DETAILED ACTION
Status of the Application
This Office Action is in response to Application Serial 18/401,341. In response to Examiner’s action mail dated December 23, 2025, Applicant submitted amendments and arguments that are mail dated January 29, 2026. The Applicant amended claim 1, 7, and 14. Applicant cancelled claim 20. Applicant added new claim 21. The claims 1-19,21 are examined below and are pending in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
Applicant did not submit an information disclosure statement for consideration by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 29, 2026 has been entered.
Response to Amendments
Claims 1-19, and 21 are pending in this application. The claims 1, 7, and 14 are amended.
Applicant’s amendments are not sufficient to overcome the 35 U.S.C. 101 rejection set forth in the previous action. The claims 1-19, and 21 are rejected under 35 U.S.C. 101, see below.
Regarding prior art, the claims 1-19, and 21 are rejected under 35 U.S.C. 103. See below.
Response to Arguments
Applicant’s arguments filed January 29, 2026, have been fully considered but they are not persuasive and/or moot in view of the revised rejections. Applicant’s argument will be considered herein below.
Rejection of claims under 35 U.S.C. 101
On pages 10-11 of the Applicant’s 35 U.S.C. 101 arguments, Applicant traverses, Examiner’s rejection. Applicant has amended representative independent claim 1 , 7, and 14. Applicant submits the amended claims recite significant improvements that transform the abstract idea into patent-eligible subject matter under 35 U.S.C. 101.
Applicant submits the amendments change the claimed invention from a generic computer implementation of scheduling to a specific technological solution that address concrete problems in automated calendar generation.
The claims provide the machine-learned model with structured domain-specific input that enables it to generate more accurate and contextually relevant calendar event recommendation. The amendments to the claims require more than routine or conventional computer activity.
The amended claims recite an improved technical process for training and utilizing machine learning models in calendar management systems.
Applicant respectfully submits that claims 1-19, as amended, are directed to patent eligible subject matter pursuant to 101 and further request that the Office reconsider and withdraw the rejection.
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 101 arguments. In light of the amendments, the claims are evaluated under 35 U.S.C. 101. The claims are not patent eligible.
At Step 2A prong one, the claims suggest calendar events based on historical activity. Suggesting calendar events is scheduling. Here, scheduling is managing personal behavior or relationships or interactions between people (including social activities, teaching, and following ruled or instructions) which is certain methods of organizing human activity. Moreover, calendaring and scheduling can be performed by human using mental evaluation and pen and paper. Because the claims recite limitations that are grouped as certain methods of organizing human activity and mental concepts, the claims therefore, are directed to a judicial exception.
At Step 2A prong two, the claims do not integrate the judicial exception into a practical application. The claims recite additional elements (e.g., communication platform, a large language model, a machine-learned model) that are used to execute the judicial exception. In limitations reciting “receiving, in response to the temporal condition being satisfied, data representing historical data including activity data between [[a]]the user profile and one or more user profiles; generating, based on inputting the historical data into a machine-learned model comprising a large language model trained on previous activity data to identify, in the historical data, one or more key words or phrases that indicate an intent for the user profile to schedule an event between two or more user identifiers, and to identify, based on the historical data, a date or time to schedule the event and one or more invitees to include in the event, a recommended calendar event that includes a first meeting and a second meeting, wherein the first meeting is associated with a first organization” are applying a computer element to perform the judicial exception. Calendaring and scheduling is abstract, the claims are fining the scheduling based in learned and historical data. Adding the words ”apply it” (or equivalent) with the judicial exception, or more instructions to implement an abstract idea on a computer, or merely uses a computer as a tool as a tool to perform an abstract idea are not indicative of integration into a practical application. See MPEP 2106.05(f).
At Step 2A prong two, the claims do not recite an improvement to the judicial exception. Calendaring and scheduling is abstract and can be performed by human using mental evaluation and pen and paper, the claims are fining the scheduling based in learned and historical data. Here, the claims are using a computer to improve scheduling. The claims do not recite an improvement that is rooted in technology.
Regarding Step 2A prong two, the claims are not integrated into a practical application.
