DETAILED ACTION
1. Claims 1-6, 8-19 and 21-22 are pending in this application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Response to Amendment
3. This office action is in response to applicant’s amendment filed on 11/26/2025 in response to the non-final rejection mailed on 10/16/2025. Claims 1, 3-5, 8, 11-12 and 18 have been amended. Claims 2, 6, 9-10, 13-17 and 19 have been kept original. Claims 7 and 20 have been cancelled. Claims 21-22 have been newly presented. Amendment has been entered.
Response to Arguments
4. Applicant's arguments, filed on 11/26/2025, with respect to the rejection of claims 1-6, 8-19 and 21-22 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant’s arguments, pages 9-14), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
The Applicant argues that the limitation “generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges;” can’t not be practical be performed in the human mind. Respectfully, the examiner disagrees. Humans naturally organize knowledge into semantic networks, where nodes represent concepts (words, phrases, or entities) and edges represent the semantic relationships between them. For example, when reading a story, your brain creates a node for "Protagonist" and an edge labeled "lives in" connected to a node for "Paris." There is nothing so complex in the limitation that could not be doing in the human mind. The tripartite graph generated here is simple; it can be formed by two nodes and two edges, making it practical to maintain in the human mind. Besides that, humans can also use pen and paper to assist in the construction of such a tripartite graph, which would make it simpler and more doable. For example, a student can simplify a complex history lesson by sketching a tripartite graph on paper, using three distinct columns for People, Dates, and Events connected by hand-drawn lines.
The Applicant argues that the limitation “identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph;” can’t not be practical be performed in the human mind. Respectfully, the examiner disagrees. A human can observe a plurality of items make judgments based on criteria such as number of occurrence and identity what item is similar one to another in a tripartite graph. For example, a human can identify similar items on a shopping list and identify those item that is similar on the list. A human can observe a tripartite graph and mentally traverse from one node to another by following its edges. For example, a chef can mentally traverse a graph to connect a Guest (Node A) to their Order (Node B), and finally to the specific Ingredient (Node C) needed to prepare it. There is nothing so complex in the limitation that could not be doing in the human mind.
The Applicant argues that “the tripartite graph is a complex
data structure that includes "vertices divided into three disjoint subsets …” Nonetheless, the current claims fail to describe the structural complexity inherent in a tripartite graph. Applicant then cited Specification paragraphs [0014], [0369]-[0040], [0056] and [0080]. However, the elements of those paragraphs are not entirely described in the claims. The claimed tripartite graph does not require billions or millions of data points; it simply requires a couple of data points to be formed.
The Applicant argues that “claim 1 improve the functioning of computing devices within the technical field of key phrase recommendation systems.” However, the claims fail to describe such an improvement in detail. A claim having broad applicability across many fields of endeavor may not provide meaningful limitations that integrate a judicial exception into a practical application or amount to significantly more, see 2106.05(f)(3). The claims, as presented, are applicable to several fields. As the applicant notes, “conventional techniques for keyphrase recommendations typically use neural networks trained using supervised techniques,” which suggests that this technology already has common areas of application. With respect to paragraph [0014] of the specification, while the description outlines potential advantages, these are not currently captured in the claims.
The Applicant argues that “The claimed solution addresses these computational limitations through significant technical improvements.” and cite specification paragraphs [0014], [0021], [0044]. However, it appears that the argument may suggest a solution that was not described in detail in the claims. See MPEP 2106.05(f)(1) – “Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished.”.
For all the above reasons, the rejection of claims 1-6, 8-19 and 21-22 under 35 U.S.C. §101 an abstract idea (mental process) is upheld.
Applicant's arguments, filed on 11/26/2025, with respect to the rejection of claims 1-6, 8-19 and 21-22 under 35 U.S.C. §103 (Applicant’s arguments, pages 15-18), have been fully considered and are but are moot because the independent claims are amended and introduce new limitations that were not previously presented newly found prior art has been applied.
Claim Rejections - 35 USC § 101
5. 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 1-6, 8-19 and 21-22 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea (Mental Process) without significantly more. The claims similarly recite steps to key phrase recommendation.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-6, 8-11 and 21-22 are directed to a method.
Claims 12-17 are directed to a system.
Claim 18-19 is directed to a non-transitory computer-readable storage medium.
Therefore, claims 1-6, 8-11 and 21-22 fall into at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per independent claim 1, the claim recites the limitations of:
“generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges;” A humans naturally organize knowledge into semantic networks, where nodes represent concepts (words, phrases, or entities) and edges represent the semantic relationships between them. For example, when reading a story, your brain creates a node for "Protagonist" and an edge labeled "lives in" connected to a node for "Paris." The tripartite graph generated here is simple; it can be formed by two nodes and two edges, making it practical to maintain in the human mind. Besides that, humans can also use pen and paper to assist in the construction of such a tripartite graph, which would make it simpler and more doable. For example, a student can simplify a complex history lesson by sketching a tripartite graph on paper, using three distinct columns for People, Dates, and Events connected by hand-drawn lines. There is nothing so complex in the limitation that could not be doing in the human mind.
“identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph;” A human can observe a plurality of items make judgments based on criteria such as number of occurrence and identity what item is similar one to another in a tripartite graph. For example, a human can identify similar items on a shopping list and identify those item that is similar on the list. A human can observe a tripartite graph and mentally traverse from one node to another by following its edges. For example, a chef can mentally traverse a graph to connect a Guest (Node A) to their Order (Node B), and finally to the specific Ingredient (Node C) needed to prepare it. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 2, the claim recites the limitation of:
“further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.” A human can observe and judge historical data and associate it with key phrases. For example, a human can observe a folder of photos and pair the photos with the date the photos were taken. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 3, the claim recites the limitation of:
“further comprising generating clusters of the key phrases, each of the clusters including the key phrases mapped via the items to a same occurrence count of the one or more seed tokens in the tripartite graph.” A human can observe and judge a document's data, select the key phrases that occur most frequently, and group them. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 4, the claim recites the limitation of:
“further comprising associating a key phrase with a highest occurrence count of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph.” A human can observe a group of key phrases that have associated scores and make judgments to order them, with the highest-scoring phrases listed first. A human can also mentally associate the highest-scoring key phrase with a single item. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 5, the claim recites the limitation of:
“wherein identifying the one or more similar items comprises filtering the clusters having the occurrence counts that are below an occurrence threshold, resulting in one or more retained clusters that include the key phrases that map to the one or more similar items in the tripartite graph.” A human can mentally observe a group of key phrases, make a judgment, and then mentally filter that group to match specific criteria. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 6, the claim recites the limitation of:
“further comprising setting the occurrence threshold at a value at which the filtering produces a number of the key phrases in the one or more retained clusters that exceeds a retention threshold.” A human can mentally set a value to be used as a filter's criterion for selecting key phrases judged good to retain. The human can then filter key phrases that are within the established criteria to form a group. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 8, the claim recites the limitation of:
“further comprising ranking candidate key phrases of the key phrases that map to the one or more similar items in the tripartite graph, the at least one key phrase representing a top-ranked subset of the candidate key phrases.” A human can observe and rank a group of key phrases based on their own judgment and criteria. The top-ranked subset is simply the group of key phrases that the human determined to be the best. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 9, the claim recites the limitation of:
“wherein the candidate key phrases are ranked in descending order of the occurrence counts associated with respective candidate key phrases.” The candidate key phrases are ranked in descending order of the occurrence counts associated with respective candidate key phrases recited above is a merely component used for to implement the mental steps recited in claim 1. Besides that, a human is also capable of ranking key phrases in descending order based on simple judgments of criteria and observations of the key phrases.
