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 .
The application has been examined. Claims 1 – 20 are pending in this office action.
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 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1 – 20 are determined to be directed to an abstract idea and not significantly more than the abstract idea itself. The rationale for this determination is explained below:
The representative claim 1 (and other independent claims 11, 20) recites a method of managing a dynamic database for providing a personalized service comprising: obtaining user input information from an electronic device; obtaining, from the user input information, context information that relates to the personalized service, wherein the context information comprises implicit information determined based on the user input information; and updating the dynamic database based on the context information.
The claims recite a certain method of organizing human activity. Before computers people have had assistants that helped keep the schedule of a user finding information request based on question along with personal knowledge of the user, their location, preferences and other criteria. Examples of cases the courts have found to recite managing interactions between people include Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018). The social activity at issue was the social activity of "’providing information to a person without interfering with the person’s primary activity.’" 896 F.3d at 1344, 127 USPQ2d 1553 (citing Interval Licensing LLC v. AOL, Inc., 193 F. Supp.3d 1184, 1188 (W.D. 2014)). The patentee claimed an attention manager for acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content. 896 F.3d at 1339-40, 127 USPQ2d at 1555. The Federal Circuit concluded that "[s]tanding alone, the act of providing someone an additional set of information without disrupting the ongoing provision of an initial set of information is an abstract idea," observing that the district court "pointed to the nontechnical human activity of passing a note to a person who is in the middle of a meeting or conversation as further illustrating the basic, longstanding practice that is the focus of the [patent ineligible] claimed invention." 896 F.3d at 1344-45, 127 USPQ2d at 1559. Additional, examples the courts have found recites an abstract idea includes filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016)
The claims additionally recite - Claim 1: a method, dynamic database, electronic device - Claim 11: an electronic device, memory, processor, dynamic database. - Claim 20: a non-transitory computer-readable recording medium, processor, electronic device.
However, the limitations merely amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). Furthermore, a method for obtaining and updating information does not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
As discussed above, the additional imitations amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f) and generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h). It is well- understood, routine, and conventional to use a computer to gather, analysis, and present information to a user and update the information, (also see court case A web browser’s back and forward button functionality, Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015). See MPEP 2106.05(d) as well as USPTO Memorandum: Revising 101 Eligibility Procedure in view of Berkheimer v. HP, Inc. (April 19, 2018). And the following court cases (See MPEP 2106.05(d). Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining "shadow accounts"); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015); Electronically scanning or extracting data from a physical document, Content Extraction and Transmission, LLC v. Wells Fargo Bank, 776 F.3d 1343, 1348, 113 USPQ2d 1354, 1358 (Fed. Cir. 2014) (optical character recognition); and Arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015).
Claims 2 – 10, and 12 – 19 further narrow the abstract idea recited in the independent claims 1, 11 and 20 and are therefore directed towards the same abstract idea. The dependent claims are directed towards further narrowing the abstract idea of providing personalized service to a user.
Claims 2 – 10, and 12 – 19 do not recite any additional elements that have not already been analyzed above. Therefore, the claims do not direct the claims to recite a practical application.
Therefore, claims 1 – 20 are rejected under U.S.C. 101.
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 (i.e., changing from AIA to pre-AIA ) 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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1 – 20 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Lee et al. (US 2022/0059088 A1) (‘Lee’ herein after) further in view of Nitz et al . (US 2014/0052681 A1) (‘Nitz’ herein after).
With respect to claim 1, 11, 20,
Lee discloses a method of managing a dynamic database for providing a personalized service comprising: obtaining user input information from an electronic device (figure 4, 10, 11, paragraph 8, 17, 18 teaches based on a user voice being input through the microphone, 54 – 56 teaches input or query could be through various input methods, Lee); obtaining, from the user input information, context information that relates to the personalized service, wherein the context information comprises implicit information determined based on the user input information (figure 11, paragraph 8, 15 – 18, paragraph 58, 81 teaching processor may obtain context information of the time when the user voice is received . The context information may include at least one of time information, location information, weather information , or schedule information at the time when the user voice is input. The time information may include information regarding a date, day and time of the point in time when the user voice is input, Lee); and updating the dynamic database based on the context information (paragraph 19 – 22 teaches when information is not present updating the knowledge base by adding information about the object to the knowledge database, paragraph 76 teaches that the AI model may learn the tendency, preference, or the like, of a user, Lee).
Lee teaches obtaining context information but does not specify as claimed that context information comprises implicit information based on the input.
