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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 11763024, 12050714. The claims of the instant application is/are similar to scope, form and content as the issued patent because though the claims at issue are not identical, they are not patentably distinct from each other because application claims 1-20 are anticipated by the above indicated patents’ claims.
Instant App. 18667031
U.S. Patent No. 12050714
U.S. Patent No. 11763024
1. A method comprising: receiving, at a virtual assistant, a query; determining, using natural language processing, whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud; in response to the determination, interpreting the query by using a first machine learning model trained on at least one machine learning technique on the public data of the public cloud or interpreting the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmitting a response to the query.
2. The method of claim 1, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising: parsing the query to determine one or more domains; and determining whether the one or more domains are in the public cloud or in the private cloud.
3. The method of claim 2, further comprising: determining whether the one or more domains has one or more intents; and determining whether the one or more intents are in the public cloud or in the private cloud.
4. The method of claim 3, further comprising: determining whether the one or more intents has one or more entities; and determining whether the one or more entities is in the public cloud or in the private cloud.
5. The method of claim 1, wherein determining whether the query is requesting to access the private data of the private cloud further comprising: receiving, at the virtual assistant, one or more domains, one or more intents, or one or more entities of the private cloud; and determining whether the one or more domains, the one or more intents, or the one or more entities are related to the query.
6. The method of claim 1, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data.
7. The method of claim 1, wherein the virtual assistant includes customizable content based on domains of the public cloud or the private cloud.
8. A virtual assistant comprising: at least one processor; and at least one memory storing instructions, which when executed by the at least one processor, causes the at least one processor to: receive a query; determine, using natural language processing, whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud; in response to the determination, interpret the query by using a first machine learning model trained on at least one machine learning technique on the public data of the public cloud or interpreting the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmit a response to the query.
9. The virtual assistant of claim 8, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the at least one processor to: parse the query to determine one or more domains; and determine whether the one or more domains are in the public cloud or in the private cloud.
10. The virtual assistant of claim 9, further comprising instructions which when executed by the at least one processor, causes the at least one processor to: determine whether the one or more domains has one or more intents; and determine whether the one or more intents are in the public cloud or in the private cloud.
11. The virtual assistant of claim 10, further comprising instructions which when executed by the at least one processor, causes the at least one processor to: determine whether the one or more intents has one or more entities; and determine whether the one or more entities is in the public cloud or in the private cloud.
12. The virtual assistant of claim 9, wherein determining whether the query is requesting to access the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the at least one processor to: receive one or more domains, one or more intents, or one or more entities of the private cloud; and determine whether the one or more domains, the one or more intents, or the one or more entities are related to the query.
13. The virtual assistant of claim 8, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data.
14. The virtual assistant of claim 9, wherein the virtual assistant includes customizable content based on domains of the public cloud or the private cloud.
15. At least one non-transitory computer readable medium storing instructions, which when executed by at least one processor of a virtual assistant, causes the virtual assistant to: receive a query; determine, using natural language processing, whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud, wherein the determining includes processing the query through a first machine learning model; in response to determining that the query is requesting to access the public data of the public cloud, respond to the query with the public data of the public cloud; in response to determining that the query is requesting to access the private data of the private cloud, interpret the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmit a response to the query.
16. The at least one non-transitory computer readable medium of claim 15, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: parse the query to determine one or more domains; and determine whether the one or more domains are in the public cloud or in the private cloud.
17. The at least one non-transitory computer readable medium of claim 16, further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: determine whether the one or more domains has one or more intents; and determine whether the one or more intents are in the public cloud or in the private cloud.
18. The at least one non-transitory computer readable medium of claim 17, further comprising instructions which when executed by the at least one processor, causes the virtual assistant processor to: determine whether the one or more intents has one or more entities; and determine whether the one or more entities is in the public cloud or in the private cloud.
19. The at least one non-transitory computer readable medium of claim 15, wherein determining whether the query is requesting to access the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: receive one or more domains, one or more intents, or one or more entities of the private cloud; and determine whether the one or more domains, the one or more intents, or the one or more entities are related to the query.
20. The at least one non-transitory computer readable medium of claim 15, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data.
