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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/412,282 filed on July 03, 2025. Claims 1-8, 11, and 15-25 are pending with the application.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202010799664.4, filed on 8/9/2020.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 11, and 15-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1, the limitations directed towards obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user; obtaining a knowledge profile of the user, wherein the knowledge profile of the user comprises a job of the user; obtaining a parameter value of a first knowledge parameter of the user based on the knowledge profile, wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user; obtaining a deep reading content list based on the deep reading keyword list, a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user; mapping content in the deep reading content list to an entry of a preset deep reading display style to generate deep reading information, is a process that, under its broadest reasonably interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components. That is, other than reciting a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device, nothing in the claim precludes these steps from practically being performed in the mind and/or by a human with pen and paper.
For example, but for the limitations stating a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device, the mention of obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user; obtaining a knowledge profile of the user, wherein the knowledge profile of the user comprises a job of the user; obtaining a parameter value of a first knowledge parameter of the user based on the knowledge profile, wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user; obtaining a deep reading content list based on the deep reading keyword list, a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user; mapping content in the deep reading content list to an entry of a preset deep reading display style to generate deep reading information, in the context of this claim, encompasses a user mentally determining keywords based on reading a document in an effort to find more documents to read as the user takes in to consideration a job title. If a claim limitation, under its broadest reasonable interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application by additional elements. In particular, a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of search) such that it amounts to no more than mere instructions to apply the exception. The recitation of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is interpreted by the examiner to be insignificant extra solution activity and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The recitation of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). To further elaborate, the additional limitations of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Claim 1 is not patent eligible.
Claims 11 and 22 recite similar limitations as in claim 1. Therefore claim 11 and 22 are rejected for the same reasons as set forth above. See claim 1 for analysis.
With respects to claims 2, 15, and 23, the limitations are directed towards wherein the obtaining a deep reading content list based on the deep reading keyword list, a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user comprises: obtaining the first knowledge graph from a preset original knowledge graph based on the deep reading keyword list; obtaining an initial content list from the content library based on the first knowledge graph; and filtering the initial content list based on the parameter value of the first knowledge parameter of the user to obtain the deep reading content list. The elements directed to further elaborate the abstract idea and the human mind and/or with pen and paper can determine a deep reading content list based on the deep reading keyword list, a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user by determining a first knowledge graph from the preset original knowledge graph based on the deep reading keyword list, determining an initial content list from a content library based on the first knowledge graph, and determining the initial content list based on the parameter value of the first knowledge parameter of the user to determine the deep reading content list. Therefore, claims 2, 15, and 23 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 3, 16, and 24, the limitations are directed towards wherein the obtaining a first knowledge graph from the preset original knowledge graph based on the deep reading keyword list comprises: finding, from the preset original knowledge graph based on the deep reading keyword list, a graph tab corresponding to a deep reading keyword in the deep reading keyword list; and extracting the first knowledge graph from the preset original knowledge graph based on the found graph tab, wherein the first knowledge graph is a partial knowledge graph that is in the preset original knowledge graph and that comprises the found graph tab. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine a first knowledge graph from the preset original knowledge graph based on the deep reading keyword list by determining, from the preset original knowledge graph based on the deep reading keyword list, a graph tab corresponding to a deep reading keyword in the deep reading keyword list; and determining the first knowledge graph from the preset original knowledge graph based on the found graph tab, wherein the first knowledge graph is a partial knowledge graph that is in the preset original knowledge graph and that comprises the found graph tab. Therefore, claims 3, 16, and 24 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 4, 17, and 25, the limitations are directed towards wherein the obtaining an initial content list from the content library based on the first knowledge graph comprises: obtaining, from the content library based on classification information comprised in the first knowledge graph, content under the classification information to obtain a first content list; obtaining, from the first content list based on graph tabs comprised in the first knowledge graph, content whose content tab hits at least one graph tab to obtain a second content list; and determining the initial content list based on the second content list. