DETAILED ACTION
1. Claims 1-6, 8-17 and 19-22 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 04/22/2026 in response to the non-final rection mailed on 01/07/2026. Claims 1-3, 5-6, 12, 14, 16-17, 20 and 22 have been amended. Claims 21-22 have been newly introduced. Claims 7 and 18 have been cancelled. Claims 4, 8-11, 13, 15, 19 and 21 have been previously presented. Amendment has been entered.
Response to Arguments
4. Applicant's arguments, filed on 04/22/2026, with respect to the rejection of claims 1-6, 8-17 and 19-22 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant’s arguments, pages 11-12), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
The applicant argues that the claims are no longer abstract because the newly introduced limitation is obtaining data. However, obtaining data using the broadest reasonable interpretation (BRI) is nothing more than getting data. This is an example of insignificant extra-solution activity of data gathering, and it can be understood as an activity incidental to the primary process or product that is merely a nominal or tangential addition to the claim (see MPEP 2106.05(g)).
The applicant argues that the claims use a system to obtain and manipulate data. Under a Broadest Reasonable Interpretation (BRI), the system claimed in the invention is treated merely as a mechanism for implementing an exception. A recitation of the words 'apply it' (or an equivalent) provides mere instructions to implement an abstract idea or other judicial exception on a computer (see MPEP 2106.05(f)).
The Applicant argues that the amendments address this issue by requiring the system to access and process stored data (including user-specific interaction histories), which cannot be performed mentally. However, the Applicant has failed to disclose in detail why these claims cannot be performed mentally. If the Applicant is referring to the use of a computer system, they must make it clear that the process cannot be done in the human mind. The courts require a clear distinction between claims that recite mental processes performed by humans and those that recite mental processes performed on a computer MPEP 2106.04(a)(2)(III).
For all the above reasons, claims 1–6, 8–17, and 19–22 continue to be rejected under 35 U.S.C. § 101 as being directed to an abstract idea (specifically, a mental process).
Applicant's arguments, filed on 04/22/2026, with respect to the rejection of claims 1-6, 8-17 and 19-22 under 35 U.S.C. §103 (Applicant’s arguments, pages 12-16), 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, 8-17 and 19-22 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 outline steps for data asset graph management.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-6 and 7-11 are directed to a method.
Claims 12-17 and 19 are directed to a computing device.
Claims 20-22 are directed to a computer program product.
Therefore, claims 1-6, 8-17 and 19-22 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 claims 1, 12 and 20, the claims similarly recite the limitations of:
“obtaining a data asset graph corresponding to the first keyword,
wherein the data asset graph is stored in the management system and
comprises nodes representing asset entities of a data asset and edges representing association relationships between the asset entities;” A human can mentally visualize a graph representing data assets. The wherein the data asset graph is stored in the management system and comprises nodes representing asset entities of a data asset and edges representing association relationships between the asset entities is merely instructions used to implement the abstract idea. There is nothing so complex in the limitation that could not be doing in the human mind.
“determining recommended exploration information for the data asset graph based on the impact factor of the data asset graph, wherein the impact factor is used to evaluate importance of a node or an edge, and wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph;” A human can observe information and make recommendations based on judgments of the observed information. The impact factor of the data asset graph is merely an element used to implement the abstract idea. The wherein the impact factor is used to evaluate importance of a node or an edge is merely instructions used to implement the abstract idea. The wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph is merely instructions used to implement the abstract idea. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 5 and 16, the claims recite the limitation of:
“wherein determining the recommended exploration information based on the impact factor of the data asset graph comprises: sorting the nodes in the data asset graph based on the impact factor and determining a first n1 nodes as the recommended exploration start node; or sorting potential extended edges of the first node based on the impact factor and determining a first n2 potential extended edges as the recommended edge;” A human can observe data describing impact factors, judge that data, and rank the results. For example, a researcher who observes how many times a scientific paper has been cited, judges the quality of those citations, and then ranks the papers by their importance. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claims 9 and 19, the claims recite the limitation of:
“wherein the feedback comprises selection or rejection of the user for the recommended exploration information.” A human can observe data that represent user feedback and mentally select or not select the data for further analysis. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 10, the claim recites the limitation of:
“generating, in response to a selection operation from the user on an intent asset in the intent asset list, the data asset graph corresponding to the intent asset.” A human can mentally choose a procedure to be applied to a plurality of data. There is nothing so complex in the limitation that could not be doing in the human mind.
Accordingly, claims 1-6, 8-17 and 19-22 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 claims 1, 12 and 20, the claims recite the additional limitation of:
“presenting the recommended exploration information to a user to guide the user to explore the data asset 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.
“obtaining an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user with the data asset graph stored in the management system, wherein the historical experience data comprises an interactive exploration experience of the user over a historical period;” 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 (obtain data using BRI is nothing more than just get data), without any further processing or analysis. The wherein the historical experience data comprises an interactive exploration experience of the user over a historical period is merely instructions used to implement the mental steps recited in claims 1 and 12.
As per dependent claims 2, 13 and 21, the claims recite the limitation of:
“wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node, wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes.” The recommended exploration start node, recommended edge, and recommended exploration target node are merely components used to implement the mental steps recited in claims 1 and 12. The wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node is merely instructions used to implement the mental steps recited in claims 1 and 12. The wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes is merely instructions used to implement the mental steps recited in claims 1 and 12.
As per dependent claims 3, 14 and 22, the claims recite the limitations of:
“wherein presenting the recommended exploration information comprises: displaying the recommended exploration start node on the graph display interface when the user triggers a recommendation control on the graph display interface;” 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.
As per dependent claims 4 and 15, the claims recite the limitation of:
“further comprising: receiving a selection of the user for a third node from the recommended exploration target node;” 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.
“displaying, on the graph display interface, an exploration path in which the second node is used as the start node and the third node is used as a target node.” 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.
As per dependent claims 6 and 17, the claims recite the limitation of:
“wherein the structure, feature is for determining structure importance of the node and the edge in the data asset; a graph, and wherein service feature is for determining service importance of the node and the edge in the data asset graph.” The wherein the structure, feature is for determining structure importance of the node and the edge in the data asset; a graph, and wherein service feature is for determining service importance of the node and the edge in the data asset graph are merely components and instructions used to implement the mental steps recited in claims 1 and 12.
