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 .
Status of The Claims
Claims 1-2, 4-12 and 14-20 are pending for examination.
Claims 1 and 11 are independent Claims.
Claims 1-2, 4-12 and 14-20 are rejected under 35 U.S.C. §§ 112(a), 112(b) and 103.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-2, 4-12 and 14-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Independent Claims 1 and 11 recites: “
generating, by one or more processors of the computing device, a multi-modal context vector comprising features extracted from considered purchases of the user, gifts of the user, signals of the user, habits of the user, work of the user, and/or family of the user, wherein the features are generated using a non-generic feature- extraction engine stored in memory;
executing, by the one or more processors, using an artificial intelligence task-ranking engine configured to fuse the task input with the multi-modal context vector to generate a machine-optimized task-execution graph that is not manually executable;
analyzing, by the one or more processors, the task input in combination with the user- specific data and the machine-optimized task-execution graph to generate a set of task-completion suggestions using at least one machine learning model;
updating, by the computing device, the machine-optimized task-execution graph based on the refined set of task-completion suggestions;”.
There are no supports for these limitations (with underlined text for highlighting) in the Applicant’s Specification.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, 4-12 and 14-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent Claims 1 and 11 recites the limitation "the refined set of task-completion suggestions" in “updating …” limitation. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 1-2, 5-12 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal (U.S. Patent Application Publication No. 2018/0109920 hereinafter Aggarwal) in view of Wang (U.S. Patent 9,299,039 hereinafter Wang) in further view of Frazer et al. (U.S. 2010/0049538 hereinafter Frazer).
As Claim 1, Aggarwal teaches a computer-implemented method for providing task management assistance for a user comprising:
anticipating (Aggarwal (¶0047 line 12-17), a machine learning model is used for anticipating the suggested action) at least one user task (Aggarwal (¶0053 line 6-11, ¶0039 line 4-8), information indicating user task is extracted from contact lists, to-do lists, user’s calendar downloaded software. User’s to-do list indicates a task that user wants to perform.) according to feedback from the user (Aggarwal (¶0048 line 5-7), model trainer trains the model based on user’s confirmation and/or denial of past suggested actions), wherein the feedback is configured to update task suggestions via a machine learning model that incorporates a feedback loop (Aggarwal (¶0048 line 5-7), model trainer trains the model based on user’s confirmation and/or denial of past suggested actions);
transmitting the task notification to a user device via the network interface (Aggarwal (¶0045 line 10-12, fig. 2 item 205A-205C), one or more task completion suggestions are displayed. Fig. 2 shows option 205 to call a dry cleaner. When user clicks the button 205C, the system initiates the action to call dry cleaner).
Aggarwal may not explicitly disclose:
receiving, via a network interface of a computing device, a task input from a user, the task input comprising at least one task description;
accessing, by one or more processors of the computing device, user-specific data from at least one of a third-party application or a user profile stored in computer-readable storage media;
refining the set of task-completion suggestions according to one or more contextual factors selected from a group consisting of task location, task priority, prior user behavior, and temporal proximity of tasks;
generating a task notification comprising at least one of a reminder, a suggested task order, or a hyperlink to an external resource for completing a task in the set of task-completion suggestions; and
Wang teaches:
receiving, via a network interface of a computing device, a task input from a user, the task input comprising at least one task description (Wang (col. 6 line 22-28), user inputs task description for Task List server 110);
accessing, by one or more processors of the computing device, user-specific data from at least one of a third-party application or a user profile stored in computer-readable storage media (Wang (col. 5 line 13-15 and 21-24), Task List server includes databases comprising a user profile database. The user profile database can include user information, user-supplied preference information …);
refining the set of task-completion suggestions according to one or more contextual factors selected from a group consisting of task location, task priority, prior user behavior, and temporal proximity of tasks (Wang (col. 7 line 31-41), actions is refined based on pre-conditions of the action, proceeding actions, user-defined preference);
generating a task notification comprising at least one of a reminder (Wang (col. 9 line 33-36), Task list server suggests booking a trip for an anniversary), a suggested task order, or a hyperlink to an external resource for completing a task in the set of task-completion suggestions (Wang (col. 7 line 7-17, fig. 7A-7B), task list server 110 identifies and provides all necessary information to execute actions as part of the task list. Fig. 7B shows hyperlink to complete the task suggestions); and
It would have been obvious for one of ordinary skill in the art before the effective filing day of the invention to have modified to-do list Aggarwal with an input task list of Wang with a reasonable expectation of success. The motivation to would be to conveniently allow “the task management services may search an online database over a network connection or a local database to enable the user to complete the task” (Wang (col. 1 line 59-62)).
