DETAILED ACTION
Status of Claims
Claim(s) 1-20 are pending and are examined herein.
Claim(s) 1, 4-7, 10, 13-16, and 19 have been Amended.
Claim(s) 1-20 are rejected under 35 U.S.C. § 101 and 103.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/02/2025 has been entered.
Response to Amendment
The amendment filed on May 02, 2025 has been entered. Claims 1-20 are pending in the application. Applicant’s amendments to claims have overcome the 35 USC § 112 (b) rejection set forth in the Final Office Action mailed on March 14, 2025. Applicant’s amendments to the claims have been fully considered and are addressed in the rejections below.
Response to Arguments
Applicant's arguments, with respect to the rejection under 35 U.S.C. § 101 filed on 04/02/2025, have been fully considered but they are not persuasive.
Applicant’s argument (Pp. 11-20 of the remarks): Applicant summarizes the 2019 Revised Patent Subject Matter Eligibility Guidance and argues that the claimed invention is a method and therefore falls within a statutory category (process) under § 101. Applicant disagrees with the office’s position that the claims are directed to an abstract idea, asserting that the claimed invention does not fall within any of the enumerated abstract idea grouping, citing Enfish v. Microsoft and argues that the inquiry is whether the claim as a whole is directed to an abstract idea, not merely whether it involves an abstract idea.
Examiner's response: The examiner respectfully disagrees. As correctly noted by the applicant and as stated in the previous Office Action, the claimed method falls within one of the four statutory categories of invention under Step 1 of the subject matter eligibility analysis. However, under Step 2A, Prong 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG), the Examiner maintains that the claim recites an abstract idea that falls within the enumerated groupings of mental processes and/or certain methods of organizing human activity.
Specifically, the claimed invention is primarily directed to the process of determining a probability of web pages being visited by a user through analysis of operational behavior based on page features and usage patterns, filter and classifying (i.e., grouping) web pages based on similarity and user operational behavior, ordering or ranking web pages based on the determined probability, managing web pages according to the ranking, and recommending web pages to the use based on the analysis. These steps amount to concepts of organizing, analyzing, and evaluating information, and making recommendations based on the analysis, which are forms of mental processes (i.e., concepts performed in the human mind including an observation, evaluation, judgment, and opinion). See MPEP § 2106.04(a)(2)(III).
With respect to the applicant’s reliance on Enfish v. Microsoft, the Examiner notes that the Enfish applies where claims are directed to a specific improvement in computer functionality rather than to an abstract idea implemented on a computer. In the present case, the claim does not recite additional elements that would amount to specific improvement to computer technology. Instead, the claim uses generic computer components merely as a tool to perform the abstract idea of analyzing and recommending web pages based on the determined likelihood and ranking of web pages.
Under Step 2A, Prong 2, the additional elements, such as the recited “using a computer ... using machine learning,” “a transformer,” and “a basic vector generation module” are considered in combination with the judicial exception but do not integrate the abstract idea into a practical application. The additional elements are recited at a high level of generality. These elements represent generic data processing and presentation operations that merely implement the abstract idea within a computer environment without imposing any meaningful limitations or providing technical implementation of the recited elements that would reflect a technological improvement.
Further, under Step 2B, the additional elements, whether considered individually or in combination, do not amount to significantly more than the identified abstract idea. The recited computer components perform generic computer functions cannot provide an inventive concept.
Accordingly, when the claim is considered as a whole, it remains directed to the abstract idea identified under Step 2A, Prong 1, and the identified additional elements evaluated under Step 2A, Prong 2, and Step 2B are not sufficient to transform the abstract idea (judicial exception) into a patent-eligible application. Therefore, the claim do not recite patent-eligible subject matter under 35 U.S.C. § 101.
Applicant’s argument (Pp. 21-22 of the remarks): Applicant further contends that, even assuming arguendo a judicial exception were present, the claims integrate the exception into a practical application, reciting specific and meaningful limitations that that integrate the judicial exception into a practical application of the exception, which can include one or more improvements to a computer or computer technology. Further, Applicant argues that the claims include significantly more than the alleged abstract idea when the subject matter of claim I is considered as a whole. Specifically, amended independent claim 1 recites, inter alia, determining a key word for each of the open web pages, and using a transformer, generating word vectors. The claim further includes classifying open web tabs which belong to a same classification in one or more queues, and marking each of the queues to show a web page has been read or unread. And the claim further includes recommending a web page to the user, in response to detecting when the user jumps web pages and goes back from a classification to a new web page which is in a different classification, the recommendation is from unread web pages from a previous classification based on a distance from a word vector of a web page to a center of the previous classification. And amended claim 1 recites generating the word vector includes inputting the word vectors into a basic vector generation module which calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively. For example, "determining a key word for each of the open web pages, and using a transformer, generating word vectors", is more than using a computer as a tool to perform an abstract idea, as the claim recites using a transformer and generating word vectors.
Examiner's response: The examiner respectfully disagrees. As noted above, the identified additional elements are not sufficient to integrate the judicial exception into a practical application under Step 2A, Prong 2, nor do they amount to significantly more under Step 2B.
Specifically, the claim limitations involving determining a keyword for each of the open web pages, classifying open web pages as read and unread, and recommending an unread web page from a related topic group when the user switches between pages from different groups, which involves generating word vectors and calculating Euclidean distance to select a page, which are all part of the abstract idea itself. These operations describes concepts that can be performed in the human mind (e.g., observation, evaluation, judgement, and decision-making) or with the aid of pen and paper.
The inclusion of mathematical computation (e.g., calculating Euclidean distance) does not alter the mental nature of the limitation. As explained in MPEP § 2106.04(a)(2)(III), “the use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation.”
The additional elements reciting a “transformer” and “a basic vector generation module” merely represent generic computer components used to perform the underlying abstract idea of generating word vectors and calculating distances. These components perform amount to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Moreover, the step of “inputting the word vectors into a basic vector generation module” constitutes insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This step merely describes data gathering in conjunction with the abstract idea of calculating Euclidean distance of vectors.
Furthermore, while Applicant asserts that the claims include meaningful limitations that improve computer functionality, the remarks do not identify any specific improvement to computer performance or describe how such improvement is achieved. Instead the recited limitations are part of the abstract idea itself and the recited computer components merely act as tools to execute the abstract idea on a computer. Here, the additional element of claim 1, such as using transformer and a basic vector generation module to generate word vectors and calculate the distance amount to generic computer components configured to perform the abstract idea and/or adding insignificant extra-solution activity (i.e., mere data gathering).
