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 .
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/989,067 filed on 12/20/2024. The preliminary amendment filed on 06/18/2025 has been entered, which cancels claim 1 and adds new claims 2-22.
Claims 2-22 are currently pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 12/20/2024, 03/17/2025, and 10/20/2025 have been reviewed and entered into the record.
Continuation
This application is a continuation of US App. No. 17/930,302 (filed 09/07/2022, now US Pat. No. 12,217,298). In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Applications are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the prosecution history of the Parent Application is relevant in this application. See e.g., Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823 (Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
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 2-22 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.
Claims 2-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the “2019 Revised Patent Subject Matter Eligibility Guidance” (published on 1/7/2019 in Fed. Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the “2019 PEG”).
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claimed method (claims 2-8), system (claims 9-15), and non-transitory computer-readable medium (claims 16-22) are directed to potentially eligible categories of subject matter (process, machine, and article of manufacture), and therefore claims 2-22 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One of the eligibility inquiry (as explained in MPEP 2106.04), it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea grouping by setting forth limitations for managing personal behavior or relationships or interactions (managing and recommending tasks for a user), and also recite activities that fall within the “Mental Processes” abstract idea grouping by reciting steps that, but for the generic computer recited in the claim, could be performed in the human mind via observation, evaluation, judgment, and/or opinion With respect to independent claim 2, the limitations reciting the abstract idea are indicated in bold below:
receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data includes a personal calendar associated with the particular user and a shared calendar associated with one or more family members of the particular user (The “receiving” step describes activity for managing personal behavior or interactions because the received data may be directly tied to activities or interactions of a user from the user’s calendar, and but for the generic computer implementation and high level API to implement the receiving, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, or with the aid of pen and paper. In addition, the “receiving” is insignificant extra-solution data gathering activity, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution data gathering activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network));
processing the calendar data using a natural-language processing (NLP) model to generate a task recommendation, wherein the task generator recommendation indicates one or more recommended tasks determined based on the personal calendar and the shared calendar, (The “processing” of the calendar data is considered activity for managing personal behavior or interactions because the processing to generate a task recommendation may be directly tied to activities or interactions of a user based on the user’s calendar data, and but for the generic computer implementation and high level use of NLP, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, or with the aid of pen and paper), wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision (The preceding “wherein” statement has been considered, but merely recites a feature that occurs outside the scope of the claim regarding how the NLP model was trained. The claim does not recite any particular function/capability/ structure or limitation resulting from how the model was trained, nor is any such function/ capability/structure or limitation inherent or reasonably understood as necessarily occurring as a result of how the model was trained. The “wherein” clause does not give meaning and purpose to the manipulative step and the training does not occur within the scope of the claim, and therefore the “wherein” statement does not impose additional patentable weight on the claim. See MPEP 2111.04. See also, Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps")); and
transmitting an indication corresponding to the task recommendation; receiving an approval to proceed with performing the one or more recommended tasks; accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks (The transmitting, receiving, and accessing steps describes activity for managing personal behavior or interactions because the transmitted/received/accessed data may be directly tied to activities or interactions of a user from the user’s calendar and/or recommendations for a user related thereto. In addition, the transmitting/receiving/accessing, when implemented by the computer, are considered insignificant extra-solution activities, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution data gathering activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)); and
updating the NLP model based on the task-execution data, wherein the NLP model is updated to generate task recommendations that accurately correlate to future updates associated with the shared calendar (The “updating” of the model is considered activity for managing personal behavior or interactions because the updated model to generate task recommendations may be directly tied to activities or interactions of a user based on the user’s calendar data, and but for the generic computer implementation and high level NLP description of the model, this step could be implemented as mental activity such as via human observation, evaluation, judgment, or opinion, or with the aid of pen and paper).
Independent claims 9 and 16 recite limitations similar to the limitations discussed above and have been determined to recite the same abstract idea(s) as claim 2.
With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP 2106.04(d) the judicial exception is not integrated into a practical application. The additional elements recited in independent claims 2, 9, and 16 include computer-implemented, receiving/transmitting/accessing, API, natural language processing (NLP) model, one or more processors, non-transitory computer-readable medium. These elements have been fully considered, but are not sufficient to integrate the abstract idea into a practical application. First, the computing elements (computer-implemented, one or more processors, non-transitory computer-readable medium) amount to 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 (network computing environment). See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Next, the API is recited at a high level of generality, is recited in a step directed to insignificant extra-solution activity, and does not provide an improvement to the functioning of a computer or to any other technology or technical field. Similarly, the use of natural language processing (NLP) is recited at a high level of generality and fails to yield a technical improvement or otherwise integrate the abstract idea into a practical application.
