DETAILD ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. Claims 1-20 have been examined and rejected. This is the first Office action on the merits.
Specification
3. The disclosure is objected to because of the following informalities:
a. In [paragraph 68], Examiner suggests changing “As shown in FIG. 5” to --As shown in FIG. 6--.
b. In [paragraph 68], Examiner suggests changing “it’s morning and kitchen and” to --it’s morning and kitchen, and--.
Appropriate correction is required.
Claim Objections
4. Claims 3, 4, 10, 13, 14, and 19 are objected to because of the following informalities:
a. In [line 2] of claim 3, Examiner suggests changing “the at one at least one” to --the at least one--.
b. Claims 13 and 19 recite similar limitations as claim 3 and are thus, objected to for similar reasons.
c. In [line 2] and [line 4] of claim 4, Examiner suggests changing “the at one at least one” to --the at least one--.
d. Claim 14 recites similar limitations as claim 4 and is thus, objected to for similar reasons.
e. In [line 4] of claim 10, Examiner suggests changing “environment associated as the environment associated” to --environment as the environment associated--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
6-1. Claim 1 recites the limitation “implementing a machine learning model comprising training data pre-trained, or trained in real-time based on historical interactions of one or more other users with data, or determined interactions with content by the one or more other users in real time” in [lines 3-5] of the claim. It is unclear whether the limitation that follows the preposition “based on” refers back to the machine learning model, the pre-trained training data, the training data trained in real-time, or generally to the training data. Based on the context of the limitation, it appears that the training data is pre-trained “based on historical interactions of one or more other users with data,” and the training data trained in real-time is based on… “determined interactions with content by the one or more other users in real time.” Examiner suggests rewording the limitation for clarification.
6-2. Claims 11 and 18 recite similar limitations as claim 1, mentioned above, and are thus, rejected for similar reasons.
6-3. Claim 3 recites the limitation, “determining whether the… at least one item of context information and the one or more contextual variables are relevant or irrelevant associated with presentation via the user interface” in [lines 2-4] of the claim. It is unclear what is meant by “associated with presentation via the user interface.”
6-4. Claims 13 and 19 recite similar limitations as claim 3, mentioned above, and are thus, rejected for similar reasons.
6-5. Claim 11 recites the limitation “the one or more users” in [line 9] of the claim. It is unclear whether “the one or more users” refers to the one or more users in [line 5] or [line 8] of the claim.
6-6. Claim 18 recites similar limitations as claim 11, mentioned above, and is thus, rejected for similar reasons.
Claim Rejections - 35 USC § 102
7. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claim 1-3, 5-7, 10-13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Wang (U.S. Patent No. 12,597,420).
8-1. Regarding claims 1, 11, and 18, Wang teaches the claim comprising: determining one or more contexts of one or more users within one or more environments, by disclosing receiving environment data that is sent to an AI system for analysis to detect physical objects in the environment and contextual information about the detected objects in the environment as well as forming a set of understandings of the overall environment [column 41, lines 10-20].
Wang teaches implementing a machine learning model comprising training data pre-trained, or trained in real-time based on historical interactions of one or more other users with data, or determined interactions with content by the one or more other users in real time, by disclosing that the AI system uses machine learning algorithms to continuously monitor user interactions and analyze the user interactions to identify patterns and trends [column 7, lines 55-67]. The AI system can acquire domain knowledge from humans through various techniques and approaches [column 9, line 53 to column 10, line 49].
Wang teaches analyzing at least one item of context information associated with the one or more contexts to determine content relevant to a user associated with an apparatus capturing content items within an environment, by disclosing that the AI system employs the machine learning algorithms to analyze the interaction data and identify patterns and trends in a user’s behavior and preferences to provide more contextually relevant and personalized recommendations [column 6, lines 53-64]. The AI system predicts the most relevant contextual information for the user [column 41, lines 21-24; column 48, lines 44-47] based on a variety of factors [column 48, line 51 to column 49, line 8].
Wang teaches analyzing the at least one item of context information or other items of context information to determine, by implementing the machine learning model, one or more contextual variables, of the one or more environments, determined as relevant to the apparatus, by disclosing that the AI system identifies relevant information through various techniques, such as machine learning algorithms [column 8, lines 1-3]. After the environment data is classified and categorized, the machine learning algorithms are used to refine the selection of the most relevant objects [column 44, lines 28-37].
Wang teaches utilizing the determined content relevant to the user and the determined one or more contextual variables determined as relevant to the apparatus to determine recommendation or action to present to a user interface, by disclosing that the AI system may generate a response or take a relevant action, such as answering a question, providing information, or executing a command [column 27, lines 20-26]. The AI system makes a decision about which contextual information to present to the user, considering the user’s current context, needs, and preferences [column 49, lines 4-8]. This allows the user to have more contextually relevant and personalized interaction with an object based on the environmental data, such as adjusting temperature settings automatically based on the user’s preferences and the current environment conditions [column 45, lines 12-20; column 49, lines 4-8].
