DETAILED ACTION
Notice of 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 .
Continued Examination Under 37 CFR 1. 114
2. 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.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 06/12/2026 has been entered.
Response to Amendment
3. Amendment filed 06/12/2026 has been considered by Examiner. Claims 1, 5, 7, 10, 12 and 16 have been amended. Claims 1-20 are pending, and likewise Claims 1- 20 have been examined.
Response to Arguments
Applicant’s amendments and arguments filed 06/12/2026, with respect to claim(s) 1-20 have been fully considered.
Applicant’s arguments in pages 13-15, filed 06/12/2026, with respect to 35 U.S.C 101 rejections of Claims 1-6, 8-13 and 15-20 have been fully considered but they are not persuasive. Applicant amended the independent claims and argued that currently amended independent claims 1, 10, and 16 recite a practical application of any alleged abstract ideas. Applicant further argued that the amended limitations, “ "determining, based on the client device activity and an activity history associated with the user account…. by determining, from data signals collected by one or more connectors from one or more software tools… source content that is not included in the content item and that the user account is predicted to locate and add to the content item", cannot be performed in the human mind, and is a practical application, at least because it improves the functioning of a computer”. Examiner respectfully disagrees. Examiner is not certain how inserting predicted modifications to a content item and finding out the source of the modification can be a practical application and an improvement to the functioning of a computer. A person can predict, based on the style, flow and content of document what addition or edit or modification can be done with the certain part of the document and where to add those. Applicant further argued that “the claims require a specific computer implemented workflow in which the system uses connector-collected data signals from software tools external to the content item to identify a predicted content-edit operation, a location within the content item for that operation, and source content not already included in the content item”. Examiner respectfully disagrees. Using “connector-collected data signals from software tools” is recited in a generic way, there is nothing innovative in there. The applicant again argued that “this ordered combination is rooted in computer technology and cannot practically be performed in the human mind, at least because it requires computer-based collection of signals from external software tools, generation of modifications for a managed content item, and automatic modification of the content item before any user acceptance or selection”. Throughout the amended claims 1, 10 and 16, no specialized or unique technology or idea have been mentioned. The use of a computer does not preclude performance of the invention via pen and paper or in a person’s mind. Also, the use of a computer or other machinery in its ordinary capacity to perform a task or simply adding a general purpose computer to an abstract idea, does not integrate a judicial exception into a practical application. Here the computer is the machine that is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. Thus, 35 U.S.C 101 rejections of Claims 1-6, 8-13 and 15-20 have been maintained.
Applicant’s arguments filed 06/12/2026, with respect to claim(s) 1-20, under 35 U.S.C. 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 8-13 and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Independent claims 1, 10 and 16 recite “detecting client device activity in relation to a content item associated with a user account of a content management system”; “determining, based on the client device activity and an activity history associated with the user account, an input prediction defining one or more predicted client device inputs predicted to be received via a client device”; “by determining, from data signals collected by one or more connectors from one or more software tools external to the content item: (i) a content-edit operation predicted to be performed by the user account, (ii) an insertion location within the content item for the content-edit operation, and (iii) source content that is not included in the content item and that the user account is predicted to locate and add to the content item;” “generating, based on the input prediction and without receiving client device input requesting the source content or selecting a predicted edit, predicted content to add to the content item in response to the one or more predicted client device inputs, the predicted content comprising one or more generated modifications for the content item;”” [[and]] automatically inserting, before presenting the predicted content for acceptance by the user account and without first receiving user selection or acceptance of the predicted content, the one or more generated modifications at the insertion location within into the content item without user interaction prompting the predicted content”;” and providing, for display via the client device, the content item comprising the predicted content”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of " detecting ... ", "determining ... ", “generating..” , “inserting..” as drafted covers mental activities. More specifically, a human can detect a device activity regarding some content, determine one or more predicted input based on the input and history, the predicted input might be related to some modification to the content, can determine what to modify, where to modify and the source of the modification, generate ( write in the paper) the predicted input and insert the modified content manually. Some of the modification idea might come from the another human, not from the original user and may be without notifying the original user. All the steps above are examples of observation and evaluation that could be performed in the human mind or with the aid of pencil and paper.