1. (Currently Amended) A method comprising: receiving, at a virtual assistant, a query; determining, using natural language processing, whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud, wherein the determining includes processing the query through a first machine learning model; in response to determining that the query is requesting to access the public data of the public cloud, responding to the query with the public data of the public cloud; in response to determining that the query is requesting to access the private data of the private cloud, interpreting the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and
transmitting a response to the query.
2. (Original) The method of claim 1, wherein the determining whether the query is
requesting to access the public data of the public cloud or the private data of the private
cloud further comprising:
parsing the query to determine one or more domains; and
determining whether the one or more domains are in the public cloud or in the
private cloud.
3. (Original) The method of claim 2, further comprising: determining whether the one or more domains has one or more intents; and
determining whether the one or more intents are in the public cloud or in the
private cloud.
4. (Original) The method of claim 3, further comprising:
determining whether the one or more intents has one or more entities; and
determining whether the one or more entities is in the public cloud or in the
private cloud.
5. (Original) The method of claim 1, wherein determining whether the query is
requesting to access private data of the private cloud further comprising:
receiving, at the virtual assistant, one or more domains, one or more intents, or
one or more entities of the private cloud; and
determining whether the one or more domains, the one or more intents, or the one
or more entities are related to the query.
6. (Cancelled)
7. (Original) The method of claim 1, wherein the virtual assistant includes customizable
content based on domains of the public cloud or the private cloud.
8. (Currently Amended) A virtual assistant comprising:
at least one processor; and at least one memory storing instructions, which when executed by the at least one
processor, causes the at least one processor to:
receive a query;
determine, using natural language processing, whether the query is
requesting to access public data of a public cloud or requesting to access private
data of a private cloud, wherein the determining includes processing the query
through a first machine learning model;
in response to determining that the query is requesting to access the public
data of the public cloud, respond to the query with the public data of the public
cloud;
in response to determining that the query is requesting to access the
private data of the private cloud, interpret the query by using a second machine
learning model trained on at least one machine learning technique on the private
data of the private cloud; and
transmit a response to the query.
9. (Original) The virtual assistant of claim 8, wherein the determining whether the query
is requesting to access the public data of the public cloud or the private data of the private
cloud further comprising instructions which when executed by the at least one processor,
causes the at least one processor to:
parse the query to determine one or more domains; and
determine whether the one or more domains are in the public cloud or in the
private cloud.
10. (Original) The virtual assistant of claim 9, further comprising instructions which
when executed by the at least one processor, causes the at least one processor to:
determine whether the one or more domains has one or more intents; and
determine whether the one or more intents are in the public cloud or in the private
cloud.
11. (Original) The virtual assistant of claim 10, further comprising instructions which
when executed by the at least one processor, causes the at least one processor to:
determine whether the one or more intents has one or more entities; and
determine whether the one or more entities is in the public cloud or in the private
cloud.
12. (Original) The virtual assistant of claim 9, wherein determining whether the query is
requesting to access private data of the private cloud further comprising instructions
which when executed by the at least one processor, causes the at least one processor to:
receive one or more domains, one or more intents, or one or more entities of the
private cloud; and
determine whether the one or more domains, the one or more intents, or the one or
more entities are related to the query.
13. (Cancelled)
14. (Original) The virtual assistant of claim 9, wherein the virtual assistant includes
customizable content based on domains of the public cloud or the private cloud.
15. (Currently Amended) At least one non-transitory computer readable medium storing
instructions, which when executed by at least one processor of a virtual assistant, causes
the virtual assistant to:
receive a query;
determine, using natural language processing, whether the query is requesting to
access public data of a public cloud or requesting to access private data of a private
cloud, wherein the determining includes processing the query through a first machine
learning model;
in response to determining that the query is requesting to access the public data of
the public cloud, respond to the query with the public data of the public cloud;
in response to determining that the query is requesting to access the private data
of the private cloud, interpret the query by using a second machine learning model
trained on at least one machine learning technique on the private data of the private
cloud; and
transmit a response to the query.
16. (Original) The at least one non-transitory computer readable medium of claim 15,
wherein the determining whether the query is requesting to access the public data of the
public cloud or the private data of the private cloud further comprising instructions which
when executed by the at least one processor, causes the virtual assistant to: parse the query to determine one or more domains; and
determine whether the one or more domains are in the public cloud or in the
private cloud.