As per dependent claim 10, the claim recites the limitation of:
“wherein the candidate key phrases associated with a same value of the occurrence counts are ranked in descending order of percentages of phrase tokens in the respective candidate key phrases that match the one or more seed tokens.” The candidate key phrases associated with the same value of the occurrence counts are ranked in descending order of percentages of phrase tokens in the respective candidate key phrases that match the one or more seed tokens recited above is a merely component used for to implement the mental steps recited in claim 1. Besides that, a human is also capable of ranking key phrases in descending order based on simple judgments of criteria and observations of the key phrases.
As per dependent claim 11, the claim recites the limitation of:
“wherein the candidate key phrases having same values of the occurrence counts and the percentages are ranked in descending order of quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph.” A human can observe a collection of key phrases and judge it to match specific criteria. Besides that, a human is also capable of ranking key phrases in descending order based on simple judgments of criteria and observations of the key phrases. There is nothing so complex in the limitation that could not be doing in the human mind.
As per independent claim 12, the claim recites the limitations of:
“generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges;” A humans naturally organize knowledge into semantic networks, where nodes represent concepts (words, phrases, or entities) and edges represent the semantic relationships between them. For example, when reading a story, your brain creates a node for "Protagonist" and an edge labeled "lives in" connected to a node for "Paris." The tripartite graph generated here is simple; it can be formed by two nodes and two edges, making it practical to maintain in the human mind. Besides that, humans can also use pen and paper to assist in the construction of such a tripartite graph, which would make it simpler and more doable. For example, a student can simplify a complex history lesson by sketching a tripartite graph on paper, using three distinct columns for People, Dates, and Events connected by hand-drawn lines. There is nothing so complex in the limitation that could not be doing in the human mind.
“traverse the tripartite graph to identify one or more similar items of the items based on occurrence counts of the one or more seed tokens connected to the one or more similar items via the first set of edges in the tripartite graph;” A human can observe a plurality of items make judgments based on criteria such as number of occurrence and identity what item is similar one to another in a tripartite graph. For example, a human can identify similar items on a shopping list and identify those item that is similar on the list. A human can observe a tripartite graph and mentally traverse from one node to another by following its edges. For example, a chef can mentally traverse a graph to connect a Guest (Node A) to their Order (Node B), and finally to the specific Ingredient (Node C) needed to prepare it. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 13, the claim recites the limitation of:
“wherein the instructions further cause the system to pair an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.” A human can observe and judge historical data and associate it with key phrases. For example, a human can observe a folder of photos and pair the photos with the date the photos were taken. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 14, the claim recites the limitation of:
“wherein the instructions further cause the system to generate clusters of the key phrases, each of the clusters including the key phrases connected via the items to a same occurrence count of the one or more seed tokens in the tripartite graph.” A human can observe and judge a document's data, select the key phrases that occur most frequently, and group them. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 15, the claim recites the limitation of:
“wherein the instructions further cause the system to associate a key phrase with a highest occurrence count of the one or more seed tokens connected to a single item to which the key phrase is connected in the tripartite graph.” A human can observe a group of key phrases that have associated scores and make judgments to order them, with the highest-scoring phrases listed first. A human can also mentally associate the highest-scoring key phrase with a single item. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 16, the claim recites the limitation of:
“wherein the instructions further cause the system to filter the clusters having the occurrence counts that are below a threshold, resulting in one or more retained clusters that include the key phrases connected to the one or more similar items in the tripartite graph.” A human can mentally observe a group of key phrases, make a judgment, and then mentally filter that group to match specific criteria. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 17, the claim recites the limitation of:
“wherein the instructions further cause the system to rank candidate key phrases of the key phrases connected to the one or more similar items in the tripartite graph based on the occurrence counts, percentages of phrase tokens in respective candidate key phrases that match the one or more seed tokens, and quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph, wherein the at least one key phrase represents a top-ranked subset of the candidate key phrases..” A human can observe and rank a group of key phrases based on their own judgment and criteria. The top-ranked subset is simply the group of key phrases that the human determined to be the best. There is nothing so complex in the limitation that could not be doing in the human mind.
As per independent claim 18, the claim recites the limitations of:
“generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges;” A humans naturally organize knowledge into semantic networks, where nodes represent concepts (words, phrases, or entities) and edges represent the semantic relationships between them. For example, when reading a story, your brain creates a node for "Protagonist" and an edge labeled "lives in" connected to a node for "Paris." The tripartite graph generated here is simple; it can be formed by two nodes and two edges, making it practical to maintain in the human mind. Besides that, humans can also use pen and paper to assist in the construction of such a tripartite graph, which would make it simpler and more doable. For example, a student can simplify a complex history lesson by sketching a tripartite graph on paper, using three distinct columns for People, Dates, and Events connected by hand-drawn lines. There is nothing so complex in the limitation that could not be doing in the human mind.
“determining, by traversing the tripartite graph, occurrence counts of the key phrases, an occurrence count of a key phrase representing a highest number of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph;” A human can observe a plurality of items make judgments based on criteria such as number of occurrence and identity what item is similar one to another in a tripartite graph. For example, a human can identify similar items on a shopping list and identify those item that is similar on the list. A human can observe a tripartite graph and mentally traverse from one node to another by following its edges. For example, a chef can mentally traverse a graph to connect a Guest (Node A) to their Order (Node B), and finally to the specific Ingredient (Node C) needed to prepare it. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 19, the claim recites the limitation of:
“the operations further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.” A human can observe and judge historical data and associate it with key phrases. For example, a human can observe a folder of photos and pair the photos with the date the photos were taken. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 21, the claim recites the limitation of:
“wherein each unique title token of the title tokens is a vertex in the tripartite graph.” The requirement wherein each unique title token is a vertex in the tripartite graph is merely an element used to implement abstract ideas.
Accordingly, claims 1-6, 8-19 and 21-22 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per dependent claim 1, the claim recites the additional elements of:
“an at least one computing device and listing platform” These elements are examples of mere instruction 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)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per dependent claim 1, the claim recites the additional limitations of:
“receiving a dataset that comprises items listed via a listing platform, titles of the items, and key phrases, wherein each of the items are paired with one or more of the key phrases;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“outputting at least one key phrase of the key phrases that maps to the one or more similar items via the second set of edges in the tripartite graph.” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering and transmission, without any further processing or analysis. Outputting information is known in the art as transmitting the gating data to be viewed on a display, or simply transmitting the data to be used in a system.
