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 .
Claims 1-20 are presented for examination.
Claim Objections
Claim 14 is objected to because of the following informalities,
Claim 14 [line 5]: “maximize cosine distances cosine distances” is repeated and should just be “maximize cosine distances”
Appropriate corrections are required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a system, claim 2 is drawn to a method and claim 15 is drawn to a non-transitory computer-readable media that executes the method of claim 2. Each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 2 and 15 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 2 and 15 recite a method of receiving user data of a user interacting with a user interface that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to receive user data of a user. Therefore, the step of receiving user data of a user interacting with a user interface is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of generating a first feature input based on the user data that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to generate feature input by extracting characteristics from user data. Therefore, the step of generating a first feature input is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of determining whether the first output corresponds to a first criterion specific to the first model that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to determine whether an output corresponds to a criterion. Therefore, the step of determining whether the first output corresponds to a first criterion is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of in response to determining the first output does not correspond to the first criterion, generating a second feature input based on the user data that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to generate an input by extracting characteristics from user data. Therefore, the step of generating a second feature input is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of determining whether the second output corresponds to a second criterion specific to the second model that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to determine whether an output corresponds to a criterion. Therefore, the step of determining whether the second output corresponds to a second criterion is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to determine a user has an intent. Therefore, the step of determining that the user has a first intent is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1, 2 and 15 recite a method of determining a first response for the first intent that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to determine a first response. Therefore, the step of determining a first response for the first intent is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1, 2 and 15 recite further a method of processing the first feature input through a first model of a dual-pathway model to generate a first output, wherein the first model comprises a deterministic word graph that generates outputs based on respective popularities of combinations of different text characters that fails to integrate the abstract idea into a practical application. The step of processing the first feature input through a first model of a dual-pathway model wherein the first model comprises a deterministic word graph is a form of insignificant input and output solution activities, where processing using a dual-pathway model and generating outputs using a deterministic word graph are necessary for all uses of the judicial exception. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1, 2 and 15 recite further a method of processing the second feature input through a second model of the dual-pathway model to generate a second output, wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences that fails to integrate the abstract idea into a practical application. The step of processing a second input using dual-pathway model wherein the model comprises a semantic autocomplete model is a form of insignificant input and output solution activities, where processing using a dual-pathway model and generating outputs using a semantic autocomplete model are necessary for all uses of the judicial exception. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision has determined that the additional elements of processing using a dual-pathway model and generating outputs using a deterministic word graph; and processing using a dual-pathway model and generating outputs using a semantic autocomplete model to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 2 and 15 are not patent eligible.
Dependent claims
Claims 3-14 and 16-20 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 2 and 15, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental and mathematical processes that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 3-14 and 16-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claim 3-14 are drawn to a method and claims 16-20 are drawn to a non-transitory computer-readable media that executes the method of claims 3-14. Each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claims 3 and 16 recite further the mental process by retrieving a plurality of available intents; and determining whether the first output corresponds to one of the plurality of available intents those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 4 and 17 recite further the mental process by determining, based on the second output, a first prediction confidence that the user has the first intent of a plurality of available intents; and determining whether the first prediction confidence equals or exceeds a first threshold confidence those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 5 and 18 recite further the mental process by determining a respective prediction confidence for each of the plurality of available intents; and comparing the respective prediction confidence for each of the plurality of available intents to the first threshold confidence those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 6 and 19 recite further the mental process by determining an accuracy metric for the second model; and determining the first threshold confidence based on the accuracy metric those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claims 7 and 20 recite further the mental process by determining a frequency at which the user has the first intent; and determining the first threshold confidence based on the frequency those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 8 recites further the mental process by generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface; receiving a first user input into the search field, wherein the first user input comprises a first textual character; determining whether the first textual character corresponds to the first text string; and in response to determining that the first textual character corresponds to the first text string, continuing to generate for display the first potential user input those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 9 recites further the mental process by generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface; receiving a first user input into the search field, wherein the first user input comprises a first textual character; determining whether the first textual character corresponds to the first text string; and in response to determining that the first textual character does not correspond to the first text string, ending display of the first potential user input those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 10 recites further the mental process by generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface; receiving a first user input into the search field, wherein the first user input comprises a first textual character; determining whether the first textual character corresponds to the first text string; and in response to determining that the first textual character does not correspond to the first text string, determining, based on the user data and the first user input, a third feature input those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 11 recites further the mental process by retrieving a plurality of responses for the first intent; determining a ranking for the plurality of responses; and selecting to determine the first response based on the ranking those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 12 recites further the mental process by determining a first user input; determining an application context; and determining a number of potential user inputs those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 13 recites further the insignificant extra solution activities by wherein processing the first feature input through the first model of the dual-pathway model to generate the first output comprises generating a directed acyclic graph with an initial vertex and a set of final vertices such that paths from the initial vertex to final vertices represent suffixes of a string based on the user data. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 14 recites further the insignificant extra solution activities by wherein processing the second feature input through the second model further comprises: training the second model to minimize cosine distances between a first set of user inputs, wherein the first set of user inputs corresponds to a single intent; and training the second model to maximize cosine distances cosine distances between a second set of user inputs, wherein the second set of user inputs corresponds to different intents. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 3-14 and 16-20 are not patent eligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sachindran et al (US 20240086489 A1) hereafter Sachindran, further in view of Galimovich et al (US 20200401638 A1) hereafter Galimovich, further in view of Hamilton et al (US 20240104091 A1) hereafter Hamilton, and further in view of Mazars (US 20210406291 A1) hereafter Mazars.