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine an initial content list from a content library based on the first knowledge graph by: determining, from the content library based on classification information comprised in the first knowledge graph, content under the classification information to obtain a first content list; determining, from the first content list based on graph tabs comprised in the first knowledge graph, content whose content tab hits at least one graph tab to obtain a second content list; and determining the initial content list based on the second content list. Therefore, claims 4, 17, and 25 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 5 and 18 the limitations are directed towards wherein the filtering the initial content list based on the parameter value of the first knowledge parameter of the user to obtain a deep reading content list comprises: calculating a first topic depth value based on the parameter value of the first knowledge parameter of the user; and selecting, from the initial content list, content whose topic depth value matches the first topic depth value to obtain the deep reading content list. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine the initial content list based on the parameter value of the first knowledge parameter of the user to determine a deep reading content list by determining a first topic depth value based on the parameter value of the first knowledge parameter of the user, and determining, from the initial content list, content whose topic depth value matches the first topic depth value to obtain the deep reading content list. Therefore, claims 5 and 18 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 6 and 19 the limitations are directed towards wherein before the mapping content in the deep reading content list to an entry of a preset deep reading display style, the method further comprises: obtaining a parameter value of first classification information of the content currently browsed by the user; and obtaining a deep reading display style corresponding to the parameter value of the first classification information. These additional elements further elaborates the abstract idea and merely confine the claim to a particular technological environment or field of use. Therefore, claims 6 and 19 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 7 and 20 the limitations are directed towards wherein the deep reading request carries a content identifier of the content currently browsed by the user; and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: searching for the deep reading keyword list from the content library based on the content identifier, wherein the found deep reading keyword list is a deep reading keyword list of content indicated by the content identifier. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine a deep reading request carries a content identifier of the content currently browsed by the user, determine, based on the deep reading request, a deep reading keyword list of content currently browsed by the user by searching for the deep reading keyword list from the content library based on the content identifier, wherein the found deep reading keyword list is a deep reading keyword list of content indicated by the content identifier. Therefore, claims 7 and 20 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
With respects to claims 8 and 21 the limitations are directed towards wherein the deep reading request carries the deep reading keyword list of the content currently browsed by the user; and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: obtaining, from the deep reading request, the deep reading keyword list of the content currently browsed by the user. These elements further elaborates the abstract idea and the human mind and/or with pen and paper can determine the deep reading request carries the deep reading keyword list of the content currently browsed by the user and the determine, based on the deep reading request, a deep reading keyword list of content currently browsed by the user by determining, from the deep reading request, the deep reading keyword list of the content currently browsed by the user. Therefore, claims 8 and 21 do not recite additional limitations which tie the abstract idea into a practical application and does not amount to significantly more than the identified judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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) 1-5, 11, and 15-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Killalea et al. (U.S. Patent No.: US 8250071 B1) hereinafter Killalea, in view of Liu (U.S. Publication No.: US 20200320086 A1) hereinafter Liu, and further in view of Cohen et al. (U.S. Publication No.: US 20100028846 A1) hereinafter Cohen.
As to claim 1:
Killalea discloses:
A content recommendation method, wherein the content recommendation method [Column 5 Lines 24-27 teach the servers 124(1)-(S) may be embodied in any number of ways, including as a single server, a cluster of servers, a server farm or data center, and so forth, although other server architectures (e.g., a mainframe architecture) may also be used. Column 11 Lines 45-48 teach Also shown on the reader page 808 is a recommendation region 832 beneath the identity tile 810. In the recommendation region 832, recommendations may be made to the reader to learn more about a particular term.] and comprises:
receiving, by a server, a request for a content list sent by an electronic device [Column 3 Lines 45-47 teach terms deemed to be of interest to readers during consumption of eBooks are collected by reader devices and aggregated at a remote service. Column 4 Lines 19-21 teach this interest may be exhibited through requesting a definition, highlighting the term], wherein the server provides content from a content library to a plurality of electronic devices [Column 3 Lines 49-53 teach each reader 102(1)-(N) employs a corresponding electronic device 106(1), . . . , 106(D) to consume one or more eBooks. The electronic devices 106(1)-(D), or generally devices 106, are each capable of rendering, playing, or otherwise presenting the eBook or other content items.];
transmitting, by the server, the content list and associated content to the electronic device [Column 3 Lines 49-53 teach each reader 102(1)-(N) employs a corresponding electronic device 106(1), . . . , 106(D) to consume one or more eBooks. The electronic devices 106(1)-(D), or generally devices 106, are each capable of rendering, playing, or otherwise presenting the eBook or other content items.]
receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives a selection operation performed by a user on a deep reading button displayed on the electronic device [Column 7 Lines 66-67 and Column 8 Line 1 teaches In FIG. 3, the reader 102(1) had requested a dictionary definition of the term "fair." Column 9 Lines 30-32 teach the controls may be selected using the keyboard 404, the navigation mechanism 408, other physical control buttons, or soft keys via a touch-responsive screen.];
obtaining, by the server, a knowledge profile of the user, wherein the knowledge profile of the user comprises a job of the user [Column 6 Lines 25-31 teach user metadata 206 may also be used by the vocabulary service 122. The user metadata 206 may include a geographic location of the user 206(1), a date of birth 206(2), educational level 206(3), occupation 206(4), friends/associates or social network 206(5), other comparable users 206(6), books previously read by the user 206(7), preferred languages/dialects 206(8) known to the user, and other information 206(U).]
obtaining, by the server, a parameter value of a first knowledge parameter of the user based on the knowledge profile [Column 6 Lines 25-31 teach user metadata 206 may also be used by the vocabulary service 122. The user metadata 206 may include a geographic location of the user 206(1), a date of birth 206(2), educational level 206(3), occupation 206(4), friends/associates or social network 206(5), other comparable users 206(6), books previously read by the user 206(7), preferred languages/dialects 206(8) known to the user, and other information 206(U).],
mapping, by the server, content in the deep reading content list to an entry of a preset deep reading display style to generate deep reading information and
transmitting, by the server, the deep reading information to the electronic device [Column 11 Llines 54-62 teaches FIG. 9 shows a second screen rendering 902 of the vocabulary information UI 806 that is presented in response to the reader activating the term control 824 for the term "yonder" (FIG. 8). The screen rendering 902 is a term page that provides a view of the vocabulary information from the term's perspective. In this example, the term page 902 is for the particular vocabulary term "yonder" as noted by the heading at the top of the page. Note: Fig. 9 also shows 904 a definition portion of the display style reads in the claims.];
Killalea discloses some of the limitations as set forth in claims 1 but does not appear to expressly disclose obtaining, by the server, based on the deep reading request, a deep reading keyword list of content currently browsed by the user obtaining, by the server, a deep reading content list based on the deep reading keyword list, obtaining a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user, wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user.
Liu discloses:
obtaining, by the server, based on the deep reading request, a deep reading keyword list of content currently browsed by the user [Paragraph 0005 teaches during operation, the system can select a content piece from a content library and extract, by a computer using a natural language processing (NLP) technique, one or more keywords from the content piece. Paragraph 0043 teaches once the domain-knowledge-based model is obtained, the system can apply this model during an online process. More specifically, the system can extract domain-knowledge-based features from online content (e.g., a webpage browsed by the user, content pieces currently displayed in the application page, content pieces in a content library, etc.) using the domain-knowledge-based model. Note: Extracting keywords that features from content pieces that currently displayed in the application page reads on the claimed obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user.];
obtaining, by the server, a deep reading content list based on the deep reading keyword list, obtaining a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user [Paragraph 0005 teaches during operation, the system can select a content piece from a content library and extract, by a computer using a natural language processing (NLP) technique, one or more keywords from the content piece. Paragraph 0011 teaches the domain knowledge can include one or more domain-knowledge graphs. A respective domain-knowledge graph can include an entity name and a number of attribute words associated with the entity. Paragraph 0025 teaches the system can also generate, for each individual user, a user attribute tag that indicates the user's preference or interest. Paragraph 0035 teaches the system can apply domain knowledge to organize extracted feature words or keywords associated with a particular domain or topic into a hierarchical structure (operation 208). For example, the hierarchical structure can be a tree structure, with the root of the tree being the domain name and leaves of the tree being words that have specific meanings in the domain. Paragraph 0043 teaches once the domain-knowledge-based model is obtained, the system can apply this model during an online process. More specifically, the system can extract domain-knowledge-based features from online content (e.g., a webpage browsed by the user, content pieces currently displayed in the application page, content pieces in a content library, etc.) using the domain-knowledge-based model. The extracted domain-knowledge-based features can be used to generate a domain-knowledge-based content label or tag, which can then be used to match users' labels or tags. Note: Generating content labels based on the keywords, the user preferences (the first knowledge parameter), and use/applying a knowledge domain that includes one or more graphs (obtaining a preset original knowledge graph) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, by incorporating generating content labels based on the keywords, user preferences, and applying knowledge domain, as taught by Liu (see Paragraph 0005, 0011, 0025, 0035, and 0043), because both applications are directed to data analysis; incorporating generating content labels based on the keywords, user preferences, and applying knowledge domain provides an improvement in the quality of the content recommendation (see Liu Paragraph 0010).