As per dependent claim 12, the claim recites the additional elements of:
“a one or more processors, memory storing instructions and listing platform” These elements are examples of mere instruction 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)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per dependent claim 12, the claim recites the additional limitations of:
“receiving a dataset that comprises items listed via a listing platform, titles of the items, and key phrases, wherein each of the items are paired with one or more of the key phrases;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“output at least one key phrase of the key phrases connected to the one or more similar items via the second set of edges in the tripartite graph.” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering and transmission, without any further processing or analysis. Outputting information is known in the art as transmitting the gating data to be viewed on a display, or simply transmitting the data to be used in a system.
As per dependent claim 18, the claim recites the additional elements of:
“a one or more processors and listing platform” These elements are examples of mere instruction 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)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
As per dependent claim 18, the claim recites the additional limitations of:
“receiving a dataset that comprises items listed via a listing platform, titles of the items, and key phrases, wherein each of the items are paired with one or more of the key phrases;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“outputting the key phrases as ranked based, in part, on the occurrence counts.” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering and transmission, without any further processing or analysis. Outputting information is known in the art as transmitting the gating data to be viewed on a display, or simply transmitting the data to be used in a system.
As per dependent claim 22, the claim recites the limitation of:
“further comprising storing, in a memory device, the tripartite graph in compressed sparse row format.” Storing the tripartite graph in a memory device using the compressed sparse row format is merely storing the graph in a specified format. Store data is a kwon form of data gathering (see MPEP § 2106.05(g)).
Therefore, claims 1-6, 8-19 and 21-22 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “receiving …; outputting …; and storing ….” are well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “receiving …; outputting …; and storing ….” as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” and/or MPEP 2106.05(d)(ii) “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);” Also see Bartz et al. (US 20080120072 A1) - a common concept to receive one or more seed terms and [Display Interface - an overview | ScienceDirect Topics, Introducing ASP.NET Web Pages - Displaying Data | Microsoft Docs, Execute DBCC PAGE command to Display Contents of Data Pages in SQL Server (kodyaz.com) and Load and display paged data | Android Developers].
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-6, 8-19 and 21-22 are not patent eligible.
Claim Rejections - 35 USC § 103
6. 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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. § 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 1, 3-6, 8-11, 18 and 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Henkin et al. (US 20110213655 A1) in view of Ramanathan et al. (US 20140229810 A1) in further view of Arici et al. (US 12086851 B1) still in further view of Kolb et al. (US 20180032930 A1).
As per claim 1, Henkin teaches a method implemented by at least one computing device, the method comprising (i.e. “methods, systems, and computer program products for facilitating on-line contextual advertising operations implemented in a computer network.”; fig. 1, para. [0051]-[0052]):
receiving a dataset (i.e. “Hybrid System 108 receives the web page content from the PUB server 104”; fig. 1, para. [0061], [0117], [0137]; Examiner note: the dataset is interpreted as the web page content)
that comprises items listed (i.e. “generate page information (e.g., page classifier data) and KeyPhrase information (e.g., list identified KeyPhrases on page which may be suitable for highlight/mark-up).”; figs. 16A, 66A, para. [0117]; Examiner note: the items listed is interpreted as the page information)
via a listing platform (i.e. “Hybrid System 108”; [0117]; Examiner note: the listing platform is interpreted as the Hybrid System),
titles of the items, and key phrases (i.e. “a web page content is obtained and displayed where the web page content comprises keyword/key phrases ant titles”; fig. 66A. Further, i.e. “Example Information kept for each phrase/phrases: [1697] text [1698] source (manual, automatic, meta KeyPhrases, title) [1699] frequency (number of docs the phrase appeared in) [1700] related phrases (e.g., Bush, George Bush, President of the United States)”; para. [1696]-[1700]),
wherein each of the items are paired with one or more of the key phrases (i.e. “the information displays in the Hybrid Management GUI has a plurality of rows and columns in a table where each row can be an items/URLs in the table”; figs. 66A. Further, i.e. figs. 70A-B also displays items/URLs associated with key phrases. Furthermore, i.e. “In one example, items are linked to a source web page (or other content item) through a keyphrase or phrase on the page. The keyphrase or phrase may be ordinary text and may be selected and converted into a link that is highlighted on the page.”; para. [0060], [0062]);
However, it is noted that the prior art of Henkin does not explicitly teaches “generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges; receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph; and outputting at least one key phrase of the key phrases that maps to the one or more similar items via the second set of edges in the tripartite graph.”
On the other hand, in the same field of endeavor, Ramanathan teaches generating a tripartite graph (i.e. “construct a logical tri-partite graph”; fig.4, para. [0046])
in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges (i.e. “a particular Wikipedia concept 354 may be associated with multiple different extracted topic 352”; fig. 5, para. [0046]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the title tokens of the titles are interpreted as the particular Wikipedia concept 354 . The items associated with the titles are interpreted as the candidate videos 356. Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge), and
the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges (i.e. “These are the Wiki concepts 354 shown in FIG. 5 that are connected to the given keyphrase 352 or video 356, as the case may be.” Fig. 5, 5a, para. [0046], [0047]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the items are the video 356. Fig. 5 shows edge 360 association of Wikipedia concepts and videos which are interpreted as the second set of edges);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanathan that teaches a topic extracted from a digital text document into the prior art of Henkin that teaches facilitating on-line contextual advertising operations. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to facilitate the extraction of noun phrases covering an entire digital text document, as these can serve as a list of topics for the subsequent selection of videos, which facilitates the process of content-matching (Ramanathan, para. [0024]).
However, it is noted that the prior arts of Henkin and Ramanathan do not explicitly teach “receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph; and outputting at least one key phrase of the key phrases that maps to the one or more similar items via the second set of edges in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens (i.e. “the similarity detection system 100 may use a component 130 for candidate selection to determine a set of candidate items 140 from the catalog 110, e.g., using analysis of token overlap 135 to the seed item 120. For example, if a seed item 120 has the title “Diet Cola Cherry Mini-Can, Fridge Pack, 10 Count,” then the similarity detection system may determine a set of candidate items 140 whose titles and/or descriptions have sufficient overlap with the terms in the title of the seed item 120.”; figs. 1-2, Column 5, Lines 1-9; Examiner note: the candidate selection 130 is receiving the seed item information (e.g. titles) from the Seed item 120, see figs. 1-2);
outputting at least one key phrase of the key phrases that maps to the one or more similar items via the second set of edges in the tripartite graph (i.e. “The interface element 480 may be displayed on a display device 495 associated with a client computing device 490 operated by a customer of the electronic catalog. The interface element 480 or its contents may be sent to the client computing device 490 via one or more networks 450, e.g., the Internet. The interface element 480 may display descriptions of at least some of the selected items 180, such as a description 485A of item I.sub.1 and a description 485Y of item I.sub.y.”; fig. 4, Column 12, Lines 65-67, Column 13, Lines 1-14; Examiner note: the outputting at least one key phrase of the key phrases that maps to the one or more similar items is interpreted as the display descriptions of at least some of the selected items 180, such as a description 485A of item I.sub.1 and a description 485Y of item I.sub.y.. It is also noted that the prior art of Ramanathan Fig. 5 shows edge 360 association of Wikipedia concepts and videos which are interpreted as the second set of edges. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, and Ramanathan that teaches a topic extracted from a digital text document. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
However, it is noted that the prior arts of Henkin, Ramanathan and Arici do not explicitly teach “identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph.”
