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 1-12, 14, 17-20 and 22-24 are pending for examination.
Claims 1-5, 9, 11, 12, 14, 17-20, 22 and 23-24 are rejected under 35 U.S.C. §102.
Claims 6-8 and 10 are rejected under 35 U.S.C. §103.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1-5, 9, 11, 12, 14, 17-20, 22 and 23-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bruckner et al. (U.S. 2019/0147760 hereinafter Bruckner)
As Claim 1, Bruckner teaches in a digital medium environment, a method implemented by at least one computing device, the method comprising:
monitoring interaction by a user with content on one or more web pages during a current browsing session (Bruckner (¶0018 last 7 lines, ¶0024 line 3-6), media usage related to books, articles, digital media from online sources);
generating user interaction information including a first description of the interaction by the user with the content and a second description of the content (Bruckner (¶0024 line 3-10, ¶0028 line 6-18), user interactions (such as media usage data) and description of the content (content organization, style and/or format) are analyzed. In another example, user interactions (gaze focus) and description of the content (visual or audio) are used to determine user learning style as visual or auditory learner);
determining, by a machine learning system (Bruckner (¶0034 line 4-7, ¶0046 line 11-12), machine learning model), a cognitive style that indicates one or more content modalities of a plurality of content modalities in which the user prefers to consume information while browsing web pages (Bruckner (¶0024 line 3-6), media usage related to books, articles, digital media from online sources) based on the user interaction information (Bruckner (¶0024 line 3-10, ¶0028 line 6-18), user interactions (such as media usage data) and description of the content (content organization, style and/or format) are analyzed. In another example, user interactions (gaze focus) and description of the content (visual or audio) are used to determine user learning style as visual or auditory learner), wherein text content is a first content modality of the plurality, image content is a second content modality of the plurality, video content is a third content modality of the plurality, Graphics Interchange Format (GIF) content is a fourth content modality of the plurality, and audio content is a fifth content modality of the plurality (Bruckner (¶0033 last 8 lines), content modalities are text, image, video, audio …);
retrieving, over a network, a webpage configuration of a webpage from a webpage hosting system (Bruckner (¶0018 last 3 lines), the application is a web-page application), the webpage hosting system including pre-configured webpage configurations of the webpage associated with different cognitive styles and configured for conveying a set of information via different content modalities of the plurality of content modalities (Bruckner (¶0020 last 8 lines, ¶0033 last 8 lines), system provides content in visual, auditory and/or tactile that is most suited for the user learning styles. User learning styles can be visual or auditory), the webpage configuration selected from the pre-configured webpage configurations as corresponding to the cognitive style (Bruckner (¶0020 last 8 lines, ¶0033 last 8 lines), system provides content in visual, auditory and/or tactile that is most suited for the user learning styles); and
causing output, via a user interface, of the webpage configuration of the webpage configured for output in the one or more content modalities rather than other ones of the pre-configured webpage configurations (Bruckner (¶0043 line 1-10, ¶0046), when user’s comprehension is inadequate. Ruther modification of the content is need in order to algin with user’s learning style).
As Claim 2, besides Claim 1, Bruckner teaches the interaction including clicking on the content, hovering over the content, or scrolling through the content (Bruckner ¶0028 line 6-18), user interactions (gaze over the content) are used to determine user learning style as visual or auditory learner).
As Claim 3, besides Claim 1, Bruckner teaches the interaction including user eye focus on the content, the user eye focus detected by tracking eye movements of the user (Bruckner ¶0028 line 6-18), user interactions (gaze over the content) are used to determine user learning style as visual or auditory learner).
As Claim 4, besides Claim 1, Bruckner teaches further comprising:
generating a first user interaction information collection including the user interaction information for a first set of multiple interactions during the current browsing session (Bruckner ¶0028 line 6-18), user interactions (gaze over the content) are used to determine user learning style as visual or auditory learner);
providing the first user interaction information collection to the machine learning system to generate a first cognitive style (Bruckner (¶0020 last 8 lines, ¶0033 last 8 lines), system provides content in visual, auditory and/or tactile that is most suited for the user learning styles);
generating a second user interaction information collection including the user interaction information for a second set of multiple interactions during the current browsing session (Bruckner (¶0043 line 1-10, ¶0046), when user’s comprehension is inadequate. Ruther modification of the content is need in order to algin with user’s learning style); and
providing the second user interaction information collection to the machine learning system to generate a second cognitive style, the first cognitive style being different than the second cognitive style (Bruckner (¶0043 line 1-10, ¶0046), when user’s comprehension is inadequate. Ruther modification of the content is need in order to algin with user’s learning style).
As Claim 5, besides Claim 1, Bruckner teaches the user interaction information for a user interaction including:
an indication of a type of the user interaction; a representation of the content; and
an indication of a content modality of the content interacted with, the content modality being one of the plurality of content modalities (Bruckner (¶0024 line 3-10, ¶0028 line 6-18), user interactions (such as media usage data) and description of the content (content organization, style and/or format) are analyzed. In another example, user interactions (gaze focus) and description of the content (visual or audio) are used to determine user learning style as visual or auditory learner).
As Claim 9, besides Claim 1, Bruckner teaches wherein the cognitive style is measured along one or more of multiple dimensions each defining a scale between two cognitive style characteristics, the multiple dimensions including two or more of: analytic vs holistic, visual vs verbal, impulsive vs deliberative, extraversion vs introversion, sensing vs intuitive, thinking vs feeling, and judging vs perceiving (Bruckner (¶0033 last 8 lines), user learning styles can be visual or auditory).