With respect to the receiving/transmitting/accessing, in addition to merely being implemented by the generic computer, these activities also fall under insignificant extra-solution activity, which is not enough to amount to a practical application. See MPEP 2106.05(g). Furthermore, these 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.
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. The additional elements recited in independent claims 2, 9, and 16 include computer-implemented, receiving/transmitting/accessing, API, natural language processing (NLP) model, one or more processors, non-transitory computer-readable medium. These elements have been fully considered, but fail to add significantly more to the claims. First, the computing elements (computer-implemented, one or more processors, non-transitory computer-readable medium) amount to 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 (network computing environment). See MPEP 2106.05(f) and 2106.05(h). See also, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Applicant's Specification describes generic and/or off-the-shelf computing devices for implementing the invention covering virtually any computing device under the sun (Spec. at paragraphs 53, 110, 367, 382, and 396: e.g., laptop computer, smartphone, desktop computer, table computer, PDA, etc., noting in par. 367 that “the computer system taking any suitable physical form”). Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the application programming interface (API) recited in the receiving step, it is noted that APIs are well-understood, routine, and conventional in the art. See, e.g., Hendrick et al., US 2013/0346234 at paragraph 16, noting that “ intake of the user activity data 104 and catalog data 106 into the recommendations system 100 occurs through the Web service application program interface (API) 118, the details of which will be known to those skilled in the art.” See also, Walsham, US 2014/0040248 at paragraph 31, noting that “New input series may be created and existing input series updated automatically via an application programming interface (hereinafter, API), APIs are known known in the art as a means for allowing content to be shared by different applications.” Therefore, the use of an API is not sufficient to integrate the abstract idea into a practical application or to add significantly more to the claims. Next, it is noted that natural language processing (NLP) is well-understood, routine, and conventional in the art, which does not amount to significantly more than the abstract idea itself. See, e.g., Morsa, US 2006/0085408 (paragraph 0144: well -known-to-the-arts natural language processing (NLP) (computational linguistics) or some other method as is well known to the arts may be used). See also, Szabo, US Pat. No. 5,966,126 (col. 6, lines 57-62 and col. 28, lines 16-19: e.g., definitions may be produced in known manner, such as by explicit definition, or through use of assistive technologies, such as natural language translators; user defines a search using prior known techniques, such as natural language searching). Accordingly, the use of natural language processing is insufficient to add significantly more to the claim.
With respect to the receiving/transmitting/accessing steps, in addition to being implemented by a generic computer, these activities also fall under insignificant extra-solution activity, and such extra-solution activities have been recognized as well-understood, routine, and conventional and thus insufficient to add significantly more to the abstract idea, as noted by the CAFC with respect to storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. See also, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). See also, OIP Technologies, 788 F.3d at 1363, 115 USPQ2d at 1092-93.
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 the ordered combination amounts to significantly more than the abstract idea itself.
Dependent claims 3-8, 12-15, and 17-22 recite the same abstract idea as recited in the independent claims under the “Certain Methods of Organizing Human Activity” and “Mental Processes” abstract idea groupings, and with the exception of the additional elements addressed below, are directed to further details that merely narrow the abstract idea when evaluated under Step 2A Prong One of the eligibility inquiry along with the same or similar generic computing elements as independent claims 2/9/16 and addressed above (which is incorporated herein), which fail to integrate the abstract idea into a practical application or add significantly more to the claims.
With respect to the computing device and graphical user interface (claims 3, 10, 17) and user computing device (claims 4, 11, and 18), computing device (claims 6, 13, and 20), and application (claims 5, 12, and 19), these additional elements fall under the realm of generic computing devices, which does not amount to a practical application or add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). See also, Affinity Labs of Texas LLC v. DirecTV LLC, 838 F.3d 1253, 1257-1258 (Fed. Cir. 2016) (mere recitation of a GUI does not make a claim patent-eligible); Intellectual Ventures I LLC v. Capital One Bank, 792 F.3d 1363, 1370 (Fed. Cir. 2015) (“the interactive interface limitation is a generic computer element”). With respect to the additional receive/display/accessing/ transmitting activities (claims 3-5, 10-12, 18-20), these amount to insignificant extra-solution activities, which is not enough to amount to a practical application (MPEP 2106.05(g)), and such extra-solution activity has also been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
With respect to the NLP model in claims 8/15/22, this additional element has been previously evaluated under Step 2A Prong Two and Step 2B above in the discussion of independent claims 2/9/16, and substantially the same rationale is applicable to is usage in claims 8/15/22.