As discussed in MPEP § 2106.04(d), the determination of whether a claim improves computer functionality must be reflected in the claim and the judicial exception alone cannot provide the improvement, the improvement can be provided by one or more additional elements. Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity.
Accordingly, when viewed as whole, the claim is directed to the abstract idea of analyzing user behavior and web page features to determine a probability of a user visiting a web page based on filtering, classifying, ranking, organizing, and recommending we pages, and the additional elements, considered individually or in combination, do not integrate the judicial exception into a practical application or amount to significantly more than the abstract idea itself.
In view of the above, the rejection under 35 U.S.C. § 101 is maintained. For more details on the subject matter eligibility analysis of the claim, the Examiner respectfully refers to the rejection under 35 U.S.C. § 101.
Applicant's arguments, with respect to the rejection under 35 U.S.C. § 103 filed on 05/02/2025 (see remarks Pp. 25-26) have been fully considered but are moot in view of the new grounds of rejection necessitated by amendments.
The examiner refers to the updated rejection under 35 U.S.C. § 103 for more details.
Claim Objections
Claim(s) 5-7 and 14-17 are objected to for claim consistency and formal clarity:
Regarding claim 5, the claim recites “The claims recites “analyzing the page features ....” where the claim should read “analyzing the web page features ...”
Regarding claim 5, the claim recites “The claims recites “analyzing the page features ....” where the claim should read “analyzing the web page features ...”
Regarding claim 6, the claim recites “The claims recites “analyzing the page features ....” where the claim should read as “analyzing the web page features ...”
Regarding claim 7, the claim recites “wherein the web page features include opening time of the page and a source of the page.” The claim should read as follows “wherein the web page features include opening time of the web page and a source of the web page.”
Regarding claims 14-17, the claims recite similar limitations as corresponding claims 5-7 and recite similar issues.
Appropriate correction is required to avoid an improper antecedent basis issue in the claims. The term “the page” should be defined as “the web page” for consistency with the previously recited term.
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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1-9 recite a method (representing a process);
Claims 10-18 recite a system (representing a machine); and
Claims 19-20 recite a computer program product (representing an article of manufacture).
Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Examiner Note: Claim(s) 19-20 are directed to a computer program product comprising computer readable storage medium, the specification defined a computer readable storage medium, as used herein, is not to be construed as being transitory signals per se (i.e., do not include a signal). See paragraphs [0116], [0118], and [0123].
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, and hence is not patent-eligible subject matter.
Regarding Amended Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
determining, a probability of each of a plurality of web pages being visited by a user, the web pages being opened by the user using a device the probability being determined using …, user operational behavior analysis based on an analysis of the web page features for each of the web pages being visited by the user and filtering the web pages by classifying the web page features; (The “determining” step is an abstract idea of Mental Process. Examiner’s note:, the “determining” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can practically performed in the human mind and/or with physical aid (e.g., pen and paper). But for the recitation of generic computer components. That is, other than reciting “using a computer components,” nothing in the claim precludes the determining step from practically being performed in the human mind. This involves calculating a probability score for each web page based on user behavior and web pages features. This step is a mental process. Additionally, filtering and grouping web pages based on user behavior information and web page features are act of evaluating information that can be performed in the human mind including observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2)(III). )
ordering, using a cognitive analysis, the open web pages in an order based on the probability of each of the web pages being visited by the user, the order ranking the plurality of web pages based on a highest probability of a web page of the plurality of web pages being visited; (An abstract idea of “a Mental Step” and/or “certain methods of organizing human activity.” The “ordering” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. Examiner note: this step involves ranking web pages based on the highest probability being revisited by a user using quantitative analysis. This is an act of evaluating and comparing web pages that can be performed in the human mind, see MPEP § 2106.04(a)(2)(III).)
managing the open web pages based on the ordering of the open web pages, (An abstract idea of “a Mental Step” and/or “certain methods of organizing human activity.” The “managing” step, covers concepts that can be performed in the human mind. This step would involve organizing the web pages based on priority (ranking). This step is an act of evaluating and organizing information that can be performed in the human mind See MPEP § 2106.04(a)(2)(III).)
determining a key word for each of the open web pages, and … generating word vectors; (An abstract idea of a “Mental Step.” The “determining” step of a key word from open web page can be practically performed in the human mind and/or with the aid of pen and paper. The key word determination step covers concepts that can be performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
classifying open web pages which belong to a same classification in one or more queues, and marking each of the queues to show each of the web pages has been read or unread; (An abstract idea of a “Mental Step.” The “classifying” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be performed in the human mind. This step involves grouping similar web pages into categories and marking them as read or unread, which is a form of evaluating information, including observation, evaluation, judgment, or opinion (see MPEP § 2106.04(a)(2), subsection III).)
recommending a web page of the open web pages to the user, in response to detecting when the user jumps between the open web pages and goes back from a classification to a web page of the open web pages which is in a different classification, the recommendation being from unread web pages from a classification based on a distance from a word vector of a web page to a center of the classification. (An abstract idea of a “Mental process.” The “recommending” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be performed in the human mind. This step involves analyzing the user’s navigation behavior and suggesting a web page based on that analysis, which is an act of evaluating and judging user actions and preferences. This falls under the mental process categories such as observation, evaluation, judgment, or opinion, see MPEP § 2106.04(a)(2)(III). The inclusion of a word vector and the distance measure is part of the analysis step that can be derived manually by an individual.)
calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively. (An abstract idea of “a Mental Process” and/or “a Mathematical Concept.” The “calculating” step, as drafted, and under its broadest reasonable interpretation, covers concepts that can be performed in the human mind. This step involves mathematical calculation of the Euclidean distance of vectors and selecting points based on the results of the nearest distance measure. Accordingly this involves mathematical determination and evaluation that can be performed in the human mind with the aid of pen and paper. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim recites that the computer-implemented method uses “machine learning” to determine the probability of web pages being visited by a user. The use of a computer to execute a machine learning model to determine the probability amounts to no more than mere instructions to apply an abstract idea on a computer. Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, see MPEP § 2106.05(f).
The claim recites “using a transformer” in the claim limitation “determining a key word for each of the open web pages, and using a transformer, generating word vectors”. The claimed used of a transformer to determine key word or generate work vectors amounts to no more than using a generic computer component to perform the abstract idea of determining key words and/or generating word vectors. The term “transformer” can be broadly interpreted as computer programed instructions that apply a mathematical transformation to convert keywords to numerical vectors. Accordingly, the transformer amount to no more than using computer component and/or computer instructions as a tool to perform the abstract idea. See MPEP § 2106.05(f).