The claims recite the additional limitation of a “processor”, “ non transitory computer readable storage medium” ,”software tool”, for performing the method. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. The current specification in paragraphs [0147],[0152],[0153] clearly specifies them as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The claims as drafted, are not patent eligible.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements 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 conventional computer implementation. Claims 1, 10
and 16 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without
significantly more than the abstract idea.
Claim 2 recites the additional limitation of “wherein determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account” , where analyzing data signals such as text, audio can be done in human mind or with the aid of pen and paper. Connectors, software tools are additional elements as shown in specification in para. [0059], [0106], which is not sufficient to amount to significantly more than the judicial exception. The claim 2 as drafted, is not patent eligible.
Claim 3 recites “wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises processing the data signals collected by the connectors as a prompt through a large language model ”. Processing the data signal, which can be a text and presenting as a prompt with the help of language model, which can be just a knowledge source, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. Large language model is an additional element, as shown in specification, para.[0036],can be a machine learning model or neural model, which is not sufficient to amount to significantly more than the judicial exception. The claim as drafted, is not patent eligible.
Claim 4 recites “wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises locating sample content from a repository and modifying the sample content based on the client device activity and the activity history”, where human can locate sample content by observation and modify the content, such as any saved information on the device, based on the activity history, manually with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 4 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 5 and 12 recite “further comprising: one or more generated modifications a post solution activity, which is not sufficient to amount to significantly more than the judicial exception. The claims 5 and 12 as drafted, are not patent eligible.
Claims 6, 13 and 19 recite the additional limitations of “wherein: detecting the client device activity in relation to the content item comprises detecting that the user account begins a composite action”; “determining the input prediction comprises determining, based on the user account beginning the composite action, that the content item relates to the composite action”; “generating the predicted content to add to the content item comprises generating draft content for a first draft of the content item”; “and inserting the predicted content within the content item comprises inserting the draft content into a template file for the content item” . Detecting that the user is doing multiple action from the device activity, determining that the input predicted content item is related to the multiple actions, generating draft and template of the content are observation, evaluation and could be performed in the human mind or with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 6, 13 and 19 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 8 recites “further comprising: determining that a composite action of the user account is paused or complete”; “and generating a summary content item comprising a summary of the at least one user interface element”. The status of the actions in a user’s account in a user’s device can be find out by looking at the device and observing the indications/notifications which shows the status of the actions such as complete, incomplete, in progress etc. User can generate a summary of different types of notifications ( user interface element), such as how many “complete” notifications, how many “on hold” or “paused” notifications, which can be done by observations and by using pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 8 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claims 9 and 15 recite “further comprising: determining that a composite action of the user account is paused or complete”; “generating, for additional user accounts of the content management system, an update notification indicating a completion status for the composite action”; “and providing, without additional interaction by the user account, the update notification to the additional user accounts for display via user interfaces of client devices associated with the additional user accounts”. The status of the actions in a user’s account in a user’s device can be find out by looking at the device and observing the indications/notifications which shows the status of the actions such as complete, incomplete, in progress etc. Same type of status indication can be find out for additional user’s account by observing the device. Displaying the notifications is a post solution activity, which is not sufficient to amount to significantly more than the judicial exception. The claims as drafted, are not patent eligible.
Claim 11 recites the additional limitation of “wherein generating the predicted content to add to the content item comprises processing, as a prompt through a large language model, data signals collected by connectors from software tools associated with the user account” . Processing the data signal, which can be a text and presenting as a prompt with the help of language model, which can be just a knowledge source, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. Connectors, software tools as shown in specification in para. [0059], [0106], also large language model, as shown in specification, para.[0036], which can be a machine learning model or neural model, are additional elements, which are not sufficient to amount to significantly more than the judicial exception. The claim 11 as drafted, is not patent eligible.
Claim 17 recites the additional limitation of “wherein: determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account”; “and generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises processing the data signals collected by the connectors as a prompt through a large language model” , where processing/ analyzing data signals such as text, audio and presenting as a prompt with the help of language model, which can be just a knowledge source, is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper.. Connectors, software tools as shown in specification in para. [0059], [0106], also large language model, as shown in specification, para.[0036], which can be a machine learning model or neural model, are additional elements, which are not sufficient to amount to significantly more than the judicial exception. The claim 17 as drafted, is not patent eligible.