With respect to the unsupervised training in claims 8/15/22, this limitation is recited at a high level of generality and the claims fail to provide any details, technique, or algorithm as to how the unsupervised training is performed, and as admitted in the Specification, the unsupervised training could be implemented with mathematical algorithms such as k-means clustering, expectation-maximization (EM), fuzzy c-means (FCM), density-based spatial clustering (DBSCAN) algorithms (Spec. at par. [0392]), and thus fall under the “Mathematical Concepts” abstract idea grouping, such that “Adding one abstract idea (math) to another abstract idea” (fundamental economic practice) “does not render the claim non-abstract.” See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea and thus subjected to further analysis in part two of the Alice/Mayo test). Nevertheless, even if evaluated as an additional element, the supervised learning is recited at a high level of generality and fails to yield a technical improvement or otherwise add a practical application. See MPEP 2106.05(f) and 2106.05(h). Furthermore, given the high-level of generality and lack of “how” as to its implementation by the claimed invention, the unsupervised training, even when implemented by a computer, is similar to merely adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (e.g., generic computing environment), which does not amount to a practical application or significantly more than the abstract idea itself. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Furthermore, under Step 2B, it is noted that unsupervised training is well-understood, routine and conventional in the art. See, e.g., Sun et al., US 2014/0372351, noting in par. [0038] that “machine learning algorithm used to implement the classifier 304 may include any machine learning algorithm known in the art, including, for example, a supervised or unsupervised learning algorithm.”
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 integrate the judicial exception into a practical application and fails to add significantly to the claims beyond the abstract idea itself.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
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.
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.
Claims 2-7, 9-14, and 16-21 are rejected under 35 U.S.C. 103 as unpatentable over Perazzo et al. (US 2016/0112362, hereinafter “Perazzo”) in view of Horvitz et al. (US 2016/0189032, hereinafter “Horvitz”) in view of Kulkarni et al. (US 2022/0107852, hereinafter “Kulkarni”).
Claims 2/9/16: As per claim 2, Perazzo teaches a computer-implemented method comprising:
receiving calendar data for a particular user of a task facilitation service through an external application programming interface (API), wherein the calendar data includes a personal calendar associated with the particular user … of the particular user (pars. 37, 46-47, 62, and 67: to-do list manager 148 can be synchronized with a user’s 102 calendar both on the client-side (the client device 120) and on the server-side; workflow management system 100 can mirror tasks to/from a user’s calendar as well such that the user does not have to manage manually two separate items—the calendar and the to-do list. In an exemplary embodiment, the calendar and task list synchronization can occur on the client-side using standard APIs associated with calendar software although this could also be done on the server-side; application or service can include any of a reminder, a calendar, a mapping algorithm, a checklist, an address book, and any application offering Application Program Interfaces (APIs); the data sources 130 may be external; transform messages or the like into calendar events);
processing the calendar data using a … model to generate a task recommendation, wherein the task generator recommendation indicates one or more recommended tasks determined based on the personal calendar … (pars. 22-25, 30, 40-48, and 67: e.g., data sources 130 can include, without limitation…calendar and meeting data; contextual messaging methods 700, 750 can transform messages or the like into calendar events (including synchronization with third party calendars), reminders, to-dos delegated to or shared with other users, etc. using a combination of…natural language processing, and parsing to extract task name and type, time, location, checklist, etc.; workflow management system 100 can mirror tasks to/from a user’s calendar; to-do list manager 148 interacts with the recommendation engine 146, the Web portal 142, the user interaction function 144, and the user management function 150 to actively manage a to-do or task list for each of the users; present focused recommendations based on the back-end processing of the data at the server; to-do list manager 148 manages tasks for each of the users 102 based on their associated roles, specializations, etc. as well as their own personal data; to-do list manager 148 interacts with the recommendation engine 146, the Web portal 142, the user interaction function 144, and the user management