The claim further recites the limitation “prompting the user to visit a web page of the open web pages in response to the ranking of an open web page in the order” and “which amounts to adding an insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). In other words, the step of presenting the result of the analysis to the user is considered a mere data outputting step in conjunction with the abstract idea. The “prompting” step does not impose meaningful limitations that can transform a claim into patent-eligible subject matter.
The claim recites “inputting the word vectors into a basic vector generation module which calculates a Euclidean distance.” The claimed element “a basic vector generation module” to calculate Euclidean distance amounts to no more than using a generic computer component and/or computer instructions to perform the abstract idea of calculating a Euclidean distance. The “inputting the word vectors into a basic vector generation module” adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). The claim additional element merely defines a generic data gathering step in conjunction with the abstract idea of calculating a Euclidean distance.
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As explained above, the recitation of using machine learning, a transformer, and a basic vector generation module to perform the abstract ideas amounts to no more than mere instructions to apply the abstract idea on a computer. Mere instructions to apply an exception cannot provide an inventive concept. Thus, the same analysis utilized under Step 2A Prong 2 is equally true in Step 2B.
The recitations of “prompting the user to visit a web page” and “inputting the word vectors into a basic vector generation module” were considered to be insignificant extra-solution activities in Step 2A Prong Two. These steps merely define generic computer functions that are recited at a high level of generality such as data gathering and outputting steps. The courts have recognized the use of a generic computer to perform generic computer functions as well-understood, routine and conventional activities. Therefore, this limitation remains insignificant extra-solution activities even upon reconsideration. See MPEP § 2106.05(d).
Accordingly, when viewed as a whole, the claim is primarily directed to the abstract idea of determining a probability of the user visiting web page, filtering, classifying, ordering, organizing, and recommending web pages. The additional elements, whether considered individually or in combination with the judicial exception, do not integrate the judicial exception into a practical application or amount to significantly more than the abstract idea itself.
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Original Claim 2,
Step 2A Prong 1: Claim 2, which incorporates the rejection of claim 1, recites further limitation such as:
analyzing, using a computer, web page features of respective web pages opened by a user using a device; filtering the web pages by classifying the web page features using a matrix of classification for each of the features; and generating a filtered web page collection based on the classifying of the web page features. (That is part of the abstract idea recited in claim 1. Analyzing, filtering, and classifying web pages to generate a filtered web pages are steps that would fall under the abstract idea of “Mental Processes” – concepts performed in the human mind (including an observation, evaluation, judgment, or opinion). See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 2 is ineligible.
Regarding Original Claim 3,
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the managing includes prompting the user to visit a web page of the open web pages having the highest probability of being visited by the user. (This limitation is part of the insignificant extra-solution activity as described in claim 1. This step involves presenting the result of the abstract analysis to the user, which does not introduce a technological improvement. It merely represents a data gathering and/or outputting step that is recited at a high level of generality.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above in step 2A, prong Two, the additional element adds insignificant extra-solution activity to the judicial exception. The data outputting step represents a generic computer function that have been recognized by the courts as well-understood, routine, and conventional function (see MPEP § 2106.05(d)).
Therefore, claim 3 is ineligible.
Regarding Amended Claim 4,
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the web page features include multiple attributes relating to each of the open web pages. (This limitation is part of the abstract idea recited claim 1. This step relates to the evaluation and/or analysis of data (i.e., attributes of the web pages), which is a mental process that can be performed in the human mind. See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 4 is ineligible.
Regarding Amended Claim 5,
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 1, recites further limitation such as:
analyzing the web page features wherein the analyzing includes classifying the features and attributes into patterns. (This limitation is part of the abstract idea recited claim 1. This step involves analyzing web pages features that are part of the web page analysis, which is a mental process that can be performed in the human mind including observation, evaluation, judgment, and opinion. See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 5 is ineligible.
Regarding Amended Claim 6,
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 1, recites further limitation such as:
analyzing the web page features wherein the analyzing includes classifying the features and attributes into patterns, wherein the patterns include a continuation regular pattern and a non-continuation pattern. (This limitation is part of the abstract idea recited claim 1. This step involves analyzing web pages features that are part of the web page analysis. The process of analyzing and classifying web pages based on content and usual practice covers concepts that can be performed in the human mind including observation, evaluation, judgment, and opinion. This is a mental process, see MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 6 is ineligible.
Regarding Amended Claim 7,
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the web page features include opening time of the page and a source of the page. (This limitation is part of the abstract idea recited claim 1. This step involves analyzing the web pages features that are part of the web page analysis, which is a mental process that can be performed in the human mind. The claim merely define the type of feature such as the time of visiting web pages. See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 7 is ineligible.
Regarding Original Claim 8,
Step 2A Prong 1: Claim 8, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the determining includes logic for switching pages; and training a learning module to learn the logic. (This limitation is part of the “apply it,” which amount to no more than mere instruction to apply the abstract idea on a computer. The claim recites a training module at high level of application, such that the claim invokes computers or other machinery in its ordinary capacity merely as a tool to perform an existing process. See MPEP § 2106.05(f).)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element amounts to mere instruction to apply the exception using a generic computer component. Thus, the same analysis utilized under Step 2A Prong 2 is equally true in Step 2B.
Therefore, claim 8 is ineligible.
Regarding Original Claim 9,
Step 2A Prong 1: Claim 9, which incorporates the rejection of claim 1, recites further limitation such as:
determining a page jump operation using the cognitive analysis, the cognitive analysis includes quantitative analysis of a visit possibility for the filtered page collection; (This limitation is part of the abstract idea recited claim 1. This step involves evaluating the user behavior with the visited web pages using quantitative analysis, which is a mental process that can be performed in the human mind, see MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
providing page jump information for the managing of the open web pages. (This limitation is part of insignificant extra-solution activity as described in claim 1. This step involves providing the result of the data analysis to the user. This represents a generic computer component (i.e., presenting information to the user) that is recited at a high level of generality and does not impose any meaningful limitation to the process.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above in step 2A, prong Two, the additional element adds insignificant extra-solution activity to the judicial exception. The providing step represents a generic computer function that is recognized by the courts as well-understood, routine, and conventional function (see MPEP § 2106.05(d)).
Therefore, claim 9 is ineligible.
