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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-17, 19 and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Brown (US20150185996) in view of Sarikaya (20170212886) .
Claim 1,Brown discloses a virtual assistant platform implemented by a computer system comprising: one or more hardware processors configured to execute computer readable instructions (Fig. 2 shows various hardware processors);
one or more memory storing the instructions; (memory 304 shown in fig. 3)
a mapping data structure stored in the one or more memory,
(Language unit 608 includes set of input response mapping)
the mapping data structure mapping a plurality of intents to respective client specific actions; (Section 0127, lines 2-3 “the language unit includes a set of input that is mapped to a response)
a network interface configured to receive a query from a user device operating in a client specific communication session with a virtual assistant in a first context, (Section 0117 figs 4-13 shows different AI assistant network interfaces for which users can submit queries for the system to provide answers)
the instructions when executed providing an Al language model comprising; (Fig. 4-13 in section 0117) a client specific language model, the client specific language model having been trained on client specific data; (Section 0043, lines 2-3 … train a virtual assistant for example train the base virtual assistants with the basketball concepts as explained in Section 0127)
a mesh language model, (Virtual assistant as shown in fig. 1 Ele. 1) the mesh language model having been trained on mesh specific data, the mesh specific data having been received by operating multiple virtual assistants in the first context, the Al language model being responsive to the query to generate an intent; (Section 0038, lines 12-16- thus the virtual assistant team 12 is a mesh AI assistant with doctor virtual assistant, financial virtual assistant, travel agent virtual assistant, professor virtual assistant and sports virtual assistant)
a function to apply the intent to the data structure and access a corresponding client specific action for delivery of a response to the user device; (Section 0126, lines 2-4- thus the unit for user queries or questions to a task or response is used to deliver response to the questions entered by the user)
and a transmission function to transmit the response to the user device.
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Brown does not disclose clearly a mapping function.
Sarikaya discloses a mapping function a mappings corresponding mappings to the schema or models as shown in Section 0038.
Sarikaya also discloses a bundled models (mesh model) ( that teaches mapping function to access corresponding intents. Sarikaya is analogous art. (Section 0051-0052).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of using a mapping function to map models with intent. The motivation is that it will make the Assistant language model very effective responding to the questions of their users.
Claim 2, Brown in view of Sarikaya discloses wherein Al language model comprises a generic language model, the generic model having been trained on general, non-context-specific data, the non-context-specific data having been received from multiple virtual assistants operating in different contexts. (Brown: Section 0127, the base language model 606 is trained based information from the basketball concept or from the sport virtual assistant 1202 as shown in Fig. 12 (e.g football, basketball or baseball etc))
Claim 3, Brown in view of Sarikaya discloses wherein the Al language model comprises an ethics model trained to recognize queries requiring an ethics response. (Brown: Section 0055, lines 8-10 learning the behaviors or track a pattern as described in Lines 8-10 shows the ethics model, Also in section 0056 lines recognizing incorrect input reads on the ethnic models)
Claim 4, Brown in view of Sarikaya discloses wherein the Al language model comprises a small talk model trained to recognize queries relating to small talk for which no intent is applied to the mapping data structure. (Brown: Section 0056, lines 8-9 thus the model recognizing the phrase “Cobo San Lucas” reads on the small talks)
Claim 5, Brown in view of Sarikaya discloses wherein the instructions, when executed, provide a data extraction function which accesses query related data stored in a logging database and removes any personal identifiers from the data. (Brown: Section 0091, lines 2-6 thus “where a sport virtual assistant may be configured to provide sports functionality, such as accessing sports websites, providing sports scores in a particular formats”)
Claim 6, Brown in view of Sarikaya discloses wherein the instructions, when executed, provide a data storage function which stores the anonymized data in a mesh data pool specific to the first context. (Brown: Section 0095, lines 1-10 thus recognize a particular number sequence with dashes as a social security number)