At Step 2B, the claims are evaluated as a whole. As stated above, the claims are using a computer to conduct the judicial exception. It is “apply it”. See MPEP 2106.05(f). Furthermore, the claims MPEP 2105(d). The claims are not patent eligible. See 35 U.S.C. 101 rejection below.
Furthermore, the Applicant argues ‘… claims provide the machine-learned model with structured domain-specific input that enables it to generate more accurate and contextually relevant calendar event recommendation …’. Examiner submits, the claims do not recite domain specific input. The Applicant’s arguments are not persuasive.
Rejection of claims under 35 U.S.C. 103
On pages 12-17 of the Applicant’s arguments, the Applicant traverses:
CLAIMS 1-4, 6-11, 13-18 WOULD NOT HAVE BEEN OBVIOUS OVER DOTAN-COHEN IN VIEW OF SCHEMERS
Applicant submits, the Office can not show that either Dotan-Cohan or Schemer teach or suggest using a “predetermined list” of key words and phrases, inputting the “predefined list” into the model and generating, based on inputting historical data … into a machine-learned model trained on previous activity data and the list, a recommended calendar event… as recited in claim (and similarly claim 7 and claim 14).
Applicant submits that the Office has failed to show that the cited documents teach or such at least “a recommended calendar event that includes a first meeting and a second meeting” … as recited in claim (and similarly claim 7 and claim 14).
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 103 arguments. Applicant’s amendments to the claims necessitate grounds for a new rejection. See below.
Claim Rejections - 35 USC§ 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 6, 21 are machine.
Claims 7-13 are manufacture.
Claims 14-19 are process.
Claim 20 is canceled.
Claims 1-19, and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims (claim 1 and similarly claim 7 and claim 14) recite, “….determining that a temporal condition that instructs … suggest calendar events has been satisfied, the temporal condition including a time at which … suggests the calendar events, wherein the temporal condition is determined based on: determining, based on historical data, a log off time of a user profile; determining a time that is a threshold period of time prior to the log off time; and setting the time as the temporal condition; receiving, in response to the temporal condition being satisfied, data representing historical data including activity data between [[a]]the user profile and one or more user profiles;
generating, based on inputting the historical data into … trained on previous activity data to identify, in the historical data, one or more key words or phrases that indicate an intent for the user profile to schedule an event between two or more user identifiers, and to identify, based on the historical data, a date or time to schedule the event and one or more invitees to include in the event, a recommended calendar event that includes a first meeting and a second meeting, wherein the first meeting is associated with a first organization and the second meeting is associated with a second organization that is different than the first organization; causing the recommended calendar event to be displayed …; receiving, in response to displaying the recommended calendar event and from the user profile, user input data representing an intent to generate an event associated with the recommended calendar event; and causing, based on the user input data, the event to be generated”. Claims 1-19, and 21, in view of the claim limitations, are directed to the abstract idea of suggest calendar events based on historical activity.
The claims recite scheduling which is managing personal behavior or relationships or interactions between people (including social activities, teaching, and following ruled or instructions). Thus, the claims recite certain methods of organizing human activity.
Moreover, calendaring and scheduling can be performed by human using mental evaluation and pen and paper. So, the claims recite an abstract idea that is a mental concept.
Because the claims recite limitations that are grouped as certain methods of organizing human activity and mental concepts, the claims therefore, are directed to a judicial exception at Step 2A prong one.
This judicial exception is/are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising”, “a communication platform, ” “a machine-learned model comprising a large language model,” and “via a user interface associated with the user profile,” in claim 1 (and similarly claim 7 and claim 14); “One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:”, “in claim 7; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f)
The dependent claims do not recite additional elements beyond what is recited in the independent claims on which it relies.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.The claims also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting transformation or reduction of a particular article to a different state or thing.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements when considered both individually and as an ordered combination do not amount to significantly more.
As discussed above the claims do not amount to significantly more. As discussed above the claims are applying a computer to conduct the judicial exception. See MPEP 2106.05(f).