Aggarwal in view of Wang may not explicitly disclose:
generating, by one or more processors of the computing device, a multi-modal context vector comprising features extracted from considered purchases of the user, gifts of the user, signals of the user, habits of the user, work of the user, and/or family of the user, wherein the features are generated using a non-generic feature- extraction engine stored in memory;
executing, by the one or more processors, using an artificial intelligence task-ranking engine configured to fuse the task input with the multi-modal context vector to generate a machine-optimized task-execution graph that is not manually executable;
analyzing, by the one or more processors, the task input in combination with the user- specific data and the machine-optimized task-execution graph to generate a set of task-completion suggestions using at least one machine learning model;
updating, by the computing device, the machine-optimized task-execution graph based on the refined set of task-completion suggestions;
Frazer teaches:
generating, by one or more processors of the computing device, a multi-modal context vector (Frazer (¶0668, fig. 23), multiple vectors represent individual customers, events and various time) comprising features extracted from considered purchases of the user, gifts of the user, signals of the user, habits of the user, work of the user, and/or family of the user, wherein the features are generated using a non-generic feature- extraction engine stored in memory (Frazer (¶0043 line 2-9, ¶0044 line 4-9), system considers user’s purchase in generating insights between entities. Frazer (¶0122-¶0123), set of nodes are built (non-generic feature-extraction engine) for customers. Edges represent the strength of relationship between pairs of nodes);
executing, by the one or more processors, using an artificial intelligence task-ranking engine configured to fuse the task input with the multi-modal context vector to generate a machine-optimized task-execution graph (Frazer (¶0076 line 7-10, ¶0077-¶0082), customer transaction data are converted into structural format. System projects decision on individual or groups transactions or customers) that is not manually executable (Frazer (¶0681 last 12 lines), instructions cause the machine to automatically execute);
analyzing, by the one or more processors, the task input (Frazer (¶0573, ¶0574), current purchase or recent purchase) in combination with the user- specific data (Frazer (¶0575), entire history of market basket) and the machine-optimized task-execution graph to generate a set of task-completion suggestions using at least one machine learning model (Frazer (¶0602), “following discussion various possible business objectives and ways to post-process or adjust the propensity scores obtained from the recommendation engines to reflect those business objectives are presented. The post-processing combines the recommendation scores with adjustment coefficients. Based on how these adjustment coefficients are derived, there are two broad types of score adjustments”);
updating, by the computing device, the machine-optimized task-execution graph based on the refined set of task-completion suggestions (Frazer (¶0046 line 7-11, fig. 1 item 116), “The method 100 also includes feeding back information regarding the occurrence of the event 116. This information is useful in determining or tweaking the relationships or insights between the entities associated with the data as well as predicting the likelihood of occurrence of a future event.”);
It would have been obvious for one of ordinary skill in the art before the effective filing day of the invention to have modified a machine learning of Aggarwal in view Wang with a machine learning graph taught by Frazer with reasonable expectation of success. The motivation to would so that “the interests of an individual consumer can be determined, then it is believed that advertising and promotions related to these interests will be more successful in obtaining a positive consumer response, such as purchases of the advertised products or services” (Frazer (¶0002)).
As Claim 2, besides Claim 1, Aggarwal in view of Wang teaches wherein the task-completion suggestion is based on one or more of a type of task, user information, user feedback, task location, and a priority designation (Aggarwal (¶0053 line 6-11, ¶0039 line 4-8), information indicating user task from (contact lists, to-do lists, user’s calendar downloaded software). User’s to-do list indicates a task that user wants to perform.).