Regarding Amended Claim 10,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 10. The only difference is that claim 1 is drawn to a method, and claim 10 is drawn to a system. The recitation of “a computer system comprising; a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor, to cause the computer system to perform the following functions…,” which is directed to the applying of computer instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements amount to no more than mere instruction to apply the abstract idea on a computer. Therefore, the additional elements do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, claim 10 is ineligible.
Regarding Original Claim 11,
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 11.
Therefore, claim 11 is ineligible.
Regarding Original Claim 12,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 12.
Therefore, claim 12 is ineligible.
Regarding Amended Claim 13,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 13.
Therefore, claim 13 is ineligible.
Regarding Amended Claim 14,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding Amended Claim 15,
The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding Amended Claim 16,
The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding Original Claim 17,
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Original Claim 18,
The claim recites similar limitations as corresponding claim 9. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 9, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Amended Claim 19,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 19. The only difference is that claim 1 is drawn to a method, and claim 19 is drawn to a computer program product. The recitation of “a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform functions...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instruction to apply the abstract idea using a generic computer component. Therefore, the additional elements do not integrate the judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, claim 19 is ineligible.
Regarding Original Claim 20,
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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 the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 9-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jitkoff et al., (Pub. No.: US 9110568 B2) in view of Arnold et al., (Pub. No.: US 20210383252 A1), further in view of Birch et al., (Pub. No.: US 20200042567 A1), and further in view of Saad et al., (Pub. No.: US 20210406593 A1).
Regarding Amended Claim 1,
Jitkoff discloses the following:
A computer-implemented method for facilitating web page selection for a user navigating multiple web pages, comprising: (Jitkoff, [Col. 1, lines 16-18] “This document relates to methods and apparatus for displaying and manipulating information in a web browser and, in particular, to browser tab management.”)
determining, using a computer, a probability of each of a plurality of web pages being visited by a user, the web pages being opened by the user using a device, the probability being determined using …, and user operational behavior analysis based on an analysis of the web page features for each of the web pages being visited by the user (Jitkoff, [col. 2, lines 38-45] A user's interaction with a plurality of online content is monitored, and user activity metadata generated by, and associated with, the user's interaction with the online content is collected. A plurality of tabs of the online content is opened in a graphical user interface, and a relative importance of each of the plurality of tabs to the user is determined based on the user activity metadata associated with the online content in the tab…) and filtering the web pages by classifying the web page features; (Jitkoff, [Col. 8, lines 26-35] “Clusters 400 and 440 can be created to indicate not only pages that are related by cross-references as discussed above, but also to indicate pages that are related by page content. Thus, a cluster could be formed to delineate tabs of pages that are categorically similar to each other, e.g., that all contain online content from news sites, that all contain online content from sports sites, that all contain online content from banks and financial sites, that all contain online content from personal dating sites, and the like.” Jitkoff, [Col. 9, lines 34-40] “The tab manipulator 516 also may be configured to organize the tabs 522, 522 b into groups or clusters of tabs according to one or more criteria which may be determined or commanded by the tab selection manager 512. In turn, the groups or clusters of tabs then may be displayed in the organized representation of the tabs 522 to the user 590 via the display 504…”)
ordering, using a cognitive analysis, the open web pages in an order based on the probability of each of the web pages being visited by the user, the order ranking the plurality of web pages based on a highest probability of a web page of the plurality of web pages being visited; (Jitkoff, [Col. 9, lines 27-30] “The tab selection manager 512 can rank the open tabs according to their relative importance to the user, which may be based at least in part on the user activity metadata associated with the online content in each tab.” Jitkoff, [Col. 7, lines 11-23 ] “… the position of the plurality of tabs 302, 304, 306, and 308 in the window 300 can be determined based, at least in part, on user activity metadata that is used to indicate the relative importance of the online content in each individual tab to the user. The determination of the relative importance of each tab 302, 304, 306, and 308 can be based on a plurality of factors, and which may be weighted differently or equally in different algorithms used to determine a relative importance to the user of each tab. For example, the tabs 302, 304, 306, and 308 can be positioned linearly on the tab bar 310 in order of their relative importance to the user, with the tab of highest importance being placed to the left and tabs of lowest importance being placed to the left right.”)
managing the open web pages based on the ordering of the open web pages, the managing including prompting the user to visit a web page of the open web pages in response to the ranking of one of the open web pages in the order; (Jitkoff, [Col. 6, lines 16-19] “… Use the historical and statistical user activity metadata it has collected to actively manage (230) the user's tabs. For example, the browser can reposition tabs based on their relative importance to the user,” Jitkoff, [Col. 7, lines 21-22] in order of their relative importance to the user, with the tab of highest importance being placed to the left …”)
As explained above, Jitkoff discloses a system and method of a web browser management for manipulation and selection of windows of a web browser including selection manager that determines a ranking or relative importance of web pages based on user activity metadata and manipulator for organizing and grouping web pages (i.e., open tabs) based on ranking. Jitkoff does not appear to explicitly teach:
the probability being determined using machine learning
determining a key word for each of the open web pages, and using a transformer, generating word vectors;
classifying open web pages which belong to a same classification in one or more queues, and marking each of the queues to show each of the web pages has been read or unread;
recommending a web page of the open web pages to the user, in response to detecting when the user jumps between the open web pages and goes back from a classification to a web page of the open web pages which is in a different classification, the recommendation being from unread web pages from a classification based on a distance from a word vector of a web page to a center of the classification; and
generating the word vector includes inputting the word vectors into a basic vector generation module which calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively.