Furthermore, the claims recite receiving, in response to displaying the recommended calendar event and from the user profile, user input data representing an intent to generate an event associated with the recommended calendar event; and causing, based on the user input data, the event to be generated. At step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 2-6 further narrow the abstract idea of independent claim 1. Dependent claims 8-13 further narrow the abstract idea of independent claim 7. Dependent claims 15-19, and 21 further narrow the abstract idea of independent claim 14. The claims 1-19, and 21 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 1-19, and 21 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-19, and 21 are rejected under 35 U.S.C.101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claim(s) 1-4, 6-11, 13-18, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dothan-Cohen (US 2019/0205839 A1) in view of Prieto (Investigating the Use of ChatGPT for Scheduling of Construction Projects) and Fan (US 2022/0398,111 A1).
Regarding Claim 1, (and similarly claim 7 and claim 14) (Currently Amended)
A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
Dotan- Cohen [004] discloses a methods and systems that may provide a user with an enhanced computing experience by determining and utilizing an optimal user-activity schedule. … based on learned behavior patterns of the user … Future activity likely to be performed by the user also may be determined or inferred and used by the computing system in conjunction with the learned activity patterns to generate one or more optimal activity schedules. A generated schedule may be considered optimal for the user because it is consistent with learned behavior patterns of the user. For example, where a user has a pattern of doing focused work at certain periods of the day, a generated optimal schedule may allocate focus-intensive work during those periods or may avoid scheduling meetings during those times. In some embodiments, a user's current context also may be used for determining or selecting optimal schedules., Dotan-Cohen [004], [051].
Although highly suggested Dotan-Cohen does not explicitly teach:
determining that a temporal condition that instructs a communication platform to suggest calendar events has been satisfied, the temporal condition including a time at which the communication platform suggests the calendar events, wherein the temporal condition is determined based on: determining, based on historical data, a log off time of a user profile; determining a time that is a threshold period of time prior to the log off time; and setting the time as the temporal condition; receiving, in response to the temporal condition being satisfied, data representing historical data including activity data between [[a]]the user profile and one or more user profiles; generating, based on inputting the historical data into a machine-learned model comprising a large language model trained on previous activity data to identify, in the historical data, one or more key words or phrases that indicate an intent for the user profile to schedule an event between two or more user identifiers,
Fan teaches:
determining that a temporal condition that instructs a communication platform to suggest calendar events has been satisfied, the temporal condition including a time at which the communication platform suggests the calendar events, wherein the temporal condition is determined based on: determining, based on historical data, a log off time of a user profile;
Fan teaches when a user 205 requests a virtual desktop session, the system can determine a predicted logoff time for the session. The predicted logoff time can be determined by the session placement manager 230, for example, using user session data that it has recorded and stored. Different algorithms, models, and methods can be used to derive the predicted logoff time for a user. an algorithm can be used that predicts a logoff time for the session based on the time the session is requested by the user, historical session data of the user 205, and/or historical data of other users in the pool 200. Such historical data can include previous logon and logoff times of the user 205 or users 200, including information regarding the sessions such as the day of the week, session duration times, calendar days, etc. For example, the algorithm can predict the logoff time based on an average of previous logoff times. The averaged previous logoff times can also be taken from the same day of the week, Fan [030], [032],[042], [Figure 3].
Examiner submits time of the session, session duration times, calendar days, and average log time taken from the same week are temporal conditions.
Fan teaches information other than historical user data can likewise be used to derive a logoff time prediction for the user 205. Such information may include information about the user from the user's profile (job position, location, etc.), information provided by an administrator, or information inputted by the user 205 (e.g., user may input an anticipated logoff time).Fan [033]. Examiner submits Fan teaches logon logoff predictions that are associated with a user profile.
