Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This action is in response to the original filing on 03/22/2024. Claims 1-21 are pending and have been considered below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-6 and 10-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 2, Claim 2 recites the limitation "the weight of the selected node" in the final clause. There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites "a weighting for one or more content items," and claim 2 separately recites "a weighting for a selected node of the plurality of nodes of the knowledge base" using the indefinite article a second time, so that two weightings stand introduced. Neither is recited as "a weight." It is therefore unclear whether "the weight of the selected node" refers to the weighting introduced in claim 1, to the separately introduced weighting of claim 2, or to some further quantity derived from one of them. Claims 3-6 depend from claim 2 and inherit the same defect. The system claims show the correction the applicant already made elsewhere: claim 15 introduces a single "a received weight for a selected node," and claims 16-19 refer back to it consistently, so the corresponding system chain raises no antecedent basis question.
For the purposes of examination, the introduced limitation of Claim 15 in regards to “a received weight for a selected node” will be used when examining Claim 2.
Regarding Claims 3 and 6, Claims 3 and 6 depend on claim 2 and incorporate by reference every limitation of claim 2, including the indefinite limitation "the weight of the selected node." Claims 3 and 6 are therefore indefinite for the reasons given for Claim 2. Neither adds an independent indefiniteness defect.
Regarding Claims 4 and 5, Claims 4 and 5 each recite the limitation "the received weight for the selected node." There is insufficient antecedent basis for this limitation in the claims. No "received weight" is recited anywhere in claim 4 or 5 or in the chain from which they depend. Claim 1 recites "a received user feedback" that "corresponds to a weighting for one or more content items"; claim 2 recites "a weighting for a selected node"; and claim 3 adds only the act of accessing a text file. "The received weight" maps cleanly to none of these, and because claims 1 and 2 each introduce a weighting with the indefinite article, the limitation has at least two candidate antecedents. The metes and bounds of the duplication step, how many times the text or the file is duplicated, therefore cannot be determined. Contrast claims 18 and 19, which recite the identical duplication limitations in the system chain and there do have proper antecedent basis, because claim 15 expressly recites "a received weight for a selected node." The disparity indicates a drafting oversight in the method chain rather than an intended difference in scope.
For the purposes of examination, the introduced limitation of Claim 15 in regards to “a received weight for a selected node” will be used when examining Claims 4 and 5.
Claims 4 and 5 additionally depend on Claim 3, which depends from claim 2, and so also inherit the indefinite limitation "the weight of the selected node" for the reasons given for claim 2.
Appropriate correction is required. Applicant may resolve the rejection by conforming the method chain to the terminology the system chain already uses, for example, by amending claim 2 to recite that the user feedback comprises "a received weight for a selected node of the plurality of nodes of the knowledge base," and by conforming the later references in claims 2, 4, and 5 to that recitation. Such an amendment appears to find support at specification paragraphs [0060] and [0066]-[0068].
Regarding Claim 10, Claim 10 recites “a selection of a content item for creation of a weighted node corresponding to the content item”. The following step then recites “configuring the user interface to receive input to create the weighted node by adjusting a weight of a node representing the selected content item”. It is ambiguous and therefore unknown if the method creates a new weighted node, or if it is modifying an existing node by assigning/changing its weight. For the purposes of examination, this limitation is interpreted as: the node comprising a modification to an existing node by a modification of its weight.
Regarding Claims 11-14, Claims 11-14 depend on claim 10 and incorporate by reference every limitation of claim 10, including the indefinite limitation "the weight of the selected node." Claims 11-14 are therefore indefinite for the reasons given for Claim 10. Neither adds an independent indefiniteness defect.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6-17, and 20-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1 and 15
Step 1: Claims 1 and 15 recite a method and system. As such, they are directed to the statutory categories of a method and a machine.
Step 2A Prong 1: The claims recite, inter alia: “generating a knowledge base including a plurality of nodes representing a respective plurality of content items;” which, under its broadest reasonable interpretation in light of the specification, amounts to no more than generation of a graph or other relational structure including multiple nodes, which is easily performed by a human with the aid of pen and paper and is thus a mental process.
The claims further recite: “updating the knowledge base based on a received user feedback, wherein the user feedback corresponds to a weighting for one or more content items;” which, under its broadest reasonable interpretation in light of the specification, amounts to no more than a mental process wherein an update or revision is made to the relational structure based on receiving a user’s assessment (See MPEP 2106.04(a)(2) Subsection III).
The claims further recite: “generating content groupings based on the updated knowledge base, the content groupings including subsets of the plurality of content items, the subsets including conceptually similar content items;” which, under its broadest reasonable interpretation in light of the specification, amounts to no more than a mental process of grouping objects or data items by similarity.
Step 2A Prong 2: The claims recite the additional elements of: “providing the generated content groupings via a user interface”. This limitation amounts to no more than mere instructions to apply an exception, particularly requiring the use of software to tailor information and provide it to the user on a generic computer (See MPEP 2106.05(f))
Step 2B: The claims do not recite significantly more than the judicial exception. The recitation of: “providing the generated content groupings via a user interface”. amounts to no more than mere instructions to apply an exception, particularly requiring the use of software to tailor information and provide it to the user on a generic computer (See MPEP 2106.05(f))
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claims 2 and 16
Step 1: Claims 2 and 16 recite a method and system. As such, they are directed to the statutory categories of a method and a machine.
