DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to correspondence filed 27 July 2024 in reference to application 18/785,613. Claims 1-20 are pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 4-11, 13-16, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 4-11, 13-16, and 20 of copending Application No. 18,789,226 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each as laid out in the chart below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 1-17, 19, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 3-17, 19 and 20 of copending Application No. 18/784,763 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other as laid out in the chart below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
Application 18/789,226
Application 18/784,763
Claim 1: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
Claim 1: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
Claim 1: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
a hardware processor executing computer-readable program code instructions for the box (OTB) artificial intelligence (AI) productivity tool to access natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system;
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system
a natural language capabilities database memory to store the natural language descriptions of capabilities in a capabilities decision tree with each capability stored under a plurality of branches as a capability node grouped under a branch of the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system in a capabilities decision tree with each capability stored under a plurality of branches as a capability node grouped under a branch of the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system in a capabilities decision tree with each capability stored under a plurality of branches as a capability node grouped under a branch of the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
the hardware processor executing computer-readable program code instructions to generate capability intent values from the natural language descriptions of the capabilities and storing the capability intent values with the capability nodes;
a hardware processor executing computer-readable program code instructions of the OTB AI productivity tool to generate capability intent values from the natural language descriptions of the capabilities and storing the capability intent values with the capability nodes;
a hardware processor executing computer-readable program code instructions of the OTB AI productivity tool to generate capability intent values from the natural language descriptions of the capabilities and storing the capability intent values with the capability nodes;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting a response by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to perform a cosine semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree to identify a best match capability node having a highest cosine semantic similarity search score with the query input intent value; and
the hardware processor executing computer-readable program code instructions to perform a breadth-first text frequency-inverted document frequency (TF-IDF) weighted cosine semantic similarity search comparing the capability intent values of each of the capability nodes in the capabilities decision tree as a semantic similarity comparison, as weighted by a TF-IDF comparison between natural language of the user query input and each of the natural language descriptions of capabilities of each of the capability nodes to identify a best match childless capability node in the capabilities decision tree having a highest TF-IDF weighted cosine semantic similarity search score with the query input intent value; and
the hardware processor executing computer-readable program code instructions to perform a text frequency-inverted document frequency (TF-IDF) weighted cosine semantic similarity search semantically comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree with the query input intent value, as weighted by a TF-IDF comparison between natural language of the user query input and each of the natural language descriptions of capabilities of the capability nodes to identify a best match capability node having a highest TF-IDF weighted cosine semantic similarity search score with the query input intent value;
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
the hardware processor executing computer-readable program code instructions for a best match capability from the best match childless capability node with a first AI productivity tool-enableable software application in response to the user query input.
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
Claim 2: The information handling system of claim 1, wherein the cosine semantic similarity search includes a parent score weighted cosine semantic similarity search that generates, for each child capability node in the branch, a parent weighted cosine similarity search score that is weighted by the cosine similarity search score determined for the parent capability node.
Claim 4: The information handling system of claim 1, wherein the breadth-first TF-IDF weighted cosine semantic similarity search includes a parent score and TF-IDF weighted cosine semantic similarity search that generates, for each child capability node, a parent and TF-IDF weighted cosine similarity search score that is weighted by the TF-IDF weighted cosine similarity search score determined for the parent capability node of that child capability node.
Claim 3: The information handling system of claim 1, wherein the TF-IDF weighted cosine semantic similarity search includes a parent score and TF-IDF weighted cosine semantic similarity search that generates, for each child capability node in the branch, a parent and TF-IDF weighted cosine similarity search score that is weighted by the TF-IDF weighted cosine similarity search score determined for the parent capability node.
Claim 3: The information handling system of claim 1, wherein the hardware processor executing computer-readable program code instructions to perform the cosine semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the cosine semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 4: The information handling system of claim 1, wherein the hardware processor executing computer-readable program code instructions to perform the TF-IDF weighted cosine semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest TF-IDF weighted cosine semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the TF-IDF weighted cosine semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 4: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to provide responsive output via text or audio.
Claim 5: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to execute the best match capability to automatically execute changes to or update one or more local software applications in response to the user query input
Claim 5: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to automatically execute changes to or update one or more local software applications in response to the user query input.