As per dependent claims 8 and 19, the claims recite the limitations of:
“receiving a feedback of the user on the recommended exploration information; 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.
“updating a recommendation parameter based on the feedback.” 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.
As per dependent claim 10, the claim recites the limitations of:
“further comprising: receiving a second keyword input by the user in the search box of the search interface;” 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.
“obtaining an intent asset list, based on the second keyword, from the data asset managed by the management system, wherein the intent asset list includes a unique identifier of the data asset that matches the second keyword;” 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.
“displaying the intent asset list to the user through the search interface;” 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.
As per dependent claim 11, the claim recites the limitations of:
“receiving an extended edge from the user;” 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.
“updating the data asset graph based on the extended edge.” 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.
As per claims 12, the claim recites the additional elements of:
“a computing device cluster, at least one computing device, a memory, and one or more processors.” This element is example 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 claims 20, the claim recites the additional elements of:
“a non-transitory computer-readable medium, a one or more processors, and a computing device cluster.” This element is example 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.
Therefore, claims 1-6, 8-17 and 19-22 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 “presenting ...; displaying ...; receiving ...; updating ...; and obtaining ....” are well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “presenting ...; displaying ...; receiving ...; updating ...; and obtaining ....” 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);” See also see [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, 8-17 and 19-22 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, 11-12 and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Circlaeys et al. (US 20190340529 A1) in view of Kale et al. (US 20180052884 A1) in further view of Grossman et al. (US 20220277047 A1).
As per claim 1, Circlaeys teaches a method (i.e. “Methods, …for organizing, storing, describing, and/or retrieving digital assets”; para. [0024])
executed by a management system (i.e. “FIG. 1A illustrates, in block diagram form, an asset management processing system that includes electronic components for performing digital asset management (DAM),”; fig. 1A, Para. [0014]; Examiner note: using a BRI the management system is interpreted as the asset management processing system), comprising:
wherein the data asset graph is stored in the management system (i.e. “The system 100 can also include memory 110 for storing and/or retrieving metadata 112, the metadata network 114”; fig 1a, para. [0042]; Examiner note: using a BRI the management system is interpreted as the system 100. Using a BRI the data asset graph is interpreted as the metadata network 114) and
comprises nodes representing asset entities of a data asset and edges representing association relationships between the asset entities (i.e. “a “multidimensional network” and its variations refer to a complex graph having multiple kinds of relationships. A multidimensional network generally includes multiple nodes and edges. For one embodiment, the nodes represent metadata, and the edges represent relationships or correlations between the metadata.”; para. [0036]; Examiner note: using a BRI the asset entities are interpreted as the metadata. Using a BRI the relationships between the asset entities are interpreted as the correlations between the metadata);
However, it is noted that the prior art of Circlaeys does not explicitly teach “receiving a first keyword input by a user in a search box of a search interface of the management system; obtaining a data asset graph corresponding to the first keyword; obtaining an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user with the data asset graph stored in the management system, wherein the historical experience data comprises an interactive exploration experience of the user over a historical period; determining recommended exploration information for the data asset graph based on the impact factor of the data asset graph, wherein the impact factor is used to evaluate importance of a node or an edge, and wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph; presenting the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Kale teaches receiving a first keyword input by a user (i.e. “a user may type the text input “Hi, can you find me a pair of red nikey shoes?”; fig. 9, para. [0096]; Examiner note: using a BRI the receiving a first keyword input by a user is interpreted as the user may type the text input “Hi, can you find me a pair of red nikey shoes?)
in a search box of a search interface of the management system (i.e. “The search component 220 can accommodate text, or Artificial Intelligence (AI) encoded voice and image inputs, and identify relevant inventory items to users based on explicit and derived query intents.”; fig. 2, para. [0041]; Examiner note: using a BRI the search box of a search interface is interpreted as the search component 220);
obtaining a data asset graph (i.e. “the knowledge graph 808 that is formulated along dimensions likely to be relevant to the user query.”; para. [0086]; Examiner note: using a BRI the obtaining a data asset graph is interpreted as the knowledge graph 808 that is formulated)
corresponding to the first keyword (i.e. “The knowledge graph 808 indicates there is a forty percent (0.4) correlation between “Shoes” and “Men's Athletic Shoes””; fig. 11a, 11b, para. [0116]; Examiner note: using a BRI the first key word is interpreted as the shoes);
obtaining an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user (i.e. “receive text, visual selectors, and image attributes”; para. [0081]. Further, i.e. “consult the knowledge graph 808 to determine the most helpful attributes for this dominant object of user interest.”; para. [0100]. Further, i.e. “the knowledge graph 808 may be based on the historical interaction of all users”; para. [0112]; Examiner note: using a BRI the obtaining is the receive. Using a BRI the impact factor is the most helpful attributes. Using a BRI the historical experience data of the user is interpreted as the historical interaction of all users)
with the data asset graph stored in the management system (Using a BRI the data asset graph stored in the management system is taught by Circlaeys),
wherein the historical experience data comprises an interactive exploration experience of the user over a historical period (i.e. “the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time.”; para. [0112]);
determining recommended exploration information for the data asset graph based on the impact factor of the data asset graph (i.e. “The knowledge graph 808 … represents a plurality of nodes…Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation”; fig. 10, para. [0104]; Examiner note: using a BRI the determining recommended exploration information is interpreted as the generate an item recommendation. Using a BRI the based on the impact factor is interpreted as the attribute/attribute value),
wherein the impact factor is used to evaluate importance of a node or an edge (i.e. “the most helpful and/or frequently specified attributes are color, brand, and size, along with corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item.”; para. [0100]. Further, i.e. “the conditional probabilities of category/attribute/attribute value interactions calculated from user behavioral patterns,”; Abstract; Examiner note: using a BRI the node is the item category. Using a BRI the evaluate importance is interpreted as the calculated corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item), and
wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph (i.e. “Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation. In this example, item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes””; fig. 10, para. [0104]; Examiner note: using a BRI the node is the item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes”);
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 Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation into the prior art of Circlaeys that teaches digital asset management (DAM). Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to train the artificial intelligence framework using sample queries, improving its quality over time (Kale, para. [0045]).