Step 2A Prong 1: The claims recite, inter alia: “updating the knowledge base based on the weighting for the selected node comprises updating a corpus representative of the plurality of content items to reflect the weight of the selected node and” which, under its broadest reasonable interpretation in light of the specification, amounts to no more than a mental process of noting the value of importance against one entry in a map or graph and updating a representation of that particular data item.
The claims further recite: “re-generating the knowledge base based on the updated corpus.” which, under its broadest reasonable interpretation in light of the specification, amounts to no more than re-drawing a mapping or graph from the revised set of source material.
Step 2A Prong 2: The claims recite the additional elements of: “the user feedback comprises a weighting for a selected node of the plurality of nodes of the knowledge base;”. This additional element is recited at a very high level of generality and amounts to no more than generally linking a judicial exception to a particular technological environment or field of use as per MPEP 2106.05(h) and is most similar to limiting the abstract idea of collecting information, analyzing it, and displaying certain results.
Step 2B: The claims do not contain significantly more than the judicial exception. The additional elements of: “the user feedback comprises a weighting for a selected node of the plurality of nodes of the knowledge base;”. This additional element is recited at a very high level of generality and amounts to no more than generally linking a judicial exception to a particular technological environment or field of use as per MPEP 2106.05(h) and is most similar to limiting the abstract idea of collecting information, analyzing it, and displaying certain results.
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claims 3 and 17
Step 1: Claims 3 and 17 recite a method and system. As such, they are directed to the statutory categories of a method and a machine.
Step 2A Prong 1: Claims 3 and 17 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claims 2 and 15 which they depend on, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claims recite the additional elements of: “wherein updating the knowledge base based on the weighting for the selected node comprises accessing a text file corresponding to a content item of the plurality of content items, the content item corresponding to the selected node”. This amounts to no more than mere insignificant extra solution activity, particularly data gathering, wherein a text file is accessed in order to update a node (See MPEP 2106.05(g)).
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering recited by: “wherein updating the knowledge base based on the weighting for the selected node comprises accessing a text file corresponding to a content item of the plurality of content items, the content item corresponding to the selected node”. This insignificant extra solution activity is well-understood, routine, and conventional activities similar to storing and retrieving information from memory (See MPEP 2106.05(d) Subsection II).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 6
Step 1: Claim 6 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 6 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 2, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claim recites the additional element of: “wherein the weighting for the selected node is received via the user interface.” This amounts to no more than generally linking the exception to a particular field of use or technological environment by specifying that the channel through which the input to the mental process arrives is generically linked to a user interface (See MPEP 2106.05(h))
Step 2B: The claims do not recite significantly more than the judicial exception. The claim recitation of: “wherein the weighting for the selected node is received via the user interface.” This amounts to no more than generally linking the exception to a particular field of use or technological environment by specifying that the channel through which the input to the mental process arrives is generically linked to a user interface (See MPEP 2106.05(h))
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claims 7 and 20
Step 1: Claims 7 and 20 recite a method and system. As such, they are directed to the statutory categories of a method and a machine.
Step 2A Prong 1: Claims 7 and 20 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claims 1 and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claim recites the additional element of: “wherein generating the content groupings based on the updated knowledge base comprises executing a clustering algorithm on the nodes of the updated knowledge base.” This amounts to no more than execution of a clustering algorithm recited at a high level of generality with no particular algorithm, parameterization, or modification of an algorithm claimed. This amounts to no more than mere instructions to implement the grouping step on a computer, equivalent to adding the words ‘apply it’ (MPEP 2106.05(f)).
Step 2B: The judicial exception is not sufficiently integrated into a practical application. The claim recites: “wherein generating the content groupings based on the updated knowledge base comprises executing a clustering algorithm on the nodes of the updated knowledge base.” This amounts to no more than execution of a clustering algorithm recited at a high level of generality with no particular algorithm, parameterization, or modification of an algorithm claimed. This amounts to no more than mere instructions to implement the grouping step on a computer, equivalent to adding the words ‘apply it’ (MPEP 2106.05(f)).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claims 8 and 21
Step 1: Claims 8 and 21 recite a method and system. As such, they are directed to the statutory categories of a method and a machine.
Step 2A Prong 1: Claims 8 and 21 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claims 1 and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claims recite the additional elements of: “wherein providing the generated content groupings via the user interface comprises displaying a visual representation of the knowledge base via the user interface” This amounts to no more than describing the output of the result of the abstract idea, and is insignificant extra solution activity (See MPEP 2105.05(g)). The user interface remains generic machinery performing under its ordinary capacity (See MPEP 2105.05(f)).
Step 2B: The claims do not recite significantly more than the judicial exception. The additional elements of: “wherein providing the generated content groupings via the user interface comprises displaying a visual representation of the knowledge base via the user interface” amount to no more than describing the output of the result of the abstract idea and is insignificant extra solution activity (See MPEP 2105.05(g)) and is most similar to the well-understood, routine, and conventional activity of presenting offers and gathering statistics (MPEP 2106.05(d) Subsection II). The user interface remains generic machinery performing under its ordinary capacity (See MPEP 2105.05(f)).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 9
Step 1: Claim 9 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 9 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claim recites the additional element of: “wherein: the user feedback comprises received text; and”, amounting to no more than a high level recitation generally linking an abstract idea to a technological environment, most similar to limiting the use of a formula (See MPEP 2106.05(h)).
