DETAILED ACTION
Notice to Applicant
The following is a FINAL Office action upon examination of application number 18/516,359 filed on 11/21/2023. Claims 1-20 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 18/516,359 filed 11/21/2023 is a Continuation in Part of application 17/838,098, filed 06/10/2022. Application 17/838,098 claims Priority from Provisional Application 63/209,864, filed 06/11/2021.
Response to Amendment
In the response file December 10, 2025, Applicant amended claims 1-18 and 20, and did not cancel any claims. No new claims were presented for examination.
Applicant's amendments to claims 8 and 18 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim objections; accordingly, these objections have been removed.
Applicant's amendments to claims 8, 11, and 18 are hereby acknowledged. The amendments are sufficient to overcome the previously issued claim rejection under 35 U.S.C. 112(b); accordingly, this rejection has been withdrawn.
Applicant's amendments to the claims are hereby acknowledged. The amendments are not sufficient to overcome the previously issued claim rejection under 35 U.S.C. 101; accordingly, this rejection has been maintained.
Response to Arguments
Applicant's arguments filed December 10, 2025, have been fully considered.
Applicant submits that the currently amended claims are directed to patent-eligible subject matter in light of the USPTO Memo of August 4th, 2025, titled "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter "Reminders Memo") and the Patent Eligibility Guideline Example 47” [Applicant’s Remarks, 12/10/2025, page 11]
The Examiner respectfully disagrees. In response, it is respectfully maintained that the eligibility analysis applied to the claims in this application is fully consistent with the guidance of the August 4, 2025 Memorandum as well as procedures set forth in MPEP 2106. The §101 rejection follows the step-by-step framework required under MPEP 2106.04(a)-(d) and MPEP 2106.05. Specifically, under Step 2A Prong One, the rejection explicitly identifies, in bold text, the specific limitations that are determined to recite the abstract idea (i.e., the certain method of organizing human activity). The rejection also identifies the additional elements, in plain text, which were separately analyzed under Step 2A, Prong Two and Step 2B to determine whether the judicial exception is integrated into a practical application or amounts to significantly more.
The Office action further provides detailed reasoning explaining why the claim elements – both individually and as an ordered combination – do not integrated the abstract idea into a practical application, and why the claims as whole do not recite significantly more than the abstract idea itself. The Office action therefore demonstrates that the claims were evaluated as whole, consistent with both the MPEP and current USPTO guidance. Applicant’s argument fails to acknowledge, discuss, or point out any alleged errors in the findings set forth in the Step 2A Prong One analysis that provides reasons why each of the specifically addressed limitations is interpreted as setting forth or describing activity falling under one or more of the abstract idea groupings. Accordingly, Applicant’s argument is not persuasive because it does not identify any specific error in the analysis or demonstrate that the rejection failed to consider the claims in its entirety.
Moreover, in response to Applicant’s citation to Example 47, the Examiner first emphasizes that the claims in Example 47 share virtually no similarities with Applicant’s invention. The Examiner emphasizes that there are virtually no similarities in subject or fact pattern as between Applicant’s claims and claim 3 of Example 47 (Anomaly Detection). As compared to Example 47 (claim 3), Applicant’s claims recite no limitations remotely similar to the limitations for “A method of using an artificial neural network (ANN) to detect malicious network packets comprising: (a) training, by a computer, the ANN based on input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; (b) detecting one or more anomalies in network traffic using the trained ANN; (c) determining at least one detected anomaly is associated with one or more malicious network packets; (d) detecting a source address associated with the one or more malicious network packets in real time; (e) dropping the one or more malicious network packets in real time; and (f) blocking future traffic from the source address.” While claim 1 was amended to recite “determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; retrieve the metadata for each of the one or more user events,” this limitation does not reflect an improvement in the technical field of network intrusion detection by providing for improved network security using information from the detection (of a source address associated with the one or more malicious network packets in real time) to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Applicant’s claim as a whole including the amended “determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; retrieve the metadata for each of the one or more user events,” in contrast to claim 3 of Example 47 does not integrate the judicial exception into a practical application such that the claim is not directed to the judicial exception.
Applicant submits “that the claims recite an improvement to computer capabilities or to a technological field (e.g., calendar and event organization platforms) and are thus patent eligible. For example, currently amended independent claim 1 recites a practical application of, in part: generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events; and determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; retrieve the metadata for each of the one or more user events; and automatically schedule each of the one or more user events based on priority information for the one or more user events, the priority information comprising the one or more user-facing priority levels and the one or more machine learning priority sublevels.” [Applicant’s Remarks, 12/10/2025, page 12]
In response to Applicant’s argument that “currently amended independent claim 1 recites a practical application,” it is noted that the additional elements in amended claim 1 are: at least one processor, a memory, a digital calendar, and a machine learning model of the system, which merely serve to tie the abstract idea to a particular technological environment (computer-based operating environment) via generic computing hardware, software/instructions, which is not sufficient to amount to a practical application, as noted in MPEP 2106.05. Applicant has provided no facts/evidence, cited any portion of the Specification, nor provided a persuasive line of reasoning showing how the additional elements are integrated with the abstract idea to integrate the abstract idea into a practical application.
It is also noted that the claims are devoid of any discernible change, transformation, or improvement to a computer (software or hardware) or any existing technology. Applicant has not shown that any specific technological improvement is achieved within the scope of the claims. It bears emphasis that no processor, memory, digital calendar, system, or technological elements are modified or improved upon in any discernible manner. Instead, the result produced by the claims is simply information relating to priority information, which is not a technical result or improvement thereof.