However, it is noted that the prior art of Circlaeys and Kale do not explicitly teach “presenting the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Grossman teaches presenting the recommended exploration information (i.e. “the graph can be used to generate displays for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028]; Examiner note: using a BRI the presenting is interpreted as the display)
through a graph display interface to the user (i.e. “The user interface output devices 214 may include a display subsystem”; para. [0041])
to guide the user to explore the data asset graph (i.e. “for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028], [0054]; Examiner note: using a BRI the to explore the data asset graph is interpreted as the learning about the graph and/or for performing queries and the like on that graph data).
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 Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), and Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to identify assets under control in a network-connected environment, as it lowers security risks (Grossman, para. [0004]-[0005]).
As per claim 11, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 1 above.
Additionally, Circlaeys teaches further comprising: receiving an extended edge from the user (i.e. “the matching of the determined at least one moment may optionally be further enhanced based, at least in part, on the sender of the message's relationship to the identified moment (e.g., whether or not the sender appears in a DA associated with the moment, whether the sender was present at the same location during the identified moment(s), whether the sender is in a particular social group with the user, etc.).”; fig. 6, para. [0070]; Examiner note: where the sender that sends message in over computer device is known as a user; the extended edge from the user is interpreted as the sender of the message's relationship to the identified moment; an extended edge is known as a way to capture new relationships or connections between existing data assets); and
updating the data asset graph based on the extended edge (i.e. “the matching of the determined at least one moment may optionally be further enhanced based, at least in part, on the sender of the message's relationship to the identified moment (e.g., whether or not the sender appears in a DA associated with the moment, whether the sender was present at the same location during the identified moment(s), whether the sender is in a particular social group with the user, etc.).”; fig. 6, para. [0070]; Examiner note: the updating the data asset graph based on the extended edge is interpreted as the enhanced based, at least in part, on the sender of the message's relationship to the identified moment).
As per claim 12, Circlaeys teaches a computing device cluster comprising (i.e. “Methods, apparatuses, computer-readable media, and systems for providing users with more intelligent and automated DA sharing suggestions”; para. [0008]; Examiner note: using a BRI the computing device cluster is interpreted as the apparatuses):
at least one computing device comprising (i.e. “computing device”; figs. 5, 7, para. [0067], [0073]):
a memory (i.e. “memory 760”; fig. 7, para. [0073]) configured to store computer-readable instructions (i.e. “Processor 705 may execute instructions necessary to carry out or control the operation of many functions performed by device 700 (e.g., such as the generation and/or processing of DAs in accordance with the various embodiments described herein).”; fig. 7, para. [0074]); and
one or more processors coupled to the memory and configured to execute the computer-readable instructions to cause the computing device cluster to (i.e. “Processor 705 may execute instructions necessary to carry out or control the operation of many functions performed by device 700 (e.g., such as the generation and/or processing of DAs in accordance with the various embodiments described herein).”; fig. 7, para. [0074]):
wherein the data asset graph is stored in the computing device cluster (i.e. “The system 100 can also include memory 110 for storing and/or retrieving metadata 112, the metadata network 114”; fig 1a, para. [0042]; Examiner note: using a BRI the computing device cluster is interpreted as the system 100. Using a BRI the data asset graph is interpreted as the metadata network 114) and
comprises nodes representing asset entities of a data asset and edges representing association relationships between the asset entities (i.e. “a “multidimensional network” and its variations refer to a complex graph having multiple kinds of relationships. A multidimensional network generally includes multiple nodes and edges. For one embodiment, the nodes represent metadata, and the edges represent relationships or correlations between the metadata.”; para. [0036]; Examiner note: using a BRI the asset entities are interpreted as the metadata. Using a BRI the relationships between the asset entities are interpreted as the correlations between the metadata);
However, it is noted that the prior art of Circlaeys does not explicitly teach “receive a first keyword input by a user in a search box of a search interface of the computing device cluster; obtain a data asset graph corresponding to the first keyword; obtain an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user with the data asset graph stored in the management system, wherein the historical experience data comprises an interactive exploration experience of the user over a historical period; determine recommended exploration information for the data asset graph based on the impact factor of the data asset graph, wherein the impact factor is used to evaluate importance of a node or an edge and wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph; present the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Kale teaches receive a first keyword input by a user (i.e. “a user may type the text input “Hi, can you find me a pair of red nikey shoes?”; fig. 9, para. [0096]; Examiner note: using a BRI the receive a first keyword input by a user is interpreted as the user may type the text input “Hi, can you find me a pair of red nikey shoes?)
in a search box of a search interface of the computing device cluster (i.e. “The search component 220 can accommodate text, or Artificial Intelligence (AI) encoded voice and image inputs, and identify relevant inventory items to users based on explicit and derived query intents.”; fig. 2, para. [0041]; Examiner note: using a BRI the search box of a search interface is interpreted as the search component 220);
obtain a data asset graph (i.e. “the knowledge graph 808 that is formulated along dimensions likely to be relevant to the user query.”; para. [0086]; Examiner note: using a BRI the obtain a data asset graph is interpreted as the knowledge graph 808 that is formulated)
corresponding to the first keyword (i.e. “The knowledge graph 808 indicates there is a forty percent (0.4) correlation between “Shoes” and “Men's Athletic Shoes””; fig. 11a, 11b, para. [0116]; Examiner note: using a BRI the first key word is interpreted as the shoes);
obtain an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user (i.e. “receive text, visual selectors, and image attributes”; para. [0081]. Further, i.e. “consult the knowledge graph 808 to determine the most helpful attributes for this dominant object of user interest.”; para. [0100]. Further, i.e. “the knowledge graph 808 may be based on the historical interaction of all users”; para. [0112]; Examiner note: using a BRI the obtain is the receive. Using a BRI the impact factor is the most helpful attributes. Using a BRI the historical experience data of the user is interpreted as the historical interaction of all users)
with the data asset graph stored in the management system (Using a BRI the data asset graph stored in the management system is taught by Circlaeys),
wherein the historical experience data comprises an interactive exploration experience of the user over a historical period (i.e. “the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time.”; para. [0112]);
determine recommended exploration information for the data asset graph based on the impact factor of the data asset graph (i.e. “The knowledge graph 808 … represents a plurality of nodes…Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation”; fig. 10, para. [0104]; Examiner note: using a BRI the determine recommended exploration information is interpreted as the generate an item recommendation. Using a BRI the based on the impact factor is interpreted as the attribute/attribute value),
wherein the impact factor is used to evaluate importance of a node or an edge (i.e. “the most helpful and/or frequently specified attributes are color, brand, and size, along with corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item.”; para. [0100]. Further, i.e. “the conditional probabilities of category/attribute/attribute value interactions calculated from user behavioral patterns,”; Abstract; Examiner note: using a BRI the node is the item category. Using a BRI the evaluate importance is interpreted as the calculated corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item), and
wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph (i.e. “Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation. In this example, item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes””; fig. 10, para. [0104]; Examiner note: using a BRI the node is the item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes”);
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 Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation into the prior art of Circlaeys that teaches digital asset management (DAM). Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to train the artificial intelligence framework using sample queries, improving its quality over time (Kale, para. [0045]).