Claim 5: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to automatically execute changes to or update one or more local software applications.
Claim 5: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to execute the best match capability to automatically execute changes to or update one or more local software applications in response to the user query input
Claim 5: The information handling system of claim 1 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to automatically execute changes to or update one or more local software applications in response to the user query input.
Claim 6: The information handling system of claim 1, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query intent input value generated from the user query input.
Claim 6: The information handling system of claim 1, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query intent input value generated from the user query input
Claim 6: The information handling system of claim 1, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query intent input value generated from the user query input.
Claim 7: The information handling system of claim 1, wherein the cosine semantic similarity search determines a degree of angular similarity between vector values for the capability intent values and the query input intent value that mathematically represent one or more phrases within the natural language descriptions for the capabilities and natural language of the user query input.
Claim 7: The information handling system of claim 1, wherein the TF-IDF weighted cosine semantic similarity search determines a degree of angular similarity between vector values for the capability intent values and the query input intent value that mathematically represent one or more phrases within the natural language descriptions for the capabilities and natural language of the user query input.
Claim 7: The information handling system of claim 1, wherein the TF-IDF weighted cosine semantic similarity search determines a degree of angular similarity between vector values for the capability intent values and the query input intent value that mathematically represent one or more phrases within the natural language descriptions for the capabilities and natural language of the user query input as weighted by the TF-IDF comparison.
Claim 8: A method for executing computer readable code instructions of an on the box (OTB) artificial intelligence (AI) productivity tool at an information handling system to respond to a user query input comprising:
Claim 8: A method for executing computer readable code instructions of an on the box (OTB) artificial intelligence (AI) productivity tool at an information handling system to respond to a user query input comprising:
Claim 8: A method for executing computer readable code instructions of an on the box (OTB) artificial intelligence (AI) productivity tool at an information handling system to respond to a user query input comprising:
executing computer-readable program code instructions, via a hardware processor, for the box (OTB) artificial intelligence (AI) productivity tool to access natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system;
Storing, in a natural language capabilities database memory, the natural language descriptions of capabilities for a plurality of AI productivity tool-enablable software applications executing on the information handling system and capability intent values generated from the natural language descriptions
storing, in a natural language capabilities database memory, the natural language descriptions of capabilities for a plurality of AI productivity tool-enablable software applications executing on the information handling system
to storing the natural language descriptions of capabilities and capability intent values generated from the natural language descriptions in capability nodes in a capabilities decision tree in a natural language capabilities database memory with each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
Storing, in a natural language capabilities database memory, the natural language descriptions of capabilities for a plurality of AI productivity tool-enablable software applications executing on the information handling system and capability intent values generated from the natural language descriptions in capability nodes in a capabilities decision tree with each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
storing, in a natural language capabilities database memory, the natural language descriptions of capabilities for a plurality of AI productivity tool-enablable software applications executing on the information handling system, and capability intent values generated from the natural language descriptions in capability nodes in a capabilities decision tree, where each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships and where metadata for each capability node identifies a child capability node or parent capability node of the capability node;
generating, via a hardware processor, a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
generating, via a hardware processor executing machine readable code instructions of the OTB AI productivity tool, a query input intent value from a user query input received via text or audio requesting a response by one of the plurality of AI productivity tool-enableable software applications;
generating, via a hardware processor executing machine readable code instructions of the OTB AI productivity tool, a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
performing, via a hardware processor, a semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree to identify a best match capability node among the capability nodes along the branch having a highest semantic similarity search score with the query input intent value; and
performing, via a hardware processor, a text frequency-inverted document frequency (TF-IDF) weighted semantic similarity search comparing the capability intent values of each of the capability nodes in the capabilities decision tree in a semantic similarity search comparison weighted by a TF-IDF comparison between the user query input and the natural language descriptions of the capability nodes in the capabilities decision tree to identify a best match childless capability node among the childless capability nodes having a highest TF-IDF weighted semantic similarity search score with the query input intent value; and
performing, via a hardware processor, a text frequency-inverted document frequency (TF-IDF) weighted semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree with the query input intent value and weighted by a TF-IDF comparison between the user query input and the natural language descriptions of the capability nodes along the branch of the plurality of branches in the capabilities decision tree to identify a best match capability node among the capability nodes along the branch having a highest TF-IDF weighted semantic similarity search score with the user query input;
executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
executing computer-readable program code instructions to perform a best match capability associated with the best match childless capability node of a first AI productivity tool-enableable software application in response to the user query input.
executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
Claim 9: The method of claim 8 wherein the semantic similarity search includes a parent score weighted semantic similarity search that generates, for each child capability node in the branch, a parent weighted similarity search score that is weighted by the similarity search score determined for the parent capability node.