Claim 18 recites “wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises identifying sample content within another content item and modifying the sample content based on the client device activity and the activity history”, where locating/ identifying sample content within another content, such as identifying any saved document within a saved set of documents on the device, and changing/editing/modifying the identified saved document based on how recently it has been used by the user, could be done by a human manually or with the aid of pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 18 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 20 recites “wherein the instructions, when executed by the at least one processor, further cause the computing device to provide the template file with the draft content for the first draft of the content item for display via a user interface of the client device”, where providing the template file with draft content for presentation can be done with the aid of pen and paper. Display via the client device is post solution activity, which is not sufficient to amount to significantly more than the judicial exception. The claim as drafted, is not patent eligible.
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.
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 1, 10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Dicklin et al. ( US 20250103867 A1), hereinafter referenced as Dicklin, in view of Doshi et al. ( WO 2023002496 A1), hereinafter referenced as Doshi.
Regarding Claim 1, Dicklin teaches a computer-implemented method comprising:
detecting client device activity in relation to a content item associated with a user account of a content management system ( Dicklin: Para.[0034],[0041],Fig. 1, the platform 100 which include a cloud content management system 110, detect an activity from user’s device ( such as text from client devices from clients 140-1,..,140-m), such as typing “passport” ( content item). Para.[0036], user’s cloud storage ( user’s account) on the cloud based content management platform consists of accessible, relevant documents) ;
determining, based on the client device activity and an activity history associated with the user account, an input prediction defining one or more predicted client device inputs predicted to be received via a client device, the one or more predicted client device inputs corresponding to content edits that the user account is predicted to add to the content item ( Dicklin: Para.[0161]-[0165], [0175], Fig. 11, At block 1110, the real-time anticipation subsystem 116 may identify an action of a user of the cloud-based content management platform 100 with respect to one or more documents of multiple documents stored at the platform 100. The action of the user may include the user selecting a document or a folder displayed by the user interface, opening a document or folder, hovering a mouse cursor over a document or folder. At block 1122, the real-time anticipation subsystem 116 may select the one or more documents stored in the cloud-based content management platform 100, based on a last modified date of a document, a last opened date of a document, or a last commented-on date of a document, documents that a user has permission to open, modify, or otherwise access. At block 1124, based on user inputting text data “ file size” into a user interface of the platform 100 and activity during the past week ( history), the real-time anticipation subsystem 116 may select a document portion from the working set, and the document portion may include a paragraph that includes information about file sizes. At block 1126, the real-time anticipation subsystem 116 may input the paragraph selected in block 1124);
Dicklin while teaching the method of claim 1, fails to explicitly teach the claimed, by determining, from data signals collected by one or more connectors from one or more software tools external to the content item: (i) a content-edit operation predicted to be performed by the user account, (ii) an insertion location within the content item for the content-edit operation, and (iii) source content that is not included in the content item and that the user account is predicted to locate and add to the content item; generating, based on the input prediction and without receiving client device input requesting the source content or selecting a predicted edit, predicted content to add to the content item in response to the one or more predicted client device inputs, the predicted content comprising one or more generated modifications for the content item; [[and]] automatically inserting, before presenting the predicted content for acceptance by the user account and without first receiving user selection or acceptance of the predicted content, the one or more generated modifications at the insertion location within the content item without user interaction prompting the predicted content; and providing, for display via the client device, the content item comprising the predicted content.