However, it is noted that the prior art of Circlaeys and Kale do not explicitly teach “present the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Grossman teaches present the recommended exploration information (i.e. “the graph can be used to generate displays for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028]; Examiner note: using a BRI the present is interpreted as the display)
through a graph display interface to the user (i.e. “The user interface output devices 214 may include a display subsystem”; para. [0041])
to guide the user to explore the data asset graph (i.e. “for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028], [0054]; Examiner note: using a BRI the to explore the data asset graph is interpreted as the learning about the graph and/or for performing queries and the like on that graph data).
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 Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), and Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to identify assets under control in a network-connected environment, as it lowers security risks (Grossman, para. [0004]-[0005]).
As per claim 20, Circlaeys teaches a computer program product (i.e. “Methods, apparatuses, computer-readable media, and systems for providing users with more intelligent and automated DA sharing suggestions”; para. [0008]; Examiner note: using a BRI the computing device cluster is interpreted as the apparatuses)
comprising computer-executable instructions that are stored on a non-transitory computer-readable medium and that, when executed by one or more processors, cause a computing device cluster to (i.e. “memory 760” and “Processor 705 may execute instructions necessary to carry out or control the operation of many functions performed by device 700 (e.g., such as the generation and/or processing of DAs in accordance with the various embodiments described herein).”; fig. 7, para. [0073]-[0074]):
wherein the data asset graph is stored in the computing device cluster (i.e. “The system 100 can also include memory 110 for storing and/or retrieving metadata 112, the metadata network 114”; fig 1a, para. [0042]; Examiner note: using a BRI the computing device cluster is interpreted as the system 100. Using a BRI the data asset graph is interpreted as the metadata network 114) and
comprises nodes representing asset entities of a data asset and edges representing association relationships between the asset entities (i.e. “a “multidimensional network” and its variations refer to a complex graph having multiple kinds of relationships. A multidimensional network generally includes multiple nodes and edges. For one embodiment, the nodes represent metadata, and the edges represent relationships or correlations between the metadata.”; para. [0036]; Examiner note: using a BRI the asset entities are interpreted as the metadata. Using a BRI the relationships between the asset entities are interpreted as the correlations between the metadata);
However, it is noted that the prior art of Circlaeys does not explicitly teach “receive a first keyword input by a user in a search box of a search interface of the computing device cluster; obtain a data asset graph corresponding to the first keyword,; obtain an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user with the data asset graph stored in the management system, wherein the historical experience data comprises an interactive exploration experience of the user over a historical period; determine recommended exploration information for the data asset graph based on an impact factor of the data asset graph, wherein the impact factor is used to evaluate importance of a node or an edge and wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph;; present the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Kale teaches receive a first keyword input by a user (i.e. “a user may type the text input “Hi, can you find me a pair of red nikey shoes?”; fig. 9, para. [0096]; Examiner note: using a BRI the receive a first keyword input by a user is interpreted as the user may type the text input “Hi, can you find me a pair of red nikey shoes?)
in a search box of a search interface of the computing device cluster (i.e. “The search component 220 can accommodate text, or Artificial Intelligence (AI) encoded voice and image inputs, and identify relevant inventory items to users based on explicit and derived query intents.”; fig. 2, para. [0041]; Examiner note: using a BRI the search box of a search interface is interpreted as the search component 220);
obtain a data asset graph (i.e. “the knowledge graph 808 that is formulated along dimensions likely to be relevant to the user query.”; para. [0086]; Examiner note: using a BRI the obtain a data asset graph is interpreted as the knowledge graph 808 that is formulated)
corresponding to the first keyword (i.e. “The knowledge graph 808 indicates there is a forty percent (0.4) correlation between “Shoes” and “Men's Athletic Shoes””; fig. 11a, 11b, para. [0116]; Examiner note: using a BRI the first key word is interpreted as the shoes);
obtain an impact factor of the data asset graph based on one or more of a service feature, a structure feature, or historical experience data of the user (i.e. “receive text, visual selectors, and image attributes”; para. [0081]. Further, i.e. “consult the knowledge graph 808 to determine the most helpful attributes for this dominant object of user interest.”; para. [0100]. Further, i.e. “the knowledge graph 808 may be based on the historical interaction of all users”; para. [0112]; Examiner note: using a BRI the obtain is the receive. Using a BRI the impact factor is the most helpful attributes. Using a BRI the historical experience data of the user is interpreted as the historical interaction of all users)
with the data asset graph stored in the management system (Using a BRI the data asset graph stored in the management system cluster is taught by Circlaeys),
wherein the historical experience data comprises an interactive exploration experience of the user over a historical period (i.e. “the knowledge graph 808 may be based on the historical interaction of all users with an electronic marketplace over a period of time.”; para. [0112]);
determine recommended exploration information for the data asset graph based on an impact factor of the data asset graph (i.e. “The knowledge graph 808 … represents a plurality of nodes…Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation”; fig. 10, para. [0104]; Examiner note: using a BRI the determine recommended exploration information is interpreted as the generate an item recommendation. Using a BRI the based on the impact factor is interpreted as the attribute/attribute value),
wherein the impact factor is used to evaluate importance of a node or an edge (i.e. “the most helpful and/or frequently specified attributes are color, brand, and size, along with corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item.”; para. [0100]. Further, i.e. “the conditional probabilities of category/attribute/attribute value interactions calculated from user behavioral patterns,”; Abstract; Examiner note: using a BRI the node is the item category. Using a BRI the evaluate importance is interpreted as the calculated corresponding conditional probability values showing the relative correlation or association strength or conditional probability of importance of each in finding a relevant item), and
wherein the recommended exploration information comprises a recommended exploration start node in the data asset graph (i.e. “Each node may represent an item category, an item attribute, or an item attribute value for the exemplary scenario of processing natural language user inputs to generate an item recommendation. In this example, item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes””; fig. 10, para. [0104]; Examiner note: using a BRI the node is the item categories include “Men's Athletic Shoes”, “Cars & Trucks”, and “Women's Athletic Shoes”);
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 Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation into the prior art of Circlaeys that teaches digital asset management (DAM). Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to train the artificial intelligence framework using sample queries, improving its quality over time (Kale, para. [0045]).