Claim 9: The method of claim 8, wherein the TF-IDF weighted semantic similarity search includes a parent score and TF-IDF weighted semantic similarity search that generates, for each child capability node in the branch, a parent and TF-IDF weighted similarity search score that is weighted by the TF-IDF weighted similarity search score determined for the parent capability node of that child capability node.
Claim 9: The method of claim 8 wherein the TF-IDF weighted semantic similarity search includes a parent score and TF-IDF weighted semantic similarity search that generates, for each child capability node in the branch, a parent and TF-IDF weighted similarity search score that is weighted by the TF-IDF weighted similarity search score determined for the parent capability node.
Claim 10: The method of claim 8, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query input intent value generated from natural language of the user query input.
Claim 10: The method of claim 8, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query input intent value generated from natural language of the user query input.
Claim 10: The method of claim 8, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the capabilities for correlation with the query input intent value generated from natural language of the user query input.
Claim 11: The method of claim 8, wherein computer-readable program code instructions of a latent semantic analysis text embedding algorithm are executed via the hardware processor for generating the capability intent values and the query input intent value.
Claim 11: The method of claim 8, wherein computer-readable program code instructions of a latent semantic analysis text embedding algorithm are executed via the hardware processor for generating the capability intent values and the query input intent value.
Claim 11: The method of claim 8, wherein computer-readable program code instructions of a latent semantic analysis text embedding algorithm are executed via the hardware processor for generating the capability intent values and the query input intent value.
Claim 12: The method of claim 8, wherein the hardware processor executing computer-readable program code instructions to perform the semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 12: The method of claim 8, wherein the hardware processor executing computer-readable program code instructions to perform the TF-IDF weighted semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest TF-IDF weighted semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the TF-IDF weighted semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 13: The method of claim 8, wherein a cosine semantic search machine learning algorithm generates the semantic similarity search score for each of the gathered capabilities by performing the semantic similarity search that is a cosine semantic similarity search comparing a degree of angular similarity between vector values for the capability intent values in capability nodes of the branch of the plurality of branches in the capabilities decision tree with the query input intent value.
Claim 13: The method of claim 8, wherein a cosine semantic search machine learning algorithm generates the TF-IDF weighted semantic similarity search score for each of the capabilities by performing a cosine semantic similarity search comparing a degree of angular similarity between vector values for the capability intent values in capability nodes of the capabilities decision tree with the query input intent value.
Claim 13: The method of claim 8, wherein a cosine semantic search machine learning algorithm generates the TF-IDF weighted semantic similarity search score for each of the capabilities by performing a cosine semantic similarity search comparing a degree of angular similarity between vector values for the capability intent values in capability nodes of the branch of the plurality of branches in the capabilities decision tree with the query input intent value and weighting the cosine semantic similarity search with the TF-IDF comparison of terms in the user query input with the natural language descriptions of each of the capability nodes.
Claim 14: The method of claim 8 further comprising: executing computer-readable program code instructions of the first AI productivity tool-enablable software application, via the hardware processor, to perform the best match capability to optimize settings for a hardware component of the information handling system.
Claim 14: The method of claim 8 further comprising: executing computer-readable program code instructions of the first AI productivity tool-enablable software application, via the hardware processor, to perform the best match capability to optimize settings for a hardware component of the information handling system.
Claim 14: The method of claim 8, wherein a cosine semantic search machine learning algorithm generates the TF-IDF weighted semantic similarity search score for each of the capabilities by performing a cosine semantic similarity search comparing a degree of angular similarity between vector values for the capability intent values in capability nodes of the branch of the plurality of branches in the capabilities decision tree with the query input intent value and weighting the cosine semantic similarity search with the TF-IDF comparison of terms in the user query input with the natural language descriptions of each of the capability nodes.