However, Jitkoff in view of Arnold teaches the following:
determining, using a computer, a probability of each of a plurality of web pages being visited by a user, the web pages being opened by the user using a device, (Arnold, [0156] “The contextual hub system 116 monitors the average view frequency by accessing the number of times a user visits a web page over a defined period of time. For instance, the contextual hub system 116 can determine the average number of times a user visits a particular web page over the course of a day, week, month, etc. [0189] “For instance, the web pages 704 can include web pages that a user has visited through a web browser and/or via the contextual hub 714.” [0162] “The contextual hub system 116 may utilize a signal processing algorithm as part of the act 504 of analyzing the usage signals. …, the contextual hub system 116 utilizes the signal processing algorithm to generate a score for a tab. Using the signal processing algorithm, the contextual hub system 116 can apply different weights to different usage signals to determine the score for a tab.” [0163] “In at least one embodiment, as part of the act 504 of analyzing tab signals, the contextual hub system 116 generates a tab score. For instance, a higher tab score corresponds to a higher probability that the user will reopen and access the particular tab. Lower tab scores correspond to a lower likelihood that the user will return to access the particular tab.”) the probability being determined using machine learning and user operational behavior analysis based on an analysis of the web page features for each of the web pages being visited by the user (Arnold, [0218] In some implementations (e.g., implementations where the contextual learning model 1004 is a probability determination neural network rather than a binary classification neural network), the ground-truth related information includes a ground truth relevance score (represented as a probability that the content is related to a contextual hub) for the training third-party source content 1016. [0235] The contextual hub system 116 inputs the training user information 1302 into the hub proposal machine learning model 1304 (e.g., a binary classification neural network or a probability determination neural network). The hub proposal machine learning model 1304 analyzes the training user information 1302 to generate and pass latent features and other data between various neurons and layers to generate predicted contextual models 1306.”) and filtering the web pages (Arnold, [0205] “… the contextual hub system 116 can also provide a sorted focused web browsing history to the user via the contextual hub. [0206] “As discussed previously, the contextual hub system 116 can generate and train various types of contextual models. For instance, the contextual model can comprise a filter as illustrated in FIG. 9.”) by classifying the web page features; (Arnold, [0121] “The contextual hub system 116 can determine the predicted contextual hub label 364 based on domain names or content within the tabs 368 a-368 c. In at least one embodiment, the contextual hub system 116 analyzes the actual content associated with the tabs 368 a-368 d to predict the contextual hub label 364. For instance, the contextual hub system 116 can extract feature vectors from URL addresses, text, images, and other data from the third-party sources. [0125] In at least one embodiment, and as illustrated in FIG. 3C, the contextual hub system 116 generates predicted tab labels 372a-372c. Generally, the predicted tab labels 372a-372c indicate content linked by the tabs 368a-368c. The contextual hub system 116 presents the predicted tab labels 372a-372c in the tabs 368a-368c. The contextual hub system 116 can generate the predicted tab labels 372a-372c by extracting a title from the URL or content of the linked web page or third-party source.”)
determining a key word for each of the open web pages, and using a transformer, generating word vectors; (Arnold, [0171] “In addition to usage patterns, the contextual hub system 116 can determine to suggest or to automatically combine two tabs in a particular contextual hub based on a content similarity score between content within a tab and contextual information related to a contextual hub. …, the contextual hub system 116 identifies key words associated with a source to generate the word vector for the source. Similarly, the contextual hub system 116 can identify key words associated with a contextual hub and generate a word vector for the contextual hub. Then, the contextual hub system 116 can compare the two word vectors to determine a difference between the word vectors, which results in the content similarity score.” Further see [0200] and [0207]-[0209].)
classifying open web pages which belong to a same classification in one or more queues, and marking each of the queues to show each of the web pages has been read or unread; (Arnold, [0167] “Additionally, the contextual hub system 116 can associate particular actions with ranges of tab scores. In one embodiment, and as illustrated in FIG. 5, the contextual hub system 116 determines to group tabs with similar scores. In particular, the contextual hub system 116 determines to move tabs 2 and 3 within the same contextual hub based on determining that the tab scores for tabs 2 and 3 fall within a predetermined range of 40-70.” [0169] “In particular, the contextual hub system 116 may determine to group tabs based on frequency of use. For example, the contextual hub system 116 may group a first set of tabs that are used on a daily basis and a second set of tabs that are used less frequently. Furthermore, the contextual hub system 116 may determine to group tabs based on common access hours.” [0171] “In addition to usage patterns, the contextual hub system 116 can determine to suggest or to automatically combine two tabs in a particular contextual hub based on a content similarity score between content within a tab and contextual information related to a contextual hub.” [0284] “As illustrated in FIG. 16, the contextual hub system 116 updates the viewing status (1618) of the web-accessible content. In particular, the contextual hub system 116 updates the viewing status to reflect the user operation.” [0295] “As illustrated in FIG. 17A, the contextual hub system 116 can present viewership notifications that indicate a viewing status via the contextual hub management graphical user interface 1704. For instance, the contextual hub system 116 can present a viewership notification together with specific content within the contextual hub. In at least one embodiment, the viewership notifications comprise a “not seen” icon, a “present” icon, and a “seen” icon. The “not seen” icon reflects that the user has not yet accessed the content. The “present” icon reflects that a user is currently viewing or accessing the content. The “seen” icon reflects that the user has accessed but is not currently viewing the content.”) [Note: Grouping tabs based on usage patterns and content similarity (e.g., tab scores, frequency of use, and web pages or tabs content features). Usage patterns include viewership notification (i.e., “not seen” and “seen”).]
recommending a web page of the open web pages to the user, in response to detecting when the user jumps between the open web pages and goes back from a classification to a web page of the open web pages which is in a different classification, (Arnold, [0047] “To illustrate, the contextual hub system can monitor usage signals associated with a tab within a contextual hub. For instance, the contextual hub system can monitor the user's view frequency, a time last accessed, the average time spent viewing, and other types of usage signals. …, the contextual hub system might determine to automatically close a tab or suggest closing the tab, add a tab or suggest adding a tab to a contextual hub, move a tab or suggest to move a tab from a first contextual hub to a second contextual hub, as will be explained in greater detail below.” [0170] “Moreover, the contextual hub system 116 may detect that a user is performing a back-and-forth tab navigation between two or more tabs as a usage pattern. In other words, by detecting that a user is accessing a first tab, then a second tab, then a first tab again within a threshold period of time (e.g., 10 minutes) the contextual hub system 116 determines that it is likely the user accessing both tabs for the same project and can suggest to the user to combine the tabs into a single contextual hub. …, In some embodiments, the contextual hub system can automatically combine the tabs and provide a notification to the user that the tabs have been combined.” [0164] “Additionally, or alternatively, the contextual hub system 116 may determine an action of sending a notification suggesting a course of action. For example, the contextual hub system 116 can provide, for display at a client device, a notification suggesting that the user close the tab or suggest a different contextual hub to which the user can move the tab.” Further see [0175].) the recommendation being from unread web pages from a classification based on ... from a word vector of a web page to a center of the classification; (Arnold, [0171] “the contextual hub system 116 can compare content within a web-accessible source (e.g., content within a third-party web site or content within a document within a content management system) to contextual information of a contextual hub to generate a content similarity score. If, for example, the content similarity score ... matches a defined threshold ... then the contextual hub system 116 can suggest adding the web-accessible source to the contextual hub. Similarly, the contextual hub system 116 can identify key words associated with a contextual hub and generate a word vector for the contextual hub. Then, the contextual hub system 116 can compare the two word vectors to determine a difference between the word vectors, which results in the content similarity score. Thus, the lower the difference, the more similar the source and the contextual hub. Other scoring systems can be used as well, including counting and/or comparing key words.”) [Examiner’s Note: web pages not yet associated with (or added to) a particular contextual hub including suggesting adding or moving tabs would read on the recommendation being from unread web pages. The suggestion is based similarity score generated by comparing word vectors to determine a difference.]