Furthermore, the additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Applicant submits “Childs, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest each limitation recited by independent claims 1, 12, and 20. For example, Childs, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest "generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events" and "determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events," as recited by currently amended independent claim 1.” [Applicant’s Remarks, 12/10/2025, pages 14-15]
In response to the Applicant’s argument that “Childs, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest each limitation recited by independent claims 1, 12, and 20. For example, Childs, whether considered singly or in combination with the other cited references, fails to describe, teach, or suggest "generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events" and "determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events," as recited by currently amended independent claim 1,” it is noted that this argument is a mere allegation of patentability by the Applicant with no supporting rationale or explanation. Merely stating that the claims do not teach a feature does not offer any insight as to why the specific sections of the prior art relied upon by the Examiner fail to disclose the claimed features. Applicant's arguments amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references. Moreover, the Examiner notes the limitations being argued by Applicant as being newly amended to the claims in the response filed 12/10/2025, which have been addressed in the updated rejection below. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “Childs discussion of a "priority associated with the event request" fails to teach or suggest generating metadata by "determining one or more user-facing priority levels associated with the one or more user events" and "determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events," as recited by amended independent claim 1.” [Applicant’s Remarks, 12/10/2025, page 15]
In response to Applicant’s argument, it is noted that Childs was not asserted as disclosing the amended limitation “determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events." With respect to the amended limitation “generating metadata by "determining one or more user-facing priority levels associated with the one or more user events. It is noted that Childs teaches and at least suggest this limitation. Specifically, Childs discloses determining a priority associated with an event request based on context data (paragraph 0028), including relationships based priority (paragraph 0029), keyword based priority (paragraph 003), and behavior based priority (paragraph 0032). These priority designations correspond to user-facing priority levels, as they reflect the relative importance of events to the user and user in scheduling decisions. Applicant’s argument has been considered, but it pertains to amendments to independent claim 1 that are believed to be addressed via the updated ground of rejection under §103 set forth in the instant Office action, which incorporates a new reference and new citations to address the amended limitations in claim and supports a conclusion of obviousness of the amended claims.
Applicant submits “Johnson merely discusses that a user may assign a priority level to each meeting and task. However, the user-assigned priority level in Johnson cannot be relied upon to teach or suggest "one or more machine learning priority sub-levels," much less generating metadata by "determining one or more user-facing priority levels associated with the one or more user events" and "determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events," as recited by amended independent claim 1.” [Applicant’s Remarks, 12/10/2025, page 16]
In response to Applicant’s argument that “Johnson cannot be relied upon to teach or suggest "one or more machine learning priority sub-levels," much less generating metadata by "determining one or more user-facing priority levels associated with the one or more user events" and "determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events," as recited by amended independent claim 1,” it is noted that Johnson was not asserted as teaching the amended limitations. Accordingly, this argument is deemed moot.
Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above, or they are
directed to features which have been newly added via amendment. Therefore, this is now the Examiner's first opportunity to consider these limitations and as such any arguments regarding these limitations would be inappropriate since they have not yet been examined. A full rejection of these limitations will be presented later in this Office Action.
Claim Rejections - 35 USC § 112
16. 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.
17. Claims 12-19 and 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 pre-AIA the applicant regards as the invention.
18. Claim 12 was amended to recite “A method for intelligent calendar management, the method comprising: receiving one or more user events for scheduling with a digital calendar associated with a user; generating metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events; and determining, utilizing a machine learning model of the system...” The limitation “the system” lacks antecedent basis because claim 12 does not introduce “a system”. Appropriate correction is required.
19. Claim 20 was amended to recite “generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events; and determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events...” The limitation “the system” lacks antecedent basis because claim 20 does not introduce “a system”. Appropriate correction is required.
20. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
21. 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.
22. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
23. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-11), method (claims 12-19), and computer program product (claim 20) are directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-20 is satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing scheduling activities, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions). With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: at least one processor; and a memory, coupled to the at least one processor, configured to store executable instructions that, when executed by the at least one processor, cause the at least one processor to: receive one or more user events for scheduling with a digital calendar associated with a user; generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events; and determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; retrieve the metadata for each of the one or more user events; and automatically schedule each of the one or more user events based on priority information for the one or more user events, the priority information comprising the one or more user-facing priority levels and the one or more machine learning priority sublevels. These steps describe managing personal behavior or relationships or interactions (e.g., social activities, following rules or instructions) and are organizing human activity because the limitations are directly tied to managing a user schedule.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to the independent claims, the additional elements are: at least one processor, a memory, coupled to the at least one processor, configured to store executable instructions, a digital calendar, and a machine learning model of the system (claim 1), a digital calendar and a machine learning model of the system (claim 12), a non-transitory computer-readable medium having computer-readable program code stored thereon, a digital calendar, and a machine learning model of the system (claim 20). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to the independent claims, the additional elements are: at least one processor, a memory, coupled to the at least one processor, configured to store executable instructions, a digital calendar, and a machine learning model of the system (claim 1), a digital calendar and a machine learning model of the system (claim 12), a non-transitory computer-readable medium having computer-readable program code stored thereon, a digital calendar, and a machine learning model of the system (claim 20). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraph [0040]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.).
Even if the machine learning model was evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of machine learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 39: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art”). See also, Anders et al., US 2020/0020015 (paragraph 101: “inferences may be performed by any combination of means known in the art, such as by pattern-matching, text analytics, semantic analytics, statistical methods, artificial intelligence, Bayesian analysis, machine learning, or keyword searching”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-11 and 13-19 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-11 recite “wherein the one or more user-facing priority levels associated with the one or more user events is defined through metadata for each of the one or more user events when a user event is created for scheduling,” “wherein the priority information for an event includes an indication of whether the event is under a control of a scheduling application for automatic scheduling by the scheduling application,” “wherein, when the event is under the control of a scheduling application for automatic scheduling by the scheduling application, the metadata includes an indication of a specific vendor of the scheduling application for controlling the automatic scheduling of the event,” “wherein the metadata for an event includes an indication of whether the event is able to move during a scheduling process,” “wherein the metadata for an event includes an indication of a user-facing priority level of the event,” “wherein the metadata for an event includes an indication of a machine learning priority sublevel of the event,” “generate a user identifier (ID) for the one or more user events, wherein the user ID, when decoded, indicates the priority information associated with the one or more user events,” “generate one or more scheduling links for sharing information associated with the digital calendar,” “generate one or more high priority scheduling links for scheduling one or more events with a high priority,” “wherein the one or more high priority scheduling links include one or more team high priority scheduling links,” however these limitations cover organizing human activity since they flow directly from the schedule management involving human interaction, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping. Accordingly, these steps are part of the same abstract idea(s) set forth in the independent claims. The additional elements recited in the dependent claims include: a scheduling application (claim 4), machine learning (claim 7), instructions (claims 8-10), one or more scheduling links (claims 9 and 19), one or more high priority scheduling links (claim 10), the one or more high priority scheduling links and one or more team high priority scheduling links (claim 11). However, each of these elements is recited at a high level of generality and fails to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application. When evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
24. 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 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.