On the other hand, in the same field of endeavor, Kolb teaches identifying, by traversing the tripartite graph, one or more similar items of the items based on occurrence counts of the one or more seed tokens that map to the one or more similar items via the first set of edges in the tripartite graph (i.e. “The search engine can traverse the graph to determine which vendor organizations provide which solutions to which problems. From the graph of FIG. 4, the search engine can determine that: two candidate problems match the buyer-user's query”; para. [0069]. Further, i.e. “FIG. 4 shows a tripartite graph”; fig. 4, para. [0068]; Examiner note: The identifying is interpreted as the to determine. The based on occurrence counts of the one or more seed tokens that map to the one or more similar items is interpreted as the two candidate problems match the buyer-user's query. It is also noted that the prior art of Ramanathan Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services into the combination of prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to provide a first object with at least one relevant link to a second object. The second object and its associated connection serve as evidence of previously provided professional services, thereby enhancing the user's ability to interpret search results as part of a powerful data retrieval system (Kolb, para. [0040]).
As per claim 3, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “further comprising generating clusters of the key phrases, each of the clusters including the key phrases mapped via the items to a same occurrence count of the one or more seed tokens in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches further comprising generating clusters of the key phrases (i.e. “If the catalog 110A includes a variety of items in the beverage category such as “Cola 12-Pack,” “Vanilla Cola Mini-Can, Fridge Pack, 10 Count,” “Diet Cola Cherry Mini-Can, Fridge Pack, 12 Count,” “Cherry Diet Soda Mini-Can, 10 Count,” “Diet Cola Cherry, 10 Count,” “6-Pack Cola, Decaf,” “Regular Cola Cherry, Fridge Pack, 10 Count,” and “12 Pack Regular Cola Thirst-Quenching,” then the candidate items 140A may exclude those catalog items that do not have sufficient overlap in their descriptions.”; fig. 2, Column 10, Lines 64-67 and Column 11, Lines 1-10; Examiner note: the generating clusters of the key phrases are intercepted as the candidate items 140A may exclude those catalog items that do not have sufficient overlap in their descriptions),
each of the clusters including the key phrases mapped via the items to a same occurrence count of the one or more seed tokens in the tripartite graph (i.e. “the importance scoring 150 may determine that “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items 140A and therefore more distinctive than the more common terms “Cola,” “Fridge Pack,” “10 Count,” and so on.”; figs. 2-3, Column 11, Lines 16-26 and Column 12, Lines 29-35; Examiner note: the same occurrence count of the one or more seed tokens in the tripartite graph is interpreted as the “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 4, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 3 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “further comprising associating a key phrase with a highest occurrence count of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches further comprising associating a key phrase with a highest occurrence count of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph (i.e. “The importance scores may be used to determine token overlap metrics in a weighted fashion, where a token overlap metric represents a similarity score between the seed item and a candidate item. For example, if the similarity detection system assigns a high importance score to the term “diet,” then candidate products containing “diet” may be assigned a high similarity score, while candidate products containing the term “regular” may be assigned a low similarity score.”; fig. 2, Column 3, Lines 10-22; Examiner note: the key phrase is interpreted as the term diet herein; the highest occurrence count of the one or more seed tokens mapped to the single item to which the key phrase is mapped in the tripartite graph is interpreted as the high importance score to the term “diet,” then candidate products containing “diet” may be assigned a high similarity score. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 5, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 3 above.
Additionally, Henkin teaches wherein identifying the one or more similar items (i.e. “a system and method for statistically analyzing web pages and other content to determine to what degree two or more items of content are related to one another.”; para. [0052]-[0054])
comprises filtering the clusters having the occurrence counts that are below an occurrence threshold (i.e. “the score for each topic may be normalized and represented by a number between 0 and 1.”; figs. 11A-B, para. [0052]-[0056]; Examiner note: this normalization process is using cosine similarity threshold which is a specific value used to determine if two items are based on their cosine similarity score, which ranges from 0 to 1. The topics that is not n the range is filter out, see para. [0056] and [1521]),
resulting in one or more retained clusters that include the key phrases that map to the one or more similar items in the tripartite graph (i.e. “The resulting list of scores is a vector representing the relatedness of the web page to the topics in the taxonomy.”; para. [0052]-[0054]; Examiner note; the resulting in one or more retained clusters that include the key phrases that map to the one or more similar items in the tripartite graph is interpreted as the resulting list of scores is a vector representing the relatedness of the web page to the topics in the taxonomy. The tripartite graph is taught by prior art of Ramanathan above).
As per claim 6, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 5 above.
Additionally, Henkin teaches further comprising setting the occurrence threshold (i.e. “define different thresholds for each Ad/related element type”; para. [0500]-[0512]; Examiner note: )
at a value (i.e. “The thresh values may be between 0-1. The default threshold example is 0.25.”; [0512])
at which the filtering produces a number of the key phrases in the one or more retained clusters that exceeds a retention threshold (i.e. “The retrieval from the index bring all (or selected ones of) the results that pass different threshold values for ads, videos and information.”; para. [0512]. Further, i.e. “As shown at 1013, one or more Identify/Score Phrases operations may be performed. (See FIG. 3D)--Selecting the actual phrases to be highlighted, by taking the phrases that maximize relevancy and yield to the source and target pages.”; [0513]. Furthermore, i.e. “the Hybrid System may generate clusters of content sources of different type (e.g., text, video, etc.) that have a relevance score to each other. Each cluster can have one or more associated topics and/or KeyPhrases.”; para. [1108]).
As per claim 8, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 1 above.
Additionally, Henkin teaches further comprising ranking candidate key phrases of the key phrases that map to the one or more similar items in the tripartite graph (i.e. “the relevancy and/or scoring values may be used to select and/or rank the most desirable and/or suitable ad candidates (e.g., 1620) for an identified source web page (e.g., 1602).”; fig. 16A, para. [0052], [0923], [0931]; Examiner note: ranking is also known as scoring in computer sciences),
the at least one key phrase representing a top-ranked subset of the candidate key phrases (i.e. “More specifically, as illustrated in the example embodiment of FIG. 16A, the final result (1620) of the ad selection process 1600 includes ad information (and related ranking information 1622) corresponding to 3 potential ad candidates (e.g., Ad2, Ad1, Ad3).”; fig. 16A, para. [0931]; Examiner note: the top-ranked subset of the candidate key phrases is interpreted as the key phrases represented in the fig. 16A:1606b).
As per claim 9, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 8 above.
Additionally, Henkin teaches wherein the candidate key phrases are ranked in descending order of the occurrence counts associated with respective candidate key phrases (i.e. “a respective score value may be calculated for each word/phrase identified in the source document according to: Score(phrase-page)=a*Frequencey+b*Title+c*MCB+d*Bold+e*Link, where: [0396] Frequency=the number of occurrences of that word/phrase in the source page”; figs. 69E-G, para. [0395], [0931]; Examiner note: where the frequence is representing the occurrence counts, see figs. 69E-G, the scores are in descending order, see figs. 69E-G).