Killalea and Liu discloses some of the limitations as set forth in claims 1 but does not appear to expressly disclose wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user.
Cohen discloses:
wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user [Paragraph 0168 teaches obtaining a baseline scored challenges score, two optional goals are achieved. First, the user and/or the user's organization can accurately assess what the user already knows (e.g., from work experience or other types of training). Note: Determining what a user already knows (first knowledge parameter/cognitive ability) based on work experience (job) of a user reads on the claims. ]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea and Liu, by incorporating determining what a user already knows based on work experience, as taught by Cohen (see Paragraph 0168), because the three publications are directed to data analysis; incorporating determining what a user already knows based on work experience improves the user's performance and the user is much more likely to continue utilizing the training system for this reason as well (see Cohen Paragraph 0067).
Claims 11 and 22 recite similar limitations as in claim 1. Therefore claims 11 and 221 are rejected for the same reasons as set forth above. See claim 1 for analysis.
As to claim 2:
Killalea, Liu, and Cohen discloses all of the limitations as set forth in claims 1.
Liu also discloses:
The content recommendation method according to claim 1, wherein the obtaining a deep reading content list based on the deep reading keyword list, a preset knowledge graph, and the parameter value of the first knowledge parameter of the user comprises: obtaining a first knowledge graph from the preset original knowledge graph based on the deep reading keyword list [Paragraph 0011 teaches the domain knowledge can include one or more domain-knowledge graphs. A respective domain-knowledge graph can include an entity name and a number of attribute words associated with the entity. Paragraph 0035 teaches the system can apply domain knowledge to organize extracted feature words or keywords associated with a particular domain or topic into a hierarchical structure (operation 208). For example, the hierarchical structure can be a tree structure, with the root of the tree being the domain name and leaves of the tree being words that have specific meanings in the domain.]; obtaining an initial content list from a content library based on the first knowledge graph [Paragraph 0070 teaches more specifically, the content-recommendation system may retrieve a relatively large number of content based on the matching between the feature tags of the content pieces and the user attribute tag. To enhance the user's reading experience, further content screening and filtering can be provided.]; and filtering the initial content list based on the parameter value of the first knowledge parameter of the user to obtain the deep reading content list [Paragraph 0025 teaches the system can also generate, for each individual user, a user attribute tag that indicates the user's preference or interest. Paragraph 0070 teaches more specifically, the content-recommendation system may retrieve a relatively large number of content based on the matching between the feature tags of the content pieces and the user attribute tag. To enhance the user's reading experience, further content screening and filtering can be provided. Note: Filtering recommended content pieces (deep reading content list) based on user tags and attributes (user profile) and a knowledge graph based on keywords wherein the user tag also represents the user preferences (first knowledge parameter of the user) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, by incorporating content piece filtering based on keywords, as taught by Liu (see Paragraph 0025, 0035, 0043, and 0070), because both applications are directed to data analysis; incorporating content piece filtering based on keywords provides an improvement in the quality of the content recommendation (see Liu Paragraph 0010).
Claims 15 and 23 recite similar limitations as in claim 2. Therefore claims 15 and 23 are rejected for the same reasons as set forth above. See claim 2 for analysis.
As to claim 3:
Killalea, Liu, and Cohen discloses all of the limitations as set forth in claims 1 and 2.