25. 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 of this title, 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.
26. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
27. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
28. Claims 1-3, 5-8, 12-14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Childs et al., Pub. No.: US 2021/0192466 A1, [hereinafter Childs], in view of Johnson et al., Pub. No.: US 2009/0089133 A1, [hereinafter Johnson], in further view of Bhattacharya et al., Pub. No.: US 2020/0293999 A1, [hereinafter Bhattacharya].
As per claim 1, Childs teaches a system for intelligent calendar management, the system (paragraph 0003) comprising:
at least one processor (paragraph 0003: “an information handling device, comprising: a processor; a memory device that stores instructions executable by the processor”); and
a memory, coupled to the at least one processor, configured to store executable instructions that, when executed by the at least one processor, cause the at least one processor (paragraph 0003: “an information handling device, comprising: a processor; a memory device that stores instructions executable by the processor”) to:
receive one or more user events for scheduling with a digital calendar associated with a user (paragraph 0003: “receive an event request for a user”; paragraph 0016: “a method for dynamically and automatically managing incoming event requests by using context data associated with the event request. In an embodiment, a user may receive an event request (e.g., a meeting request, an activity request, etc.)”);
generate metadata for the one or more user events by: determining one or more user-facing priority levels associated with the one or more user events (paragraph 0016: “a method for dynamically and automatically managing incoming event requests by using context data associated with the event request. In an embodiment, a user may receive an event request (e.g., a meeting request, an activity request, etc.). An embodiment may then determine a priority associated with the event request by accessing available context data.”; paragraph 0028, discussing that an embodiment may determine a priority associated with the event request by accessing available context data. An embodiment may review a permissions list to determine the types of context data a user has allowed the email application access to; paragraph 0029, discussing that the context data may correspond to a relationship between the event requestor and the user and the priority may be determined based upon the relationship. Stated differently, an event request received from an event requestor determined to have a close relationship with the user may be prioritized over events whose creators have a more distant relationship with the user. For example, an event request received from a user's boss, an important client, their spouse, etc. may be prioritized over an event request received from the user's friend. In an embodiment, a user may assign a relationship to each event requestor and also assign a priority designation to each type of relationship…; paragraph 0031, discussing that event requests comprising one or more keywords may be automatically prioritized over other event requests containing no keywords. In situations where two or more event requests both comprise keywords, an embodiment may be able to prioritize one event request over another based upon reference to a keyword priority designation database. In this database, different keywords may be assigned different priority levels, where a higher priority assignment indicates a more important keyword. Accordingly, the event request having the higher priority keyword may be assigned a higher priority than the event request having the lower priority keyword. Additionally or alternatively, an embodiment may also weigh the number of identified keywords in an event request in the priority decision. For example, an event request having three identified keywords may be prioritized over another event request having only a single identified keyword; paragraph 0032, discussing that the context data may correspond to the past behavior of the user with respect to how they have managed and/or reacted to particular types of event requests and the priority may be based on this past behavior. The type of event request may relate to the nature of the event (e.g., a work-based event, a leisure-based event, etc.), the identity of the event requestor (e.g., a work-based requestor, a family-based requestor, a friend requestor, etc.), the activity level of the event (e.g., a meeting vs. a physical activity, etc.), and the like...An embodiment may assign a priority to a new event request based upon the identified past behavior of the user with respect to similar types of event requests. For example, a meeting request received from the user's boss may be assigned a high priority designation because an embodiment may identify that a user has quickly accepted past meeting requests from their boss. As another example, an after work team-building activity request may be assigned a low priority designation because an embodiment may identify that a user has declined or not responded to prior team-building activity requests);
retrieve the metadata for each of the one or more user events (paragraph 0027: “a method for dynamically managing incoming event requests by utilizing context data. At 301, an embodiment may receive an event request directed to a user. In an embodiment, the event request may be created by an event requestor and may contain one or more of: a description of an event, a date of an event, a location of an event, an event recipient list, and the like.”; paragraph 0028: “an embodiment may determine a priority associated with the event request by accessing available context data.”; paragraph 0016); and
the priority information comprising the one or more user-facing priority levels (paragraph 0029, discussing that the context data may correspond to a relationship between the event requestor and the user and the priority may be determined based upon the relationship. Stated differently, an event request received from an event requestor determined to have a close relationship with the user may be prioritized over events whose creators have a more distant relationship with the user. For example, an event request received from a user's boss, an important client, their spouse, etc. may be prioritized over an event request received from the user's friend. In an embodiment, a user may assign a relationship to each event requestor and also assign a priority designation to each type of relationship…; paragraph 0032, discussing that the context data may correspond to the past behavior of the user with respect to how they have managed and/or reacted to particular types of event requests and the priority may be based on this past behavior. The type of event request may relate to the nature of the event (e.g., a work-based event, a leisure-based event, etc.), the identity of the event requestor (e.g., a work-based requestor, a family-based requestor, a friend requestor, etc.), the activity level of the event (e.g., a meeting vs. a physical activity, etc.), and the like...An embodiment may assign a priority to a new event request based upon the identified past behavior of the user with respect to similar types of event requests. For example, a meeting request received from the user's boss may be assigned a high priority designation because an embodiment may identify that a user has quickly accepted past meeting requests from their boss. As another example, an after work team-building activity request may be assigned a low priority designation because an embodiment may identify that a user has declined or not responded to prior team-building activity requests; paragraphs 0028, 0031).