With respect to claim 1, Sachindran teaches a system for selecting outputs from dual-pathway models based on model-specific criteria (the search system clusters search results into result subsets based on clustering criteria, such as entity type [par. 0025]), the system comprising:
one or more processors (one or more processors included to execute the pre-fetcher and to perform operations in the system [par. 0037, 0038]): and
one or more non-transitory, computer-readable mediums comprising instructions recorded thereon that when executed by the one or more processors cause operations (an apparatus comprising a non-transitory machine-readable storage medium [par. 0118]) comprising:
processing user data through a first model of a dual-pathway model to generate a first output (user interface can be used to input search queries and view and perceive output that includes data produced. The predictive model determines and output user intent data that are correlated with inputs [par. 0039, 0045]);
determining whether the first output corresponds to a first criterion specific to the first model (the search system clusters the results into subsets based on clustering criteria. Result clusters are sets of search results generated by search system back end that are grouped by a clustering criterion such as entity type [par. 0025, 0044-0046]), wherein determining whether the first output corresponds to the first criterion further comprises:
retrieving a plurality of available intents (a predictive model is used to determine the intent data, such as a semantic label to apply to a search term. Search system back end uses predictive model to determine intent data [par. 0020, 0021, 0036, 0045, 0046]); and
determining whether the first output corresponds to one of the plurality of available intents (the intent data is used to pre-construct a result page that corresponds to the intent data, so that when the user initiates the query, a set of search results is already available [par. 0020, 0021, 0029, 0036, 0045, 0046]);
in response to determining the first output does not correspond to the first criterion, processing the user data through a second model of the dual-pathway model to generate a second output (search system clusters search results into result subsets based on clustering data. the search system groups search results into a job cluster that contains job postings. If the rank has a result that contains job postings higher than other results that do not contain job postings, pre-fetch the job result that do not contain job postings or which correspond to other entity types [par. 0025, 0044-0046]);
determining whether the second output corresponds to a second criterion specific to the second model wherein determining whether the second output corresponds to the second criterion further comprises:
determining a first prediction confidence that the user has the first intent of the plurality of available intents (a confidence value is associated with the session entity type that indicates a likelihood the session entity type matches the user’s actual intent [par. 0064-0066]); and
determining whether the first prediction confidence equals or exceeds a first threshold confidence (if the session entity type confidence value satisfies a threshold session entity type confidence value, pre-fetcher provides partial search inputs and portions of context data. if the session entity type confidence value exceeds a predetermined threshold confidence value, the session entity type satisfies the pre-fetch threshold [par. 0064-066]); and
generating for display, on a user interface, a first potential user input corresponding to the first intent, wherein the first potential user input comprises a first text string for entry into a search field of the user interface (user can enter a search term including few characters of a word into a text input box of a search interface. A frontend search interface includes a text input box [par. 0012, 0083, 0089, 0095, 0096, 0123]).
However, Sachindran does not explicitly teach wherein the first model comprises a deterministic word graph that generates outputs based on respective popularities of combinations of different text characters; wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences; in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents.