Liu also discloses:
The content recommendation method according to claim 2, wherein the obtaining a first knowledge graph from the preset original knowledge graph based on the deep reading keyword list comprises: finding, from the preset original knowledge graph based on the deep reading keyword list, a graph tab corresponding to a deep reading keyword in the deep reading keyword list [Paragraph 0037 teaches the next level nodes can include keywords that can be used to label different categories in the domain. Paragraph 0038 teaches the hierarchical domain knowledge can facilitate the identification of individual words that have strong and clear meanings in a particular domain, thus making these words valuable in expressing the true meaning of content pieces. Note Leaf nodes (graph tabs) that are associated with keywords from a hierarchical domain knowledge graph (preset original knowledge graph) reads on the claims. ]; and extracting the first knowledge graph from the preset original knowledge graph based on the found graph tab, wherein the first knowledge graph is a partial knowledge graph that is in then preset original knowledge graph and that comprises the found graph tab [Paragraph 0041 teaches entity node 402 can identify an entity name “driver license,” and the attribute nodes can each include a word or phrase that describes an attribute of the entity “driver license,” such as “point deduction,” “renewal,” “new regulation,” etc…. Domain-knowledge graph 400 can specify a number of combination words, including “driver license-renewal,” “driver license-new regulation,” etc., with each combination word combining the entity word with one of its attribute. The meaning of each combination word is clearly defined in the domain, thus allowing the combination word to closely express the meaning of content pieces. Note: Determining a combination of nodes (first knowledge graph) from a hierarchical domain knowledge graph (preset original knowledge graph), wherein the combination nodes is based on a keyword leaf node (found graph tab) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, by incorporating knowledge graph data used to identify a combination of nodes from within a graph associated with keywords, as taught by Liu (see Paragraph 0037, 0038, and 0041), because both applications are directed to data analysis; incorporating knowledge graph data used to identify a combination of nodes from within a graph associated with keywords provides an improvement in the quality of the content recommendation (see Liu Paragraph 0010).
Claims 16 and 24 recite similar limitations as in claim 3. Therefore claims 16 and 24 are rejected for the same reasons as set forth above. See claim 3 for analysis.
As to claim 4:
Killalea, Liu, and Cohen discloses all of the limitations as set forth in claims 1 and 2.
Liu also discloses:
The content recommendation method according to claim 2, wherein the obtaining an initial content list from a content library based on the first knowledge graph comprises: obtaining, from the content library based on classification information comprised in the first knowledge graph, content under the classification information to obtain a first content list [Paragraph 0007 teaches a category associated with the content piece.]; obtaining, from the first content list based on graph tabs comprised in the first knowledge graph, content whose content tab hits at least one graph tab to obtain a second content list [Paragraph 0041 teaches entity node 402 can identify an entity name “driver license,” and the attribute nodes can each include a word or phrase that describes an attribute of the entity “driver license,” such as “point deduction,” “renewal,” “new regulation,” etc…. Domain-knowledge graph 400 can specify a number of combination words, including “driver license-renewal,” “driver license-new regulation,” etc., with each combination word combining the entity word with one of its attribute. The meaning of each combination word is clearly defined in the domain, thus allowing the combination word to closely express the meaning of content pieces. Paragraph 0070 teaches more specifically, the content-recommendation system may retrieve a relatively large number of content based on the matching between the feature tags of the content pieces and the user attribute tag. To enhance the user's reading experience, further content screening and filtering can be provided. Note: Filtering recommended content pieces (deep reading content list) based on user tags and attributes (user profile) and a knowledge graph based on keywords of the knowledge graph (first knowledge graph) nodes (graph tabs) to obtain filtered out recommended content pieces (second content list) reads on the claims.] and;
determining the initial content list based on the second content list [Paragraph 0070 teaches more specifically, the content-recommendation system may retrieve a relatively large number of content based on the matching between the feature tags of the content pieces and the user attribute tag. To enhance the user's reading experience, further content screening and filtering can be provided. Note: The remaining content pieces that better enhance the user’s reading experience (initial content list) based on the content pieces that were filtered out (second content list) wherein there are reasonably further filtering steps to obtain further filtered content pieces reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, by incorporating content piece filtering based on keywords, as taught by Liu (see Paragraph 0035, 0043, and 0070), because both applications are directed to data analysis; incorporating content piece filtering based on keywords provides an improvement in the quality of the content recommendation (see Liu Paragraph 0010).
Claims 17 and 25 recite similar limitations as in claim 4. Therefore claims 17 and 25 are rejected for the same reasons as set forth above. See claim 4 for analysis.
As to claim 5:
Killalea, Liu, and Cohen discloses all of the limitations as set forth in claims 1 and 2.
