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 .
Current status/standing of the claims.
Claims 1-20 (Cancelled).
Independent claims 21, 30 and 35 (New).
Claims 22-29, 31-34 and 36-40 are dependent on the cancelled claims and therefore are not considered/examined at this time.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 21, 30 and 35 are provisionally rejected on the ground of provisional nonstatutory double patenting as being unpatentable over claim 1 of co-pending Application No. 18/436,497. Although the claims at issue are not identical, they are not patentably distinct from each other as it is representatively demonstrated below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
The instant application: 18/582,292
Co-pending Application: 18/436,497
21. (New) A method comprising:
receiving, by a computing device via a user interface, a user input associated with an application;
receiving, by the computing device via a data interface, stored information associated with the user, wherein the stored information includes learned user preferences;
determining, via an artificial intelligence (Al) model based on the user input and the stored information, one or more actions;
wherein, using learned user information and preferences and other data, the Al native operating system wrapper predicts desired application behavior based on the user information, preferences, and other data, and the Al based model provides user information and sensor data as inputs and predicts desired application behavior based on those inputs, and then automates application tasks based on this prediction;
performing the one or more actions on the application; and
transmitting output from the application to the user interface.
1. (Currently Amended) A method comprising:
receiving, by a computing device comprising a processor and a non-transitory memory via a user interface, a user input associated with an application;
receiving, by the computing device via a data interface, stored information associated with a user;
determining, via an artificial intelligence (Al) model based on the user input and the stored information, one or more actions, the determining including evaluating a user defined policy specifying permissible actions for the application;
performing the one or more actions on the application; and
transmitting output from the application to the user interface.
Regarding the additional feature in Italic font, the new references address such feature.
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(s) 21, 30 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Bueche in view of Hwang et al (US 2020/0349938) OR Pandya et al (US 2022/0414527).
Claims 21 and 30, Bueche teaches a method and a computing device comprising:
receiving, by a computing device via a user interface, a user input associated with an application; (The analytic server 110 may receive a request to schedule an appointment from a customer electronic device 150, [0018]);
receiving, by the computing device via a data interface, stored information associated with the user, (the analytic server may display a graphical user interface (GUI) requesting the customers or service providers to enter required information. The analytic server may store the entered information into customer database 130a or the service provider database 130b., [0021]).
wherein the stored information includes learned user preferences; (The analytic server may utilize the historical customer data and customer appointments from existing customers and train the historical data to learn hidden patterns in the historical data to build a predictive model…For example, based on the historical data on existing customers, the analytic server may learn that women at age 30 to 40 prefer to go to salon Store A in a certain area, [0047 OR The analytic server may also learn the ingredients the customer likes for the pizza. When the analytic server receives a request asking for ordering a pizza, the analytic server may determine that pizza restaurant B is a potential service provider, [0048]);
determining, via an artificial intelligence (Al) model based on the user input and the stored information, one or more actions; (the analytic server may execute an artificial intelligence model to predict the user preferences and service request attributes…see Abstract, [0046-0048] per stored data, [0021-0022]).
wherein, using learned user information and preferences and other data, the Al native operating system wrapper predicts desired application behavior based on the user information, preferences, and other data, and the Al based model provides user information and sensor data as inputs and predicts desired application behavior based on those inputs, and then automates application tasks based on this prediction;
(As demonstrated in the previous step, Bueche utilizes an analytic server as a based/standardized model to apply predictive modeling or machine learning techniques, including but are not limited to, neural networks (NNs), support vector machine (SVMs), decision trees, k-nearest neighbor, linear and logistic regression, clustering, association rules, and scorecards, to learn the patterns hidden in historical data, [0046-0048], Bueche does not specify the use and type of AI-based model as required by the current Specs, [0046]:… “the AI based model may be a deep learning based model. In some embodiments, the AI based model may include machine learning components and/or heuristics used in making predictions”.
Examiner wishes to provide additional references addressing the claim requirement.
Hwang, via Fig. 3, teaches an operation of generating an event-emotion-based response model (or an AI model) through deep learning, according to an embodiment, [0088-0101].