While Childs describes automatically responding to the event request based on the priority (paragraph 0016), Childs does not explicitly teach determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; and automatically schedule each of the one or more user events based on priority information for the one or more user events, the priority information comprising the one or more machine learning priority sublevels. Johnson in the analogous art of scheduling system teaches:
automatically schedule each of the one or more user events based on priority information for the one or more user events (paragraph 0027, discussing a mechanism for integrating a calendar and task scheduler to enable automatic scheduling of meetings and assignment of tasks based on priority. A calendar tool is provided which allows a user to schedule meetings and appointments; paragraph 0029, discussing that the user may also assign a priority level to each meeting. The priority level may be a numerical value which indicates the importance of a particular meeting…; paragraph 0002).
Childs is directed to a method for event request prioritization. Johnson relates to an integrated calendar and task scheduler. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Childs with Johnson because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying Childs to include Johnson’s feature for including automatically schedule each of the one or more user events based on priority information for the one or more user events, in the manner claimed, would serve the motivation of enabling automatic scheduling of meetings and assignment of tasks based on priority (Johnson at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Childs-Johnson combination does not explicitly teach determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events; and the priority information comprising the one or more machine learning priority sublevels. However, Bhattacharya in the analogous art of calendar event conflict resolution systems teaches these concepts. Bhattacharya teaches:
determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events (paragraph 0004, discussing systems, methods and devices for prioritizing calendar events with artificial intelligence…A statistical machine learning model, such as a feature selection model, may be applied to a plurality of factors associated with each of the events. Those factors may include event parameters (e.g., duration of event, time and day of event, location of event, etc.). Those factors may also include one or more attributes of potential attendees of the events (e.g., seniority of attendees, organizational title of attendees, office location of attendees, etc.). An event priority score can then be generated for each of the events, and those events can be ranked according to their relative scores. The lower-ranked events can be replaced with higher ranked events that conflict with them. The machine learning model may utilize a feedback loop that takes user feedback about the machine-rankings into account, and the machine learning model may thus be modified accordingly; paragraph 0017, discussing mechanisms for prioritizing events on users' calendars, alone and/or in the context of other users, such that meetings can be ranked and processed for moving appropriately when conflicts arise. In some examples, when a new event conflicts with one or more previously scheduled/existing events on a user's calendar, the electronic calendar service may identify one, two, three or more times for scheduling the new event and present those times to the user. The electronic calendar service may only present times to users for previously booked events that have lower event priority scores compared with the new event. The event priority score for each event may be generated by the electronic calendar service via application of one or more machine learning models…; paragraph 0025, discussing that in generating the event priority score for an event, the electronic calendar service may apply a machine learning model to one or more of the event attendee attribute datapoints and/or one or more of the event parameter datapoints…The machine learning model may be trained to generate a score reflecting the relative importance of a calendar event to a user—that score (i.e., the event priority score) is indicative of how likely a user is to prioritize the event when another event conflicts with it. In additional examples, a confidence score may be applied an event corresponding to a likelihood that a user would prefer to replace that event with a different event having a specific event priority score; paragraphs 0030, 0033); and
the priority information comprising the one or more machine learning priority sublevels (paragraph 0017, discussing mechanisms for prioritizing events on users' calendars, alone and/or in the context of other users, such that meetings can be ranked and processed for moving appropriately when conflicts arise. In some examples, when a new event conflicts with one or more previously scheduled/existing events on a user's calendar, the electronic calendar service may identify one, two, three or more times for scheduling the new event and present those times to the user. The electronic calendar service may only present times to users for previously booked events that have lower event priority scores compared with the new event. The event priority score for each event may be generated by the electronic calendar service via application of one or more machine learning models…; paragraph 0025, discussing that in generating the event priority score for an event, the electronic calendar service may apply a machine learning model to one or more of the event attendee attribute datapoints and/or one or more of the event parameter datapoints…The machine learning model may be trained to generate a score reflecting the relative importance of a calendar event to a user—that score (i.e., the event priority score) is indicative of how likely a user is to prioritize the event when another event conflicts with it. In additional examples, a confidence score may be applied an event corresponding to a likelihood that a user would prefer to replace that event with a different event having a specific event priority score; paragraph 0035, discussing that on the left side of machine learning training sub-environment 408, each of the meetings (including new meting D) are arranged under events (pre-training) element 410 based on their ranks according to their event priority scores. Thus, meeting C is first with an event priority score of 7.6, meeting A is second with an event priority score of 6.6, meeting D is third with an event priority score of 5.7, and meeting B is fourth with an event priority score of 4.1..); paragraph 0053, discussing that the event ranking engine may perform one or more operations associated with generating event priority scores for new and existing events. The event priority scores may be generated based on application of one or more machine learning models to the extracted features (e.g., event features and attendee attributes). Conflict resolution engine may perform one or more operations associated with ranking new and existing events and prioritizing those events based on the rankings).
The Childs-Johnson combination describes features related to calendar and event management. Bhattacharya relates systems, methods and devices for prioritizing calendar events with artificial intelligence. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson combination with because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson combination to include Bhattacharya’s features for including determining, utilizing a machine learning model of the system, one or more machine learning priority sub-levels associated with the one or more user events, and the priority information comprising the one or more machine learning priority sublevels, in the manner claimed, would serve the motivation of the making the prioritization of meetings more efficient (Bhattacharya at paragraph 0019); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Childs suggests wherein the one or more user-facing priority levels associated with the one or more user events is defined through metadata for the one or more user events when a user event is created for scheduling (paragraph 0029, discussing that the context data may correspond to a relationship between the event requestor and the user and the priority may be determined based upon the relationship. Stated differently, an event request received from an event requestor determined to have a close relationship with the user may be prioritized over events whose creators have a more distant relationship with the user. For example, an event request received from a user's boss, an important client, their spouse, etc. may be prioritized over an event request received from the user's friend. In an embodiment, a user may assign a relationship to each event requestor and also assign a priority designation to each type of relationship; paragraph 0036, discussing that the priority determination may be facilitated by analyzing one or more times of available context data; paragraphs 0028, 0030, 0032).