However, Nitz teaches that context information comprises implicit information based on the input in paragraphs 53 stating that the monitoring module monitors the user's implicit or explicit responses to the actions that are presented or executed by the user, paragraph 66 and 80 teach rules that may be automatically created over time as the system learns about the user's activity, some of the rules may be modifiable by the user or the system, these may be changed based on explicit user behavior, by implicit user responses (e.g., the user simply ignores a system-generated candidate action), based on details the system learns over time (e.g., whenever the user is driving a car, the user prefers candidate actions to be presented audibly), or based on the user's current situation and/or context (e.g., if the user has a busy day, no candidate actions should be offered).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention because the automated learning and dynamic method of Nitz improves on the accuracy and customization along with assistance to the user. The learning module observes in an automated fashion, the real-time inputs, the stored user-specific information, the operation of the inference engine in applying the knowledge base to generate current context inferences over time, and the user's explicit and/or implicit responses to system-generated candidate actions. The learning module applies artificial intelligence-based machine learning algorithms to continuously improve the knowledge base, adapt the knowledge base to the mobile user's personal schedule, routine, or rhythm of activity, and to allow the system to improve its ability to offer personalized, timely and helpful candidate actions to the user at appropriate times and using appropriate presentation methods.
With respect to claim 2, 12,
Lee as modified discloses the method of claim 1, wherein the context information is information used for knowledge graph based reasoning for providing the personalized service (paragraph 51 – 53 teaches that the knowledge information may be stored in a graph model, Lee).
With respect to claim 3, 13,
Lee as modified discloses the method of claim 1, wherein the obtaining the context information comprises: obtaining one or more pieces of explicit information from the user input information; and determining the implicit information based on a generative model using at least one of: a context determined from the user input information or the one or more pieces of explicit information (paragraph 5 and 58 – 63, Lee).
With respect to claim 4, 14,
Lee as modified discloses the method of claim 1, wherein the implicit information is determined based on a context indicated by the user input information (paragraph 58, 76, Lee).
With respect to claim 5, 15,
Lee as modified discloses the method of claim 3, wherein the one or more pieces of explicit information comprises event information and at least one of subject information, location information, or temporal information, and wherein the implicit information comprises reason information indicating a reason for an event corresponding to the event information (figure 1, 3, 5, paragraph 9, 21, 94, 113 – 115, Nitz).
With respect to claim 6, 16,
Lee as modified discloses the method of claim 5, wherein the updating the dynamic database comprises storing latest pieces of information corresponding to the subject information, the event information, the location information, the temporal information, and the reason information, in the dynamic database (figure 1, 3, 5, paragraph 9, 21, 94 113 – 115, Nitz).
With respect to claim 7, 17,
Lee as modified discloses the method of claim 5, wherein the dynamic database comprises a plurality of nodes corresponding to the subject information, the event information, the location information, the temporal information, and the reason information (figure 1, 3, 5, paragraph 9, 21, 94 113 – 115, Nitz).
With respect to claim 8, 18,
Lee as modified discloses the method of claim 1, wherein the updating the dynamic database comprises storing information for controlling another device in the dynamic database based on a usage history of the electronic device indicating the electronic device has controlled the other device (paragraph 5 and 58 – 63, Lee).
With respect to claim 9, 19,
Lee as modified discloses the method of claim 1, wherein the user input information comprises at least one of: information input to the electronic device by a user, information based on a usage pattern of the electronic device by the user, information based on an action performed on the electronic device by the user, or information displayed on a screen of the electronic device based on usage of the electronic device by the user (figure 4, 10, 11, paragraph 5 and 58 – 63, Lee).
With respect to claim 10,
Lee as modified discloses the method of claim 1, wherein the obtaining the user input information comprises: storing a user input by a user to the electronic device or identifying a screen displayed on the electronic device (figure 10, 11, paragraph 90 – 91, 156 – 157 and 162 – 163, Lee).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180018373 A1 teaches a context based digital assistant collecting information, using one or more input sensor devices, about the plurality of users within a physical environment. The operation includes analyzing the collected information to determine a present situational context for the plurality of users that are currently present within the physical environment. An action to perform is determined based on the determined present situational context. The determined action is executed using the one or more output devices.
US 20170034649 A1 teaches user's availability to receive a communication can be inferred through the analysis of signal data that describes a present context of the mobile device and/or the mobile device's user. Upon determining a present level of availability, the technology described herein can take several different actions.
US 20250284721 A1 teaches relates to the use of machine learning (ML) models and techniques to improve the speed and quality of generative artificial intelligence (AI) applications in a database system and, more particularly, to personalization and training of machine learning models using existing user data and using model predictions to filter context that is passed to large language models.
US 20240412720 A1 teaches an artificial intelligence (AI) assistant system and a method for providing a contextualized response to a user using AI are disclosed. The system receives voice input, determines user identification, updates conversational context data with user identification and a tokenized representation of the voice input, processes the voice input using a transformer-based language model to generate a response, update the conversational context data with a tokenized representation of the generated response, and output the response via the audio output device.
US 10475446 B2 teaches a virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAVNEET K GMAHL whose telephone number is (571)272-5636.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SANJIV SHAH can be reached on . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NAVNEET GMAHL/Examiner, Art Unit 2166 Dated: 5/30/2026
/SANJIV SHAH/Supervisory Patent Examiner, Art Unit 2166
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