However, Doshi does teach the claimed, by determining, from data signals collected by one or more connectors from one or more software tools external to the content item: (i) a content-edit operation predicted to be performed by the user account ( Doshi: Para.[17], Fig. 1 illustrates a process of adaptive video lecture delivery, where web cam or other sensory devise 100 is used to capture various facial expressions and cognitive responses of viewer. Para. [18], Fig. 2, based on the attentiveness of the viewer ( from the continuous feedback 210) are detected from the client side while playing the lecture segment, the Next Segment Predictor 206 decides how to modify/edit the video content),
(ii) an insertion location within the content item for the content-edit operation ( Doshi: Para. [19], Fig.3, Knowledge Bank 301 has time position at which the video can be stopped to insert new videos i.e. break points ( insertion location). The attentiveness received from the viewer triggers the decision unit (303) to check if any insertions are to be made to the main video to maintain the viewer's attention and interest levels),
and (iii) source content that is not included in the content item and that the user account is predicted to locate and add to the content item ( Doshi: Para.[28], Fig. 1, If the viewer is not attentive or attentiveness is decreasing, MCQ ( multiple choice questions) or some interesting video segment is presented to the viewer related to the topic ( was not included in the content)),
generating, based on the input prediction and without receiving client device input requesting the source content or selecting a predicted edit, predicted content to add to the content item in response to the one or more predicted client device inputs, the predicted content comprising one or more generated modifications for the content item ( Doshi: Para.[29]-[31], Fig.2 , The server 202 receives the attentiveness score as continuous feedback 210 to find if there should be any change in the content sequence. The Next Segment Predictor (206) decides the final content segments (209) that needs to be inserted . As soon as the current segment is completed, new segment (209) received from the server is first served before proceeding with its pre-decided lecture sequence. Para.[11], attentiveness analysis is dynamically done without a break and without letting the user know) ;
[[and]] automatically inserting, before presenting the predicted content for acceptance by the user account and without first receiving user selection or acceptance of the predicted content, the one or more generated modifications at the insertion location within ( Doshi: Para.[27], [31], Fig. 2, The smart decision of what content type should be selected for what kind of attentiveness trend is derived from incremental training model developed with Al/ML technique. For this the viewer's feedback in terms of attentiveness level for the newly inserted content segment 209 is observed. The change in attentiveness is stored for future reference. This data is then fed to the next segment predictor 206 to calculate if sending a particular content segment is desirable or not. Once this decision is made, new content segments are selected and automatically inserted. The new content segments 209 to be inserted are received on the client side 211. As soon as the next break point is hit the new content segments are played. This happens without any notification to the viewer/user. For the user it seems to be a pre-decided sequence but in real it has been modified to suit the temperament of the user/viewer) ;
and providing, for display via the client device, the content item comprising the predicted content ( Doshi: Para.[19],[27],Fig. 2, video with the new content segment ( predicted) is played to the user on display 203).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Doshi’s teaching of Smart E-Learning system using adaptive video lecture delivery based on attentiveness of the viewer, into the system and method of real-time anticipation of user interest in information contained in documents in cloud storage, taught by Dicklin, because, this smart learning techniques by incorporating modifying content without the intervention from user would result effective learning. (Doshi, Para.[4],[5]).
Claim 10 is system claim comprising: at least one processor ( Dicklin: Para.[0193], Fig.14, processor 1402); and at least one non-transitory computer-readable storage medium comprising instructions that, when executed by the at least one processor, cause the system to (Dicklin: Para.[0195], Fig. 14, non-transitory machine-readable storage medium 1424), perform the steps in method claim 1 above and as such, claim 10 is similar in scope and content to claim 1 and therefore, claim 10 is rejected under similar rationale as presented against claim 1 above.
Claim 16 is non-transitory computer-readable storage medium claim comprising instructions that, when executed by at least one processor, cause a computing device to ( Dicklin: Para.[0193], Fig.14, processor 1402. Para.[0195], Fig. 14, non-transitory machine-readable storage medium 1424), perform the steps in method claim 1 above and as such, claim 16 is similar in scope and content to claim 1 and therefore, claim 16 is rejected under similar rationale as presented against claim 1 above.
Claims 2-6, 9, 11-13, 15 and 17-20 and are rejected under 35 U.S.C. 103 as being unpatentable over Dicklin et al. ( US 20250103867 A1), hereinafter referenced as Dicklin, in view of Doshi et al. ( WO 2023002496 A1), hereinafter referenced as Doshi, further in view of Kulkarni et al. (US 11567812 B2), hereinafter referenced as Kulkarni.
Regarding Claim 2, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, wherein determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account.