Although not explicitly taught Childs, Johnson in the analogous art of scheduling systems teaches wherein the one or more user-facing priority levels associated with the one or more user events is defined through metadata for the one or more user events when a user event is created for scheduling (paragraph 0043, discussing that the user may use preference window to set other preferences, such as the priority level of meeting. The priority level may be a numerical value which indicates the importance of a particular meeting; paragraph 0053, discussing that the user also sets a priority level for the new meeting or appointment. The priority level indicates the importance of the meeting or appointment. The priority level set by the user may override any priority level already associated with the meeting or appointment. For example, when a meeting coordinator sends a meeting invitation to the user, the coordinator may assign a priority level to the meeting. However, when the user receives and accepts the invitation, the user may override the existing priority level assigned to the meeting and assign a higher or lower priority to the meeting based on policies defined for the calendar or the time of accepting the invitation).
Childs is directed to a method for event request prioritization. Johnson relates to an integrated calendar and task scheduler. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Childs with Johnson because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying Childs to include Johnson’s feature for including wherein the one or more user-facing priority levels associated with the one or more user events is defined through metadata for the one or more user events when a user event is created for scheduling, in the manner claimed, would serve the motivation of enabling automatic scheduling of meetings and assignment of tasks based on priority (Johnson at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Although not explicitly taught by Childs, Johnson in the analogous art of scheduling systems teaches wherein the priority information for an event includes an indication of whether the event is under a control of a scheduling application for automatic scheduling by the scheduling application (paragraph 0002: “enable automatic scheduling of meetings and assignment of tasks based on priority”; paragraph 0033, discussing that as meetings and appointments are added to or removed from the user's calendar, the blocks of time which are allocated to scheduled tasks are automatically rescheduled based on priorities assigned to the meetings and the tasks; paragraph 0048, discussing that the user may also use preference window 316 to specify that the scheduling algorithm may automatically call a specified function or a policy to change the priority level assigned to each task when necessary. For example, the function or policy may raise the priority level for a task to the highest priority level if the task's estimated completion date is less than two work days before the task's due date. In another example, the priority level of a task may be raised by one priority level incrementally, up to the highest priority level, if the task's schedule has been published since the task's previous reschedule. Preference window 316 may also provide a means for the user to indicate that a task is very important. These very important tasks may be flagged such that their priority levels may not be automatically altered by the function or policy).
Childs is directed to a method for event request prioritization. Johnson relates to an integrated calendar and task scheduler. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Childs with Johnson because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying Childs to include Johnson’s feature for including wherein the priority information for an event includes an indication of whether the event is under a control of a scheduling application for automatic scheduling by the scheduling application, in the manner claimed, would serve the motivation of enabling automatic scheduling of meetings and assignment of tasks based on priority (Johnson at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Although not explicitly taught by Childs, Johnson in the analogous art of scheduling systems teaches wherein the metadata for an event includes an indication of whether the event is able to move during a scheduling process (paragraph 0048, discussing that the user may also use preference window 316 to specify that the scheduling algorithm may automatically call a specified function or a policy to change the priority level assigned to each task when necessary. For example, the function or policy may raise the priority level for a task to the highest priority level if the task's estimated completion date is less than two work days before the task's due date. In another example, the priority level of a task may be raised by one priority level incrementally, up to the highest priority level, if the task's schedule has been published since the task's previous reschedule. Preference window 316 may also provide a means for the user to indicate that a task is very important. These very important tasks may be flagged such that their priority levels may not be automatically altered by the function or policy; paragraph 0073, discussing that if the time block to be scheduled for the added meeting conflicts with the time block for an existing scheduled task, the scheduling algorithm schedules the added meeting in the time block specified by the user and reschedules all tasks from that point forward in time. In this manner, the time block assigned to a lower priority meeting may overlap a higher priority task which cannot be moved without missing its due date. Thus, while the lower priority meeting is scheduled at the specified time block, the lower priority meeting is flagged as conflicting with a higher priority task. In contrast, lower priority tasks which may overlap with higher priority tasks are rescheduled to a different time block by the scheduling algorithm. In an alternative embodiment, the scheduling algorithm may not schedule the lower priority meeting if it conflicts with a higher priority task which cannot be moved without missing its due date. Instead, the scheduling algorithm may send a notice to the meeting coordinator or other meeting participants declining the meeting invitation).
Childs is directed to a method for event request prioritization. Johnson relates to an integrated calendar and task scheduler. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Childs with Johnson because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying Childs to include Johnson’s feature for including wherein the metadata for an event includes an indication of whether the event is able to move during a scheduling process, in the manner claimed, would serve the motivation of enabling automatic scheduling of meetings and assignment of tasks based on priority (Johnson at paragraph 0002); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 6, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Childs further teaches wherein the metadata for an event includes an indication of a user-facing priority level of the event (paragraph 0030, discussing that the context data may correspond to a keyword present in the event request and the priority may be based on the presence or absence of the keyword. Non-limiting examples of keywords include relevant project names, acronyms, importance-indicating trigger words (e.g., “important”, “high priority”, “urgent”, etc.), and the like; paragraph 0031, discussing that event requests comprising one or more keywords may be automatically prioritized over other event requests containing no keywords. In situations where two or more event requests both comprise keywords, an embodiment may be able to prioritize one event request over another based upon reference to a keyword priority designation database. In this database, different keywords may be assigned different priority levels, where a higher priority assignment indicates a more important keyword. Accordingly, the event request having the higher priority keyword may be assigned a higher priority than the event request having the lower priority keyword. Additionally or alternatively, an embodiment may also weigh the number of identified keywords in an event request in the priority decision. For example, an event request having three identified keywords may be prioritized over another event request having only a single identified keyword; paragraphs 0028, 0032).