Claim 7, Brown in view of Sarikaya discloses wherein the instructions, when executed, extract data from a plurality of logging databases, each logging database holding data specific to a client, the clients all belonging to the first context. . (Brown: Section 0091, lines 2-6 thus “where a sport virtual assistant may be configured to provide sports functionality, such as accessing sports websites, providing sports scores in a particular formats”)
Claim 8, Brown in view of Sarikaya discloses wherein the instructions, when executed, cause data to be extracted from a second plurality of logging databases, each logging database of the second plurality being associated with clients operating in a second context, (Brown: Section 0102 virtual assistant is healthcare (e.g doctor) is the first context, finance (e.g accounting, taxes, banking) will be the second context) ) and to store anonymized data from the second plurality of logging databases in a second mesh data pool. (Brown: Section 0095, lines 1-10 thus recognize a particular number sequence with dashes as a social security number)
Claim 9, Brown in view of Sarikaya discloses when executed, provide an analytics function which analyses the anonymized data in the one or more mesh data pool, determines when retraining of the mesh language model is required, and, when determined, causes the mesh language model to be retrained and updated. (Brown: Section 0042, lines 8-13- thus “it is determined that the term “hoop” is not yet associated with basketball and therefore the state “no means there should be a training needed”)
Claim 10, Brown discloses a method of configuring a set of virtual assistants assigned to a common context and operating at different client locations, (Section 0126, lines 1-3, Fig. 6 show a virtual assistant interface 600) the method comprising:
monitoring operation of the set of virtual assistants, (Section 0042, lines 5-6 “the virtual assistant service may the interactions and make updates”) monitor each virtual assistant configured to receive a query from a user and generate an intent derived by natural language processing of the query, (Section 0126, lines 5-6 user queries and answers) by a client specific machine learning model (Sports virtual assistant 602- See Section 0126, lines 7-8) and a context specific machine learning model, (Basketball concept 604 in Section 0126 lines 9-10) the client specific model having been trained on client specific data while operating at a client location, (Section 0127, lines 1-3 thus the base language model is trained with the basketball concept 604 to answer basketball questions).
and the context specific model having been trained on context specific data received from multiple virtual assistants operating in the context; (Section 0127 ,lines 9-11, the sports virtual assistant is trained to respond to sports/basketball questions) detecting one or more anomaly from one or more of the virtual assistants;
categorizing the anomaly; (Section 0042, lines 5-8 “since the term hoop is not yet associate with basketball” reads on the anomaly)
retraining the context specific machine learning model to remove the anomaly; ( Section 0042, lines 10-11selecting the term hoops to be associated with basketball tasks removes the anomaly because the model is trained to recognize the term hoops) and delivering the retrained context specific machine learning model to each of the set of virtual assistants. (Section 0042, lines 11-13- thus the trainer has updated a virtual assistant is delivered by it been updated)
Brown does not disclose clearly a mapping function.
Sarikaya discloses a mapping function a mappings corresponding mappings to the schema or models as shown in Section 0038.
Sarikaya also discloses a bundled models (mesh model) that teaches mapping function to access corresponding intents. Sarikaya is analogous art. (Section 0051-0052).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to include the teaching of using a mapping function to map models with intent. The motivation is that it will make the Assistant language model very effective responding to the questions of their users.
Claim 11, Brown in view of Sarikaya discloses further comprising configuring a second set of virtual assistants operating in a second common context,
the method comprising: operating the virtual assistants of the second set in the second context; (Brown: Section 0127, lines 19- thus find a score of a game read on the second context) determining one or more anomaly from one or more of the virtual assistants of the second set and categorizing the one or more anomaly detected from the virtual assistants of the second set; (Brown: Section 0127 reads on the different anomaly such as “adding content to the set of input, updating the response and adding an additional language unit”)
retraining a second context specific machine learning model specific to the second common context to remove the anomaly; (Brown: Section 0128, lines 4-5 the model is retrained by reconfiguring the concepts by creating new associations).