The claims also recite: “the weighting is based on the received text”, amounting to no more than a high level recitation generally linking an abstract idea to a technological environment, most similar to limiting the use of a formula (See MPEP 2106.05(h)).
Step 2B: The claims do not recite significantly more than the judicial exception. The additional element of: “wherein: the user feedback comprises received text; and”, amounting to no more than a high level recitation generally linking an abstract idea to a technological environment, most similar to limiting the use of a formula (See MPEP 2106.05(h))
The claims also recite: “the weighting is based on the received text”, amounting to no more than a high level recitation generally linking an abstract idea to a technological environment, most similar to limiting the use of a formula (See MPEP 2106.05(h))
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 10
Step 1: Claim 10 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: The claim recites: "a selection of a content item for creation of a weighted node corresponding to the content item" recites the mentally performable act of picking out an item to be emphasized.
The claim further recites the limitation "create the weighted node by adjusting a weight of a node representing the selected content item, the node representing the content item in a knowledge base including a plurality of nodes representing a plurality of content items" which is the mentally performable process, with the aid of pen and paper, of assigning a numerical emphasis to an entry in a map.
The claim further recites: "the content groupings being determined based on the plurality of nodes and the weighted node" which is the mentally performable process of sorting the items into groups in light of that emphasis.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of "receiving, via a user interface," "configuring the user interface to receive input," and "displaying, via the user interface." Receiving a selection through a user interface is insignificant extra solution data gathering, and displaying a representation of the resulting groupings is insignificant extra-solution output (MPEP 2106.05(g)). Configuring a user interface to receive input is recited purely functionally: claim 10 specifies no structure, layout, control, or behavior of the interface beyond the fact that it accepts a weight value. This is generic computer machinery performing in its ordinary capacity, accepting input and presenting output, and amounts to mere instructions to implement the exception on a computer (MPEP 2106.05(f)), while generally linking the exception to a computerized content-management environment (MPEP 2106.05(h)).
Step 2B: Claim 10 does not amount to significantly more. The user interface, individually and as an ordered combination with the recited steps, is used only to gather the input to the abstract idea and to report its result. The specification establishes its conventionality at [0029] and [0051].
Claim 11
Step 1: Claim 11 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 11 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 10, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claim recites the additional element of: “wherein the representation of the content groupings comprises tags corresponding with keywords representative of each of the content groupings.”. This limitation amounts to no more than merely generally linking an exception to a particular field of use or technological environment, most similar to the case of limitation of a database index to only XML tags (see Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937) and (MPEP 2106.05(h)).
Step 2B: The claim does not recite significantly more than the judicial exception. The additional element of: “wherein the representation of the content groupings comprises tags corresponding with keywords representative of each of the content groupings.”, amounts to no more than merely generally linking an exception to a particular field of use or technological environment, most similar to the case of limitation of a database index to only XML tags (see Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d at 1328-29, 121 USPQ2d at 1937) and (MPEP 2106.05(h)).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 12
Step 1: Claim 12 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 12 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claims 10, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claims recite the additional elements of: “displaying, via the user interface, a visual representation of the knowledge base with the representation of the content groupings of the plurality of content items in the knowledge base.” This amounts to no more than describing the output of the result of the abstract idea, and is insignificant extra solution activity (See MPEP 2105.05(g)). The user interface remains generic machinery performing under its ordinary capacity (See MPEP 2105.05(f)).
Step 2B: The claims do not recite significantly more than the judicial exception. The additional elements of: “displaying, via the user interface, a visual representation of the knowledge base with the representation of the content groupings of the plurality of content items in the knowledge base.” amount to no more than describing the output of the result of the abstract idea and is insignificant extra solution activity (See MPEP 2105.05(g)) and is most similar to the well-understood, routine, and conventional activity of receiving or transmitting data (MPEP 2106.05(d) Subsection II). The user interface remains generic machinery performing under its ordinary capacity (See MPEP 2105.05(f)).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 13
Step 1: Claim 13 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 13 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 10, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claim recites the additional element of: “wherein the content groupings are determined by executing a clustering algorithm on the knowledge base including the weighted node.” This amounts to no more than execution of a clustering algorithm recited at a high level of generality with no particular algorithm, parameterization, or modification of an algorithm claimed. This amounts to no more than mere instructions to implement the grouping step on a computer, equivalent to adding the words ‘apply it’ (MPEP 2106.05(f)).
Step 2B: The judicial exception is not sufficiently integrated into a practical application. The claim recites: “wherein the content groupings are determined by executing a clustering algorithm on the knowledge base including the weighted node.” This amounts to no more than execution of a clustering algorithm recited at a high level of generality with no particular algorithm, parameterization, or modification of an algorithm claimed. This amounts to no more than mere instructions to implement the grouping step on a computer, equivalent to adding the words ‘apply it’ (MPEP 2106.05(f)).
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim 14
Step 1: Claim 14 recites a method. As such, the claim is directed to the statutory category of a method.
Step 2A Prong 1: Claim 14 merely narrows the previously recited abstract limitations. For the reasons described above with respect to Claim 10, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claim above and does not provide anything more than the mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper and mathematical concepts that are achievable through mathematical computation.