function; provide recommendations to users in the form of a task list or to-do list; automatically generate contextually-derived tasks for the user)), wherein the NLP model was initially trained with a training dataset using unsupervised training and without user supervision (The preceding “wherein” statement has been considered, but does not impose any additional limitation on the “processing” or the claimed method/system/ medium, but merely recites a feature that occurs outside the scope of the claim regarding how the NLP model was trained, which is not required to be performed by the claim. The claim does not recite any particular function/capability/ structure or limitation resulting from how the model was previously trained, nor is any such function/capability/structure or limitation inherent or reasonably understood as necessarily occurring as a result of how the model was trained. The “wherein” clause does not give meaning and purpose to the manipulative step and the unsupervised training does not occur within the scope of the claim, and therefore the “wherein” statement does not impose additional patentable weight on the claim. See MPEP 2111.04. See also, Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps"));
transmitting an indication corresponding to the task recommendation (pars. 43, 78 and Fig. 13: The recommendation engine 146 is an intelligent engine that creates tasks, encoding workflow directly into tasks that are presented via the to-do list manager; The recommendation engine 146 is an intelligent engine that creates tasks, encoding workflow directly into tasks that are presented via the to-do list manager; FIG. 13 illustrates Paolo's mobile device 120 after receiving the message from Sandhya. Here, Paolo can accept, decline, or propose changes);
receiving an approval to proceed with performing the one or more recommended tasks (pars. 43, 78 and Fig. 13: Paolo's mobile device 120 after receiving the message from Sandhya. Here, Paolo can accept, decline, or propose changes);
accessing task-execution data associated with the one or more recommended tasks, wherein the task-execution data identifies performance statuses associated with the one or more recommended tasks (pars. 44, 46-48, and 51: e.g., break down tasks in the event specific tasks are “stuck” meaning little progress is being made for complex tasks; break down tasks in the event specific tasks are “stuck” meaning little progress is being made for complex tasks; meeting attendees can automatically be tracked and a task to attend a meeting can be automatically marked as completed or in-progress); and
updating the … model based on the task-execution data, wherein the … model is updated to generate task recommendations that accurately correlate to future updates associated with the shared calendar (pars. 25, 27, 29, 48, 51, 56, and 69: e.g., updating the to-do list based on changes in the user's context, as determined by the workflow management system based on the location from the mobile device, and ongoing analysis of the data; can update the task list dynamically; workflow management system 100 can have built-in intelligence related to tasks. For example, if a current task is to attend a meeting at a certain time, the workflow management system 100 can determine that the user 102 (and other users 102) are at the meeting based on location information from their associated client devices 120. Here, the user 102 would not need to update the task list; instead it can be automatically set to complete or in-progress based on this passive data capture; Additionally, the to-do list manager 146 can update the task list order based on the location information to provide more relevance and efficiency).
Perazzo does not explicitly teach:
shared calendar associated with one or more family members;
recommended tasks determined based on…the shared calendar;
natural language processing (NLP) model;
updating the NLP model.
Horvitz teaches:
shared calendar associated with one or more family members (par. 41: calendar program 42, including user's shared calendar called “FAMILY CALENDAR”);
recommended tasks determined based on…the shared calendar (par. 41: calendar program 42, including user's shared calendar called “FAMILY CALENDAR”, the personal agent program 10 has learned a behavioral pattern that the user has taken a 2 week family vacation each August in each of the last 3 years. It is now July, and the user has a shared calendar item from the FAMILY CALENDAR for August 2-August 16 that reads simply “BARCELONA.” Additionally, by monitoring user activity 48′ from the browser 48, the personal agent program 10 learns that the user recently purchased a “LEARN SPANISH” audio book from an online book provider. Based on these contextual factors, the personal agent program 10 makes an inference that the user is again planning a family trip in August, this time to Barcelona, Spain from August 2-August 16. The recommendation engine 24 in the personal agent program 10 may also create an additional user recommendation preference 62 based on this behavioral pattern).