As per claim 7, the Childs-Johnson-Bhattacharya combination teaches the system of claim 6. Childs further teaches wherein the metadata for an event includes an indication of a priority sublevel of the event (paragraph 0031, discussing that event requests comprising one or more keywords may be automatically prioritized over other event requests containing no keywords. In situations where two or more event requests both comprise keywords, an embodiment may be able to prioritize one event request over another based upon reference to a keyword priority designation database. In this database, different keywords may be assigned different priority levels, where a higher priority assignment indicates a more important keyword. Accordingly, the event request having the higher priority keyword may be assigned a higher priority than the event request having the lower priority keyword. Additionally or alternatively, an embodiment may also weigh the number of identified keywords in an event request in the priority decision. For example, an event request having three identified keywords may be prioritized over another event request having only a single identified keyword; paragraph 0032, discussing that the context data may correspond to the past behavior of the user with respect to how they have managed and/or reacted to particular types of event requests and the priority may be based on this past behavior. The type of event request may relate to the nature of the event, the identity of the event requestor, the activity level of the event, and the like. In an embodiment, common behaviors of users responsive to receiving the event request may include immediately accepting the event request, immediately declining the event request, accepting or declining the event request after a prolonged period of time, not responding to the event request, requesting to reschedule the event request, and the like. An embodiment may assign a priority to a new event request based upon the identified past behavior of the user with respect to similar types of event requests. For example, a meeting request received from the user's boss may be assigned a high priority designation because an embodiment may identify that a user has quickly accepted past meeting requests from their boss. As another example, an after work team-building activity request may be assigned a low priority designation because an embodiment may identify that a user has declined or not responded to prior team-building activity requests).
The Childs-Johnson combination does not explicitly teach wherein the metadata for an event includes an indication of a machine learning priority sublevel of the event. However, Bhattacharya in the analogous art of calendar event conflict resolution systems teaches this concept. Bhattacharya teaches:
wherein the metadata for an event includes an indication of a machine learning priority sublevel of the event (paragraph 0004, discussing systems, methods and devices for prioritizing calendar events with artificial intelligence…A statistical machine learning model, such as a feature selection model, may be applied to a plurality of factors associated with each of the events. Those factors may include event parameters (e.g., duration of event, time and day of event, location of event, etc.). Those factors may also include one or more attributes of potential attendees of the events (e.g., seniority of attendees, organizational title of attendees, office location of attendees, etc.). An event priority score can then be generated for each of the events, and those events can be ranked according to their relative scores. The lower-ranked events can be replaced with higher ranked events that conflict with them. The machine learning model may utilize a feedback loop that takes user feedback about the machine-rankings into account, and the machine learning model may thus be modified accordingly; paragraph 0017, discussing mechanisms for prioritizing events on users' calendars, alone and/or in the context of other users, such that meetings can be ranked and processed for moving appropriately when conflicts arise. In some examples, when a new event conflicts with one or more previously scheduled/existing events on a user's calendar, the electronic calendar service may identify one, two, three or more times for scheduling the new event and present those times to the user. The electronic calendar service may only present times to users for previously booked events that have lower event priority scores compared with the new event. The event priority score for each event may be generated by the electronic calendar service via application of one or more machine learning models…; paragraph 0025, discussing that in generating the event priority score for an event, the electronic calendar service may apply a machine learning model to one or more of the event attendee attribute datapoints and/or one or more of the event parameter datapoints…The machine learning model may be trained to generate a score reflecting the relative importance of a calendar event to a user—that score (i.e., the event priority score) is indicative of how likely a user is to prioritize the event when another event conflicts with it. In additional examples, a confidence score may be applied an event corresponding to a likelihood that a user would prefer to replace that event with a different event having a specific event priority score; paragraphs 0030, 0033, 0035, 0053).
The Childs-Johnson combination describes features related to calendar and event management. Bhattacharya relates systems, methods and devices for prioritizing calendar events with artificial intelligence. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson combination with because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson combination to include Bhattacharya’s feature for including wherein the metadata for an event includes an indication of a machine learning priority sublevel of the event, in the manner claimed, would serve the motivation of the making the prioritization of meetings more efficient (Bhattacharya at paragraph 0019); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 8, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Childs further teaches wherein, the executable instructions further include instructions that, when executed by the at least one processor, cause the at least one processor to: generate a user identifier (ID) for the one or more user events, wherein the user ID, when decoded, indicates the priority information associated with the one or more user events (paragraph 0030, discussing that the context data may correspond to a keyword present in the event request and the priority may be based on the presence or absence of the keyword. Non-limiting examples of keywords include relevant project names, acronyms, importance-indicating trigger words (e.g., “important”, “high priority”, “urgent”, etc.), and the like. In an embodiment, the keyword may be present in virtually any portion of the email. Identification of the keyword may be facilitated by analyzing the event request (e.g., using one or more conventional textual analysis techniques, etc.) and thereafter comparing one or more identified words in the event request to a predetermined list of known keywords. If a match is identified, then the matched word may be considered a keyword. In an embodiment, the known keywords may be designated by a user).
Claims 12 and 20 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. As per claim 12, the Childs-Johnson-Bhattacharya combination teaches a method for intelligent calendar management (Childs, paragraph 0016). As per claim 20, the Childs-Johnson combination teaches a computer program product for intelligent calendar management, the computer program product comprising a non-transitory computer-readable medium having computer-readable program code stored thereon (Childs, paragraphs 0037, 0038).
Claim 13 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 2, as discussed above.
Claim 14 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 3, as discussed above.
Claim 16 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 5, as discussed above.
Claim 17 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claims 6 and 7, as discussed above.
Claim 18 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 8, as discussed above.
29. Claims 4, 9, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Childs in view of Johnson, in view of Bhattacharya, in further view of Lee, Pub. No.: US 2010/0010864 A1, [hereinafter Lee].