Step 2A Prong 2: The claims recite the additional elements of: “wherein the selection of the content item is received as the content item is being added to the knowledge base” This amounts to no more than describing with high level of generality the selection of an item as it is simultaneously being added to a map or graph, amounting to no more than mere instructions to apply an exception (MPEP 2106.05(f)).
Step 2B: The claims do not recite significantly more than the judicial exception. The additional elements of: “wherein the selection of the content item is received as the content item is being added to the knowledge base” amounts to no more than describing with high level of generality the selection of an item as it is simultaneously being added to a map or graph, amounting to no more than mere instructions to apply an exception (MPEP 2106.05(f)). It is considered to be well-understood, routine, and conventional activity in the art, most similar to electronic recordkeeping (See MPEP 2106.05(d) Subsection II)
Considering the additional elements individually and in combination, the claims are directed to judicial exceptions without significantly more.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6, 10, 12, and 15-16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WALSH et al. (US 20210406534 A1, hereinafter Walsh).
Regarding Claim 1, Walsh teaches a method (Paragraph [0002] According to some implementations, a method may include) comprising:
generating a knowledge base including a plurality of nodes representing a respective plurality of content items; (Paragraph [0043] As shown in FIG. 1A, and by reference number 115, the labelling system may determine (or generate) a substance knowledge graph embedding (KGE) associated with the substance and a historical KGE associated with the knowledge base. For example, the labelling system may generate the substance KGE (or a KGE for the substance) based on the related substance information (e.g., based on the related substances and the relationships associated with the related substances). As an example, nodes of the substance KGE may represent the substance, the related substances, and/or the effects of the interactions. The connections between the nodes may represent the relationships (e.g., the triples). (Generation of a knowledge base with nodes acting as representations of content items.))
updating the knowledge base based on a received user feedback, wherein the user feedback corresponds to a weighting for one or more content items; (Paragraph [0043] For example, the relationships may correspond to embeddings of the substance KGE. In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. Paragraph [0061] In this regard, the labelling system (e.g., using the validation module) may provide the misaligned substance data to a device associated with a user for validation by the user. For example, the labelling system may transmit a request, to the device, to validate the misaligned substance data and provide the misaligned substance data to the device via a user interface. In this regard, the labelling system may provide the misaligned substance data as recommendations for updating the knowledge base and/or updating the substance KGE (and, consequently, the substance description) to include the misaligned substance data (Updating the knowledge base upon receiving user feedback via the labeling system (validation module), the updating corresponding to the KGE made up of weights between nodes representing content items.))
generating content groupings based on the updated knowledge base, the content groupings including subsets of the plurality of content items, the subsets including conceptually similar content items; and (Paragraph [0050] In some implementations, when comparing the substance KGE and the historical KGE, the labelling system may generate, based on the substance KGE, a substance coherency cluster associated with the substance KGE (or a substance KGE cluster) and generate, based the historical KGE, a historical coherency cluster associated with the historical KGE (a historical KGE cluster). The labelling system may determine the similarity score based on an alignment analysis of the substance KGE cluster and the historical KGE cluster. In this regard, the similarity score may comprise a coherence similarity score that is generated based on a cosine similarity analysis. [0072] For example, the labelling system may generate, based on the new substance KGE, a new substance KGE cluster and generate, based the new historical KGE, a new historical KGE cluster, in a manner similar to the manner described in connection with FIG. 1A (Generation of content clusters based on the new (updated) KGE, the clusters consisting of coherent (similar) items))
providing the generated content groupings via a user interface. (Paragraph [0052] The labelling system may perform an alignment analysis of the substance KGE cluster and the historical KGE cluster and, based on the alignment analysis, may detect substances that are aligned and substances that are misaligned, as explained in more detail below. Paragraph [0061] In this regard, the labelling system (e.g., using the validation module) may provide the misaligned substance data to a device associated with a user for validation by the user. For example, the labelling system may transmit a request, to the device, to validate the misaligned substance data and provide the misaligned substance data to the device via a user interface (Misaligned data in the cluster is provided to the user through a user interface))
Regarding Claim 2, Walsh teaches all of the limitations as claimed in Claim 1, including: the user feedback comprises a weighting for a selected node of the plurality of nodes of the knowledge base; and (Paragraph [0032] As an example, the labelling system may use a long short-term memory (LSTM) machine learning model (e.g., a bi-directional LSTM) to identify the relationships associated with the related substances. For example, the labelling system may determine interaction data and relationship data from the substance description using a neural network model (e.g., a bi-directional LSTM), display the interaction data and the relationship data to a user, receive feedback data from the user, and modify the neural network model based on the feedback data. Paragraph [0043] As shown in FIG. 1A, and by reference number 115, the labelling system may determine (or generate) a substance knowledge graph embedding (KGE) associated with the substance and a historical KGE associated with the knowledge base. For example, the labelling system may generate the substance KGE (or a KGE for the substance) based on the related substance information (e.g., based on the related substances and the relationships associated with the related substances). For instance, the substance KGE may include information identifying the related substances and the relationships (e.g., the triples). As an example, nodes of the substance KGE may represent the substance, the related substances, and/or the effects of the interactions. The connections between the nodes may represent the relationships (e.g., the triples). For example, the relationships may correspond to embeddings of the substance KGE. In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. For instance, the greater the weight of the connection, the closer the relationship between the two nodes, and vice versa. Similarly, the shorter the distance of the connection, the closer the relationship between the two nodes, and vice versa. (The user provides feedback of relationships between received data and then is able to modify the network based on that feedback. Paragraph [0043] makes clear that these relationships are expressed as weighted connections between nodes. It follows then that when the user provides feedback on these relationships, the user is also providing a weight on the measure of closeness between nodes due to their interchangeable/corresponding nature))
updating the knowledge base based on the weighting for the selected node comprises updating a corpus representative of the plurality of content items to reflect the weight of the selected node and (Paragraph [0024] The knowledge base may include a data structure (e.g., a database, a linked list, a table, and/or the like) that stores historical data (e.g., obtained from a corpus of documents) regarding different historical substances and historical data regarding relationships between the historical substances. Paragraph [0061] For example, the labelling system may transmit a request, to the device, to validate the misaligned substance data and provide the misaligned substance data to the device via a user interface. In this regard, the labelling system may provide the misaligned substance data as recommendations for updating the knowledge base and/or updating the substance KGE (and, consequently, the substance description) to include the misaligned substance data Paragraph [0066] The labelling system may update the knowledge base data structure to include the substance data associated with Related substance 1. (The knowledge base stores historical data from a received corpus of data items. Based on user feedback (including weights expressed as relationships), the knowledge base including its data structure, which in turn means its stored representation of the corpus, are updated.))