However, Kulkarni does teach the claimed, wherein determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account (Kulkarni: Column 16, lines 8-17, software tools can be applied to content creation associated with user account corresponds to a content creator segment based on the predicted activity event 206. Column 33, lines 50-67, Fig. 9 illustrates computing device 900 ( content management system may comprise one or more device such as 900) and the communication interface 908, 910 ( to collect data signals to be analyzed)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Regarding Claim 3, Dicklin in view of Doshi, further in view of Kulkarni teach the computer-implemented method of claim 2. Dicklin further teaches, wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises processing the data signals collected by the connectors as a prompt through a large language model ( Dicklin: Para.[0050], Fig. 1, the real-time anticipation subsystem 116 may be configured to predict, in real-time, a user's interests and generate generative MLM prompts. Para.[0054], the generative MLM 120 may include a machine learning model configured to predict the next word and may include a transformer- based large language model (LLM). Para. [0194], network interface 1408).
Claim 11 is system claim performing the steps in method claim 3 above and as such, claim 11 is similar in scope and content to claim 3 and therefore, claim 11 is rejected under similar rationale as presented against claim 3 above.
Regarding Claim 4, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises locating sample content from a repository and modifying the sample content based on the client device activity and the activity history.
However, Kulkarni does teach the claimed, wherein generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises locating sample content from a repository and modifying the sample content based on the client device activity and the activity history ( Kulkarni: Column 23, lines 44-66, Fig. 5, user activity sequence system 104 may sample raw event data for a percentage of user accounts of the content management system 103 or may sample raw event data for a predetermined time period. At pre-processing act 502, the user activity sequence system 104 may filter raw event data ( remove unreliable data or filter out duplicates)) .
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Claim 18 is non-transitory computer-readable storage medium claim performing the steps in method claim 4 above and as such, claim 18 is similar in scope and content to claim 4 and therefore, claim 18 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 5, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, further comprising: saving the content item as modified with the one or more generated modifications; and providing, for display via
However, Kulkarni does teach the claimed, further comprising: saving the content item as modified with the one or more generated modifications ( Kulkarni: Column 12, lines 33-47, user activity sequence system 104 receives an indication that edits to a new/opened document at the client device 106a have been saved in the content management system 103);
and providing, for display via ( Kulkarni: Column 7, lines 50-57, client applications can present or display information to respective users associated with the client devices, including information or content responsive to a predicted activity event such as view, annotate, edit, send, or share a digital content item).0065 , bank
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Claim 12 is system claim performing the steps in method claim 5 above and as such, claim 12 is similar in scope and content to claim 5 and therefore, claim 12 is rejected under similar rationale as presented against claim 5 above.
Regarding Claim 6, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, wherein: detecting the client device activity in relation to the content item comprises detecting that the user account begins a composite action; determining the input prediction comprises determining, based on the user account beginning the composite action, that the content item relates to the composite action; generating the predicted content to add to the content item comprises generating draft content for a first draft of the content item; and inserting the predicted content within the content item comprises inserting the draft content into a template file for the content item.
However, Kulkarni does teach the claimed, wherein: detecting the client device activity in relation to the content item comprises detecting that the user account begins a composite action ( Kulkarni: Column 29, lines 46-58, Fig. 8,act 802 shows identification of sequence of activity events ( composite action) associated with a user account ( or a group of user accounts) of a content management system);
determining the input prediction comprises determining, based on the user account beginning the composite action, that the content item relates to the composite action ( Kulkarni: Column 30, lines 28-47, Fig. 8, at 808, performing the action based on the predicted activity event, display on a first and second client device associated with the first and second user account, a first and second action suggestion related to a digital content item accessible by the group of user accounts, wherein the action suggestions are based on the first predicted activity event );
generating the predicted content to add to the content item comprises generating draft content for a first draft of the content item ( Kulkarni: Column 13, lines 20-22, generating draft content);
and inserting the predicted content within the content item comprises inserting the draft content into a template file for the content item ( Kulkarni: Column 16, lines 22-33, the user activity sequence system 104 may auto populate ( inserting) one or more entry fields of a template with the previous documents/ mining digital content data);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Claim 13 is system claim performing the steps in method claim 6 above and as such, claim 13 is similar in scope and content to claim 6 and therefore, claim 13 is rejected under similar rationale as presented against claim 6 above.