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determining a time that is a threshold period of time prior to the log off time;
and setting the time as the temporal condition; receiving, in response to the temporal condition being satisfied, data representing historical data including activity data between [[a]]the user profile and one or more user profiles;
See above Fan [030],[032], [042], [Figure 3]. Examiner submits time of the session, session duration times, calendar days, and average log time taken from the same week are temporal conditions.
generating, based on inputting the historical data into a machine-learned model comprising … trained on previous activity data to identify, in the historical data,
Fan [041] teaches determine the buckets based on historical user session data of the users in the pool 200 (e.g., statistics of session logon and logoff times in the user pool 200) using an algorithm or session placement bucket model. Such a model can be run periodically (e.g., daily, or twice per day) to update the buckets (e.g., to re-calculate the logoff time range assigned to each bucket). Fan [042] teaches for example, the algorithm can analyze historical session data of the pool of users and determine the bucket time ranges based on this data. The algorithm can analyze session logoff data for a defined period, e.g., a day. The data can be taken from the same day of the week as the current day to eliminate weekday-dependent discrepancies, and the data can be averaged from several past days or periods. Fan [041]-[042].
Fan [034] teaches prediction can be made using a machine learning algorithm that takes into account numerous features (or variables) that can be obtained from historical user 205 session data, such as previous logon times, logoff times, days of the week, etc., Fan [034], [044], [Figure 3].
Dotan-Cohen determines a set of activity features associated with the user activity based on the monitored user activity to optimal[ly] schedule user activities. Fan models log times and session data. It would have been obvious to combine before the effective filing date, determining a set of activity patterns based on analysis of the user activity, as taught by Dotan-Cohen, with analyze historical session data, as taught by Fan, to create look-ahead plans from the description of activities in the master schedule. Prieto [Introduction].
Prieto teaches:
… a large language model … one or more key words or phrases …
Prieto teaches scheduling using ChatGPT and extracting information from construction documents.
Dotan-Cohen determines a set of activity features associated with the user activity based on the monitored user activity to optimal[ly] schedule user activities. Prieto teaches ChatGPT as an aid in project management. It would have been obvious to combine before the effective filing date, determining a set of activity patterns based on analysis of the user activity, as taught by Dotan-Cohen, with using ChatGPT to extract information from construction documents, as taught by Prieto, to analyze construction site data such as progress reports, safety inspections, quality control reports, and code compliance checking and extract insights that can help improve project efficiency and mitigate risks. Prieto [Introduction].
Dotan-Cohen teaches:
to schedule an event between two or more user identifiers, and to identify, based on the historical data, a date or time to schedule the event and one or more invitees to include in the event, a recommended calendar event that includes a first meeting and a second meeting, wherein the first meeting is associated with a first organization and the second meeting is associated with a second organization that is different than the first organization; causing the recommended calendar event to be displayed via a user interface associated with the user profile; receiving, in response to displaying the recommended calendar event and from the user profile, user input data representing an intent to generate an event associated with the recommended calendar event; and causing, based on the user input data, the event to be generated.
Dotan -Cohen [0110] discloses predicted future user activity information included in an optimal activity schedule may be determined from the content of user-related communications (e.g., emails, text or instant messages, voice messages, video and audio recordings, Snapchat® communications, Slack® communications, Skype® communications, Jabber® communications, etc.). In this regard, some embodiments of optimal schedule generator 290 may include one or more analytical applications or services configured to review the content of user communications (both user-initiated and communications received from other persons) to designate communications and content thereof that include probable or likely future user activities that should be included in an optimal activity schedule. These analytical applications or services may utilize topic modeling or automatic summarization (which may include extraction or abstraction) to analyze the content and extract information indicating action items or similar user activity from the content of the communications. In some embodiments, a weighting factor may be applied based on the topic, the persons involved, the level of relation to the user, a frequency of occurrence of the subject in communications, and/or other factors … an optimal schedule may be generated in which a proposed meeting to discuss the proposal is included. The optimal schedule may be utilized by an optimal-schedule consumer application, such as consumer application 271 discussed further below, to provide a calendar invite for the meeting that may be automatically generated and/or sent to all participants.; Dotan-Cohen [0149] depicts an example calendar application. Dotan -Cohen [0110], [0149]-[0150], [Figure 6A-6C].
Dotan-Cohen [0120] discloses the optimal schedule logic 235 can take many different forms depending on the user activities determined to be likely to occur. For example, some embodiments of optimal schedule logic 235 may employ machine-learning mechanisms or other statistical measures to classify future activity events as types of activity (e.g., a task requiring browsing or light reading may be classified as non-focus-intensive activity) for appropriate allocation in an optimal schedule, or for determining an optimal schedule based on user context, preferences, and/or feedback.