re-generating the knowledge base based on the updated corpus. (Paragraph [0071] Accordingly, the labelling system may update the substance KGE to include the substance data associated with Historical substance 2 (e.g., thereby generating a new substance KGE, in a manner similar to the manner described in connection with FIG. 1A (reference number 115)). (When information is updated, the knowledge base is generated once again.))
Regarding Claim 6, Walsh teaches all of the limitations as claimed in Claim 2, including: wherein the weighting for the selected node is received via the user interface. (Paragraph [0002] obtaining, by the device and based on the similarity score, validation information associated with a representation of the related substance within the substance KGE, wherein the validation information indicates a degree of confidence associated with the representation of the related substance within the substance KGE; and performing, by the device and based on the validation information, an action associated with the substance description or a knowledge base that is associated with the historical ontology data. Paragraph [0043] For instance, the substance KGE may include information identifying the related substances and the relationships (e.g., the triples). As an example, nodes of the substance KGE may represent the substance, the related substances, and/or the effects of the interactions. The connections between the nodes may represent the relationships (e.g., the triples). For example, the relationships may correspond to embeddings of the substance KGE. In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. For instance, the greater the weight of the connection, the closer the relationship between the two nodes, and vice versa. (Information requisite to the validation of the knowledge base is received from a user through a device with a UI, including weights for nodes))
Regarding Claim 10, Walsh teaches a method (Paragraph [0002] According to some implementations, a method may include) comprising:
receiving, via a user interface, a selection of a content item for creation of a weighted node corresponding to the content item; (Paragraph [0002] According to some implementations, a method may include receiving, by a device, a substance description of a substance; identifying, by the device and from the substance description, Paragraph [0027] In some implementations, the labelling system may obtain the substance description from a device (e.g. a user device, a server device, and/or the like), from a data structure, and/or the like. (Receiving content from a user device (which contains a UI) in order to generate a weighted node map))
configuring the user interface to receive input to create the weighted node by adjusting a weight of a node representing the selected content item, (Paragraph [0027] In some implementations, the labelling system may obtain the substance description from a device (e.g. a user device, a server device, and/or the like), from a data structure, and/or the like Paragraph [0032] As an example, the labelling system may use a long short-term memory (LSTM) machine learning model (e.g., a bi-directional LSTM) to identify the relationships associated with the related substances. For example, the labelling system may determine interaction data and relationship data from the substance description using a neural network model (e.g., a bi-directional LSTM), display the interaction data and the relationship data to a user, receive feedback data from the user, and modify the neural network model based on the feedback data. (The user interface (via a device) is configured to receive user input which is then used to adjust the weight of connections between nodes (expressed as relationships between data points, see paragraph [0043])))
the node representing the content item in a knowledge base including a plurality of nodes representing a plurality of content items; (Paragraph [0023] The labelling system may be hosted by a cloud computing environment or by one or more server devices, and may be associated with one or more user devices and/or data structures (e.g., including the knowledge base discussed above). Paragraph [0043] For example, the relationships may correspond to embeddings of the substance KGE. In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. For instance, the greater the weight of the connection, the closer the relationship between the two nodes, and vice versa. Similarly, the shorter the distance of the connection, the closer the relationship between the two nodes, and vice versa. (The received information is used for a knowledge base wherein each node is weighted))
displaying, via the user interface, a representation of content groupings of the plurality of content items in the knowledge base, the content groupings being determined based on the plurality of nodes and the weighted node. (Paragraph [0032] For example, the labelling system may determine interaction data and relationship data from the substance description using a neural network model (e.g., a bi-directional LSTM), display the interaction data and the relationship data to a user, receive feedback data from the user, and modify the neural network model based on the feedback data. Paragraph [0034] In some implementations, the labelling system may express the relationships in the form of subject-predicate-object (or a triple). Paragraph [0043] For instance, the substance KGE may include information identifying the related substances and the relationships (e.g., the triples). As an example, nodes of the substance KGE may represent the substance, the related substances, and/or the effects of the interactions. The connections between the nodes may represent the relationships (e.g., the triples). For example, the relationships may correspond to embeddings of the substance KGE. In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. (Displaying relationship data of the knowledge graph to a user, wherein the displayed relationships are expressed as connections between nodes (paragraph [0043])))
Regarding Claim 12, Walsh teaches all of the limitations as claimed in Claim 10, including: displaying, via the user interface, a visual representation of the knowledge base with the representation of the content groupings of the plurality of content items in the knowledge base. (Paragraph [0032] For example, the labelling system may determine interaction data and relationship data from the substance description using a neural network model (e.g., a bi-directional LSTM), display the interaction data and the relationship data to a user, receive feedback data from the user, and modify the neural network model based on the feedback data. (Displaying data of the knowledge base visually))
Regarding Claims 15 and 16, Claims 15 and 16 are system claims corresponding to the method of Claims 1 and 2. As such, they are rejected for the same reasons above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-5, 11, 14, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Walsh, as applied in claims 2, 10 and 15 above, in view of Black et al. (US 11429686 B2, hereinafter Black).