Pandya teaches, “the disclosure provides a system for offering predictive and automated guidance to a user of software, the system comprising one or more computers and one or more storage devices storing instructions that may be operable, when executed by the one or more computers, to cause the one or more computers to: (1) automatically collect user activity data related to performing a first in-app task from a plurality of computing devices each running the software over a period of time that occurs prior to a first time; (2) train a deep learning model configured to identify the first in-app task using the collected user activity data; (3) receive, from a first user device, first data representing in-app behavior for a first user at a first time; (4) determine, via the deep learning model, that the first data corresponds to a high likelihood of the first user engaging in a first step of the first in-app task; (5) automatically identify, via the deep learning model, a first sequence of in-app steps that can be used to perform the first in-app task based on the collected user activity data; and (6) present, via the first user device, a first assistance message providing guidance about the first in-app task including information about a second step that is directly subsequent to the first step in the sequence, [0007].
performing the one or more actions on the application; (See previous steps OR the analytic server may display a GUI requesting the customer to enter credential information such as username, password, certificate, and biometrics…the analytic server may access the customer database storing user credentials, which the analytic server may be configured to reference in order to determine whether a set of credentials match an appropriate set of credentials that identify and authenticate the customer…see par. 22…the analytic server may execute an artificial intelligence model to predict the user preferences and service request attributes…[0046-0048]); and
transmitting output from the application to the user interface. (The analytic server may send the electronic message in the form of text message, instant message, email, voicemail, or any other electronic message. The electronic message may include a GUI that displays…[0052]).
Claim 35. (New) A system comprising:
a plurality of edge devices configured to collect user data; and
a central secure platform communicatively connected via a communications channel to each edge device of the plurality of edge devices, (the customer electronic devices may be any computing device allowing a customer to interact with the analytic server …the customer electronic devices may comprise any number of input and output devices supporting various types of data, such as text, image, audio, video, and the like…the customer electronic device may execute an appointment-scheduling program, which may include a user interface that renders an interactive layout, schematic, or other elements for the user to input a request…for example, the user interface may include a text-based interface allowing the user to enter manual command…fig.1 and [0025]) wherein the central secure platform is configured to:
store user data received from the plurality of edge devices in stored information, wherein the stored information includes learned user preferences;
provide the stored user data to one or more service providers based on a policy defined by a user; and perform one or more actions based on at least one of a user input or the stored user data, wherein, using learned user information and preferences and other data, the AI native operating system wrapper predicts desired application behavior based on the user information, preferences, and other data, and the AI based model provides user information and sensor data as inputs and predicts desired application behavior based on those inputs, and then automates application tasks based on this prediction. (This claim is rejected in the same manner as demonstrated in claim 21 that is by Bueche in view of Hwang or Pandya).
It would have been obvious to the ordinary artisan before the effective filing date to incorporate the teaching of Hwang or Pandya into the teaching of Bueche who already teaches multiple learning models for the purpose of specifying deep learning model, one of the most current and powerful state-of-the-art artificial intelligence to identify, analyze and predict human emotion and/or the user’s next action likely to be.
Response to Arguments
Applicant’s arguments with respect to claim(s) 2/27/26 have been 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.
Applicant argues that §102 Anticipation over Bueche (US 2022/0172176)
The Office Action rejected several former claims under § 102 as anticipated by Bueche. The § 102 mapping in the Office Action focuses on the prior claim set's generic steps (receiving user input, receiving stored information, determining actions via Al model, performing actions, transmitting output).
The amended independent claims now require additional limitations not addressed in the Office Action's anticipation mapping. Specifically, that the stored information includes learned user preferences and that, using learned user information and preferences and other data, the Al native operating system wrapper predicts desired application behavior and automates application tasks based on that prediction using user information and sensor data as inputs. Because these limitations are not treated in the Office Action's anticipation analysis, withdrawal of the § 102 rejection is respectfully requested at least for the amended independent claims.
Examiner is respectfully providing the new references to address the amendment.
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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUNG-HOANG J. NGUYEN whose telephone number is (571)270-1949. The examiner can normally be reached Reg. Sched. 6:00-3:00.
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/PHUNG-HOANG J NGUYEN/Primary Examiner, Art Unit 2691
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