However, it is noted that the prior art of Circlaeys and Kale do not explicitly teach “present the recommended exploration information through a graph display interface to the user to guide the user to explore the data asset graph.”
On the other hand, in the same field of endeavor, Grossman teaches present the recommended exploration information (i.e. “the graph can be used to generate displays for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028]; Examiner note: using a BRI the present is interpreted as the display)
through a graph display interface to the user (i.e. “The user interface output devices 214 may include a display subsystem”; para. [0041])
to guide the user to explore the data asset graph (i.e. “for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028], [0054]; Examiner note: using a BRI the to explore the data asset graph is interpreted as the learning about the graph and/or for performing queries and the like on that graph data).
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 Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), and Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to identify assets under control in a network-connected environment, as it lowers security risks (Grossman, para. [0004]-[0005]).
8. Claims 2, 5-6, 10, 13, 16-17 and 21 are rejected under 35 U.S.C. § 103 as being unpatentable over Circlaeys et al. (US 20190340529 A1) in view of Kale et al. (US 20180052884 A1) in further view of Grossman et al. (US 20220277047 A1) still in further view of Raghavan et al. (US 20130218899 A1).
As per claim 2, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node, wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the recommended exploration information further comprises a recommended edge, or a recommended exploration target node (i.e. “At block 330, the server adds each of the nodes to a queue for processing, in the order by which they were sorted in block 320, with the highest priority node at the head of the queue.”; fig.3, para. [0161]; Examiner note: the recommended exploration target node is the highest priority node),
wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes (i.e. “the ordering may also or instead be based on a popularity-based scoring of the nodes, such as how often each node has been returned as part of a result set for a query, or how often each node has been a member of a search result subgraph that has actually been selected by a user in a search result listing.”; para.[0121]. Further, i.e. “create size_of(K) ordered lists to track paths from root to leaf in SRT”; para. [0176]; Examiner note: the path is interpreted as the paths from root to leaf).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 5, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 2 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein determining the recommended exploration information based on the impact factor of the data asset graph comprises at least one of: sorting the nodes in the data asset graph based on the impact factor and determining a first n1 nodes as the recommended exploration start node; or sorting potential extended edges of the first node based on the impact factor and determining a first n2 potential extended edges as the recommended edge.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein determining the recommended exploration information (i.e. “the search result subgraph identification process may be optimized to consider higher-priority candidate nodes ahead of lower-priority candidates, as determined in block 275.”; fig. 2, para. [0133]; Examiner note: using a BRI the recommended exploration information is interpreted as the higher-priority candidate nodes)
based on the impact factor of the data asset graph (i.e. “based on the average candidate node priority score for each set. Other optimization strategies for higher-priority candidate nodes are also possible.”; fig. 2, para. [0133]; Examiner note: using a BRI the impact factor is interpreted as the node priority score)
comprises at least one of: sorting the nodes in the data asset graph based on the impact factor and determining a first n1 nodes as the recommended exploration start node; or sorting potential extended edges of the first node based on the impact factor and determining a first n2 potential extended edges as the recommended edge (i.e. “the server sorts the nodes by their likelihood of being part of a highly relevant search result subgraph.”; fig. 3, para. [0160]. Further, i.e. “in the order by which they were sorted in block 320, with the highest priority node at the head of the queue.”; para. [0161]; Examiner note: using a BRI the recommended exploration start node is interpreted as the highest priority node at the head of the queue).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 6, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 5 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the structure feature is for determining structure importance of the node and the edge in the data asset graph, and wherein the service feature is for determining service importance of the node and the edge in the data asset graph.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the structure feature is for determining structure importance of the node and the edge in the data asset graph (i.e. “The server calculates priority scores for the data objects in the candidate sets based at least in part on one or more of: a link analysis of the graph; or metadata describing structural constraints upon the data objects.”; para. [0064]; Examiner note: using a BRI The structure feature for determining structure importance is interpreted as the metadata describing structural constraints), and
wherein the service feature is for determining service importance of the node and the edge in the data asset graph (i.e. “The server calculates priority scores for the data objects in the candidate sets based at least in part on one or more of: a link analysis of the graph; or metadata describing structural constraints upon the data objects.”; para. [0064]; Examiner note: using a BRI The service feature is for determining service importance is interpreted as the link analysis of the graph).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 10, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “further comprising: receiving a second keyword input by the user in the search box of the search interface; obtaining an intent asset list, based on the second keyword, from the data asset managed by the management system, wherein the intent asset list includes a unique identifier of the data asset that matches the second keyword; displaying the intent asset list to the user through the search interface; and generating, in response to a selection operation from the user on an intent asset in the intent asset list, the data asset graph corresponding to the intent asset.”