Examiner’s Note: Although Perazzo teaches the following limitation, it is further noted that Horvitz also teaches receiving an approval to proceed with performing the one or more recommended tasks (pars. 39-43 and Fig. 3: See, e.g., Fig. 3, which displays a recommendation and “Click Here For Coupon” or “Click Here to Make a Reservation” buttons, which function as approvals to proceed with performance of these recommended tasks).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Perazzo with Horvitz because the references are analogous since they are both directed to automated features for recommending tasks for a user, which is within Applicant’s field of endeavor of providing task recommendations to users, and because modifying Perazzo to include Horvitz’s shared calendar features, as claimed, would be a compatible and beneficial extension of Perazzo’s existing features for sharing calendar/task information between users (Perazzo at par. 67); 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.
Perazzo and Horvitz do not explicitly teach:
natural language processing (NLP) model;
updating the NLP model.
Kulkarni teaches:
natural language processing (NLP) model (pars. 133, 138, 140, 144, and Fig. 8: In some embodiments, the NLP model comprises a long short-term memory neural network; provide the sequence of activity event vectors to a natural language processing (NLP) model; receive, from the NLP model, one or more predicted activity events; and provide, for display on a client device associated with the user, one or more suggestions based on the one or more predicted activity event);
updating the NLP model (pars. 14, 101, and 108-109: e.g., training and tuning a natural language model; training the natural language model … including updating the learned parameters; return such loss data to the natural language model 202 where the user activity sequence system 104 can adjust the learned parameters 512 to improve the quality of predictions (by narrowing the difference between predicted activity events and the ground truth). Moreover, the training/learning iteration just described can be an iterative process, as shown by the return arrow between act 514 and the natural language model 202 such that the user activity sequence system 104 can continually adjust the learned parameters 512 over learning cycles).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Perazzo/Horvitz with Kulkarni because the references are analogous since they are both directed to automated features for recommending tasks for a user, which is within Applicant’s field of endeavor of providing task recommendations to users, and because modifying Perazzo/Horvitz to include Kulkarni’s NLP model and updating of the NLP model, as claimed, would be a compatible and beneficial extension of Perazzo’s existing features for using natural language processing (NLP) for parsing and extracting calendar and task related information pursuant to providing recommendations (Perazzo at pars. 67-68 and 70), and would provide the benefit of extracting relevant information from regular sentences/content of users (Perazzo at par. 68); 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.
Claims 9 and 16 are directed to a system and non-transitory computer-readable medium that encompass substantially similar limitations as those set forth in claim 2 and addressed above. Perazzo, in view of Horvitz/Kulkarni, teaches a system and non-transitory computer-readable medium for performing the limitations discussed above (Perazzo at paragraphs 21 and 80: systems; embodiments may be implemented as a non-transitory computer-readable storage medium having computer readable code stored thereon for programming a computer, server, appliance, device, etc. each of which may include a processor to perform methods as described and claimed herein; See also, Horvitz at par. 63 and Fig. 1; See also, Kulkarni at pars. 7 and 128), and claims 9 and 16 are therefore rejected using the same reference and for substantially the same reasons as set forth above.
Claims 3/10/17: Each of Perazzo and Horvitz further teaches wherein when the indication is received by a computing device, the computing device displays a graphical user interface configured to receive the approval (Perazzo at pars. 43, 78 and Fig. 13: recommendation engine 146 is an intelligent engine that creates tasks, encoding workflow directly into tasks that are presented via the to-do list manager; FIG. 13 illustrates Paolo's mobile device 120 after receiving the message from Sandhya. Here, Paolo can accept, decline, or propose changes; See also, Horvitz at pars. 39-43 and Fig. 3: See, e.g., Fig. 3, which is a graphical user interface configured to receive a user’s approval of a displayed recommendation by selecting the “Click Here For Coupon” or “Click Here to Make a Reservation” button, which are approvals to proceed with performance of either of these recommended tasks).
Claims 4/11/18: Perazzo further teaches wherein the personal calendar is accessed from a user computing device corresponding to the particular user (pars. 25-27, 55-57, and Fig. 1: system, implemented through a server, to a client device associated with a user to automatically derive a context associated with the user, wherein the client device is a mobile device; receiving data capture from a client device of the user and integrating associated data in the workflow management system), but does not explicitly teach the shared calendar is accessed from another different device corresponding to the one or more family members.
