiple communication software service modalities facilitated by the UCaaS platform, generate user-specific calendar rules for the user and assert the user-specific calendar rules against a virtual calendar of the user according to a set of pending tasks of the user, wherein one or more tasks of the set of pending tasks is obtained from a third party application integrated with the UCaaS platform using an application programming interface call to the third party application, wherein the trend data corresponds to trends in user participation and non- participation across the communications and the priority data corresponds to determined importances of the communications, and wherein the virtual calendar is facilitated by a client application that enables the user to connect to the communications; facilitate, by server-side conferencing software of the UCaaS platform, a video conference between the user and one or more other users of the UCaaS platform, wherein to facilitate the video conference comprises to: receive, by the server-side conferencing software, audio and video data captured using hardware components of a user device connected to the video conference via the client application; and generate, based on the audio and video data, user participation data indicative of low participation of the user in the video conference, wherein the video conference is one of a recurring series of video conferences; cause, based on respective data of the trend data indicating that the user disabled video obtained via a camera of the hardware components during past video conferences of the recurring series and that the user remained silent during the past video conferences, the machine learning model to generate the user-specific calendar rules; cause the machine learning model to assert the user-specific calendar rules against the virtual calendar to automatically adjust the virtual calendar, wherein to automatically adjust the virtual calendar includes to: determine a first priority level for each pending task of the set of pending tasks; determine, using the user participation data, a second priority level for a scheduled item already included in the virtual calendar at a scheduled time and representing a next video conference of the recurring series of video conferences; determine that a first priority level of a pending task of the set of pending tasks is higher than the second priority level; and transmit, to the client application, data generated based on the determination that the first priority level of the pending task is higher than the second priority level to cause an automatic adjustment to the virtual calendar to remove the scheduled item and add a new event corresponding to the pending task at the scheduled time and to cause a graphical user interface output at the user device to show the automatic adjustment to the virtual calendar; and retrain the machine learning model to generate and assert the user-specific calendar rules based on whether the pending task is completed during the scheduled time (as required by independent claims 1, 11, and 16)”, thus rendering claims 1, 11, 16 and their dependent claims as allowable over the prior art.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625