On the other hand, in the same field of endeavor, Raghavan teaches further comprising: receiving a second keyword input by the user in the search box of the search interface (i.e. “The search request comprises search criteria, including one or more terms (or “keywords”) ... The terms may have been entered, for instance, via user input received at any suitable search interface presented by a client computer, such as one of clients 130.”; fig. 2; para. [0082]; Examiner note: the receiving a second keyword input by a user is interpreted as the terms may have been entered, for instance, via user input received where the term is keyword);
obtaining an intent asset list (i.e. “At block 230, the server builds an index”; fig.2, para. [0089]; Examiner note: the intent asset list is interpreted as the index),
based on the second keyword (i.e. “terms associated with the interpreted data objects.”; fig.2, para. [0089]),
from the data asset managed by the management system (i.e. “indexed terms may further be selected from certain types of related data objects.”; para. [0089]; Examiner note: the data asset is interpreted as the data objects),
wherein the intent asset list includes a unique identifier of the data asset that matches the second keyword (i.e. “the indexed terms are selected only from content associated with certain fields or tags.”; para. [0089]; Examiner note: the unique identifier is interpreted as the certain fields or tags);
displaying the intent asset list to the user through the search interface (i.e. “a client may look up and retrieve any information necessary to generate its own display of information about the data objects in each search result subgraph in the result set.”; fig. 2, para. [141]); and
generating, in response to a selection operation from the user on an intent asset in the intent asset list, the data asset graph corresponding to the intent asset (i.e. “At block 240, the server generates a graph describing relationships between each of the interpreted data objects.”; fig.2, para. [0092]).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 13, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 12 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node, wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node (i.e. “At block 330, the server adds each of the nodes to a queue for processing, in the order by which they were sorted in block 320, with the highest priority node at the head of the queue.”; fig.3, para. [0161]; Examiner note: the recommended exploration target node is the highest priority node),
wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes (i.e. “the ordering may also or instead be based on a popularity-based scoring of the nodes, such as how often each node has been returned as part of a result set for a query, or how often each node has been a member of a search result subgraph that has actually been selected by a user in a search result listing.”; para.[0121]. Further, i.e. “create size_of(K) ordered lists to track paths from root to leaf in SRT”; para. [0176]).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 16, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 13 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to sort nodes along paths starting from the second node based on the impact factor and determining a first n3 nodes as the recommended exploration target node.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to (i.e. “The components thereof may, for example, be implemented by one or more hardware processors of those one or more computer systems, configured to execute instructions for performing the various functions described herein”; para. [0080])
sort nodes along paths starting from the second node based on the impact factor and determining a first n3 nodes as the recommended exploration target node (i.e. “the search result subgraph identification process may be optimized to consider higher-priority candidate nodes ahead of lower-priority candidates, as determined in block 275.”; fig. 2, para. [0133]; Examiner note: using a BRI the recommended exploration information is interpreted as the higher-priority candidate nodes. Further, i.e. “based on the average candidate node priority score for each set. Other optimization strategies for higher-priority candidate nodes are also possible.”; fig. 2, para. [0133]; Examiner note: using a BRI the impact factor is interpreted as the node priority score. Furthermore, i.e. “the server sorts the nodes by their likelihood of being part of a highly relevant search result subgraph.”; fig. 3, para. [0160]. Furthermore, i.e. “in the order by which they were sorted in block 320, with the highest priority node at the head of the queue.”; para. [0161]).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 17, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 16 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the structure feature is for determining structure importance of a node and an edge in the data asset graph and wherein the service feature is for determining service importance of the node and the edge in the data asset graph.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the structure feature is for determining structure importance of a node and an edge in the data asset graph (i.e. “The server calculates priority scores for the data objects in the candidate sets based at least in part on one or more of: a link analysis of the graph; or metadata describing structural constraints upon the data objects.”; para.[0064]; Examiner note: using a BRI The structure feature for determining structure importance is interpreted as the metadata describing structural constraints)
and wherein the service feature is for determining service importance of the node and the edge in the data asset graph (i.e. “The server calculates priority scores for the data objects in the candidate sets based at least in part on one or more of: a link analysis of the graph; or metadata describing structural constraints upon the data objects.”; para. [0064]; Examiner note: using a BRI The service feature for determining service importance is interpreted as the link analysis of the graph).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
As per claim 21, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 20 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node, wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the recommended exploration information further comprises a recommended edge or a recommended exploration target node (i.e. “At block 330, the server adds each of the nodes to a queue for processing, in the order by which they were sorted in block 320, with the highest priority node at the head of the queue.”; fig.3, para. [0161]; Examiner note: the recommended exploration target node is the highest priority node),
wherein the recommended edge comprises a potential extended edge of a first node selected by the user from the recommended exploration start node, and wherein the recommended exploration target node comprises a node through which a path on which a second node selected by the user from the recommended exploration start node is a start node passes (i.e. “the ordering may also or instead be based on a popularity-based scoring of the nodes, such as how often each node has been returned as part of a result set for a query, or how often each node has been a member of a search result subgraph that has actually been selected by a user in a search result listing.”; para.[0121]. Further, i.e. “create size_of(K) ordered lists to track paths from root to leaf in SRT”; para. [0176]).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
9. Claims 3-4, 14-15 and 22 are rejected under 35 U.S.C. § 103 as being unpatentable over Circlaeys et al. (US 20190340529 A1) in view of Kale et al. (US 20180052884 A1) in further view of Grossman et al. (US 20220277047 A1) still in view of Raghavan et al. (US 20130218899 A1) still in further view of Hirmer et al. (US 20200081917 A1).
As per claim 3, Circlaeys, Kale, Grossman and Raghavan teach all the limitations as discussed in claim 2 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein presenting the recommended exploration information comprises:”
On the other hand, in the same field of endeavor, Raghavan teaches wherein presenting the recommended exploration information (i.e. “the graph can be used to generate displays for users interested in learning about the graph and/or for performing queries and the like on that graph data.”; para. [0028]; Examiner note: the presenting is interpreted as the display) comprises:
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to utilize a common underlying format to access database data, as it standardizes data structures to ensure consistency and efficiency (Raghavan, para. [0002]).
However, it is noted that the prior art of Circlaeys, Kale, Grossman and Raghavan do not explicitly teach “displaying the recommended exploration start node on the graph display interface when the user triggers a recommendation control on the graph display interface.”
On the other hand, in the same field of endeavor, Hirmer teaches displaying the recommended exploration start node on the graph display interface when the user triggers a recommendation control on the graph display interface (i.e. “The exemplary user interface 700 depicts only a single moment identified with keyword tag 708 “Paris—Trocadero, Dec. 21, 2015” with associated multimedia content icon 706. Selection of the moment multimedia content icon 606 will return the digital assets associated with that moment.”; figs. 2, 6-7, para. [0103]-[0105]; Examiner note: using a BRI the graph display interface is interpreted as the user interface; using a BRI when the user triggers a recommendation control on the graph display interface is interpreted as the selection of the moment multimedia content icon).