In the same field of endeavor, Galimovich teaches wherein the first model comprises a deterministic word graph that generates outputs based on respective popularities of combinations of different text characters (some techniques used for ranking search results including how popular a given search query is in searches, or how often a particular search query is typically used with determinative terms by other users. For example, any determinative terms like images, movies or weather [par. 0092, 0114, 0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of generating a search query completion suggestion by a search engine as suggested by Galimovich into the concept of returning search results by pre-fetching using the session confidence value as suggested by Sachindran because both of these systems addressing the process of generating results based on search queries and suggestions. Doing so would be desirable because the system of Sachindran would be more efficient by using dynamic search query completion suggestion to narrow down the search query to common suggestions (Galimovich, [par. 0058-0062]).
However, the combination of Sachindran and Galimovich does not teach wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences; in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents.
In the same field of endeavor, Hamilton teaches wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences (a search platform comprises multiple ranking mechanisms to generate autocomplete suggestions for a search query. The system relates to generating personalized autocomplete suggestions (or autocomplete prediction based on user-specific data. The query generating user profile may comprise data in the form of text string(s), numerical character(s), etc. [par. 0017-0019]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of performing personalized autocomplete predictions as suggested by Hamilton into the combination of Sachindran and Galimovich because all of these systems addressing the process of generating results based on search queries and suggestion models. Doing so would be desirable because the combination of Sachindran and Galimovich would be more efficient by performing autocomplete prediction and generating a session-agnostic autocomplete score for the candidate search query to identify a corresponding search result cluster for the candidate search result (Hamilton, [par. 0002-0005]).
However, the combination of Sachindran, Galimovich and Hamilton does not disclose in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents.
In the same field of endeavor, Mazars teaches in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents (an initial set of information is classified based on one criterion, and a question is generated based on at least one criterion. A second intent of the user is classified based on the response, and a subset of the initial set of information is selected based on at least the classified second intent that the output is provided based on the subset [par. 0006, 0007, 0015, 0044-0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of conducting a dialogue-based search as suggested by Mazars into the combination of Sachindran, Galimovich and Hamilton because all of these systems addressing the process of generating results based on search queries and suggestion models. Doing so would be desirable because the combination of Sachindran, Galimovich and Hamilton would be more efficient by classifying a first intent of the user based on user’s request and based on at least one criterion, and selecting a subset of initial set of information of user based on the classified second intent (Mazars, [par. 0006-0015]).
With respect to claim 2, Sachindran teaches a method for selecting outputs from dual-pathway models based on model-specific criteria, the method comprising (the search system clusters search results into result subsets based on clustering criteria, such as entity type [par. 0025]):
receiving user data of a user interacting with a user interface (a user provides search terms through the search interface. User interface used to capture user interactions such as inputs, page loads, and clicks, etc. [par. 0002, 0012-0016, 0038, 0039, 0042]);
generating a first feature input based on the user data (user provides search terms through the interface, and the user then inputs a signal that tells the search engine to initiate the search. The search engine formulates a search query based on the input [par. 0002, 0026, 0027]);
processing the first feature input through a first model of a dual-pathway model to generate a first output (user interface can be used to input search queries and view and perceive output that includes data produced. The predictive model determines and output user intent data that are correlated with inputs [par. 0039, 0045]);
determining whether the first output corresponds to a first criterion specific to the first model (the search system clusters the results into subsets based on clustering criteria. Result clusters are sets of search results generated by search system back end that are grouped by a clustering criterion such as entity type [par. 0025, 0044-0046]);
in response to determining the first output does not correspond to the first criterion, generating a second feature input based on the user data (search system clusters search results into result subsets based on clustering data. the search system groups search results into a job cluster that contains job postings. If the rank has a result that contains job postings higher than other results that do not contain job postings, pre-fetch the job result that do not contain job postings or which correspond to other entity types [par. 0025, 0044-0046]);
processing the second feature input through a second model of the dual-pathway model to generate a second output (search system clusters search results into result subsets based on clustering data. the search system groups search results into a job cluster that contains job postings. If the rank has a result that contains job postings higher than other results that do not contain job postings, pre-fetch the job result that do not contain job postings or which correspond to other entity types [par. 0025, 0044-0046]);
determining whether the second output corresponds to a second criterion specific to the second model (the search system clusters the results into subsets based on clustering criteria. Result clusters are sets of search results generated by search system back end that are grouped by a clustering criterion such as entity type [par. 0025, 0044-0046]).