Regarding Claim 3, Walsh teaches the invention as claimed in Claim 2 above. Walsh does not teach: wherein updating the knowledge base based on the weighting for the selected node comprises accessing a text file corresponding to a content item of the plurality of content items, the content item corresponding to the selected node.
In the same field of endeavor, Black teaches: wherein updating the knowledge base based on the weighting for the selected node comprises accessing a text file corresponding to a content item of the plurality of content items, the content item corresponding to the selected node. (Col. 5 Lines 36-45 In selected embodiments, a knowledge base 18 may include text recognition database 20i. That is, in certain embodiments, a robot 12 may be configured or programmed to parse text on a webpage. (Col. 13 Lines 29-41) A learning cell 66 may be changed when a robot 12 deviates from a current node list or workflow 28 in order to achieve its assigned goal. For example, when a goal is achieved, a weight function within a learning cell 66 may be modified to allow the newly discovered path to receive a higher ranking going forward. This may be accomplished by adding the newly discovered path to a node network 22 and weighting one or more functions in a learning cell 66 so the sequence taken may be more likely to be used in the future. (Col. 13-14 Lines 61-8) In selected embodiments, from a functional perspective, a robot 12 may be either in learning mode or in fast mode. A learning mode may be one in which a robot 12 is continuously defining new rules, new nodes 24, or anything else that is not contained within its knowledge base 18. (Accessing text on a webpage, the webpage being the content item of a node in a knowledge base. These webpages act as nodes in the network, and actions taken when navigating to these nodes (accessing a content item corresponding to a node in the form of webpages) are weighted in order to encourage the robot to repeat said actions. The robot also learns new nodes when not contained in the knowledge base, serving as an update))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated updating the knowledge base based on weights of selected nodes as taught by Black into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve and reduce the time needed for a human being to spend performing various actions when browsing the internet (Black Col. 1 Lines 24-35).
Regarding Claim 4, the combination of Walsh and Black teaches all the limitations of Claim 3, including: wherein updating the knowledge base based on the weighting for the selected node further comprises duplicating text in the text file corresponding to the content item based on the received weight for the selected node. (Col. 11 Lines 10-23 Accordingly, as a robot 12 “views” a webpage 58, it may look for elements 64 on the webpage 58 that correspond or relate to fields 64c, buttons 64d, or the like with standard phrases such as “name” or “submit.” Once a robot 12 successfully classifies the elements 64 on a webpage 58, it may fill in the necessary data, acquire information from the webpage 58, push the appropriate buttons 64d, and so forth. Thus, as a robot 12 transitions from node 24 to node 24 in its advance toward an assigned goal, it may map out the nodes 24 in of a network 22. Thus, the robot 12 may update one or more appropriate databases 20 within a knowledge base 18. (Col. 13 Lines 29-41) A learning cell 66 may be changed when a robot 12 deviates from a current node list or workflow 28 in order to achieve its assigned goal. For example, when a goal is achieved, a weight function within a learning cell 66 may be modified to allow the newly discovered path to receive a higher ranking going forward. This may be accomplished by adding the newly discovered path to a node network 22 and weighting one or more functions in a learning cell 66 so the sequence taken may be more likely to be used in the future. (Col. 19 Lines 29-48) Referring to FIG. 9, in an alternative first phase 83b of a scanning process 76, a robot 12 may probe 94 a first location on a webpage 58 with a pointer. This may enable a robot 12 to identify 78 an element 64 below the first location. Accordingly, the element 64 may be added 100 to a list of elements corresponding to the webpage 58 and the boundaries of the element 64 may be obtained 106 from the underlying code 60. The robot 12 may then determine 98 whether the first phase 83b of the scanning process 76 of the website 58 is complete. (Updating a knowledge base with nodes comprising webpages. The pages are first scanned in a process involving probing, wherein parts of the page are 'probed' and stored in a database, then webpages are later analyzed to determine if an update is necessary. This is tantamount to copying the text in the page, as storing it in the database to later check means that the current version must be stored in its original form. Each node is also weighted, and the robot makes its choices to update or ignore based on this weighting))