Claim 19 is non-transitory computer-readable storage medium claim performing the steps in method claim 6 above and as such, claim 19 is similar in scope and content to claim 6 and therefore, claim 19 is rejected under similar rationale as presented against claim 6 above.
Regarding Claim 9, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, further comprising: determining that a composite action of the user account is paused or complete; generating, for additional user accounts of the content management system, an update notification indicating a completion status for the composite action; and providing, without additional interaction by the user account, the update notification to the additional user accounts for display via user interfaces of client devices associated with the additional user accounts.
However, Kulkarni does teach the claimed, further comprising: determining that a composite action of the user account is paused or complete ( Kulkarni: Column 15, lines 14-22, the predicted activity event 206 may indicate that a user account has completed work on digital content items and/or has moved onto new/different digital content items);
generating, for additional user accounts of the content management system, an update notification indicating a completion status for the composite action ( Kulkarni: Column 16, lines 34-50, the user activity sequence system 104 can generate one or more activity highlights based on the predicted activity events 206, may prioritize and/or focus on highlights ( such as completion) of various user account activities performed based on the particular predicted activity
event 206. Column 5, lines 3-9, sending notification);
and providing, without additional interaction by the user account, the update notification to the additional user accounts for display via user interfaces of client devices associated with the additional user accounts ( Kulkarni: Column 30, lines 28-47, displaying on client devices different suggestions/updates related to a digital content item accessible by the group of user accounts).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Claim 15 is system claim performing the steps in method claim 9 above and as such, claim 15 is similar in scope and content to claim 9 and therefore, claim 15 is rejected under similar rationale as presented against claim 9 above.
Regarding Claim 17, Dicklin in view of Doshi teach the non-transitory computer-readable storage medium of claim 16. Dicklin in view of Doshi fail to explicitly teach the claimed, wherein: determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account.
However, Kulkarni does teach the claimed, wherein: determining the input prediction defining the one or more predicted client device inputs comprises analyzing data signals collected by connectors from software tools associated with the user account (Kulkarni: Column 16, lines 8-17, software tools can be applied to content creation associated with user account corresponds to a content creator segment based on the predicted activity event 206. Column 33, lines 50-67, Fig. 9 illustrates computing device 900 ( content management system may comprise one or more device such as 900) and the communication interface 908, 910 ( to collect data signals to be analyzed)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Dicklin further teaches, and generating the predicted content to add to the content item in response to the one or more predicted client device inputs comprises processing the data signals collected by the connectors as a prompt through a large language model ( Dicklin: Para.[0050], Fig. 1, the real-time anticipation subsystem 116 may be configured to predict, in real-time, a user's interests and generate generative MLM prompts. Para.[0054], the generative MLM 120 may include a machine learning model configured to predict the next word and may include a transformer- based large language model (LLM). Para. [0194], network interface 1408).
Regarding Claim 20, Dicklin in view of Doshi, further in view of Kulkarni teach the non-transitory computer-readable storage medium of claim 19. Kulkarni further teaches, wherein the instructions, when executed by the at least one processor, further cause the computing device to provide the template file with the draft content for the first draft of the content item for display via a user interface of the client device ( Kulkarni: Column 13, lines 20-22, generating draft content. Column 16, lines 22-33, the user activity sequence system 104 may auto populate one or more entry fields of a template with the previous documents/ mining digital content data. Column 30, lines 28-47, displaying on client devices).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Claims 7, 8, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Dicklin et al. ( US 20250103867 A1), hereinafter referenced as Dicklin, in view of Doshi et al. ( WO 2023002496 A1), hereinafter referenced as Doshi, further in view of Kulkarni et al. (US 11567812 B2), hereinafter referenced as Kulkarni, further in view of Mansour et al. ( US 20250005263 A1), hereinafter referenced as Mansour.
Regarding Claim 7, Dicklin in view of Doshi teach the computer-implemented method of claim 1. Dicklin in view of Doshi fail to explicitly teach the claimed, further comprising: providing, for display via the client device, the content item comprising the predicted content; receiving at least one user interface element associated with a different content item.