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the method/system of Jitkoff to incorporate the contextual hub system as taught by Arnold. One would have been motivated to make such a combination in order to improve web search, browsing, and communication. Doing so would provide improvements to accuracy, efficiency, and flexibility relative to conventional systems (Arnold [0059]).
While Jitkoff in view of Arnold describes web browser contextual hub system that maintains groups of related pages (contextual hubs) using content similarity scores between pages by identifying keywords, generating word vectors, and comparing word vectors to determine the difference. Jitkoff in view of Arnold does not define the similarity scores and comparison of word vectors based on distance from a word vector. Jitkoff in view of Arnold do not appear to explicitly teach:
the recommendation being from unread web pages from a classification based on a distance from a word vector of a web page to a center of the classification; and
generating the word vector includes inputting the word vectors into a basic vector generation module which calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively.
However, Birch, in combination with Jitkoff and Arnold, teaches:
recommending a web page of the open web pages to the user, in response to detecting when the user jumps between the open web pages and goes back from a classification to a web page of the open web pages which is in a different classification, the recommendation being from unread web pages from a classification based on a distance from a word vector of a web page to a center of the classification; (Birch, [0052] “a web browser implementing the techniques discussed herein may use a similarity analysis to determine which web pages are topically related to a particular task … , and, when the user's browser activity relates to that task, the web browser may automatically display pages relating to the task (and/or other navigation suggestions, such as a suggested page or a suggested action that helps the user accomplish the task). For example, in some implementations, the navigation suggestion includes a list of previously-rendered web pages in which the user is likely to return to. In some implementations, the navigation suggestion includes a suggested web page (e.g., a web page in which the user has not yet visited).” [0056] “In addition, user reaction to the navigation suggestions can be monitored to create a feedback loop to refine further recommendations. …, In some implementations, the user may be provided with controls allowing the user to make an election as to both if and when the web browser and/or the system may enable the monitoring of user reaction to navigation suggestions.” [0069] “In some implementations, the navigation suggestion 128 includes a list (or a subset) of the web pages 121 of the second group 118. In some implementations, the web pages 121 of the second group 118 are filtered or sorted based on the times in which the web pages 121 were accessed or rendered, and/or the level of user engagement with the web pages 121, and the filtered or sorted list of web pages 121 of the second group 118 are provided as one or more navigation suggestions 128 on the user interface 124 of the web browser 122, which provides the web pages 121 that the user is likely to re-visit to complete or continue with the second task. In some implementations, the navigation suggestion 128 includes a suggested web page (e.g., not part of the second group 118) but topically-related to the second group 118 to help the user with the second task. In some implementations, the navigation suggestion 128 includes a suggested course of action to help the user with the second task.” [0088]-[0089] “The similarity analyzer 108 may create a feature vector based on the keywords and their probabilities. …,The similarity analyzer 108 may create a feature vector for each navigation tree 114 using the keywords and probabilities, and determine distance (or similarity) vectors based on comparisons of the feature vectors.” Further see [0121].)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Jitkoff and Arnold before them, to incorporate the Browser-based navigation suggestions system as taught by Birch. One would have been motivated to make such a combination in order reduces the amount of computational resource of the computing device consumed by the web browser by programmatically providing one or more navigation suggestions to a user in order to accomplish and/or complete a task associated with use of the web browser. Doing so would allow more accurate navigation suggestions over time (Birch [0053]-[0056]).
While Birch teaches similarity analyzer that creates feature vectors based on keywords of web pages and determines the distance based on comparisons of the feature vectors. The combination of Jitkoff, Arnold, and Birch does not appear to explicitly suggest:
calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively.
However, Saad, in combination with Jitkoff, Arnold, and Birch, teaches:
generating the word vector includes inputting the word vectors into a basic vector generation module which calculates a Euclidean distance, when the number of the word vectors is greater than 1, choosing a minimum instance for each point to vectors, respectively. (Saad, [0070]-[0071] “the content-prediction application applies a neural network (e.g., an artificial neural network) to the set of web pages to generate vectors representative of the respective web pages. In some instances, the neural network (e.g., a multilayer deep neural network) trained with website data can be applied to a web page (e.g., a web page) of the previous interaction data. For example, each word appearing in the web page is assigned as an input value for a set of input values, and the input values are applied to the neural network to generate an m-dimensional vector representative of the web page.” [0073]-[0074] “the content-prediction application identifies one or more colors based on positions of the vectors in the embedding space. In some instances, the color can be identified by calculating distances between vectors (e.g., Euclidean, Minkowski distance, Hamming distance, cosine distance/similarity) and identifying a position in which the nearest distance among the vectors, ... the neural network generates a plurality of vectors that represent the web page, instead of a single vector representing the web page as the above implementation. After the plurality of vectors for each web page are plotted in the embedding space, the EMD between two vectors representing two web pages can be determined based on semantic distances between words in the respective web pages, where words can be accessed from an electronic lexical database called WordNet. Once the semantic distances between words are obtained, the EMD calculates the similarity between two web pages with a many-to-many matching between words.” [0094]-[0097] “the Euclidean distance d(A, B) is calculated for a color-palette vector representative of the color palette A=[a1, a2, . . . , an] and a content vector representative of a content item of the catalog B=[b1, b2, . . . bn]. A lower Euclidean distance value may indicate a higher degree of similarity between vectors A and B. ... the set of items can be ranked based on their respective distance metrics, from the lowest distance metric value to the highest distance metric value. The catalog can be generated such that the set of items are presented in accordance with the determined rankings. ... An average or a median value of the set of distance metrics can be calculated, which is then identified whether such value is less than the predetermined threshold.”) [Examiner’s Note: Saad teaches the process of generating multiple word embedding vectors and comparing at least two vectors when computing the similarity or Euclidean distances between two or more word vectors to select the nearest. The limitation “when the number of the word vectors is greater than 1,” defines a condition that is inherently met when computing similarity or distance between two or more vectors. Therefore, it would have been obvious that the number of word vectors is greater than 1 to compute the distance between two vectors and select the nearest.]