Claim 15: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
Claim 15: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
Claim 15: An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool comprising:
a hardware processor executing computer-readable program code instructions for the box (OTB) artificial intelligence (AI) productivity tool to access natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system;
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system, capability intent values generated from the natural language descriptions for each capability
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system,
a natural language capabilities database memory to store the natural language descriptions of capabilities, capability intent values generated from the natural language descriptions, and a capability identification in capability nodes in a capabilities decision tree with each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system, capability intent values generated from the natural language descriptions for each capability and a capability identification in capability nodes in a capabilities decision tree with each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node
a natural language capabilities database memory to store natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system, capability intent values generated from the natural language descriptions for each capability, and a capability identification in capability nodes in a capabilities decision tree with each capability node grouped under a branch of a plurality of branches in the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting response by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting an action by one of the plurality of AI productivity tool-enableable software applications;
the hardware processor executing computer-readable program code instructions to perform a semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree to identify a best match capability node among the capability nodes along the branch having a highest semantic similarity search score with the query input intent value; and
the hardware processor executing computer-readable code instructions for performing a cosine semantic similarity search comparing the capability intent values in the capabilities decision tree to the query input intent value and a text frequency-inverted document frequency (TF-IDF) comparison between natural language of the user query input and each of the natural language descriptions of capabilities to generate a TF-IDF weighted cosine semantic similarity search score for each capability node;
the hardware processor executing computer-readable code instructions for performing a text frequency-inverted document frequency (TF-IDF) comparison between natural language of the user query input and each of the natural language descriptions of the capabilities along the branch of the capabilities decision tree for TF-IDF similarity values; and the hardware processor executing computer-readable program code instructions to perform a TF-IDF weighted cosine semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree and weighted by the TF-IDF similarity values to identify a best match capability node having a highest TF-IDF weighted cosine semantic similarity search score with the query input intent value;
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match childless capability node to execute an associated best match capability in response to the user query input.
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.
Claim 16: The information handling system of claim 15, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the gathered capabilities for correlation with the natural language of the query input intent value generated from the natural language of the user query input.
Claim 16: The information handling system of claim 15, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the gathered capabilities for correlation with the natural language of the query input intent value generated from the natural language of the user query input.
Claim 16: The information handling system of claim 15, wherein the capability intent values are generated by execution of code instructions for a text embedding algorithm and mathematically represent semantic meaning for words or phrases within the natural language descriptions for the gathered capabilities for correlation with the natural language of the query input intent value generated from the natural language of the user query input.
Claim 17: The information handling system of claim 15, wherein the hardware processor executes computer-readable program code instructions of a cosine semantic search machine learning algorithm for generating the semantic similarity search score for each of the gathered capabilities by performing the semantic similarity search that is a cosine semantic similarity search comparing the capability intent values in the branch of the plurality of branches in the capabilities decision tree to the query input intent value.
Claim 17: The information handling system of claim 15, wherein the hardware processor executes computer-readable program code instructions of a cosine semantic search machine learning algorithm for generating the TF-IDF weighted semantic similarity search score for each of the capabilities by comparing the capability intent values in the branch of the plurality of branches in the capabilities decision tree to the query input intent value.
Claim 18: The information handling system of claim 15, wherein computer-readable program code instructions of a Word2Vec neural network text embedding algorithm are executed via the hardware processor for generating the capability intent values and the query input intent value.
Claim 19: The information handling system of claim 15, wherein the hardware processor executing computer-readable program code instructions to perform the semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 19: The information handling system of claim 15, wherein the hardware processor executing computer-readable program code instructions to perform the TF-IDF weighted semantic similarity search compares the capability intent values of plural capability nodes along a first level of the capabilities decision tree to determine a parent capability node having a highest TF-IDF weighted semantic similarity search score on the first level of the capabilities tree for selecting the branch of the plurality of branches in the capabilities decision tree having child capability nodes to be further analyzed by the TF-IDF weighted semantic similarity search to identify the best match capability node among the capability nodes along the branch.
Claim 20: The information handling system of claim 15 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to provide hardware component adjustment for a hardware component of the information handling system in response to the user query input.