17. (Original) The at least one non-transitory computer readable medium of claim 16,
further comprising instructions which when executed by the at least one processor, causes
the virtual assistant to:
determine whether the one or more domains has one or more intents; and
determine whether the one or more intents are in the public cloud or in the private
cloud.
18. (Original) The at least one non-transitory computer readable medium of claim 17,
further comprising instructions which when executed by the at least one processor, causes
the virtual assistant processor to:
determine whether the one or more intents has one or more entities; and
determine whether the one or more entities is in the public cloud or in the private
cloud.
19. (Original) The at least one non-transitory computer readable medium of claim 15,
wherein determining whether the query is requesting to access private data of the private
cloud further comprising instructions which when executed by the at least one processor,
causes the virtual assistant to: receive one or more domains, one or more intents, or one or more entities of the
private cloud; and
determine whether the one or more domains, the one or more intents, or the one or
more entities are related to the query.
20. (Cancelled)
21. (New) The method of claim 1, wherein the private data available on the private cloud
is confidential data, and wherein the second machine learning model is trained on the
confidential data.
22. (New) The virtual assistant of claim 8, wherein the private data available on the
private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data.
23. (New) The at least one non-transitory computer readable medium of claim 15,
wherein the private data available on the private cloud is confidential data, and wherein
the second machine learning model is trained on the confidential data.
1. (Currently Amended) A method comprising:
receiving, at a virtual assistant at a public cloud having access to public data of the public cloud,
a query;
determining whether the query is requesting to access the public data of the public cloud by
processing the query through a first machine learning model trained [[by]] using the public data
of the public cloud;
in response to determining that the query is not requesting to access the public data of the public cloud, determining whether the query is a request to access private data of a private cloud;
in response to determining that the query is requesting to access the private data of the private
cloud, transmitting the query to a virtual assistant at the private cloud; and
in response to transmitting the query to the virtual assistant of the private cloud, interpreting the
query by [[the]] using a second machine learning model trained on at least one machine learning
technique using the private data of the private cloud; and
receiving, at the virtual assistant of the public cloud from the virtual assistant of the private
cloud, a response to the query.
11. (Currently Amended) A virtual assistant at a public cloud having access to public data of the
public cloud, the virtual assistant comprising: at least one processor; and at least one memory
storing instructions, which when executed by the at least one processor, causes the at least one
processor to:
receive a query;
determine whether the query is requesting to access the public data of the public cloud by
processing the query through a first machine learning model trained by using the public data of
the public cloud;
in response to determining that the query is not requesting to access the public data of the public
cloud, determine whether the query is a request to access private data of a private cloud;
in response to determining that the query is requesting to access the private data of the private
cloud, transmit the query to a virtual assistant at the private cloud; and
in response to transmitting the query to the virtual assistant of the private cloud, interpret the
query by the using a second machine learning model trained on at least one machine learning technique on using the private data of the private cloud; and
receive, from the virtual assistant of the private cloud, a response to the query.
17. (Currently Amended) At least one non-transitory computer readable medium storing
instructions, which when executed by at least one processor of a virtual assistant at a public
cloud having access to public data of the public, causes the at least one processor to:
receive a query;
determine whether the query is requesting to access the public data of the public cloud by
processing the query through a first machine learning model trained by using the public data of
the public cloud;
in response to determining that the query is not requesting to access the public data of the public
cloud, determine whether the query is a request to access private data of a private cloud;
in response to determining that the query is requesting to access the private data of the private
cloud, transmit the query to a virtual assistant at the private cloud; and
in response to transmitting the query to the virtual assistant of the private cloud, interpret the
query by [[the]] using a second machine learning model trained on at least one machine learning
technique on using the private data of the private cloud; and
receive, from the virtual assistant of the private cloud, a response to the query.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berk et al (US Pub. #: 20090094223), hereafter Berk and Sanchez et al (US Pub. #: 20150356427), hereafter San.