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 Hirmer that teaches automatically labelling the digital assets assists a user in organizing and sharing the digital assets with friends and family into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity, and Raghavan that teaches enhancing search results for unstructured queries on normalized data. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to enable a user to quickly and easily filter digital assets in a digital asset collection because it can provide an overall improved user experience (Hirmer, para. [0003]).
As per claim 4, Circlaeys, Kale, Grossman, Raghavan and Hirmer teach all the limitations as discussed in claim 3 above.
However, it is noted that the prior art of Circlaeys, Kale, Grossman, and Hirmer do not explicitly teach “further comprising: receiving a selection of the user for a third node from the recommended exploration target node; and displaying, on the graph display interface, an exploration path in which the second node is used as the start node and the third node is used as a target node.”
On the other hand, in the same field of endeavor, Raghavan teaches further comprising: receiving a selection of the user for a third node from the recommended exploration target node (i.e. “The ordering may also or instead be based on a popularity-based scoring of the nodes, such as how often each node has been returned as part of a result set for a query, or how often each node has been a member of a search result subgraph that has actually been selected by a user in a search result listing”; para. [0161]); and
displaying, on the graph display interface (i.e. “The user interface output devices 214 may include a display subsystem”; para. [0041]),
an exploration path in which the second node is used as the start node and the third node is used as a target node (i.e. “create size_of(K) ordered lists to track paths from root to leaf in SRT”; para. [0176]; Examiner note: the exploration path is interpreted as the track paths from root to leaf).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity, and Hirmer that teaches automatically labelling the digital assets assists a user in organizing and sharing the digital assets with friends and family. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to enable a user to quickly and easily filter digital assets in a digital asset collection because it can provide an overall improved user experience (Hirmer, para. [0003]).
As per claim 14, Circlaeys, Kale, Grossman and Raghavan teach all the limitations as discussed in claim 13 above.
However, it is noted that the prior art of Circlaeys, Kale, Grossman and Raghavan do not explicitly teach “wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to display the recommended edge on the graph display interface when the user selects the first node and triggers an edge recommendation operation on the graph display interface.”
On the other hand, in the same field of endeavor, Hirmer teaches wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to (i.e. “The computer readable medium can store a plurality of instructions that, when executed by one or more processors of a computing device, cause the one or more processors to perform various operations.”; fig. 16A, 16B, para. [0096])
display the recommended edge on the graph display interface when the user selects the first node and triggers an edge recommendation operation on the graph display interface (i.e. “The exemplary user interface 700 depicts only a single moment identified with keyword tag 708 “Paris—Trocadero, Dec. 21, 2015” with associated multimedia content icon 706. Selection of the moment multimedia content icon 606 will return the digital assets associated with that moment.”; figs. 2, 6-7, para. [0103]-[0105]; Examiner note: using a BRI the graph display interface is interpreted as the user interface; using a BRI when the user triggers a recommendation control on the graph display interface is interpreted as the selection of the moment multimedia content icon).
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 Hirmer that teaches automatically labelling the digital assets assists a user in organizing and sharing the digital assets with friends and family into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity, and Raghavan that teaches enhancing search results for unstructured queries on normalized data. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to enable a user to quickly and easily filter digital assets in a digital asset collection because it can provide an overall improved user experience (Hirmer, para. [0003]).
As per claim 15, Circlaeys, Kale, Grossman, Raghavan, and Hirmer teach all the limitations as discussed in claim 14 above.
However, it is noted that the prior art of Circlaeys, Kale, Grossman, and Hirmer do not explicitly teach “wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to: receive a selection of the user for a third node from the recommended exploration target node; and display, on the graph display interface, an exploration path in which the second node is used as the start node and the third node is used as a target node.”
On the other hand, in the same field of endeavor, Raghavan teaches wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to (i.e. “The computer readable medium can store a plurality of instructions that, when executed by one or more processors of a computing device, cause the one or more processors to perform various operations.”; fig. 16A, 16B, para. [0096]):
receive a selection of the user for a third node from the recommended exploration target node (i.e. “selecting the moment content icon 930 for “Cupertino Village” would return digital assets with metadata associated a moment for Cupertino Village captured on Aug. 22, 2016. In addition to the moment multimedia content icons 930 and associated keyword tags 932, asset counts 940 are displayed for each moment.”; fig. 2, 9, para. [0072]; Examiner note: the third node from the recommended exploration target node is interpreted as the moment content icon 930 for “Cupertino Village”); and
display, on the graph display interface, an exploration path in which the second node is used as the start node and the third node is used as a target node (i.e. “Selection of any one of these moment multimedia content icons 930 would further limit the search of the digital asset collection and return only digital assets that relate to both the location and temporal limitation associated with the moment.”; fig. 2, 9, para. [0072]; Examiner note: the second path from the second node to the third node is interpreted as the return only digital assets that relate to both the location and temporal limitation associated with the moment).
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 Raghavan that teaches enhancing search results for unstructured queries on normalized data into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity, and Hirmer that teaches automatically labelling the digital assets assists a user in organizing and sharing the digital assets with friends and family. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to enable a user to quickly and easily filter digital assets in a digital asset collection because it can provide an overall improved user experience (Hirmer, para. [0003]).
As per claim 22, Circlaeys, Kale, Raghavan and Grossman teach all the limitations as discussed in claim 21 above.
However, it is noted that the prior art of Circlaeys, Kale, Grossman and Raghavan do not explicitly teach “wherein when executed by the one or more processors, the computer-executable instructions further cause the computing device cluster to display the recommended exploration target node on the graph display interface when the user triggers a target recommendation operation on the graph display interface.”