As Claim 11, Bruckner teaches in digital medium environment, a computing device comprising:
a processor (Bruckner (¶0054 line 2-4), processor); and
computer-readable storage media having stored thereon multiple instructions that, responsive to execution by the processor (Bruckner (¶0054 line 2-4), memory), cause the processor to perform operations including:
receiving user feedback interacting with content that is presented via a webpage based on the cognitive style the webpage configuration of the webpage, the user feedback indicating a known cognitive style for the user (Bruckner (¶0048, ¶0049), student provides feedback on area of confusion); and
training the machine learning system by updating the machine learning system based on a difference between the cognitive style and the known cognitive style for the user (Bruckner (¶0032 last 1-5 lines), machine learning model constantly adapts to user input).
The rest of the limitation(s) are rejected for the same reasons as Claim 1.
As Claim 12, the Claim is rejected for the same reasons as Claim 2.
As Claim 14, the Claim is rejected for the same reasons as Claim 5.
As Claim 17, the Claim is rejected for the same reasons as Claim 9.
As Claim 18, the Claim is rejected for the same reasons as Claim 1.
As Claim 19, the Claim is rejected for the same reasons as Claim 10.
As Claim 20, the Claim is rejected for the same reasons as Claim 5.
As Claim 22, the Claim is rejected for the same reasons as Claim 3.
As Claim 23, besides Claim 1, Bruckner teaches wherein each of the pre-configured webpage configurations convey a same set of information in different content modalities of the plurality of content modalities (Bruckner (¶0020 last 8 lines, ¶0033 last 8 lines), system provides content in visual, auditory and/or tactile that is most suited for the user learning styles).
As Claim 24, besides Claim 1, Bruckner teaches wherein generating the user interaction information includes: generating a vector describing the interaction by the user with the content; and generating one or more additional vectors, each additional vector representing a different content modality of the content interacted with by the user (Bruckner (¶0024 line 3-10, ¶0028 line 6-18), user interactions (such as media usage data) and description of the content (content organization, style and/or format) are analyzed. In another example, user interactions (gaze focus) and description of the content (visual or audio) are used to determine user learning style as visual or auditory learner).
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) 6-8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bruckner in view of Hassan et al. (U.S. 2021/0166104 hereinafter Hassan).
As Claim 6, besides Claim 1, Bruckner does not explicitly disclose:
the machine learning system comprising a Bi-directional Long Short Term Memory network to extract features from the user interaction information.
Hassan teaches:
the machine learning system comprising a Bi-directional Long Short Term Memory network to extract features from the user interaction information (Hassan (¶0063 line 3-6), system uses LSTM units layer).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify model system of Bruckner instead be the LSTM model taught by Hassan, with a reasonable expectation of success. The motivation would be so that “the output generated from the LSTM layer may be input into filtering layer to improve results provided based on the learning model by excluding input randomly from the training process” (Hassan (¶0070 line 1-4)) (Teaching suggestion motivation).
As Claim 7, besides Claim 6, Bruckner in view of Hassan teaches further comprising using, for the current browsing session, weights or values of hidden states of the Bi-directional Long Short Term Memory network determined during a previous browsing session (Hassan (¶0063 line 3-6), system uses LSTM units layer. LSTM units layer is weight and bias of the trained machine model).
As Claim 8, besides Claim 6, Bruckner in view of Hassan teaches the machine learning system including multiple fully connected layers followed by a sigmoid activation to classify the features to generate the cognitive style (Hassan (¶0064, ¶0073), system uses neural network with stacked layer and activation function).
As Claim 10, besides Claim 1, Bruckner may not explicitly disclose:
the machine learning system having been trained by providing training data to the machine learning system for a training user, comparing the cognitive style generated by the machine learning system to a known cognitive style of the training data for the training user, and updating weights or values of hidden states of the machine learning system to minimize a loss between the cognitive style of the training data and the known cognitive style for the training user.
Hassan teaches the machine learning system having been trained by providing training data to the machine learning system for a training user (Hassan (¶0056 line 1-6), product list is customized for a particular user A), comparing the cognitive style generated by the machine learning system to a known cognitive style of the training data for the training user (Hassan (¶0067), product ID is converted into meaningful embedding vectors), and updating weights or values of hidden states of the machine learning system to minimize a loss between the cognitive style of the training data and the known cognitive style for the training user (Hassan (¶0052 last 5 lines, ¶0062 last 7 lines, ¶0076 line 3-9, ¶0070 line 2-4), output form LSTM units layer is improved with a filtering layer. Model is trained based on user profile and interest in order to provide product with higher chance of being selected).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify model system of Bruckner instead be the LSTM model taught by Hassan, with a reasonable expectation of success. The motivation would be so that “the output generated from the LSTM layer may be input into filtering layer to improve results provided based on the learning model by excluding input randomly from the training process” (Hassan (¶0070 line 1-4)) (Teaching suggestion motivation).
Response to Arguments
§101 Rejection – Software Per Se:
Claims 18-20 are amended; therefore, §101 Rejections under Software Per Se are respectfully withdrawn.
§101 Rejection:
Step 2A prong 2: Applicants argue that the Claimed features go beyond the judicial exception to a particular technology environment because claim 1 recite “a cognitive style that indicates one or more content modality” (last paragraph of page 16 in the remarks). Applicant further clarify Example 37 where “the additional elements recite a specific manner of automatically display icons to the user based on the usage which provides a specific improvement over prior systems …” (2019 PEG Examples page 2).
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Applicant’s arguments are persuasive therefore §101 rejection(s) are respectfully withdrawn.
Claims rejections:
As Claim 1, Applicants argue that Hassan does not disclose various limitations in the Claims (pages 22-29).
Applicant’s arguments are moot because new reference Bruckner is currently use to reject the Claims. See current rejection(s) for details.
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