As per claim 4, the Childs-Johnson-Bhattacharya combination teaches the system of claim 3, but it does not explicitly teach wherein, when the event is under the control of a scheduling application for automatic scheduling by the scheduling application, the metadata includes an indication of a specific vendor of the scheduling application for controlling the automatic scheduling of the event. However, Lee in the analogous art of scheduling systems teaches this concept. Lee teaches:
wherein, when the event is under the control of a scheduling application for automatic scheduling by the scheduling application, the metadata includes an indication of a specific vendor of the scheduling application for controlling the automatic scheduling of the event (paragraph 0068, discussing a record indicating access to the electronic calendar then may be stored…The record may be in any format and include any data structure storing a list of customers who have provided access to their electronic calendars. Upon the scheduling of the event, configured data regarding the event, may be generated. The configured data may be in any format compatible with the electronic calendar. For example, the API's specified function call format may identify the required configured data format, perhaps in a standard or modified iCalendar, vCalendar, vCal, or any other specified format that may be compatible with the electronic calendar 100 or the API 170. The configured calendar data may relate to the event's description, topic, objective, date, time, location, participants, subject matter, priority, relative importance, resources required for said event, or any combination thereof. The specified format for the configured calendar data may or may not require additional approval from the first party before the event is docketed with the electronic calendar; paragraph 0095, discussing that a customer may wish to purchase an airplane ticket for an upcoming vacation, perhaps from ACME AIRLINES. The customer, who may use a web-based electronic calendar, such as GODADDY.COM ONLINE GROUP CALENDAR, may access the electronic calendar on his client, which may be a desktop computer. If he has not already done so, the customer may add ACME AIRLINES to a trust list via a profile manager on his electronic calendar, perhaps by selecting ACME AIRLINES from the list of airlines listed in the user interface. A profile database, which may be a component of the electronic calendar, may then be updated to include ACME AIRLINES on the trust list; paragraph 0096, discussing that ACME AIRLINES then may be granted access to the API of the customer's electronic calendar, possibly by providing ACME AIRLINES with the API's 150 function call specification, requiring ACME AIRLINES to include properly-formatted identifying information in any function call, and granting access only when such information is included. The electronic calendar then may electronically notify ACME AIRLINES that is has been granted access to the customer's electronic calendar's API...Once ACME AIRLINES receives the electronic notification, it may store a record, perhaps in a customer database, indicating that it now has access to this specific customer's electronic calendar should the need arise to schedule an event; paragraph 0108, discussing that the profile database may include a list of businesses, customers, contacts, or other users, which may be provided access to the profile manager, user interface, profile database, electronic calendar and/or client-side application. The profile database may also store each business', customer's, contact's or other user's assigned priority. This list may be maintained to automate the scheduling of incoming calendar requests for the event; paragraph 0110).
The Childs-Johnson-Bhattacharya combination describes features related to calendar and event management. Lee relates to a systems for coordinating schedules. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson-Bhattacharya combination with Lee because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson-Bhattacharya combination to include Lee’s feature for including wherein, when the event is under the control of a scheduling application for automatic scheduling by the scheduling application, the metadata includes an indication of a specific vendor of the scheduling application for controlling the automatic scheduling of the event, in the manner claimed, would serve the motivation of finding and recommending an ideal match (Lee at paragraph 0121); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1, but it does not explicitly teach wherein, the executable instructions further include instructions that, when executed by the at least one processor, cause the at least processor to: generate one or more scheduling links for sharing information associated with the digital calendar. However, Lee in the analogous art of scheduling systems teaches this concept. Lee teaches:
wherein, the executable instructions further include instructions that, when executed by the at least one processor, cause the at least one processor to: generate one or more scheduling links for sharing information associated with the digital calendar (paragraph 0047, discussing that the API may be exposed to the second party by any method known in the art or developed in the future including, but not limited to, pointing the second party to a web server to make an HTTP request in the proper function call format. The API's specification may be provided to the second party, which may define the function call format required by the API. The specified function call format may require identifying information from the second party that may allow the electronic calendar to determine whether the second party attempting to access the API has been granted access by the first party. Access to the API then may be governed by an access-protected URL that permits access only to properly-identified entities; paragraph 0060, discussing that several different methods may be used for granting electronic calendar access to a second party via an exposed API….A second party is granted access to an electronic calendar of a first party to schedule an event by exposing the electronic calendar's API to the second party. The API may be exposed to the second party by any method known in the art or developed in the future including, but not limited to, providing the API's specification to the second party. The specification may define the function call format required by the API. The specified function call format may require identifying information from the second party that may allow the electronic calendar to determine whether the second party attempting to access the API has been granted access by the first party. Access to the API then may be governed by an access-protected URL that only permits access to properly-identified entities; paragraph 0096, discussing that ACME AIRLINES then may be granted access to the API of the customer's electronic calendar, possibly by providing ACME AIRLINES with the API's function call specification, requiring ACME AIRLINES to include properly-formatted identifying information in any function call, and granting access (perhaps via an access-protected URL) only when such information is included. The electronic calendar then may electronically notify ACME AIRLINES that is has been granted access to the customer's electronic calendar's API...Once ACME AIRLINES receives the electronic notification, it may store a record indicating that it now has access to this specific customer's electronic calendar should the need arise to schedule an event).
The Childs-Johnson-Bhattacharya combination describes features related to calendar and event management. Lee relates to a systems for coordinating schedules. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson-Bhattacharya combination with Lee because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson-Bhattacharya combination to include Lee’s feature for including generating one or more scheduling links for sharing information associated with the digital calendar, in the manner claimed, would serve the motivation of finding and recommending an ideal match (Lee at paragraph 0121); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 15 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 4, as discussed above.
Claim 19 recites substantially similar limitations that stand rejected via the art citations and
rationale applied to claim 9, as discussed above.
30. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Childs in view of Johnson, in view of Bhattacharya, in further view of Gelbart et al., Pub. No.: US 2019/0266032 A1, [hereinafter Gelbart].
As per claim 10, the Childs-Johnson-Bhattacharya combination teaches the system of claim 1. Although not explicitly taught by the Childs-Johnson combination, Gelbart in the analogous art of scheduling systems teaches wherein, the executable instructions further include instructions that, when executed by the at least one processor, cause the at least one processor to: generate one or more high priority scheduling links for scheduling one or more events with a high priority (paragraph 0022, discussing that the interface enables easy, one click linking of calendar events into an intelligent learning system that enables automated tracking of time focused on priorities, and yields intelligent insights and progress benchmarking. Example embodiments may transform the functionality of a digital calendar system interface from a transactional tool into a strategic work-tool or strategy execution tool which, instead of merely automating calendaring functions, improves user performance, helping users to function more effectively, and pushes users to think about the calendaring actions they are taking with regard to themselves and to others. Example embodiments may also automate linking calendar events to their priorities as a learning system and may give users the ability to scan and modify these automated links directly in the calendar system interface; paragraph 0073, discussing that priority portion transforms the functionality of the digital calendar interface from a transactional tool to a strategy execution platform and strategic work-tool which, instead of merely automating calendaring functions, improves user performance, helping users to execute, align and function more effectively, with more strategic information, in the right place, at the right time, and pushes users to think about the calendaring actions they are taking with regard to themselves and to others. The invention also automates linking calendar events to their priorities as a learning system and gives users the ability to scan and modify these automated links directly in the calendar. Because priorities are displayed in priority portion 205 right alongside calendar entries in calendar portion, users are more likely and easily able to link these priorities to calendar entries. For example, a user may simply drag a priority from priority portion 205 onto a calendar entry in calendar portion 210 (or vice versa) in order to link a calendar entry to a particular priority, or be triggered to proactively block time by creating new events to ensure that priorities are afforded necessary attention. Furthermore, this linking may be done without leaving the digital calendar system user interface and/or without selecting a different view within the digital calendar user interface; paragraphs 0084-0085, discussing that the user interface includes native interface modules, which may provide native digital calendar system user interface features, and priority related data modules, which may adapt the framework provided by native interface modules to provide a strategy execution tool that helps the user to: easily link calendar entries to priorities within a modified calendar view without leaving their calendar screen).