On the other hand, in the same field of endeavor, Hirmer teaches wherein when executed by the one or more processors, the computer-executable instructions further cause the computing device cluster to (i.e. “The computer readable medium can store a plurality of instructions that, when executed by one or more processors of a computing device, cause the one or more processors to perform various operations.”; fig. 16A, 16B, para. [0096])
display the recommended exploration target node on the graph display interface when the user triggers a target recommendation operation on the graph display interface (i.e. “The exemplary user interface 700 depicts only a single moment identified with keyword tag 708 “Paris—Trocadero, Dec. 21, 2015” with associated multimedia content icon 706. Selection of the moment multimedia content icon 606 will return the digital assets associated with that moment.”; figs. 2, 6-7, para. [0103]-[0105]; Examiner note: using a BRI the graph display interface is interpreted as the user interface; when the user triggers a recommendation control on the graph display interface is interpreted as the selection of the moment multimedia content icon. Further, “User interface 800 depicts the moment with multimedia content icon 806 and keyword tag 808 for “Paris-Trocadero” from the search described for FIG. 7. As depicted in FIG. 8, related searches can be conducted by selecting one of the multimedia content icon 806 for the collections for Groups and People.”; figs. 2, 7-10, para. [0105]-[0106]; Examiner note: using a BRI the target node is interpreted as the selecting one of the multimedia content icon; using a BRI the triggers the edge recommendation operation is interpreted as the keyword tag).
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 Hirmer that teaches automatically labelling the digital assets assists a user in organizing and sharing the digital assets with friends and family into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity, and Raghavan that teaches enhancing search results for unstructured queries on normalized data. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to enable a user to quickly and easily filter digital assets in a digital asset collection because it can provide an overall improved user experience (Hirmer, para. [0003]).
10. Claims 8-9 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Circlaeys et al. (US 20190340529 A1) in view of Kale et al. (US 20180052884 A1) in further view of Grossman et al. (US 20220277047 A1) still in further view of Strauss (US 20180253661 A1).
As per claim 8, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 1 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “further comprising: receiving a feedback of the user on the recommended exploration information; and updating a recommendation parameter based on the feedback.”
On the other hand, in the same field of endeavor, Strauss teaches further comprising: receiving a feedback of the user on the recommended exploration information (i.e. “The content review interface 410 presents the user-provided content 420, typically through a client device 210 accessed by a user of the online system 240 who has been granted review privileges.”; fig. 4, para. [0062]-[0064]); and
updating a recommendation parameter based on the feedback (i.e. “For example, the online system 240 identifies rejected content items from the sampled subset that received a rejection review decision from the quality review interface and modifies parameters of the machine learning model by using the identified rejected content items as training inputs to the machine learning model.”; para. [0076]. Further, i.e. “In response to selecting a control 430 to reject the content, the content review interface 410 may prompt the reviewing user to select one or more reasons 440 for rejection.”; fig. 4, para. [0062]-[0064]).
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 Strauss that teaches validating performance of a machine learning classifier by efficiently identifying members of a minority class among a population with a class imbalance into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to provide the sampled content for human review through a review interface because it can evaluate and improve the effectiveness of the content review (Strauss, para. [0059]).
As per claim 9, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 8 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the feedback comprises selection or rejection of the user for the recommended exploration information.”
On the other hand, in the same field of endeavor, Strauss teaches wherein the feedback comprises selection or rejection of the user for the recommended exploration information (i.e. “In response to selecting a control 430 to reject the content, the content review interface 410 may prompt the reviewing user to select one or more reasons 440 for rejection.”; fig. 4, para. [0062]-[0064]).
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 Strauss that teaches validating performance of a machine learning classifier by efficiently identifying members of a minority class among a population with a class imbalance into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to provide the sampled content for human review through a review interface because it can evaluate and improve the effectiveness of the content review (Strauss, para. [0059]).
As per claim 19, Circlaeys, Kale and Grossman teach all the limitations as discussed in claim 12 above.
However, it is noted that the prior art of Circlaeys, Kale and Grossman do not explicitly teach “wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to: receive a feedback of the user on the recommended exploration information, wherein the feedback comprises selection or rejection of the user for the recommended exploration information; and update a recommendation parameter based on the feedback.”
On the other hand, in the same field of endeavor, Strauss teaches wherein the one or more processors are further configured to execute the computer-readable instructions to cause the computing device cluster to (i.e. “The content review interface 410 presents the user-provided content 420, typically through a client device 210 accessed by a user of the online system 240 who has been granted review privileges.”; fig. 4, para. [0062]-[0064]):
receive a feedback of the user on the recommended exploration information (i.e. “The content review interface 410 presents the user-provided content 420, typically through a client device 210 accessed by a user of the online system 240 who has been granted review privileges.”; fig. 4, para. [0062]-[0064]),
wherein the feedback comprises selection or rejection of the user for the recommended exploration information (i.e. “In response to selecting a control 430 to reject the content, the content review interface 410 may prompt the reviewing user to select one or more reasons 440 for rejection.”; fig. 4, para. [0062]-[0064]); and
update a recommendation parameter based on the feedback (i.e. “For example, the online system 240 identifies rejected content items from the sampled subset that received a rejection review decision from the quality review interface and modifies parameters of the machine learning model by using the identified rejected content items as training inputs to the machine learning model.”; para. [0076]. Further, i.e. “In response to selecting a control 430 to reject the content, the content review interface 410 may prompt the reviewing user to select one or more reasons 440 for rejection.”; fig. 4, para. [0062]-[0064]).
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 Strauss that teaches validating performance of a machine learning classifier by efficiently identifying members of a minority class among a population with a class imbalance into the combination of prior arts of Circlaeys that teaches digital asset management (DAM), Kale that teaches processing natural language user inputs into a more formal, machine-readable, structured query representation used for making an item recommendation, and Grossman that teaches techniques for performing searches of network-connected assets to identify assets under control of an entity. Additionally, this can improve computer functionality by enabling computing systems that use one or more embodiments of the digital asset management (DAM) systems.
The motivation for doing so would be to provide the sampled content for human review through a review interface because it can evaluate and improve the effectiveness of the content review (Strauss, para. [0059]).
Prior Art of Record
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Riscutia et al. (US 20210406263 A1), teaches a database is an organized collection of data.
Xia et al. (US 20200356599 A1), teaches a computer-implemented method of determining data lineage based on database queries.
Costabello et al. (US 20190287006 A1), teaches a system for providing integrated monitoring and communications of diagnostic equipment.
Conclusion
12. 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
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