Horvitz teaches the shared calendar is accessed from another different device corresponding to the one or more family members (pars. 17-20, 41, and 56: plurality of computer programs used by the user on the user computing device 12 and/or one or more other user computing devices, such as user computing device 52 and user computing device 54; monitoring engine 22 may monitor friend activity 57 in a calendar program, a mobile device location tracking program and a social networking program that are executed on the friend computing device; monitor user activity across a plurality of computer programs used by the user on a user computing device and one or more other user computing devices; user's GPS-enabled mobile communication device and mobile device location tracking program 46, and user activity 42′ from the calendar program 42, including user's shared calendar called “FAMILY CALENDAR”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Perazzo/Horvitz/Kulkarni to incorporate Horvitz’s feature for accessing a shared calendar from another user (e.g., family member), as claimed, in order to provide the benefit of cross-device calendar and task management, which would be appreciated as a more flexible and accurate approach as compared to single-device access and tracking; 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.
Claims 5/12/19: Perazzo further teaches wherein the calendar data includes details for a calendar item of the calendar (pars. 47, 59, 62, 65, and 74: e.g., calendar items such as appointments and tasks; calendar event and the one or more additional details include a date and/or time; location and the one or more additional details include a geographic location, store, restaurant, or landmark), and wherein receiving the approval further includes: transmitting an update for application data of the calendar application to indicate that the one or more recommended tasks have been generated for the calendar item (pars. 67, 76, 78-79, and Figs. 13 and 15: modifying and/or updating a calendar event; Once Sandhya accepts, the mobile devices 120 for both users trigger applications or services to add calendar events with the people; additional options such as a reminder and whether or not to add to the calendar. In this step, the unique character or picture 814 is the people icon and the date and time icon and the one or more additional details are the people (Sandhya and Paolo) and the date and time (July 15.sup.th, 7 PM) along with the options (reminder and add to calendar); can accept, decline, or propose changes; after receiving the message from Sandhya. Here, in this example, Paolo accepts the request).
Claims 6/13/20: Each of Perazzo and Horvitz further teaches wherein when the indication is received by a computing device, the computing device is enabled to approve the task recommendation (Perazzo at pars. 25-29, 76-79, and Figs. 13-15: contextually-derived tasks are relevant, selective, and actionable based on the context to optimize workflow and efficiency of the user; managing a to-do list for the user including the contextually-derived tasks and user-defined tasks, wherein the contextually-derived tasks are automatically derived by the recommendation engine and the user-defined tasks are created by the user; can accept, decline, or propose changes; See also, Horvitz at pars. 39-43 and Fig. 3: See, e.g., Fig. 3, which displays a recommendation and “Click Here For Coupon” or “Click Here to Make a Reservation” buttons, which function as computer executed indications of approvals of these recommendations).
Claims 7/14/21: With respect to the limitation of wherein the training dataset includes training data associated with other users, it is noted that neither claims 7/14/21 nor parent claims 2/9/16 recite or require any step, function, structure, or other limitation involving a training data set within the scope of these claims because, as discussed above in the rejection of claims 2/9/16, the “wherein” statement in these claims refers to something that previously occurred outside the scope of the claim and the details described in the “wherein” do not impose any additional limitation on the “processing” or the claimed method/system/ medium, such that the claim does not recite any particular function/capability/structure or limitation resulting from how the model was previously trained, nor is any such function/capability/structure or limitation inherent or reasonably understood as necessarily occurring as a result of how the model was trained, and therefore the additional details in the “wherein” statement of claims 7/14/21, similar to their parent claims, do not give meaning and purpose to the manipulative step since the unsupervised training does not occur within the scope of the claim, and therefore these subsequent “wherein” statements do not impose additional patentable weight on the claim. See MPEP 2111.04. See also, Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps"). Nevertheless, it is noted that Perazzo teaches training data associated with other users (pars. 21, 24, 41, 48, 50, 58, and 67: recommendation engine 146 is an artificial intelligence function that parses data in the data sources; messaging platform acts as an Artificial Intelligence (AI)-based anticipatory assistant; workflow management system 100 can have built-in intelligence related to tasks. For example, if a current task is to attend a meeting at a certain time, the workflow management system 100 can determine that the user 102 (and other users 102) are at the meeting based on location information from their associated client devices).
Claims 8, 15, and 22 are rejected under 35 U.S.C. 103 as unpatentable over Perazzo et al. (US 2016/0112362, hereinafter “Perazzo”) in view of Horvitz et al. (US 2016/0189032, hereinafter “Horvitz”) in view of Kulkarni et al. (US 2022/0107852, hereinafter “Kulkarni”), as applied to claims 2, 9, and 16 above, and further in view of Shaya et al. (US 2020/0302404, hereinafter “Shaya”).