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Jitkoff, Arnold, and Birch before them, to incorporate the methods that facilitate prediction of content based on vector data structures generated from web pages as taught by Saad. One would have been motivated to make such a combination in order to generate vectors based on content object, facilitates prediction of content based on vector data structures, and select the color palettes based on a color-palette preference predicted for the user. Doing so would an improvement of user-interface systems that can be achieved based on automatic relocation of user-interface elements based on prediction of content items in response to a color-palette selection (Saad [0027] & [0059]).
Regarding Original Claim 2, the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
analyzing web page features of respective web pages opened by a user; (Arnold, [0122] “The contextual hub system 116 can determine the predicted contextual hub label 364 based on domain names or content within the tabs 368 a-368 c. In at least one embodiment, the contextual hub system 116 analyzes the actual content associated with the tabs 368 a-368 d to predict the contextual hub label 364. For instance, the contextual hub system 116 can extract feature vectors from URL addresses, text, images, and other data from the third-party sources. The contextual hub system 116 can compare the feature vectors from the third-party sources to identify common themes, and the contextual hub system 116 can generate the contextual hub label 364 based on the common themes.” [0150] “Additionally, or alternatively, the contextual hub system 116 can monitor contextual hub features in determining an action to perform with respect to a tab. In particular, the contextual hub system 116 can analyze contextual hub features such as the number of tabs within the contextual hub, content within the contextual hub, and other features.”[Note: the contextual information including analyzing contextual features of content, see [0067]- [0068].]) filtering the web pages by classifying the web page features using a matrix of classification for each of the features, (Arnold, [0125] “The contextual hub system 116 can generate the predicted tab labels 372 a-372 c by extracting a title from the URL or content of the linked web page or third-party source.” [0140] “As illustrated in FIG. 3F, the contextual hub system 116 presents identified tab labels 375 a-375 b within the predicted results 391. The contextual hub system 116 may present, within the predicted results 391, any and all potential results. In at least one embodiment, the contextual hub system 116 lists a specific number of identified tabs within the predicted results 391 and presents the specific number of the most relevant tabs.” [0149] “Generally, the contextual hub system 116 receives and analyzes signals that indicate which tabs should remain open within a contextual hub and which tabs should be closed or otherwise deleted from a contextual hub.” [0206] “As discussed previously, the contextual hub system 116 can generate and train various types of contextual models. For instance, the contextual model can comprise a filter as illustrated in FIG. 9.” [0216] “the contextual hub system 116 can identify other characteristics by which to define the contextual hub filter. For instance, the contextual hub system 116 may utilize a machine learning model to determine latent features and characteristics by which the contextual hub filter may more accurately identify related content.” Further see [0209]. [Note: determining whether content contains information that is contextually relevant to a contextual hub, generating predicted tab labels based on the content or features of the linked web page, and using contextual model (e.g., machine learning model) to determine latent features (feature vector).]) and generating a filtered web page collection based on the classifying of the web page features. (Arnold, [0142] “ the contextual hub system 116 can list the identified tab labels 375 a-375 b grouped by contextual hub. …,For instance, the contextual hub system 116 can list identified tabs located within the travel contextual hub first because the travel contextual hub is currently displayed within the tab menu 340.” [0171] “the contextual hub system 116 can determine to suggest or to automatically combine two tabs in a particular contextual hub based on a content similarity score between content within a tab and contextual information related to a contextual hub.” [0184] “To provide this functionality, the contextual hub system 116 can generate a contextual model that identifies related web-accessible content to group relevant content within a single contextual hub.” Further see [0095].)
Regarding Original Claim 3, the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
wherein the managing includes prompting the user to visit a web page of the open web pages having the highest probability of being visited by the user. (Jitkoff, [Col. 6, lines 16-23] “The browser can then use the historical and statistical user activity metadata it has collected to actively manage (230) the user's tabs. For example, the browser can reposition tabs based on their relative importance to the user and/or move tabbed pages that appear to be of relatively low current interest to the user to a position that indicates a low priority for the tabbed page (e.g., to the right-hand side of a tab bar of a browser window).” [Col.7, lines 15-22] “The determination of the relative importance of each tab 302, 304, 306, and 308 can be based on a plurality of factors, and which may be weighted differently or equally in different algorithms used to determine a relative importance to the user of each tab. For example, the tabs 302, 304, 306, and 308 can be positioned linearly on the tab bar 310 in order of their relative importance to the user, with the tab of highest importance being placed to the left ...”)
Regarding Amended Claim 4, the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
wherein the web page features include multiple attributes relating to each of the open web pages. (Jitkoff, [Col. 4, lines 3-23 ] “Some examples of intrinsic (or content) metadata include the web page's title, the URL at which the page is stored... Some examples of extrinsic or activity metadata include the number of times the page has been accessed by the user, the date and time of last access to the page by the user...”)
Regarding Amended Claim 5,
the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
wherein the web page features include multiple attributes relating to each of the open web pages; (Jitkoff, [Col. 4, lines 3-23 ] “Some examples of intrinsic (or content) metadata include the web page's title, the URL at which the page is stored... Some examples of extrinsic or activity metadata include the number of times the page has been accessed by the user, the date and time of last access to the page by the user...”)
and analyzing the web page features wherein the analyzing includes classifying the features and attributes into patterns. (Arnold, [0168] “… the contextual hub system 116 calculates usage patterns by determining the length of average usage session for each hour of the day for a determined period of time (e.g., day, week, month, year, etc.).” [0169] “The contextual hub system 116 may determine to move a tab from one contextual hub to another based on usage patterns. …, In particular, the contextual hub system 116 may determine to group tabs based on frequency of use. For example, the contextual hub system 116 may group a first set of tabs that are used on a daily basis and a second set of tabs that are used less frequently.”)
Regarding Amended Claim 6,
the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
wherein the web page features include multiple attributes relating to each of the open web pages. (Jitkoff, [Col. 4, lines 3-23 ] “Some examples of intrinsic (or content) metadata include the web page's title, the URL at which the page is stored... Some examples of extrinsic or activity metadata include the number of times the page has been accessed by the user, the date and time of last access to the page by the user...”)
analyzing the web page features wherein the analyzing includes classifying the features and attributes into patterns, wherein the patterns include a continuation regular pattern and a noncontinuation pattern. (Arnold, [0168] “In one or more embodiments, the contextual hub system 116 determines to perform an action based on usage patterns associated with a tab. In particular, the contextual hub system 116 can determine patterns of a user accessing one or more tabs to determine two tabs are related.” [0169] “… the contextual hub system 116 may determine to group tabs based on frequency of use. For example, the contextual hub system 116 may group a first set of tabs that are used on a daily basis and a second set of tabs that are used less frequently. Furthermore, the contextual hub system 116 may determine to group tabs based on common access hours. …etc.” [0170] “Moreover, the contextual hub system 116 may detect that a user is performing a back-and-forth tab navigation between two or more tabs as a usage pattern. …etc.”) [Examiner’s Note: Usage patterns including frequent and less frequent.]