Liu also discloses:
The content recommendation method according to claim 2, wherein the filtering the initial content list based on the parameter value of the first knowledge parameter of the user to obtain a deep reading content list comprises:
calculating a first topic depth value based on the parameter value of the first knowledge parameter of the user [Paragraph 0043 teaches the extracted domain-knowledge-based features can be used to generate a domain-knowledge-based content label or tag, which can then be used to match users' labels or tags. The users' label can include similar domain-knowledge-based feature words. Paragraph 0072 teaches each feature word in the feature tag that matches a feature word in the user's attribute tag can be assigned a score based on its weight, and the total score of the content piece can be the weighted sum of all matching feature words. The ranking of the content pieces can be determined based on their scores. Note: Obtaining a user’s attribute (a parameter value of a first knowledge parameter of the user) and tagging the attribute (calculating) with a tag (a first topic depth value based on the parameter value of the first knowledge parameter of the user) reads on the claims.]; and selecting, from the initial content list, content whose topic depth value matches the first topic depth value to obtain the deep reading content list [Paragraph 0071 teaches when the user is reading a news story in a news application, additional news stories having features matching the user's attribute tag can be recommended and displayed to the user on a side panel of the news application.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, by incorporating content piece filtering based on keywords, as taught by Liu (see Paragraph 0035, 0043, and 0070), because both applications are directed to data analysis; incorporating content piece filtering based on keywords provides an improvement in the quality of the content recommendation (see Liu Paragraph 0010).
Claim 18 recites similar limitations as in claim 5. Therefore claim 18 is rejected for the same reasons as set forth above. See claim 5 for analysis.
Claim(s) 6-8 and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Killalea et al. (U.S. Patent No.: US 8250071 B1) hereinafter Killalea, in view of Liu (U.S. Publication No.: US 20200320086 A1) hereinafter Liu, in view of Cohen et al. (U.S. Publication No.: US 20100028846 A1) hereinafter Cohen, and further in view of Reitter et al. (U.S. Publication No.: US 20070288431 A1) hereinafter Reitter.
As to claim 6:
Killalea, Liu, and Cohen discloses some of the limitations as set forth in claims 1 but does not appear to expressly disclose wherein before the mapping content in the deep reading content list to an entry of a preset deep reading display style, the method further comprises: obtaining a parameter value of first classification information of the content currently browsed by the user and obtaining a deep reading display style corresponding to the parameter value of the first classification information.
Reitter discloses:
The content recommendation method according to claim 1, wherein before the mapping content in the deep reading content list to an entry of a preset deep reading display style, the method further comprises: obtaining a parameter value of first classification information of the content currently browsed by the user [Paragraph 0141 teaches Keyword Extractor (KE) currently suggests keyword+category combinations only if the keyword appears on a page that it processes. Note: Obtaining category (classification information) from web pages that it processes (currently browsed) by the user reads on the claims.]; and obtaining a deep reading display style corresponding to the parameter value of the first classification information [Paragraph 0298-0303 teaches determining how categories should be displayed (a deep reading display style) corresponding to the extraction of a category information using KE (keyword extractor) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, Liu, and Cohen, by incorporating obtaining category from web pages that it processes by the user, as taught by Reitter (see Paragraphs 0141 and 0298-0303), because the four publications are directed to data analysis; incorporating obtaining category from web pages that it processes by the user improves performance of ads over time (see Reitter Paragraph 0010).
Claim 19 recites similar limitations as in claim 6. Therefore claim 19 is rejected for the same reasons as set forth above. See claim 6 for analysis.
As to claim 7:
Killalea, Liu, and Cohen discloses some of the limitations as set forth in claims 1 but does not appear to expressly disclose wherein the deep reading request carries a content identifier of the content currently browsed by the user and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: searching for the deep reading keyword list from a content library based on the content identifier, wherein the found deep reading keyword list is a deep reading keyword list of content indicated by the content identifier.
Reitter discloses:
The content recommendation method according to claim 1, wherein the deep reading request carries a content identifier of the content currently browsed by the user [Paragraph 0536 teaches detection of an end-user interaction with a Web page, generating keywords based on the end-user interaction with the Web page.]; and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: searching for the deep reading keyword list from a content library based on the content identifier, wherein the found deep reading keyword list is a deep reading keyword list of content indicated by the content identifier [Paragraph 0063 teaches this database contains the information that is generated by the Extractor Service for each URL and returns the data. Note: Utilizing a database to lookup keywords (deep reading keyword list) for each URL (content identifier) reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, Liu, and Cohen, by incorporating utilizing a database to lookup keywords (deep reading keyword list) for each URL (content identifier), as taught by Reitter (see Paragraphs 0063 and 0536), because the four publications are directed to data analysis; incorporating utilizing a database to lookup keywords (deep reading keyword list) for each URL (content identifier) improves performance of ads over time (see Reitter Paragraph 0010).
