DETAILED ACTION
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 21-40 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, and 7 of U.S. Patent No. 12,192,020 in view of Dunne et al. (US 2019/0095844), hereinafter Dunne, in view of Rangarajan et al. (U.S. 10,033,756), hereinafter Rangarajan, in view of Dotan-Cohen et al. (U.S. 10,623,364), hereinafter Dotan-Cohen
With respect to Claim 1,
Prior Patent – 12,192,020
Application – 18/973,769
1. A computer-implemented machine learning method for improving a collaboration environment, the method comprising:
21. A computer-implemented machine learning method for improving a collaboration environment, the method comprising:
receiving text data for one or more users of the collaboration environment;
receiving user data and a statement, wherein the statement is derived from text data;
generating a statement by partitioning the text data;
No corresponding limitation
determining an act associated with the statement using a trained machine learning model;
determining an act associated with the statement;
generating an actor list using at least the statement and the act;
and generating an actor list from the plurality of actors using at least the thread.
and determining at least one actionable item for an actor from the actor list,
No corresponding limitation
wherein the at least one actionable item is sent directly to a display module without storage in a database.
No corresponding limitation
further comprising generating a thread using at least the statement and the act; and wherein generating the actor list includes generating the actor list using the at least the statement, the act, and the thread (claim 3)
determining a thread based on the statement, the act, and the user data, wherein the thread is indicative of communications between a plurality of actors;
With respect to Claim 21, Although the conflicting claims are not identical, they are not patentably distinct from each other because the claim is redrafted from a method to an apparatus claim, adding necessary hardware for the apparatus to function, and changing the scope of the new independent claims by adding some of the limitations from Claim 3 of the parent case to the claims of the current case.
It has been held that the omission of an element and its function is an obvious expedient if the remaining elements perform the same function as before. In re karlson, 136 USPQ 184 (CCPA). Also, note Ex Parte Raine 168 USPQ 376 (bd. App 1969); omission of a reference whose function is not needed would be obvious to one skilled in the art.
With respect to Claim 22, the parent patent fails to explicitly teach wherein the statement is further derived using security data.
Rangarajan teaches the statement is further derived using security data (Column 18, lines 10-20 “As another example, the action statement (“actionStatement”: “additionally log details of trust level computation in an external repository”) instructs trust management engine 13 to additionally record all details of the trust level computation in log datastore 17 in addition to log datastore 20 (e.g., including the integrity measurements, security assessments, the evaluation of rule statements, etc.).”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing of the application to combine the invention of to combine the system of the parent patent with the system of Rangarajan in order to incorporate security information in the derivation as it would increase the security of the overall system.
With respect to Claim 23, the parent patent fails to explicitly teach wherein the user data comprises text shared in another chat
Dunne from the same or similar field of endeavor teaches the user data comprises text shared in another chat (Dunne, ¶ [0077], text in a first chat channel, and ¶ [0034], “However, the given user typically does not know who is available, and more importantly, who is most available and most qualified. The illustrative embodiments solve this computer-centric issue by analyzing chat activity in the chat channel, determining which user is the most qualified and most available in real time, and then transmitting an automatic message to that most qualified and most available user requesting that person to perform the task.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing of the application to combine the invention of to use text data from a chat to prompt an trained AI models to assign tasks to users instead of the user directly querying an AI as it allows better incorporation of the AI into daily usage.
With respect to Claim 24, the parent patent fails to explicitly teach wherein the user data comprises an organizational chart.
Dotan-Cohen teaches wherein the user data comprises an organizational chart (column 20, lines 20-40, “As examples, the system may identify the user from text of the message body and/or an attachment to the user message, based on another user (e.g., a send or recipient of the user message) forwarding the user message to the user, or otherwise based on the user being associated with another user (e.g., a known assistant, coworker, boss, etc.). The user could be identified in an organizational chart as being associated with the other user.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing of the application to combine the invention of to use an organizational chart in the system of the parent patent as taught by Dotan-Cohen as using an organizational chart will better allow the assignment as it is a list of the people in the organization that would be able to complete tasks.