However, Kulkarni does teach the claimed, further comprising: providing, for display via the client device, the content item comprising the predicted content ( Kulkarni: Column 7, lines 50-57, client applications 108 can present or display information to respective users associated with the client devices 106, including information or content responsive to a predicted activity event) ;
receiving at least one user interface element associated with a different content item ( Kulkarni: Column 31, lines 43-49, receiving/generating digital reminder ( user interface element) associated with different content items);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Dicklin in view of Doshi, further in view of Kulkarni, while teaching the method of claim 7, fail to explicitly teach the claimed, and based on determining that the at least one user interface element is unrelated to the content item, suppressing the at least one user interface element from display via the client device.
However, Mansour does teach the claimed, and based on determining that the at least one user interface element is unrelated to the content item, suppressing the at least one user interface element from display via the client device ( Mansour: Para.[0251], Fig. 5, the edit control 512 ( user interface element) in the graphical user interface 500 get suppressed, if the user does not have a permissions profile that allows edit permissions with respect to the currently displayed electronic document or page ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mansour’s teaching of systems and methods for automatically generating content, structuring user-generated content, and/or generating structured content in collaboration platforms, into the system and method, taught by Dicklin in view of Doshi, further in view of Kulkarni, because, this would improve the efficiency of an organization by establishing a collaborative work environment with access to, a suite of discrete software platforms or services to facilitate cooperation and completion of work.(Mansour, Para.[0003],[0004]).
Regarding Claim 8, Dicklin in view of Doshi, further in view of Kulkarni, further in view of Mansour teach the computer-implemented method of claim 7. Kulkarni further teaches, further comprising: determining that a composite action of the user account is paused or complete ( Kulkarni: Column 15, lines 14-22, the predicted activity event 206 may indicate that a user account has completed work on a digital content item and/or has moved onto a new/different digital content item);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in view of Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Dicklin further teaches and generating a summary content item comprising a summary of the at least one user interface element ( Dicklin: Para.[0183], the user interface may display a context menu, and the context menu may include the selectable option of "Summarize this document”).
Regarding Claim 14, Dicklin in view of Doshi teach the system of claim 10. Dicklin in view of Doshi fail to explicitly teach the claimed, wherein the instructions, when executed by the at least one processor, further cause the system to: receive one or more user interface elements associated with a different content item.
However, Kulkarni does teach the claimed, wherein the instructions, when executed by the at least one processor, further cause the system to: receive one or more user interface elements associated with a different content item ( Kulkarni: Column 31, lines 43-49, receiving/generating digital reminder ( user interface element) associated with different content items);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Kulkarni’s teaching of natural language model to determine a most probable candidate sequence of tokens and thereby generate a predicted user activity, into the system and method, taught by Dicklin in Doshi, because, this would improve the accuracy of predicted activity events for different users of a content management system.(Kulkarni [ Column 5, lines 30-65]).
Dicklin in view of Doshi, further in view of Kulkarni, while teaching the claim 14, fail to explicitly teach the claimed, based on determining that the one or more user interface elements are unrelated to the content item, suppress the one or more user interface elements from display via the client device;
However, Mansour does teach the claimed, based on determining that the one or more user interface elements are unrelated to the content item, suppress the one or more user interface elements from display via the client device ( Mansour: Para.[0251], Fig. 5, the edit control 512 ( user interface element) in the graphical user interface 500 get suppressed, if the user does not have a permissions profile that allows edit permissions with respect to the currently displayed electronic document or page );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Mansour’s teaching of systems and methods for automatically generating content, structuring user-generated content, and/or generating structured content in collaboration platforms, into the system and method, taught by Dicklin in view of Doshi, further in view of Kulkarni, because, this would improve the efficiency of an organization by establishing a collaborative work environment with access to, a suite of discrete software platforms or services to facilitate cooperation and completion of work.(Mansour, Para.[0003],[0004]).
Dicklin further teaches, and generate a summary content item comprising a summary of the one or more user interface elements ( Dicklin: Para.[0183], the user interface may display a context menu, and the context menu may include the selectable option of "Summarize this document”).
Conclusion
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/NADIRA SULTANA/Examiner, Art Unit 2653
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