However, Sachindran does not disclose wherein the first model comprises a deterministic word graph that generates outputs based on respective popularities of combinations of different text characters; wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences; in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents; and determining a first response for the first intent.
In the same field of endeavor, Galimovich teaches wherein the first model comprises a deterministic word graph that generates outputs based on respective popularities of combinations of different text characters (some techniques used for ranking search results including how popular a given search query is in searches, or how often a particular search query is typically used with determinative terms by other users. For example, any determinative terms like images, movies or weather [par. 0092, 0114, 0115]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of generating a search query completion suggestion by a search engine as suggested by Galimovich into the concept of returning search results by pre-fetching using the session confidence value as suggested by Sachindran because both of these systems addressing the process of generating results based on search queries and suggestions. Doing so would be desirable because the system of Sachindran would be more efficient by using dynamic search query completion suggestion to narrow down the search query to common suggestions (Galimovich, [par. 0058-0062]).
However, the combination of Sachindran and Galimovich does not disclose wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences; in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents; and determining a first response for the first intent.
In the same field of endeavor, Hamilton teaches wherein the second model comprises a semantic autocomplete model that generates outputs based on predicted confidences (a search platform comprises multiple ranking mechanisms to generate autocomplete suggestions for a search query. The system relates to generating personalized autocomplete suggestions (or autocomplete prediction based on user-specific data. The query generating user profile may comprise data in the form of text string(s), numerical character(s), etc. [par. 0017-0019]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of performing personalized autocomplete predictions as suggested by Hamilton into the combination of Sachindran and Galimovich because all of these systems addressing the process of generating results based on search queries and suggestion models. Doing so would be desirable because the combination of Sachindran and Galimovich would be more efficient by performing autocomplete prediction and generating a session-agnostic autocomplete score for the candidate search query to identify a corresponding search result cluster for the candidate search result (Hamilton, [par. 0002-0005]).
However, the combination of Sachindran, Galimovich and Hamilton does not disclose in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents; and determining a first response for the first intent.
In the same field of endeavor, Mazars teaches in response to determining the second output does correspond to the second criterion, determining that the user has a first intent of a plurality of intents (an initial set of information is classified based on one criterion, and a question is generated based on at least one criterion. A second intent of the user is classified based on the response, and a subset of the initial set of information is selected based on at least the classified second intent that the output is provided based on the subset [par. 0006, 0007, 0015, 0044-0047]); and
determining a first response for the first intent (a first response based on the initial intent is received from user, wherein a question is generated based on the at least one criterion [par. 0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of conducting a dialogue-based search as suggested by Mazars into the combination of Sachindran, Galimovich and Hamilton because all of these systems addressing the process of generating results based on search queries and suggestion models. Doing so would be desirable because the combination of Sachindran, Galimovich and Hamilton would be more efficient by classifying a first intent of the user based on user’s request and based on at least one criterion, and selecting a subset of initial set of information of user based on the classified second intent (Mazars, [par. 0006-0015]).
With respect to claim 3, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches wherein determining whether the first output corresponds to the first criterion further comprises:
retrieving a plurality of available intents (Sachindran, a predictive model is used to determine the intent data, such as a semantic label to apply to a search term. Search system back end uses predictive model to determine intent data [par. 0020, 0021, 0036, 0045, 0046]); and
determining whether the first output corresponds to one of the plurality of available intents (Sachindran, the intent data is used to pre-construct a result page that corresponds to the intent data, so that when the user initiates the query, a set of search results is already available [par. 0020, 0021, 0029, 0036, 0045, 0046]);
With respect to claim 4, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches wherein determining whether the second output corresponds to the second criterion further comprises:
determining, based on the second output, a first prediction confidence that the user has the first intent of a plurality of available intents (Sachindran, a confidence value is associated with the session entity type that indicates a likelihood the session entity type matches the user’s actual intent [par. 0064-0066]); and
determining whether the first prediction confidence equals or exceeds a first threshold confidence (Sachindran, if the session entity type confidence value satisfies a threshold session entity type confidence value, pre-fetcher provides partial search inputs and portions of context data. if the session entity type confidence value exceeds a predetermined threshold confidence value, the session entity type satisfies the pre-fetch threshold [par. 0064-066]).