The Childs-Johnson-Bhattacharya combination describes features related to calendar and event management. Gelbart relates to a system and method for transforming a digital calendar into a strategic tool. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson-Bhattacharya combination with Gelbart because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson-Bhattacharya combination to include Gelbart’s feature for including wherein, the executable instructions further include instructions that, when executed by the at least one processor, cause the at least one processor to: generate one or more high priority scheduling links for scheduling one or more events with a high priority, in the manner claimed, would serve the motivation of improving user performance, helping users to execute, align and function more effectively (Gelbart at paragraph 0073); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
31. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Childs in view of Johnson, in view of Bhattacharya, in view of Gelbart, in further view of Jaynes et al., Pub. No.: US 2021/0250194 A1, [hereinafter Jaynes].
As per claim 11, the Childs-Johnson-Bhattacharya-Gelbart combination teaches the system of claim 10. Although not explicitly taught by the Childs-Johnson combination, Jaynes in the analogous art of scheduling systems teaches wherein the one or more high priority scheduling links include one or more team high priority scheduling links (paragraph 0033, discussing that meeting metadata may specify a vendor, meeting start time, meeting end time, and a link to join the meeting; paragraph 0040, discussing that the calendar parser generates a user meeting packet. The user meeting packet includes metadata defining one or more of the meetings extracted from the calendar by the calendar parser. The metadata includes any information of the extracted meeting desired, such as but not limited to meeting title, meeting date, meeting time, meeting organizer, meeting host, meeting participants, meeting members contact information, meeting application and connection requirements (e.g., Zoom, BlueJeans, Google Hangout, meeting equipment requirements and other information known in the art. User meeting packet is then transmitted via the communication device and a communication link to the host controller from the client device. In embodiments, the user meeting packet is additionally or alternatively transmitted, either directly from the client device, or relayed from the host controller, to a server that is externally located from the room. The server may function as a room management server that controls meetings across multiple rooms for a given organization, building, entity, etc.; paragraph 0047, discussing that the room manager may further analyze the metadata in the user meeting packet to identify user information associated with the user meeting to identify if the user meeting has a higher priority than the pre-scheduled room meeting. For example, the room manager may identify the user meeting has having precedence over a pre-scheduled room meeting when the user meeting includes a C-level or other higher priority attendee. For example, the room manager may identify the user meeting has having precedence over a pre-scheduled room meeting when the user meeting has a higher number of participants; paragraph 0063, discussing comparing relative priorities of conflicting meetings. In an example, room manager 326 may further analyze the metadata in user meeting packet to identify user information associated with the user meeting and determine if the user meeting has a higher priority than the pre-scheduled room meeting. For example, the room manager may identify the user meeting has having precedence over a pre-scheduled room meeting when the user meeting includes a C-level or other higher priority attendee. For example, the room manager may identify the user meeting has having precedence over a pre-scheduled room meeting when the user meeting has a higher number of participants; paragraph 0064, discussing a method that transmits meeting updates to associated participants. For example, when a user meeting did not previously identify a particular room for a given meeting, the room manager (either from the host controller or the server) may transmit a meeting update to all participants identified in the user meeting packet. Meeting update may have a variety of forms, including email or instant messaging such as SMS, Slack or Microsoft Teams, for example. The meeting update may include a variety of content, such as the room location or other identifying information, a start time or other initiation message, and/or connection information (e.g., one or more of dial-in information for the given meeting, and the URL and/or IP address of the host controller). The update may automatically change other participants calendars to reflect the room information and associated equipment connection information. Alternatively, the meeting update may be transmitted from the client device. Although examples of messages have been given, these are not limiting and other types of information may be sent to meeting participants, managers, or other groups of users such as staff or departments; paragraph 0079, discussing that the method of resolving conflicts further comprises determining at least one of a user's permission to start a meeting, a permission to use a room, and a priority of meeting types).
The Childs-Johnson-Bhattacharya-Gelbart combination describes features related to calendar and event management. Jaynes relates to a calendaring system. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing schedules. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Childs-Johnson-Bhattacharya-Gelbart combination with Jaynes because the references are analogous art because they are both directed to solutions for scheduling, which falls within applicant’s field of endeavor (schedule management systems), and because modifying the Childs-Johnson-Bhattacharya-Gelbart combination to include Jaynes’ feature for including wherein the one or more high priority scheduling links include one or more team high priority scheduling links, in the manner claimed, would serve the motivation of facilitating scheduling (Jaynes at paragraph 0035); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patel et al., Pub. No.: US 2022/0271962 A1 – describes an intelligent meeting assistant system that employs machine learning algorithm(s) to learn a user's behavior or preferences relating to meetings (e.g., the user's behavior in response to notifications provided by the intelligent meeting assistant system) and adjust future actions performed by, or outputs provided by, the intelligent meeting assistant system to enhance the user's meeting experiences.
Singh et al., Pub. No.: US 2022/0066800 A1 – describes systems and methods for executing a virtual assistant system.
Qayyum, Shamaila, and Ahsan Qureshi. "A survey on machine learning based requirement prioritization techniques." Proceedings of the 2018 International Conference on Computational Intelligence and Intelligent Systems. 2018 – describes that machine learning had been used to automate the process of requirements prioritization.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 extension fee 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.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625