As per claim 10, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 9 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “wherein the candidate key phrases associated with a same value of the occurrence counts are ranked in descending order of percentages of phrase tokens in the respective candidate key phrases that match the one or more seed tokens.”
On the other hand, in the same field of endeavor, Arici teaches wherein the candidate key phrases associated with a same value of the occurrence counts (i.e. “the importance scoring 150 may determine that “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items 140A and therefore more distinctive than the more common terms “Cola,” “Fridge Pack,” “10 Count,” and so on.”; figs. 2-3; Column 11, Lines 1-26; Examiner note: the three of candidate items illustrated in figure 2:140 has the same value of count) are ranked in descending order of percentages of phrase tokens in the respective candidate key phrases that match the one or more seed tokens (i.e. “Higher importance scores may be assigned to “Diet,” “Cherry,” and “Mini-Can.” As a result of the context-dependent importance scores 160A, the similar item selection 170 may output the similar items 180A including “Diet Cola Cherry Mini-Can, Fridge Pack, 12 Count” and “Cherry Diet Soda Mini-Can, 10 Count.””; figs. 2-5, Column 11, Lines 16-26. Further, i.e. “The vocabulary-based score may use a set of thresholds based on the raw count or percentage of items for which a token occurs. For example, a word appearing at least ninety times in a hundred candidate items may be assigned a high score (e.g., one), a word appearing more than five times but less than ninety times may be assigned a low but nonzero score (e.g., 0.01), and a word appearing no more than five times may be assigned an even lower score (e.g., zero).”; figs. 3-5, Column 8, Lines 2-10).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 11, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 10 above.
Additionally, Henkin teaches wherein the candidate key phrases (i.e. “the occurrence of keywords/keyphrases on the page that relate to each topic.”; figs. 69E-G, para. [0053]-[0054], [0395])
having same values of the occurrence counts (i.e. “Frequency=the number of occurrences of that word/phrase in the source page”; figs. 69E-G, para. [0395]; Examiner note: the tripartite graph/table illustrated in figs. 69E-G disclose the count values for the phrases “2.4” and “USB cable” as 1 where the 1 is the count value of the frequence those word/phrase appears in the text) and
the percentages are ranked in descending order of quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph (i.e. “a respective score value may be calculated for each word/phrase identified in the source document according to: Score(phrase-page)=a*Frequencey+b*Title+c*MCB+d*Bold+e*Link”; figs. 69E-G; para. [00394]-[0395], [0415]; Examiner note: the tripartite graph/table illustrated in figs. 69E-G disclose the count values for the phrases “2.4” and “USB cable” which are has PNP Proximity value as true and respective scores in ranked in descending order of quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph. The tripartite graph is taught by prior art of Ramanathan above).
As per claim 12, Henkin teaches a system comprising (i.e. “methods, systems, and computer program products for facilitating on-line contextual advertising operations implemented in a computer network.”; fig. 1, para. [0051]-[0052]):
one or more processors (i.e. “one or more processors 262”; para. [0172]); and
memory storing instructions that (i.e. “In at least one embodiment, different portions of memory 264 may be configured or designed for different uses such as, for example, caching and/or storing data, programming instructions, and/or other types of information.”; para. [0177]),
when executed by the one or more processors (i.e. “Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.”; para. [1745]), cause the system to:
receive a dataset (i.e. “Hybrid System 108 receives the web page content from the PUB server 104”; fig. 1, para. [0061], [0117], [0137]; Examiner note: the dataset is interpreted as the web page content)
that comprises items listed (i.e. “generate page information (e.g., page classifier data) and KeyPhrase information (e.g., list identified KeyPhrases on page which may be suitable for highlight/mark-up).”; figs. 16A, 66A, para. [0117]; Examiner note: the items listed is interpreted as the page information)
via a listing platform (i.e. “Hybrid System 108”; [0117]; Examiner note: the listing platform is interpreted as the Hybrid System),
titles of the items, and key phrases (i.e. “a web page content is obtained and displayed where the web page content comprises keyword/key phrases ant titles”; fig. 66A. Further, i.e. “Example Information kept for each phrase/phrases: [1697] text [1698] source (manual, automatic, meta KeyPhrases, title) [1699] frequency (number of docs the phrase appeared in) [1700] related phrases (e.g., Bush, George Bush, President of the United States)”; para. [1696]-[1700]),
wherein each of the items are paired with one or more of the key phrases (i.e. “the information displays in the Hybrid Management GUI has a plurality of rows and columns in a table where each row can be an items/URLs in the table”; figs. 66A. Further, i.e. figs. 70A-B also displays items/URLs associated with key phrases. Furthermore, i.e. “In one example, items are linked to a source web page (or other content item) through a keyphrase or phrase on the page. The keyphrase or phrase may be ordinary text and may be selected and converted into a link that is highlighted on the page.”; para. [0060], [0062]);
However, it is noted that the prior art of Henkin does not explicitly teaches “generate a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges; receive a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; traverse the tripartite graph to identify one or more similar items of the items based on occurrence counts of the one or more seed tokens connected to the one or more similar items via the first set of edges in the tripartite graph; and output at least one key phrase of the key phrases connected to the one or more similar items via the second set of edges in the tripartite graph.”
On the other hand, in the same field of endeavor, Ramanathan teaches generate a tripartite graph (i.e. “construct a logical tri-partite graph”; fig.4, para. [0046])
in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges (i.e. “a particular Wikipedia concept 354 may be associated with multiple different extracted topic 352”; fig. 5, para. [0046]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the title tokens of the titles are interpreted as the particular Wikipedia concept 354 . The items associated with the titles are interpreted as the candidate videos 356. Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge), and
the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges (i.e. “These are the Wiki concepts 354 shown in FIG. 5 that are connected to the given keyphrase 352 or video 356, as the case may be.” Fig. 5, 5a, para. [0046], [0047]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the items are the video 356. Fig. 5 shows edge 360 association of Wikipedia concepts and videos which are interpreted as the second set of edges);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanathan that teaches a topic extracted from a digital text document into the prior art of Henkin that teaches facilitating on-line contextual advertising operations. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to facilitate the extraction of noun phrases covering an entire digital text document, as these can serve as a list of topics for the subsequent selection of videos, which facilitates the process of content-matching (Ramanathan, para. [0024]).