Claim 1: Berk teaches a method comprising: receiving, at a virtual assistant, a query; ([012, 016] The facility can receive a search query from a user).
determining, [using natural language processing], whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud; ([012, 016, Fig. 1] The facility can also decompose the search query into constituent parts and perform one or more of a set of evaluations of the individual constituent parts to determine likely classifications… [013] determines whether one or more query classes associated with one or more of the exactly matched rules from the first set are likely query classes. Set of evaluations also includes evaluating the search query against a second set of rules to determine if the search query matches one or more of the second set of rules according to a regular expression pattern match, determines whether one or more query classes associated with one or more of the regular expression pattern matched rules from the second set are likely query classes, evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers. The facility determines likely query classes from these evaluations; The content acquisition component uses the zero or more query classes and/or other data in order to determine which local or remote services and/or content sources to access (such as a public or private network... i.e., private or public cloud) to obtain content);
in response to the determination, interpreting the query by using a first machine learning model trained on at least one machine learning technique on the public data of the public cloud or interpreting the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmitting a response to the query. ([013-16, 22] The set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers. Each of the one or more ranked arbitrated query classifications is mapped to one or more external and / or internal data sources... (such as a public or private network... i.e., private or public cloud). The facility retrieves content from the mapped one or more data sources and services using the user's original search query... The facility places retrieved content corresponding to each of the ranked arbitrated query classifications in a display region for display to the user; [042] the facility ranked the query class corresponding to the "A" widget first, the query class corresponding to the "B" widget second, and the query class corresponding to the "C" widget third).
Berk is silent on using natural language processing.
But analogous art San teaches using natural language processing. ([032, Fig. 2] the cognitive inference and learning system (CILS) implements natural language processing).
Therefore it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of using natural language processing as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Claim 2: the combination of Berk and San teaches the method of claim 1, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising: parsing the query to determine one or more domains; and determining whether the one or more domains are in the public cloud or in the private cloud. (Berk: [024] facility defines one or more custom code-based classifiers to classify search queries that shall not readily classify to one or more query classes. The facility evaluates a search query against the one or more custom code-based classifiers to determine that the search query matches a domain name... [033] the facility maps the ranked query classes to external and/or internal data sources...).
Claim 3: the combination of Berk and San teaches the method of claim 2, further comprising: determining whether the one or more domains has one or more intents; and determining whether the one or more intents are in the public cloud or in the private cloud (Berk: [0013] the set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers; The evaluations performed by the facility enable the facility to understand the semantic nature of the user's search query (i.e., intent); [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 4: the combination of Berk and San teaches the method of claim 3, further comprising: determining whether the one or more intents has one or more entities; and determining whether the one or more entities is in the public cloud or in the private cloud. (Berk: [018, 22] the facility classifies search queries to zero or more of the query classes; the facility can evaluate the search query against the model's data to determine a statistical likelihood, or probability, that each topic (i.e., entities) is relevant to the search query; [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 5: the combination of Berk and San teaches the method of claim 1, wherein determining whether the query is requesting to access the private data of the private cloud further comprising: receiving, at the virtual assistant, one or more domains, one or more intents, or one or more entities of the private cloud; and determining whether the one or more domains, the one or more intents, or the one or more entities are related to the query. (Berk: [034] the facility retrieves content from the mapped internal data sources by searching the mapped internal data sources with the user's original search query. The facility also uses retrieved context data to search the mapped internal data sources).
Claim 6: the combination of Berk and Lun teaches the method of claim 1, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data. (San [0122]: the private cognitive platform accesses knowledge elements stored in the hosted and private universal knowledge repositories and data stored in the repositories of licensed data, and private data (i.e., confidential data)).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of having private confidential data as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Claim 7: the combination of Berk and San teaches the method of claim 1, wherein the virtual assistant includes customizable content based on domains of the public cloud or the private cloud. (Berk: [024] uses custom code-based classifiers is that they provide flexibility and customization as to their inputs, outputs and methods used to determine likely query classes; facility defines one or more custom code-based classifiers to classify search queries that shall not readily classify to one or more query classes. The facility evaluates a search query against the one or more custom code-based classifiers to determine that the search query matches a domain name... [033] the facility maps the ranked query classes to external and/or internal data sources ...).