Claim 20: The information handling system of claim 15 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to provide hardware component adjustment for a hardware component of the information handling system in response to the user query input.
Claim 20: The information handling system of claim 15 further comprising: the hardware processor executing computer-readable program code instructions of the first AI productivity tool-enablable software application to perform the best match capability to provide hardware component adjustment for a hardware component of the information handling system in response to the user query input.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended or a terminal disclaimer filed to overcome the rejection(s) under double patenting doctrine, set forth in this Office action. The following is a statement of reasons for the indication of allowable subject matter:
Consider claim 1, Wang et al. (US Patent 11,501,177) teaches An information handling system executing computer readable code instructions for an on the box (OTB) artificial intelligence (AI) productivity tool (abstract) comprising:
a hardware processor executing computer-readable program code instructions for the box (OTB) artificial intelligence (AI) productivity tool to access natural language descriptions of capabilities associated with each of a plurality of AI productivity tool-enablable software applications executing on the information handling system (Wang column 2, lines 20-30, Accordingly, the model repository stack may provide a link between human descriptions of an (in some cases, complex and/or multi-part) AI application and machine descriptions of such an AI application.);
Wang does not specifically teach
a hardware processor executing computer-readable program code instructions of the OTB AI productivity tool to generate capability intent values from the natural language descriptions of the capabilities and storing the capability intent values with the capability nodes
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting a response by one of the plurality of AI productivity tool-enableable software applications;
In the same field of natural language processing, Van Hoof (US PAP 2018/0075131) teaches a hardware processor executing computer-readable program code instructions of the OTB AI productivity tool to generate capability intent values from the natural language descriptions of the capabilities and storing the capability intent values with the capability nodes ([0005] In another aspect of the tools and techniques, a natural language query can be processed via a main natural language processor, with the processing comprising producing a plurality of requests to produce an intent of the query. The requests to produce the intent of the query can be dispatched from the main natural language processor to a set of extension natural language processors, with the set of extension natural language processors each being configured to generate an intent of the query independently of the main natural language processor.)
the hardware processor executing computer-readable program code instructions to generate a query input intent value from a user query input received via text or audio requesting a response by one of the plurality of AI productivity tool-enableable software applications ([0005] In another aspect of the tools and techniques, a natural language query can be processed via a main natural language processor, with the processing comprising producing a plurality of requests to produce an intent of the query. The requests to produce the intent of the query can be dispatched from the main natural language processor to a set of extension natural language processors, with the set of extension natural language processors each being configured to generate an intent of the query independently of the main natural language processor.);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wang to obtain above limitation based on the teachings of Van Hoof for the purpose of receiving an intent of the natural language query from each of the extension natural language processors in the set, in response to the sending of the requests to produce an intent of the extension natural language processor.
However the prior art of record does not teach or fairly suggest the limitation of
“a natural language capabilities database memory to store the natural language descriptions of capabilities in a capabilities decision tree with each capability stored under a plurality of branches as a capability node grouped under a branch of the capabilities decision tree according to logical topics in hierarchical parent-child relationships, wherein metadata for each capability node identifies a child capability node or parent capability node of the capability node…
the hardware processor executing computer-readable program code instructions to perform a cosine semantic similarity search comparing the capability intent values of the capability nodes along the branch of the plurality of branches in the capabilities decision tree to identify a best match capability node having a highest cosine semantic similarity search score with the query input intent value; and
the hardware processor executing computer-readable program code instructions for a first AI productivity tool-enableable software application having the best match capability node to execute an associated best match capability in response to the user query input.”
While the prior art certainly suggests matching applications using intent values, it does not combine the tree structure along with the weighted cosine semantic similarity search as claimed. Therefore claim 1 contains allowable subject matter.
Claims 8 and 15 contain similar subject matter as claim 1 and therefore contains allowable subject matter as well.
Claims 2-7, 9-14, and 16-20 depend on and further limit claims 1, 8 and 15 and therefore contains allowable subject matter as well.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mittal et al. (US PAP 2019/0163818) teaches using tree structures to process queries, but does not use the same search strategies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DOUGLAS C GODBOLD whose telephone number is (571)270-1451. The examiner can normally be reached 6:30am-5pm Monday-Thursday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/ Primary Examiner, Art Unit 2655