Claim 20 recites similar limitations as in claim 7. Therefore claim 20 is rejected for the same reasons as set forth above. See claim 7 for analysis.
As to claim 8:
Killalea, Liu, and Cohen discloses some of the limitations as set forth in claims 1 but does not appear to expressly disclose wherein the deep reading request carries the deep reading keyword list of the content currently browsed by the user and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: obtaining, from the deep reading request, the deep reading keyword list of the content currently browsed by the user.
Reitter discloses:
The content recommendation method according to claim 1, wherein the deep reading request carries the deep reading keyword list of the content currently browsed by the user [Paragraph 0536 teaches detection of an end-user interaction with a Web page, generating keywords based on the end-user interaction with the Web page. Note: Currently browsed web page that is associated with user interaction, wherein the web page must also include the keywords (carries the dep reading keyword list) reads on the claims.]; and the obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user comprises: obtaining, from the deep reading request, the deep reading keyword list of the content currently browsed by the user [Paragraph 0536 teaches detection of an end-user interaction with a Web page, generating keywords based on the end-user interaction with the Web page.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Killalea, Liu, and Cohen, by incorporating currently browsed web page that is associated with user interaction, wherein the web page must also include the keywords (carries the dep reading keyword list), as taught by Reitter (see Paragraphs 0536), because the four publications are directed to data analysis; incorporating currently browsed web page that is associated with user interaction, wherein the web page must also include the keywords (carries the dep reading keyword list) improves performance of ads over time (see Reitter Paragraph 0010).
Claim 21 recites similar limitations as in claim 8. Therefore claim 21 is rejected for the same reasons as set forth above. See claim 8 for analysis.
Response to Arguments
Applicant’s arguments with respect to 35 USC § 103 rejections directed to claim 1 have been considered but are moot because the new ground of rejection does not rely on any combinations of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant presents the following arguments in July 3, 2025 remarks pages 10-11:
“…Applicant respectfully submits that the amended claims recite eligible subject matter...”
Examiner respectfully presents the following response to Applicant’s remarks:
Applicant’s arguments have been fully considered but they are not persuasive. Regarding independent claim 1, but for the limitations stating a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device, the mention of obtaining, based on the deep reading request, a deep reading keyword list of content currently browsed by the user; obtaining a knowledge profile of the user, wherein the knowledge profile of the user comprises a job of the user; obtaining a parameter value of a first knowledge parameter of the user based on the knowledge profile, wherein the first knowledge parameter of the user comprises a cognitive ability of the user, and the cognitive ability of the user is determined based on at least the job of the user in the knowledge profile of the user; obtaining a deep reading content list based on the deep reading keyword list, a preset original knowledge graph, and the parameter value of the first knowledge parameter of the user; mapping content in the deep reading content list to an entry of a preset deep reading display style to generate deep reading information, in the context of this claim, encompasses a user mentally determining keywords based on reading a document in an effort to find more documents to read as the user takes in to consideration a job title. If a claim limitation, under its broadest reasonable interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the examiner maintains the claim recites an abstract idea.
The judicial exception is not integrated into a practical application by additional elements. In particular, a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of search) such that it amounts to no more than mere instructions to apply the exception. The recitation of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is interpreted by the examiner to be insignificant extra solution activity and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea. The examiner maintains these elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use for data gathering in conjunction with the abstract idea.
This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The recitation of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device is interpreted to be well understood, routine and conventional activity (Receiving or transmitting data over a network, Symantec (see MPEP 2106.06(d))). To further elaborate, the additional limitations of a content recommendation method, wherein the content recommendation method comprises: receiving, by a server, a request for a content list sent by an electronic device, wherein the server provides content from a content library to a plurality of electronic devices; transmitting, by the server, the content list and associated content to the electronic device; receiving, by the server, a deep reading request sent by the electronic device, wherein the deep reading request is sent when the electronic device receives selection operation performed by a user on a deep reading button displayed on the electronic device, and transmitting, by the server, the deep reading information to the electronic device does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use. Therefore the examiner maintains claim 1 is not patent eligible.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EARL ELIAS whose telephone number is (571)272-9762. The examiner can normally be reached Monday - Friday (IFP).
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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