and delivering the retrained second context specific machine learning model to each of the second set of virtual assistants. (Brown: Section 0130, lines 6-8 thus saving the changes made deliver the updates to the system for it to be made)
Claim 12, Brown in view of Sarikaya discloses wherein the step of monitoring operation of the first and second sets of virtual assistants comprises logging user queries in association with responses from the respective virtual assistant models for each context, (Brown: Section 0042 lines 5-6 “Virtual assistant monitoring interactions”) and generating a first dataset of queries and responses for the first context and a second dataset of queries and responses for the second context of virtual models. (Brown: Section 0090, lines 3-9 thus sports virtual assistant and the medical virtual assistant is trained by dataset such as accessing sports web and medical virtual assistant is trained by fill medical prescriptions and medical diagnostic)
Claim 13, Brown in view of Sarikaya discloses where the method further comprising, prior to the step of delivering the context specific machine learning model, the step of providing a candidate update to a client location (Brown: Section 0164 ratings associated with a trainer reads on the candidate update) and receiving selection of one or more candidate updates from the client location. (Brown: Section 0143, updated with different types of sports virtual assistants where candidate updates are football, basketball and baseball)
Claim 14, Brown in view of Sarikaya discloses wherein the step of categorizing the anomaly comprises at least one of identifying that a new intent is needed identifying that an existing intent needs updating; identifying that a new intent variance of a query is needed; identifying that an answer update is needed; and detecting that there has been an ethical breach. (Brown: Section 0127 reads on the different anomaly such as “adding content to the set of input, updating the response and adding an additional language unit”)
Claim 15, Canceled
Claim 16, Canceled
Claim 17, Brown in view of Sarikaya discloses comprising transmitting an intent output from a virtual assistant model to a client location and receiving an answer from that client location and delivering the answer to the user. (Brown: Section 0076, lines user locations are being used to deliver answers to user questions)
Claim 18, Canceled
Claim 19, Brown in view of Sarikaya discloses wherein each virtual assistant comprises an ethics module configured to manage the ethical behavior of the virtual assistant. (Brown: Section 0193, thus the new team of virtual assistants created to determine if updates are needed reads on the function of the ethnics module)
Claim 20, Canceled
Claim 21, Brown in view of Sarikaya wherein the step of categorizing an anomaly comprises identifying that frustration of a user has been detected by natural language processing of the user queries. (Brown: Section 0121 thus response that are inappropriate reads on anomaly that are frustrating)
Claim 22, Brown in view of Sarikaya wherein each virtual assistant model comprises a generic model, the generic model having been trained on general non context-specific data received from multiple virtual assistants operating in different contexts. (Brown: Section 0093, lines based language model being trained to respond to sports questions selected from a group of virtual assistants)
Claim 23, Brown in view of Sarikaya a method according to claim 10, comprising receiving a request to instantiate a context specific virtual assistant; delivering a context specific virtual assistant comprising a generic model, (Brown: Section 0093 “A base language model reads on the generic model) the generic model having been trained on general non context-specific data received from multiple virtual assistants operating in different contexts and a context specific model; (Brown: Section 0127, lines 4-8 thus the base language model is updated by adding content and updating the response adding additional language model) and training the instantiated virtual assistant on client specific data. (Brown: Section 0120, lines 2-6 thus base language model of virtual assistants)
Claim 24, Brown in view of Sarikaya comprising allocating the instantiated virtual assistant to at least one of a horizontal mesh and a vertical mesh, the vertical mesh comprising a plurality of industry specific contexts (Brown: Section 0029, lines 4-6 thus secretary virtual assistant) and the horizontal mesh comprising a plurality of function specific contexts. (Brown: Section 0155, lines 10-11 virtual assistant to interpret user input such associating to the concepts of “hoops and basketball” reads on the horizontal mesh)
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sgobba (US20220198292) teaches a question and answer pair is received from an external knowledge base. From the question, a set of intents is extracted.
Etchart (US20230068798) teaches a system can operate a speech-controlled device to perform active speaker detection to detect an utterance using image data showing a user speaking the utterance.
Conclusion
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/AKWASI M SARPONG/SPE, Art Unit 2681 11/05/2025
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