As Claim 5, besides Claim 1, Aggarwal in view of Wang in further view of Yu teaches wherein information indicative of a user task is received from at least one of a third-party application and user input (Aggarwal (¶0053 line 6-11, ¶0039 line 4-8), information indicating user task is extracted from contact lists, to-do lists, user’s calendar downloaded software. User’s to-do list indicates a task that user wants to perform.).
As Claim 6, besides Claim 1, Aggarwal in view of Wang teaches wherein the task-completion suggestion determination utilizes machine learning methods (Aggarwal (¶0047 line 12-17), a personal assistant (machine learning model) is used to determined task completion suggestion.).
As Claim 7, besides Claim 1, Aggarwal in view of Wang teaches further comprising queuing the at least one user task to determine an order for determining the task-completion suggestion (Aggarwal (¶0057 line 8-10), user tasks is queued based on the most preferable from user’s references.).
As Claim 8, besides Claim 1, Aggarwal in view of Wang teaches wherein queueing is based on one more of an indicated priority, time, date, or user information of the task (Aggarwal (¶0057 line 8-10), user tasks are queued based on the most preferable from user’s references.).
As Claim 9, besides Claim 1, Aggarwal in view of Wang teaches wherein the notification is a push notification (Aggarwal (¶0045 line 10-12, fig. 2 item 205A-205C), one or more task completion suggestions are displayed. Fig. 2 shows option 205 to call a dry cleaner. When user clicks the button 205C, the system initiates the action to call dry cleaner).
As Claim 10, besides Claim 1, Aggarwal in view of Wang teaches wherein method comprises:
receiving feedback from the user (Aggarwal (¶0048 line 5-7), user’s past activities collected by the model trainer); and
using the feedback to determine a new task-completion suggestion (Aggarwal (¶0048 line 22-25), new task-completion suggestions are determined based on the user’s past activities).
As Claims 11-12 and 15-19, Claims 11-12 and 14-19 are in the same scope as Claim 1-2 and 4-9 and are rejected for the same reasons.
As Claim 20, besides Claim 11, Aggarwal in view of Ballard in further view of Yu teaches wherein the mobile application is operation in a mobile phone (Aggarwal (¶0017 line 4-5), the application is executed on a mobile phone).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aggarwal and Wang in view of Frazer in further view of Toulotte (U.S. Patent Application Publication No. 2007/0219874 hereinafter Toulotte).
As Claim 4, besides Claim 1, Aggarwal in view of Wang in further view of Frazer may not explicitly disclose:
wherein assistance information includes one or more of an informative article, a purchase option, a service provider, a reminder, advice, and a purchase suggestion
Toulotte teaches wherein assistance information includes one or more of an informative article, a purchase option, a service provider, a reminder, advice, and a purchase suggestion (Toulotte (¶0074 line 6-11), assistant information includes a link to an online store.).
It would have been obvious for one of ordinary skill in the art before the effective filing day of the invention to have combined Aggarwal in view of Wang in further view of Frazer’s assistance information instead be Toulotte’s gift suggestion with a reasonable expectation of success. The motivation to replace Aggarwal’s assistance information with Toulotte’s gift suggestion would be to effectively remind people of upcoming events and provide efficient means for the people to purchase items for the upcoming events (Toulotte (¶0003)).
As Claim 14, Claim 14 is in the same scope as Claim 4 and is/are rejected for the same reasons.
Response to Arguments
Rejections under 35 U.S.C. §101:
Applicants argue that current amendment(s) recite specific technical architecture (last 2 paragraphs of page 7 in the remarks).
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Applicants’ arguments are persuasive; therefore, 35 U.S.C. §101 rejections are respectfully withdrawn.
Rejections under 35 U.S.C. § §103:
As Claim 1 and 11, Applicants argue that cited references does not disclose “multi-modal feature vectorization” (fourth and fifth paragraph of page 8 in the remarks).
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Applicants’ arguments are not persuasive because the limitation(s) are not supported by the Applicant’s specification. Besides, Applicants’ arguments are also moot because new reference Frazer teaches the amended limitation(s).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147