Claim 8: Berk teaches a virtual assistant comprising: at least one processor; and at least one memory storing instructions, which when executed by the at least one processor, causes the at least one processor to (Fig. 1): receive a query; determine, [using natural language processing], whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud; in response to the determination, interpret the query by using a first machine learning model trained on at least one machine learning technique on the public data of the public cloud or interpreting the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmit a response to the query. ([012, 016] The facility can receive a search query from a user; [012, 016, Fig. 1] The facility can also decompose the search query into constituent parts and perform one or more of a set of evaluations of the individual constituent parts to determine likely classifications… The content acquisition component uses the zero or more query classes and/or other data in order to determine which local or remote services and/or content sources to access (i.e., private or public) to obtain content; [013-16, 22] The set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers. each of the one or more ranked arbitrated query classifications is mapped to one or more external and / or internal data sources... (such as a public or private network... i.e., private or public cloud). The facility retrieves content from the mapped one or more data sources and services using the user's original search query... The facility places retrieved content corresponding to each of the ranked arbitrated query classifications in a display region for display to the user; [042] the facility ranked the query class corresponding to the "A" widget first, the query class corresponding to the "B" widget second, and the query class corresponding to the "C" widget third).
Berk is silent on using natural language processing.
But analogous art San teaches using natural language processing. ([032, Fig. 2] the CILS implements natural language processing).
Therefore it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of using natural language processing as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Claim 9: the combination of Berk and San teaches the virtual assistant of claim 8, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the at least one processor to: parse the query to determine one or more domains; and determine whether the one or more domains are in the public cloud or in the private cloud. (Berk: [024] facility defines one or more custom code-based classifiers to classify search queries that shall not readily classify to one or more query classes. The facility evaluates a search query against the one or more custom code-based classifiers to determine that the search query matches a domain name... [033] the facility maps the ranked query classes to external and/or internal data sources...).
Claim 10: the combination of Berk and San teaches the virtual assistant of claim 9, further comprising instructions which when executed by the at least one processor, causes the at least one processor to: determine whether the one or more domains has one or more intents; and determine whether the one or more intents are in the public cloud or in the private cloud. (Berk: [0013] the set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers; The evaluations performed by the facility enable the facility to understand the semantic nature of the user's search query (i.e., intent); [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 11: the combination of Berk and San teaches the virtual assistant of claim 10, further comprising instructions which when executed by the at least one processor, causes the at least one processor to: determine whether the one or more intents has one or more entities; and determine whether the one or more entities is in the public cloud or in the private cloud. (Berk: [018, 22] the facility classifies search queries to zero or more of the query classes; the facility can evaluate the search query against the model's data to determine a statistical likelihood, or probability, that each topic (i.e., entities) is relevant to the search query; [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 12: the combination of Berk and San teaches the virtual assistant of claim 9, wherein determining whether the query is requesting to access the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the at least one processor to: receive one or more domains, one or more intents, or one or more entities of the private cloud; and determine whether the one or more domains, the one or more intents, or the one or more entities are related to the query. (Berk: [034] the facility retrieves content from the mapped internal data sources by searching the mapped internal data sources with the user's original search query. The facility also uses retrieved context data to search the mapped internal data sources).
Claim 13: the combination of Berk and Lun teaches the virtual assistant of claim 8, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data. (San [0122]: the private cognitive platform accesses knowledge elements stored in the hosted and private universal knowledge repositories and data stored in the repositories of licensed data, and private data (i.e., confidential data)).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of having private confidential data as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Claim 14: the combination of Berk and San teaches the virtual assistant of claim 9, wherein the virtual assistant includes customizable content based on domains of the public cloud or the private cloud. (Berk: [024] uses custom code-based classifiers is that they provide flexibility and customization as to their inputs, outputs and methods used to determine likely query classes; facility defines one or more custom code-based classifiers to classify search queries that shall not readily classify to one or more query classes. The facility evaluates a search query against the one or more custom code-based classifiers to determine that the search query matches a domain name... [033] the facility maps the ranked query classes to external and/or internal data sources...).