With respect to claim 5, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
determining a respective prediction confidence for each of the plurality of available intents (Mazars, nodes of the graph are estimated by the knowledge selection module 202, within a threshold level of confidence. Each node is scanned and ranked based on a level of confidence that the node matches the user’s intent [par. 0039, 0043, 0044, 0054]); and
comparing the respective prediction confidence for each of the plurality of available intents to the first threshold confidence (Mazars, the next turn generation module 204 evaluates the selected nodes and determines whether the results should be returned. The number of selected nodes being lower than a threshold number while the confidence of the node are above a confidence threshold [par. 0054, 0057, 0077]).
With respect to claim 6, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
determining an accuracy metric for the second model (Sachindran, the predictive model used to determine session entity type outputs a session entity type and a confidence value. The confidence value is a probabilistic value or a statistical value to indicate the likelihood that a session entity type matches a user’s intent [par. 0064]); and
determining the first threshold confidence based on the accuracy metric (Sachindran, if the confidence value generated satisfies a confidence threshold, pre-fetcher generates a search query. If the confidence value exceeds a pre-defined confidence threshold value, the confidence value satisfies the confidence threshold [par. 0064-0066]).
With respect to claim 7, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
determining a frequency at which the user has the first intent (Sachindran, pre-fetching a result subset requires determining the user’s intended search based on partial search input and context data, fetching a first subset of search results that has high probability of matching the user’s intent [par. 0017, 0025]); and
determining the first threshold confidence based on the frequency (Sachindran, the predictive model generates the confidence value associated with the intent data. The confidence value is a probabilistic value indicating a likelihood that the intent data matches user’s actual intent. If the confidence value generated by the model satisfies the confidence threshold, the pre-fetcher generates a search query [par. 0066]).
With respect to claim 8, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface (Sachindran, a user may enter a search term by typing a few characters of a word into a text input box of a search interface. Result subsets are pre-fetched and result pages are preconstructed each time the user enters an additional text character in the search input box. An example of a web browser transmits an HTTP request in response to user input that is received through a user interface [par. 0012, 0027, 0052, 0083]);
receiving a first user input into the search field, wherein the first user input comprises a first textual character (Sachindran, the processing device receives user first input from a search session of a user device, the context data includes a quantity of text in the first input and a search history associated [par. 0083, 0089, 0095, 0096]);
determining whether the first textual character corresponds to the first text string (Sachindran, the processing device determines whether a combination of the first input and the context data satisfies a pre-fetch threshold by determining a threshold quantity of text based on the search history and determining that the quantity of text in the first input satisfies the threshold quantity of text [par. 0096]); and
in response to determining that the first textual character corresponds to the first text string, continuing to generate for display the first potential user input (Sachindran, if the combination of the first input and the context data satisfies the pre-fetch threshold, the processing device proceeds to operation 506 [par. 0096]).
With respect to claim 9, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface (Sachindran, a user may enter a search term by typing a few characters of a word into a text input box of a search interface. Result subsets are pre-fetched and result pages are preconstructed each time the user enters an additional text character in the search input box. An example of a web browser transmits an HTTP request in response to user input that is received through a user interface [par. 0012, 0027, 0052, 0083]);
receiving a first user input into the search field, wherein the first user input comprises a first textual character (Sachindran, the processing device receives user first input from a search session of a user device, the context data includes a quantity of text in the first input and a search history associated [par. 0083, 0089, 0095, 0096]);
determining whether the first textual character corresponds to the first text string (Sachindran, the processing device determines whether a combination of the first input and the context data satisfies a pre-fetch threshold by determining a threshold quantity of text based on the search history and determining that the quantity of text in the first input satisfies the threshold quantity of text [par. 0096]); and
in response to determining that the first textual character does not correspond to the first text string, ending display of the first potential user input (Sachindran, if the combination of the first input and the context data does not satisfy the pre-fetch threshold, the processing device may end the method [par. 0096]).