Regarding Claim 5, the combination of Walsh and Black teaches all the limitations of Claim 3, including: wherein updating the knowledge base based on the weighting for the selected node further comprises duplicating the text file corresponding to the content item based on the received weight for the selected node. (Col. 3-4, Lines 65-5 For example, state changes in webpage logic or actual code changes in the webpage itself may render one or more replay files or sequences obsolete. Accordingly, in such situations, a robot 12 may perform a full scan of the node in order to move forward toward an assigned goal. The results of this new scan and a corresponding replay file may then be stored in a knowledge base 18 for future use by robots 12 that encounter the node. (Col. 15 Lines 56-64) In selected embodiments, scanning 76 may comprise scanning an entire webpage 58. In such embodiments, a robot 12 may identify 78 and define 80 the boundaries of each element 64 on a webpage 58. Alternatively, a robot 12 may apply one or more rules within a knowledge base 18 to determine which portion or portions of a webpage 58 to scan 76. (Col. 18 Lines 28-39) Referring to FIG. 8, in selected embodiments, scanning 76 a node 24 may comprise a systematic march from location to location across a webpage 58, probing the webpage 58 at each location. Probing 94 at that first location may enable a robot 12 to read or otherwise obtain an identification of the element 64 located below the pointer. In selected embodiments, the identification may be obtained from the underlying code 60 or from a document object model corresponding thereto or based thereon. (Col. 18 Lines 40-49) Based on the probing 94, a robot 12 may determine 96 whether an element 64 below the pointer has been probed before. Conversely, if the element 64 below the pointer has not been probed before, the element 64 may be added 100 to a list of elements corresponding to the webpage 58. (Col. 22 Lines 15-25) In certain embodiments or situations, a robot 12 may encounter a node 24 that it does not have in its node map 22 or does not recognize. In such embodiments or situations, a further operation may be performed. First, a corresponding robot 12 may attempt to find the node 24 by comparing its fields 64c, buttons 64d, URL, or the like or a combination or sub-combination thereof to information in its node database 20b. Failing this, the robot 12 may assign the node 24 a name and store it as a new node that is unknown. (The webpage in its entirety is scanned and stored in a database, then later used to update the knowledge base if needed. The scanning process comprises probing elements of the page, including the underlying code of elements found within that page. An example embodiment is outlined wherein the entire webpage is scanned and added to the knowledge base, meaning that the entire page (file) is duplicated from its current “live” state on its respective webpage URL and then added to the knowledge base as a node, where it can later be used as a reference point to determine if any changes have been made to the “live” page.))
Regarding Claim 11, Walsh teaches the invention as claimed in Claim 10 above. Walsh does not teach: wherein the representation of the content groupings comprises tags corresponding with keywords representative of each of the content groupings.
In the same field of endeavor, Black teaches: wherein the representation of the content groupings comprises tags corresponding with keywords representative of each of the content groupings. (Col. 5 Lines 13-25 In selected embodiments, a knowledge base 18 may include a key phrase and/or keyword database 20g. Various important key phrases and/or keywords may be stored within such a database 20g In selected embodiments, the node may be identified by finding the names or labels corresponding to one or more buttons and/or fields and comparing them to a database list of key phrases or keywords. (The knowledge base including key words))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated tagging keywords representing content inside a knowledge base as taught by Black into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve and reduce the time needed for a human being to spend performing various actions when browsing the internet (Black Col. 1 Lines 24-35).
Regarding Claim 14, Walsh teaches the invention as claimed in Claim 10 above. Walsh does not teach: wherein the selection of the content item is received as the content item is being added to the knowledge base.
In the same field of endeavor, Black teaches: wherein the selection of the content item is received as the content item is being added to the knowledge base. (Col. 4 Lines 40-50 In selected embodiments, a knowledge base 18 may include a field database 20e. A field may be an input field that a user may encounter on a webpage. Certain fields may enable users of a webpage to type in data such as name, address, telephone number, credit card information, shipping address, billing address, coupon code, web address (URL), password, or the like. Other fields may enable users of a webpage to select, from an array of predefined choices, data such as size, color, shipping method, payment method, or the like. (Selection of user inputted content as it is added to the knowledge base))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated selection of content items as they are received as taught by Black into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve and reduce the time needed for a human being to spend performing various actions when browsing the internet (Black Col. 1 Lines 24-35).
Regarding Claims 17-19, Claims 17-19 are system claims similar to the method of Claims 3-5. As such, they are rejected for the same reasons above.
Claims 7-9, 13, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Walsh, as applied in claims 1, 10 and 15 above, in view of Agrawal et al. (US 20240221060 A1, hereinafter Agrawal).
Regarding Claim 7, Walsh teaches the invention as claimed in Claim 1 above. Walsh does not teach: wherein generating the content groupings based on the updated knowledge base comprises executing a clustering algorithm on the nodes of the updated knowledge base.