Regarding Claim 2, (and similarly claim 8 and claim 15) (Previously Presented)
The system of claim 1, wherein generating the recommended calendar event is based on: determining that the historical data indicates that the recommended calendar event is to be scheduled; determining, based on the historical data, two or more user profiles to include in the recommended calendar event, the two or more user profiles including the user profile and at least one user profile included in the one or more user profiles; determining, based on the historical data, a time and a day to associate with the recommended calendar event;
See claim 1 - Dotan -Cohen [0110], [0149]-[0150], [Figure 6A-6C].
Dotan-Cohen [0116] discloses contextual information may be used to determine potential user commitments that are likely to become future user activity that is then included in an optimal schedule (e.g., for suggestion as a calendar item). …. For example, a user may indicate in an email to another person that the user will respond, provide content, schedule a meeting.
and generating, based on the historical data indicating the event is to be scheduled, the two or more user profiles to include in the recommended calendar event, the time, and the day, the recommended calendar event.
Dotan -Cohen [0115] discloses it may be determined that a user needs to schedule a meeting with another person (e.g., based on communication context indicating such a need). In one circumstance, activity patterns of the user determined or inferred by the activity pattern inference engine 260 may indicate that the user typically schedules meetings to last one hour, and typically in the afternoon. In another circumstance, a context extracted from a user communication by the user activity monitor 280 indicates the user has a preferred time and duration for the meeting (e.g., the following day at 3-4 PM).; Dotan-Cohen [0117] discloses a user's activity events and activity patterns may be parsed to determine which user activities have occurred in the past and which are likely to occur again in the future. This may be determined, in part, from an analysis of the user's past activities or calendar events, commitments, indicated preferences, and other patterns of user activity. For example, a type of user activity (e.g., work-related, personal-related, activity-related, content-related, or another type of user activity) may be determined, a frequency of occurrence of the user activity (e.g., recurring weekly being more likely to occur again in the future) may be determined, a context of the user activity may be determined (e.g., location of the user, persons the user is interacting with, or other context) and any user preferences (e.g., a user accepting or declining certain scheduled user activities in the past) may be used to tailor or tune the selection of user activities included in an optimal schedule.
(Examiner submits it would be obvious to one of ordinary skill in the art a meeting that is scheduled considers two or more meeting attendees /participants schedules.)
Regarding Claim 3, (and similarly claim 9 and claim 16) (Previously Presented)
The system of claim 1, wherein the historical data includes at least one of: one or more past calendar events, one or more existing calendar events, one or more voice calls, one or more video calls, one or more exchanged messages, one or more actions performed by the one or more user profiles, or one or more timelines associated with the one or more user profiles.
Dotan-Cohen [043] discloses user-activity information (for example: application usage, online activity, searches, voice data such as automatic speech recognition, activity logs, communications data including calls, texts, instant messages, and emails, website posts, other user data associated with communication events, or other types of user activity information) including, in some embodiments, user activity that occurs over more than one user device, user history, session logs, application data, contacts data, calendar and schedule data, notification data, and social-network data, user-account(s) data (which may include data from user preferences provided from user preferences 246 or feedback component 250, or settings associated with a personal assistant application or service), home-sensor data.
Within claim 3, Dotan- Cohen discloses user-activity information, e-mails, instant message, voice data, session logs, calendar and schedule data …, and thus, Dotan-Cohen discloses one or more past calendar events, one or more existing calendar events, one or more voice calls. Claim 3 a "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
Regarding Claim 4, (and similarly claim 11 and 18) (Previously Presented)
The system of claim 1, wherein the recommended calendar event comprises a first event and a second event, the operations further comprising: causing the first event to be displayed via the user interface associated with the user profile; and causing, based on the first event being associated with the second event, the second event to be displayed via the user interface associated with the user profile.