With respect to Claim 25, the claim corresponds to Claim 7 of the parent patent.
With respect to Claim 26, the claim corresponds to Claim 7 of the parent patent.
With respect to Claim 27, the claim readds limitation from Claim 1 of the parent patent as shown above.
With respect to Claims 28-34, the claim are the machine learning system corresponding to the computer-implemented machine learning method of claims 21-27.
With respect to Claims 35-40, the claims are the non-transitory, computer-readable medium corresponding to the computer-implemented machine learning method of claims 21-27
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, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 21, 23, 25-28, 30, 32-35, 37, 39 and 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dunne et al. (US 2019/0095844), hereinafter Dunne, in view of Min et al. (US 2019/0235916), hereinafter Min.
With respect to Claim 21, Dunne in teaches a computer-implemented machine learning method for improving a collaboration environment (Dunne, ¶ 0101, computer-readable program instructions);, the method comprising:
receiving user data and a statement, wherein the statement is derived from text data (Dunne, ¶ [0077], text in a first chat channel, and ¶ [0034], “However, the given user typically does not know who is available, and more importantly, who is most available and most qualified. The illustrative embodiments solve this computer-centric issue by analyzing chat activity in the chat channel, determining which user is the most qualified and most available in real time, and then transmitting an automatic message to that most qualified and most available user requesting that person to perform the task.”)
determining an act associated with the statement (Dunne, ¶ 0021, 0043, the illustrative embodiments may employ a learning module which can infer with precision a probabilistic determination of whether an individual can complete a given task.)
Dunne fails to explicitly teach determining a thread based on the statement, the act, and the user data, wherein the thread is indicative of communications between a plurality of actors; and generating an actor list from the plurality of actors using at least the thread.
Min teaches determining a thread based on the statement, the act, and the user data, wherein the thread is indicative of communications between a plurality of actors; and generating an actor list from the plurality of actors using at least the thread (Min, ¶ 0080, “Natural language processing may include matching one or more parts of the second user input 518 to key phrases or to predetermined tasks (e.g., “Send text message,” “Add a reminder,” and the like) to ascertain the subject matter of the task. The natural language processing may yield multiple subject matters or multiple user-initiated task-requests from the second user input 518, and in response, the VA 140 divides the second user input 518 according to the quantity of user-initiated task-requests.”)
The manner of combining the inventions of Dunne and Min is to use the natural language processing and task matching of Min in the system of Dunne. As the chat of Dunne would contain multiple inputs, matching them to other statements, turning them into a thread of multiple tasks as taught by Min in order to better determine the task and action and who to perform it in the system of Dunne.
With respect to Claim 23, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne teaches wherein the user data comprises text shared in another chat (Dunne, ¶ [0077], text in a first chat channel, and ¶ [0034], “However, the given user typically does not know who is available, and more importantly, who is most available and most qualified. The illustrative embodiments solve this computer-centric issue by analyzing chat activity in the chat channel, determining which user is the most qualified and most available in real time, and then transmitting an automatic message to that most qualified and most available user requesting that person to perform the task.”)
With respect to Claim 25, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne fails to explicitly teach wherein determining the thread based on the statement, the act and the user data includes determining the thread using a trained machine learning model
Min teaches wherein determining the thread based on the statement, the act and the user data includes determining the thread using a trained machine learning model (Min, ¶ 0080, “Natural language processing may include matching one or more parts of the second user input 518 to key phrases or to predetermined tasks (e.g., “Send text message,” “Add a reminder,” and the like) to ascertain the subject matter of the task. The natural language processing may yield multiple subject matters or multiple user-initiated task-requests from the second user input 518, and in response, the VA 140 divides the second user input 518 according to the quantity of user-initiated task-requests.”)