However, it is noted that the prior arts of Henkin and Ramanathan do not explicitly teach “receive a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; traverse the tripartite graph to identify one or more similar items of the items based on occurrence counts of the one or more seed tokens connected to the one or more similar items via the first set of edges in the tripartite graph; and output at least one key phrase of the key phrases connected to the one or more similar items via the second set of edges in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches receive a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens (i.e. “the similarity detection system 100 may use a component 130 for candidate selection to determine a set of candidate items 140 from the catalog 110, e.g., using analysis of token overlap 135 to the seed item 120. For example, if a seed item 120 has the title “Diet Cola Cherry Mini-Can, Fridge Pack, 10 Count,” then the similarity detection system may determine a set of candidate items 140 whose titles and/or descriptions have sufficient overlap with the terms in the title of the seed item 120.”; figs. 1-2, Column 5, Lines 1-9; Examiner note: the candidate selection 130 is receiving the seed item information (e.g. titles) from the Seed item 120, see figs. 1-2);
output at least one key phrase of the key phrases connected to the one or more similar items via the second set of edges in the tripartite graph (i.e. “The interface element 480 may be displayed on a display device 495 associated with a client computing device 490 operated by a customer of the electronic catalog. The interface element 480 or its contents may be sent to the client computing device 490 via one or more networks 450, e.g., the Internet. The interface element 480 may display descriptions of at least some of the selected items 180, such as a description 485A of item I.sub.1 and a description 485Y of item I.sub.y.”; fig. 4, Column 12, Lines 65-67, Column 13, Lines 1-14; Examiner note: the outputting at least one key phrase of the key phrases that maps to the one or more similar items is interpreted as the display descriptions of at least some of the selected items 180, such as a description 485A of item I.sub.1 and a description 485Y of item I.sub.y.. It is also noted that the prior art of Ramanathan Fig. 5 shows edge 360 association of Wikipedia concepts and videos which are interpreted as the second set of edges. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, and Ramanathan that teaches a topic extracted from a digital text document. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
However, it is noted that the prior arts of Henkin, Ramanathan and Arici do not explicitly teach “traverse the tripartite graph to identify one or more similar items of the items based on occurrence counts of the one or more seed tokens connected to the one or more similar items via the first set of edges in the tripartite graph;”
On the other hand, in the same field of endeavor, Kolb teaches traverse the tripartite graph to identify one or more similar items of the items based on occurrence counts of the one or more seed tokens connected to the one or more similar items via the first set of edges in the tripartite graph (i.e. “The search engine can traverse the graph to determine which vendor organizations provide which solutions to which problems. From the graph of FIG. 4, the search engine can determine that: two candidate problems match the buyer-user's query”; para. [0069]. Further, i.e. “FIG. 4 shows a tripartite graph”; fig. 4, para. [0068]; Examiner note: The identifying is interpreted as the to determine. The based on occurrence counts of the one or more seed tokens that map to the one or more similar items is interpreted as the two candidate problems match the buyer-user's query. It is also noted that the prior art of Ramanathan Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services into the combination of prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to provide a first object with at least one relevant link to a second object. The second object and its associated connection serve as evidence of previously provided professional services, thereby enhancing the user's ability to interpret search results as part of a powerful data retrieval system (Kolb, para. [0040]).
As per claim 14, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “wherein the instructions further cause the system to generate clusters of the key phrases, each of the clusters including the key phrases connected via the items to a same occurrence count of the one or more seed tokens in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches wherein the instructions further cause the system to generate clusters of the key phrases (i.e. “If the catalog 110A includes a variety of items in the beverage category such as “Cola 12-Pack,” “Vanilla Cola Mini-Can, Fridge Pack, 10 Count,” “Diet Cola Cherry Mini-Can, Fridge Pack, 12 Count,” “Cherry Diet Soda Mini-Can, 10 Count,” “Diet Cola Cherry, 10 Count,” “6-Pack Cola, Decaf,” “Regular Cola Cherry, Fridge Pack, 10 Count,” and “12 Pack Regular Cola Thirst-Quenching,” then the candidate items 140A may exclude those catalog items that do not have sufficient overlap in their descriptions.”; fig. 2, Column 10, Lines 64-67 and Column 11, Lines 1-10; Examiner note: the generating clusters of the key phrases are intercepted as the candidate items 140A may exclude those catalog items that do not have sufficient overlap in their descriptions),
each of the clusters including the key phrases connected via the items to a same occurrence count of the one or more seed tokens in the tripartite graph (i.e. “the importance scoring 150 may determine that “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items 140A and therefore more distinctive than the more common terms “Cola,” “Fridge Pack,” “10 Count,” and so on.”; figs. 2-3, Column 11, Lines 16-26 and Column 12, Lines 29-35; Examiner note: the same occurrence count of the one or more seed tokens in the tripartite graph is interpreted as the “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 15, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 14 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “wherein the instructions further cause the system to associate a key phrase with a highest occurrence count of the one or more seed tokens connected to a single item to which the key phrase is connected in the tripartite graph.”
On the other hand, in the same field of endeavor, Arici teaches wherein the instructions further cause the system to associate a key phrase with a highest occurrence count of the one or more seed tokens connected to a single item to which the key phrase is connected in the tripartite graph (i.e. “The importance scores may be used to determine token overlap metrics in a weighted fashion, where a token overlap metric represents a similarity score between the seed item and a candidate item. For example, if the similarity detection system assigns a high importance score to the term “diet,” then candidate products containing “diet” may be assigned a high similarity score, while candidate products containing the term “regular” may be assigned a low similarity score.”; fig. 2, Column 3, Lines 10-22; Examiner note: the key phrase is interpreted as the term diet herein; the highest occurrence count of the one or more seed tokens connected to a single item to which the key phrase is connected in the tripartite graph is interpreted as the high importance score to the term “diet,” then candidate products containing “diet” may be assigned a high similarity score. The tripartite graph is taught by prior art of Ramanathan above)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 16, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 14 above.
Additionally, Henkin teaches wherein the instructions further cause the system to filter the clusters having the occurrence counts that are below a threshold (i.e. “the score for each topic may be normalized and represented by a number between 0 and 1.”; figs. 11A-B, para. [0052]-[0056]; Examiner note: this normalization process is using cosine similarity threshold which is a specific value used to determine if two items are based on their cosine similarity score, which ranges from 0 to 1. The topics that is not n the range is filter out, see para. [0056] and [1521]),
resulting in one or more retained clusters that include the key phrases connected to the one or more similar items in the tripartite graph (i.e. “The resulting list of scores is a vector representing the relatedness of the web page to the topics in the taxonomy.”; para. [0052]-[0054]; Examiner note; the resulting in one or more retained clusters that include the key phrases connected to the one or more similar items is interpreted as the resulting list of scores is a vector representing the relatedness of the web page to the topics in the taxonomy. The tripartite graph is taught by prior art of Ramanathan above).
As per claim 17, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of prior arts of Henkin, Ramanathan and Kolb do not explicitly teach “wherein the instructions further cause the system to rank candidate key phrases of the key phrases connected to the one or more similar items in the tripartite graph based on the occurrence counts, percentages of phrase tokens in respective candidate key phrases that match the one or more seed tokens, and quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph, wherein the at least one key phrase represents a top-ranked subset of the candidate key phrases.”