8-2. Regarding claims 2 and 12, Wang teaches all the limitations of claims 1 and 11 respectively, wherein the apparatus comprises a head mounted device, by disclosing that the AI system may be integrated with virtual reality/augmented reality headsets [column 5, lines 12-14].
8-3. Regarding claims 3, 13, and 19, Wang teaches all the limitations of claims 1, 11, and 18 respectively, further comprising: determining whether the at one at least one item of context information and the one or more contextual variables are relevant or irrelevant associated with presentation via the user interface, by disclosing using machine learning algorithms to refine the selection of the most relevant objects [column 44, lines 28-37] wherein information determined to be irrelevant is removed [column 45, lines 45-47].
8-4. Regarding claims 5 and 15, Wang teaches all the limitations of claims 3 and 13 respectively, further comprising: generating explainable data associated with at least one item of context information or at least a subset of the one or more items of contextual variables that are determined relevant for the presentation, by disclosing integrating an explainable AI into the AI system [column 11, lines 17-43].
8-5. Regarding claims 6 and 16, Wang teaches all the limitations of claims 5 and 15 respectively, further comprising: presenting the explainable data to the user interface to enable the user to view, or interact with, the explainable data, by disclosing displaying to the user the inputs considered in the decision-making process, the features with higher weights, and the calculation of the final outputs [column 11, lines 23-26].
8-6. Regarding claims 7, 17, and 20, Wang teaches all the limitations of claims 1, 11, and 18 respectively, further comprising: determining that the content relevant to the user is beneficial for the user to consider for presentation via the user interface even though the user is unaware of the content relevant to the user, by disclosing that the AI system may generate dynamic responses to user questions [column 19, lines 41-43; column 20, lines 12-23; column 67, lines 41-59].
8-7. Regarding claim 10, Wang teaches all the limitations of claim 1, further comprising: determining that the determined one or more contextual variables determined as relevant to the apparatus comprises detected information associated with a second user associated with a same environment associated as the environment associated with the user or another environment of the one or more environments, by disclosing that the AI system uses machine learning algorithms to continuously monitor user interactions and analyze the user interactions to identify patterns and trends [column 7, lines 55-67]. The AI system can acquire domain knowledge from humans through various techniques and approaches [column 9, line 53 to column 10, line 49]. Contextual information may be based on the preferences and behaviors of similar users [column 49, lines 48-52; column 50, lines 22-28].
Claim Rejections - 35 USC § 103
9. 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.
10. Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (U.S. Patent No. 12,597,420) in view of Gupta et al (U.S. Patent No. 10,372,774).
10-1. Regarding claims 4 and 14, Wang teaches all the limitations of claims 3 and 13 respectively. Wang does not expressly teach the claim further comprising: determining that the at one at least one item of context information and the one or more contextual variables are relevant in response to determining that corresponding determined scores, associated with the at one at least one item of context information and the one or more contextual variables, equal or exceed a predetermined threshold. Gupta discloses receiving a context indication corresponding to a user, the context indication indicative of the real-time context or the environment of the user [column 10, lines 34-38]. Numerous context indications may be received, and a determination is made whether the context indication is a significant context indication [column 11, lines 36-39] based on a significance score exceeding a threshold [column 11, lines 47-49]. Significant context indications are then used to generate notification content for presentation to the user [column 12, lines 21-31, 51-59]. This would help conserve processing power by reducing the amount of data the system has to process. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine relevant context indications based on a significance score exceeding a threshold, as taught by Gupta. This would help conserve processing power by reducing the amount of data the system has to process.
11. Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (U.S. Patent No. 12,597,420) in view of Hwang et al (Pub. No. US 2020/0045163).
11-1. Regarding claim 8, Wang teaches all the limitations of claim 1. Wang does not expressly teach the claim further comprising: generating at least one application associated with the determined at least one recommendation to present via the user interface. Hwang discloses applying context information including at least one of environmental information collected through a sensor or a network and usage information generated by use of the electronic devices 100a and 100b to machine learning based learning models [paragraph 307] and displaying a first shortcut related to an application determined based on the result of applying the context information to the learning model [paragraph 309]. This would save time by allowing relevant applications to be easily selectable when needed. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow the AI system of Wang to provide an application based on analysis of environmental data, as taught by Hwang. This would save time by allowing relevant applications to be easily selectable when needed.
11-2. Regarding claim 9, Wang-Hwang teach all the limitations of claim 8, further comprising: generating at least one task associated with the action and the application to present via the user interface, by disclosing that some of the applications provided to the user correspond to tasks that the user performs based on context [Hwang, paragraphs 289, 295].
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALVIN H TAN whose telephone number is (571)272-8595. The examiner can normally be reached M-F 10AM-6PM.
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/ALVIN H TAN/Primary Examiner, Art Unit 2118
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