The manner of combining the inventions of Dunne and Min is to use the natural language processing and task matching of Min in the system of Dunne. As the chat of Dunne would contain multiple inputs, matching them to other statements, turning them into a thread of multiple tasks as taught by Min in order to better determine the task and action and who to perform it in the system of Dunne.
With respect to Claim 26, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne teaches wherein determining the act includes determining the act using a trained machine learning model (Dunne, ¶ [0035], “vector modeling, in accordance with an illustrative embodiment. Vectors 200 shown in FIG. 2 are used to evaluate which user is most available and most qualified to perform a task, as described above with respect to FIG. 1.”)
With respect to Claim 27, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne teaches wherein generating the actor list includes generating the actor list further using the statement and the act (Dunne, ¶ [0077], text in a first chat channel, and ¶ [0034], “However, the given user typically does not know who is available, and more importantly, who is most available and most qualified. The illustrative embodiments solve this computer-centric issue by analyzing chat activity in the chat channel, determining which user is the most qualified and most available in real time, and then transmitting an automatic message to that most qualified and most available user requesting that person to perform the task.”)
With respect to Claims 28, 30, 32-34, the claim are the machine learning system corresponding to the computer-implemented machine learning method of claims 21, 23 and 25-27.
With respect to Claims 35, 37, 39 and 40, the claims are the non-transitory, computer-readable medium corresponding to the computer-implemented machine learning method of claims 21, 23 and 25-27.
Claim(s) 22, 29 and 36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dunne et al. (US 2019/0095844), hereinafter Dunne, in view of Min et al. (US 2019/0235916), hereinafter Min, in view of Rangarajan et al. (U.S. 10,033,756), hereinafter Rangarajan.
With respect to Claim 22, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne in view of Min fails to explicitly teach wherein the statement is further derived using security data.
Rangarajan teaches the statement is further derived using security data (Column 18, lines 10-20 “As another example, the action statement (“actionStatement”: “additionally log details of trust level computation in an external repository”) instructs trust management engine 13 to additionally record all details of the trust level computation in log datastore 17 in addition to log datastore 20 (e.g., including the integrity measurements, security assessments, the evaluation of rule statements, etc.).”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing of the application to combine the invention of to combine the system of Dunne with the system of Rangarajan in order to incorporate security information in the derivation as it would increase the security of the overall system.
With respect to Claim 29, the claim is the machine learning system corresponding to the computer-implemented machine learning method of claim 22.
With respect to Claim 36, the claim is the non-transitory, computer-readable medium corresponding to the computer-implemented machine learning method of claim 22.
Claim(s) 24, 31, and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dunne et al. (US 2019/0095844), hereinafter Dunne, in view of Min et al. (US 2019/0235916), hereinafter Min, in view of Dotan-Cohen et al. (U.S. 10,623,364), hereinafter Dotan-Cohen.
With respect to Claim 24, Dunne in view of Min teaches the computer-implemented method of claim 21, Dunne in view of Min fails to explicitly teach wherein the user data comprises an organizational chart.
Dotan-Cohen teaches wherein the user data comprises an organizational chart (column 20, lines 20-40, “As examples, the system may identify the user from text of the message body and/or an attachment to the user message, based on another user (e.g., a send or recipient of the user message) forwarding the user message to the user, or otherwise based on the user being associated with another user (e.g., a known assistant, coworker, boss, etc.). The user could be identified in an organizational chart as being associated with the other user.”)
Therefore, it would have been obvious to one of ordinary skill in the art prior to the filing of the application to combine the invention of to use an organizational chart in the system of Dunne as taught by Dotan-Cohen, as Dunne teaches determining who to assign a task, and organizational chart will better allow the assignment as it is a list of the people in the organization that would be able to complete tasks.
With respect to Claim 31, the claim is the machine learning system corresponding to the computer-implemented machine learning method of claim 24.
With respect to Claim 38, the claim is the non-transitory, computer-readable medium corresponding to the computer-implemented machine learning method of claim 24.
Conclusion
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/ANGELA NGUYEN/Primary Examiner, Art Unit 2479