Regarding Amended Claim 7,
the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
wherein the web page features include opening time of the page and a source of the page. (Jitkoff, [Col. 4, lines 3-23 ] “Some examples of intrinsic (or content) metadata include the web page's title, the URL at which the page is stored... Some examples of extrinsic or activity metadata include the number of times the page has been accessed by the user, the date and time of last access to the page by the user...”)
Regarding Original Claim 9, the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
determining a page jump operation using the cognitive analysis, the cognitive analysis includes quantitative analysis of a visit possibility for the filtered page collection; and providing page jump information for the managing of the open web pages. (Arnold, [0149]-[0162] “The series of acts 400 includes the act 402 of monitoring usage signals associated with a tab. Generally, the contextual hub system 116 receives and analyzes signals .... the contextual hub system 116 can receive event reports from a contextual hub and analyze signals within the event report, such as view frequency, time last accessed, the type of content, the time spent viewing, and other signals. ... the contextual hub system 116 assigns a usage score to each of the tabs .... the contextual hub system 116 can apply different weights to different usage signals to determine the score for a tab.” [0169]-[0170] “The contextual hub system 116 may determine to move a tab from one contextual hub to another based on usage patterns. Generally, the contextual hub system 116 can group tabs within a contextual hub to consolidate tabs that the user will likely access at the same time. In particular, the contextual hub system 116 may determine to group tabs based on frequency of use. ... the contextual hub system 116 may detect that a user is performing a back-and-forth tab navigation between two or more tabs as a usage pattern. ... the contextual hub system 116 determines that it is likely the user accessing both tabs for the same project and can suggest to the user to combine the tabs into a single contextual hub.” [0110] “the contextual hub management graphical user interface 304 includes elements for navigating, organizing, and managing tabs within a contextual hub. As illustrated in FIG. 3A, the tab menu 340 includes information specific to the contextual hub represented by the contextual hub label 322. For instance, the tab menu 340 displays contextual hub label 322, the tab elements 338 a-338 d, a contextual hub description 324, an open tabs element 332, and a recently closed tabs element 336. Generally, the contextual hub description 324 includes a brief summary describing the source and/or content within the contextual hub linked to the contextual hub label 322. As illustrated, based on user selection of the open tabs element 332 or the recently closed tabs element 336, the contextual hub system 116 can present currently open tabs or recently closed tabs associated with the contextual hub label 322, respectively.” [0164] “the contextual hub system 116 performs the act 506 of determining an action. In particular, the act 506 comprises determining an action to perform with respect to the contextual hub. The contextual hub system 116 may determine to perform at least the following actions on a tab: close the tab, keep the tab open, move the tab to a new or different contextual hub, and reorder the tab within the same contextual hub.” Further described [0366]-[0373].)
Regarding Amended Claim 10,
The claim recites substantially similar limitation as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a computer-implemented method, and claim 10 is directed to a computer system comprising; a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor, to cause the computer system to perform the following functions...
Jitkoff also discloses “systems and techniques to manage and display information in a user interface.” FIG. 5 is an exemplary block diagram of a system for managing multiple tabs of online content.
Regarding Original Claim 11,
The claim recites substantially similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Original Claim 12,
The claim recites substantially similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Amended Claim 13,
The claim recites substantially similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding Amended Claim 14,
The claim recites substantially similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Amended Claim 15,
The claim recites substantially similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding Amended Claim 16,
The claim recites substantially similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Regarding Original Claim 18,
The claim recites substantially similar limitations as corresponding claim 9 and is rejected for similar reasons as claim 9 using similar teachings and rationale.
Regarding Amended Claim 19,
The claim recites substantially similar limitation as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a computer-implemented method, and claim 19 is directed to a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform functions, by the computer, comprising the functions... .
Jitkoff also discloses “A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory 704, the storage device 706, or memory on processor 702.” See [Co. 10, Lines 55-65].
Regarding Original Claim 20,
The claim recites substantially similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Claim(s) 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Jitkoff, Arnold, Birch, and Saad as described above, and further in view of Nie et al., (Pub. No.: US 20200110998 A1).
Regarding Original Claim 8,
the combination of Jitkoff, Arnold, Birch, and Saad teaches the elements of claim 1 as outlined above, and further teaches:
While the combination of Jitkoff, Arnold, Birch, and Saad teaches the determination of the probability of web pages being viewed or revisited by the user and Birch discloses the tab switcher interface of the web browser that includes a logic for switching between multiple browsing tabs (i.e., auto-tab list) to select one of the opened browser tabs.
the combination of Jitkoff, Arnold, Birch, and Saad do not appear to explicitly teach:
the determining includes logic for switching pages; and training a learning module to learn the logic.
However, Nie, in combination with Jitkoff, Arnold, Birch, and Saad, teaches the limitations:
the determining includes logic for switching pages; and training a learning module to learn the logic. (Nie, [0025] “A user session flow includes a series of web pages visited by the user during a user session. Each user session's activities are treated as sequence data. A learning model (e.g., Long Short-Term Memory) is built on top of this sequence data to predict the user's next activity. [0026] The web server generates a learning model using a neural network based on the plurality of users' sessions. The learning model is configured to predict a next user activity based on a current page flow of a current user session. The next user activity indicates one of continuing the current user session by visiting another web page provided by the web server and ending the current user session.” Further see [0062].)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective date of the claimed invention, having the combination of Jitkoff, Arnold, Birch, and Saad, to incorporate the method/system for improving user engagement based on user session analysis as taught by Nie. One would have been motivated to make such a combination in order to dynamically modify a web page in response to the user's activities and a prediction based on the user's activities. Doing so would improve user engagement with the web browsing (Nie [0040]).
Regarding Original Claim 17,
The claim recites substantially similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Pub. No.: US 20210373728 A1) – “Justin James WAGLE” relates to “Machine learning-assisted graphical user interface for content organization.”
(Pub. No.: US 20180373723 A1) – “LEVI; Thomas Scott” relates to “Method and system for applying a machine learning approach to ranking webpages' performance relative to their nearby peers.”
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/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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