On the other hand, in the same field of endeavor, Arici teaches wherein the instructions further cause the system to rank candidate key phrases of the key phrases connected to the one or more similar items in the tripartite graph based on the occurrence counts (i.e. “the importance scoring 150 may determine that “Diet,” “Cherry,” and “Mini-Can” are less commonly occurring in the set of candidate items 140A and therefore more distinctive than the more common terms “Cola,” “Fridge Pack,” “10 Count,” and so on.”; figs. 2-3; Column 11, Lines 1-26; Examiner note: the three of candidate items illustrated in figure 2:140 has the same value of count which infer them to be similar items. The tripartite graph is taught by prior art of Ramanathan above),
percentages of phrase tokens in respective candidate key phrases that match the one or more seed tokens, and quantities of the one or more similar items to which the respective candidate key phrases are mapped in the tripartite graph, wherein the at least one key phrase represents a top-ranked subset of the candidate key phrases (i.e. “Higher importance scores may be assigned to “Diet,” “Cherry,” and “Mini-Can.” As a result of the context-dependent importance scores 160A, the similar item selection 170 may output the similar items 180A including “Diet Cola Cherry Mini-Can, Fridge Pack, 12 Count” and “Cherry Diet Soda Mini-Can, 10 Count.””; figs. 2-5, Column 11, Lines 16-26. Further, i.e. “The vocabulary-based score may use a set of thresholds based on the raw count or percentage of items for which a token occurs. For example, a word appearing at least ninety times in a hundred candidate items may be assigned a high score (e.g., one), a word appearing more than five times but less than ninety times may be assigned a low but nonzero score (e.g., 0.01), and a word appearing no more than five times may be assigned an even lower score (e.g., zero).”; figs. 3-5, Column 8, Lines 2-10; Examiner note: the top-ranked subset of the candidate key phrases is interpreted as the subset of key phrases “Diet,” “Cherry,” and “Mini-Can.”. The tripartite graph is taught by prior art of Ramanathan above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
As per claim 18, Henkin teaches a non-transitory computer-readable storage medium storing instructions (i.e. “… volatile memory (e.g., RAM) … In at least one embodiment, different portions of memory 264 may be configured or designed for different uses such as, for example, caching and/or storing data, programming instructions, and/or other types of information.”; para. [0177]; Examiner note: the non-transitory computer-readable storage medium is interpreted as the memory) that,
when executed by one or more processors (i.e. “one or more processors 262”; para. [0172]),
cause the one or more processors to perform operations (i.e. “Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.”; para. [1745]) comprising:
receiving a dataset (i.e. “Hybrid System 108 receives the web page content from the PUB server 104”; fig. 1, para. [0061], [0117], [0137]; Examiner note: the dataset is interpreted as the web page content)
that comprises items listed (i.e. “generate page information (e.g., page classifier data) and KeyPhrase information (e.g., list identified KeyPhrases on page which may be suitable for highlight/mark-up).”; figs. 16A, 66A, para. [0117]; Examiner note: the items listed is interpreted as the page information)
via a listing platform (i.e. “Hybrid System 108”; [0117]; Examiner note: the listing platform is interpreted as the Hybrid System),
titles of the items, and key phrases (i.e. “a web page content is obtained and displayed where the web page content comprises keyword/key phrases ant titles”; fig. 66A. Further, i.e. “Example Information kept for each phrase/phrases: [1697] text [1698] source (manual, automatic, meta KeyPhrases, title) [1699] frequency (number of docs the phrase appeared in) [1700] related phrases (e.g., Bush, George Bush, President of the United States)”; para. [1696]-[1700]),
wherein each of the items are paired with one or more of the key phrases (i.e. “the information displays in the Hybrid Management GUI has a plurality of rows and columns in a table where each row can be an items/URLs in the table”; figs. 66A. Further, i.e. figs. 70A-B also displays items/URLs associated with key phrases. Furthermore, i.e. “In one example, items are linked to a source web page (or other content item) through a keyphrase or phrase on the page. The keyphrase or phrase may be ordinary text and may be selected and converted into a link that is highlighted on the page.”; para. [0060], [0062]);
outputting the key phrases as ranked based, in part, on the occurrence counts (i.e. “the application is doing a word count, the map function would break the line into words and output the word as the key and "1" as the value.”; figs. 70A-B, 72-74, 78, para. [0433], [0592], [1825]).
However, it is noted that the prior art of Henkin does not explicitly teaches “generating a tripartite graph in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges, and the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges; receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; determining, by traversing the tripartite graph, occurrence counts of the key phrases, an occurrence count of a key phrase representing a highest number of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph;”
On the other hand, in the same field of endeavor, Ramanathan teaches generating a tripartite graph (i.e. “construct a logical tri-partite graph”; fig.4, para. [0046])
in which title tokens of the titles are mapped to the items associated with the titles via a first set of edges (i.e. “a particular Wikipedia concept 354 may be associated with multiple different extracted topic 352”; fig. 5, para. [0046]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the title tokens of the titles are interpreted as the particular Wikipedia concept 354 . The items associated with the titles are interpreted as the candidate videos 356. Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge), and
the items are mapped to the key phrases that are paired with the items in the dataset via a second set of edges (i.e. “These are the Wiki concepts 354 shown in FIG. 5 that are connected to the given keyphrase 352 or video 356, as the case may be.” Fig. 5, 5a, para. [0046], [0047]; Further, i.e. “The relationship between key phrases 352, Wikipedia concept 354 and candidate videos 356 are represented by edges 360.”; fig. 5, para. [0046]; Examiner note: the items are the video 356. Fig. 5 shows edge 360 association of Wikipedia concepts and videos which are interpreted as the second set of edges);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanathan that teaches a topic extracted from a digital text document into the prior art of Henkin that teaches facilitating on-line contextual advertising operations. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to facilitate the extraction of noun phrases covering an entire digital text document, as these can serve as a list of topics for the subsequent selection of videos, which facilitates the process of content-matching (Ramanathan, para. [0024]).
However, it is noted that the prior arts of Henkin and Ramanathan do not explicitly teach “receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens; determining, by traversing the tripartite graph, occurrence counts of the key phrases, an occurrence count of a key phrase representing a highest number of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph;”
On the other hand, in the same field of endeavor, Arici teaches receiving a seed title of a seed item listed via the listing platform, the seed title including one or more seed tokens (i.e. “the similarity detection system 100 may use a component 130 for candidate selection to determine a set of candidate items 140 from the catalog 110, e.g., using analysis of token overlap 135 to the seed item 120. For example, if a seed item 120 has the title “Diet Cola Cherry Mini-Can, Fridge Pack, 10 Count,” then the similarity detection system may determine a set of candidate items 140 whose titles and/or descriptions have sufficient overlap with the terms in the title of the seed item 120.”; figs. 1-2, Column 5, Lines 1-9; Examiner note: the candidate selection 130 is receiving the seed item information (e.g. titles) from the Seed item 120, see figs. 1-2);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, and Ramanathan that teaches a topic extracted from a digital text document. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to improve the ability of computers to display relevant information to users in a space-constrained user interface because it can facilitate online contextual advertising operations (Arici, Column 3, Lines 23-38).