Claim 15: Berk teaches at least one non-transitory computer readable medium storing instructions, which when executed by at least one processor of a virtual assistant, causes the virtual assistant to (Fig. 1): receive a query; determine, using natural language processing, whether the query is requesting to access public data of a public cloud or requesting to access private data of a private cloud, wherein the determining includes processing the query through a first machine learning model; in response to determining that the query is requesting to access the public data of the public cloud, respond to the query with the public data of the public cloud; in response to determining that the query is requesting to access the private data of the private cloud, interpret the query by using a second machine learning model trained on at least one machine learning technique on the private data of the private cloud; and transmit a response to the query. ([012, 016] The facility can receive a search query from a user; [012, 016, Fig. 1] The facility can also decompose the search query into constituent parts and perform one or more of a set of evaluations of the individual constituent parts to determine likely classifications… The content acquisition component uses the zero or more query classes and/or other data in order to determine which local or remote services and/or content sources to access (i.e., private or public) to obtain content; [013-16, 22] The set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers. each of the one or more ranked arbitrated query classifications is mapped to one or more external and / or internal data sources... (such as a public or private network... i.e., private or public cloud). The facility retrieves content from the mapped one or more data sources and services using the user's original search query... The facility places retrieved content corresponding to each of the ranked arbitrated query classifications in a display region for display to the user; [042] the facility ranked the query class corresponding to the "A" widget first, the query class corresponding to the "B" widget second, and the query class corresponding to the "C" widget third).
Berk is silent on using natural language processing.
But analogous art San teaches using natural language processing. ([032, Fig. 2] the CILS implements natural language processing).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of using natural language processing as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Claim 16: the combination of Berk and San teaches the at least one non-transitory computer readable medium of claim 15, wherein the determining whether the query is requesting to access the public data of the public cloud or the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: parse the query to determine one or more domains; and determine whether the one or more domains are in the public cloud or in the private cloud. (Berk: [024] facility defines one or more custom code-based classifiers to classify search queries that shall not readily classify to one or more query classes. The facility evaluates a search query against the one or more custom code-based classifiers to determine that the search query matches a domain name... [033] the facility maps the ranked query classes to external and/or internal data sources...).
Claim 17: the combination of Berk and San teaches the at least one non-transitory computer readable medium of claim 16, further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: determine whether the one or more domains has one or more intents; and determine whether the one or more intents are in the public cloud or in the private cloud. (Berk: [0013] the set of evaluations also include evaluating the search query against one or more data models, against one or more indexes and against custom code-based classifiers; The evaluations performed by the facility enable the facility to understand the semantic nature of the user's search query (i.e., intent); [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 18: the combination of Berk and San teaches the at least one non-transitory computer readable medium of claim 17, further comprising instructions which when executed by the at least one processor, causes the virtual assistant processor to: determine whether the one or more intents has one or more entities; and determine whether the one or more entities is in the public cloud or in the private cloud. (Berk: [018, 22] the facility classifies search queries to zero or more of the query classes; the facility can evaluate the search query against the model's data to determine a statistical likelihood, or probability, that each topic (i.e., entities) is relevant to the search query; [014] each of the one or more ranked arbitrated query classifications is mapped to one or more external and/or internal data sources and services).
Claim 19: the combination of Berk and San teaches the at least one non-transitory computer readable medium of claim 15, wherein determining whether the query is requesting to access the private data of the private cloud further comprising instructions which when executed by the at least one processor, causes the virtual assistant to: receive one or more domains, one or more intents, or one or more entities of the private cloud; and determine whether the one or more domains, the one or more intents, or the one or more entities are related to the query. (Berk: [034] the facility retrieves content from the mapped internal data sources by searching the mapped internal data sources with the user's original search query. The facility also uses retrieved context data to search the mapped internal data sources).
Claim 20: the combination of Berk and Lun teaches the at least one non-transitory computer readable medium of claim 15, wherein the private data available on the private cloud is confidential data, and wherein the second machine learning model is trained on the confidential data. (San [0122]: the private cognitive platform accesses knowledge elements stored in the hosted and private universal knowledge repositories and data stored in the repositories of licensed data, and private data (i.e., confidential data)).
Therefore, it is prima facie obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Berk to include the idea of having private confidential data as taught by San thus they are also typically able to learn a knowledge domain based upon the best available data and get better, and more immersive, over time [028].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See form PTO-892.
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/BADRINARAYANAN /Primary Examiner, Art Unit 2494.