Dotan-Cohen discloses a user schedules a meeting for an hour. And the presences indicator indicates “busy: for that hour, the same presence indicator status may also be applied to the 30 minutes period before the meeting, based on activity patterns indicating that user usually prepares for meeting for this amount of time., Dotan- Cohen [0109], [Figure 6C]
(Examiner interprets, the first event: 30 minute period meeting prep and second event: meeting A)
Regarding Claim 6, [and similarly claim 13 and claim 20] (Currently Amended)
The system of claim 1, wherein generating the event comprises: determining that the recommended calendar event includes one or more event parameters, the one or more event parameters including a list of invitees, a date, a time, or a description;
Dotan-Cohen [0110] discloses the optimal schedule may be utilized by an optimal-schedule consumer application, such as consumer application 271 discussed further below, to provide a calendar invite for the meeting that may be automatically generated and/or sent to all participants.;
Dotan-Cohen [0114] discloses participants, types of participants (e.g., boss, co-worker), a topic are pattern is user activity that may be used for scheduling … when generating an optimal schedule. Dotan-Cohen [0111], [0114]
determining that the user input data includes one or more modified event parameters, the one or more modified event parameters including a modified representation of at least one of the list of invitees, the date, the time, or the description; and generating the event based on the one or more modified event parameters.
Dotan-Cohen [0114] discloses and patterns of user activity may be used for scheduling, rescheduling, and/or cancelling certain user activities when generating an optimal schedule. , Dotan-Cohen [0114], [0117]
Within claim 6, Dotan- Cohen discloses user-activity information scheduling data , types of participants, and thus, Dotan-Cohen discloses a list of invitees, the date, and time. Claim 3 a "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
Regarding Claim 21, (New)
The system of claim 1, wherein the recommended calendar event includes a reason that the machine-learned model recommended the recommended calendar event.
See Claim 1 Dotan-Cohen [0111], [0114]
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
Claims 5, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dothan-Cohen (US 2019/0205839 A1) in view of Prieto (Investigating the Use of ChatGPT for Scheduling of Construction Projects) and Fan (US 2022/0398,111 A1) and in further view of Neckermann (US 2021/0,407,520 A1).
Regarding Claim 5, [and similarly claim 12] (Previously Presented)
The system of claim 1, wherein generating the recommended calendar event comprises: determining that the historical data is associated with audio input uttered by a second user profile in a previous event; and generating, based on inputting the text data into the machine-learned model, the recommended calendar event.
See above, Dotan -Cohen [0115], [0117], [0120] discloses calendar events using machine learning and historical data. Dotan [0116] discloses contextual information may be obtained from user communications (e.g., voice messages), which may be analyzed for potential user commitment., Dotan [0116], [0110] , [045]
Although highly suggested, Dotan-Cohan does not explicitly teach:
… converting the audio input into text data …
Neckermann teaches:
… converting the audio input into text data
Neckerman teaches interpretive data can be used to provide context to user data, which can support determinations or inferences made by the components or subcomponents of system 200, such as venue information from a location, a text corpus from user speech (i.e., speech-to-text), or aspects of spoken natural language understanding. Moreover, it is contemplated that for some embodiments, the components or subcomponents of system 200 may use user data and/or user data in combination with interpretive data for carrying out the objectives of the subcomponents, Neckermann [051],
Neckermann [071] discloses analyzing actual utterances that occur in an event.
Dotan-Cohen discloses determine a set of activity features associated with the user activity based on the monitored user activity to optimal[ly] schedule user activities. Neckermann teaches contextual data can be utterances in a meeting that indicate an identity of users. It would have been obvious to combine before the effective filing date, determine a set of activity patterns based on analysis of the user activity, as taught by Dotan-Cohen, with speech-to-text services, as taught by Neckermann, to determin[e] an identity of one or more users that use a same audio source., Neckermann [abstract].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Miao (2020, Integrated Parallel System for Audio Conferencing Voice Transcription and Speaker Identification)
Mallipali (US 2019/005,458 A) suggest calendar events.
Ferrydiansyah (US 2018/052,824 A1) teaches domain and intent.
Liu (US 11860473 B2) teaches LLM and intent.
JP 2024506985 A AI Natural Language scheduling.
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/THEA LABOGIN/Examiner, Art Unit 3624
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