Claims 8/15/22: Perazzo does not teach the limitations of claims 8/15/22.
Kulkarni further teaches wherein updating includes adjusting one or more weights of the NLP model … and wherein the one or more weights of the NLP model are adjusted until a corresponding logarithmic loss exceeds a predetermined threshold (pars. 106-109: e.g., cause the natural language model 202 to execute a training iteration on training data; training the natural language model …including updating the learned parameters 512 to reflect one or more smoothing parameters, an unknown word (UNK) parameter, etc.; implement different evaluation metrics and/or threshold standards; In particular, the loss function (e.g., as part of a back-propagation process for a neural network) can return such loss data to the natural language model 202 where the user activity sequence system 104 can adjust the learned parameters 512 to improve the quality of predictions (by narrowing the difference between predicted activity events and the ground truth). Moreover, the training/learning iteration just described can be an iterative process, as shown by the return arrow between act 514 and the natural language model; loss function can include a classification loss function (e.g., a hinge loss/multi-class SVM loss function, cross entropy loss/negative log likelihood function, etc.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the combination of Perazzo/Horvitz/Kulkarni to incorporate Kulkarni’s weighting and adjusting of the NLP model until a log loss exceeds a predetermined threshold, as claimed, in order to provide the benefit of tuning the natural language model (Kulkarni at par. 101), which would yield the expected benefit of a more accurately and continuously improving model; 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.
Perazzo, Horvitz, and Kulkarni do not explicitly teach using the unsupervised training and without user supervision.
Shaya teaches using the unsupervised training and without user supervision (pars. 31, 39, 51, 65, and 195 e.g., machine learning is used to predict the appropriate existing task list for a new task item; may include a task input field for natural language; unsupervised methods such as k-means clustering).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Perazzo/Horvitz/Kulkarni with Shaya because the references are analogous since they are both directed to automated features for recommending/suggesting tasks, which is within Applicant’s field of endeavor of providing task recommendations to users, and because modifying Perazzo/Horvitz/Kulkarni to include Shaya’s unsupervised learning, as claimed, would provide the benefit of an efficient technique for automatically training/improving the model; 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:
Calendar-aware proactive email recommendation. Zhao, Qian; Bennett, Paul N.; Fourney, Adam; Thompson, Anne Loomis; Williams, Shane; et al. 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018: 655-664. Association for Computing Machinery, Inc. (Jun 27, 2018): discloses features of an e-mail recommender system to predict the usefulness of emails, including the effects of different machine learning models.
A personalized health recommendation system based on smartphone calendar events. Katariya, Sharvil; Bose, Joy; Reddy, Mopuru Vinod; Sharma, Amritansh; Tappashetty, Shambhu. Springer Verlag, 2018: discloses the use of a trained model for classifying calendar events in order to provide personalized recommendations for a user.
An intelligent personal assistant for task and time management. Myers, Karen; Berry, Pauline; Blythe, Jim; Conley, Ken; Gervasio, Melinda; et al. AI Magazine28.2: 47(15). American Association for Artificial Intelligence. (Jul 2007 - Sep 2007): discloses an automated time and task management tool that employs AI technologies to aid users with time/task management by providing recommendations to users, including integration with commercial calendaring tools.
P. Anthony and O. Y. Wooi, "Calendar agent with commonsense reasoning," 2011 Malaysian Conference in Software Engineering, Johor Bahru, Malaysia, 2011, pp. 399-403: discloses computer implemented features, such as natural language processing (NLP), for automated calendar management features, including analyzing user behavior to provide better recommendations for tasks, meeting, events, and the like.
Wang et al. (US 2015/0019642): discloses a calendar-event recommendation system, including updating a calendar based on acceptance of recommended events (at least paragraphs 88-89).
Deole et al. (US 2022/0327494): discloses features for providing electronic event attendance mode recommendations.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Timothy A. Padot whose telephone number is 571.270.1252. The Examiner can normally be reached on Monday-Friday, 8:30 - 5:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Brian Epstein can be reached at 571.270.5389. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/TIMOTHY PADOT/
Primary Examiner, Art Unit 3625
01/22/2026