However, it is noted that the prior arts of Henkin, Ramanathan and Arici do not explicitly teach “determining, by traversing the tripartite graph phrases, occurrence counts of the key phrases, an occurrence count of a key phrase representing a highest number of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph;”
On the other hand, in the same field of endeavor, Kolb teaches determining, by traversing the tripartite graph phrases, occurrence counts of the key phrases (Taught by the prior art of Henkin), an occurrence count of a key phrase representing a highest number of the one or more seed tokens mapped to a single item to which the key phrase is mapped in the tripartite graph (i.e. “For example, for a vendor in the results, the number of its relevant case studies that are connected to that vendor by a ‘solved-by’ edge are counted (and similarly for number of its relevant clients, number of its solution nodes connected to the starting problem node).”; fig. 7, para. [0119]. Further, i.e. “The search engine can traverse the graph to determine which vendor organizations provide which solutions to which problems. From the graph of FIG. 4, the search engine can determine that: two candidate problems match the buyer-user's query”; para. [0069]. Further, i.e. “FIG. 4 shows a tripartite graph”; fig. 4, para. [0068]. Furthermore, i.e. “The NLP technique is exemplified in FIG. 10A with an example string 91 received at the web server. The string is tokenized into nine words 95 by identifying white space and hyphenated words. A NER module looks-up the words (as N-grams) in a database (such as index 14) of known entities, including company names, industry names, city names and names of data object types, such as node types or edge types. From the comparison, the systems assigns the words to one or more known entities (Table 96), each with an entity confidence score.”; fig. 10A-D, para. [0099]; Examiner note: Examiner note: the occurrence count of the key phrase representing a highest number of the one or more seed tokens mapped to the single item to which the key phrase is mapped in the tripartite graph is interpreted as the term with higher confidence score illustrated in figures 1A-D. It is also noted that the prior art of Ramanathan Fig. 5 shows edge 359 association of Wikipedia concepts and different extracted topic/key phrases which is interpreted as the second set of edge);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services into the combination of prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, and Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to provide a first object with at least one relevant link to a second object. The second object and its associated connection serve as evidence of previously provided professional services, thereby enhancing the user's ability to interpret search results as part of a powerful data retrieval system (Kolb, para. [0040]).
As per claim 21, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Henkin, Ramanathan, Arici and Kolb do not explicitly teach “wherein each unique title token of the title tokens is a vertex in the tripartite graph.”
On the other hand, in the same field of endeavor, Ramanathan teaches wherein each unique title token of the title tokens is a vertex in the tripartite graph (i.e. “a particular Wikipedia concept 354 may be associated with multiple different extracted topic 352 and may be covered by multiple different candidate videos 356.”; fig. 5, para. [0046]; Examiner note: the unique title token is interpreted as the particular Wikipedia concept 354 which is a vertex).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ramanathan that teaches a topic extracted from a digital text document into the combination of prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to facilitate the extraction of noun phrases covering an entire digital text document, as these can serve as a list of topics for the subsequent selection of videos, which facilitates the process of content-matching (Ramanathan, para. [0024]).
8. Claims 2, 13 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Henkin et al. (US 20110213655 A1) in view of Ramanathan et al. (US 20140229810 A1) in further view of Arici et al. (US 12086851 B1) still in further view of Wolny et al. (US 20180300407 A1).
As per claim 2, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Henkin, Ramanathan, Arici and Kolb do not explicitly teach “further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.”
On the other hand, in the same field of endeavor, Wolny teaches further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform (i.e. “The related term can be determined based on a predictive model trained on historical user interactions (e.g., queries) with a social media dataset of topics. The historical user interaction can include query terms and associated topics returned as query results.”; fig. 6, para. [0036], [0039], [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wolny that teaches assisting a user to enhance a query based on related terms generated automatically in response to inputs from the user into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to use text clustering techniques to organize search terms in a search because it can reduce false positives (Wolny, para. [0005]).
As per claim 13, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 12 above.
However, it is noted that the combination of the prior arts of Henkin, Ramanathan, Arici and Kolb do not explicitly teach “wherein the instructions further cause the system to pair an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.”
On the other hand, in the same field of endeavor, Wolny teaches wherein the instructions further cause the system to pair an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform (i.e. “The related term can be determined based on a predictive model trained on historical user interactions (e.g., queries) with a social media dataset of topics. The historical user interaction can include query terms and associated topics returned as query results.”; fig. 6, para. [0036], [0039], [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wolny that teaches assisting a user to enhance a query based on related terms generated automatically in response to inputs from the user into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to use text clustering techniques to organize search terms in a search because it can reduce false positives (Wolny, para. [0005]).
As per claim 19, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 18 above.
However, it is noted that the combination of the prior arts of Henkin, Ramanathan, Arici and Kolb do not explicitly teach “the operations further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform.”
On the other hand, in the same field of endeavor, Wolny teaches the operations further comprising pairing an item with a key phrase in the dataset based on historical engagement with the item in response to the key phrase being searched via the listing platform (i.e. “The related term can be determined based on a predictive model trained on historical user interactions (e.g., queries) with a social media dataset of topics. The historical user interaction can include query terms and associated topics returned as query results.”; fig. 6, para. [0036], [0039], [0043]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wolny that teaches assisting a user to enhance a query based on related terms generated automatically in response to inputs from the user into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to use text clustering techniques to organize search terms in a search because it can reduce false positives (Wolny, para. [0005]).
9. Claims 22 is rejected under 35 U.S.C. § 103 as being unpatentable over Henkin et al. (US 20110213655 A1) in view of Ramanathan et al. (US 20140229810 A1) in further view of Arici et al. (US 12086851 B1) still in further view of Zhu et al. (US 20250181888 A1).
As per claim 22, Henkin, Ramanathan, Arici and Kolb teach all the limitations as discussed in claim 1 above.
However, it is noted that the combination of the prior arts of Henkin, Ramanathan, Arici and Kolb do not explicitly teach “further comprising storing, in a memory device, the tripartite graph in compressed sparse row format.”
On the other hand, in the same field of endeavor, Zhu teaches further comprising storing, in a memory device, the tripartite graph in compressed sparse row format (i.e. “first-order neighbors of all nodes are sorted based on the connecting edge types, and connecting edge data stored in the compressed sparse row format can be used as a type index of a heterogeneous graph.”; figs. 4-5, para. [0046], [0053]-[0055]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Zhu that teaches a shard storage for a graph and a subgraph into the combination of the prior arts of Henkin that teaches facilitating on-line contextual advertising operations, Ramanathan that teaches a topic extracted from a digital text document, Arici that teaches dynamically generating relevant questions in natural language via semantic knowledge representation of a generic ontology of an organization to guide a user in starting a data analytics process, and Kolb that teaches analyzing data in an online professional social network to identify and rank organizations with regard to providing professional services. Additionally, this facilitates on-line contextual analysis and/or advertising operations implemented in a computer network.
The motivation for doing so would be to integrate subgraph storage, querying, and sampling into a single service within a distributed system. This approach effectively reduces memory usage and improves sampling efficiency (Zhu, para. [0038]).
Prior Art of Record
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dey et al. (US 20250322016 A1), teaches key phrase recommendation techniques identify and suggest words or phrases that enhance user experience, visibility, and engagement of content items.
Madhavan et al. (US 20130268517 A1), teaches search queries, information identifying aspects of entities identified in the search queries, and using the aspects in presenting information in response to the search queries.
Chen et al. (US 20080033915 A1), teaches search results are ranked by applying sub-relevancies within a search result list.
Zamir et al. (US 20060224587 A1), teaches systems and methods of using user information to customize a user's searching and browsing environment.
Kelong Mao et al. (Kelong Mao et al. SIGIR '20,July 25-30, 2020, Virtual Event, All Pages. (Year: 2020)), teaches Tagging has been recognized as a successful practice to boost relevance matching for information retrieval (IR), especially when items lack rich textual descriptions.
Conclusion
11. 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 ANTONIO CAIA DO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30.
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/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164