With respect to claim 10, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
generating for display, on the user interface, a first potential user input corresponding to the first response, wherein the first potential user input comprises a first text string for entry into a search field of the user interface (Sachindran, a user may enter a search term by typing a few characters of a word into a text input box of a search interface. Result subsets are pre-fetched and result pages are preconstructed each time the user enters an additional text character in the search input box. An example of a web browser transmits an HTTP request in response to user input that is received through a user interface [par. 0012, 0027, 0052, 0083]);
receiving a first user input into the search field, wherein the first user input comprises a first textual character (Sachindran, the processing device receives user first input from a search session of a user device, the context data includes a quantity of text in the first input and a search history associated [par. 0083, 0089, 0095, 0096]);
determining whether the first textual character corresponds to the first text string (Sachindran, the processing device determines whether a combination of the first input and the context data satisfies a pre-fetch threshold by determining a threshold quantity of text based on the search history and determining that the quantity of text in the first input satisfies the threshold quantity of text [par. 0096]); and
in response to determining that the first textual character does not correspond to the first text string, determining, based on the user data and the first user input, a third feature input (Sachindran, if the combination of the first input and the context data does not satisfy the pre-fetch threshold, the processing device may wait for additional input [par. 0096]).
With respect to claim 11, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches further comprising:
retrieving a plurality of responses for the first intent (Mazars, the system may narrow the user’s intent based on the user’s responses [par. 0027]);
determining a ranking for the plurality of responses (Mazars, responses returned based on the user query that satisfy one or more criteria, such as the confidence level is above the threshold, may be ranked by the clustering algorithm [par. 0043, 0044]); and
selecting to determine the first response based on the ranking (Mazars, the features corresponding to the responses are ranked according to their contribution in generating clusters. A particular feature may then be selected for generating a question for user [par. 0044, 0049]).
With respect to claim 12, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches wherein generating the first feature input based on the user data further comprises:
determining a first user input (Sachindran, an initial search input may be determined to generate a corresponding search query [par. 0017-0020]);
determining an application context (Sachindran, a context data is associated with the search input [par. 0017-0020]); and
determining a number of potential user inputs (Sachindra, examples of search inputs are individual characters or groups of individual characters of one or more search terms [par. 0017-0020]).
With respect to claim 13, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches wherein processing the first feature input through the first model of the dual-pathway model to generate the first output comprises generating a directed acyclic graph with an initial vertex and a set of final vertices such that paths from the initial vertex to final vertices represent suffixes of a string based on the user data (Hamilton, an autocomplete prediction generation computing entity may generate a general search result corpus trie data object. A general search result corpus trie data object may describe a tree data structure comprising a plurality of nodes arranged in a hierarchical manner, wherein a node may comprise one or more characters that extends a character sequence represented by the parent node [par. 0074]).
With respect to claim 14, the combination of Sachindran, Galimovich, Hamilton and Mazars teaches wherein processing the second feature input through the second model further comprises:
training the second model to minimize cosine distances between a first set of user inputs, wherein the first set of user inputs corresponds to a single intent; and training the second model to maximize cosine distances cosine distances between a second set of user inputs, wherein the second set of user inputs corresponds to different intents (Hamilton, the autocomplete prediction generation computing entity performs one or more clustering operations on a fixed-size representations to generate search results based on the similarity measures, such as cosine similarity measures or Euclidean-based distance measure [par. 0093]).
With respect to claim 15, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 16, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 3. Therefore, it is rejected for the same reason as claimed in claim 3 above.
With respect to claim 17, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 4. Therefore, it is rejected for the same reason as claimed in claim 4 above.
With respect to claim 18, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 5. Therefore, it is rejected for the same reason as claimed in claim 5 above.
With respect to claim 19, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 6. Therefore, it is rejected for the same reason as claimed in claim 6 above.
With respect to claim 20, it is a non-transitory computer-readable media claim that is corresponding to the method of claim 7. Therefore, it is rejected for the same reason as claimed in claim 7 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chandrababu et al (US 20240193657 A1) disclosed an online concierge system generates an order including multiple items based on unstructured data received from a user through a chat interface instead of manually adding items to the order. The user provides unstructured data to the online concierge system through the chat interface, and the online concierge system extracts an intent from the unstructured data using a natural language process. Based on the intent, the online concierge system identifies a group of items associated with the intent and selects a group of items. The online concierge system generates an order for the user that includes the items comprising the selected group of items.
Dunn et al (US 20200394360 A1) disclosed systems and methods for analyzing intent. Intents may be analyzed to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication.
Le et al (US 20220358396 A1) disclosed methods and systems for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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