In the same field of endeavor, Agrawal teaches: wherein generating the content groupings based on the updated knowledge base comprises executing a clustering algorithm on the nodes of the updated knowledge base. (Paragraph [0059] Models 316 may be the output and result of AI modeling using the data collected from the one or more data sources 309. AI modeling may refer to the creation, training and deployment of machine learning algorithms that may emulate decision-making based on data available within the knowledge corpus 311 of the AI system 310 and/or using data that may be available outside of the knowledge corpus 311. The AI models 316 may provide the AI system 310 with a foundation to support advanced intelligence methodologies, such as real-time analytics, predictive analytics and/or augmented analytics, which can be utilized when identifying location clusters for each user, Paragraph [0065] Embodiments of corpus creation module 603 may perform tasks, functions and/or processes of the object replacement module 307 that may be directed toward creating or updating user records within the knowledge corpus 311 based on the data collected from the one or more data sources 309. (Using an algorithm to generate clusters based on data available in the updated knowledge corpus (base)))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated generating content groupings using a clustering algorithm as taught by Agrawal into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve the user experience of customers when traveling to new areas of the world through use of maintained augmented reality (AR) devices (Agrawal Paragraphs [0002]-[0003]).
Regarding Claim 8, Walsh teaches the invention as claimed in Claim 1 above. Walsh does not teach: wherein providing the generated content groupings via the user interface comprises displaying a visual representation of the knowledge base via the user interface.
In the same field of endeavor, Agrawal teaches: wherein providing the generated content groupings via the user interface comprises displaying a visual representation of the knowledge base via the user interface. (Paragraph [0060] User interface 314 may refer to an interface provided between AI system 310 and human users. For example, end users operating an AR device 301 can experience an AR environment provided by augmented reality service 320. The user interface 314 utilized by AI system 310 may be a command line interface (CLI), menu-driven interface, graphical user interface (GUI), a touchscreen GUI, etc. Programs and applications 150 provided by AI system 310, such as the augmented reality service 320, may include any type of application that may incorporate and leverage the use of artificial intelligence to complete one or more tasks, operations or provide an environment to a user. (Providing information to the user about the application, which includes the knowledge base, through a UI))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated providing generated content groupings to a user through a visual interface as taught by Agrawal into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve the user experience of customers when traveling to new areas of the world through use of maintained augmented reality (AR) devices (Agrawal Paragraphs [0002]-[0003]).
Regarding Claim 9, Walsh teaches the invention as claimed in Claim 1 above, including: the weighting is based on the received text. (Paragraph [0043] In some instances, attributes of a connection (e.g., a weight, a length, and/or the like) of two nodes may represent a measure of closeness of the relationship between the two nodes. For instance, the greater the weight of the connection, the closer the relationship between the two nodes, and vice versa. Similarly, the shorter the distance of the connection, the closer the relationship between the two nodes, and vice versa. (The weighting between nodes is determinant on user feedback))
Walsh does not teach: wherein: the user feedback comprises received text; and
In the same field of endeavor, Agrawal teaches: wherein: the user feedback comprises received text; and (Paragraph [0058] For example, AI engine 312 can analyze the data collected from the data sources 309 with respect to each user's interactions with their surroundings, each user's mobility patterns, shopping patterns of each user and resulting behaviors based on those shopping patterns, attributes of products and objects purchased or of interest to the user including, but not limited to the color, shape, size, price, etc., as well as sentiment and natural language processing of product reviews and other types of written feedback created by each user. (Feedback from a user comprising written text))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated user feedback comprising received text as taught by Agrawal into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve the user experience of customers when traveling to new areas of the world through use of maintained augmented reality (AR) devices (Agrawal Paragraphs [0002]-[0003]).
Regarding Claim 13, Walsh teaches the invention as claimed in Claim 10 above. Walsh does not teach: wherein the content groupings are determined by executing a clustering algorithm on the knowledge base including the weighted node.
In the same field of endeavor, Agrawal teaches: wherein the content groupings are determined by executing a clustering algorithm on the knowledge base including the weighted node. (Paragraph [0059] Models 316 may be the output and result of AI modeling using the data collected from the one or more data sources 309. AI modeling may refer to the creation, training and deployment of machine learning algorithms that may emulate decision-making based on data available within the knowledge corpus 311 of the AI system 310 and/or using data that may be available outside of the knowledge corpus 311. The AI models 316 may provide the AI system 310 with a foundation to support advanced intelligence methodologies, such as real-time analytics, predictive analytics and/or augmented analytics, which can be utilized when identifying location clusters for each user, Paragraph [0065] Embodiments of corpus creation module 603 may perform tasks, functions and/or processes of the object replacement module 307 that may be directed toward creating or updating user records within the knowledge corpus 311 based on the data collected from the one or more data sources 309. (Using a clustering algorithm to generate clusters based on data available in the updated knowledge corpus (base)))
It would have been obvious to one having ordinary skill in the art before the filing date of the claimed invention to have incorporated providing content groupings through use of a clustering algorithm as taught by Agrawal into Walsh as both are in the same field of maintaining knowledge bases, and the combination would be desirable as a way to improve the user experience of customers when traveling to new areas of the world through use of maintained augmented reality (AR) devices (Agrawal Paragraphs [0002]-[0003]).
Regarding Claims 20 and 21, Claims 20 and 21 are system claims corresponding to the method of Claims 7 and 8. As such, they are rejected for the same reasons above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Boxwell et al. (US 20200134088 A1) discusses assembling a corpus and providing the user with a plurality of answers to a submitted question with responses from a knowledge base.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN A CARDOSO whose telephone number is (571)272-8512. The examiner can normally be reached M-F 7:30 - 5:00, alternate Friday's off.
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/JUSTIN CARDOSO/
Patent Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
Prosecution Insights